2023-01-11T21:14:25.1603730Z Requested labels: linux.2xlarge
2023-01-11T21:14:25.1603874Z Job defined at: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/trunk/91627
2023-01-11T21:14:25.1604133Z Reusable workflow chain:
2023-01-11T21:14:25.1604181Z pytorch/pytorch/.github/workflows/trunk.yml@refs/tags/ciflow/trunk/91627 (8419ddda87c8a47eacc63b54bc7ec98c1f27c26e)
2023-01-11T21:14:25.1604256Z -> pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/trunk/91627 (8419ddda87c8a47eacc63b54bc7ec98c1f27c26e)
2023-01-11T21:14:25.1604278Z Waiting for a runner to pick up this job...
2023-01-11T21:14:25.8717641Z Job is about to start running on the runner: i-0c70567fa7dfd6397 (organization)
2023-01-11T21:14:29.4563735Z Current runner version: '2.300.2'
2023-01-11T21:14:29.4569071Z Runner name: 'i-0c70567fa7dfd6397'
2023-01-11T21:14:29.4569519Z Runner group name: 'Default'
2023-01-11T21:14:29.4570213Z Machine name: 'ip-10-0-0-26'
2023-01-11T21:14:29.4572140Z ##[group]GITHUB_TOKEN Permissions
2023-01-11T21:14:29.4572893Z Actions: write
2023-01-11T21:14:29.4573169Z Checks: write
2023-01-11T21:14:29.4573487Z Contents: write
2023-01-11T21:14:29.4573806Z Deployments: write
2023-01-11T21:14:29.4574090Z Discussions: write
2023-01-11T21:14:29.4574426Z Issues: write
2023-01-11T21:14:29.4574772Z Metadata: read
2023-01-11T21:14:29.4575049Z Packages: write
2023-01-11T21:14:29.4575357Z Pages: write
2023-01-11T21:14:29.4575702Z PullRequests: write
2023-01-11T21:14:29.4576021Z RepositoryProjects: write
2023-01-11T21:14:29.4576379Z SecurityEvents: write
2023-01-11T21:14:29.4576705Z Statuses: write
2023-01-11T21:14:29.4576972Z ##[endgroup]
2023-01-11T21:14:29.4580209Z Secret source: Actions
2023-01-11T21:14:29.4580894Z Prepare workflow directory
2023-01-11T21:14:29.7129396Z Prepare all required actions
2023-01-11T21:14:29.7298052Z Getting action download info
2023-01-11T21:14:30.0292834Z Download action repository 'pytorch/test-infra@main' (SHA:2c225610d00fb13c04fcd60389d3e4d8326167c3)
2023-01-11T21:14:30.3038169Z Download action repository 'pytorch/pytorch@master' (SHA:c5836153f5332ca83d5cacde38f2829a4d54793e)
2023-01-11T21:14:33.0847293Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2023-01-11T21:14:33.3677148Z Getting action download info
2023-01-11T21:14:33.5550710Z Download action repository 'malfet/checkout@silent-checkout' (SHA:c7b8fef48edfe1bca0044a44b1f7f7c4318a3076)
2023-01-11T21:14:33.7874082Z Getting action download info
2023-01-11T21:14:33.9554166Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2023-01-11T21:14:34.1005857Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml
2023-01-11T21:14:34.1007577Z ##[group] Inputs
2023-01-11T21:14:34.1008006Z   build-environment: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:14:34.1009248Z   test-matrix: { include: [
  { config: "default", shard: 1, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "default", shard: 2, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "default", shard: 3, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "default", shard: 4, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "functorch", shard: 1, num_shards: 1, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "nogpu_AVX512", shard: 1, num_shards: 1, runner: "linux.2xlarge" },
  { config: "nogpu_NO_AVX2", shard: 1, num_shards: 1, runner: "linux.2xlarge" },
  { config: "jit_legacy", shard: 1, num_shards: 1, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "distributed", shard: 1, num_shards: 3, runner: "linux.8xlarge.nvidia.gpu" },
  { config: "distributed", shard: 2, num_shards: 3, runner: "linux.8xlarge.nvidia.gpu" },
  { config: "distributed", shard: 3, num_shards: 3, runner: "linux.8xlarge.nvidia.gpu" },
]}

2023-01-11T21:14:34.1010572Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:14:34.1011048Z   sync-tag: 
2023-01-11T21:14:34.1011827Z   timeout-minutes: 240
2023-01-11T21:14:34.1012114Z   use-gha: 
2023-01-11T21:14:34.1012446Z ##[endgroup]
2023-01-11T21:14:34.1013126Z Complete job name: linux-bionic-cuda11.7-py3.10-gcc7 / test (nogpu_NO_AVX2, 1, 1, linux.2xlarge)
2023-01-11T21:14:34.1733108Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2023-01-11T21:14:34.1733421Z with:
2023-01-11T21:14:34.1733962Z   github-secret: ***
2023-01-11T21:14:34.1734322Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2023-01-11T21:14:34.1734689Z   activate-with-label: false
2023-01-11T21:14:34.1735024Z   label: with-ssh
2023-01-11T21:14:34.1735246Z   remove-existing-keys: true
2023-01-11T21:14:34.1735454Z env:
2023-01-11T21:14:34.1735660Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:34.1735880Z ##[endgroup]
2023-01-11T21:14:34.2505773Z ciflow reference detected, attempting to extract PR number
2023-01-11T21:14:34.6477848Z Grabbing public ssh keys from https://github.com/pytorch-bot[bot].keys
2023-01-11T21:14:34.7232933Z No SSH keys found for user pytorch-bot[bot]
2023-01-11T21:14:34.7233308Z Grabbing public ssh keys from https://github.com/LucaLumetti.keys
2023-01-11T21:14:34.8086980Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh
2023-01-11T21:14:34.8099611Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys
2023-01-11T21:14:34.8124214Z Login using: ssh ec2-user@ec2-52-23-220-178.compute-1.amazonaws.com
2023-01-11T21:14:34.8124879Z All testing is done inside the container, to start an interactive session run:
2023-01-11T21:14:34.8125251Z   docker exec -it $(docker container ps --format '{{.ID}}') bash
2023-01-11T21:14:34.8345242Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@master
2023-01-11T21:14:34.8345511Z with:
2023-01-11T21:14:34.8345672Z   submodules: recursive
2023-01-11T21:14:34.8345857Z   fetch-depth: 0
2023-01-11T21:14:34.8346024Z env:
2023-01-11T21:14:34.8346180Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:34.8346367Z ##[endgroup]
2023-01-11T21:14:34.8553689Z ##[group]Run retry () {
2023-01-11T21:14:34.8553954Z retry () {
2023-01-11T21:14:34.8554182Z   $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
2023-01-11T21:14:34.8554401Z }
2023-01-11T21:14:34.8554578Z echo "${GITHUB_WORKSPACE}"
2023-01-11T21:14:34.8554804Z if [ -z "${NO_SUDO}" ]; then
2023-01-11T21:14:34.8555034Z   retry sudo rm -rf "${GITHUB_WORKSPACE}"
2023-01-11T21:14:34.8555228Z else
2023-01-11T21:14:34.8555432Z   retry rm -rf "${GITHUB_WORKSPACE}"
2023-01-11T21:14:34.8555633Z fi
2023-01-11T21:14:34.8555846Z mkdir "${GITHUB_WORKSPACE}"
2023-01-11T21:14:34.8571211Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:14:34.8571445Z env:
2023-01-11T21:14:34.8571628Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:34.8571813Z   NO_SUDO: 
2023-01-11T21:14:34.8571971Z ##[endgroup]
2023-01-11T21:14:34.8665354Z /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:14:37.1376015Z ##[group]Run malfet/checkout@silent-checkout
2023-01-11T21:14:37.1376271Z with:
2023-01-11T21:14:37.1376477Z   ref: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:14:37.1376683Z   fetch-depth: 0
2023-01-11T21:14:37.1376868Z   submodules: recursive
2023-01-11T21:14:37.1377058Z   quiet-checkout: true
2023-01-11T21:14:37.1377247Z   repository: pytorch/pytorch
2023-01-11T21:14:37.1377578Z   token: ***
2023-01-11T21:14:37.1377754Z   ssh-strict: true
2023-01-11T21:14:37.1377951Z   persist-credentials: true
2023-01-11T21:14:37.1378144Z   clean: true
2023-01-11T21:14:37.1378314Z   lfs: false
2023-01-11T21:14:37.1378500Z   set-safe-directory: true
2023-01-11T21:14:37.1378669Z env:
2023-01-11T21:14:37.1378845Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:37.1379037Z ##[endgroup]
2023-01-11T21:14:37.2469074Z Syncing repository: pytorch/pytorch
2023-01-11T21:14:37.2470650Z ##[group]Getting Git version info
2023-01-11T21:14:37.2471126Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2023-01-11T21:14:37.2471595Z [command]/usr/bin/git version
2023-01-11T21:14:37.2471808Z git version 2.38.1
2023-01-11T21:14:37.2472386Z ##[endgroup]
2023-01-11T21:14:37.2483920Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/e8b51ab1-7ca3-4fd4-bb05-0fa628c8aad6' before making global git config changes
2023-01-11T21:14:37.2484837Z Adding repository directory to the temporary git global config as a safe directory
2023-01-11T21:14:37.2489743Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:14:37.2527915Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2023-01-11T21:14:37.2532603Z ##[group]Initializing the repository
2023-01-11T21:14:37.2535822Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:14:37.2564546Z hint: Using 'master' as the name for the initial branch. This default branch name
2023-01-11T21:14:37.2565086Z hint: is subject to change. To configure the initial branch name to use in all
2023-01-11T21:14:37.2565645Z hint: of your new repositories, which will suppress this warning, call:
2023-01-11T21:14:37.2566041Z hint: 
2023-01-11T21:14:37.2566523Z hint: 	git config --global init.defaultBranch <name>
2023-01-11T21:14:37.2566847Z hint: 
2023-01-11T21:14:37.2567313Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2023-01-11T21:14:37.2567948Z hint: 'development'. The just-created branch can be renamed via this command:
2023-01-11T21:14:37.2568342Z hint: 
2023-01-11T21:14:37.2568864Z hint: 	git branch -m <name>
2023-01-11T21:14:37.2569527Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/
2023-01-11T21:14:37.2575104Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch
2023-01-11T21:14:37.2604021Z ##[endgroup]
2023-01-11T21:14:37.2604650Z ##[group]Disabling automatic garbage collection
2023-01-11T21:14:37.2607885Z [command]/usr/bin/git config --local gc.auto 0
2023-01-11T21:14:37.2634806Z ##[endgroup]
2023-01-11T21:14:37.2635355Z ##[group]Setting up auth
2023-01-11T21:14:37.2642044Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2023-01-11T21:14:37.2671610Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :
2023-01-11T21:14:37.2917222Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2023-01-11T21:14:37.2948894Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :
2023-01-11T21:14:37.3191960Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2023-01-11T21:14:37.3239427Z ##[endgroup]
2023-01-11T21:14:37.3240056Z ##[group]Fetching the repository
2023-01-11T21:14:37.3246612Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --quiet --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2023-01-11T21:15:30.9503955Z [command]/usr/bin/git rev-parse --verify --quiet 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e^{object}
2023-01-11T21:15:30.9533015Z 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:15:30.9539292Z ##[endgroup]
2023-01-11T21:15:30.9539899Z ##[group]Determining the checkout info
2023-01-11T21:15:30.9540776Z ##[endgroup]
2023-01-11T21:15:30.9541322Z ##[group]Checking out the ref
2023-01-11T21:15:30.9546334Z [command]/usr/bin/git checkout --quiet --force 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:15:32.2858328Z ##[endgroup]
2023-01-11T21:15:32.2858762Z ##[group]Setting up auth for fetching submodules
2023-01-11T21:15:32.2864852Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2023-01-11T21:15:32.2962240Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2023-01-11T21:15:32.2999860Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2023-01-11T21:15:32.3035768Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2023-01-11T21:15:32.3070284Z ##[endgroup]
2023-01-11T21:15:32.3070969Z ##[group]Fetching submodules
2023-01-11T21:15:32.3075318Z [command]/usr/bin/git submodule sync --recursive
2023-01-11T21:15:32.3362482Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2023-01-11T21:15:32.3646015Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni'
2023-01-11T21:15:32.3646693Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16'
2023-01-11T21:15:32.3647485Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv'
2023-01-11T21:15:32.3649870Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK'
2023-01-11T21:15:32.3651910Z Submodule 'third_party/QNNPACK' (https://github.com/pytorch/QNNPACK) registered for path 'third_party/QNNPACK'
2023-01-11T21:15:32.3654368Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator'
2023-01-11T21:15:32.3656751Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK'
2023-01-11T21:15:32.3659334Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark'
2023-01-11T21:15:32.3661948Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo'
2023-01-11T21:15:32.3665006Z Submodule 'third_party/cub' (https://github.com/NVlabs/cub.git) registered for path 'third_party/cub'
2023-01-11T21:15:32.3667618Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend'
2023-01-11T21:15:32.3670954Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass'
2023-01-11T21:15:32.3674089Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen'
2023-01-11T21:15:32.3677260Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm'
2023-01-11T21:15:32.3680603Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers'
2023-01-11T21:15:32.3683931Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'
2023-01-11T21:15:32.3687488Z Submodule 'third_party/foxi' (https://github.com/houseroad/foxi.git) registered for path 'third_party/foxi'
2023-01-11T21:15:32.3691132Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp'
2023-01-11T21:15:32.3694714Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo'
2023-01-11T21:15:32.3698715Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest'
2023-01-11T21:15:32.3703287Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep'
2023-01-11T21:15:32.3707189Z Submodule 'third_party/ios-cmake' (https://github.com/Yangqing/ios-cmake.git) registered for path 'third_party/ios-cmake'
2023-01-11T21:15:32.3710883Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi'
2023-01-11T21:15:32.3715093Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto'
2023-01-11T21:15:32.3719388Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl'
2023-01-11T21:15:32.3723780Z Submodule 'third_party/neon2sse' (https://github.com/intel/ARM_NEON_2_x86_SSE.git) registered for path 'third_party/neon2sse'
2023-01-11T21:15:32.3728244Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann'
2023-01-11T21:15:32.3732764Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx'
2023-01-11T21:15:32.3737574Z Submodule 'third_party/onnx-tensorrt' (https://github.com/onnx/onnx-tensorrt) registered for path 'third_party/onnx-tensorrt'
2023-01-11T21:15:32.3742308Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft'
2023-01-11T21:15:32.3747634Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf'
2023-01-11T21:15:32.3752755Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd'
2023-01-11T21:15:32.3757891Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool'
2023-01-11T21:15:32.3763034Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11'
2023-01-11T21:15:32.3768609Z Submodule 'third_party/python-enum' (https://github.com/PeachPy/enum34.git) registered for path 'third_party/python-enum'
2023-01-11T21:15:32.3773859Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy'
2023-01-11T21:15:32.3779359Z Submodule 'third_party/python-six' (https://github.com/benjaminp/six.git) registered for path 'third_party/python-six'
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2023-01-11T21:16:59.7364928Z ##[endgroup]
2023-01-11T21:16:59.7365525Z ##[group]Persisting credentials for submodules
2023-01-11T21:16:59.7371774Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :
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2023-01-11T21:16:59.9805048Z [command]/usr/bin/git submodule foreach --recursive git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url
2023-01-11T21:17:00.0081170Z Entering 'android/libs/fbjni'
2023-01-11T21:17:00.0112955Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
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2023-01-11T21:17:00.0160076Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
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2023-01-11T21:17:00.0209412Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
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2023-01-11T21:17:00.0254518Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
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2023-01-11T21:17:00.0300800Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/QNNPACK/config	remote.origin.url
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2023-01-11T21:17:00.0346417Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2023-01-11T21:17:00.0360937Z Entering 'third_party/XNNPACK'
2023-01-11T21:17:00.0392923Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
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2023-01-11T21:17:00.0450379Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
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2023-01-11T21:17:00.0498595Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
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2023-01-11T21:17:00.0546314Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cub/config	remote.origin.url
2023-01-11T21:17:00.0560903Z Entering 'third_party/cudnn_frontend'
2023-01-11T21:17:00.0592796Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
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2023-01-11T21:17:00.0644892Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
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2023-01-11T21:17:00.0697888Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
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2023-01-11T21:17:00.0746244Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2023-01-11T21:17:00.0760822Z Entering 'third_party/fbgemm/third_party/asmjit'
2023-01-11T21:17:00.0792552Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
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2023-01-11T21:17:00.0838438Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
2023-01-11T21:17:00.0853357Z Entering 'third_party/fbgemm/third_party/googletest'
2023-01-11T21:17:00.0885333Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
2023-01-11T21:17:00.0899395Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2023-01-11T21:17:00.0932295Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
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2023-01-11T21:17:00.0979362Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
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2023-01-11T21:17:00.1027326Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
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2023-01-11T21:17:00.1074342Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/foxi/config	remote.origin.url
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2023-01-11T21:17:00.1123008Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2023-01-11T21:17:00.1136954Z Entering 'third_party/gloo'
2023-01-11T21:17:00.1170289Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
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2023-01-11T21:17:00.1216569Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2023-01-11T21:17:00.1231557Z Entering 'third_party/ideep'
2023-01-11T21:17:00.1263923Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2023-01-11T21:17:00.1277235Z Entering 'third_party/ideep/mkl-dnn'
2023-01-11T21:17:00.1309298Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2023-01-11T21:17:00.1325797Z Entering 'third_party/ideep/mkl-dnn/third_party/oneDNN'
2023-01-11T21:17:00.1358265Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/modules/third_party/oneDNN/config	remote.origin.url
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2023-01-11T21:17:00.1412431Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ios-cmake/config	remote.origin.url
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2023-01-11T21:17:00.1458631Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
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2023-01-11T21:17:00.1505184Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2023-01-11T21:17:00.1518550Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2023-01-11T21:17:00.1551287Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config	remote.origin.url
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2023-01-11T21:17:00.1598297Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config	remote.origin.url
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2023-01-11T21:17:00.1645624Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config	remote.origin.url
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2023-01-11T21:17:00.1693022Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/neon2sse/config	remote.origin.url
2023-01-11T21:17:00.1707508Z Entering 'third_party/nlohmann'
2023-01-11T21:17:00.1739936Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config	remote.origin.url
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2023-01-11T21:17:00.1787433Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config	remote.origin.url
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2023-01-11T21:17:00.1847225Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/benchmark/config	remote.origin.url
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2023-01-11T21:17:00.1894245Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T21:17:00.1911274Z Entering 'third_party/onnx-tensorrt'
2023-01-11T21:17:00.1944021Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/config	remote.origin.url
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2023-01-11T21:17:00.1990696Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/config	remote.origin.url
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2023-01-11T21:17:00.2041471Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/benchmark/config	remote.origin.url
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2023-01-11T21:17:00.2090112Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T21:17:00.2104387Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang'
2023-01-11T21:17:00.2136132Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
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2023-01-11T21:17:00.2186814Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config	remote.origin.url
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2023-01-11T21:17:00.2237295Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config	remote.origin.url
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2023-01-11T21:17:00.2287238Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config	remote.origin.url
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2023-01-11T21:17:00.2333823Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config	remote.origin.url
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2023-01-11T21:17:00.2382859Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config	remote.origin.url
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2023-01-11T21:17:00.2428840Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config	remote.origin.url
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2023-01-11T21:17:00.2474771Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config	remote.origin.url
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2023-01-11T21:17:00.2521688Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-enum/config	remote.origin.url
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2023-01-11T21:17:00.2613945Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-six/config	remote.origin.url
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2023-01-11T21:17:00.2660275Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config	remote.origin.url
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2023-01-11T21:17:00.2706900Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tbb/config	remote.origin.url
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2023-01-11T21:17:00.2754517Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config	remote.origin.url
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2023-01-11T21:17:00.2800712Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config	remote.origin.url
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2023-01-11T21:17:00.2849498Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config	remote.origin.url
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2023-01-11T21:17:00.2895637Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config	remote.origin.url
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2023-01-11T21:17:00.2943529Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config	remote.origin.url
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2023-01-11T21:17:00.2990885Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
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2023-01-11T21:17:00.6118949Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
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2023-01-11T21:17:01.1073475Z ##[endgroup]
2023-01-11T21:17:01.1111613Z [command]/usr/bin/git log -1 --format='%H'
2023-01-11T21:17:01.1136662Z '8419ddda87c8a47eacc63b54bc7ec98c1f27c26e'
2023-01-11T21:17:01.1256077Z Prepare all required actions
2023-01-11T21:17:01.1281595Z ##[group]Run ./.github/actions/setup-linux
2023-01-11T21:17:01.1281796Z env:
2023-01-11T21:17:01.1281972Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:01.1282145Z ##[endgroup]
2023-01-11T21:17:01.1327962Z ##[group]Run set -euo pipefail
2023-01-11T21:17:01.1328193Z set -euo pipefail
2023-01-11T21:17:01.1328405Z function get_ec2_metadata() {
2023-01-11T21:17:01.1328675Z   # Pulled from instance metadata endpoint for EC2
2023-01-11T21:17:01.1329020Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2023-01-11T21:17:01.1329323Z   category=$1
2023-01-11T21:17:01.1329564Z   curl -fsSL "http://169.254.169.254/latest/meta-data/${category}"
2023-01-11T21:17:01.1329790Z }
2023-01-11T21:17:01.1329982Z echo "ami-id: $(get_ec2_metadata ami-id)"
2023-01-11T21:17:01.1330314Z echo "instance-id: $(get_ec2_metadata instance-id)"
2023-01-11T21:17:01.1330736Z echo "instance-type: $(get_ec2_metadata instance-type)"
2023-01-11T21:17:01.1331128Z echo "system info $(uname -a)"
2023-01-11T21:17:01.1343652Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:01.1343880Z env:
2023-01-11T21:17:01.1344043Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:01.1344232Z ##[endgroup]
2023-01-11T21:17:01.1421454Z ami-id: ami-096198a0bccc6bad4
2023-01-11T21:17:01.1469711Z instance-id: i-0c70567fa7dfd6397
2023-01-11T21:17:01.1515638Z instance-type: c5.2xlarge
2023-01-11T21:17:01.1521896Z system info Linux ip-10-0-0-26.ec2.internal 4.14.252-195.483.amzn2.x86_64 #1 SMP Mon Nov 1 20:58:46 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
2023-01-11T21:17:01.1536638Z ##[group]Run if systemctl is-active --quiet docker; then
2023-01-11T21:17:01.1536908Z if systemctl is-active --quiet docker; then
2023-01-11T21:17:01.1537156Z     echo "Docker daemon is running...";
2023-01-11T21:17:01.1537369Z else
2023-01-11T21:17:01.1537591Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2023-01-11T21:17:01.1537816Z fi
2023-01-11T21:17:01.1548990Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:01.1549194Z env:
2023-01-11T21:17:01.1549368Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:01.1549553Z ##[endgroup]
2023-01-11T21:17:01.1589972Z Docker daemon is running...
2023-01-11T21:17:01.1603313Z ##[group]Run AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2023-01-11T21:17:01.1603653Z AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2023-01-11T21:17:01.1603937Z retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T21:17:01.1604330Z retry aws ecr get-login*** "$AWS_DEFAULT_REGION" | docker login --username AWS \
2023-01-11T21:17:01.1604684Z     --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
2023-01-11T21:17:01.1615173Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:01.1615377Z env:
2023-01-11T21:17:01.1615555Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:01.1615754Z   AWS_RETRY_MODE: standard
2023-01-11T21:17:01.1615929Z   AWS_MAX_ATTEMPTS: 5
2023-01-11T21:17:01.1616126Z   AWS_DEFAULT_REGION: us-east-1
2023-01-11T21:17:01.1616317Z ##[endgroup]
2023-01-11T21:17:01.8979915Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2023-01-11T21:17:01.8980322Z Configure a credential helper to remove this warning. See
2023-01-11T21:17:01.8980789Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2023-01-11T21:17:01.8981089Z 
2023-01-11T21:17:01.8981222Z Login Succeeded
2023-01-11T21:17:01.9008977Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T21:17:01.9009268Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T21:17:01.9009611Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T21:17:01.9020871Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:01.9021079Z env:
2023-01-11T21:17:01.9021260Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:01.9021451Z ##[endgroup]
2023-01-11T21:17:01.9091542Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2023-01-11T21:17:01.9091788Z with:
2023-01-11T21:17:01.9092163Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:17:01.9092508Z env:
2023-01-11T21:17:01.9092684Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:01.9092873Z ##[endgroup]
2023-01-11T21:17:01.9104337Z ##[group]Run retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T21:17:01.9104596Z retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T21:17:01.9104856Z # ignore output since only exit code is used for conditional
2023-01-11T21:17:01.9105142Z # only pull docker image if it's not available locally
2023-01-11T21:17:01.9105435Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2023-01-11T21:17:01.9105727Z   retry docker pull "${DOCKER_IMAGE}"
2023-01-11T21:17:01.9105926Z fi
2023-01-11T21:17:01.9116114Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:01.9116335Z env:
2023-01-11T21:17:01.9116513Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:01.9116893Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:17:01.9117261Z ##[endgroup]
2023-01-11T21:17:02.2094644Z fd224c2e6c79d7fdec6408da598bf52bc5b201dd: Pulling from pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7
2023-01-11T21:17:02.2095252Z fb668870d8a7: Pulling fs layer
2023-01-11T21:17:02.2095554Z 4542784317be: Pulling fs layer
2023-01-11T21:17:02.2095915Z e0bec5df5af5: Pulling fs layer
2023-01-11T21:17:02.2096254Z 4053f75740ab: Pulling fs layer
2023-01-11T21:17:02.2096613Z 57e09105cdfd: Pulling fs layer
2023-01-11T21:17:02.2096956Z 606761d225e5: Pulling fs layer
2023-01-11T21:17:02.2097273Z 69473a703fb4: Pulling fs layer
2023-01-11T21:17:02.2097623Z a08ab4e0594b: Pulling fs layer
2023-01-11T21:17:02.2097963Z 4cd507bccac2: Pulling fs layer
2023-01-11T21:17:02.2098277Z fa92f16621a4: Pulling fs layer
2023-01-11T21:17:02.2098628Z 6dc2b05bd224: Pulling fs layer
2023-01-11T21:17:02.2098969Z ce4a87d45645: Pulling fs layer
2023-01-11T21:17:02.2099314Z 41860ea59b6c: Pulling fs layer
2023-01-11T21:17:02.2099627Z 87d0ffa55850: Pulling fs layer
2023-01-11T21:17:02.2099991Z f9f75aaba8d7: Pulling fs layer
2023-01-11T21:17:02.2100288Z 0c06be5c20e0: Pulling fs layer
2023-01-11T21:17:02.2100459Z 69473a703fb4: Waiting
2023-01-11T21:17:02.2100647Z d23c0a07b67c: Pulling fs layer
2023-01-11T21:17:02.2100842Z 1001f0d2f3d0: Pulling fs layer
2023-01-11T21:17:02.2101173Z a08ab4e0594b: Waiting
2023-01-11T21:17:02.2101361Z e1c655e7ec0e: Pulling fs layer
2023-01-11T21:17:02.2101555Z a11b4b5fd784: Pulling fs layer
2023-01-11T21:17:02.2101743Z bc41eab7f454: Pulling fs layer
2023-01-11T21:17:02.2101929Z 606761d225e5: Waiting
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2023-01-11T21:17:02.2102293Z 4cd507bccac2: Waiting
2023-01-11T21:17:02.2102758Z f410dcc9d0be: Pulling fs layer
2023-01-11T21:17:02.2102945Z 57e09105cdfd: Waiting
2023-01-11T21:17:02.2103110Z fa92f16621a4: Waiting
2023-01-11T21:17:02.2103296Z 90d8f9bbe048: Pulling fs layer
2023-01-11T21:17:02.2103494Z eedfbaa04e4f: Pulling fs layer
2023-01-11T21:17:02.2103675Z 6dc2b05bd224: Waiting
2023-01-11T21:17:02.2103855Z 2f2308643d60: Pulling fs layer
2023-01-11T21:17:02.2104050Z c1a92fad2c2c: Pulling fs layer
2023-01-11T21:17:02.2104221Z ce4a87d45645: Waiting
2023-01-11T21:17:02.2104395Z 41860ea59b6c: Waiting
2023-01-11T21:17:02.2104620Z 47037a50f270: Pulling fs layer
2023-01-11T21:17:02.2104793Z 87d0ffa55850: Waiting
2023-01-11T21:17:02.2104974Z 1a2fd7b216d7: Pulling fs layer
2023-01-11T21:17:02.2105167Z 765839304d2e: Pulling fs layer
2023-01-11T21:17:02.2105341Z f9f75aaba8d7: Waiting
2023-01-11T21:17:02.2105530Z e51794baeb92: Pulling fs layer
2023-01-11T21:17:02.2105821Z ea4bfeaa0fc7: Pulling fs layer
2023-01-11T21:17:02.2105996Z 0c06be5c20e0: Waiting
2023-01-11T21:17:02.2106178Z d8065d17513d: Pulling fs layer
2023-01-11T21:17:02.2106374Z 6d83ca3dedf3: Pulling fs layer
2023-01-11T21:17:02.2106546Z d23c0a07b67c: Waiting
2023-01-11T21:17:02.2106734Z 12ddc57b99eb: Pulling fs layer
2023-01-11T21:17:02.2106946Z 1001f0d2f3d0: Waiting
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2023-01-11T21:17:02.2107508Z 29a7c0d5fa4c: Pulling fs layer
2023-01-11T21:17:02.2107700Z 16825bb02017: Pulling fs layer
2023-01-11T21:17:02.2107880Z bdf297d7f88c: Pulling fs layer
2023-01-11T21:17:02.2108064Z 90d8f9bbe048: Waiting
2023-01-11T21:17:02.2108253Z 885c12efa4ae: Pulling fs layer
2023-01-11T21:17:02.2108433Z 28c5689cb975: Pulling fs layer
2023-01-11T21:17:02.2108699Z cca768f96df4: Pulling fs layer
2023-01-11T21:17:02.2108899Z 904b81494b5e: Pulling fs layer
2023-01-11T21:17:02.2109073Z e1c655e7ec0e: Waiting
2023-01-11T21:17:02.2109259Z eedfbaa04e4f: Waiting
2023-01-11T21:17:02.2109452Z 61eecfa8b34e: Pulling fs layer
2023-01-11T21:17:02.2109628Z a11b4b5fd784: Waiting
2023-01-11T21:17:02.2109812Z 95c1ac011645: Pulling fs layer
2023-01-11T21:17:02.2110053Z 07cee023724c: Pulling fs layer
2023-01-11T21:17:02.2110229Z c1a92fad2c2c: Waiting
2023-01-11T21:17:02.2110416Z 195d560d8cf6: Pulling fs layer
2023-01-11T21:17:02.2110642Z b8f759fd0191: Waiting
2023-01-11T21:17:02.2110802Z 47037a50f270: Waiting
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2023-01-11T21:17:02.2111698Z a399389c7f8e: Pulling fs layer
2023-01-11T21:17:02.2111885Z 1a2fd7b216d7: Waiting
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2023-01-11T21:17:02.2112567Z bdf297d7f88c: Waiting
2023-01-11T21:17:02.2112743Z 6d83ca3dedf3: Waiting
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2023-01-11T21:17:02.2113081Z 0d8aeb1421f9: Pulling fs layer
2023-01-11T21:17:02.2113275Z 02048a597c22: Pulling fs layer
2023-01-11T21:17:02.2113468Z 25d615d8a5e2: Pulling fs layer
2023-01-11T21:17:02.2113638Z 885c12efa4ae: Waiting
2023-01-11T21:17:02.2113814Z 12ddc57b99eb: Waiting
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2023-01-11T21:17:02.2116398Z 8afbc57dfec9: Waiting
2023-01-11T21:17:02.3342872Z 4542784317be: Verifying Checksum
2023-01-11T21:17:02.3343221Z 4542784317be: Download complete
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2023-01-11T21:17:15.7973096Z d23c0a07b67c: Download complete
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2023-01-11T21:19:47.9921012Z Digest: sha256:0da23f4faf0ce20770149c4a783e08eaa91c07112511dc5511c77937c66edb24
2023-01-11T21:19:48.0258451Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:19:48.0446702Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:19:48.0705215Z ##[group]Run python3 -m pip install psutil==5.9.1
2023-01-11T21:19:48.0705592Z python3 -m pip install psutil==5.9.1
2023-01-11T21:19:48.0705922Z python3 -m pip install pynvml==11.4.1
2023-01-11T21:19:48.0706273Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2023-01-11T21:19:48.0706627Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2023-01-11T21:19:48.6345071Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:19:48.6345330Z env:
2023-01-11T21:19:48.6345540Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:19:48.6345755Z ##[endgroup]
2023-01-11T21:19:52.0304391Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T21:19:52.0574583Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.7/site-packages (5.9.1)
2023-01-11T21:19:52.4949179Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T21:19:52.5127475Z Requirement already satisfied: pynvml==11.4.1 in /home/ec2-user/.local/lib/python3.7/site-packages (11.4.1)
2023-01-11T21:19:52.7223532Z Prepare all required actions
2023-01-11T21:19:52.7223798Z Getting action download info
2023-01-11T21:19:53.0782714Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:4a8bfae15cc25cc0785c1603ee87a9da8fd442ea)
2023-01-11T21:19:53.2793725Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a)
2023-01-11T21:19:53.4003005Z ##[group]Run ./.github/actions/download-build-artifacts
2023-01-11T21:19:53.4003242Z with:
2023-01-11T21:19:53.4003441Z   name: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:19:53.4003647Z env:
2023-01-11T21:19:53.4003823Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:19:53.4003997Z ##[endgroup]
2023-01-11T21:19:53.4025483Z ##[group]Run seemethere/download-artifact-s3@v4
2023-01-11T21:19:53.4025703Z with:
2023-01-11T21:19:53.4025895Z   name: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:19:53.4026123Z   s3-bucket: gha-artifacts
2023-01-11T21:19:53.4026314Z   region: us-east-1
2023-01-11T21:19:53.4026499Z env:
2023-01-11T21:19:53.4026657Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:19:53.4026845Z ##[endgroup]
2023-01-11T21:19:53.8386781Z Found 1 objects with prefix pytorch/pytorch/3896346758/linux-bionic-cuda11.7-py3.10-gcc7/
2023-01-11T21:19:53.8387310Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2023-01-11T21:20:04.3430047Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2023-01-11T21:20:04.3430292Z 
2023-01-11T21:20:04.3443922Z ##[warning]The `set-output` command is deprecated and will be disabled soon. Please upgrade to using Environment Files. For more information see: https://github.blog/changelog/2022-10-11-github-actions-deprecating-save-state-and-set-output-commands/
2023-01-11T21:20:04.3451720Z Artifact download has finished successfully
2023-01-11T21:20:04.3578940Z ##[group]Run unzip -o artifacts.zip
2023-01-11T21:20:04.3579176Z unzip -o artifacts.zip
2023-01-11T21:20:04.3590758Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:20:04.3590982Z env:
2023-01-11T21:20:04.3591163Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:20:04.3591342Z ##[endgroup]
2023-01-11T21:20:04.3660051Z Archive:  artifacts.zip
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2023-01-11T21:20:13.0706091Z ##[group]Run df -H
2023-01-11T21:20:13.0706359Z df -H
2023-01-11T21:20:13.0717389Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:20:13.0717594Z env:
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2023-01-11T21:20:13.0717960Z ##[endgroup]
2023-01-11T21:20:13.0784116Z Filesystem      Size  Used Avail Use% Mounted on
2023-01-11T21:20:13.0784493Z devtmpfs        8.2G     0  8.2G   0% /dev
2023-01-11T21:20:13.0784805Z tmpfs           8.2G   12M  8.2G   1% /dev/shm
2023-01-11T21:20:13.0785156Z tmpfs           8.2G  431k  8.2G   1% /run
2023-01-11T21:20:13.0785436Z tmpfs           8.2G     0  8.2G   0% /sys/fs/cgroup
2023-01-11T21:20:13.0785937Z /dev/nvme0n1p1  162G   35G  127G  22% /
2023-01-11T21:20:13.0818512Z ##[group]Run .github/scripts/parse_ref.py
2023-01-11T21:20:13.0818748Z .github/scripts/parse_ref.py
2023-01-11T21:20:13.0830140Z shell: /usr/bin/bash -e {0}
2023-01-11T21:20:13.0830334Z env:
2023-01-11T21:20:13.0830499Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:20:13.0830699Z ##[endgroup]
2023-01-11T21:20:13.1129614Z ##[group]Run set -x
2023-01-11T21:20:13.1129888Z set -x
2023-01-11T21:20:13.1130056Z 
2023-01-11T21:20:13.1130250Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2023-01-11T21:20:13.1130494Z   TEST_COMMAND=.jenkins/pytorch/multigpu-test.sh
2023-01-11T21:20:13.1130757Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2023-01-11T21:20:13.1130994Z   TEST_COMMAND=.jenkins/onnx/test.sh
2023-01-11T21:20:13.1131178Z else
2023-01-11T21:20:13.1131378Z   TEST_COMMAND=.jenkins/pytorch/test.sh
2023-01-11T21:20:13.1131574Z fi
2023-01-11T21:20:13.1131716Z 
2023-01-11T21:20:13.1131948Z COMMIT_MESSAGES=$(git cherry -v "origin/${GIT_DEFAULT_BRANCH:-master}")
2023-01-11T21:20:13.1132187Z 
2023-01-11T21:20:13.1132404Z # sanitize the input commit message and PR body here:
2023-01-11T21:20:13.1132606Z #
2023-01-11T21:20:13.1132901Z # trim all new lines from commit messages + PR_BODY to avoid issues with batch environment
2023-01-11T21:20:13.1133282Z # variable copying. see https://github.com/pytorch/pytorch/pull/80043#issuecomment-1167796028
2023-01-11T21:20:13.1133583Z COMMIT_MESSAGES="${COMMIT_MESSAGES//[$'\n\r']}"
2023-01-11T21:20:13.1133814Z PR_BODY="${PR_BODY//[$'\n\r']}"
2023-01-11T21:20:13.1134118Z 
2023-01-11T21:20:13.1134367Z # then trim all special characters like single and double quotes to avoid unescaped inputs to
2023-01-11T21:20:13.1134652Z # wreak havoc internally
2023-01-11T21:20:13.1134891Z export COMMIT_MESSAGES="${COMMIT_MESSAGES//[\'\"]}"
2023-01-11T21:20:13.1135134Z export PR_BODY="${PR_BODY//[\'\"]}"
2023-01-11T21:20:13.1135385Z 
2023-01-11T21:20:13.1135616Z # detached container should get cleaned up by teardown_ec2_linux
2023-01-11T21:20:13.1135913Z # TODO: Stop building test binaries as part of the build phase
2023-01-11T21:20:13.1136178Z # Used for GPU_FLAG since that doesn't play nice
2023-01-11T21:20:13.1136420Z # shellcheck disable=SC2086,SC2090
2023-01-11T21:20:13.1136645Z container_name=$(docker run \
2023-01-11T21:20:13.1136835Z   ${GPU_FLAG:-} \
2023-01-11T21:20:13.1137037Z   -e BUILD_ENVIRONMENT \
2023-01-11T21:20:13.1137238Z   -e PR_NUMBER \
2023-01-11T21:20:13.1137433Z   -e GITHUB_ACTIONS \
2023-01-11T21:20:13.1137612Z   -e BASE_SHA \
2023-01-11T21:20:13.1137796Z   -e BRANCH \
2023-01-11T21:20:13.1137976Z   -e SHA1 \
2023-01-11T21:20:13.1138153Z   -e AWS_DEFAULT_REGION \
2023-01-11T21:20:13.1138351Z   -e IN_WHEEL_TEST \
2023-01-11T21:20:13.1138543Z   -e SHARD_NUMBER \
2023-01-11T21:20:13.1138720Z   -e TEST_CONFIG \
2023-01-11T21:20:13.1138911Z   -e NUM_TEST_SHARDS \
2023-01-11T21:20:13.1139099Z   -e PR_BODY \
2023-01-11T21:20:13.1139362Z   -e COMMIT_MESSAGES \
2023-01-11T21:20:13.1139573Z   -e CONTINUE_THROUGH_ERROR \
2023-01-11T21:20:13.1139788Z   -e PYTORCH_RETRY_TEST_CASES \
2023-01-11T21:20:13.1140184Z   -e PYTORCH_OVERRIDE_FLAKY_SIGNAL \
2023-01-11T21:20:13.1140391Z   -e PR_LABELS \
2023-01-11T21:20:13.1140592Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2023-01-11T21:20:13.1140796Z   -e SCCACHE_BUCKET \
2023-01-11T21:20:13.1140985Z   -e SCCACHE_S3_KEY_PREFIX \
2023-01-11T21:20:13.1141179Z   -e XLA_CUDA \
2023-01-11T21:20:13.1141387Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2023-01-11T21:20:13.1141609Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2023-01-11T21:20:13.1141851Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2023-01-11T21:20:13.1142108Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2023-01-11T21:20:13.1142329Z   --ulimit stack=10485760:83886080 \
2023-01-11T21:20:13.1142798Z   --security-opt seccomp=unconfined \
2023-01-11T21:20:13.1143032Z   --cap-add=SYS_PTRACE \
2023-01-11T21:20:13.1143215Z   --ipc=host \
2023-01-11T21:20:13.1143506Z   --shm-size="${SHM_SIZE}" \
2023-01-11T21:20:13.1143692Z   --tty \
2023-01-11T21:20:13.1143861Z   --detach \
2023-01-11T21:20:13.1144047Z   --name="${container_name}" \
2023-01-11T21:20:13.1144248Z   --user jenkins \
2023-01-11T21:20:13.1144484Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2023-01-11T21:20:13.1144725Z   -w /var/lib/jenkins/workspace \
2023-01-11T21:20:13.1144932Z   "${DOCKER_IMAGE}"
2023-01-11T21:20:13.1145105Z )
2023-01-11T21:20:13.1145311Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2023-01-11T21:20:13.1145641Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2023-01-11T21:20:13.1156883Z shell: /usr/bin/bash -e {0}
2023-01-11T21:20:13.1157054Z env:
2023-01-11T21:20:13.1157229Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:20:13.1157481Z   BUILD_ENVIRONMENT: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:20:13.1157708Z   PR_NUMBER: 
2023-01-11T21:20:13.1157972Z   BRANCH: 
2023-01-11T21:20:13.1158181Z   SHA1: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:13.1158432Z   BASE_SHA: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:13.1158646Z   PYTORCH_RETRY_TEST_CASES: 1
2023-01-11T21:20:13.1158856Z   PYTORCH_OVERRIDE_FLAKY_SIGNAL: 1
2023-01-11T21:20:13.1159065Z   TEST_CONFIG: nogpu_NO_AVX2
2023-01-11T21:20:13.1159241Z   SHARD_NUMBER: 1
2023-01-11T21:20:13.1159408Z   NUM_TEST_SHARDS: 1
2023-01-11T21:20:13.1159571Z   PR_BODY: 
2023-01-11T21:20:13.1159741Z   CONTINUE_THROUGH_ERROR: False
2023-01-11T21:20:13.1159981Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2023-01-11T21:20:13.1160217Z   SCCACHE_S3_KEY_PREFIX: trunk
2023-01-11T21:20:13.1160386Z   SHM_SIZE: 2g
2023-01-11T21:20:13.1160761Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:20:13.1161226Z   XLA_CUDA: 
2023-01-11T21:20:13.1161488Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2023-01-11T21:20:13.1161762Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2023-01-11T21:20:13.1161985Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2023-01-11T21:20:13.1162185Z ##[endgroup]
2023-01-11T21:20:13.1187026Z + [[ nogpu_NO_AVX2 == \m\u\l\t\i\g\p\u ]]
2023-01-11T21:20:13.1187492Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *onnx* ]]
2023-01-11T21:20:13.1187748Z + TEST_COMMAND=.jenkins/pytorch/test.sh
2023-01-11T21:20:13.1190101Z ++ git cherry -v origin/master
2023-01-11T21:20:13.7508222Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into '\''input'\''
2023-01-11T21:20:13.7508641Z + 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch
2023-01-11T21:20:13.7509325Z + 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e '\''other'\'' instead of '\''output'\'' in documentation'
2023-01-11T21:20:13.7510188Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into '\''input'\''+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e '\''other'\'' instead of '\''output'\'' in documentation'
2023-01-11T21:20:13.7510628Z + PR_BODY=
2023-01-11T21:20:13.7511272Z + export 'COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation'
2023-01-11T21:20:13.7512236Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation'
2023-01-11T21:20:13.7512657Z + export PR_BODY=
2023-01-11T21:20:13.7512818Z + PR_BODY=
2023-01-11T21:20:13.7518591Z +++ nproc --ignore=2
2023-01-11T21:20:13.7551522Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e PR_BODY -e COMMIT_MESSAGES -e CONTINUE_THROUGH_ERROR -e PYTORCH_RETRY_TEST_CASES -e PYTORCH_OVERRIDE_FLAKY_SIGNAL -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS --env-file=/tmp/github_env_3896346758 --ulimit stack=10485760:83886080 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=2g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:20:24.0374992Z + container_name=a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T21:20:24.0375544Z + echo DOCKER_CONTAINER_ID=a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T21:20:24.0378813Z ++ echo dist/torch-2.0.0a0+git8419ddd-cp310-cp310-linux_x86_64.whl
2023-01-11T21:20:24.0379876Z + docker exec -t a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256 sh -c 'pip install dist/torch-2.0.0a0+git8419ddd-cp310-cp310-linux_x86_64.whl[opt-einsum] && .jenkins/pytorch/test.sh'
2023-01-11T21:20:24.5825297Z Processing ./dist/torch-2.0.0a0+git8419ddd-cp310-cp310-linux_x86_64.whl
2023-01-11T21:20:25.2984093Z Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (1.11.1)
2023-01-11T21:20:25.2987135Z Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (2.6.3)
2023-01-11T21:20:25.2991066Z Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (4.4.0)
2023-01-11T21:20:25.3003161Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (3.3.0)
2023-01-11T21:20:25.3056258Z Requirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.10/site-packages (from opt-einsum>=3.3->torch==2.0.0a0+git8419ddd) (1.21.2)
2023-01-11T21:20:25.3198403Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.0.0a0+git8419ddd) (1.2.1)
2023-01-11T21:20:25.9243749Z Installing collected packages: torch
2023-01-11T21:20:32.8135000Z Successfully installed torch-2.0.0a0+git8419ddd
2023-01-11T21:20:32.9481269Z + echo 'Environment variables:'
2023-01-11T21:20:32.9481709Z Environment variables:
2023-01-11T21:20:32.9481884Z + env
2023-01-11T21:20:32.9499590Z SHARD_NUMBER=1
2023-01-11T21:20:32.9499997Z NV_LIBCUBLAS_DEV_VERSION=11.10.1.25-1
2023-01-11T21:20:32.9500352Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-7
2023-01-11T21:20:32.9500715Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2023-01-11T21:20:32.9501086Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.13.4-1+cuda11.7
2023-01-11T21:20:32.9501315Z UCC_HOME=/usr
2023-01-11T21:20:32.9502677Z BUILD_ENVIRONMENT=linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:20:32.9503101Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2023-01-11T21:20:32.9503635Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-7=11.7.3.21-1
2023-01-11T21:20:32.9503927Z INSTALLED_DB=yes
2023-01-11T21:20:32.9504151Z HOSTNAME=a71a466242c1
2023-01-11T21:20:32.9504340Z GITHUB_REF_NAME=ciflow/trunk/91627
2023-01-11T21:20:32.9504581Z GITHUB_API_URL=https://api.github.com
2023-01-11T21:20:32.9504885Z GITHUB_REPOSITORY_OWNER_ID=21003710
2023-01-11T21:20:32.9505081Z OPENSSL_DIR=/opt/openssl
2023-01-11T21:20:32.9509216Z UCC_COMMIT=1c7a7127186e7836f73aafbd7697bbc274a77eee
2023-01-11T21:20:32.9510836Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9511396Z CUDA_PATH=/usr/local/cuda
2023-01-11T21:20:32.9512058Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2023-01-11T21:20:32.9512518Z GITHUB_RUN_ATTEMPT=1
2023-01-11T21:20:32.9512834Z TEST_CONFIG=nogpu_NO_AVX2
2023-01-11T21:20:32.9513207Z NV_LIBNPP_VERSION=11.7.3.21-1
2023-01-11T21:20:32.9513658Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-7=11.7.50-1
2023-01-11T21:20:32.9514049Z GITHUB_REPOSITORY_OWNER=pytorch
2023-01-11T21:20:32.9514381Z GITHUB_ACTIONS=true
2023-01-11T21:20:32.9514688Z NVIDIA_VISIBLE_DEVICES=all
2023-01-11T21:20:32.9515057Z NV_NVPROF_VERSION=11.7.50-1
2023-01-11T21:20:32.9515421Z NV_LIBCUSPARSE_VERSION=11.7.3.50-1
2023-01-11T21:20:32.9515920Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/trunk.yml@refs/tags/ciflow/trunk/91627
2023-01-11T21:20:32.9516377Z NVIDIA_PRODUCT_NAME=CUDA
2023-01-11T21:20:32.9516677Z CI=true
2023-01-11T21:20:32.9516982Z PYTORCH_OVERRIDE_FLAKY_SIGNAL=1
2023-01-11T21:20:32.9517478Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-7=11.10.1.25-1
2023-01-11T21:20:32.9517819Z BRANCH=
2023-01-11T21:20:32.9518090Z GITHUB_HEAD_REF=
2023-01-11T21:20:32.9518454Z UCX_COMMIT=31e74cac7bee0ef66bef2af72e7d86d9c282e5ab
2023-01-11T21:20:32.9518947Z GITHUB_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:32.9519357Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
2023-01-11T21:20:32.9519720Z GITHUB_ACTION_REF=
2023-01-11T21:20:32.9520065Z NCCL_VERSION=2.13.4-1
2023-01-11T21:20:32.9520392Z GITHUB_ACTION=__self
2023-01-11T21:20:32.9543970Z GITHUB_REF_PROTECTED=false
2023-01-11T21:20:32.9544620Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2023-01-11T21:20:32.9545138Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2023-01-11T21:20:32.9546016Z ***
2023-01-11T21:20:32.9546335Z INSTALLED_VISION=yes
2023-01-11T21:20:32.9546663Z NVARCH=x86_64
2023-01-11T21:20:32.9546942Z NV_LIBCUSPARSE_DEV_VERSION=11.7.3.50-1
2023-01-11T21:20:32.9547132Z HOME=/var/lib/jenkins
2023-01-11T21:20:32.9547546Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9547856Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2023-01-11T21:20:32.9548048Z NVIDIA_CUDA_END_OF_LIFE=1
2023-01-11T21:20:32.9548242Z GITHUB_ACTION_REPOSITORY=
2023-01-11T21:20:32.9548429Z GITHUB_REF_TYPE=tag
2023-01-11T21:20:32.9548636Z NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:32.9548837Z GITHUB_RETENTION_DAYS=90
2023-01-11T21:20:32.9549122Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2023-01-11T21:20:32.9549426Z NV_LIBNCCL_PACKAGE=libnccl2=2.13.4-1+cuda11.7
2023-01-11T21:20:32.9549905Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9550365Z DEBIAN_FRONTEND=noninteractive
2023-01-11T21:20:32.9550627Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
2023-01-11T21:20:32.9550838Z GITHUB_REF=refs/tags/ciflow/trunk/91627
2023-01-11T21:20:32.9551070Z NV_CUDA_LIB_VERSION=11.7.0-1
2023-01-11T21:20:32.9551308Z GITHUB_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9551522Z INSTALLED_PROTOBUF=yes
2023-01-11T21:20:32.9551717Z GITHUB_REPOSITORY_ID=65600975
2023-01-11T21:20:32.9551907Z GITHUB_RUN_ID=3896346758
2023-01-11T21:20:32.9552148Z NV_LIBNPP_PACKAGE=libnpp-11-7=11.7.3.21-1
2023-01-11T21:20:32.9552366Z NV_LIBNCCL_PACKAGE_NAME=libnccl2
2023-01-11T21:20:32.9552583Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs
2023-01-11T21:20:32.9552794Z NV_NVTX_VERSION=11.7.50-1
2023-01-11T21:20:32.9552987Z CONTINUE_THROUGH_ERROR=False
2023-01-11T21:20:32.9553208Z GITHUB_SERVER_URL=https://github.com
2023-01-11T21:20:32.9553398Z MAX_JOBS=6
2023-01-11T21:20:32.9553574Z GITHUB_ACTOR_ID=54816060
2023-01-11T21:20:32.9553794Z NV_LIBCUBLAS_VERSION=11.10.1.25-1
2023-01-11T21:20:32.9554131Z NV_LIBCUBLAS_PACKAGE=libcublas-11-7=11.10.1.25-1
2023-01-11T21:20:32.9554506Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2023-01-11T21:20:32.9554762Z UCX_HOME=/usr
2023-01-11T21:20:32.9554951Z PYTORCH_RETRY_TEST_CASES=1
2023-01-11T21:20:32.9555181Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2023-01-11T21:20:32.9555446Z BASE_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9555707Z NV_CUDA_CUDART_DEV_VERSION=11.7.60-1
2023-01-11T21:20:32.9555887Z PR_BODY=
2023-01-11T21:20:32.9556058Z GITHUB_BASE_REF=
2023-01-11T21:20:32.9556227Z TERM=xterm
2023-01-11T21:20:32.9556372Z XLA_CUDA=
2023-01-11T21:20:32.9556569Z NV_NVML_DEV_VERSION=11.7.50-1
2023-01-11T21:20:32.9556767Z TORCH_CUDA_ARCH_LIST=Maxwell
2023-01-11T21:20:32.9556942Z CUDA_VERSION=11.7.0
2023-01-11T21:20:32.9557194Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-7
2023-01-11T21:20:32.9557414Z OPENSSL_ROOT_DIR=/opt/openssl
2023-01-11T21:20:32.9557819Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9558115Z GITHUB_JOB=test
2023-01-11T21:20:32.9558307Z SCCACHE_S3_KEY_PREFIX=trunk
2023-01-11T21:20:32.9558781Z COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation
2023-01-11T21:20:32.9559232Z NVIDIA_DRIVER_CAPABILITIES=compute,utility
2023-01-11T21:20:32.9559443Z NUM_TEST_SHARDS=1
2023-01-11T21:20:32.9559616Z PR_NUMBER=
2023-01-11T21:20:32.9560001Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9560285Z SHLVL=1
2023-01-11T21:20:32.9560541Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-7
2023-01-11T21:20:32.9562634Z GITHUB_REPOSITORY=pytorch/pytorch
2023-01-11T21:20:32.9563624Z NVIDIA_REQUIRE_CUDA=cuda>=11.7 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511
2023-01-11T21:20:32.9565047Z NV_LIBNPP_DEV_VERSION=11.7.3.21-1
2023-01-11T21:20:32.9565319Z SHA1=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9565543Z GITHUB_EVENT_NAME=push
2023-01-11T21:20:32.9565772Z NV_CUDA_CUDART_VERSION=11.7.60-1
2023-01-11T21:20:32.9566110Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2023-01-11T21:20:32.9566313Z GITHUB_RUN_NUMBER=22986
2023-01-11T21:20:32.9566508Z GITHUB_WORKFLOW=trunk
2023-01-11T21:20:32.9566823Z PATH=/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:32.9567147Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:32.9567409Z GITHUB_WORKFLOW_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9567765Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:20:32.9568071Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:32.9568277Z _=/usr/bin/env
2023-01-11T21:20:32.9568563Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2023-01-11T21:20:32.9670260Z + TORCH_INSTALL_DIR=/opt/conda/lib/python3.10/site-packages/torch
2023-01-11T21:20:32.9671281Z + TORCH_BIN_DIR=/opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T21:20:32.9671950Z + TORCH_LIB_DIR=/opt/conda/lib/python3.10/site-packages/torch/lib
2023-01-11T21:20:32.9672375Z + TORCH_TEST_DIR=/opt/conda/lib/python3.10/site-packages/torch/test
2023-01-11T21:20:32.9672598Z + BUILD_DIR=build
2023-01-11T21:20:32.9672796Z + BUILD_RENAMED_DIR=build_renamed
2023-01-11T21:20:32.9673036Z + BUILD_BIN_DIR=build/bin
2023-01-11T21:20:32.9673226Z + export VALGRIND=ON
2023-01-11T21:20:32.9673406Z + VALGRIND=ON
2023-01-11T21:20:32.9673604Z + export TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T21:20:32.9673855Z + TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T21:20:32.9674168Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *clang9* ]]
2023-01-11T21:20:32.9674573Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *bazel* ]]
2023-01-11T21:20:32.9675452Z ++ realpath build/custom_test_artifacts
2023-01-11T21:20:32.9704070Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2023-01-11T21:20:32.9706542Z ++ dirname .jenkins/pytorch/test.sh
2023-01-11T21:20:32.9712801Z + source .jenkins/pytorch/common.sh
2023-01-11T21:20:32.9722015Z +++ dirname .jenkins/pytorch/common.sh
2023-01-11T21:20:32.9731361Z ++ source .jenkins/pytorch/common_utils.sh
2023-01-11T21:20:32.9738705Z +++ declare -f -t trap_add
2023-01-11T21:20:32.9743917Z ++ set -ex
2023-01-11T21:20:32.9744381Z ++ [[ linux-bionic-cuda11.7-py3.10-gcc7 == *rocm* ]]
2023-01-11T21:20:32.9744666Z ++ BUILD_TEST_LIBTORCH=0
2023-01-11T21:20:32.9744979Z + echo 'Environment variables'
2023-01-11T21:20:32.9745172Z Environment variables
2023-01-11T21:20:32.9745350Z + env
2023-01-11T21:20:32.9752398Z SHARD_NUMBER=1
2023-01-11T21:20:32.9752800Z NV_LIBCUBLAS_DEV_VERSION=11.10.1.25-1
2023-01-11T21:20:32.9753492Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-7
2023-01-11T21:20:32.9753960Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2023-01-11T21:20:32.9754512Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.13.4-1+cuda11.7
2023-01-11T21:20:32.9754871Z UCC_HOME=/usr
2023-01-11T21:20:32.9755366Z BUILD_ENVIRONMENT=linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:20:32.9755838Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2023-01-11T21:20:32.9756334Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-7=11.7.3.21-1
2023-01-11T21:20:32.9756711Z INSTALLED_DB=yes
2023-01-11T21:20:32.9757023Z HOSTNAME=a71a466242c1
2023-01-11T21:20:32.9757355Z GITHUB_REF_NAME=ciflow/trunk/91627
2023-01-11T21:20:32.9757715Z GITHUB_API_URL=https://api.github.com
2023-01-11T21:20:32.9758125Z GITHUB_REPOSITORY_OWNER_ID=21003710
2023-01-11T21:20:32.9758493Z OPENSSL_DIR=/opt/openssl
2023-01-11T21:20:32.9758877Z UCC_COMMIT=1c7a7127186e7836f73aafbd7697bbc274a77eee
2023-01-11T21:20:32.9759733Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9760132Z CUDA_PATH=/usr/local/cuda
2023-01-11T21:20:32.9760622Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2023-01-11T21:20:32.9761048Z GITHUB_RUN_ATTEMPT=1
2023-01-11T21:20:32.9761370Z TEST_CONFIG=nogpu_NO_AVX2
2023-01-11T21:20:32.9761942Z NV_LIBNPP_VERSION=11.7.3.21-1
2023-01-11T21:20:32.9762429Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-7=11.7.50-1
2023-01-11T21:20:32.9762849Z GITHUB_REPOSITORY_OWNER=pytorch
2023-01-11T21:20:32.9763208Z GITHUB_ACTIONS=true
2023-01-11T21:20:32.9763518Z NVIDIA_VISIBLE_DEVICES=all
2023-01-11T21:20:32.9763908Z NV_NVPROF_VERSION=11.7.50-1
2023-01-11T21:20:32.9764315Z NV_LIBCUSPARSE_VERSION=11.7.3.50-1
2023-01-11T21:20:32.9764828Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/trunk.yml@refs/tags/ciflow/trunk/91627
2023-01-11T21:20:32.9765321Z NVIDIA_PRODUCT_NAME=CUDA
2023-01-11T21:20:32.9765608Z CI=true
2023-01-11T21:20:32.9765876Z PYTORCH_OVERRIDE_FLAKY_SIGNAL=1
2023-01-11T21:20:32.9766304Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-7=11.10.1.25-1
2023-01-11T21:20:32.9766695Z BRANCH=
2023-01-11T21:20:32.9766979Z GITHUB_HEAD_REF=
2023-01-11T21:20:32.9767243Z UCX_COMMIT=31e74cac7bee0ef66bef2af72e7d86d9c282e5ab
2023-01-11T21:20:32.9767521Z GITHUB_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:32.9767974Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
2023-01-11T21:20:32.9768375Z GITHUB_ACTION_REF=
2023-01-11T21:20:32.9768708Z NCCL_VERSION=2.13.4-1
2023-01-11T21:20:32.9768894Z GITHUB_ACTION=__self
2023-01-11T21:20:32.9769060Z VALGRIND=ON
2023-01-11T21:20:32.9769243Z GITHUB_REF_PROTECTED=false
2023-01-11T21:20:32.9769631Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2023-01-11T21:20:32.9769920Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2023-01-11T21:20:32.9770197Z ***
2023-01-11T21:20:32.9770364Z INSTALLED_VISION=yes
2023-01-11T21:20:32.9770545Z NVARCH=x86_64
2023-01-11T21:20:32.9770757Z NV_LIBCUSPARSE_DEV_VERSION=11.7.3.50-1
2023-01-11T21:20:32.9770963Z HOME=/var/lib/jenkins
2023-01-11T21:20:32.9771372Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9771672Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2023-01-11T21:20:32.9771877Z NVIDIA_CUDA_END_OF_LIFE=1
2023-01-11T21:20:32.9772079Z GITHUB_ACTION_REPOSITORY=
2023-01-11T21:20:32.9772262Z GITHUB_REF_TYPE=tag
2023-01-11T21:20:32.9772482Z NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:32.9772689Z GITHUB_RETENTION_DAYS=90
2023-01-11T21:20:32.9772964Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2023-01-11T21:20:32.9773275Z NV_LIBNCCL_PACKAGE=libnccl2=2.13.4-1+cuda11.7
2023-01-11T21:20:32.9773698Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9774004Z DEBIAN_FRONTEND=noninteractive
2023-01-11T21:20:32.9774257Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
2023-01-11T21:20:32.9774484Z GITHUB_REF=refs/tags/ciflow/trunk/91627
2023-01-11T21:20:32.9774714Z NV_CUDA_LIB_VERSION=11.7.0-1
2023-01-11T21:20:32.9774938Z GITHUB_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9775165Z INSTALLED_PROTOBUF=yes
2023-01-11T21:20:32.9775362Z GITHUB_REPOSITORY_ID=65600975
2023-01-11T21:20:32.9775545Z GITHUB_RUN_ID=3896346758
2023-01-11T21:20:32.9775806Z NV_LIBNPP_PACKAGE=libnpp-11-7=11.7.3.21-1
2023-01-11T21:20:32.9776030Z NV_LIBNCCL_PACKAGE_NAME=libnccl2
2023-01-11T21:20:32.9776238Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs
2023-01-11T21:20:32.9776464Z NV_NVTX_VERSION=11.7.50-1
2023-01-11T21:20:32.9776666Z CONTINUE_THROUGH_ERROR=False
2023-01-11T21:20:32.9776880Z GITHUB_SERVER_URL=https://github.com
2023-01-11T21:20:32.9777087Z MAX_JOBS=6
2023-01-11T21:20:32.9777268Z GITHUB_ACTOR_ID=54816060
2023-01-11T21:20:32.9777479Z NV_LIBCUBLAS_VERSION=11.10.1.25-1
2023-01-11T21:20:32.9777763Z NV_LIBCUBLAS_PACKAGE=libcublas-11-7=11.10.1.25-1
2023-01-11T21:20:32.9778134Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2023-01-11T21:20:32.9778382Z UCX_HOME=/usr
2023-01-11T21:20:32.9778570Z PYTORCH_RETRY_TEST_CASES=1
2023-01-11T21:20:32.9778816Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2023-01-11T21:20:32.9779089Z BASE_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9779337Z NV_CUDA_CUDART_DEV_VERSION=11.7.60-1
2023-01-11T21:20:32.9779589Z PR_BODY=
2023-01-11T21:20:32.9779763Z GITHUB_BASE_REF=
2023-01-11T21:20:32.9779924Z TERM=xterm
2023-01-11T21:20:32.9780113Z TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T21:20:32.9780303Z XLA_CUDA=
2023-01-11T21:20:32.9780490Z NV_NVML_DEV_VERSION=11.7.50-1
2023-01-11T21:20:32.9780697Z TORCH_CUDA_ARCH_LIST=Maxwell
2023-01-11T21:20:32.9780893Z CUDA_VERSION=11.7.0
2023-01-11T21:20:32.9781138Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-7
2023-01-11T21:20:32.9781363Z OPENSSL_ROOT_DIR=/opt/openssl
2023-01-11T21:20:32.9781784Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9782070Z GITHUB_JOB=test
2023-01-11T21:20:32.9782264Z SCCACHE_S3_KEY_PREFIX=trunk
2023-01-11T21:20:32.9783058Z COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation
2023-01-11T21:20:32.9783535Z NVIDIA_DRIVER_CAPABILITIES=compute,utility
2023-01-11T21:20:32.9783738Z NUM_TEST_SHARDS=1
2023-01-11T21:20:32.9783913Z PR_NUMBER=
2023-01-11T21:20:32.9784325Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_b048ddbb-aaa9-4b9f-9784-3beb8ea60621
2023-01-11T21:20:32.9784603Z SHLVL=1
2023-01-11T21:20:32.9784862Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-7
2023-01-11T21:20:32.9785102Z GITHUB_REPOSITORY=pytorch/pytorch
2023-01-11T21:20:32.9785999Z NVIDIA_REQUIRE_CUDA=cuda>=11.7 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511
2023-01-11T21:20:32.9786847Z NV_LIBNPP_DEV_VERSION=11.7.3.21-1
2023-01-11T21:20:32.9787080Z SHA1=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9787288Z GITHUB_EVENT_NAME=push
2023-01-11T21:20:32.9787546Z NV_CUDA_CUDART_VERSION=11.7.60-1
2023-01-11T21:20:32.9787807Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2023-01-11T21:20:32.9788008Z GITHUB_RUN_NUMBER=22986
2023-01-11T21:20:32.9788197Z GITHUB_WORKFLOW=trunk
2023-01-11T21:20:32.9788503Z PATH=/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:32.9788829Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:32.9789093Z GITHUB_WORKFLOW_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:32.9789451Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:20:32.9789816Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:32.9790023Z _=/usr/bin/env
2023-01-11T21:20:32.9790235Z + echo 'Testing pytorch'
2023-01-11T21:20:32.9790407Z Testing pytorch
2023-01-11T21:20:32.9790605Z + export LANG=C.UTF-8
2023-01-11T21:20:32.9790799Z + LANG=C.UTF-8
2023-01-11T21:20:32.9859023Z + PR_NUMBER=
2023-01-11T21:20:32.9859400Z + [[ nogpu_NO_AVX2 == \d\e\f\a\u\l\t ]]
2023-01-11T21:20:32.9859771Z + [[ nogpu_NO_AVX2 == \d\i\s\t\r\i\b\u\t\e\d ]]
2023-01-11T21:20:32.9860106Z + [[ nogpu_NO_AVX2 == \s\l\o\w ]]
2023-01-11T21:20:32.9860681Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *slow-gradcheck* ]]
2023-01-11T21:20:32.9861290Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T21:20:32.9861758Z + export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2023-01-11T21:20:32.9862008Z + PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2023-01-11T21:20:32.9862325Z + [[ nogpu_NO_AVX2 == *crossref* ]]
2023-01-11T21:20:32.9862667Z + [[ nogpu_NO_AVX2 == *dynamo* ]]
2023-01-11T21:20:32.9862856Z + [[ nogpu_NO_AVX2 == *inductor* ]]
2023-01-11T21:20:32.9863147Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *rocm* ]]
2023-01-11T21:20:32.9863477Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *-bazel-* ]]
2023-01-11T21:20:32.9863759Z + pip_install --user ninja==1.10.2
2023-01-11T21:20:32.9864039Z + pip install --progress-bar off --user ninja==1.10.2
2023-01-11T21:20:33.3740813Z Collecting ninja==1.10.2
2023-01-11T21:20:33.3917303Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2023-01-11T21:20:33.9705544Z Installing collected packages: ninja
2023-01-11T21:20:33.9776768Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2023-01-11T21:20:33.9777721Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2023-01-11T21:20:33.9826631Z Successfully installed ninja-1.10.2
2023-01-11T21:20:34.0468287Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:34.0468994Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:34.0469839Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *asan* ]]
2023-01-11T21:20:34.0470243Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-tsan* ]]
2023-01-11T21:20:34.0470512Z + [[ nogpu_NO_AVX2 == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2023-01-11T21:20:34.0470744Z + export ATEN_CPU_CAPABILITY=default
2023-01-11T21:20:34.0470946Z + ATEN_CPU_CAPABILITY=default
2023-01-11T21:20:34.0476886Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *tbb* ]]
2023-01-11T21:20:34.0488950Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *libtorch* ]]
2023-01-11T21:20:34.0489567Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-bazel-* ]]
2023-01-11T21:20:34.0489914Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-tsan* ]]
2023-01-11T21:20:34.0491937Z + cd test
2023-01-11T21:20:34.0508171Z + python -c 'import torch; print(torch.__config__.show())'
2023-01-11T21:20:35.3558287Z PyTorch built with:
2023-01-11T21:20:35.3558834Z   - GCC 7.5
2023-01-11T21:20:35.3559233Z   - C++ Version: 201703
2023-01-11T21:20:35.3559707Z   - Intel(R) oneAPI Math Kernel Library Version 2022.0-Product Build 20211112 for Intel(R) 64 architecture applications
2023-01-11T21:20:35.3560123Z   - Intel(R) MKL-DNN v2.7.2 (Git Hash fbec3e25a559ee252022ae066817b204e106a6ba)
2023-01-11T21:20:35.3560427Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2023-01-11T21:20:35.3560705Z   - LAPACK is enabled (usually provided by MKL)
2023-01-11T21:20:35.3560949Z   - NNPACK is enabled
2023-01-11T21:20:35.3561171Z   - CPU capability usage: NO AVX
2023-01-11T21:20:35.3563702Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Werror -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, FORCE_FALLBACK_CUDA_MPI=1, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=ON, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 
2023-01-11T21:20:35.3565840Z 
2023-01-11T21:20:35.9374298Z + cd test
2023-01-11T21:20:35.9374753Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2023-01-11T21:20:37.0657033Z ATen/Parallel:
2023-01-11T21:20:37.0668984Z 	at::get_num_threads() : 4
2023-01-11T21:20:37.0669257Z 	at::get_num_interop_threads() : 4
2023-01-11T21:20:37.0669473Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2023-01-11T21:20:37.0669771Z 	omp_get_max_threads() : 4
2023-01-11T21:20:37.0670290Z Intel(R) oneAPI Math Kernel Library Version 2022.0-Product Build 20211112 for Intel(R) 64 architecture applications
2023-01-11T21:20:37.0670578Z 	mkl_get_max_threads() : 4
2023-01-11T21:20:37.0670903Z Intel(R) MKL-DNN v2.7.2 (Git Hash fbec3e25a559ee252022ae066817b204e106a6ba)
2023-01-11T21:20:37.0671394Z std::thread::hardware_concurrency() : 8
2023-01-11T21:20:37.0671607Z Environment variables:
2023-01-11T21:20:37.0671801Z 	OMP_NUM_THREADS : [not set]
2023-01-11T21:20:37.0671982Z 	MKL_NUM_THREADS : [not set]
2023-01-11T21:20:37.0672182Z ATen parallel backend: OpenMP
2023-01-11T21:20:37.0672307Z 
2023-01-11T21:20:37.2787515Z + [[ nogpu_NO_AVX2 == *backward* ]]
2023-01-11T21:20:37.2787867Z + [[ nogpu_NO_AVX2 == *xla* ]]
2023-01-11T21:20:37.2788226Z + [[ nogpu_NO_AVX2 == \j\i\t\_\l\e\g\a\c\y ]]
2023-01-11T21:20:37.2788901Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *libtorch* ]]
2023-01-11T21:20:37.2789144Z + [[ nogpu_NO_AVX2 == distributed ]]
2023-01-11T21:20:37.2789352Z + [[ nogpu_NO_AVX2 == deploy ]]
2023-01-11T21:20:37.2789569Z + [[ nogpu_NO_AVX2 == *inductor_distributed* ]]
2023-01-11T21:20:37.2789822Z + [[ nogpu_NO_AVX2 == *dynamo* ]]
2023-01-11T21:20:37.2790018Z + [[ nogpu_NO_AVX2 == *dynamo* ]]
2023-01-11T21:20:37.2790236Z + [[ nogpu_NO_AVX2 == *inductor_huggingface* ]]
2023-01-11T21:20:37.2790478Z + [[ nogpu_NO_AVX2 == *inductor_timm* ]]
2023-01-11T21:20:37.2790691Z + [[ nogpu_NO_AVX2 == *inductor_torchbench* ]]
2023-01-11T21:20:37.2791002Z + [[ nogpu_NO_AVX2 == *inductor* ]]
2023-01-11T21:20:37.2791183Z + [[ 1 == 1 ]]
2023-01-11T21:20:37.2791414Z + [[ 1 -gt 1 ]]
2023-01-11T21:20:37.2791670Z + [[ 1 == 2 ]]
2023-01-11T21:20:37.2791881Z + [[ 1 -gt 2 ]]
2023-01-11T21:20:37.2792141Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *vulkan* ]]
2023-01-11T21:20:37.2792473Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-bazel-* ]]
2023-01-11T21:20:37.2792857Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-mobile-lightweight-dispatch* ]]
2023-01-11T21:20:37.2793207Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-tsan* ]]
2023-01-11T21:20:37.2793438Z + [[ nogpu_NO_AVX2 = docs_test ]]
2023-01-11T21:20:37.2793643Z + [[ nogpu_NO_AVX2 == *functorch* ]]
2023-01-11T21:20:37.2793832Z + install_torchvision
2023-01-11T21:20:37.2794008Z + local commit
2023-01-11T21:20:37.2794190Z ++ get_pinned_commit vision
2023-01-11T21:20:37.2794396Z ++ cat .github/ci_commit_pins/vision.txt
2023-01-11T21:20:37.2818656Z + commit=32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:37.2819461Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:37.2820036Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:37.6142964Z Collecting git+https://github.com/pytorch/vision.git@32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:37.6148906Z   Cloning https://github.com/pytorch/vision.git (to revision 32d254bbfcf14975f846765775584e61ef25a5bc) to /tmp/pip-req-build-fx6dww2u
2023-01-11T21:20:37.6317253Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-fx6dww2u
2023-01-11T21:20:39.8634918Z   Running command git rev-parse -q --verify 'sha^32d254bbfcf14975f846765775584e61ef25a5bc'
2023-01-11T21:20:39.8653255Z   Running command git fetch -q https://github.com/pytorch/vision.git 32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:40.8144553Z   Running command git checkout -q 32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:41.4138317Z   Resolved https://github.com/pytorch/vision.git to commit 32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:43.2827923Z   Preparing metadata (setup.py) ... [?25l- done
2023-01-11T21:20:43.2912257Z [?25hRequirement already satisfied: typing_extensions in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (4.4.0)
2023-01-11T21:20:43.2917626Z Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (1.21.2)
2023-01-11T21:20:43.2921828Z Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (2.28.1)
2023-01-11T21:20:43.2928893Z Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (2.0.0a0+git8419ddd)
2023-01-11T21:20:43.2937549Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (9.3.0)
2023-01-11T21:20:43.3112356Z Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (2.0.4)
2023-01-11T21:20:43.3117690Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (1.26.13)
2023-01-11T21:20:43.3123445Z Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (3.4)
2023-01-11T21:20:43.3130432Z Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (2022.12.7)
2023-01-11T21:20:43.3170634Z Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->torchvision==0.15.0a0+32d254b) (1.11.1)
2023-01-11T21:20:43.3174146Z Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->torchvision==0.15.0a0+32d254b) (2.6.3)
2023-01-11T21:20:43.3462563Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->torchvision==0.15.0a0+32d254b) (1.2.1)
2023-01-11T21:20:43.3513967Z Building wheels for collected packages: torchvision
2023-01-11T21:21:36.2454400Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done
2023-01-11T21:21:36.2487176Z [?25h  Created wheel for torchvision: filename=torchvision-0.15.0a0+32d254b-cp310-cp310-linux_x86_64.whl size=1003187 sha256=cb53fcc86fcc15f7231c78e9a501fda3a377c068517f98a1c73f50f160945a01
2023-01-11T21:21:36.2487939Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/ca/33/ae/1f7c8972d058d079236e7ca0a30b53b050afb405820b9ed787
2023-01-11T21:21:36.2522966Z Successfully built torchvision
2023-01-11T21:21:36.7755660Z Installing collected packages: torchvision
2023-01-11T21:21:37.1560251Z Successfully installed torchvision-0.15.0a0+32d254b
2023-01-11T21:21:37.2614615Z + install_triton
2023-01-11T21:21:37.2614875Z + local commit
2023-01-11T21:21:37.2615069Z + [[ nogpu_NO_AVX2 == *rocm* ]]
2023-01-11T21:21:37.2617792Z ++ get_pinned_commit triton
2023-01-11T21:21:37.2617994Z ++ cat .github/ci_commit_pins/triton.txt
2023-01-11T21:21:37.2653721Z + commit=0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:37.2654382Z + pip_install --user git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T21:21:37.2654972Z + pip install --progress-bar off --user git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T21:21:37.6099665Z Collecting git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T21:21:37.6104474Z   Cloning https://github.com/openai/triton (to revision 0d7e7532279e45672555e344646f5c19c3972331) to /tmp/pip-req-build-q7ry5r63
2023-01-11T21:21:37.6121323Z   Running command git clone --filter=blob:none --quiet https://github.com/openai/triton /tmp/pip-req-build-q7ry5r63
2023-01-11T21:21:38.3260639Z   Running command git rev-parse -q --verify 'sha^0d7e7532279e45672555e344646f5c19c3972331'
2023-01-11T21:21:38.3278917Z   Running command git fetch -q https://github.com/openai/triton 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:38.6653042Z   Running command git checkout -q 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:39.0000353Z   Resolved https://github.com/openai/triton to commit 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:39.0001397Z   Running command git submodule update --init --recursive -q
2023-01-11T21:21:39.5005870Z   Preparing metadata (setup.py) ... [?25l- done
2023-01-11T21:21:39.6469957Z [?25hCollecting cmake
2023-01-11T21:21:39.6691355Z   Downloading cmake-3.25.0-py2.py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.7 MB)
2023-01-11T21:21:39.9501940Z Collecting filelock
2023-01-11T21:21:39.9548426Z   Downloading filelock-3.9.0-py3-none-any.whl (9.7 kB)
2023-01-11T21:21:39.9587416Z Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from triton==2.0.0) (2.0.0a0+git8419ddd)
2023-01-11T21:21:39.9761105Z Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->triton==2.0.0) (1.11.1)
2023-01-11T21:21:39.9763978Z Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->triton==2.0.0) (2.6.3)
2023-01-11T21:21:39.9767140Z Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch->triton==2.0.0) (4.4.0)
2023-01-11T21:21:39.9907929Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->triton==2.0.0) (1.2.1)
2023-01-11T21:21:39.9955670Z Building wheels for collected packages: triton
2023-01-11T21:22:21.3448030Z   Building wheel for triton (setup.py) ... [?25l- \ | / - \ | / - \ | / - done
2023-01-11T21:22:21.3813075Z [?25h  Created wheel for triton: filename=triton-2.0.0-cp310-cp310-linux_x86_64.whl size=15377935 sha256=1afa21e5959155ed2bd815b9f7982252c103b5a50cf26a592ee40063a2f21ae8
2023-01-11T21:22:21.3813552Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/3f/1d/23/1c2bc47d618a44f9c949aea4b7e355e737a1f1ed208f009295
2023-01-11T21:22:21.3830265Z Successfully built triton
2023-01-11T21:22:21.9768130Z Installing collected packages: cmake, filelock, triton
2023-01-11T21:22:23.0445749Z Successfully installed cmake-3.25.0 filelock-3.9.0 triton-2.0.0
2023-01-11T21:22:23.1854107Z + pip_install --user jinja2
2023-01-11T21:22:23.1854621Z + pip install --progress-bar off --user jinja2
2023-01-11T21:22:23.6080539Z Collecting jinja2
2023-01-11T21:22:23.6280251Z   Downloading Jinja2-3.1.2-py3-none-any.whl (133 kB)
2023-01-11T21:22:23.6439124Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2) (2.1.1)
2023-01-11T21:22:24.2461970Z Installing collected packages: jinja2
2023-01-11T21:22:24.3269914Z Successfully installed jinja2-3.1.2
2023-01-11T21:22:24.4304798Z + install_monkeytype
2023-01-11T21:22:24.4305052Z + pip_install MonkeyType
2023-01-11T21:22:24.4305475Z + pip install --progress-bar off MonkeyType
2023-01-11T21:22:24.8190636Z Collecting MonkeyType
2023-01-11T21:22:24.8389865Z   Downloading MonkeyType-22.2.0-py3-none-any.whl (37 kB)
2023-01-11T21:22:24.9005126Z Collecting libcst>=0.3.7
2023-01-11T21:22:24.9071371Z   Downloading libcst-0.4.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB)
2023-01-11T21:22:24.9452578Z Requirement already satisfied: mypy-extensions in /opt/conda/lib/python3.10/site-packages (from MonkeyType) (0.4.3)
2023-01-11T21:22:24.9624800Z Requirement already satisfied: pyyaml>=5.2 in /opt/conda/lib/python3.10/site-packages (from libcst>=0.3.7->MonkeyType) (6.0)
2023-01-11T21:22:24.9631407Z Requirement already satisfied: typing-extensions>=3.7.4.2 in /opt/conda/lib/python3.10/site-packages (from libcst>=0.3.7->MonkeyType) (4.4.0)
2023-01-11T21:22:24.9778591Z Collecting typing-inspect>=0.4.0
2023-01-11T21:22:24.9837999Z   Downloading typing_inspect-0.8.0-py3-none-any.whl (8.7 kB)
2023-01-11T21:22:25.6148815Z Installing collected packages: typing-inspect, libcst, MonkeyType
2023-01-11T21:22:26.1451348Z Successfully installed MonkeyType-22.2.0 libcst-0.4.9 typing-inspect-0.8.0
2023-01-11T21:22:26.2466711Z + test_python
2023-01-11T21:22:26.2467499Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --verbose
2023-01-11T21:22:27.9231267Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:28.0857736Z Ignoring disabled issues:  []
2023-01-11T21:22:28.1717226Z /var/lib/jenkins/workspace/test/run_test.py:1169: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
2023-01-11T21:22:28.1717707Z   if torch.version.cuda is not None and LooseVersion(torch.version.cuda) >= "11.6":
2023-01-11T21:22:28.1756220Z Selected tests:
2023-01-11T21:22:28.1756529Z  backends/xeon/test_launch
2023-01-11T21:22:28.1756856Z  benchmark_utils/test_benchmark_utils
2023-01-11T21:22:28.1758523Z  distributions/test_distributions
2023-01-11T21:22:28.1758876Z  dynamo/test_aot_autograd
2023-01-11T21:22:28.1759166Z  dynamo/test_aot_cudagraphs
2023-01-11T21:22:28.1759459Z  dynamo/test_comptime
2023-01-11T21:22:28.1759743Z  dynamo/test_dynamic_shapes
2023-01-11T21:22:28.1760170Z  dynamo/test_export
2023-01-11T21:22:28.1760467Z  dynamo/test_export_mutations
2023-01-11T21:22:28.1760946Z  dynamo/test_functions
2023-01-11T21:22:28.1761302Z  dynamo/test_global
2023-01-11T21:22:28.1761650Z  dynamo/test_global_declaration
2023-01-11T21:22:28.1762026Z  dynamo/test_minifier
2023-01-11T21:22:28.1762326Z  dynamo/test_misc
2023-01-11T21:22:28.1762643Z  dynamo/test_model_output
2023-01-11T21:22:28.1762970Z  dynamo/test_modules
2023-01-11T21:22:28.1763281Z  dynamo/test_nops
2023-01-11T21:22:28.1763591Z  dynamo/test_optimizations
2023-01-11T21:22:28.1763934Z  dynamo/test_optimizers
2023-01-11T21:22:28.1764276Z  dynamo/test_python_autograd
2023-01-11T21:22:28.1764592Z  dynamo/test_recompile_ux
2023-01-11T21:22:28.1764883Z  dynamo/test_replay_record
2023-01-11T21:22:28.1765194Z  dynamo/test_repros
2023-01-11T21:22:28.1765519Z  dynamo/test_skip_non_tensor
2023-01-11T21:22:28.1765839Z  dynamo/test_subgraphs
2023-01-11T21:22:28.1766163Z  dynamo/test_torchxla_integration
2023-01-11T21:22:28.1766523Z  dynamo/test_torchxla_num_output
2023-01-11T21:22:28.1766818Z  dynamo/test_torchxla_util
2023-01-11T21:22:28.1767128Z  dynamo/test_unspec
2023-01-11T21:22:28.1767427Z  dynamo/test_verify_correctness
2023-01-11T21:22:28.1783252Z  inductor/test_minifier
2023-01-11T21:22:28.1783553Z  inductor/test_perf
2023-01-11T21:22:28.1783884Z  inductor/test_smoke
2023-01-11T21:22:28.1784131Z  inductor/test_torchinductor
2023-01-11T21:22:28.1784342Z  inductor/test_torchinductor_opinfo
2023-01-11T21:22:28.1784677Z  lazy/test_bindings
2023-01-11T21:22:28.1784983Z  lazy/test_debug_util
2023-01-11T21:22:28.1785274Z  lazy/test_extract_compiled_graph
2023-01-11T21:22:28.1785603Z  lazy/test_meta_kernel
2023-01-11T21:22:28.1785902Z  lazy/test_reuse_ir
2023-01-11T21:22:28.1786142Z  lazy/test_step_closures
2023-01-11T21:22:28.1786335Z  lazy/test_ts_opinfo
2023-01-11T21:22:28.1786520Z  nn/test_convolution
2023-01-11T21:22:28.1786687Z  nn/test_dropout
2023-01-11T21:22:28.1786866Z  nn/test_embedding
2023-01-11T21:22:28.1787039Z  nn/test_init
2023-01-11T21:22:28.1787202Z  nn/test_lazy_modules
2023-01-11T21:22:28.1787387Z  nn/test_module_hooks
2023-01-11T21:22:28.1787580Z  nn/test_multihead_attention
2023-01-11T21:22:28.1787765Z  nn/test_packed_sequence
2023-01-11T21:22:28.1787965Z  nn/test_parametrization
2023-01-11T21:22:28.1788155Z  nn/test_pooling
2023-01-11T21:22:28.1788692Z  nn/test_pruning
2023-01-11T21:22:28.1789062Z  profiler/test_memory_profiler
2023-01-11T21:22:28.1789396Z  profiler/test_profiler
2023-01-11T21:22:28.1789587Z  profiler/test_profiler_tree
2023-01-11T21:22:28.1789783Z  test_ao_sparsity
2023-01-11T21:22:28.1790038Z  test_autocast
2023-01-11T21:22:28.1790211Z  test_autograd
2023-01-11T21:22:28.1790389Z  test_binary_ufuncs
2023-01-11T21:22:28.1790575Z  test_bundled_inputs
2023-01-11T21:22:28.1790757Z  test_comparison_utils
2023-01-11T21:22:28.1791022Z  test_complex
2023-01-11T21:22:28.1791304Z  test_cpp_api_parity
2023-01-11T21:22:28.1791683Z  test_cpp_extensions_aot_ninja
2023-01-11T21:22:28.1792050Z  test_cpp_extensions_aot_no_ninja
2023-01-11T21:22:28.1792421Z  test_cpp_extensions_jit
2023-01-11T21:22:28.1792820Z  test_cpp_extensions_open_device_registration
2023-01-11T21:22:28.1793163Z  test_cuda
2023-01-11T21:22:28.1793470Z  test_cuda_nvml_based_avail
2023-01-11T21:22:28.1793697Z  test_cuda_primary_ctx
2023-01-11T21:22:28.1793868Z  test_cuda_sanitizer
2023-01-11T21:22:28.1794157Z  test_cuda_trace
2023-01-11T21:22:28.1794339Z  test_dataloader
2023-01-11T21:22:28.1794565Z  test_datapipe
2023-01-11T21:22:28.1794848Z  test_decomp
2023-01-11T21:22:28.1795143Z  test_deploy
2023-01-11T21:22:28.1795417Z  test_dispatch
2023-01-11T21:22:28.1795713Z  test_dlpack
2023-01-11T21:22:28.1796020Z  test_dynamic_shapes
2023-01-11T21:22:28.1796285Z  test_expanded_weights
2023-01-11T21:22:28.1796602Z  test_fake_tensor
2023-01-11T21:22:28.1796901Z  test_foreach
2023-01-11T21:22:28.1797192Z  test_function_schema
2023-01-11T21:22:28.1797523Z  test_functional_autograd_benchmark
2023-01-11T21:22:28.1797847Z  test_functional_optim
2023-01-11T21:22:28.1798169Z  test_functionalization
2023-01-11T21:22:28.1798490Z  test_futures
2023-01-11T21:22:28.1798778Z  test_fx
2023-01-11T21:22:28.1799059Z  test_fx_experimental
2023-01-11T21:22:28.1799383Z  test_fx_passes
2023-01-11T21:22:28.1799713Z  test_fx_reinplace_pass
2023-01-11T21:22:28.1799977Z  test_hub
2023-01-11T21:22:28.1800147Z  test_import_stats
2023-01-11T21:22:28.1800332Z  test_indexing
2023-01-11T21:22:28.1800486Z  test_itt
2023-01-11T21:22:28.1800643Z  test_jit
2023-01-11T21:22:28.1800809Z  test_jit_autocast
2023-01-11T21:22:28.1800974Z  test_jit_cuda_fuser
2023-01-11T21:22:28.1801154Z  test_jit_disabled
2023-01-11T21:22:28.1801331Z  test_jit_fuser_te
2023-01-11T21:22:28.1801495Z  test_jit_llga_fuser
2023-01-11T21:22:28.1801673Z  test_jiterator
2023-01-11T21:22:28.1801849Z  test_legacy_vmap
2023-01-11T21:22:28.1802008Z  test_license
2023-01-11T21:22:28.1802174Z  test_linalg
2023-01-11T21:22:28.1802341Z  test_logging
2023-01-11T21:22:28.1802494Z  test_masked
2023-01-11T21:22:28.1802665Z  test_maskedtensor
2023-01-11T21:22:28.1802844Z  test_matmul_cuda
2023-01-11T21:22:28.1803002Z  test_meta
2023-01-11T21:22:28.1803170Z  test_mkl_verbose
2023-01-11T21:22:28.1803338Z  test_mkldnn
2023-01-11T21:22:28.1803501Z  test_mkldnn_fusion
2023-01-11T21:22:28.1803683Z  test_mkldnn_verbose
2023-01-11T21:22:28.1803867Z  test_mobile_optimizer
2023-01-11T21:22:28.1804042Z  test_model_dump
2023-01-11T21:22:28.1804220Z  test_module_init
2023-01-11T21:22:28.1804390Z  test_modules
2023-01-11T21:22:28.1804544Z  test_monitor
2023-01-11T21:22:28.1804721Z  test_multiprocessing
2023-01-11T21:22:28.1804922Z  test_multiprocessing_spawn
2023-01-11T21:22:28.1805102Z  test_namedtensor
2023-01-11T21:22:28.1805291Z  test_namedtuple_return_api
2023-01-11T21:22:28.1805486Z  test_native_functions
2023-01-11T21:22:28.1805654Z  test_native_mha
2023-01-11T21:22:28.1805832Z  test_nestedtensor
2023-01-11T21:22:28.1806002Z  test_nn
2023-01-11T21:22:28.1806163Z  test_numba_integration
2023-01-11T21:22:28.1806350Z  test_numpy_interop
2023-01-11T21:22:28.1806532Z  test_nvfuser_dynamo
2023-01-11T21:22:28.1806715Z  test_nvfuser_frontend
2023-01-11T21:22:28.1806880Z  test_openmp
2023-01-11T21:22:28.1807069Z  test_ops
2023-01-11T21:22:28.1807248Z  test_ops_fwd_gradients
2023-01-11T21:22:28.1807421Z  test_ops_gradients
2023-01-11T21:22:28.1807593Z  test_ops_jit
2023-01-11T21:22:28.1807760Z  test_optim
2023-01-11T21:22:28.1808007Z  test_overrides
2023-01-11T21:22:28.1808184Z  test_package
2023-01-11T21:22:28.1808368Z  test_per_overload_api
2023-01-11T21:22:28.1808536Z  test_prims
2023-01-11T21:22:28.1808711Z  test_proxy_tensor
2023-01-11T21:22:28.1808896Z  test_pruning_op
2023-01-11T21:22:28.1809068Z  test_public_bindings
2023-01-11T21:22:28.1809261Z  test_python_dispatch
2023-01-11T21:22:28.1809437Z  test_pytree
2023-01-11T21:22:28.1809601Z  test_quantization
2023-01-11T21:22:28.1809781Z  test_reductions
2023-01-11T21:22:28.1809970Z  test_scatter_gather_ops
2023-01-11T21:22:28.1810149Z  test_schema_check
2023-01-11T21:22:28.1810339Z  test_serialization
2023-01-11T21:22:28.1810549Z  test_set_default_mobile_cpu_allocator
2023-01-11T21:22:28.1810740Z  test_shape_ops
2023-01-11T21:22:28.1810924Z  test_show_pickle
2023-01-11T21:22:28.1811110Z  test_sort_and_select
2023-01-11T21:22:28.1811274Z  test_sparse
2023-01-11T21:22:28.1811453Z  test_sparse_csr
2023-01-11T21:22:28.1811640Z  test_spectral_ops
2023-01-11T21:22:28.1811886Z  test_stateless
2023-01-11T21:22:28.1812063Z  test_subclass
2023-01-11T21:22:28.1812250Z  test_tensor_creation_ops
2023-01-11T21:22:28.1812426Z  test_tensorboard
2023-01-11T21:22:28.1812606Z  test_tensorexpr
2023-01-11T21:22:28.1812795Z  test_tensorexpr_pybind
2023-01-11T21:22:28.1812965Z  test_testing
2023-01-11T21:22:28.1813137Z  test_torch
2023-01-11T21:22:28.1813313Z  test_transformers
2023-01-11T21:22:28.1813479Z  test_type_hints
2023-01-11T21:22:28.1813656Z  test_type_info
2023-01-11T21:22:28.1813840Z  test_type_promotion
2023-01-11T21:22:28.1814012Z  test_unary_ufuncs
2023-01-11T21:22:28.1814185Z  test_utils
2023-01-11T21:22:28.1814356Z  test_view_ops
2023-01-11T21:22:28.1814513Z  test_vulkan
2023-01-11T21:22:28.1814676Z  test_weak
2023-01-11T21:22:28.1814857Z  test_xnnpack_integration
2023-01-11T21:22:28.1815026Z  doctests
2023-01-11T21:22:28.2358761Z Prioritized test from test file changes.
2023-01-11T21:22:28.2359257Z reordering tests for PR:
2023-01-11T21:22:28.2360626Z prioritized: ['dynamo/test_export', 'dynamo/test_misc', 'dynamo/test_optimizations', 'dynamo/test_repros', 'dynamo/test_torchxla_integration', 'dynamo/test_unspec', 'inductor/test_torchinductor', 'inductor/test_torchinductor_opinfo', 'profiler/test_profiler_tree', 'test_ao_sparsity', 'test_autograd', 'test_cpp_extensions_jit', 'test_cuda', 'test_fake_tensor', 'test_foreach', 'test_function_schema', 'test_jit', 'test_masked', 'test_meta', 'test_nn', 'test_overrides', 'test_proxy_tensor', 'test_public_bindings', 'test_python_dispatch', 'test_scatter_gather_ops', 'test_sort_and_select', 'test_sparse', 'test_sparse_csr', 'test_stateless', 'test_testing', 'test_torch', 'test_transformers', 'test_utils']
2023-01-11T21:22:28.2366731Z the rest: ['backends/xeon/test_launch', 'benchmark_utils/test_benchmark_utils', 'distributions/test_distributions', 'dynamo/test_aot_autograd', 'dynamo/test_aot_cudagraphs', 'dynamo/test_comptime', 'dynamo/test_dynamic_shapes', 'dynamo/test_export_mutations', 'dynamo/test_functions', 'dynamo/test_global', 'dynamo/test_global_declaration', 'dynamo/test_minifier', 'dynamo/test_model_output', 'dynamo/test_modules', 'dynamo/test_nops', 'dynamo/test_optimizers', 'dynamo/test_python_autograd', 'dynamo/test_recompile_ux', 'dynamo/test_replay_record', 'dynamo/test_skip_non_tensor', 'dynamo/test_subgraphs', 'dynamo/test_torchxla_num_output', 'dynamo/test_torchxla_util', 'dynamo/test_verify_correctness', 'inductor/test_minifier', 'inductor/test_perf', 'inductor/test_smoke', 'lazy/test_bindings', 'lazy/test_debug_util', 'lazy/test_extract_compiled_graph', 'lazy/test_meta_kernel', 'lazy/test_reuse_ir', 'lazy/test_step_closures', 'lazy/test_ts_opinfo', 'nn/test_convolution', 'nn/test_dropout', 'nn/test_embedding', 'nn/test_init', 'nn/test_lazy_modules', 'nn/test_module_hooks', 'nn/test_multihead_attention', 'nn/test_packed_sequence', 'nn/test_parametrization', 'nn/test_pooling', 'nn/test_pruning', 'profiler/test_memory_profiler', 'profiler/test_profiler', 'test_autocast', 'test_binary_ufuncs', 'test_bundled_inputs', 'test_comparison_utils', 'test_complex', 'test_cpp_api_parity', 'test_cpp_extensions_aot_ninja', 'test_cpp_extensions_aot_no_ninja', 'test_cpp_extensions_open_device_registration', 'test_cuda_nvml_based_avail', 'test_cuda_primary_ctx', 'test_cuda_sanitizer', 'test_cuda_trace', 'test_dataloader', 'test_datapipe', 'test_decomp', 'test_deploy', 'test_dispatch', 'test_dlpack', 'test_dynamic_shapes', 'test_expanded_weights', 'test_functional_autograd_benchmark', 'test_functional_optim', 'test_functionalization', 'test_futures', 'test_fx', 'test_fx_experimental', 'test_fx_passes', 'test_fx_reinplace_pass', 'test_hub', 'test_import_stats', 'test_indexing', 'test_itt', 'test_jit_autocast', 'test_jit_cuda_fuser', 'test_jit_disabled', 'test_jit_fuser_te', 'test_jit_llga_fuser', 'test_jiterator', 'test_legacy_vmap', 'test_license', 'test_linalg', 'test_logging', 'test_maskedtensor', 'test_matmul_cuda', 'test_mkl_verbose', 'test_mkldnn', 'test_mkldnn_fusion', 'test_mkldnn_verbose', 'test_mobile_optimizer', 'test_model_dump', 'test_module_init', 'test_modules', 'test_monitor', 'test_multiprocessing', 'test_multiprocessing_spawn', 'test_namedtensor', 'test_namedtuple_return_api', 'test_native_functions', 'test_native_mha', 'test_nestedtensor', 'test_numba_integration', 'test_numpy_interop', 'test_nvfuser_dynamo', 'test_nvfuser_frontend', 'test_openmp', 'test_ops', 'test_ops_fwd_gradients', 'test_ops_gradients', 'test_ops_jit', 'test_optim', 'test_package', 'test_per_overload_api', 'test_prims', 'test_pruning_op', 'test_pytree', 'test_quantization', 'test_reductions', 'test_schema_check', 'test_serialization', 'test_set_default_mobile_cpu_allocator', 'test_shape_ops', 'test_show_pickle', 'test_spectral_ops', 'test_subclass', 'test_tensor_creation_ops', 'test_tensorboard', 'test_tensorexpr', 'test_tensorexpr_pybind', 'test_type_hints', 'test_type_info', 'test_type_promotion', 'test_unary_ufuncs', 'test_view_ops', 'test_vulkan', 'test_weak', 'test_xnnpack_integration', 'doctests']
2023-01-11T21:22:28.2369982Z 
2023-01-11T21:22:28.2370411Z Downloading https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json
2023-01-11T21:22:28.2553754Z Downloading https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2023-01-11T21:22:28.2732733Z parallel (file granularity) tests:
2023-01-11T21:22:28.2733066Z  dynamo/test_export
2023-01-11T21:22:28.2733418Z  dynamo/test_misc
2023-01-11T21:22:28.2733692Z  dynamo/test_optimizations
2023-01-11T21:22:28.2734030Z  dynamo/test_repros
2023-01-11T21:22:28.2734397Z  dynamo/test_torchxla_integration
2023-01-11T21:22:28.2734668Z  dynamo/test_unspec
2023-01-11T21:22:28.2734862Z  inductor/test_torchinductor
2023-01-11T21:22:28.2735075Z  profiler/test_profiler_tree
2023-01-11T21:22:28.2735255Z  test_ao_sparsity
2023-01-11T21:22:28.2735427Z  test_foreach
2023-01-11T21:22:28.2735605Z  test_function_schema
2023-01-11T21:22:28.2735785Z  test_jit
2023-01-11T21:22:28.2735945Z  test_masked
2023-01-11T21:22:28.2736111Z  test_meta
2023-01-11T21:22:28.2736264Z  test_proxy_tensor
2023-01-11T21:22:28.2736446Z  test_public_bindings
2023-01-11T21:22:28.2736631Z  test_python_dispatch
2023-01-11T21:22:28.2736807Z  test_scatter_gather_ops
2023-01-11T21:22:28.2736996Z  test_sort_and_select
2023-01-11T21:22:28.2737168Z  test_sparse
2023-01-11T21:22:28.2737322Z  test_stateless
2023-01-11T21:22:28.2737492Z  test_testing
2023-01-11T21:22:28.2737661Z  test_transformers
2023-01-11T21:22:28.2737816Z  test_utils
2023-01-11T21:22:28.2738001Z  backends/xeon/test_launch
2023-01-11T21:22:28.2738208Z  benchmark_utils/test_benchmark_utils
2023-01-11T21:22:28.2738399Z  dynamo/test_aot_autograd
2023-01-11T21:22:28.2738593Z  dynamo/test_aot_cudagraphs
2023-01-11T21:22:28.2738781Z  dynamo/test_comptime
2023-01-11T21:22:28.2738957Z  dynamo/test_dynamic_shapes
2023-01-11T21:22:28.2739156Z  dynamo/test_export_mutations
2023-01-11T21:22:28.2739488Z  dynamo/test_functions
2023-01-11T21:22:28.2739658Z  dynamo/test_global
2023-01-11T21:22:28.2739849Z  dynamo/test_global_declaration
2023-01-11T21:22:28.2740043Z  dynamo/test_minifier
2023-01-11T21:22:28.2740216Z  dynamo/test_model_output
2023-01-11T21:22:28.2740402Z  dynamo/test_modules
2023-01-11T21:22:28.2740579Z  dynamo/test_nops
2023-01-11T21:22:28.2740745Z  dynamo/test_optimizers
2023-01-11T21:22:28.2740941Z  dynamo/test_python_autograd
2023-01-11T21:22:28.2741138Z  dynamo/test_recompile_ux
2023-01-11T21:22:28.2741320Z  dynamo/test_replay_record
2023-01-11T21:22:28.2741515Z  dynamo/test_skip_non_tensor
2023-01-11T21:22:28.2741702Z  dynamo/test_subgraphs
2023-01-11T21:22:28.2741884Z  dynamo/test_torchxla_num_output
2023-01-11T21:22:28.2742082Z  dynamo/test_torchxla_util
2023-01-11T21:22:28.2742282Z  dynamo/test_verify_correctness
2023-01-11T21:22:28.2742678Z  inductor/test_minifier
2023-01-11T21:22:28.2742867Z  inductor/test_perf
2023-01-11T21:22:28.2743050Z  inductor/test_smoke
2023-01-11T21:22:28.2743218Z  lazy/test_bindings
2023-01-11T21:22:28.2743483Z  lazy/test_debug_util
2023-01-11T21:22:28.2743682Z  lazy/test_extract_compiled_graph
2023-01-11T21:22:28.2743879Z  lazy/test_meta_kernel
2023-01-11T21:22:28.2744045Z  lazy/test_reuse_ir
2023-01-11T21:22:28.2744225Z  lazy/test_step_closures
2023-01-11T21:22:28.2744410Z  lazy/test_ts_opinfo
2023-01-11T21:22:28.2744572Z  nn/test_dropout
2023-01-11T21:22:28.2744745Z  nn/test_embedding
2023-01-11T21:22:28.2744915Z  nn/test_init
2023-01-11T21:22:28.2745072Z  nn/test_lazy_modules
2023-01-11T21:22:28.2745255Z  nn/test_module_hooks
2023-01-11T21:22:28.2745442Z  nn/test_multihead_attention
2023-01-11T21:22:28.2745622Z  nn/test_packed_sequence
2023-01-11T21:22:28.2745813Z  nn/test_parametrization
2023-01-11T21:22:28.2745994Z  nn/test_pruning
2023-01-11T21:22:28.2746171Z  profiler/test_memory_profiler
2023-01-11T21:22:28.2746367Z  profiler/test_profiler
2023-01-11T21:22:28.2746546Z  test_autocast
2023-01-11T21:22:28.2746702Z  test_binary_ufuncs
2023-01-11T21:22:28.2746889Z  test_bundled_inputs
2023-01-11T21:22:28.2747071Z  test_comparison_utils
2023-01-11T21:22:28.2747235Z  test_complex
2023-01-11T21:22:28.2747402Z  test_cuda_sanitizer
2023-01-11T21:22:28.2747574Z  test_dataloader
2023-01-11T21:22:28.2747731Z  test_datapipe
2023-01-11T21:22:28.2747895Z  test_decomp
2023-01-11T21:22:28.2748056Z  test_deploy
2023-01-11T21:22:28.2748203Z  test_dlpack
2023-01-11T21:22:28.2748373Z  test_dynamic_shapes
2023-01-11T21:22:28.2748555Z  test_expanded_weights
2023-01-11T21:22:28.2748749Z  test_functional_autograd_benchmark
2023-01-11T21:22:28.2748949Z  test_functional_optim
2023-01-11T21:22:28.2749136Z  test_functionalization
2023-01-11T21:22:28.2749305Z  test_futures
2023-01-11T21:22:28.2749478Z  test_fx_experimental
2023-01-11T21:22:28.2749654Z  test_fx_passes
2023-01-11T21:22:28.2749818Z  test_fx_reinplace_pass
2023-01-11T21:22:28.2750053Z  test_hub
2023-01-11T21:22:28.2750226Z  test_import_stats
2023-01-11T21:22:28.2750383Z  test_itt
2023-01-11T21:22:28.2750548Z  test_jit_autocast
2023-01-11T21:22:28.2750726Z  test_jit_fuser_te
2023-01-11T21:22:28.2750886Z  test_jit_llga_fuser
2023-01-11T21:22:28.2751064Z  test_jiterator
2023-01-11T21:22:28.2751235Z  test_legacy_vmap
2023-01-11T21:22:28.2751392Z  test_license
2023-01-11T21:22:28.2751558Z  test_logging
2023-01-11T21:22:28.2751727Z  test_maskedtensor
2023-01-11T21:22:28.2751888Z  test_matmul_cuda
2023-01-11T21:22:28.2752060Z  test_mkl_verbose
2023-01-11T21:22:28.2752226Z  test_mkldnn
2023-01-11T21:22:28.2752381Z  test_mkldnn_fusion
2023-01-11T21:22:28.2752559Z  test_mkldnn_verbose
2023-01-11T21:22:28.2752733Z  test_model_dump
2023-01-11T21:22:28.2752947Z  test_module_init
2023-01-11T21:22:28.2753100Z  test_monitor
2023-01-11T21:22:28.2753267Z  test_namedtensor
2023-01-11T21:22:28.2753440Z  test_native_functions
2023-01-11T21:22:28.2753602Z  test_native_mha
2023-01-11T21:22:28.2753779Z  test_nestedtensor
2023-01-11T21:22:28.2753959Z  test_numba_integration
2023-01-11T21:22:28.2754128Z  test_numpy_interop
2023-01-11T21:22:28.2754388Z  test_nvfuser_dynamo
2023-01-11T21:22:28.2754570Z  test_nvfuser_frontend
2023-01-11T21:22:28.2754731Z  test_openmp
2023-01-11T21:22:28.2754895Z  test_optim
2023-01-11T21:22:28.2755057Z  test_package
2023-01-11T21:22:28.2755219Z  test_per_overload_api
2023-01-11T21:22:28.2755398Z  test_pruning_op
2023-01-11T21:22:28.2755570Z  test_pytree
2023-01-11T21:22:28.2755728Z  test_quantization
2023-01-11T21:22:28.2755904Z  test_schema_check
2023-01-11T21:22:28.2756083Z  test_serialization
2023-01-11T21:22:28.2756270Z  test_set_default_mobile_cpu_allocator
2023-01-11T21:22:28.2756466Z  test_shape_ops
2023-01-11T21:22:28.2756633Z  test_subclass
2023-01-11T21:22:28.2756789Z  test_tensorboard
2023-01-11T21:22:28.2756978Z  test_tensorexpr_pybind
2023-01-11T21:22:28.2757157Z  test_type_hints
2023-01-11T21:22:28.2757315Z  test_type_info
2023-01-11T21:22:28.2757494Z  test_type_promotion
2023-01-11T21:22:28.2757674Z  test_unary_ufuncs
2023-01-11T21:22:28.2757831Z  test_view_ops
2023-01-11T21:22:28.2757998Z  test_vulkan
2023-01-11T21:22:28.2758234Z  test_weak
2023-01-11T21:22:28.2758398Z  test_xnnpack_integration
2023-01-11T21:22:28.2758609Z serial (file granularity) tests:
2023-01-11T21:22:28.2758825Z  inductor/test_torchinductor_opinfo
2023-01-11T21:22:28.2759008Z  test_autograd
2023-01-11T21:22:28.2759190Z  test_cpp_extensions_jit
2023-01-11T21:22:28.2759368Z  test_cuda
2023-01-11T21:22:28.2759522Z  test_fake_tensor
2023-01-11T21:22:28.2759687Z  test_nn
2023-01-11T21:22:28.2759847Z  test_overrides
2023-01-11T21:22:28.2760004Z  test_sparse_csr
2023-01-11T21:22:28.2760169Z  test_torch
2023-01-11T21:22:28.2760363Z  distributions/test_distributions
2023-01-11T21:22:28.2760550Z  nn/test_convolution
2023-01-11T21:22:28.2760732Z  nn/test_pooling
2023-01-11T21:22:28.2760912Z  test_cpp_api_parity
2023-01-11T21:22:28.2761101Z  test_cpp_extensions_aot_ninja
2023-01-11T21:22:28.2761312Z  test_cpp_extensions_aot_no_ninja
2023-01-11T21:22:28.2761542Z  test_cpp_extensions_open_device_registration
2023-01-11T21:22:28.2761748Z  test_cuda_nvml_based_avail
2023-01-11T21:22:28.2761948Z  test_cuda_primary_ctx
2023-01-11T21:22:28.2762133Z  test_cuda_trace
2023-01-11T21:22:28.2762290Z  test_dispatch
2023-01-11T21:22:28.2762456Z  test_fx
2023-01-11T21:22:28.2762623Z  test_indexing
2023-01-11T21:22:28.2762781Z  test_jit_cuda_fuser
2023-01-11T21:22:28.2762962Z  test_jit_disabled
2023-01-11T21:22:28.2763131Z  test_linalg
2023-01-11T21:22:28.2763291Z  test_mobile_optimizer
2023-01-11T21:22:28.2763468Z  test_modules
2023-01-11T21:22:28.2763646Z  test_multiprocessing
2023-01-11T21:22:28.2763833Z  test_multiprocessing_spawn
2023-01-11T21:22:28.2764040Z  test_namedtuple_return_api
2023-01-11T21:22:28.2764224Z  test_ops
2023-01-11T21:22:28.2764384Z  test_ops_fwd_gradients
2023-01-11T21:22:28.2764565Z  test_ops_gradients
2023-01-11T21:22:28.2764736Z  test_ops_jit
2023-01-11T21:22:28.2764890Z  test_prims
2023-01-11T21:22:28.2765059Z  test_reductions
2023-01-11T21:22:28.2765232Z  test_show_pickle
2023-01-11T21:22:28.2765394Z  test_spectral_ops
2023-01-11T21:22:28.2765590Z  test_tensor_creation_ops
2023-01-11T21:22:28.2765775Z  test_tensorexpr
2023-01-11T21:22:28.2765926Z  doctests
2023-01-11T21:22:29.9623092Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:29.9751835Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:30.0457193Z Ignoring disabled issues:  []
2023-01-11T21:22:30.0562220Z Ignoring disabled issues:  []
2023-01-11T21:22:30.0595959Z Running dynamo/test_export ... [2023-01-11 21:22:30.059287]
2023-01-11T21:22:30.0598191Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_export.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:30.059579]
2023-01-11T21:22:30.0703143Z Running dynamo/test_misc ... [2023-01-11 21:22:30.069904]
2023-01-11T21:22:30.0704234Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_misc.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:30.070198]
2023-01-11T21:22:38.7001641Z 
2023-01-11T21:22:38.7002469Z Expand the folded group to see the log file of dynamo/test_export
2023-01-11T21:22:38.7010885Z ##[group]PRINTING LOG FILE of dynamo/test_export (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_u8euuxo2)
2023-01-11T21:22:38.7011497Z 
2023-01-11T21:22:38.7011781Z Running tests...
2023-01-11T21:22:38.7012300Z ----------------------------------------------------------------------
2023-01-11T21:22:38.7012716Z Test results will be stored in test-reports/python-unittest/dynamo.test_export
2023-01-11T21:22:38.7013079Z   test_dict_return (__main__.ExportTests) ... ok (3.920s)
2023-01-11T21:22:38.7013593Z   test_dict_return_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:38.7014110Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:38.7014367Z ok (0.076s)
2023-01-11T21:22:38.7014793Z   test_dupes (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7015309Z ok (0.010s)
2023-01-11T21:22:38.7015848Z   test_dupes_2 (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7016453Z ok (0.010s)
2023-01-11T21:22:38.7017079Z   test_dupes_2_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7017420Z ok (0.023s)
2023-01-11T21:22:38.7017814Z   test_dupes_and_bypass (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7018170Z ok (0.011s)
2023-01-11T21:22:38.7018645Z   test_dupes_and_bypass_reorder_with_non_tensor_arg (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7018959Z ok (0.099s)
2023-01-11T21:22:38.7019480Z   test_dupes_and_bypass_reorder_with_non_tensor_arg_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7019878Z ok (0.024s)
2023-01-11T21:22:38.7020335Z   test_dupes_and_bypass_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7020662Z ok (0.024s)
2023-01-11T21:22:38.7021127Z   test_dupes_and_bypass_with_non_tensor_arg (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7021471Z ok (0.012s)
2023-01-11T21:22:38.7021940Z   test_dupes_and_bypass_with_non_tensor_arg_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7022300Z ok (0.024s)
2023-01-11T21:22:38.7023089Z   test_dupes_and_bypass_with_non_tensor_output (__main__.ExportTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:38.7023441Z ok (0.016s)
2023-01-11T21:22:38.7023904Z   test_dupes_and_bypass_with_non_tensor_output_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:38.7024319Z ok (0.015s)
2023-01-11T21:22:38.7024797Z   test_dupes_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7025091Z ok (0.022s)
2023-01-11T21:22:38.7025360Z   test_export (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:38.7025803Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:22:38.7026057Z ok (0.123s)
2023-01-11T21:22:38.7072724Z   test_export_compare_optimize_with_make_fx (__main__.ExportTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:38.7073232Z ok (0.281s)
2023-01-11T21:22:38.7073722Z   test_export_decomp (__main__.ExportTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:38.7074440Z ok (0.081s)
2023-01-11T21:22:38.7074828Z   test_export_decomp_asserts_bad_args (__main__.ExportTests) ... ok (0.001s)
2023-01-11T21:22:38.7075331Z   test_export_decomp_asserts_bad_args_mode (__main__.ExportTests) ... ok (0.001s)
2023-01-11T21:22:38.7076114Z   test_export_graph_bypass (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7076544Z ok (0.013s)
2023-01-11T21:22:38.7077244Z   test_export_graph_bypass_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7077722Z ok (0.026s)
2023-01-11T21:22:38.7078269Z   test_export_graph_with_complex_reorder (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:38.7078544Z ok (0.018s)
2023-01-11T21:22:38.7079260Z   test_export_graph_with_complex_reorder_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:38.7079648Z ok (0.044s)
2023-01-11T21:22:38.7080209Z   test_export_graph_with_list (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7080572Z ok (0.015s)
2023-01-11T21:22:38.7081133Z   test_export_graph_with_list_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7081504Z ok (0.027s)
2023-01-11T21:22:38.7082033Z   test_export_meta_val (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:38.7082382Z ok (0.224s)
2023-01-11T21:22:38.7082916Z   test_export_mismatched_out (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7083326Z ok (0.011s)
2023-01-11T21:22:38.7083921Z   test_export_mismatched_out_2 (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7084349Z ok (0.010s)
2023-01-11T21:22:38.7085036Z   test_export_mismatched_out_2_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7085469Z ok (0.023s)
2023-01-11T21:22:38.7086108Z   test_export_mismatched_out_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7086559Z ok (0.024s)
2023-01-11T21:22:38.7087103Z   test_export_shape_control_flow_1 (__main__.ExportTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:38.7087722Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:38.7088088Z ok (0.067s)
2023-01-11T21:22:38.7088468Z   test_export_with_aten_graph (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:38.7089054Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:22:38.7089398Z ok (0.391s)
2023-01-11T21:22:38.7090046Z   test_export_with_constant_dict_values (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:38.7090566Z ok (0.012s)
2023-01-11T21:22:38.7091256Z   test_export_with_constant_free_function (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:38.7091731Z ok (0.017s)
2023-01-11T21:22:38.7092454Z   test_export_with_constant_free_function_and_class_method (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:38.7092964Z ok (0.014s)
2023-01-11T21:22:38.7093681Z   test_export_with_constant_free_function_and_class_method_multiarg (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:38.7094159Z ok (0.017s)
2023-01-11T21:22:38.7094694Z   test_export_with_constant_free_function_and_class_method_multiarg_diff (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:38.7095195Z ok (0.009s)
2023-01-11T21:22:38.7095612Z   test_export_with_constant_list_nonzero (__main__.ExportTests) ... stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:38.7095889Z ok (0.020s)
2023-01-11T21:22:38.7096317Z   test_export_with_constant_list_nonzero_free_function (__main__.ExportTests) ... stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:38.7096610Z ok (0.019s)
2023-01-11T21:22:38.7097004Z   test_export_with_constant_method_on_module (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:38.7097296Z ok (0.014s)
2023-01-11T21:22:38.7097723Z   test_export_with_constant_method_on_module_invoke_twice (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:38.7098032Z ok (0.016s)
2023-01-11T21:22:38.7098482Z   test_export_with_constant_none_control_flow (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:38.7098775Z ok (0.008s)
2023-01-11T21:22:38.7099195Z   test_export_with_constant_none_control_flow_free_func (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:38.7099496Z ok (0.007s)
2023-01-11T21:22:38.7099899Z   test_export_with_constant_not_none_control_flow (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:38.7100190Z ok (0.009s)
2023-01-11T21:22:38.7100626Z   test_export_with_constant_not_none_control_flow_free_func (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:38.7100915Z ok (0.009s)
2023-01-11T21:22:38.7101341Z   test_export_with_constant_not_none_control_flow_pos (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:38.7101638Z ok (0.009s)
2023-01-11T21:22:38.7102041Z   test_export_with_constant_not_return_const (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:38.7102315Z ok (0.007s)
2023-01-11T21:22:38.7103042Z   test_export_with_constant_tuple_nonzero (__main__.ExportTests) ... stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:38.7103337Z ok (0.020s)
2023-01-11T21:22:38.7103556Z   test_export_with_module_layer (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:38.7103924Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:38.7104149Z ok (0.030s)
2023-01-11T21:22:38.7104525Z   test_export_with_stack_trace (__main__.ExportTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:38.7104808Z ok (0.115s)
2023-01-11T21:22:38.7105044Z   test_func_return (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:38.7105394Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:38.7105613Z ok (0.016s)
2023-01-11T21:22:38.7105854Z   test_func_return_with_aten_graph (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:38.7106221Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:38.7106436Z ok (0.050s)
2023-01-11T21:22:38.7106825Z   test_input_container_type (__main__.ExportTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:38.7107102Z ok (0.071s)
2023-01-11T21:22:38.7107456Z   test_list_unpack (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7107727Z ok (0.015s)
2023-01-11T21:22:38.7108123Z   test_list_unpack_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:38.7108549Z ok (0.027s)
2023-01-11T21:22:38.7108949Z   test_zeroes_in_and_out_different_shape_on_test (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:38.7109242Z ok (0.016s)
2023-01-11T21:22:38.7109679Z   test_zeroes_in_and_out_different_shape_on_test_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:38.7109968Z ok (0.041s)
2023-01-11T21:22:38.7110468Z   test_zeroes_in_new_shape_scalar_out (__main__.ExportTests) ... stats [('calls_captured', 16), ('fusions_possible', 14), ('unique_graphs', 2)]
2023-01-11T21:22:38.7110756Z ok (0.023s)
2023-01-11T21:22:38.7111169Z   test_zeroes_in_new_shape_scalar_out_permute (__main__.ExportTests) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:22:38.7111446Z ok (0.027s)
2023-01-11T21:22:38.7111934Z   test_zeroes_in_new_shape_scalar_out_permute_dupe_and_bypass (__main__.ExportTests) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:22:38.7112246Z ok (0.028s)
2023-01-11T21:22:38.7112357Z 
2023-01-11T21:22:38.7112555Z ----------------------------------------------------------------------
2023-01-11T21:22:38.7112805Z Ran 60 tests in 6.335s
2023-01-11T21:22:38.7112920Z 
2023-01-11T21:22:38.7112986Z OK
2023-01-11T21:22:38.7113079Z 
2023-01-11T21:22:38.7113169Z Generating XML reports...
2023-01-11T21:22:38.7113571Z Generated XML report: test-reports/python-unittest/dynamo.test_export/TEST-ExportTests-20230111212231.xml
2023-01-11T21:22:38.7113806Z 
2023-01-11T21:22:38.7114212Z ##[endgroup]
2023-01-11T21:22:38.7114619Z FINISHED PRINTING LOG FILE of dynamo/test_export (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_u8euuxo2)
2023-01-11T21:22:38.7114832Z 
2023-01-11T21:22:40.3333290Z 
2023-01-11T21:22:40.3333727Z Expand the folded group to see the log file of dynamo/test_misc
2023-01-11T21:22:40.3336195Z ##[group]PRINTING LOG FILE of dynamo/test_misc (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_misc_a8pqkth5)
2023-01-11T21:22:40.3336622Z 
2023-01-11T21:22:40.3336756Z Running tests...
2023-01-11T21:22:40.3337436Z ----------------------------------------------------------------------
2023-01-11T21:22:40.3349358Z Test results will be stored in test-reports/python-unittest/dynamo.test_misc
2023-01-11T21:22:40.3352850Z   test_allow_in_graph (__main__.MiscTests) ... ok (3.915s)
2023-01-11T21:22:40.3353257Z   test_autocast (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3356807Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:40.3357052Z skip: requires cuda (0.001s)
2023-01-11T21:22:40.3357455Z   test_autocast_cpu (__main__.MiscTests) ... stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:40.3357736Z ok (0.079s)
2023-01-11T21:22:40.3357984Z   test_autocast_device (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:40.3358274Z   test_autocast_float64 (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:40.3358577Z   test_autograd_function_equivalence (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3358944Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:22:40.3359162Z ok (0.108s)
2023-01-11T21:22:40.3359600Z   test_autograd_profiler (__main__.MiscTests) ... STAGE:2023-01-11 21:22:35 1293:1293 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:40.3360085Z STAGE:2023-01-11 21:22:35 1293:1293 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:40.3360542Z STAGE:2023-01-11 21:22:35 1293:1293 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:40.3360955Z [2023-01-11 21:22:35,944] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:22:40.3361254Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3361648Z unimplemented []
2023-01-11T21:22:40.3361875Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:22:40.3362201Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:40.3362434Z ok (0.028s)
2023-01-11T21:22:40.3362855Z   test_autograd_profiler_enabled (__main__.MiscTests) ... STAGE:2023-01-11 21:22:35 1293:1293 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:40.3363347Z STAGE:2023-01-11 21:22:35 1293:1293 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:40.3363796Z STAGE:2023-01-11 21:22:35 1293:1293 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:40.3364110Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:40.3364291Z unimplemented []
2023-01-11T21:22:40.3364604Z graph_break [('torch.autograd._profiler_enabled not supported yet', 1)]
2023-01-11T21:22:40.3364972Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:40.3365246Z ok (0.014s)
2023-01-11T21:22:40.3365609Z   test_boolarg (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:40.3365870Z ok (0.013s)
2023-01-11T21:22:40.3366078Z   test_build_tuple_unpack (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3366430Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:40.3366659Z ok (0.015s)
2023-01-11T21:22:40.3366984Z   test_builder_for_class_with_metaclass (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3367343Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3367566Z ok (0.006s)
2023-01-11T21:22:40.3367936Z   test_builtin_isinstance (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3368187Z ok (0.013s)
2023-01-11T21:22:40.3368434Z   test_builtin_subclasses_as_method_on_class_type (__main__.MiscTests) ... ok (0.003s)
2023-01-11T21:22:40.3368747Z   test_builtin_subclasses_as_method_on_var (__main__.MiscTests) ... ok (0.004s)
2023-01-11T21:22:40.3369045Z   test_call_parent_non_class_methods_from_child (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3369413Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3369644Z ok (0.010s)
2023-01-11T21:22:40.3370005Z   test_callpacked (__main__.MiscTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:40.3370253Z ok (0.012s)
2023-01-11T21:22:40.3370545Z   test_cell_output1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3370893Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3371104Z ok (0.006s)
2023-01-11T21:22:40.3371398Z   test_cell_output2 (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3371631Z unimplemented []
2023-01-11T21:22:40.3372007Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:22:40.3372424Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3372651Z ok (0.010s)
2023-01-11T21:22:40.3373379Z   test_change_backends (__main__.MiscTests) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:22:40.3373990Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:22:40.3374333Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:40.3374614Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3374795Z ok (0.063s)
2023-01-11T21:22:40.3375058Z   test_cond (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3375331Z inline_call []
2023-01-11T21:22:40.3375632Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3375843Z ok (0.015s)
2023-01-11T21:22:40.3376060Z   test_cond_export (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3376398Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3376608Z ok (0.018s)
2023-01-11T21:22:40.3376836Z   test_cond_export_single_arg (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3377188Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3377413Z ok (0.011s)
2023-01-11T21:22:40.3377687Z   test_cond_nested (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3377918Z inline_call []
2023-01-11T21:22:40.3378212Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3378418Z ok (0.017s)
2023-01-11T21:22:40.3378693Z   test_cond_side_effects (__main__.MiscTests) ... expected failure (0.001s)
2023-01-11T21:22:40.3379066Z   test_config_getattr_default (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:40.3379423Z stats [('calls_captured', 21), ('fusions_possible', 18), ('unique_graphs', 3)]
2023-01-11T21:22:40.3379654Z ok (0.038s)
2023-01-11T21:22:40.3379947Z   test_config_log_level (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3380303Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3380516Z ok (0.008s)
2023-01-11T21:22:40.3380803Z   test_config_obj (__main__.MiscTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:40.3381149Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:22:40.3381362Z ok (0.025s)
2023-01-11T21:22:40.3381599Z   test_const_dict_variable_python_type (__main__.MiscTests) ... ok (0.001s)
2023-01-11T21:22:40.3382205Z   test_cross_entropy_loss_fancy_ctor (__main__.MiscTests) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:22:40.3382936Z   warnings.warn(warning.format(ret))
2023-01-11T21:22:40.3383205Z ok (0.002s)
2023-01-11T21:22:40.3383567Z   test_cross_entropy_loss_simple_ctor (__main__.MiscTests) ... ok (0.002s)
2023-01-11T21:22:40.3384097Z   test_dataclass_fields (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3384451Z inline_call []
2023-01-11T21:22:40.3384811Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:40.3385038Z ok (0.038s)
2023-01-11T21:22:40.3385338Z   test_dict_mutation_side_effect (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3385825Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3386171Z ok (0.006s)
2023-01-11T21:22:40.3386637Z   test_dict_reconstruct_keeps_original_order (__main__.MiscTests) ... frames [('total', 13), ('ok', 12)]
2023-01-11T21:22:40.3387109Z unimplemented [("Guard setup for uninitialized class <class 'torch.nn.modules.container.ModuleDict'>", 1)]
2023-01-11T21:22:40.3388739Z graph_break [('UnspecializedNNModuleVariable missing add_module', 3), ('construct nn.Module: ReLU', 1), ('call_function in skip_files /opt/conda/lib/python3.10/collections/__init__.py', 1), ('construct nn.Module: ModuleDict', 1), ('Patched init cannot be inlined.', 1), ('construct nn.Module: Linear', 1), ('construct nn.Module: Sigmoid', 1), ('call_method ConstDictVariable() update [TupleVariable()] {}', 1)]
2023-01-11T21:22:40.3389443Z inline_call [('inline __setitem__', 2), ('Patched init cannot be inlined.', 1)]
2023-01-11T21:22:40.3389681Z ok (0.039s)
2023-01-11T21:22:40.3389953Z   test_dictcomp (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3390267Z inline_call []
2023-01-11T21:22:40.3390577Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3391035Z ok (0.009s)
2023-01-11T21:22:40.3391385Z   test_disable_flag (__main__.MiscTests) ... ok (0.002s)
2023-01-11T21:22:40.3391863Z   test_disable_optimize (__main__.MiscTests) ... ok (0.002s)
2023-01-11T21:22:40.3392241Z   test_disallow_in_graph (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3392481Z unimplemented []
2023-01-11T21:22:40.3392866Z graph_break [('call_function UserDefinedObjectVariable(sub) [TensorVariable(), ConstantVariable(int)] {}', 1)]
2023-01-11T21:22:40.3393264Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:40.3393492Z ok (0.013s)
2023-01-11T21:22:40.3393712Z   test_dunder_methods (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3394056Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:40.3394267Z ok (0.022s)
2023-01-11T21:22:40.3394494Z   test_duplicate_graph_break_warning (__main__.MiscTests) ... break
2023-01-11T21:22:40.3394773Z break
2023-01-11T21:22:40.3394973Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:22:40.3395313Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:22:40.3395570Z unimplemented []
2023-01-11T21:22:40.3395883Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 4)]
2023-01-11T21:22:40.3396257Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:22:40.3396483Z ok (0.031s)
2023-01-11T21:22:40.3396703Z   test_dynamo_min_operator_with_shape (__main__.MiscTests) ... ok (0.003s)
2023-01-11T21:22:40.3397051Z   test_empty_list (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3397601Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:40.3397828Z ok (0.009s)
2023-01-11T21:22:40.3398197Z   test_enum_no_graphbreaks (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:40.3398477Z ok (0.012s)
2023-01-11T21:22:40.3398783Z   test_error_on_nested_fx_trace (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3399133Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3399358Z ok (0.006s)
2023-01-11T21:22:40.3399709Z   test_fold (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3399962Z ok (0.005s)
2023-01-11T21:22:40.3400339Z   test_frozenset_torch_func_contains (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:40.3400620Z ok (0.011s)
2023-01-11T21:22:40.3400994Z   test_function_annotation (__main__.MiscTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:40.3401247Z ok (0.010s)
2023-01-11T21:22:40.3401600Z   test_generate_tensor_from_list_of_numpy_primitive_type (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3401873Z unimplemented []
2023-01-11T21:22:40.3402080Z graph_break [('numpy', 1)]
2023-01-11T21:22:40.3402397Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3402620Z ok (0.007s)
2023-01-11T21:22:40.3402833Z   test_get_device (__main__.MiscTests) ... skip: requires cuda (0.000s)
2023-01-11T21:22:40.3403168Z   test_grad (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3403394Z unimplemented []
2023-01-11T21:22:40.3403637Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:22:40.3403950Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:40.3404177Z ok (0.013s)
2023-01-11T21:22:40.3404471Z   test_grad_mode_guard (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3404692Z unimplemented []
2023-01-11T21:22:40.3404930Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:22:40.3405252Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:40.3405508Z ok (0.014s)
2023-01-11T21:22:40.3405798Z   test_graph_break (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:40.3406030Z unimplemented []
2023-01-11T21:22:40.3406389Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 2)]
2023-01-11T21:22:40.3406791Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:22:40.3407018Z ok (0.022s)
2023-01-11T21:22:40.3407385Z   test_guard_failure_fn (__main__.MiscTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:40.3407637Z ok (0.017s)
2023-01-11T21:22:40.3408003Z   test_guard_failure_fn2 (__main__.MiscTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:40.3408267Z ok (0.014s)
2023-01-11T21:22:40.3408671Z   test_id_of_nn_module (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:40.3408924Z ok (0.011s)
2023-01-11T21:22:40.3409287Z   test_if_cond_nn_mod (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:40.3409546Z ok (0.012s)
2023-01-11T21:22:40.3409827Z   test_inference_mode (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3410184Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3410409Z ok (0.006s)
2023-01-11T21:22:40.3410699Z   test_inline_dict_mutation (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3410933Z inline_call []
2023-01-11T21:22:40.3411233Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3411460Z ok (0.019s)
2023-01-11T21:22:40.3411776Z   test_inline_func_jump_on_tensor_condition (__main__.MiscTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:40.3412117Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:40.3412341Z unimplemented []
2023-01-11T21:22:40.3412593Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:40.3412934Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:40.3413159Z ok (0.014s)
2023-01-11T21:22:40.3413448Z   test_inline_list_mutation (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3413680Z inline_call []
2023-01-11T21:22:40.3413973Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3414185Z ok (0.017s)
2023-01-11T21:22:40.3414538Z   test_inplace (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3414794Z ok (0.013s)
2023-01-11T21:22:40.3415168Z   test_inplace_param_update (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:40.3415424Z ok (0.008s)
2023-01-11T21:22:40.3415721Z   test_is_compiling (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3416075Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3416289Z ok (0.010s)
2023-01-11T21:22:40.3416655Z   test_is_floating_point (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3416920Z ok (0.008s)
2023-01-11T21:22:40.3417276Z   test_is_floating_point2 (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3417540Z ok (0.007s)
2023-01-11T21:22:40.3417893Z   test_is_tensor (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3418145Z ok (0.007s)
2023-01-11T21:22:40.3418420Z   test_is_tensor2 (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3418768Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:40.3419031Z ok (0.014s)
2023-01-11T21:22:40.3419374Z   test_is_tensor_like (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:40.3419636Z ok (0.015s)
2023-01-11T21:22:40.3419928Z   test_is_tensor_like2 (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:40.3420148Z unimplemented []
2023-01-11T21:22:40.3420472Z graph_break [('call_function args: UserDefinedObjectVariable(MyTensor) ', 1)]
2023-01-11T21:22:40.3420850Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:40.3421073Z ok (0.018s)
2023-01-11T21:22:40.3421411Z   test_item (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3421662Z ok (0.019s)
2023-01-11T21:22:40.3422020Z   test_item_changes (__main__.MiscTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:40.3422265Z ok (0.036s)
2023-01-11T21:22:40.3422964Z   test_item_changes_new_shape (__main__.MiscTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:40.3423241Z ok (0.036s)
2023-01-11T21:22:40.3423448Z   test_large_reduction_list (__main__.MiscTests) ... ok (0.013s)
2023-01-11T21:22:40.3423714Z   test_linetable_writer (__main__.MiscTests) ... ok (0.001s)
2023-01-11T21:22:40.3424071Z   test_list_append_return_none (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3424436Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3424651Z ok (0.006s)
2023-01-11T21:22:40.3424933Z   test_list_mul (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3425153Z ok (0.002s)
2023-01-11T21:22:40.3425421Z   test_listcomp (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3425642Z inline_call []
2023-01-11T21:22:40.3425942Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:40.3426158Z ok (0.010s)
2023-01-11T21:22:40.3426402Z   test_lnotab_writer (__main__.MiscTests) ... skip: use lnotab when python < 3.10 (0.000s)
2023-01-11T21:22:40.3426837Z   test_manual_seed (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3427097Z ok (0.008s)
2023-01-11T21:22:40.3427436Z   test_matmul1 (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3427688Z ok (0.005s)
2023-01-11T21:22:40.3427901Z   test_module_complex_iter (__main__.MiscTests) ... ok (0.010s)
2023-01-11T21:22:40.3428238Z   test_module_deepcopy (__main__.MiscTests) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:40.3428473Z unimplemented []
2023-01-11T21:22:40.3428796Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 2)]
2023-01-11T21:22:40.3429027Z inline_call []
2023-01-11T21:22:40.3429325Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:40.3429556Z ok (0.051s)
2023-01-11T21:22:40.3429755Z   test_named_parameters (__main__.MiscTests) ... ok (0.023s)
2023-01-11T21:22:40.3430162Z   test_namedtuple1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3430519Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3430746Z ok (0.008s)
2023-01-11T21:22:40.3431023Z   test_namedtuple2 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3431380Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3431602Z ok (0.008s)
2023-01-11T21:22:40.3431880Z   test_namedtuple3 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3432230Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3432454Z ok (0.006s)
2023-01-11T21:22:40.3432714Z   test_nan (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3433126Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3433349Z ok (0.005s)
2023-01-11T21:22:40.3433554Z   test_nested_closure (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3433900Z stats [('calls_captured', 9), ('fusions_possible', 7), ('unique_graphs', 2)]
2023-01-11T21:22:40.3434126Z ok (0.036s)
2023-01-11T21:22:40.3434353Z   test_nested_closure_mutation (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3434688Z stats [('calls_captured', 11), ('fusions_possible', 9), ('unique_graphs', 2)]
2023-01-11T21:22:40.3434917Z ok (0.028s)
2023-01-11T21:22:40.3435406Z   test_nested_disable_decorator (__main__.MiscTests) ... [2023-01-11 21:22:37,082] torch._dynamo.convert_frame: [ERROR] WON'T CONVERT fn3 /var/lib/jenkins/workspace/test/dynamo/test_misc.py line 1197 
2023-01-11T21:22:40.3435722Z due to: 
2023-01-11T21:22:40.3435905Z Traceback (most recent call last):
2023-01-11T21:22:40.3436316Z   File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 67, in unimplemented
2023-01-11T21:22:40.3436581Z     raise Unsupported(msg)
2023-01-11T21:22:40.3436934Z torch._dynamo.exc.Unsupported: call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f81b936c280>
2023-01-11T21:22:40.3437199Z 
2023-01-11T21:22:40.3437270Z from user code:
2023-01-11T21:22:40.3437515Z    File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1199, in fn3
2023-01-11T21:22:40.3437739Z     return fn2(x)
2023-01-11T21:22:40.3437980Z   File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1192, in fn2
2023-01-11T21:22:40.3438227Z     x = fn1(x)  # graph break
2023-01-11T21:22:40.3438345Z 
2023-01-11T21:22:40.3438462Z Set torch._dynamo.config.verbose=True for more information
2023-01-11T21:22:40.3438625Z 
2023-01-11T21:22:40.3438629Z 
2023-01-11T21:22:40.3438756Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3438946Z unimplemented []
2023-01-11T21:22:40.3439381Z graph_break [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f81b936c280>', 1)]
2023-01-11T21:22:40.3439805Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:40.3440290Z inline_call [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f81b936c280>', 1)]
2023-01-11T21:22:40.3440588Z ok (0.015s)
2023-01-11T21:22:40.3440957Z   test_nested_optimize (__main__.MiscTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:40.3441213Z ok (0.016s)
2023-01-11T21:22:40.3441444Z   test_nested_optimize_decorator (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3441797Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:40.3442010Z ok (0.009s)
2023-01-11T21:22:40.3442384Z   test_nested_optimize_run (__main__.MiscTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:40.3442654Z ok (0.014s)
2023-01-11T21:22:40.3443019Z   test_nn_functional_reduction (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3443291Z ok (0.007s)
2023-01-11T21:22:40.3443517Z   test_nn_sequential_invocation (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3443870Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3444081Z ok (0.024s)
2023-01-11T21:22:40.3444333Z   test_nn_sequential_invocation_reposition_indices (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3444705Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3444920Z ok (0.019s)
2023-01-11T21:22:40.3445231Z   test_no_error_on_nested_fx_trace (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3445603Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3445866Z ok (0.007s)
2023-01-11T21:22:40.3446222Z   test_no_grad (__main__.MiscTests) ... stats [('calls_captured', 40), ('fusions_possible', 32), ('unique_graphs', 8)]
2023-01-11T21:22:40.3446480Z ok (0.062s)
2023-01-11T21:22:40.3446767Z   test_not_dynamic_scope (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3446997Z inline_call []
2023-01-11T21:22:40.3447291Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3447522Z ok (0.006s)
2023-01-11T21:22:40.3447858Z   test_numel (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3448115Z ok (0.007s)
2023-01-11T21:22:40.3448413Z   test_numpy_int_constant (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3448756Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3448985Z ok (0.007s)
2023-01-11T21:22:40.3449349Z   test_numpy_variable_isinstance (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3449703Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3449928Z ok (0.005s)
2023-01-11T21:22:40.3450147Z   test_object_classmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3450489Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3450700Z ok (0.012s)
2023-01-11T21:22:40.3450919Z   test_object_staticmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3451264Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3451474Z ok (0.012s)
2023-01-11T21:22:40.3451859Z   test_onnx_shape_as_tensor (__main__.MiscTests) ... stats [('calls_captured', 15), ('fusions_possible', 10), ('unique_graphs', 5)]
2023-01-11T21:22:40.3452128Z ok (0.077s)
2023-01-11T21:22:40.3452490Z   test_optimize_on_module (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3452756Z ok (0.006s)
2023-01-11T21:22:40.3453100Z   test_pair (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:40.3453352Z ok (0.021s)
2023-01-11T21:22:40.3453627Z   test_python_slice (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3453848Z ok (0.007s)
2023-01-11T21:22:40.3454137Z   test_raise_on_backend_error (__main__.MiscTests) ... frames [('total', 1)]
2023-01-11T21:22:40.3454445Z stats [('calls_captured', 3), ('fusions_possible', 2)]
2023-01-11T21:22:40.3454648Z ok (0.006s)
2023-01-11T21:22:40.3454927Z   test_raises (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:40.3455140Z unimplemented []
2023-01-11T21:22:40.3455451Z graph_break [('call_function BuiltinVariable(str) [TensorVariable()] {}', 1)]
2023-01-11T21:22:40.3455819Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3456035Z ok (0.010s)
2023-01-11T21:22:40.3456253Z   test_rand (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:40.3456516Z   test_range_input (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3456855Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3457066Z ok (0.008s)
2023-01-11T21:22:40.3457387Z   test_recursive_inline_list_mutation (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3457631Z inline_call []
2023-01-11T21:22:40.3457920Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:40.3458150Z ok (0.015s)
2023-01-11T21:22:40.3458452Z   test_release_input_memory (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3458795Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3459019Z ok (0.004s)
2023-01-11T21:22:40.3459326Z   test_release_module_memory (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3459714Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3459938Z ok (0.009s)
2023-01-11T21:22:40.3460337Z   test_repro_graph_breaks_in__get_item_by_idx (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3460625Z ok (0.011s)
2023-01-11T21:22:40.3460917Z   test_restore_graphstate (__main__.MiscTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:40.3461244Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:40.3461466Z unimplemented []
2023-01-11T21:22:40.3461716Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:40.3462056Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:22:40.3462281Z ok (0.022s)
2023-01-11T21:22:40.3462942Z   test_restore_graphstate_internals (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3463231Z ok (0.009s)
2023-01-11T21:22:40.3463539Z   test_return_nested_function (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3463903Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 2)]
2023-01-11T21:22:40.3464122Z ok (0.016s)
2023-01-11T21:22:40.3464337Z   test_sample_input (__main__.MiscTests) ... ok (1.315s)
2023-01-11T21:22:40.3464683Z   test_setattr_mutation1 (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3465116Z unimplemented [('call_method UserDefinedObjectVariable(member_descriptor) __mul__ [ConstantVariable(int)] {}', 1)]
2023-01-11T21:22:40.3465645Z graph_break [("isinstance called on UserDefinedClass UserDefinedObjectVariable(member_descriptor) <class 'torch.Tensor'>", 1)]
2023-01-11T21:22:40.3465998Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3466300Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:22:40.3466527Z ok (0.019s)
2023-01-11T21:22:40.3466826Z   test_setattr_mutation2 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3467061Z inline_call []
2023-01-11T21:22:40.3467341Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:40.3467561Z ok (0.015s)
2023-01-11T21:22:40.3467858Z   test_setattr_mutation3 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3468071Z inline_call []
2023-01-11T21:22:40.3468367Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:40.3468589Z ok (0.016s)
2023-01-11T21:22:40.3468863Z   test_shape_unpack (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3469218Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3469444Z ok (0.005s)
2023-01-11T21:22:40.3469764Z   test_side_effects_codegen_update_mutated (__main__.MiscTests) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:40.3470094Z unimplemented []
2023-01-11T21:22:40.3470333Z graph_break [('Tensor.item', 4)]
2023-01-11T21:22:40.3470652Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:22:40.3470861Z ok (0.144s)
2023-01-11T21:22:40.3471150Z   test_size_input (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3471502Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:40.3471720Z ok (0.009s)
2023-01-11T21:22:40.3472080Z   test_slice_input (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:40.3472340Z ok (0.035s)
2023-01-11T21:22:40.3472636Z   test_tensor_build_list_unpack (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3473000Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3473224Z ok (0.018s)
2023-01-11T21:22:40.3473570Z   test_tensor_data (__main__.MiscTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3473896Z ok (0.015s)
2023-01-11T21:22:40.3474185Z   test_tensor_dict1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3474536Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3474742Z ok (0.007s)
2023-01-11T21:22:40.3475028Z   test_tensor_dict2 (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:40.3475379Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:40.3475588Z ok (0.038s)
2023-01-11T21:22:40.3475907Z   test_tensor_dot_grad_no_graph_break (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3476151Z unimplemented []
2023-01-11T21:22:40.3476381Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:22:40.3476704Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:40.3476922Z ok (0.016s)
2023-01-11T21:22:40.3477256Z   test_tensor_is_contiguous (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3477604Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:40.3477827Z ok (0.078s)
2023-01-11T21:22:40.3478129Z   test_tensor_item_capture (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3478474Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3478692Z ok (0.008s)
2023-01-11T21:22:40.3478995Z   test_tensor_item_no_capture (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3479222Z unimplemented []
2023-01-11T21:22:40.3479453Z graph_break [('Tensor.item', 1)]
2023-01-11T21:22:40.3479771Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3479977Z ok (0.009s)
2023-01-11T21:22:40.3480338Z   test_tensor_layout (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3480605Z ok (0.018s)
2023-01-11T21:22:40.3480896Z   test_tensor_types (__main__.MiscTests) ... frames [('total', 10), ('ok', 10)]
2023-01-11T21:22:40.3481241Z stats [('calls_captured', 10), ('unique_graphs', 10), ('fusions_possible', 0)]
2023-01-11T21:22:40.3481465Z ok (0.095s)
2023-01-11T21:22:40.3481839Z   test_top_package_import (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3482096Z ok (0.006s)
2023-01-11T21:22:40.3482473Z   test_torch_cuda_is_available (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3482741Z ok (0.006s)
2023-01-11T21:22:40.3482972Z   test_torch_cudnn_is_acceptable (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:40.3483301Z   test_torch_cudnn_is_acceptable_bad_inputs (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:40.3483705Z   test_torch_nn_parameter_isinstance (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3484076Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3484287Z ok (0.014s)
2023-01-11T21:22:40.3485051Z   test_torch_profiler (__main__.MiscTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91868 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:40.3486130Z   test_torch_seed (__main__.MiscTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91867 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:40.3487154Z   test_torch_size (__main__.MiscTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91866 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:40.3487832Z   test_type_copy (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3488171Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:40.3488402Z ok (0.016s)
2023-01-11T21:22:40.3488720Z   test_typing_variable_isinstance (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3489090Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3489301Z ok (0.006s)
2023-01-11T21:22:40.3489659Z   test_unpack4 (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:40.3489921Z ok (0.017s)
2023-01-11T21:22:40.3490291Z   test_unpack5 (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:40.3490547Z ok (0.016s)
2023-01-11T21:22:40.3490885Z   test_update_locals_and_stack_uses_shared_cache (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:40.3491124Z inline_call []
2023-01-11T21:22:40.3491304Z unimplemented []
2023-01-11T21:22:40.3491634Z graph_break [('call_method ListVariable() extend [ListIteratorVariable()] {}', 1)]
2023-01-11T21:22:40.3491865Z ok (0.008s)
2023-01-11T21:22:40.3492247Z   test_user_defined_class_name (__main__.MiscTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3492519Z ok (0.010s)
2023-01-11T21:22:40.3492769Z   test_user_function_variable_supports_enum_argument (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3492996Z ok (0.005s)
2023-01-11T21:22:40.3493252Z   test_user_function_variable_supports_function_argument (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3493637Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3493849Z ok (0.006s)
2023-01-11T21:22:40.3494109Z   test_user_function_variable_supports_type_abcmeta_argument (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3494491Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:40.3494703Z ok (0.007s)
2023-01-11T21:22:40.3494994Z   test_user_getattr1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3495221Z inline_call []
2023-01-11T21:22:40.3495516Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3495725Z ok (0.007s)
2023-01-11T21:22:40.3496016Z   test_user_getattr2 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3496239Z inline_call []
2023-01-11T21:22:40.3496520Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3496747Z ok (0.009s)
2023-01-11T21:22:40.3497035Z   test_user_property (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.3497245Z inline_call []
2023-01-11T21:22:40.3497539Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3497764Z ok (0.007s)
2023-01-11T21:22:40.3497972Z   test_usr_cls_classmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3498318Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3498540Z ok (0.009s)
2023-01-11T21:22:40.3498748Z   test_usr_cls_staticmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3499091Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.3499317Z ok (0.009s)
2023-01-11T21:22:40.3499520Z   test_version_ci (__main__.MiscTests) ... ok (0.001s)
2023-01-11T21:22:40.3499776Z   test_write_to_closures_in_inlining (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:40.3500171Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.3500391Z ok (0.010s)
2023-01-11T21:22:40.3500579Z   test_jit_save (__main__.TestTracer) ... ok (0.045s)
2023-01-11T21:22:40.3500726Z 
2023-01-11T21:22:40.3500925Z ----------------------------------------------------------------------
2023-01-11T21:22:40.3501168Z Ran 161 tests in 7.770s
2023-01-11T21:22:40.3501283Z 
2023-01-11T21:22:40.3501363Z OK (skipped=11, expected failures=1)
2023-01-11T21:22:40.3501493Z 
2023-01-11T21:22:40.3501576Z Generating XML reports...
2023-01-11T21:22:40.3501972Z Generated XML report: test-reports/python-unittest/dynamo.test_misc/TEST-MiscTests-20230111212231.xml
2023-01-11T21:22:40.3502684Z Generated XML report: test-reports/python-unittest/dynamo.test_misc/TEST-TestTracer-20230111212231.xml
2023-01-11T21:22:40.3502914Z 
2023-01-11T21:22:40.3503285Z ##[endgroup]
2023-01-11T21:22:40.3503753Z FINISHED PRINTING LOG FILE of dynamo/test_misc (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_misc_a8pqkth5)
2023-01-11T21:22:40.3503978Z 
2023-01-11T21:22:40.8367346Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:40.9309409Z Ignoring disabled issues:  []
2023-01-11T21:22:40.9450373Z Running dynamo/test_optimizations ... [2023-01-11 21:22:40.944652]
2023-01-11T21:22:40.9451750Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_optimizations.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:40.944958]
2023-01-11T21:22:42.4765685Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:42.5455767Z Ignoring disabled issues:  []
2023-01-11T21:22:42.5592327Z Running dynamo/test_repros ... [2023-01-11 21:22:42.558894]
2023-01-11T21:22:42.5593554Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_repros.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:42.559168]
2023-01-11T21:22:43.5577496Z 
2023-01-11T21:22:43.5579560Z Expand the folded group to see the log file of dynamo/test_optimizations
2023-01-11T21:22:43.5580837Z ##[group]PRINTING LOG FILE of dynamo/test_optimizations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizations_fis_ekyn)
2023-01-11T21:22:43.5581369Z 
2023-01-11T21:22:43.5581501Z Running tests...
2023-01-11T21:22:43.5582146Z ----------------------------------------------------------------------
2023-01-11T21:22:43.5583042Z Test results will be stored in test-reports/python-unittest/dynamo.test_optimizations
2023-01-11T21:22:43.5583584Z   test_inplace_normalize (__main__.NormalizeIRTests) ... ok (0.321s)
2023-01-11T21:22:43.5584047Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:43.5584552Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:43.5585032Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:43.5585708Z   test_example_inputs (__main__.TestOptimizations) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:43.5586199Z ok (0.092s)
2023-01-11T21:22:43.5586945Z   test_example_inputs_runtime_use (__main__.TestOptimizations) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:43.5587433Z ok (0.009s)
2023-01-11T21:22:43.5587820Z   test_has_mutation (__main__.TestOptimizations) ... ok (0.019s)
2023-01-11T21:22:43.5588522Z   test_has_mutation_factory (__main__.TestOptimizations) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:43.5589178Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:43.5589567Z ok (0.018s)
2023-01-11T21:22:43.5590248Z   test_inplacifier (__main__.TestOptimizations) ... optimizations [('out', 1), ('inplace', 1)]
2023-01-11T21:22:43.5590691Z ok (0.017s)
2023-01-11T21:22:43.5591078Z   test_ipex_bf16 (__main__.TestOptimizations) ... skip: requires ipex (0.001s)
2023-01-11T21:22:43.5591612Z   test_ipex_fp32 (__main__.TestOptimizations) ... skip: requires ipex (0.000s)
2023-01-11T21:22:43.5592311Z   test_log_conv_args (__main__.TestOptimizations) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:43.5593186Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:43.5593573Z ok (0.153s)
2023-01-11T21:22:43.5593752Z 
2023-01-11T21:22:43.5594115Z ----------------------------------------------------------------------
2023-01-11T21:22:43.5594529Z Ran 9 tests in 0.631s
2023-01-11T21:22:43.5594725Z 
2023-01-11T21:22:43.5594845Z OK (skipped=2)
2023-01-11T21:22:43.5595028Z 
2023-01-11T21:22:43.5595174Z Generating XML reports...
2023-01-11T21:22:43.5595972Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizations/TEST-NormalizeIRTests-20230111212242.xml
2023-01-11T21:22:43.5596981Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizations/TEST-TestOptimizations-20230111212242.xml
2023-01-11T21:22:43.5597435Z 
2023-01-11T21:22:43.5597879Z ##[endgroup]
2023-01-11T21:22:43.5598803Z FINISHED PRINTING LOG FILE of dynamo/test_optimizations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizations_fis_ekyn)
2023-01-11T21:22:43.5599257Z 
2023-01-11T21:22:45.5260526Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:45.6145867Z Ignoring disabled issues:  []
2023-01-11T21:22:45.6287693Z Running dynamo/test_torchxla_integration ... [2023-01-11 21:22:45.628456]
2023-01-11T21:22:45.6289515Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_torchxla_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:45.628759]
2023-01-11T21:22:47.5376849Z 
2023-01-11T21:22:47.5377591Z Expand the folded group to see the log file of dynamo/test_torchxla_integration
2023-01-11T21:22:47.5378850Z ##[group]PRINTING LOG FILE of dynamo/test_torchxla_integration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_integration_r08poec8)
2023-01-11T21:22:47.5379338Z 
2023-01-11T21:22:47.5379474Z Running tests...
2023-01-11T21:22:47.5380163Z ----------------------------------------------------------------------
2023-01-11T21:22:47.5380954Z Test results will be stored in test-reports/python-unittest/dynamo.test_torchxla_integration
2023-01-11T21:22:47.5381713Z   test_basic (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:47.5382493Z   test_inplace_update (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:47.5382971Z   test_linear (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:47.5383428Z   test_matmul (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:47.5383669Z 
2023-01-11T21:22:47.5383862Z ----------------------------------------------------------------------
2023-01-11T21:22:47.5384111Z Ran 4 tests in 0.003s
2023-01-11T21:22:47.5384232Z 
2023-01-11T21:22:47.5384306Z OK (skipped=4)
2023-01-11T21:22:47.5384413Z 
2023-01-11T21:22:47.5384499Z Generating XML reports...
2023-01-11T21:22:47.5384964Z Generated XML report: test-reports/python-unittest/dynamo.test_torchxla_integration/TEST-TorchXLAReuseGraphTest-20230111212247.xml
2023-01-11T21:22:47.5385234Z 
2023-01-11T21:22:47.5385573Z ##[endgroup]
2023-01-11T21:22:47.5386032Z FINISHED PRINTING LOG FILE of dynamo/test_torchxla_integration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_integration_r08poec8)
2023-01-11T21:22:47.5386271Z 
2023-01-11T21:22:49.6165125Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:49.7041159Z Ignoring disabled issues:  []
2023-01-11T21:22:49.7185171Z Running dynamo/test_unspec ... [2023-01-11 21:22:49.718198]
2023-01-11T21:22:49.7187373Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_unspec.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:49.718476]
2023-01-11T21:22:51.0829676Z 
2023-01-11T21:22:51.0830583Z Expand the folded group to see the log file of dynamo/test_repros
2023-01-11T21:22:51.0832363Z ##[group]PRINTING LOG FILE of dynamo/test_repros (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_repros_55ysf7ip)
2023-01-11T21:22:51.0832621Z 
2023-01-11T21:22:51.0832704Z Running tests...
2023-01-11T21:22:51.0833122Z ----------------------------------------------------------------------
2023-01-11T21:22:51.0833616Z Test results will be stored in test-reports/python-unittest/dynamo.test_repros
2023-01-11T21:22:51.0834029Z   test_Size (__main__.ReproTests) ... ok (0.353s)
2023-01-11T21:22:51.0834645Z   test_abc_setattr (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0835341Z inline_call [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:51.0835775Z unimplemented []
2023-01-11T21:22:51.0836578Z graph_break [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:51.0837233Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:51.0837669Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:51.0837955Z unimplemented []
2023-01-11T21:22:51.0838715Z graph_break [('setattr(UserDefinedObjectVariable) <function ReproTests.test_abc_setattr.<locals>.Derived.__setattr__ at 0x7efdcbc42290>', 1)]
2023-01-11T21:22:51.0839164Z inline_call []
2023-01-11T21:22:51.0839475Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0839694Z ok (0.011s)
2023-01-11T21:22:51.0840078Z   test_avoid_dupe_specialization (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0840647Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:51.0841159Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0841351Z ok (0.051s)
2023-01-11T21:22:51.0841576Z   test_batch_norm_act (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0841916Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:51.0842146Z ok (0.087s)
2023-01-11T21:22:51.0842496Z   test_batchnorm_e2e (__main__.ReproTests) ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:51.0842843Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0843032Z inline_call []
2023-01-11T21:22:51.0843333Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:51.0843625Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0843802Z ok (1.284s)
2023-01-11T21:22:51.0844121Z   test_bigbird_unsqueeze_inplace (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0844493Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:51.0844769Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0844962Z ok (0.036s)
2023-01-11T21:22:51.0845174Z   test_boxes_len (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0845508Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0845735Z ok (0.010s)
2023-01-11T21:22:51.0845956Z   test_chunk_reformer_ff (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0846400Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:51.0846629Z ok (0.077s)
2023-01-11T21:22:51.0847000Z   test_class_member (__main__.ReproTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:51.0847268Z ok (0.011s)
2023-01-11T21:22:51.0847584Z   test_convert_boxes_to_pooler_format (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:51.0847830Z inline_call []
2023-01-11T21:22:51.0848009Z unimplemented []
2023-01-11T21:22:51.0848277Z graph_break [('dynamic shapes: repeat_interleave', 2)]
2023-01-11T21:22:51.0848627Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:51.0848968Z ok (0.062s)
2023-01-11T21:22:51.0849198Z   test_create_rand_mask_from_inputs (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0849565Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:22:51.0849793Z ok (0.190s)
2023-01-11T21:22:51.0849993Z   test_dict_iter (__main__.ReproTests) ... ok (0.005s)
2023-01-11T21:22:51.0850340Z   test_dict_list_values (__main__.ReproTests) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:51.0850583Z unimplemented []
2023-01-11T21:22:51.0851039Z graph_break [('call_function in skip_files Builtin count', 2), ('call_function BuiltinVariable(zip) [UserDefinedObjectVariable(count), ListVariable()] {}', 2)]
2023-01-11T21:22:51.0851346Z ok (0.018s)
2023-01-11T21:22:51.0851651Z   test_do_paste_mask (__main__.ReproTests) ... frames [('total', 12), ('ok', 11)]
2023-01-11T21:22:51.0851980Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:51.0852329Z graph_break [('dynamic shapes: arange', 6), ('Dynamic slicing not supported', 4)]
2023-01-11T21:22:51.0852743Z stats [('calls_captured', 159), ('fusions_possible', 148), ('unique_graphs', 11)]
2023-01-11T21:22:51.0852979Z ok (1.524s)
2023-01-11T21:22:51.0853364Z   test_dynamic_shapes_right_side (__main__.ReproTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:51.0853646Z ok (0.085s)
2023-01-11T21:22:51.0854005Z   test_ellipsis (__main__.ReproTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:51.0854270Z ok (0.019s)
2023-01-11T21:22:51.0854554Z   test_exec_import (__main__.ReproTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:51.0854936Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0855191Z unimplemented []
2023-01-11T21:22:51.0855505Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0855752Z ok (0.004s)
2023-01-11T21:22:51.0856065Z   test_exec_wildcard_import (__main__.ReproTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:51.0856438Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0856693Z unimplemented []
2023-01-11T21:22:51.0857019Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0857402Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:51.0857622Z ok (0.016s)
2023-01-11T21:22:51.0857933Z   test_for_loop_graph_break (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0858171Z inline_call []
2023-01-11T21:22:51.0858539Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:51.0858956Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0859190Z ok (0.012s)
2023-01-11T21:22:51.0859500Z   test_for_loop_graph_break_before (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0859753Z unimplemented []
2023-01-11T21:22:51.0860132Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:51.0860409Z inline_call []
2023-01-11T21:22:51.0860706Z stats [('calls_captured', 100), ('fusions_possible', 99), ('unique_graphs', 1)]
2023-01-11T21:22:51.0860939Z ok (0.134s)
2023-01-11T21:22:51.0861178Z   test_get_parameter_dtype (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0861519Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:51.0861749Z ok (0.015s)
2023-01-11T21:22:51.0862008Z   test_grad_mode_carrying_correct_state_after_graph_break (__main__.ReproTests) ... Break
2023-01-11T21:22:51.0862296Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:51.0862731Z unimplemented []
2023-01-11T21:22:51.0863075Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0863524Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:51.0863754Z ok (0.011s)
2023-01-11T21:22:51.0864071Z   test_guard_fail_nested_tuple (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0864441Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:51.0864657Z ok (0.021s)
2023-01-11T21:22:51.0864977Z   test_guard_fail_tensor_bool (__main__.ReproTests) ... frames [('total', 12), ('ok', 12)]
2023-01-11T21:22:51.0865353Z unimplemented [('FOR_ITER UserDefinedObjectVariable(product)', 1)]
2023-01-11T21:22:51.0865963Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_guard_fail_tensor_bool.<locals>.fn.<locals>.get_expected at 0x7efe5e4e9c60>', 5), ('data dependent operator: aten.allclose.default', 5)]
2023-01-11T21:22:51.0866477Z stats [('calls_captured', 5), ('unique_graphs', 5), ('fusions_possible', 0)]
2023-01-11T21:22:51.0866713Z ok (0.079s)
2023-01-11T21:22:51.0866998Z   test_guard_ordering_shape_fail (__main__.ReproTests) ... ok (0.002s)
2023-01-11T21:22:51.0867275Z   test_hf_model_output (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0867625Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:22:51.0867853Z ok (0.050s)
2023-01-11T21:22:51.0868062Z   test_hf_t5_forward (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0868411Z stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:22:51.0868644Z ok (0.638s)
2023-01-11T21:22:51.0868939Z   test_indexing_with_list (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:51.0869235Z inline_call [('Tensor.numpy', 1)]
2023-01-11T21:22:51.0869442Z unimplemented []
2023-01-11T21:22:51.0869666Z graph_break [('Tensor.numpy', 1)]
2023-01-11T21:22:51.0869989Z stats [('unique_graphs', 2), ('calls_captured', 0), ('fusions_possible', -2)]
2023-01-11T21:22:51.0870309Z ok (0.022s)
2023-01-11T21:22:51.0870548Z   test_is_symbolic_tracing (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0870891Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0871121Z ok (0.007s)
2023-01-11T21:22:51.0871345Z   test_isinstance_dtype (__main__.ReproTests) ... ok (0.003s)
2023-01-11T21:22:51.0871951Z   test_isinstance_storage (__main__.ReproTests) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:22:51.0872548Z   bools = torch.BoolStorage.from_buffer(f, "big")
2023-01-11T21:22:51.0872816Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:22:51.0873011Z unimplemented []
2023-01-11T21:22:51.0873483Z graph_break [('call_function BuiltinVariable(bytearray) [ListVariable()] {}', 1), ('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:51.0874016Z inline_call [('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:51.0874424Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0874676Z expected failure (0.012s)
2023-01-11T21:22:51.0874907Z   test_issue1466_size_aot_autograd (__main__.ReproTests) ... arf
2023-01-11T21:22:51.0875123Z arf
2023-01-11T21:22:51.0875334Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:51.0875516Z unimplemented []
2023-01-11T21:22:51.0875850Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0876230Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:51.0876510Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0876705Z ok (0.032s)
2023-01-11T21:22:51.0876960Z   test_issue175 (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0877306Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:22:51.0877525Z ok (0.126s)
2023-01-11T21:22:51.0877905Z   test_longformer_chunk (__main__.ReproTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:51.0878179Z ok (0.161s)
2023-01-11T21:22:51.0878404Z   test_maml_item_capture (__main__.ReproTests) ... expected failure (0.001s)
2023-01-11T21:22:51.0878780Z   test_maml_no_item_capture (__main__.ReproTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:51.0879157Z inline_call [('inlining disallowed: <function grad at 0x7efdd52fd240>', 1)]
2023-01-11T21:22:51.0879387Z unimplemented []
2023-01-11T21:22:51.0879840Z graph_break [('Tensor.item', 2), ('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 1), ('inlining disallowed: <function grad at 0x7efdd52fd240>', 1)]
2023-01-11T21:22:51.0880317Z stats [('calls_captured', 29), ('fusions_possible', 25), ('unique_graphs', 4)]
2023-01-11T21:22:51.0880553Z ok (0.143s)
2023-01-11T21:22:51.0880899Z   test_modules (__main__.ReproTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:51.0881167Z ok (0.022s)
2023-01-11T21:22:51.0881470Z   test_multi_dot_import (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:51.0881912Z inline_call [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:51.0882208Z unimplemented []
2023-01-11T21:22:51.0882600Z graph_break [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:51.0883021Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0883237Z ok (0.011s)
2023-01-11T21:22:51.0883481Z   test_multi_import (__main__.ReproTests) ... skip: requires detectron2 (0.000s)
2023-01-11T21:22:51.0883927Z   test_named_buffers (__main__.ReproTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:51.0884189Z ok (0.011s)
2023-01-11T21:22:51.0884490Z   test_nn_parameter (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0884889Z inline_call [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:51.0885141Z unimplemented []
2023-01-11T21:22:51.0885485Z graph_break [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:51.0885874Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:51.0886104Z ok (0.014s)
2023-01-11T21:22:51.0886322Z   test_norm_dtype (__main__.ReproTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:51.0886734Z   test_not_rewrite_assert (__main__.ReproTests) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:51.0886995Z ok (0.005s)
2023-01-11T21:22:51.0887326Z   test_not_rewrite_assert_for_other_errors (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0887708Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:51.0887938Z ok (0.008s)
2023-01-11T21:22:51.0888219Z   test_numpy_list (__main__.ReproTests) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:51.0888448Z unimplemented []
2023-01-11T21:22:51.0888883Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_numpy_list.<locals>.rand_gen at 0x7efe5e51ef80>', 1)]
2023-01-11T21:22:51.0889201Z expected failure (0.020s)
2023-01-11T21:22:51.0889517Z   test_optimized_deepcopy (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0889886Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0890116Z ok (0.013s)
2023-01-11T21:22:51.0890396Z   test_primtorch (__main__.ReproTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:51.0890877Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:51.0891169Z unimplemented []
2023-01-11T21:22:51.0891547Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:51.0891825Z ok (0.005s)
2023-01-11T21:22:51.0892292Z   test_primtorch_no_graph_break (__main__.ReproTests) ... inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:51.0892641Z expected failure (0.004s)
2023-01-11T21:22:51.0892945Z   test_recursive_map (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0893175Z inline_call []
2023-01-11T21:22:51.0893479Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:51.0893697Z ok (0.013s)
2023-01-11T21:22:51.0893917Z   test_reformer_eval (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0894297Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:22:51.0894522Z ok (0.046s)
2023-01-11T21:22:51.0894751Z   test_reformer_min_chunk_len (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0895104Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:51.0895334Z ok (0.015s)
2023-01-11T21:22:51.0895548Z   test_reformer_remove_unused_args (__main__.ReproTests) ... foo
2023-01-11T21:22:51.0895766Z foo
2023-01-11T21:22:51.0895976Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:51.0896301Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0896561Z unimplemented []
2023-01-11T21:22:51.0896891Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:22:51.0897258Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:51.0897549Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0897746Z ok (0.024s)
2023-01-11T21:22:51.0897955Z   test_reformer_sorting (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:51.0898307Z stats [('calls_captured', 14), ('fusions_possible', 13), ('unique_graphs', 1)]
2023-01-11T21:22:51.0898537Z ok (0.039s)
2023-01-11T21:22:51.0898822Z   test_reformer_train (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:51.0899274Z inline_call [('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:51.0899573Z unimplemented []
2023-01-11T21:22:51.0900048Z graph_break [('autograd.Function with requires_grad', 1), ('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:51.0900499Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:51.0900729Z ok (0.065s)
2023-01-11T21:22:51.0901023Z   test_reinplacing (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0901378Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:51.0901672Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0901865Z ok (0.329s)
2023-01-11T21:22:51.0902236Z   test_relative_import (__main__.ReproTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:51.0902705Z ok (0.014s)
2023-01-11T21:22:51.0903122Z   test_relative_import_no_modulename (__main__.ReproTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:51.0903417Z ok (0.006s)
2023-01-11T21:22:51.0903791Z   test_rewrite_assert_noop (__main__.ReproTests) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:51.0904070Z ok (0.022s)
2023-01-11T21:22:51.0904455Z   test_rewrite_assert_with_fstring_msg (__main__.ReproTests) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:51.0904734Z ok (0.005s)
2023-01-11T21:22:51.0905191Z   test_rewrite_assert_with_msg (__main__.ReproTests) ... stats [('calls_captured', 18), ('fusions_possible', 15), ('unique_graphs', 3)]
2023-01-11T21:22:51.0905473Z ok (0.026s)
2023-01-11T21:22:51.0905870Z   test_rewrite_assert_without_msg (__main__.ReproTests) ... stats [('calls_captured', 12), ('fusions_possible', 10), ('unique_graphs', 2)]
2023-01-11T21:22:51.0906139Z ok (0.019s)
2023-01-11T21:22:51.0906435Z   test_rng_state (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:51.0906838Z unimplemented [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 1)]
2023-01-11T21:22:51.0907257Z graph_break [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 2)]
2023-01-11T21:22:51.0907646Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:51.0907878Z ok (0.031s)
2023-01-11T21:22:51.0908301Z   test_seq_append_list (__main__.ReproTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:51.0908561Z ok (0.024s)
2023-01-11T21:22:51.0909263Z   test_sigmoid_out (__main__.ReproTests) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:51.0909958Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:51.0910940Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:22:51.0911537Z   warnings.warn(msg)
2023-01-11T21:22:51.0912211Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:51.0912856Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:51.0913089Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:51.0913412Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:51.0913655Z ok (0.033s)
2023-01-11T21:22:51.0914037Z   test_slice_into_list_mutable (__main__.ReproTests) ... stats [('calls_captured', 30), ('fusions_possible', 29), ('unique_graphs', 1)]
2023-01-11T21:22:51.0914316Z ok (0.082s)
2023-01-11T21:22:51.0914637Z   test_slicing_dynamic_shape (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0914966Z unimplemented [('Dynamic slicing not supported', 2)]
2023-01-11T21:22:51.0915316Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:51.0915551Z ok (0.019s)
2023-01-11T21:22:51.0915878Z   test_slicing_dynamic_shape_setitem (__main__.ReproTests) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:51.0916212Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:51.0916521Z graph_break [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:51.0916859Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0917141Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0917372Z ok (0.013s)
2023-01-11T21:22:51.0918073Z   test_sort_out (__main__.ReproTests) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:51.0918766Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:51.0919820Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:51.0920483Z   return node.target(*args, **kwargs)
2023-01-11T21:22:51.0921151Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:51.0921807Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:51.0922489Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:51.0923146Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:51.0923413Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:51.0923720Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:51.0923955Z ok (0.041s)
2023-01-11T21:22:51.0924268Z   test_specialized_stride (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0924625Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0924855Z ok (0.011s)
2023-01-11T21:22:51.0925168Z   test_swin_base_tensor_attr (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0925528Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:51.0925760Z ok (0.015s)
2023-01-11T21:22:51.0926074Z   test_tensor_isinstance_tuple (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:51.0926439Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0926654Z ok (0.010s)
2023-01-11T21:22:51.0926950Z   test_tokenization (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:51.0927357Z inline_call [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:51.0927617Z unimplemented []
2023-01-11T21:22:51.0927970Z graph_break [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:51.0928224Z ok (0.009s)
2023-01-11T21:22:51.0928584Z   test_torch_ops_aten (__main__.ReproTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0928890Z ok (0.012s)
2023-01-11T21:22:51.0929270Z   test_vdd_duplicate_error (__main__.ReproTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:51.0929543Z ok (0.014s)
2023-01-11T21:22:51.0929842Z   test_while_loop_graph_break (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:51.0930082Z inline_call []
2023-01-11T21:22:51.0930466Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:51.0930862Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:51.0931092Z ok (0.007s)
2023-01-11T21:22:51.0931320Z   test_with_on_graph_break_inst (__main__.ReproTests) ... Hello world
2023-01-11T21:22:51.0931533Z Hello world
2023-01-11T21:22:51.0931753Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:51.0932126Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:51.0932394Z unimplemented []
2023-01-11T21:22:51.0932749Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2), ('Tensor.backward', 1)]
2023-01-11T21:22:51.0933154Z stats [('calls_captured', 11), ('fusions_possible', 7), ('unique_graphs', 4)]
2023-01-11T21:22:51.0933387Z ok (0.029s)
2023-01-11T21:22:51.0933482Z 
2023-01-11T21:22:51.0933684Z ----------------------------------------------------------------------
2023-01-11T21:22:51.0933933Z Ran 76 tests in 6.405s
2023-01-11T21:22:51.0934048Z 
2023-01-11T21:22:51.0934144Z OK (skipped=2, expected failures=4)
2023-01-11T21:22:51.0934275Z 
2023-01-11T21:22:51.0934349Z Generating XML reports...
2023-01-11T21:22:51.0934762Z Generated XML report: test-reports/python-unittest/dynamo.test_repros/TEST-ReproTests-20230111212244.xml
2023-01-11T21:22:51.0934992Z 
2023-01-11T21:22:51.0935327Z ##[endgroup]
2023-01-11T21:22:51.0935724Z FINISHED PRINTING LOG FILE of dynamo/test_repros (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_repros_55ysf7ip)
2023-01-11T21:22:51.0935956Z 
2023-01-11T21:22:52.9032229Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:52.9688600Z Ignoring disabled issues:  []
2023-01-11T21:22:52.9820891Z Running inductor/test_torchinductor ... [2023-01-11 21:22:52.981766]
2023-01-11T21:22:52.9822206Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:52.982018]
2023-01-11T21:22:59.1007161Z 
2023-01-11T21:22:59.1007918Z Expand the folded group to see the log file of dynamo/test_unspec
2023-01-11T21:22:59.1008979Z ##[group]PRINTING LOG FILE of dynamo/test_unspec (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_unspec_sghh16j8)
2023-01-11T21:22:59.1009486Z 
2023-01-11T21:22:59.1009601Z Running tests...
2023-01-11T21:22:59.1010203Z ----------------------------------------------------------------------
2023-01-11T21:22:59.1010877Z Test results will be stored in test-reports/python-unittest/dynamo.test_unspec
2023-01-11T21:22:59.1011509Z   test_access_by_keys_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.337s)
2023-01-11T21:22:59.1012068Z   test_basicmodule1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1012741Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:59.1013414Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1013820Z ok (0.018s)
2023-01-11T21:22:59.1014580Z   test_basicmodule2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1015121Z ok (0.013s)
2023-01-11T21:22:59.1015614Z   test_call_fn_with_non_const_inputs_safe_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1016310Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1016806Z ok (0.023s)
2023-01-11T21:22:59.1017223Z   test_cfgmod_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:59.1017512Z ok (0.027s)
2023-01-11T21:22:59.1017913Z   test_children_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1018206Z ok (0.018s)
2023-01-11T21:22:59.1018619Z   test_constloop_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:59.1018900Z ok (0.026s)
2023-01-11T21:22:59.1019148Z   test_densenet_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1019522Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:59.1019753Z ok (0.017s)
2023-01-11T21:22:59.1020069Z   test_enumvalues_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1020452Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:59.1020680Z ok (0.016s)
2023-01-11T21:22:59.1021102Z   test_fnmember_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1021411Z ok (0.012s)
2023-01-11T21:22:59.1021961Z   test_fnmembercmp1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1022244Z ok (0.012s)
2023-01-11T21:22:59.1022903Z   test_fnmembercmp2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1023201Z ok (0.012s)
2023-01-11T21:22:59.1023462Z   test_forward_directly_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1023840Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1024070Z ok (0.020s)
2023-01-11T21:22:59.1024321Z   test_generation_tag_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.002s)
2023-01-11T21:22:59.1024797Z   test_hasattr_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1025085Z ok (0.009s)
2023-01-11T21:22:59.1025494Z   test_intarg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1025783Z ok (0.013s)
2023-01-11T21:22:59.1026180Z   test_iseval1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1026467Z ok (0.012s)
2023-01-11T21:22:59.1026868Z   test_iseval2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1027135Z ok (0.012s)
2023-01-11T21:22:59.1027552Z   test_isnonelayer_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1027846Z ok (0.010s)
2023-01-11T21:22:59.1028255Z   test_istraining1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1028537Z ok (0.012s)
2023-01-11T21:22:59.1028946Z   test_istraining2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1029234Z ok (0.012s)
2023-01-11T21:22:59.1029629Z   test_layerlist_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1029921Z ok (0.016s)
2023-01-11T21:22:59.1030735Z   test_lazy_module_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:59.1031306Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:59.1032039Z [2023-01-11 21:22:51,981] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7fa5b7e2dfc0> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:22:59.1032799Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:59.1033299Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:59.1033950Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:59.1034442Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:59.1035097Z [2023-01-11 21:22:52,075] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7fa5b7e2dfc0> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:22:59.1035850Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:59.1036347Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:59.1036642Z frames [('total', 16), ('ok', 14)]
2023-01-11T21:22:59.1037387Z inline_call [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2), ('call_function UserDefinedObjectVariable(_infer_parameters) [NNModuleVariable(), TupleVariable()] {}', 1), ('call_function UserDefinedObjectVariable(_infer_parameters) [UnspecializedNNModuleVariable(LazyModule), TupleVariable()] {}', 1)]
2023-01-11T21:22:59.1038071Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.LazyModule'>", 2)]
2023-01-11T21:22:59.1038481Z graph_break [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2)]
2023-01-11T21:22:59.1038852Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1039070Z ok (0.239s)
2023-01-11T21:22:59.1039350Z   test_module_attribute_precedence_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1039748Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1039967Z ok (0.011s)
2023-01-11T21:22:59.1040235Z   test_module_class_method_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1040623Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:59.1040853Z ok (0.028s)
2023-01-11T21:22:59.1041222Z   test_module_forward_has_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 2)]
2023-01-11T21:22:59.1041502Z inline_call []
2023-01-11T21:22:59.1041787Z unimplemented [('reconstruct: ConstantVariable(dict)', 2)]
2023-01-11T21:22:59.1042236Z graph_break [('call_function BuiltinVariable(dict) [ListIteratorVariable()] {}', 1), ('call_method NNModuleVariable() buffers [] {}', 1)]
2023-01-11T21:22:59.1042666Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:59.1042897Z ok (0.075s)
2023-01-11T21:22:59.1043318Z   test_module_name_string_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1043654Z ok (0.014s)
2023-01-11T21:22:59.1043913Z   test_module_property_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1044294Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1044513Z ok (0.006s)
2023-01-11T21:22:59.1044777Z   test_module_static_method_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1045159Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:59.1045373Z ok (0.028s)
2023-01-11T21:22:59.1045793Z   test_moduledict_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1046092Z ok (0.011s)
2023-01-11T21:22:59.1046506Z   test_modulelist_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 40), ('fusions_possible', 39), ('unique_graphs', 1)]
2023-01-11T21:22:59.1046825Z ok (0.108s)
2023-01-11T21:22:59.1047083Z   test_modulemethod1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1047467Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:59.1047687Z ok (0.028s)
2023-01-11T21:22:59.1047945Z   test_modulemethod2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1048325Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:59.1048538Z ok (0.028s)
2023-01-11T21:22:59.1048973Z   test_nn_moduledict_contains_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:22:59.1049339Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:59.1049619Z inline_call [('Patched init cannot be inlined.', 1)]
2023-01-11T21:22:59.1050072Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.NNModuleTests.test_nn_moduledict_contains.<locals>.M'>", 1)]
2023-01-11T21:22:59.1050567Z graph_break [('Patched init cannot be inlined.', 1)]
2023-01-11T21:22:59.1050779Z ok (0.018s)
2023-01-11T21:22:59.1051021Z   test_parameters1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1051402Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1051632Z ok (0.009s)
2023-01-11T21:22:59.1051884Z   test_parameters2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1052248Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1052483Z ok (0.009s)
2023-01-11T21:22:59.1052901Z   test_parameters3_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:59.1053181Z ok (0.023s)
2023-01-11T21:22:59.1053603Z   test_self_mutating1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:59.1053900Z ok (0.038s)
2023-01-11T21:22:59.1054292Z   test_seq_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1054576Z ok (0.019s)
2023-01-11T21:22:59.1054839Z   test_simple_torch_function_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1055222Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1055438Z ok (0.013s)
2023-01-11T21:22:59.1055857Z   test_stringmember_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1056157Z ok (0.012s)
2023-01-11T21:22:59.1056397Z   test_submodules1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1056820Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:59.1057047Z ok (0.024s)
2023-01-11T21:22:59.1057300Z   test_submodules2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1057660Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:59.1057891Z ok (0.024s)
2023-01-11T21:22:59.1058136Z   test_super1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1058495Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1058722Z ok (0.015s)
2023-01-11T21:22:59.1058965Z   test_super2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1059322Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1059555Z ok (0.014s)
2023-01-11T21:22:59.1059815Z   test_super_class_method_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1060233Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1060451Z ok (0.007s)
2023-01-11T21:22:59.1060865Z   test_tensorlist_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1061158Z ok (0.011s)
2023-01-11T21:22:59.1061586Z   test_torch_function_with_closure_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1061892Z ok (0.008s)
2023-01-11T21:22:59.1062261Z   test_unsupportedmethod_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1062920Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:22:59.1063206Z unimplemented []
2023-01-11T21:22:59.1063596Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:22:59.1064017Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:22:59.1064235Z ok (0.028s)
2023-01-11T21:22:59.1064604Z   test_unsupportedmodule_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1065082Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:22:59.1065362Z unimplemented []
2023-01-11T21:22:59.1065747Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:22:59.1066170Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:22:59.1066399Z ok (0.030s)
2023-01-11T21:22:59.1066644Z   test_viamodulecall_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1067026Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1067258Z ok (0.015s)
2023-01-11T21:22:59.1067582Z   test_Size_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1068016Z inline_call [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:59.1068292Z unimplemented []
2023-01-11T21:22:59.1068648Z graph_break [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:59.1069030Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1069263Z ok (0.014s)
2023-01-11T21:22:59.1069613Z   test_abc_setattr_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1069866Z unimplemented []
2023-01-11T21:22:59.1070366Z graph_break [('setattr(UserDefinedObjectVariable) <function ReproTests.test_abc_setattr.<locals>.Derived.__setattr__ at 0x7fa5ae2fba30>', 1)]
2023-01-11T21:22:59.1070769Z inline_call []
2023-01-11T21:22:59.1071066Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1071295Z ok (0.009s)
2023-01-11T21:22:59.1071672Z   test_avoid_dupe_specialization_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1072078Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:59.1072359Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1072548Z ok (0.043s)
2023-01-11T21:22:59.1072902Z   test_batch_norm_act_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1073290Z inline_call [('data dependent operator: aten._local_scalar_dense.default', 2)]
2023-01-11T21:22:59.1073541Z unimplemented []
2023-01-11T21:22:59.1073857Z graph_break [('data dependent operator: aten._local_scalar_dense.default', 2)]
2023-01-11T21:22:59.1074260Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1074493Z ok (0.029s)
2023-01-11T21:22:59.1074894Z   test_batchnorm_e2e_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:59.1075236Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1075412Z inline_call []
2023-01-11T21:22:59.1075714Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1076005Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1076181Z ok (0.922s)
2023-01-11T21:22:59.1076556Z   test_bigbird_unsqueeze_inplace_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1076955Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1077233Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1077425Z ok (0.035s)
2023-01-11T21:22:59.1077679Z   test_boxes_len_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1078039Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1078272Z ok (0.009s)
2023-01-11T21:22:59.1078531Z   test_chunk_reformer_ff_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1078908Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1079126Z ok (0.075s)
2023-01-11T21:22:59.1079546Z   test_class_member_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1079841Z ok (0.011s)
2023-01-11T21:22:59.1080206Z   test_convert_boxes_to_pooler_format_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1080480Z inline_call []
2023-01-11T21:22:59.1080660Z unimplemented []
2023-01-11T21:22:59.1080928Z graph_break [('dynamic shapes: repeat_interleave', 2)]
2023-01-11T21:22:59.1081282Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:59.1081512Z ok (0.058s)
2023-01-11T21:22:59.1081781Z   test_create_rand_mask_from_inputs_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1082161Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:22:59.1082391Z ok (0.133s)
2023-01-11T21:22:59.1082635Z   test_dict_iter_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.005s)
2023-01-11T21:22:59.1083052Z   test_dict_list_values_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:59.1083319Z unimplemented []
2023-01-11T21:22:59.1083773Z graph_break [('call_function in skip_files Builtin count', 2), ('call_function BuiltinVariable(zip) [UserDefinedObjectVariable(count), ListVariable()] {}', 2)]
2023-01-11T21:22:59.1084089Z ok (0.017s)
2023-01-11T21:22:59.1084432Z   test_do_paste_mask_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 13), ('ok', 12)]
2023-01-11T21:22:59.1084862Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:59.1085225Z graph_break [('dynamic shapes: arange', 7), ('Dynamic slicing not supported', 4)]
2023-01-11T21:22:59.1085589Z stats [('calls_captured', 131), ('fusions_possible', 119), ('unique_graphs', 12)]
2023-01-11T21:22:59.1085822Z ok (1.444s)
2023-01-11T21:22:59.1086269Z   test_dynamic_shapes_right_side_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1086563Z ok (0.081s)
2023-01-11T21:22:59.1086976Z   test_ellipsis_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1087433Z ok (0.020s)
2023-01-11T21:22:59.1087782Z   test_exec_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:59.1088222Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1088480Z unimplemented []
2023-01-11T21:22:59.1088810Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1089043Z ok (0.004s)
2023-01-11T21:22:59.1089406Z   test_exec_wildcard_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:59.1089818Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1090070Z unimplemented []
2023-01-11T21:22:59.1090383Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1090757Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:59.1090988Z ok (0.017s)
2023-01-11T21:22:59.1091349Z   test_for_loop_graph_break_before_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1091632Z unimplemented []
2023-01-11T21:22:59.1092014Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:59.1092289Z inline_call []
2023-01-11T21:22:59.1092584Z stats [('calls_captured', 100), ('fusions_possible', 99), ('unique_graphs', 1)]
2023-01-11T21:22:59.1092820Z ok (0.135s)
2023-01-11T21:22:59.1093185Z   test_for_loop_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1093434Z inline_call []
2023-01-11T21:22:59.1093814Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:59.1094229Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1094445Z ok (0.013s)
2023-01-11T21:22:59.1094709Z   test_get_parameter_dtype_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1095101Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1095330Z ok (0.018s)
2023-01-11T21:22:59.1095610Z   test_grad_mode_carrying_correct_state_after_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... Break
2023-01-11T21:22:59.1095940Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1096133Z unimplemented []
2023-01-11T21:22:59.1096451Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1096831Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:59.1097060Z ok (0.016s)
2023-01-11T21:22:59.1097417Z   test_guard_fail_nested_tuple_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1097810Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:59.1098034Z ok (0.023s)
2023-01-11T21:22:59.1098392Z   test_guard_fail_tensor_bool_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 12), ('ok', 12)]
2023-01-11T21:22:59.1098835Z unimplemented [('FOR_ITER UserDefinedObjectVariable(product)', 1)]
2023-01-11T21:22:59.1099455Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_guard_fail_tensor_bool.<locals>.fn.<locals>.get_expected at 0x7fa640f535b0>', 5), ('data dependent operator: aten.allclose.default', 5)]
2023-01-11T21:22:59.1099964Z stats [('calls_captured', 5), ('unique_graphs', 5), ('fusions_possible', 0)]
2023-01-11T21:22:59.1100184Z ok (0.097s)
2023-01-11T21:22:59.1100455Z   test_guard_ordering_shape_fail_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.001s)
2023-01-11T21:22:59.1100803Z   test_hf_model_output_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1101183Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:22:59.1101400Z ok (0.052s)
2023-01-11T21:22:59.1101688Z   test_hf_t5_forward_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1102076Z stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:22:59.1102296Z ok (0.505s)
2023-01-11T21:22:59.1102852Z   test_indexing_with_list_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1103178Z inline_call [('Tensor.numpy', 1)]
2023-01-11T21:22:59.1103371Z unimplemented []
2023-01-11T21:22:59.1103608Z graph_break [('Tensor.numpy', 1)]
2023-01-11T21:22:59.1103933Z stats [('unique_graphs', 2), ('calls_captured', 0), ('fusions_possible', -2)]
2023-01-11T21:22:59.1104164Z ok (0.022s)
2023-01-11T21:22:59.1104415Z   test_is_symbolic_tracing_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1104797Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1105024Z ok (0.006s)
2023-01-11T21:22:59.1105269Z   test_isinstance_dtype_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.003s)
2023-01-11T21:22:59.1105951Z   test_isinstance_storage_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:22:59.1106571Z   bools = torch.BoolStorage.from_buffer(f, "big")
2023-01-11T21:22:59.1106842Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:22:59.1107024Z unimplemented []
2023-01-11T21:22:59.1107506Z graph_break [('call_function BuiltinVariable(bytearray) [ListVariable()] {}', 1), ('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:59.1108044Z inline_call [('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:59.1108451Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1108684Z expected failure (0.013s)
2023-01-11T21:22:59.1123330Z   test_issue1466_size_aot_autograd_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... arf
2023-01-11T21:22:59.1123598Z arf
2023-01-11T21:22:59.1123875Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1124070Z unimplemented []
2023-01-11T21:22:59.1124411Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1124796Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:59.1125078Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1125273Z ok (0.033s)
2023-01-11T21:22:59.1125525Z   test_issue175_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1125890Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:22:59.1126302Z ok (0.127s)
2023-01-11T21:22:59.1126734Z   test_longformer_chunk_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:59.1127026Z ok (0.160s)
2023-01-11T21:22:59.1127299Z   test_maml_item_capture_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... expected failure (0.002s)
2023-01-11T21:22:59.1127757Z   test_maml_no_item_capture_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:59.1128159Z inline_call [('inlining disallowed: <function grad at 0x7fa5b7d7a050>', 1)]
2023-01-11T21:22:59.1128391Z unimplemented []
2023-01-11T21:22:59.1128848Z graph_break [('Tensor.item', 2), ('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 1), ('inlining disallowed: <function grad at 0x7fa5b7d7a050>', 1)]
2023-01-11T21:22:59.1129292Z stats [('calls_captured', 29), ('fusions_possible', 25), ('unique_graphs', 4)]
2023-01-11T21:22:59.1129579Z ok (0.164s)
2023-01-11T21:22:59.1129993Z   test_modules_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:59.1130287Z ok (0.025s)
2023-01-11T21:22:59.1130641Z   test_multi_dot_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1131112Z inline_call [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:59.1131407Z unimplemented []
2023-01-11T21:22:59.1131806Z graph_break [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:59.1132209Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1132442Z ok (0.013s)
2023-01-11T21:22:59.1132722Z   test_multi_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... skip: requires detectron2 (0.000s)
2023-01-11T21:22:59.1133249Z   test_named_buffers_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:59.1133541Z ok (0.013s)
2023-01-11T21:22:59.1133893Z   test_nn_parameter_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1134320Z inline_call [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:59.1134572Z unimplemented []
2023-01-11T21:22:59.1134916Z graph_break [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:59.1135305Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1135536Z ok (0.015s)
2023-01-11T21:22:59.1135789Z   test_norm_dtype_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... skip: requires cuda (0.001s)
2023-01-11T21:22:59.1136267Z   test_not_rewrite_assert_for_other_errors_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1136677Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1136894Z ok (0.009s)
2023-01-11T21:22:59.1137297Z   test_not_rewrite_assert_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:59.1137591Z ok (0.005s)
2023-01-11T21:22:59.1137925Z   test_numpy_list_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:59.1138189Z unimplemented []
2023-01-11T21:22:59.1138609Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_numpy_list.<locals>.rand_gen at 0x7fa640f52440>', 1)]
2023-01-11T21:22:59.1138922Z expected failure (0.030s)
2023-01-11T21:22:59.1139287Z   test_optimized_deepcopy_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1139726Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1139958Z ok (0.014s)
2023-01-11T21:22:59.1140460Z   test_primtorch_no_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:59.1140833Z expected failure (0.003s)
2023-01-11T21:22:59.1141197Z   test_primtorch_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1141658Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:59.1141933Z unimplemented []
2023-01-11T21:22:59.1142319Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:59.1142760Z ok (0.005s)
2023-01-11T21:22:59.1143169Z   test_recursive_map_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1143435Z inline_call []
2023-01-11T21:22:59.1143739Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1143966Z ok (0.013s)
2023-01-11T21:22:59.1144207Z   test_reformer_eval_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1144584Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:22:59.1144816Z ok (0.046s)
2023-01-11T21:22:59.1145068Z   test_reformer_min_chunk_len_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1145450Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1145680Z ok (0.016s)
2023-01-11T21:22:59.1145927Z   test_reformer_remove_unused_args_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... foo
2023-01-11T21:22:59.1146173Z foo
2023-01-11T21:22:59.1146382Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1146714Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1146975Z unimplemented []
2023-01-11T21:22:59.1147307Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:22:59.1147687Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:59.1147965Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1148159Z ok (0.026s)
2023-01-11T21:22:59.1148421Z   test_reformer_sorting_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:59.1148794Z stats [('calls_captured', 14), ('fusions_possible', 13), ('unique_graphs', 1)]
2023-01-11T21:22:59.1149025Z ok (0.040s)
2023-01-11T21:22:59.1149382Z   test_reformer_train_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1149860Z inline_call [('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:59.1150232Z unimplemented []
2023-01-11T21:22:59.1150713Z graph_break [('autograd.Function with requires_grad', 1), ('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:59.1151177Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:59.1151395Z ok (0.065s)
2023-01-11T21:22:59.1151752Z   test_reinplacing_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1152142Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1152423Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1152614Z ok (0.297s)
2023-01-11T21:22:59.1153058Z   test_relative_import_no_modulename_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1153367Z ok (0.008s)
2023-01-11T21:22:59.1153832Z   test_relative_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1154129Z ok (0.007s)
2023-01-11T21:22:59.1154555Z   test_rewrite_assert_noop_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:59.1154843Z ok (0.022s)
2023-01-11T21:22:59.1155265Z   test_rewrite_assert_with_fstring_msg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:59.1155565Z ok (0.005s)
2023-01-11T21:22:59.1156006Z   test_rewrite_assert_with_msg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 18), ('fusions_possible', 15), ('unique_graphs', 3)]
2023-01-11T21:22:59.1156298Z ok (0.024s)
2023-01-11T21:22:59.1156778Z   test_rewrite_assert_without_msg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 12), ('fusions_possible', 10), ('unique_graphs', 2)]
2023-01-11T21:22:59.1157082Z ok (0.017s)
2023-01-11T21:22:59.1157413Z   test_rng_state_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1157839Z unimplemented [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 1)]
2023-01-11T21:22:59.1158264Z graph_break [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 2)]
2023-01-11T21:22:59.1158643Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:59.1158860Z ok (0.031s)
2023-01-11T21:22:59.1159280Z   test_seq_append_list_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:59.1159583Z ok (0.025s)
2023-01-11T21:22:59.1160315Z   test_sigmoid_out_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:59.1161041Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:59.1161939Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:22:59.1162541Z   warnings.warn(msg)
2023-01-11T21:22:59.1163214Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:59.1163856Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:59.1164103Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:59.1164406Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:59.1164640Z ok (0.035s)
2023-01-11T21:22:59.1165078Z   test_slice_into_list_mutable_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 30), ('fusions_possible', 29), ('unique_graphs', 1)]
2023-01-11T21:22:59.1165402Z ok (0.087s)
2023-01-11T21:22:59.1165782Z   test_slicing_dynamic_shape_setitem_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:59.1166156Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:59.1166452Z graph_break [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:59.1166798Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1167093Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1167283Z ok (0.013s)
2023-01-11T21:22:59.1167633Z   test_slicing_dynamic_shape_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1168000Z unimplemented [('Dynamic slicing not supported', 2)]
2023-01-11T21:22:59.1168349Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:59.1168572Z ok (0.020s)
2023-01-11T21:22:59.1169328Z   test_sort_out_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:59.1170050Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:59.1171065Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:59.1171723Z   return node.target(*args, **kwargs)
2023-01-11T21:22:59.1172409Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:59.1173064Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:59.1173731Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:59.1174384Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:59.1174644Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:59.1174962Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:59.1175184Z ok (0.043s)
2023-01-11T21:22:59.1175550Z   test_specialized_stride_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1175955Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1176175Z ok (0.012s)
2023-01-11T21:22:59.1176542Z   test_swin_base_tensor_attr_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1176966Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:59.1177195Z ok (0.016s)
2023-01-11T21:22:59.1177555Z   test_tensor_isinstance_tuple_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1177950Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1178178Z ok (0.011s)
2023-01-11T21:22:59.1178515Z   test_tokenization_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1178948Z inline_call [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:59.1179213Z unimplemented []
2023-01-11T21:22:59.1179563Z graph_break [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:59.1179803Z ok (0.010s)
2023-01-11T21:22:59.1180264Z   test_torch_ops_aten_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1180571Z ok (0.012s)
2023-01-11T21:22:59.1180989Z   test_vdd_duplicate_error_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:59.1181290Z ok (0.014s)
2023-01-11T21:22:59.1181658Z   test_while_loop_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:59.1181913Z inline_call []
2023-01-11T21:22:59.1182291Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:59.1182886Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1183118Z ok (0.008s)
2023-01-11T21:22:59.1183367Z   test_with_on_graph_break_inst_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... Hello world
2023-01-11T21:22:59.1183629Z Hello world
2023-01-11T21:22:59.1183853Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:59.1184181Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:59.1184445Z unimplemented []
2023-01-11T21:22:59.1184817Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2), ('Tensor.backward', 1)]
2023-01-11T21:22:59.1185205Z stats [('calls_captured', 11), ('fusions_possible', 7), ('unique_graphs', 4)]
2023-01-11T21:22:59.1185435Z ok (0.032s)
2023-01-11T21:22:59.1185688Z   test_builtin_functions_on_cuda (__main__.UnspecTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:59.1186055Z   test_builtin_getitem (__main__.UnspecTests) ... frames [('total', 1)]
2023-01-11T21:22:59.1186283Z expected failure (0.009s)
2023-01-11T21:22:59.1186601Z   test_builtin_max_min (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1186961Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:59.1187181Z ok (0.011s)
2023-01-11T21:22:59.1187519Z   test_feed_random_values_into_graph_only (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1187896Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:59.1188112Z ok (0.016s)
2023-01-11T21:22:59.1188469Z   test_multiple_consecutive_random_calls_before_graph (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1188861Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1189085Z ok (0.031s)
2023-01-11T21:22:59.1189379Z   test_no_recompilations (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1189744Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1189975Z ok (0.007s)
2023-01-11T21:22:59.1190330Z   test_numpy_correctness (__main__.UnspecTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:59.1190764Z unimplemented [('reconstruct: ConstantVariable(float64)', 1)]
2023-01-11T21:22:59.1191078Z graph_break [('Tensor.numpy', 2), ('numpy', 2)]
2023-01-11T21:22:59.1191401Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1191632Z ok (0.025s)
2023-01-11T21:22:59.1191958Z   test_random_call_with_while_loop (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1192331Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:59.1192545Z ok (0.008s)
2023-01-11T21:22:59.1192871Z   test_random_values_with_graph_break (__main__.UnspecTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:59.1193126Z unimplemented []
2023-01-11T21:22:59.1193350Z graph_break [('Tensor.item', 2)]
2023-01-11T21:22:59.1193674Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:22:59.1193903Z ok (0.021s)
2023-01-11T21:22:59.1194251Z   test_unspec_float_precision (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:59.1194629Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:59.1194852Z ok (0.109s)
2023-01-11T21:22:59.1194958Z 
2023-01-11T21:22:59.1195148Z ----------------------------------------------------------------------
2023-01-11T21:22:59.1195401Z Ran 137 tests in 7.194s
2023-01-11T21:22:59.1195515Z 
2023-01-11T21:22:59.1195613Z OK (skipped=3, expected failures=5)
2023-01-11T21:22:59.1195746Z 
2023-01-11T21:22:59.1195834Z Generating XML reports...
2023-01-11T21:22:59.1196255Z Generated XML report: test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecNNModuleTests-20230111212251.xml
2023-01-11T21:22:59.1196800Z Generated XML report: test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecReproTests-20230111212251.xml
2023-01-11T21:22:59.1197316Z Generated XML report: test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecTests-20230111212251.xml
2023-01-11T21:22:59.1197542Z 
2023-01-11T21:22:59.1197888Z ##[endgroup]
2023-01-11T21:22:59.1198295Z FINISHED PRINTING LOG FILE of dynamo/test_unspec (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_unspec_sghh16j8)
2023-01-11T21:22:59.1198525Z 
2023-01-11T21:23:01.1473391Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:01.2369600Z Ignoring disabled issues:  []
2023-01-11T21:23:01.2509711Z Running profiler/test_profiler_tree ... [2023-01-11 21:23:01.250586]
2023-01-11T21:23:01.2511054Z Executing ['/opt/conda/bin/python', '-bb', 'profiler/test_profiler_tree.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:01.250879]
2023-01-11T21:23:03.3241547Z 
2023-01-11T21:23:03.3242194Z Expand the folded group to see the log file of profiler/test_profiler_tree
2023-01-11T21:23:03.3243394Z ##[group]PRINTING LOG FILE of profiler/test_profiler_tree (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_tree_ha4oif61)
2023-01-11T21:23:03.3243665Z 
2023-01-11T21:23:03.3243742Z Running tests...
2023-01-11T21:23:03.3244323Z ----------------------------------------------------------------------
2023-01-11T21:23:03.3245020Z Test results will be stored in test-reports/python-unittest/profiler.test_profiler_tree
2023-01-11T21:23:03.3246635Z   test_profiler_experimental_tree (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82499 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.244s)
2023-01-11T21:23:03.3247846Z   test_profiler_experimental_tree_cuda (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.001s)
2023-01-11T21:23:03.3248673Z   test_profiler_experimental_tree_cuda_detailed (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.001s)
2023-01-11T21:23:03.3249514Z   test_profiler_experimental_tree_cuda_with_stream (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.001s)
2023-01-11T21:23:03.3250728Z   test_profiler_experimental_tree_with_memory (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82501 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:23:03.3251583Z   test_profiler_experimental_tree_with_memory_and_stack (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3252112Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3252566Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3253090Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3253511Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3253963Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3254428Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3254860Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3255289Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3255558Z ok (0.015s)
2023-01-11T21:23:03.3256376Z   test_profiler_experimental_tree_with_record_function (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/83246 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:23:03.3257236Z   test_profiler_experimental_tree_with_stack_and_modules (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3257740Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3258189Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3258628Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3259055Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3259488Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3259936Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3260353Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3260795Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3261058Z ok (0.014s)
2023-01-11T21:23:03.3261556Z   test_profiler_experimental_tree_with_stack_and_torch_dispatch (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3262064Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3262698Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3263143Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3263640Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3264072Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3264506Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3264934Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3265362Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3265625Z ok (0.006s)
2023-01-11T21:23:03.3266120Z   test_profiler_experimental_tree_with_stack_and_torch_function (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3266691Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3267126Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3267558Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3267989Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3268430Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3268849Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:23:03.3269274Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:23:03.3269718Z STAGE:2023-01-11 21:23:02 1532:1532 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:23:03.3269969Z ok (0.005s)
2023-01-11T21:23:03.3270074Z 
2023-01-11T21:23:03.3270348Z ----------------------------------------------------------------------
2023-01-11T21:23:03.3270597Z Ran 10 tests in 0.289s
2023-01-11T21:23:03.3270712Z 
2023-01-11T21:23:03.3270786Z OK (skipped=6)
2023-01-11T21:23:03.3270879Z 
2023-01-11T21:23:03.3270964Z Generating XML reports...
2023-01-11T21:23:03.3271404Z Generated XML report: test-reports/python-unittest/profiler.test_profiler_tree/TEST-TestProfilerTree-20230111212302.xml
2023-01-11T21:23:03.3271656Z 
2023-01-11T21:23:03.3271941Z ##[endgroup]
2023-01-11T21:23:03.3272359Z FINISHED PRINTING LOG FILE of profiler/test_profiler_tree (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_tree_ha4oif61)
2023-01-11T21:23:03.3272604Z 
2023-01-11T21:23:05.3170166Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:05.4078730Z Ignoring disabled issues:  []
2023-01-11T21:23:05.4214392Z Running test_ao_sparsity ... [2023-01-11 21:23:05.421127]
2023-01-11T21:23:05.4215998Z Executing ['/opt/conda/bin/python', '-bb', 'test_ao_sparsity.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:05.421397]
2023-01-11T21:23:13.6317547Z 
2023-01-11T21:23:13.6318545Z Expand the folded group to see the log file of test_ao_sparsity
2023-01-11T21:23:13.6319902Z ##[group]PRINTING LOG FILE of test_ao_sparsity (/var/lib/jenkins/workspace/test/test-reports/test_ao_sparsity_8pf9rcmq)
2023-01-11T21:23:13.6320272Z 
2023-01-11T21:23:13.6320706Z Running tests...
2023-01-11T21:23:13.6321520Z ----------------------------------------------------------------------
2023-01-11T21:23:13.6322151Z Test results will be stored in test-reports/python-unittest/test_ao_sparsity
2023-01-11T21:23:13.6322528Z   test_activation_sparsifier (ao.sparsity.test_activation_sparsifier.TestActivationSparsifier)
2023-01-11T21:23:13.6322902Z Simulates the workflow of the activation sparsifier, starting from object creation ... ok (0.308s)
2023-01-11T21:23:13.6323427Z   test_constructor (ao.sparsity.test_data_scheduler.TestBaseDataScheduler)
2023-01-11T21:23:13.6323759Z Checks if the warning is thrown if the scheduler step is called ... ok (0.002s)
2023-01-11T21:23:13.6324093Z   test_order_of_steps (ao.sparsity.test_data_scheduler.TestBaseDataScheduler) ... ok (0.007s)
2023-01-11T21:23:13.6324468Z   test_state_dict (ao.sparsity.test_data_scheduler.TestBaseDataScheduler) ... ok (0.003s)
2023-01-11T21:23:13.6324828Z   test_step (ao.sparsity.test_data_scheduler.TestBaseDataScheduler) ... ok (0.008s)
2023-01-11T21:23:13.6325607Z   test_nn_embeddings (ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier) ... /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py:104: UserWarning: Replacing existing data of the same name. - Did you mean a different name?
2023-01-11T21:23:13.6326231Z   warnings.warn("Replacing existing data of the same name. - Did you mean a different name?")
2023-01-11T21:23:13.6326484Z ok (0.021s)
2023-01-11T21:23:13.6326839Z   test_nn_parameters (ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier) ... ok (0.015s)
2023-01-11T21:23:13.6327221Z   test_tensors (ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier) ... ok (0.015s)
2023-01-11T21:23:13.6327569Z   test_constructor (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:13.6327923Z   test_mask_squash (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.001s)
2023-01-11T21:23:13.6328290Z   test_mask_squash_with_params1 (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:13.6328649Z   test_mask_squash_with_params2 (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:13.6329018Z   test_mask_squash_with_params3 (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:13.6329378Z   test_prepare_config (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.001s)
2023-01-11T21:23:13.6329732Z   test_state_dict (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.003s)
2023-01-11T21:23:13.6330064Z   test_step (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.001s)
2023-01-11T21:23:13.6330435Z   test_complex_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier)
2023-01-11T21:23:13.6330791Z Test fusion for models that contain Conv2d & Linear modules. ... ok (0.090s)
2023-01-11T21:23:13.6331144Z   test_constructor (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.004s)
2023-01-11T21:23:13.6331571Z   test_prepare_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.028s)
2023-01-11T21:23:13.6332005Z   test_prepare_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.012s)
2023-01-11T21:23:13.6332451Z   test_prune_conv2d_activation_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.152s)
2023-01-11T21:23:13.6332890Z   test_prune_conv2d_bias_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.092s)
2023-01-11T21:23:13.6333334Z   test_prune_conv2d_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.050s)
2023-01-11T21:23:13.6333779Z   test_prune_conv2d_padding_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.267s)
2023-01-11T21:23:13.6334226Z   test_prune_conv2d_pool_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.043s)
2023-01-11T21:23:13.6334663Z   test_prune_linear_activation_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.061s)
2023-01-11T21:23:13.6335114Z   test_prune_linear_bias_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.061s)
2023-01-11T21:23:13.6335545Z   test_prune_linear_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier)
2023-01-11T21:23:13.6335936Z test pruning linear-> linear modules ... ok (0.060s)
2023-01-11T21:23:13.6336327Z   test_step_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.025s)
2023-01-11T21:23:13.6336841Z   test_step_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.009s)
2023-01-11T21:23:13.6337691Z   test_convert_without_squash_mask (ao.sparsity.test_composability.TestComposability) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:23:13.6338183Z   warnings.warn(
2023-01-11T21:23:13.6338344Z ok (0.288s)
2023-01-11T21:23:13.6338621Z   test_fusion_before_s_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.284s)
2023-01-11T21:23:13.6338994Z   test_q_prep_before_s_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.293s)
2023-01-11T21:23:13.6339401Z   test_qat_prep_before_s_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.011s)
2023-01-11T21:23:13.6339786Z   test_s_prep_before_fusion (ao.sparsity.test_composability.TestComposability) ... ok (0.293s)
2023-01-11T21:23:13.6340155Z   test_s_prep_before_q_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.299s)
2023-01-11T21:23:13.6340532Z   test_s_prep_before_qat_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.012s)
2023-01-11T21:23:13.6340884Z   test_constructor (ao.sparsity.test_scheduler.TestCubicScheduler) ... ok (0.002s)
2023-01-11T21:23:13.6341691Z   test_step (ao.sparsity.test_scheduler.TestCubicScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/scheduler/base_scheduler.py:125: UserWarning: Detected call of `scheduler.step()` before `sparsifier.step()`. You have to make sure you run the sparsifier.step() BEFORE any calls to the scheduer.step().
2023-01-11T21:23:13.6342264Z   warnings.warn("Detected call of `scheduler.step()` before `sparsifier.step()`. "
2023-01-11T21:23:13.6342658Z ok (0.003s)
2023-01-11T21:23:13.6342919Z   test_jit_trace (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.085s)
2023-01-11T21:23:13.6343283Z   test_masking_logic (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.002s)
2023-01-11T21:23:13.6343658Z   test_state_dict_preserved (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.005s)
2023-01-11T21:23:13.6344031Z   test_weights_parametrized (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.002s)
2023-01-11T21:23:13.6344412Z   test_q_prep_fx_before_s_prep (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:13.6344860Z This test checks that the ordering of prepare_fx -> sparse prepare -> convert_fx ... ok (0.309s)
2023-01-11T21:23:13.6345220Z   test_q_prep_fx_s_prep_ref_conv (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:13.6345657Z This checks that the ordering: prepare_fx -> sparse prepare -> convert_to_reference_fx ... ok (0.295s)
2023-01-11T21:23:13.6346030Z   test_s_prep_before_q_prep_fx (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:13.6346462Z This test checks that the ordering of sparse prepare -> prepare_fx -> convert_fx ... ok (0.321s)
2023-01-11T21:23:13.6346821Z   test_s_prep_before_qat_prep_fx (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:13.6347258Z This test checks that the ordering of sparse prepare -> prepare_qat_fx -> convert_fx ... ok (0.029s)
2023-01-11T21:23:13.6347618Z   test_s_prep_q_prep_fx_ref (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:13.6348058Z This checks that the ordering: sparse prepare -> prepare_fx -> convert_to_reference_fx ... ok (0.294s)
2023-01-11T21:23:13.6348429Z   test_constructor (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.002s)
2023-01-11T21:23:13.6348827Z   test_mask_squash (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.002s)
2023-01-11T21:23:13.6349222Z   test_prepare (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.001s)
2023-01-11T21:23:13.6349683Z   test_sparsity_levels (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.437s)
2023-01-11T21:23:13.6350063Z   test_step (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.259s)
2023-01-11T21:23:13.6350508Z   test_nn_embeddings (ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers) ... ok (0.276s)
2023-01-11T21:23:13.6350895Z   test_nn_parameters (ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers) ... ok (0.225s)
2023-01-11T21:23:13.6351262Z   test_tensors (ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers) ... ok (0.245s)
2023-01-11T21:23:13.6351682Z   test_ptq_quantize_first (ao.sparsity.test_data_sparsifier.TestQuantizationUtils)
2023-01-11T21:23:13.6352005Z The expectation is post_training_sparse_quantize function ... ok (0.019s)
2023-01-11T21:23:13.6352339Z   test_ptq_sparsify_first (ao.sparsity.test_data_sparsifier.TestQuantizationUtils)
2023-01-11T21:23:13.6352716Z The expectation is post_training_sparse_quantize function ... ok (0.007s)
2023-01-11T21:23:13.6353256Z   test_sparse_qlinear (ao.sparsity.test_kernels.TestQuantizedSparseKernels) ... 2023-01-11 21:23:12,642 - root - INFO - static sparse qlinear is only available in fbgemm
2023-01-11T21:23:13.6353740Z 2023-01-11 21:23:12,656 - root - INFO - static sparse qlinear is only available in fbgemm
2023-01-11T21:23:13.6354131Z 2023-01-11 21:23:12,666 - root - INFO - dynamic sparse qlinear is only available in qnnpack
2023-01-11T21:23:13.6354514Z 2023-01-11 21:23:12,669 - root - INFO - dynamic sparse qlinear is only available in qnnpack
2023-01-11T21:23:13.6354736Z ok (0.060s)
2023-01-11T21:23:13.6355333Z   test_sparse_qlinear (ao.sparsity.test_kernels.TestQuantizedSparseLayers) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:23:13.6355760Z   warnings.warn(
2023-01-11T21:23:13.6356096Z [W qlinear_dynamic.cpp:247] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator())
2023-01-11T21:23:13.6356705Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:302: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:23:13.6357037Z   warnings.warn(
2023-01-11T21:23:13.6357207Z ok (0.107s)
2023-01-11T21:23:13.6357479Z   test_sparse_qlinear_serdes (ao.sparsity.test_kernels.TestQuantizedSparseLayers) ... ok (0.139s)
2023-01-11T21:23:13.6357840Z   test_constructor (ao.sparsity.test_scheduler.TestScheduler) ... ok (0.001s)
2023-01-11T21:23:13.6358174Z   test_lambda_scheduler (ao.sparsity.test_scheduler.TestScheduler) ... ok (0.001s)
2023-01-11T21:23:13.6358490Z   test_order_of_steps (ao.sparsity.test_scheduler.TestScheduler)
2023-01-11T21:23:13.6358781Z Checks if the warning is thrown if the scheduler step is called ... ok (0.005s)
2023-01-11T21:23:13.6359093Z   test_step (ao.sparsity.test_scheduler.TestScheduler) ... ok (0.001s)
2023-01-11T21:23:13.6359432Z   test_fqn_to_module (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6359734Z Tests that fqn_to_module operates as inverse ... ok (0.004s)
2023-01-11T21:23:13.6360054Z   test_fqn_to_module_fail (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6360374Z Tests that fqn_to_module returns None when it tries to ... ok (0.002s)
2023-01-11T21:23:13.6360691Z   test_fqn_to_module_for_tensors (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6361036Z Tests that fqn_to_module works for tensors, actually all parameters ... ok (0.005s)
2023-01-11T21:23:13.6361390Z   test_get_arg_info_from_tensor_fqn (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6361745Z Tests that get_arg_info_from_tensor_fqn works for all parameters of the model. ... ok (0.004s)
2023-01-11T21:23:13.6362101Z   test_get_arg_info_from_tensor_fqn_fail (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6362584Z Tests that get_arg_info_from_tensor_fqn works as expected for invalid tensor_fqn ... ok (0.003s)
2023-01-11T21:23:13.6362930Z   test_module_to_fqn (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6363264Z Tests that module_to_fqn works as expected when compared to known good ... ok (0.003s)
2023-01-11T21:23:13.6363594Z   test_module_to_fqn_fail (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6363995Z Tests that module_to_fqn returns None when an fqn that doesn't ... ok (0.002s)
2023-01-11T21:23:13.6364326Z   test_module_to_fqn_root (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:13.6364731Z Tests that module_to_fqn returns '' when model and target module are the same ... ok (0.002s)
2023-01-11T21:23:13.6365087Z   test_constructor (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.001s)
2023-01-11T21:23:13.6365509Z   test_mask_squash (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.001s)
2023-01-11T21:23:13.6365882Z   test_prepare (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.001s)
2023-01-11T21:23:13.6366245Z   test_sparsity_levels (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.028s)
2023-01-11T21:23:13.6366620Z   test_step (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.228s)
2023-01-11T21:23:13.6366984Z   test_step_2_of_4 (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.013s)
2023-01-11T21:23:13.6367183Z 
2023-01-11T21:23:13.6367370Z ----------------------------------------------------------------------
2023-01-11T21:23:13.6367613Z Ran 79 tests in 6.264s
2023-01-11T21:23:13.6367727Z 
2023-01-11T21:23:13.6367790Z OK
2023-01-11T21:23:13.6367880Z 
2023-01-11T21:23:13.6367965Z Generating XML reports...
2023-01-11T21:23:13.6368469Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_activation_sparsifier.TestActivationSparsifier-20230111212306.xml
2023-01-11T21:23:13.6369156Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_scheduler.TestBaseDataScheduler-20230111212306.xml
2023-01-11T21:23:13.6369815Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier-20230111212306.xml
2023-01-11T21:23:13.6370457Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestBaseSparsifier-20230111212306.xml
2023-01-11T21:23:13.6371126Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier-20230111212306.xml
2023-01-11T21:23:13.6371796Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestComposability-20230111212306.xml
2023-01-11T21:23:13.6372479Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestCubicScheduler-20230111212306.xml
2023-01-11T21:23:13.6373115Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_parametrization.TestFakeSparsity-20230111212306.xml
2023-01-11T21:23:13.6373751Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestFxComposability-20230111212306.xml
2023-01-11T21:23:13.6374417Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier-20230111212306.xml
2023-01-11T21:23:13.6375093Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers-20230111212306.xml
2023-01-11T21:23:13.6375757Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestQuantizationUtils-20230111212306.xml
2023-01-11T21:23:13.6376401Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseKernels-20230111212306.xml
2023-01-11T21:23:13.6377087Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseLayers-20230111212306.xml
2023-01-11T21:23:13.6377703Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestScheduler-20230111212306.xml
2023-01-11T21:23:13.6378330Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions-20230111212306.xml
2023-01-11T21:23:13.6378972Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestWeightNormSparsifier-20230111212306.xml
2023-01-11T21:23:13.6379264Z 
2023-01-11T21:23:13.6379554Z ##[endgroup]
2023-01-11T21:23:13.6379948Z FINISHED PRINTING LOG FILE of test_ao_sparsity (/var/lib/jenkins/workspace/test/test-reports/test_ao_sparsity_8pf9rcmq)
2023-01-11T21:23:13.6380170Z 
2023-01-11T21:23:15.5453168Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:15.6309806Z Ignoring disabled issues:  []
2023-01-11T21:23:15.6447541Z Running test_foreach ... [2023-01-11 21:23:15.644493]
2023-01-11T21:23:15.6449452Z Executing ['/opt/conda/bin/python', '-bb', 'test_foreach.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:15.644744]
2023-01-11T21:23:18.1671444Z 
2023-01-11T21:23:18.1671995Z Expand the folded group to see the log file of test_foreach
2023-01-11T21:23:18.1672987Z ##[group]PRINTING LOG FILE of test_foreach (/var/lib/jenkins/workspace/test/test-reports/test_foreach_j11d3hc8)
2023-01-11T21:23:18.1673227Z 
2023-01-11T21:23:18.1673328Z Running tests...
2023-01-11T21:23:18.1673729Z ----------------------------------------------------------------------
2023-01-11T21:23:18.1674006Z 
2023-01-11T21:23:18.1674220Z ----------------------------------------------------------------------
2023-01-11T21:23:18.1674458Z Ran 0 tests in 0.000s
2023-01-11T21:23:18.1674602Z 
2023-01-11T21:23:18.1674651Z OK
2023-01-11T21:23:18.1674741Z 
2023-01-11T21:23:18.1674825Z Generating XML reports...
2023-01-11T21:23:18.1675150Z Test results will be stored in test-reports/python-unittest/test_foreach
2023-01-11T21:23:18.1675327Z 
2023-01-11T21:23:18.1676836Z ##[endgroup]
2023-01-11T21:23:18.1677242Z FINISHED PRINTING LOG FILE of test_foreach (/var/lib/jenkins/workspace/test/test-reports/test_foreach_j11d3hc8)
2023-01-11T21:23:18.1677453Z 
2023-01-11T21:23:20.0443319Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:20.1091514Z Ignoring disabled issues:  []
2023-01-11T21:23:20.1226044Z Running test_function_schema ... [2023-01-11 21:23:20.122355]
2023-01-11T21:23:20.1228585Z Executing ['/opt/conda/bin/python', '-bb', 'test_function_schema.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:20.122611]
2023-01-11T21:23:22.2027574Z 
2023-01-11T21:23:22.2028260Z Expand the folded group to see the log file of test_function_schema
2023-01-11T21:23:22.2029110Z ##[group]PRINTING LOG FILE of test_function_schema (/var/lib/jenkins/workspace/test/test-reports/test_function_schema_jpsmuh2e)
2023-01-11T21:23:22.2029387Z 
2023-01-11T21:23:22.2029484Z Running tests...
2023-01-11T21:23:22.2029883Z ----------------------------------------------------------------------
2023-01-11T21:23:22.2030378Z Test results will be stored in test-reports/python-unittest/test_function_schema
2023-01-11T21:23:22.2030708Z   test_backward_compatible_arguments (__main__.TestFunctionSchema) ... ok (0.227s)
2023-01-11T21:23:22.2031087Z   test_backward_compatible_outputs (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2031410Z   test_backward_compatible_structure (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2031788Z   test_backward_compatible_with_smart_serialization (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2032145Z   test_forward_compatible_arguments_real_use_case (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2032671Z   test_forward_compatible_arguments_with_out (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2033011Z   test_forward_compatible_arguments_without_out (__main__.TestFunctionSchema) ... ok (0.002s)
2023-01-11T21:23:22.2033351Z   test_out_schema (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2033626Z   test_schema_error (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2033919Z   test_serialize_and_deserialize (__main__.TestFunctionSchema) ... ok (0.212s)
2023-01-11T21:23:22.2034290Z   test_string_optional_parameter_default_value (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2034632Z   test_tensor_list_alias_annotation_properly_parsed (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2035024Z   test_tensor_option_arguments_properly_parsed (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:22.2035214Z 
2023-01-11T21:23:22.2035500Z ----------------------------------------------------------------------
2023-01-11T21:23:22.2035771Z Ran 13 tests in 0.448s
2023-01-11T21:23:22.2035882Z 
2023-01-11T21:23:22.2035944Z OK
2023-01-11T21:23:22.2036034Z 
2023-01-11T21:23:22.2036116Z Generating XML reports...
2023-01-11T21:23:22.2036592Z Generated XML report: test-reports/python-unittest/test_function_schema/TEST-TestFunctionSchema-20230111212321.xml
2023-01-11T21:23:22.2036836Z 
2023-01-11T21:23:22.2037125Z ##[endgroup]
2023-01-11T21:23:22.2037527Z FINISHED PRINTING LOG FILE of test_function_schema (/var/lib/jenkins/workspace/test/test-reports/test_function_schema_jpsmuh2e)
2023-01-11T21:23:22.2037755Z 
2023-01-11T21:23:24.0476100Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:24.1114251Z Ignoring disabled issues:  []
2023-01-11T21:23:24.1246781Z Running test_jit ... [2023-01-11 21:23:24.124425]
2023-01-11T21:23:24.1249043Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:24.124687]
2023-01-11T21:25:38.7972898Z 
2023-01-11T21:25:38.7973406Z Expand the folded group to see the log file of test_jit
2023-01-11T21:25:38.8024064Z ##[group]PRINTING LOG FILE of test_jit (/var/lib/jenkins/workspace/test/test-reports/test_jit_k9iebijm)
2023-01-11T21:25:38.8024424Z CUDA not available, skipping tests
2023-01-11T21:25:38.8024559Z 
2023-01-11T21:25:38.8024634Z Running tests...
2023-01-11T21:25:38.8025074Z ----------------------------------------------------------------------
2023-01-11T21:25:38.8025708Z Test results will be stored in test-reports/python-unittest/test_jit
2023-01-11T21:25:38.8065394Z   test_becomes_wildcard_annotations (jit.test_alias_analysis.TestAliasAnalysis) ... ok (0.002s)
2023-01-11T21:25:38.8066088Z   test_nested_list_construct_not_wildcard (jit.test_alias_analysis.TestAliasAnalysis) ... ok (0.010s)
2023-01-11T21:25:38.8066706Z   test_recursive_calls (jit.test_alias_analysis.TestAliasAnalysis) ... ok (0.014s)
2023-01-11T21:25:38.8067295Z   test_async_future_type_python (jit.test_async.TestAsync) ... ok (0.001s)
2023-01-11T21:25:38.8067804Z   test_async_grad_guard_no_grad (jit.test_async.TestAsync) ... ok (0.043s)
2023-01-11T21:25:38.8068339Z   test_async_grad_guard_with_grad (jit.test_async.TestAsync) ... ok (0.006s)
2023-01-11T21:25:38.8068838Z   test_async_kwargs (jit.test_async.TestAsync) ... ok (0.192s)
2023-01-11T21:25:38.8069337Z   test_async_parsing (jit.test_async.TestAsync) ... ok (0.008s)
2023-01-11T21:25:38.8069795Z   test_async_python (jit.test_async.TestAsync) ... ok (0.002s)
2023-01-11T21:25:38.8072986Z   test_async_script (jit.test_async.TestAsync) ... ok (0.006s)
2023-01-11T21:25:38.8073878Z   test_async_script_capture (jit.test_async.TestAsync) ... ok (0.008s)
2023-01-11T21:25:38.8074844Z   test_async_script_error (jit.test_async.TestAsync) ... ok (0.030s)
2023-01-11T21:25:38.8075388Z   test_async_script_multi_forks (jit.test_async.TestAsync) ... ok (0.014s)
2023-01-11T21:25:38.8075934Z   test_async_script_multi_waits (jit.test_async.TestAsync) ... ok (0.006s)
2023-01-11T21:25:38.8077492Z   test_async_script_nested (jit.test_async.TestAsync) ... ok (0.009s)
2023-01-11T21:25:38.8077969Z   test_async_script_no_script_mod (jit.test_async.TestAsync) ... ok (0.001s)
2023-01-11T21:25:38.8078419Z   test_async_script_trace (jit.test_async.TestAsync) ... ok (0.032s)
2023-01-11T21:25:38.8078809Z   test_future_subtyping (jit.test_async.TestAsync)
2023-01-11T21:25:38.8079210Z Test that futures subtype each other properly. ... ok (0.009s)
2023-01-11T21:25:38.8079617Z   test_no_future_subtype_message (jit.test_async.TestAsync) ... ok (0.001s)
2023-01-11T21:25:38.8080033Z   test_trace_fork_wait (jit.test_async.TestAsync) ... ok (0.010s)
2023-01-11T21:25:38.8080452Z   test_trace_fork_wait_inline (jit.test_async.TestAsync) ... ok (0.007s)
2023-01-11T21:25:38.8080876Z   test_trace_fork_wait_leaking (jit.test_async.TestAsync) ... ok (0.002s)
2023-01-11T21:25:38.8081346Z   test_trace_fork_wait_list_modulecalls (jit.test_async.TestAsync) ... ok (0.024s)
2023-01-11T21:25:38.8134922Z   test_trace_modulecalls_with_different_output_types (jit.test_async.TestAsync) ... ok (0.015s)
2023-01-11T21:25:38.8135559Z   test_aten_pow_zero_negative_exponent (jit.test_aten_pow.TestAtenPow)
2023-01-11T21:25:38.8136019Z 1. Testing a = int, b = int ... ok (0.021s)
2023-01-11T21:25:38.8136513Z   test_autodiff_requires_grad_nograd (jit.test_autodiff.TestAutodiffJit) ... ok (0.083s)
2023-01-11T21:25:38.8137091Z   test_requires_grad_outputs (jit.test_autodiff.TestAutodiffJit) ... ok (0.051s)
2023-01-11T21:25:38.8137709Z   test_requires_grad_outputs_profiled_twice (jit.test_autodiff.TestAutodiffJit) ... ok (0.118s)
2023-01-11T21:25:38.8138340Z   test_requires_grad_outputs_side_effects (jit.test_autodiff.TestAutodiffJit) ... ok (0.116s)
2023-01-11T21:25:38.8138937Z   test_undefined_tensor_lists (jit.test_autodiff.TestAutodiffJit) ... ok (0.116s)
2023-01-11T21:25:38.8139567Z   test_aliased_outputs (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.003s)
2023-01-11T21:25:38.8140285Z   test_bias_as_arg (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.365s)
2023-01-11T21:25:38.8141007Z   test_bias_as_module_attr (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.310s)
2023-01-11T21:25:38.8141734Z   test_chunk_constant_script_ad (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.004s)
2023-01-11T21:25:38.8142640Z   test_constructed_bias (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.023s)
2023-01-11T21:25:38.8143394Z   test_diff_graph_inline_threshold (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.007s)
2023-01-11T21:25:38.8144323Z   test_differentiable_graph_ops_requires_grad (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... skip: disable until we property handle tensor lists with undefined gradients (0.001s)
2023-01-11T21:25:38.8145223Z   test_does_not_create_cycles (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:38.8145965Z   test_does_not_merge_unrelated (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:38.8146912Z   test_has_profiled_info_aliasing_outputs (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.001s)
2023-01-11T21:25:38.8147686Z   test_merge_respects_aliasing (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.009s)
2023-01-11T21:25:38.8148419Z   test_merges_dense (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:38.8149109Z   test_merges_down (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:38.8149814Z   test_merges_up (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:38.8150515Z   test_merges_without_cycles (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:38.8151710Z   test_prune_grad (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... skip: Simple Executor doesn't support gradients (0.001s)
2023-01-11T21:25:38.8152677Z   test_requires_grad_for_tensor_list (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:38.8153453Z   test_respects_lexical_scoping (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.007s)
2023-01-11T21:25:38.8154196Z   test_simple_merge (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:38.8154904Z   test_simple_no_merge (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:38.8155919Z   test_errors (jit.test_backends.TestBackends) ... [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T21:25:38.8156765Z ok (0.143s)
2023-01-11T21:25:38.8157750Z   test_execution (jit.test_backends.TestBackends) ... [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T21:25:38.8158505Z ok (0.136s)
2023-01-11T21:25:38.8159290Z   test_save_load (jit.test_backends.TestBackends) ... [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T21:25:38.8160026Z ok (0.245s)
2023-01-11T21:25:38.8160477Z   test_errors (jit.test_backends.TestBackendsWithCompiler) ... ok (0.045s)
2023-01-11T21:25:38.8161081Z   test_execution (jit.test_backends.TestBackendsWithCompiler) ... ok (0.067s)
2023-01-11T21:25:38.8161641Z   test_batch_mm_no_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.006s)
2023-01-11T21:25:38.8162197Z   test_batch_mm_permitted_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.006s)
2023-01-11T21:25:38.8162777Z   test_batch_mm_prohibited_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.006s)
2023-01-11T21:25:38.8163354Z   test_batch_mm_prohibited_mutation_if_node (jit.test_batch_mm.TestBatchMM) ... ok (0.008s)
2023-01-11T21:25:38.8163930Z   test_batch_mm_prohibited_mutation_multiple_adds (jit.test_batch_mm.TestBatchMM) ... ok (0.008s)
2023-01-11T21:25:38.8164537Z   test_batch_mm_side_permitted_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.008s)
2023-01-11T21:25:38.8165140Z   test_batch_mm_side_prohibited_mutation_common_side (jit.test_batch_mm.TestBatchMM) ... ok (0.009s)
2023-01-11T21:25:38.8165747Z   test_batch_mm_side_prohibited_mutation_uncommon_side (jit.test_batch_mm.TestBatchMM) ... ok (0.009s)
2023-01-11T21:25:38.8166245Z   test_del (jit.test_builtins.TestBuiltins) ... ok (0.006s)
2023-01-11T21:25:38.8166773Z   test_del_multiple_operands (jit.test_builtins.TestBuiltins) ... ok (0.008s)
2023-01-11T21:25:38.8167305Z   test_has_attr (jit.test_builtins.TestBuiltins) ... ok (0.018s)
2023-01-11T21:25:38.8167806Z   test_has_attr_invalid_args (jit.test_builtins.TestBuiltins) ... ok (0.008s)
2023-01-11T21:25:38.8168353Z   test_cast_overloads (jit.test_class_type.TestClassType) ... ok (0.036s)
2023-01-11T21:25:38.8168888Z   test_class_attribute_wrong_type (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8169480Z Test that the error message displayed when convering a class type ... ok (0.019s)
2023-01-11T21:25:38.8170003Z   test_class_constant (jit.test_class_type.TestClassType) ... ok (0.032s)
2023-01-11T21:25:38.8170553Z   test_class_constructs_itself (jit.test_class_type.TestClassType) ... ok (0.017s)
2023-01-11T21:25:38.8171109Z   test_class_inheritance (jit.test_class_type.TestClassType) ... ok (0.123s)
2023-01-11T21:25:38.8171637Z   test_class_inheritance_implicit (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8172143Z Test that inheritance is detected in ... ok (0.031s)
2023-01-11T21:25:38.8172742Z   test_class_sorting (jit.test_class_type.TestClassType) ... ok (0.085s)
2023-01-11T21:25:38.8173295Z   test_class_specialization (jit.test_class_type.TestClassType) ... ok (0.079s)
2023-01-11T21:25:38.8173841Z   test_class_type_as_param (jit.test_class_type.TestClassType) ... ok (0.017s)
2023-01-11T21:25:38.8174360Z   test_classmethod (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8174812Z Test classmethods on class types. ... ok (0.023s)
2023-01-11T21:25:38.8175282Z   test_conditional_set_attr (jit.test_class_type.TestClassType) ... ok (0.134s)
2023-01-11T21:25:38.8175802Z   test_custom_delete (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8176306Z Test that del can be called on an instance of a class that ... ok (0.044s)
2023-01-11T21:25:38.8176804Z   test_default_args (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8177298Z Test that methods on class types can have default arguments. ... ok (0.099s)
2023-01-11T21:25:38.8177924Z   test_get_attr (jit.test_class_type.TestClassType) ... ok (0.014s)
2023-01-11T21:25:38.8178463Z   test_get_attr_not_initialized (jit.test_class_type.TestClassType) ... ok (0.012s)
2023-01-11T21:25:38.8178992Z   test_get_with_method (jit.test_class_type.TestClassType) ... ok (0.001s)
2023-01-11T21:25:38.8179517Z   test_imported_classes (jit.test_class_type.TestClassType) ... ok (0.031s)
2023-01-11T21:25:38.8180036Z   test_in (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:38.8180541Z   test_init_compiled_first (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:38.8181059Z   test_interface (jit.test_class_type.TestClassType) ... ok (0.269s)
2023-01-11T21:25:38.8181588Z   test_optional_type_promotion (jit.test_class_type.TestClassType) ... ok (0.030s)
2023-01-11T21:25:38.8182131Z   test_out_of_order_methods (jit.test_class_type.TestClassType) ... ok (0.138s)
2023-01-11T21:25:38.8182787Z   test_overloaded_fn (jit.test_class_type.TestClassType) ... ok (0.148s)
2023-01-11T21:25:38.8183314Z   test_properties (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8183821Z Test that a scripted class can make use of the @property decorator. ... ok (0.072s)
2023-01-11T21:25:38.8184415Z   test_py_class_to_ivalue_missing_attribute (jit.test_class_type.TestClassType) ... ok (0.021s)
2023-01-11T21:25:38.8184976Z   test_python_interop (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:38.8185502Z   test_recursive_class (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8186071Z Recursive class types not yet supported. We should give a good error message. ... ok (0.137s)
2023-01-11T21:25:38.8186651Z   test_recursive_script_builtin_type_resolution (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8187533Z Test resolution of built-in torch types(e.g. torch.Tensor, torch.device) when a class is recursively compiled. ... ok (0.053s)
2023-01-11T21:25:38.8191702Z   test_recursive_script_module_builtin_type_resolution (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8192614Z Test resolution of built-in torch types(e.g. torch.Tensor, torch.device) when a class is recursively compiled ... ok (0.028s)
2023-01-11T21:25:38.8193251Z   test_recursive_scripting (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8193887Z Test that class types are recursively scripted when an Python instance of one ... ok (0.025s)
2023-01-11T21:25:38.8194485Z   test_recursive_scripting_failed (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8195008Z Test that class types module attributes that fail to script ... ok (0.045s)
2023-01-11T21:25:38.8195546Z   test_reference_semantics (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8196106Z Test that modifications made to a class instance in TorchScript ... ok (0.016s)
2023-01-11T21:25:38.8196650Z   test_save_load_with_classes (jit.test_class_type.TestClassType) ... ok (0.018s)
2023-01-11T21:25:38.8197229Z   test_save_load_with_classes_nested (jit.test_class_type.TestClassType) ... ok (0.162s)
2023-01-11T21:25:38.8197991Z   test_save_load_with_classes_returned (jit.test_class_type.TestClassType) ... ok (0.019s)
2023-01-11T21:25:38.8198529Z   test_schema_human_readable (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8199170Z Make sure that the schema is human readable, ie the mode parameter should read "nearest" instead of being displayed in octal ... ok (0.232s)
2023-01-11T21:25:38.8199832Z   test_self_referential_method (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8200399Z Test that a scripted class can have a method that refers to the class itself ... ok (0.026s)
2023-01-11T21:25:38.8200955Z   test_set_attr_in_method (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:38.8201487Z   test_set_attr_non_initialized (jit.test_class_type.TestClassType) ... ok (0.012s)
2023-01-11T21:25:38.8202062Z   test_set_attr_type_mismatch (jit.test_class_type.TestClassType) ... ok (0.011s)
2023-01-11T21:25:38.8202602Z   test_staticmethod (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8203163Z Test static methods on class types. ... ok (0.032s)
2023-01-11T21:25:38.8203615Z   test_type_annotation (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8204157Z Test that annotating container attributes with types works correctly ... ok (0.148s)
2023-01-11T21:25:38.8204697Z   test_type_annotations (jit.test_class_type.TestClassType) ... ok (0.012s)
2023-01-11T21:25:38.8205264Z   test_unresolved_class_attributes (jit.test_class_type.TestClassType) ... ok (0.022s)
2023-01-11T21:25:38.8205789Z   test_unused_method (jit.test_class_type.TestClassType)
2023-01-11T21:25:38.8206254Z Test unused methods on scripted classes. ... ok (0.026s)
2023-01-11T21:25:38.8206740Z   test_binary_op_complex_tensor (jit.test_complex.TestComplex) ... ok (0.018s)
2023-01-11T21:25:38.8207281Z   test_comparison_ops (jit.test_complex.TestComplex) ... ok (0.013s)
2023-01-11T21:25:38.8207802Z   test_complex_constants_and_ops (jit.test_complex.TestComplex) ... ok (0.449s)
2023-01-11T21:25:38.8208329Z   test_complex_constructor (jit.test_complex.TestComplex) ... ok (0.048s)
2023-01-11T21:25:38.8208867Z   test_complex_list_sum (jit.test_complex.TestComplex) ... ok (0.003s)
2023-01-11T21:25:38.8209376Z   test_complex_parse (jit.test_complex.TestComplex) ... ok (0.007s)
2023-01-11T21:25:38.8209863Z   test_complexdict (jit.test_complex.TestComplex) ... ok (0.004s)
2023-01-11T21:25:38.8210362Z   test_complexlist (jit.test_complex.TestComplex) ... ok (0.003s)
2023-01-11T21:25:38.8210847Z   test_div (jit.test_complex.TestComplex) ... ok (0.003s)
2023-01-11T21:25:38.8211330Z   test_infj_nanj_pickle (jit.test_complex.TestComplex) ... ok (0.007s)
2023-01-11T21:25:38.8211811Z   test_pickle (jit.test_complex.TestComplex) ... ok (0.007s)
2023-01-11T21:25:38.8212287Z   test_script (jit.test_complex.TestComplex) ... ok (0.003s)
2023-01-11T21:25:38.8212787Z   test_tensor_attributes (jit.test_complex.TestComplex) ... ok (0.007s)
2023-01-11T21:25:38.8213346Z   test_torch_complex_constructor_with_tensor (jit.test_complex.TestComplex) ... ok (0.039s)
2023-01-11T21:25:38.8213999Z   test_calling_scripted_custom_op (jit.test_custom_operators.TestCustomOperators) ... ok (0.004s)
2023-01-11T21:25:38.8214669Z   test_calling_traced_custom_op (jit.test_custom_operators.TestCustomOperators) ... ok (0.005s)
2023-01-11T21:25:38.8215334Z   test_default_arguments_are_used (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8215972Z   test_dynamic_op_registry (jit.test_custom_operators.TestCustomOperators) ... ok (0.002s)
2023-01-11T21:25:38.8216596Z   test_generic_list (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8217230Z   test_passing_and_returning_lists (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8217913Z   test_passing_one_positional_but_not_the_second (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8218561Z   test_passing_too_few_args (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8219332Z   test_passing_too_many_args (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8219967Z   test_passing_unknown_kwargs (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8220628Z   test_script_graph_contains_custom_op (jit.test_custom_operators.TestCustomOperators) ... ok (0.002s)
2023-01-11T21:25:38.8221471Z   test_script_graph_for_custom_ops_matches_traced_graph (jit.test_custom_operators.TestCustomOperators) ... skip: Need to figure out default dtype differences between fbcode and oss (0.000s)
2023-01-11T21:25:38.8222286Z   test_simply_calling_an_operator (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:38.8223136Z   test_where_no_scalar (jit.test_custom_operators.TestCustomOperators) ... ok (0.003s)
2023-01-11T21:25:38.8223698Z   test_setattr_no_aliasdb (jit.test_dce.TestDCE) ... ok (0.005s)
2023-01-11T21:25:38.8224174Z   test_setattr_removed (jit.test_dce.TestDCE) ... ok (0.008s)
2023-01-11T21:25:38.8225139Z   test_python_submodule_script (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:25:38.8226033Z   test_shared_module (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:25:38.8226973Z   test_tensor_sharing (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:25:38.8227855Z   test_tensor_sharing_with_forward (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:25:38.8228743Z   test_traced_module (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:25:38.8229353Z   test__post_init__ (jit.test_dataclasses.TestDataclasses) ... ok (3.396s)
2023-01-11T21:25:38.8229921Z   test_comparators (jit.test_dataclasses.TestDataclasses) ... ok (4.813s)
2023-01-11T21:25:38.8230461Z   test_custom__eq__ (jit.test_dataclasses.TestDataclasses) ... ok (0.018s)
2023-01-11T21:25:38.8231106Z   test_default_factories (jit.test_dataclasses.TestDataclasses) ... ok (0.005s)
2023-01-11T21:25:38.8231683Z   test_init_vars (jit.test_dataclasses.TestDataclasses) ... ok (0.084s)
2023-01-11T21:25:38.8232194Z   test_no_source (jit.test_dataclasses.TestDataclasses) ... ok (0.018s)
2023-01-11T21:25:38.8232799Z   test_use_unregistered_dataclass_raises (jit.test_dataclasses.TestDataclasses) ... ok (0.002s)
2023-01-11T21:25:38.8233435Z   test_custom_device_op (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.008s)
2023-01-11T21:25:38.8234041Z   test_device_apply (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.002s)
2023-01-11T21:25:38.8234614Z   test_device_arg (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:38.8235223Z   test_device_if_propagation (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.004s)
2023-01-11T21:25:38.8235840Z   test_if_loop_mix (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.004s)
2023-01-11T21:25:38.8236430Z   test_loop_device_change (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:38.8237022Z   test_loop_simple (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:38.8237595Z   test_mobilenet (jit.test_device_analysis.TestDeviceAnalysis) ... ok (3.484s)
2023-01-11T21:25:38.8238181Z   test_nested_loops (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.008s)
2023-01-11T21:25:38.8238745Z   test_set_dtype (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:38.8239310Z   test_simple (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.005s)
2023-01-11T21:25:38.8239899Z   test_tensor_as_fns (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.009s)
2023-01-11T21:25:38.8240461Z   test_while_change (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:38.8241052Z   test_zerodim_cpu (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.012s)
2023-01-11T21:25:38.8241661Z   test_zerodim_gpu (jit.test_device_analysis.TestDeviceAnalysis) ... skip: No CUDA (0.000s)
2023-01-11T21:25:38.8242386Z   test_zerodim_no_device (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.023s)
2023-01-11T21:25:38.8242909Z   test_aug_assign (jit.test_list_dict.TestDict) ... ok (0.019s)
2023-01-11T21:25:38.8243375Z   test_basic (jit.test_list_dict.TestDict) ... ok (0.023s)
2023-01-11T21:25:38.8243827Z   test_clear (jit.test_list_dict.TestDict) ... ok (0.005s)
2023-01-11T21:25:38.8244249Z   test_copy (jit.test_list_dict.TestDict) ... ok (0.006s)
2023-01-11T21:25:38.8244701Z   test_del (jit.test_list_dict.TestDict) ... ok (0.009s)
2023-01-11T21:25:38.8245180Z   test_dict_bool_conversion (jit.test_list_dict.TestDict) ... ok (0.027s)
2023-01-11T21:25:38.8245673Z   test_dict_preserves_order (jit.test_list_dict.TestDict) ... ok (0.077s)
2023-01-11T21:25:38.8246189Z   test_dict_to_python (jit.test_list_dict.TestDict) ... ok (0.024s)
2023-01-11T21:25:38.8246646Z   test_dict_variance (jit.test_list_dict.TestDict)
2023-01-11T21:25:38.8247195Z `Dict[T1, _]` is not a subtype of `Dict[T2, _]`, even if `T1` is ... ok (0.013s)
2023-01-11T21:25:38.8247641Z   test_get (jit.test_list_dict.TestDict) ... ok (0.009s)
2023-01-11T21:25:38.8248094Z   test_get_boolkey (jit.test_list_dict.TestDict) ... ok (0.010s)
2023-01-11T21:25:38.8248549Z   test_items (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:38.8248988Z   test_key_type (jit.test_list_dict.TestDict) ... ok (0.002s)
2023-01-11T21:25:38.8249434Z   test_keys (jit.test_list_dict.TestDict) ... ok (0.006s)
2023-01-11T21:25:38.8249866Z   test_len (jit.test_list_dict.TestDict) ... ok (0.004s)
2023-01-11T21:25:38.8250286Z   test_loop (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:38.8250744Z   test_membership (jit.test_list_dict.TestDict) ... ok (0.008s)
2023-01-11T21:25:38.8251222Z   test_mutability (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:38.8251720Z   test_optional_dict_construct (jit.test_list_dict.TestDict) ... ok (0.012s)
2023-01-11T21:25:38.8252216Z   test_ordered_dict (jit.test_list_dict.TestDict) ... ok (0.014s)
2023-01-11T21:25:38.8252683Z   test_pop (jit.test_list_dict.TestDict) ... ok (0.012s)
2023-01-11T21:25:38.8253164Z   test_popitem (jit.test_list_dict.TestDict) ... ok (0.004s)
2023-01-11T21:25:38.8253602Z   test_setdefault (jit.test_list_dict.TestDict) ... ok (0.009s)
2023-01-11T21:25:38.8254115Z   test_type_annotation_missing_contained_type (jit.test_list_dict.TestDict)
2023-01-11T21:25:38.8254667Z Test that the use of a Dict type annotation without contained ... ok (0.003s)
2023-01-11T21:25:38.8255169Z   test_update (jit.test_list_dict.TestDict) ... ok (0.011s)
2023-01-11T21:25:38.8255643Z   test_update_existing_key (jit.test_list_dict.TestDict) ... ok (0.006s)
2023-01-11T21:25:38.8256125Z   test_values (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:38.8256577Z   test_view (jit.test_list_dict.TestDict) ... ok (0.067s)
2023-01-11T21:25:38.8257079Z   test_binary_scalar (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.010s)
2023-01-11T21:25:38.8258536Z   test_binary_tensors (jit.test_dtype_analysis.TestDtypeAnalysis) ... /var/lib/jenkins/workspace/test/jit/test_dtype_analysis.py:165: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:25:38.8259523Z   rand_tensor = torch.rand(shape, dtype=dtype)
2023-01-11T21:25:38.8259880Z ok (0.077s)
2023-01-11T21:25:38.8260297Z   test_combined (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.008s)
2023-01-11T21:25:38.8260882Z   test_conv_no_mixed_args (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.016s)
2023-01-11T21:25:38.8261470Z   test_custom_rules (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.016s)
2023-01-11T21:25:38.8262033Z   test_unary (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.034s)
2023-01-11T21:25:38.8262715Z   test_closed_over_enum_constant (jit.test_enum.TestEnum) ... ok (0.010s)
2023-01-11T21:25:38.8263215Z   test_enum_as_const (jit.test_enum.TestEnum) ... ok (0.008s)
2023-01-11T21:25:38.8263845Z   test_enum_as_module_attribute (jit.test_enum.TestEnum) ... ok (0.009s)
2023-01-11T21:25:38.8264306Z   test_enum_comp (jit.test_enum.TestEnum) ... ok (0.007s)
2023-01-11T21:25:38.8264769Z   test_enum_comp_diff_classes (jit.test_enum.TestEnum) ... ok (0.010s)
2023-01-11T21:25:38.8265272Z   test_enum_explicit_script (jit.test_enum.TestEnum) ... ok (0.001s)
2023-01-11T21:25:38.8265756Z   test_enum_iterate (jit.test_enum.TestEnum) ... ok (0.011s)
2023-01-11T21:25:38.8266205Z   test_enum_ivalue_type (jit.test_enum.TestEnum) ... ok (0.008s)
2023-01-11T21:25:38.8266695Z   test_enum_module_return (jit.test_enum.TestEnum) ... ok (0.010s)
2023-01-11T21:25:38.8267155Z   test_enum_name (jit.test_enum.TestEnum) ... ok (0.008s)
2023-01-11T21:25:38.8267585Z   test_enum_return (jit.test_enum.TestEnum) ... ok (0.009s)
2023-01-11T21:25:38.8268038Z   test_enum_value (jit.test_enum.TestEnum) ... ok (0.007s)
2023-01-11T21:25:38.8268614Z   test_enum_value_types (jit.test_enum.TestEnum) ... ok (0.024s)
2023-01-11T21:25:38.8269128Z   test_heterogenous_value_type_enum_error (jit.test_enum.TestEnum) ... ok (0.005s)
2023-01-11T21:25:38.8269656Z   test_non_existent_enum_value (jit.test_enum.TestEnum) ... ok (0.006s)
2023-01-11T21:25:38.8270176Z   test_string_enum_as_module_attribute (jit.test_enum.TestEnum) ... ok (0.009s)
2023-01-11T21:25:38.8270838Z   test_freeze_interface_swapping_two_methods (jit.test_freezing.TestFreezing) ... ok (0.198s)
2023-01-11T21:25:38.8271471Z   test_freeze_interface_within_object (jit.test_freezing.TestFreezing) ... expected failure (0.041s)
2023-01-11T21:25:38.8272061Z   test_freeze_module (jit.test_freezing.TestFreezing) ... ok (0.014s)
2023-01-11T21:25:38.8272625Z   test_freeze_module_detach_gradient (jit.test_freezing.TestFreezing) ... ok (0.012s)
2023-01-11T21:25:38.8273203Z   test_freeze_module_in_training_mode (jit.test_freezing.TestFreezing) ... ok (0.243s)
2023-01-11T21:25:38.8273766Z   test_freeze_module_inlining (jit.test_freezing.TestFreezing) ... ok (0.040s)
2023-01-11T21:25:38.8274332Z   test_freeze_module_no_forward (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:38.8274902Z   test_freeze_module_return_self (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8275459Z   test_freeze_module_return_sub_module (jit.test_freezing.TestFreezing) ... ok (0.014s)
2023-01-11T21:25:38.8276041Z   test_freeze_module_with_aliased_attr (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8276632Z   test_freeze_module_with_aliased_attr2 (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8277196Z   test_freeze_module_with_aliased_attr3 (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:38.8277806Z   test_freeze_module_with_aliased_tensor_attr (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:38.8278423Z   test_freeze_module_with_aliased_tensor_attr2 (jit.test_freezing.TestFreezing) ... ok (0.010s)
2023-01-11T21:25:38.8279018Z   test_freeze_module_with_aliased_tensor_attr3 (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8279617Z   test_freeze_module_with_aliased_tensor_attr4 (jit.test_freezing.TestFreezing) ... ok (0.009s)
2023-01-11T21:25:38.8280216Z   test_freeze_module_with_call_method (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8280780Z   test_freeze_module_with_fork (jit.test_freezing.TestFreezing) ... ok (0.012s)
2023-01-11T21:25:38.8281332Z   test_freeze_module_with_fork2 (jit.test_freezing.TestFreezing) ... ok (0.010s)
2023-01-11T21:25:38.8281919Z   test_freeze_module_with_fork_calling_module_method (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:38.8282531Z   test_freeze_module_with_helperfunction (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:38.8283142Z   test_freeze_module_with_inplace_mutable (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:38.8283710Z   test_freeze_module_with_list (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8284658Z   test_freeze_module_with_mutable_dict (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8285249Z   test_freeze_module_with_mutable_list (jit.test_freezing.TestFreezing) ... ok (0.005s)
2023-01-11T21:25:38.8285840Z   test_freeze_module_with_mutable_tensor (jit.test_freezing.TestFreezing) ... ok (0.005s)
2023-01-11T21:25:38.8286427Z   test_freeze_module_with_nested_fork (jit.test_freezing.TestFreezing) ... ok (0.020s)
2023-01-11T21:25:38.8287025Z   test_freeze_module_with_nestedaliasing (jit.test_freezing.TestFreezing) ... ok (0.023s)
2023-01-11T21:25:38.8287645Z   test_freeze_module_with_nestedaliasingscalar (jit.test_freezing.TestFreezing) ... ok (0.019s)
2023-01-11T21:25:38.8288285Z   test_freeze_module_with_non_static_module_container_index (jit.test_freezing.TestFreezing)
2023-01-11T21:25:38.8289047Z Test that Modules containing non-static ModuleDict or ModuleList ... ok (0.183s)
2023-01-11T21:25:38.8289642Z   test_freeze_module_with_overlapping_attrs (jit.test_freezing.TestFreezing) ... ok (0.009s)
2023-01-11T21:25:38.8290705Z   test_freeze_module_with_preserve_sub_module (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:38.8343049Z   test_freeze_module_with_preserve_sub_module_and_mutation (jit.test_freezing.TestFreezing) ... ok (0.014s)
2023-01-11T21:25:38.8343729Z   test_freeze_module_with_sharedclasstype (jit.test_freezing.TestFreezing) ... ok (0.024s)
2023-01-11T21:25:38.8344339Z   test_freeze_module_with_submodule (jit.test_freezing.TestFreezing) ... ok (0.014s)
2023-01-11T21:25:38.8344903Z   test_freeze_module_with_tensor (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8345456Z   test_freeze_module_with_tuple (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8346063Z   test_freeze_module_with_tupleoutput_submodule (jit.test_freezing.TestFreezing) ... ok (0.010s)
2023-01-11T21:25:38.8346690Z   test_freeze_module_with_user_preserved_attr (jit.test_freezing.TestFreezing) ... ok (0.005s)
2023-01-11T21:25:38.8347361Z   test_freeze_module_with_user_preserved_attribute_on_submodule (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:38.8348069Z   test_freeze_module_with_user_preserved_attribute_on_unused_submodule (jit.test_freezing.TestFreezing) ... ok (0.008s)
2023-01-11T21:25:38.8348736Z   test_freeze_module_with_user_preserved_method (jit.test_freezing.TestFreezing) ... ok (0.008s)
2023-01-11T21:25:38.8349364Z   test_freeze_module_with_user_preserved_method2 (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:38.8349999Z   test_freeze_module_with_user_preserved_method_on_submodule (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:38.8350607Z   test_freeze_no_forward (jit.test_freezing.TestFreezing) ... ok (0.010s)
2023-01-11T21:25:38.8351246Z   test_freeze_non_interface_module_swap (jit.test_freezing.TestFreezing) ... ok (0.015s)
2023-01-11T21:25:38.8351823Z   test_freeze_non_module_class_getattr (jit.test_freezing.TestFreezing) ... ok (0.045s)
2023-01-11T21:25:38.8352401Z   test_freeze_recursive_interfaces (jit.test_freezing.TestFreezing) ... ok (0.209s)
2023-01-11T21:25:38.8353012Z   test_freeze_recursive_interfaces_same_name (jit.test_freezing.TestFreezing) ... ok (0.090s)
2023-01-11T21:25:38.8353644Z   test_freeze_recursive_interfaces_with_reassignment (jit.test_freezing.TestFreezing) ... ok (0.215s)
2023-01-11T21:25:38.8354258Z   test_freeze_with_interface_mutable (jit.test_freezing.TestFreezing) ... ok (0.161s)
2023-01-11T21:25:38.8354832Z   test_freeze_with_swapping_interfaces (jit.test_freezing.TestFreezing) ... ok (0.053s)
2023-01-11T21:25:38.8355438Z   test_module_getattr_indirection (jit.test_freezing.TestFreezing) ... ok (0.040s)
2023-01-11T21:25:38.8356024Z   test_module_with_shared_type_instances (jit.test_freezing.TestFreezing) ... ok (0.084s)
2023-01-11T21:25:38.8356581Z   test_dictionary_as_example_inputs_for_jit_trace (__main__.TestFrontend) ... ok (0.071s)
2023-01-11T21:25:38.8357095Z   test_instancing_error (__main__.TestFrontend) ... ok (0.406s)
2023-01-11T21:25:38.8357831Z   test_collapse_adjacent_conversions (jit.test_freezing.TestFrozenOptimizations) ... ok (0.015s)
2023-01-11T21:25:38.8358499Z   test_conv_add_folding (jit.test_freezing.TestFrozenOptimizations) ... ok (1.337s)
2023-01-11T21:25:38.8359101Z   test_conv_bn_folding (jit.test_freezing.TestFrozenOptimizations) ... ok (0.365s)
2023-01-11T21:25:38.8359857Z   test_conv_bn_folding_autocast_scenario_cuda (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:38.8360615Z   test_conv_bn_folding_not_forward (jit.test_freezing.TestFrozenOptimizations) ... ok (0.012s)
2023-01-11T21:25:38.8361231Z   test_conv_hardswish (jit.test_freezing.TestFrozenOptimizations) ... ok (3.241s)
2023-01-11T21:25:38.8361849Z   test_conv_mul_add_bn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.060s)
2023-01-11T21:25:38.8362457Z   test_conv_to_mkldnn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.038s)
2023-01-11T21:25:38.8363243Z   test_conv_to_mkldnn_no_mkldnn (jit.test_freezing.TestFrozenOptimizations) ... skip: Testing no mkldnn (0.001s)
2023-01-11T21:25:38.8363950Z   test_freeze_conv_relu_fusion (jit.test_freezing.TestFrozenOptimizations) ... skip: requires CUDNN (0.001s)
2023-01-11T21:25:38.8364696Z   test_freeze_conv_relu_fusion_not_forward (jit.test_freezing.TestFrozenOptimizations) ... skip: requires CUDNN (0.001s)
2023-01-11T21:25:38.8365386Z   test_freeze_mkdlnn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.006s)
2023-01-11T21:25:38.8366019Z   test_freeze_remove_dropout (jit.test_freezing.TestFrozenOptimizations) ... ok (0.005s)
2023-01-11T21:25:38.8366658Z   test_freeze_remove_feature_dropout (jit.test_freezing.TestFrozenOptimizations) ... ok (0.006s)
2023-01-11T21:25:38.8367329Z   test_hardswish_hardsigmoid (jit.test_freezing.TestFrozenOptimizations) ... ok (0.008s)
2023-01-11T21:25:38.8367998Z   test_incompatible_perf_formats (jit.test_freezing.TestFrozenOptimizations) ... ok (0.191s)
2023-01-11T21:25:38.8368626Z   test_linear_bn_folding (jit.test_freezing.TestFrozenOptimizations) ... ok (1.032s)
2023-01-11T21:25:38.8369395Z   test_linear_bn_folding_autocast_scenario_cuda (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.003s)
2023-01-11T21:25:38.8370225Z   test_linear_concat (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:38.8370923Z   test_linear_concat_complex (jit.test_freezing.TestFrozenOptimizations)
2023-01-11T21:25:38.8371600Z Testing that the interleaving of multiple optimizations does not ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:38.8372282Z   test_linear_concat_different_input (jit.test_freezing.TestFrozenOptimizations)
2023-01-11T21:25:38.8372966Z There should be no change to the graph due to the optimization pass ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:38.8373758Z   test_linear_multiple_blocks (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.002s)
2023-01-11T21:25:38.8374485Z   test_linear_non_constant_weight (jit.test_freezing.TestFrozenOptimizations) ... ok (0.007s)
2023-01-11T21:25:38.8375135Z   test_linear_transpose (jit.test_freezing.TestFrozenOptimizations) ... ok (0.007s)
2023-01-11T21:25:38.8375767Z   test_maxpool_mkldnn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.338s)
2023-01-11T21:25:38.8376391Z   test_mkldnn_fuser_broadcasting (jit.test_freezing.TestFrozenOptimizations) ... ok (0.023s)
2023-01-11T21:25:38.8377055Z   test_mkldnn_inplace_removal (jit.test_freezing.TestFrozenOptimizations) ... ok (0.015s)
2023-01-11T21:25:38.8377731Z   test_numel_less_than_size_with_padding (jit.test_freezing.TestFrozenOptimizations) ... ok (0.019s)
2023-01-11T21:25:38.8378396Z   test_optimize_freeze_module (jit.test_freezing.TestFrozenOptimizations) ... ok (0.019s)
2023-01-11T21:25:38.8379006Z   test_pool2d_batchnorm (jit.test_freezing.TestFrozenOptimizations) ... ok (0.402s)
2023-01-11T21:25:38.8379748Z   test_pool3d_batchnorm (jit.test_freezing.TestFrozenOptimizations) ... ok (1.305s)
2023-01-11T21:25:38.8380365Z   test_remove_detach (jit.test_freezing.TestFrozenOptimizations) ... ok (0.004s)
2023-01-11T21:25:38.8380982Z   test_remove_detach_not_applied (jit.test_freezing.TestFrozenOptimizations) ... ok (0.003s)
2023-01-11T21:25:38.8381620Z   test_scalar_mul (jit.test_freezing.TestFrozenOptimizations) ... ok (0.049s)
2023-01-11T21:25:38.8382234Z   test_subgraph_creation (jit.test_functional_blocks.TestFunctionalBlocks) ... ok (0.077s)
2023-01-11T21:25:38.8383075Z   test_check_no_type_promotion (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (0.226s)
2023-01-11T21:25:38.8383873Z   test_functional_to_inplace_activation (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (0.056s)
2023-01-11T21:25:38.8384691Z   test_no_functional_to_inplace (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (0.008s)
2023-01-11T21:25:38.8385614Z   test_resnet18_correctness (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (3.442s)
2023-01-11T21:25:38.8386299Z   test_getattr_with_default (jit.test_attr.TestGetDefaultAttr) ... ok (0.007s)
2023-01-11T21:25:38.8386886Z   test_fuse_linear (jit.test_graph_rewrite_passes.TestGraphRewritePasses) ... ok (0.029s)
2023-01-11T21:25:38.8387431Z   test_hash_bool (jit.test_hash.TestHash) ... ok (0.007s)
2023-01-11T21:25:38.8387890Z   test_hash_device (jit.test_hash.TestHash) ... ok (0.007s)
2023-01-11T21:25:38.8388347Z   test_hash_float (jit.test_hash.TestHash) ... ok (0.009s)
2023-01-11T21:25:38.8388765Z   test_hash_int (jit.test_hash.TestHash) ... ok (0.007s)
2023-01-11T21:25:38.8389199Z   test_hash_none (jit.test_hash.TestHash) ... ok (0.003s)
2023-01-11T21:25:38.8389645Z   test_hash_string (jit.test_hash.TestHash) ... ok (0.006s)
2023-01-11T21:25:38.8390049Z   test_hash_tensor (jit.test_hash.TestHash)
2023-01-11T21:25:38.8390453Z Tensors should hash by identity ... ok (0.005s)
2023-01-11T21:25:38.8390978Z   test_hash_tuple (jit.test_hash.TestHash) ... ok (0.007s)
2023-01-11T21:25:38.8391466Z   test_hash_tuple_nested_unhashable_type (jit.test_hash.TestHash) ... ok (0.003s)
2023-01-11T21:25:38.8392007Z   test_forward_tuple_input (jit.test_hooks.TestHooks) ... ok (0.017s)
2023-01-11T21:25:38.8392535Z   test_hook_compilation_hint (jit.test_hooks.TestHooks) ... skip:  (0.000s)
2023-01-11T21:25:38.8393048Z   test_hook_hook_name_collision (jit.test_hooks.TestHooks) ... ok (0.008s)
2023-01-11T21:25:38.8393577Z   test_hook_method_name_collision (jit.test_hooks.TestHooks) ... ok (0.007s)
2023-01-11T21:25:38.8394112Z   test_module_direct_forward_invocation (jit.test_hooks.TestHooks) ... ok (0.012s)
2023-01-11T21:25:38.8394663Z   test_module_forward_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.022s)
2023-01-11T21:25:38.8395203Z   test_module_forward_single_input (jit.test_hooks.TestHooks) ... ok (0.019s)
2023-01-11T21:25:38.8395732Z   test_module_hook_return_nothing (jit.test_hooks.TestHooks) ... ok (0.018s)
2023-01-11T21:25:38.8396298Z   test_module_multiple_hooks_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.031s)
2023-01-11T21:25:38.8396852Z   test_module_multiple_hooks_single_input (jit.test_hooks.TestHooks) ... ok (0.028s)
2023-01-11T21:25:38.8397405Z   test_module_no_forward_input (jit.test_hooks.TestHooks) ... ok (0.016s)
2023-01-11T21:25:38.8397921Z   test_module_same_hook_repeated (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:38.8398487Z   test_submodule_called_directly_with_hooks (jit.test_hooks.TestHooks) ... ok (0.014s)
2023-01-11T21:25:38.8399054Z   test_submodule_direct_forward_invocation (jit.test_hooks.TestHooks) ... ok (0.020s)
2023-01-11T21:25:38.8399633Z   test_submodule_forward_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.033s)
2023-01-11T21:25:38.8400183Z   test_submodule_forward_single_input (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:38.8400753Z   test_submodule_forward_single_input_return_not_tupled (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:38.8401480Z   test_submodule_hook_return_nothing (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:38.8402062Z   test_submodule_multiple_hooks_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.034s)
2023-01-11T21:25:38.8402642Z   test_submodule_multiple_hooks_single_input (jit.test_hooks.TestHooks) ... ok (0.030s)
2023-01-11T21:25:38.8403179Z   test_submodule_no_forward_input (jit.test_hooks.TestHooks) ... ok (0.016s)
2023-01-11T21:25:38.8403712Z   test_submodule_same_hook_repeated (jit.test_hooks.TestHooks) ... ok (0.023s)
2023-01-11T21:25:38.8404229Z   test_wrong_hook_signatures (jit.test_hooks.TestHooks) ... ok (0.035s)
2023-01-11T21:25:38.8404734Z   test_wrong_pre_hook_signatures (jit.test_hooks.TestHooks) ... ok (0.042s)
2023-01-11T21:25:38.8405294Z   test_add_out_ignorable_args (jit.test_ignorable_args.TestIgnorableArgs) ... ok (0.003s)
2023-01-11T21:25:38.8405911Z   test_slice_ignorable_args_for_slice (jit.test_ignorable_args.TestIgnorableArgs) ... ok (0.004s)
2023-01-11T21:25:38.8406695Z   test_with_ignore_context_manager_with_inp_out (jit.test_ignore_context_manager.TestIgnoreContextManager) ... ok (0.013s)
2023-01-11T21:25:38.8407453Z   test_with_ignore_context_manager_with_just_inp (jit.test_ignore_context_manager.TestIgnoreContextManager) ... ok (0.004s)
2023-01-11T21:25:38.8408191Z   test_with_ignore_context_manager_with_just_out (jit.test_ignore_context_manager.TestIgnoreContextManager) ... ok (0.005s)
2023-01-11T21:25:38.8408988Z   test_inplace_to_functional_activation (jit.test_convert_activation.TestInplaceToFunctionalActivation) ... ok (0.039s)
2023-01-11T21:25:38.8409771Z   test_resnet18_correctness (jit.test_convert_activation.TestInplaceToFunctionalActivation) ... ok (3.320s)
2023-01-11T21:25:38.8410399Z   test_bool (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:38.8410910Z   test_dict (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:38.8411444Z   test_dict_nested (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:38.8412006Z   test_dict_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.002s)
2023-01-11T21:25:38.8412558Z   test_dict_tensor (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:38.8413137Z   test_empty_container_special_cases (jit.test_isinstance.TestIsinstance) ... ok (0.008s)
2023-01-11T21:25:38.8413768Z   test_empty_container_throws_warning_in_eager (jit.test_isinstance.TestIsinstance) ... ok (0.001s)
2023-01-11T21:25:38.8414323Z   test_float (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:38.8414833Z   test_if_else (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:38.8415354Z   test_in_if (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:38.8415858Z   test_in_while_loop (jit.test_isinstance.TestIsinstance) ... ok (0.007s)
2023-01-11T21:25:38.8416384Z   test_int (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:38.8416884Z   test_list (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:38.8417406Z   test_list_nested (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:38.8417973Z   test_list_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.002s)
2023-01-11T21:25:38.8418530Z   test_list_tensor (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:38.8419075Z   test_list_tensor_type_true (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:38.8419668Z   test_nontuple_container_rhs_throws_in_eager (jit.test_isinstance.TestIsinstance) ... ok (0.001s)
2023-01-11T21:25:38.8420254Z   test_optional (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:38.8420802Z   test_optional_nested (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:38.8421369Z   test_optional_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.002s)
2023-01-11T21:25:38.8421938Z   test_optional_none (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:38.8422656Z   test_tensor_type_false (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:38.8423309Z   test_tuple (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:38.8423808Z   test_tuple_nested (jit.test_isinstance.TestIsinstance) ... ok (0.007s)
2023-01-11T21:25:38.8424360Z   test_tuple_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.002s)
2023-01-11T21:25:38.8424917Z   test_tuple_rhs (jit.test_isinstance.TestIsinstance) ... ok (0.007s)
2023-01-11T21:25:38.8425432Z   test_tuple_tensor (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:38.8425965Z   test_type_refinement (jit.test_isinstance.TestIsinstance) ... ok (0.016s)
2023-01-11T21:25:38.8426431Z   test_ModuleList (__main__.TestJit) ... ok (0.054s)
2023-01-11T21:25:38.8426839Z   test_Sequential (__main__.TestJit) ... ok (0.023s)
2023-01-11T21:25:38.8427216Z   test_T_mT_H_mH (__main__.TestJit) ... ok (0.020s)
2023-01-11T21:25:38.8427641Z   test_add_relu_fusion (__main__.TestJit) ... ok (0.026s)
2023-01-11T21:25:38.8428211Z   test_arg_configurations (__main__.TestJit)
2023-01-11T21:25:38.8428789Z Different arg configurations should trigger different traces ... skip: Need to be adjusted to Graph Executor (0.001s)
2023-01-11T21:25:38.8429309Z   test_attrs (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8429684Z   test_batchnorm (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8430095Z   test_big (__main__.TestJit) ... skip: Requires a lot of RAM (0.001s)
2023-01-11T21:25:38.8430551Z   test_conj_transpose (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8431062Z   test_constant_insertion (__main__.TestJit) ... ok (3.635s)
2023-01-11T21:25:38.8431535Z   test_constant_prop_aliasing_type (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:38.8432003Z   test_constant_prop_exception (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8432470Z   test_constant_prop_if_constant (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8432936Z   test_constant_prop_if_inline (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8433410Z   test_constant_prop_loop_constant (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8433893Z   test_constant_prop_nested (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8434346Z   test_constant_prop_none (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8434779Z   test_constant_prop_print (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8435233Z   test_constant_prop_rand (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8435698Z   test_constant_prop_remove_output (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8436172Z   test_constant_prop_simple (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8436593Z   test_constants_pkl (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:38.8436992Z   test_cpp (__main__.TestJit) ... ok (0.029s)
2023-01-11T21:25:38.8437372Z   test_cse (__main__.TestJit) ... ok (0.007s)
2023-01-11T21:25:38.8437792Z   test_cse_not_introduce_aliasing (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:38.8438258Z   test_cu_escaped_number (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8438751Z   test_cuda_export_restore (__main__.TestJit) ... skip: requires CUDA (0.001s)
2023-01-11T21:25:38.8439246Z   test_debug_flush_compilation_cache (__main__.TestJit) ... ok (0.014s)
2023-01-11T21:25:38.8439717Z   test_decompose_addmm (__main__.TestJit) ... ok (0.037s)
2023-01-11T21:25:38.8440179Z   test_device_not_equal (__main__.TestJit) ... skip: requires CUDA (0.001s)
2023-01-11T21:25:38.8440686Z   test_diff_subgraph_clones_constants (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8441116Z   test_disabled (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8441520Z   test_dropout (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:38.8441995Z   test_dropout_cuda (__main__.TestJit) ... skip: test_dropout_cuda require CUDA (0.001s)
2023-01-11T21:25:38.8442938Z   test_dropout_func_requires_grad (__main__.TestJit) ... STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:38.8443940Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:38.8444756Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:38.8445537Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:38.8446304Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:38.8447109Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:38.8447886Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:38.8448659Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:38.8449443Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:38.8450318Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:38.8451085Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:38.8451868Z STAGE:2023-01-11 21:24:08 1925:1925 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:38.8452331Z ok (0.101s)
2023-01-11T21:25:38.8452793Z   test_dropout_module_requires_grad (__main__.TestJit) ... skip: Testing differentiable graph (0.001s)
2023-01-11T21:25:38.8453296Z   test_einsum (__main__.TestJit) ... ok (0.025s)
2023-01-11T21:25:38.8453691Z   test_element_size (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8454139Z   test_expand_fold_quant_inputs (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8454606Z   test_expand_quantlint (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8455163Z   test_export_batchnorm (__main__.TestJit) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:38.8455711Z   test_export_dropout (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:38.8456132Z   test_export_lstm (__main__.TestJit) ... ok (0.041s)
2023-01-11T21:25:38.8456541Z   test_export_opnames (__main__.TestJit) ... ok (0.012s)
2023-01-11T21:25:38.8456968Z   test_export_rnn (__main__.TestJit) ... ok (0.070s)
2023-01-11T21:25:38.8457483Z   test_flags (__main__.TestJit) ... skip: Need to instrument GraphExecutors a bit more (0.001s)
2023-01-11T21:25:38.8458013Z   test_function_default_values (__main__.TestJit) ... ok (0.039s)
2023-01-11T21:25:38.8458478Z   test_hide_source_ranges_context_manager (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8458945Z   test_import_method (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8459385Z   test_inferred_as_tensor (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:38.8459785Z   test_layout (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8460207Z   test_matrix_conj_transpose (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8460667Z   test_matrix_transpose (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8461107Z   test_module_default_values (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8461571Z   test_mutable_default_values (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8462086Z   test_native_dropout_corner_case (__main__.TestJit) ... skip: test requires CUDA (0.001s)
2023-01-11T21:25:38.8462703Z   test_nn_conv (__main__.TestJit) ... ok (0.242s)
2023-01-11T21:25:38.8463096Z   test_nn_lp_pool1d (__main__.TestJit) ... ok (0.102s)
2023-01-11T21:25:38.8463505Z   test_nn_lp_pool2d (__main__.TestJit) ... ok (0.139s)
2023-01-11T21:25:38.8463917Z   test_nn_padding (__main__.TestJit) ... ok (0.265s)
2023-01-11T21:25:38.8464343Z   test_nn_padding_functional (__main__.TestJit) ... ok (0.024s)
2023-01-11T21:25:38.8464805Z   test_no_erroneous_warnings (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8465381Z   test_non_ascii_string (__main__.TestJit) ... skip: temporarily disable the test for fwd compatibility (0.001s)
2023-01-11T21:25:38.8466037Z   test_numel (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8466461Z   test_pattern_based_module_rewrite (__main__.TestJit) ... ok (0.036s)
2023-01-11T21:25:38.8466925Z   test_pattern_based_rewrite (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:38.8467444Z   test_pattern_based_rewrite_with_source_range_preserved (__main__.TestJit) ... ok (0.019s)
2023-01-11T21:25:38.8468272Z   test_peephole_optimize_shape_ops (__main__.TestJit) ... skip: Simple executor doesn't have shape information (0.002s)
2023-01-11T21:25:38.8468848Z   test_permute_inputs_binding (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8469301Z   test_pretty_printer (__main__.TestJit) ... ok (0.038s)
2023-01-11T21:25:38.8469726Z   test_print_classes_module (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8470156Z   test_print_op_module (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8470598Z   test_print_torch_ops_modules (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8472225Z   test_profiler (__main__.TestJit) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/65521 for allplatform(s) . If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:25:38.8473167Z   test_python_bindings (__main__.TestJit) ... ok (0.225s)
2023-01-11T21:25:38.8473588Z   test_python_ir (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8474000Z   test_python_ir_utils (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8474443Z   test_python_ir_utils_graph (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:38.8474935Z   test_python_ivalue (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:38.8475376Z   test_pytorch_jit_env_off (__main__.TestJit) ... ok (1.575s)
2023-01-11T21:25:38.8475806Z   test_recursive_cse (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8476263Z   test_repeat_interleave_script (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8476715Z   test_restore_device (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:38.8477225Z   test_restore_device_cuda (__main__.TestJit) ... skip: restore device requires CUDA (0.001s)
2023-01-11T21:25:38.8477832Z   test_restore_shared_storage_on_cuda (__main__.TestJit) ... skip: restore device requires CUDA (0.001s)
2023-01-11T21:25:38.8478345Z   test_script_autograd_grad (__main__.TestJit) ... ok (0.373s)
2023-01-11T21:25:38.8478784Z   test_script_backward (__main__.TestJit) ... ok (0.011s)
2023-01-11T21:25:38.8479852Z   test_script_backward_twice (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_script.py:1243: UserWarning: `optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead
2023-01-11T21:25:38.8480515Z   warnings.warn(
2023-01-11T21:25:38.8480813Z ok (0.014s)
2023-01-11T21:25:38.8481161Z   test_script_fn_pkl (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8481598Z   test_script_tensor_type (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8482063Z   test_shape_analysis_broadcast (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8482556Z   test_shape_analysis_masked_select (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8483068Z   test_shape_analysis_unsqueeze_in_loop (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8483531Z   test_sparse_csr_tensors (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8483969Z   test_sparse_tensors (__main__.TestJit) ... ok (0.032s)
2023-01-11T21:25:38.8486026Z   test_torch_complex (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: An output with one or more elements was resized since it had shape [3, 4], which does not match the required output shape [2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:25:38.8487395Z   return callable(*args, **kwargs)
2023-01-11T21:25:38.8489280Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: An output with one or more elements was resized since it had shape [5, 2], which does not match the required output shape [2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:25:38.8490483Z   return callable(*args, **kwargs)
2023-01-11T21:25:38.8490785Z ok (0.026s)
2023-01-11T21:25:38.8491494Z   test_torch_load_error (__main__.TestJit) ... skip: TODO: re-enable with https://github.com/pytorch/pytorch/pull/29339 (0.001s)
2023-01-11T21:25:38.8492191Z   test_torch_load_zipfile_check (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8492661Z   test_torch_ops_kwonly (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:38.8493088Z   test_torch_ops_overloaded (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:38.8493519Z   test_torch_sum (__main__.TestJit) ... ok (0.010s)
2023-01-11T21:25:38.8493945Z   test_trace_retains_train (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:38.8494352Z   test_train_eval (__main__.TestJit) ... ok (0.044s)
2023-01-11T21:25:38.8494763Z   test_transpose (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:38.8495185Z   test_unchecked_cast (__main__.TestJit) ... ok (0.011s)
2023-01-11T21:25:38.8495604Z   test_unique_state_dict (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:38.8496149Z   test_verify (__main__.TestJit) ... skip: verify needs to be updated to work with GraphExecutors (0.001s)
2023-01-11T21:25:38.8496667Z   test_warnings (__main__.TestJit) ... ok (0.009s)
2023-01-11T21:25:38.8497168Z   test_nn_AdaptiveAvgPool1d (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8497743Z   test_nn_AdaptiveAvgPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8498362Z   test_nn_AdaptiveAvgPool1d_one_output (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8498975Z   test_nn_AdaptiveAvgPool2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8499549Z   test_nn_AdaptiveAvgPool2d_single (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:38.8500164Z   test_nn_AdaptiveAvgPool2d_single_1x1output (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:38.8500771Z   test_nn_AdaptiveAvgPool2d_tuple (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8501375Z   test_nn_AdaptiveAvgPool2d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:38.8501970Z   test_nn_AdaptiveAvgPool3d_last_dim (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8502884Z   test_nn_AdaptiveAvgPool3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:38.8503306Z   test_nn_AdaptiveAvgPool3d_single (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:38.8503637Z   test_nn_AdaptiveAvgPool3d_tuple (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:38.8503988Z   test_nn_AdaptiveAvgPool3d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:38.8504330Z   test_nn_AdaptiveMaxPool1d (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:38.8504673Z   test_nn_AdaptiveMaxPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:38.8505008Z   test_nn_AdaptiveMaxPool2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:38.8505357Z   test_nn_AdaptiveMaxPool2d_single (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8505705Z   test_nn_AdaptiveMaxPool2d_tuple (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8506171Z   test_nn_AdaptiveMaxPool2d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:38.8506501Z   test_nn_AdaptiveMaxPool3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:38.8506842Z   test_nn_AdaptiveMaxPool3d_single (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8507191Z   test_nn_AdaptiveMaxPool3d_single_nonatomic (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:38.8507523Z   test_nn_AdaptiveMaxPool3d_tuple (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8507873Z   test_nn_AdaptiveMaxPool3d_tuple_nonatomic (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8508222Z   test_nn_AdaptiveMaxPool3d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:38.8508543Z   test_nn_AvgPool1d (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8508887Z   test_nn_AvgPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8509209Z   test_nn_AvgPool1d_stride (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8509525Z   test_nn_AvgPool1d_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8509818Z   test_nn_AvgPool2d (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8510123Z   test_nn_AvgPool2d_divisor (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8510442Z   test_nn_AvgPool2d_divisor_stride (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:38.8510815Z   test_nn_AvgPool2d_divisor_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:38.8511131Z   test_nn_AvgPool2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:38.8511446Z   test_nn_AvgPool2d_stride (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:38.8511763Z   test_nn_AvgPool2d_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:38.8512066Z   test_nn_AvgPool3d (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8512373Z   test_nn_AvgPool3d_divisor (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8512695Z   test_nn_AvgPool3d_divisor_stride (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8513039Z   test_nn_AvgPool3d_divisor_stride1_pad0_gpu_input (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8513378Z   test_nn_AvgPool3d_divisor_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8513741Z   test_nn_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8514124Z   test_nn_AvgPool3d_divisor_stride_pad_gpu_general_output (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8514496Z   test_nn_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8514848Z   test_nn_AvgPool3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8515173Z   test_nn_AvgPool3d_stride (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8515502Z   test_nn_AvgPool3d_stride1_pad0_gpu_input (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8515822Z   test_nn_AvgPool3d_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8516164Z   test_nn_AvgPool3d_stride_pad_gpu_fixedkw_output (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8516525Z   test_nn_AvgPool3d_stride_pad_gpu_general_output (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8516877Z   test_nn_AvgPool3d_stride_pad_gpu_input_nooverlap (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8517202Z   test_nn_BCELoss (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8517511Z   test_nn_BCELoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8517875Z   test_nn_BCELoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8518187Z   test_nn_BCELoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8518503Z   test_nn_BCELoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8518820Z   test_nn_BCELoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8519136Z   test_nn_BCELoss_scalar_weights (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8519450Z   test_nn_BCELoss_weights (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8519770Z   test_nn_BCELoss_weights_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8520105Z   test_nn_BCELoss_weights_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8520422Z   test_nn_BCEWithLogitsLoss (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8520808Z   test_nn_BCEWithLogitsLoss_legacy_enum (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8521161Z   test_nn_BCEWithLogitsLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8521510Z   test_nn_BCEWithLogitsLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8521849Z   test_nn_BCEWithLogitsLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8522192Z   test_nn_BCEWithLogitsLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8522544Z   test_nn_BCEWithLogitsLoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8522885Z   test_nn_BCEWithLogitsLoss_scalar_weights (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8523234Z   test_nn_BCEWithLogitsLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8523566Z   test_nn_BatchNorm1d_3d_input (__main__.TestJitGeneratedModule) ... ok (0.088s)
2023-01-11T21:25:38.8523898Z   test_nn_BatchNorm1d_3d_input_not_affine (__main__.TestJitGeneratedModule) ... ok (0.089s)
2023-01-11T21:25:38.8524211Z   test_nn_BatchNorm1d_affine (__main__.TestJitGeneratedModule) ... ok (0.090s)
2023-01-11T21:25:38.8524544Z   test_nn_BatchNorm1d_affine_simple_average (__main__.TestJitGeneratedModule) ... ok (0.092s)
2023-01-11T21:25:38.8524877Z   test_nn_BatchNorm1d_not_affine (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:38.8525191Z   test_nn_BatchNorm1d_not_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.085s)
2023-01-11T21:25:38.8525519Z   test_nn_BatchNorm1d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.089s)
2023-01-11T21:25:38.8525830Z   test_nn_BatchNorm2d (__main__.TestJitGeneratedModule) ... ok (0.086s)
2023-01-11T21:25:38.8526158Z   test_nn_BatchNorm2d_2d_simple_average (__main__.TestJitGeneratedModule) ... ok (0.091s)
2023-01-11T21:25:38.8526472Z   test_nn_BatchNorm2d_momentum (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:38.8526796Z   test_nn_BatchNorm2d_not_affine (__main__.TestJitGeneratedModule) ... ok (0.086s)
2023-01-11T21:25:38.8527125Z   test_nn_BatchNorm2d_not_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.083s)
2023-01-11T21:25:38.8527443Z   test_nn_BatchNorm2d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:38.8527751Z   test_nn_BatchNorm3d (__main__.TestJitGeneratedModule) ... ok (0.090s)
2023-01-11T21:25:38.8528070Z   test_nn_BatchNorm3d_3d_simple_average (__main__.TestJitGeneratedModule) ... ok (0.090s)
2023-01-11T21:25:38.8528398Z   test_nn_BatchNorm3d_momentum (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:38.8528703Z   test_nn_BatchNorm3d_not_affine (__main__.TestJitGeneratedModule) ... ok (0.085s)
2023-01-11T21:25:38.8529036Z   test_nn_BatchNorm3d_not_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.081s)
2023-01-11T21:25:38.8529366Z   test_nn_BatchNorm3d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.084s)
2023-01-11T21:25:38.8529697Z   test_nn_Bilinear (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8553893Z   test_nn_CELU (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8554223Z   test_nn_CELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8554527Z   test_nn_CELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8554898Z   test_nn_CTCLoss_2d_int_target_lengths_intlists (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.002s)
2023-01-11T21:25:38.8555279Z   test_nn_CTCLoss_2d_int_target_lengths_tensors (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8555620Z   test_nn_CTCLoss_2d_lengths_tensors (__main__.TestJitGeneratedModule) ... ok (0.024s)
2023-01-11T21:25:38.8555969Z   test_nn_CTCLoss_lengths_intlists (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.002s)
2023-01-11T21:25:38.8556443Z   test_nn_CTCLoss_lengths_tensors (__main__.TestJitGeneratedModule) ... ok (0.024s)
2023-01-11T21:25:38.8556770Z   test_nn_ConstantPad1d (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8557075Z   test_nn_ConstantPad1d_batch (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8557404Z   test_nn_ConstantPad1d_complex (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8557724Z   test_nn_ConstantPad2d (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8559064Z   test_nn_ConstantPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8559381Z   test_nn_ConstantPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8559698Z   test_nn_ConstantPad3d (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8560014Z   test_nn_ConstantPad3d_complex (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8560343Z   test_nn_ConstantPad3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8560639Z   test_nn_Conv1d (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:38.8560949Z   test_nn_Conv1d_circular_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.059s)
2023-01-11T21:25:38.8561263Z   test_nn_Conv1d_dilated (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8561552Z   test_nn_Conv1d_groups (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8561849Z   test_nn_Conv1d_pad1 (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8562150Z   test_nn_Conv1d_pad1size1 (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8562438Z   test_nn_Conv1d_pad2 (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8562739Z   test_nn_Conv1d_pad2size1 (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8563043Z   test_nn_Conv1d_pad_same (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8563350Z   test_nn_Conv1d_pad_same2 (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8563648Z   test_nn_Conv1d_pad_same_dilated (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8563960Z   test_nn_Conv1d_pad_valid (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8564279Z   test_nn_Conv1d_reflect_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:38.8564602Z   test_nn_Conv1d_replicate_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:38.8564914Z   test_nn_Conv1d_stride (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8565214Z   test_nn_Conv1d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:38.8565527Z   test_nn_Conv1d_zeros_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:38.8565817Z   test_nn_Conv2d (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8566124Z   test_nn_Conv2d_circular_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:38.8566516Z   test_nn_Conv2d_depthwise (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8566820Z   test_nn_Conv2d_depthwise_dilated (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8567152Z   test_nn_Conv2d_depthwise_padded (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8567481Z   test_nn_Conv2d_depthwise_strided (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8567814Z   test_nn_Conv2d_depthwise_with_multiplier (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8568121Z   test_nn_Conv2d_dilated (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8568422Z   test_nn_Conv2d_groups (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8568733Z   test_nn_Conv2d_groups_thnn (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8569022Z   test_nn_Conv2d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8569370Z   test_nn_Conv2d_pad_same (__main__.TestJitGeneratedModule) ... ok (0.065s)
2023-01-11T21:25:38.8569686Z   test_nn_Conv2d_pad_same_dilated (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8569998Z   test_nn_Conv2d_pad_valid (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8570290Z   test_nn_Conv2d_padding (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:38.8570605Z   test_nn_Conv2d_reflect_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:38.8570938Z   test_nn_Conv2d_replicate_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.060s)
2023-01-11T21:25:38.8571240Z   test_nn_Conv2d_strided (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:38.8571542Z   test_nn_Conv2d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:38.8571855Z   test_nn_Conv2d_zeros_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:38.8572162Z   test_nn_Conv3d (__main__.TestJitGeneratedModule) ... ok (0.059s)
2023-01-11T21:25:38.8572447Z   test_nn_Conv3d_1x1x1_no_bias (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:38.8572763Z   test_nn_Conv3d_circular_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:38.8573078Z   test_nn_Conv3d_dilated (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:38.8573375Z   test_nn_Conv3d_dilated_strided (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8573683Z   test_nn_Conv3d_groups (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8573979Z   test_nn_Conv3d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8574279Z   test_nn_Conv3d_pad_same (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8574581Z   test_nn_Conv3d_pad_same_dilated (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8574890Z   test_nn_Conv3d_pad_valid (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8575217Z   test_nn_Conv3d_replicate_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:38.8575519Z   test_nn_Conv3d_stride (__main__.TestJitGeneratedModule) ... ok (0.063s)
2023-01-11T21:25:38.8575832Z   test_nn_Conv3d_stride_padding (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8576142Z   test_nn_Conv3d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8576453Z   test_nn_Conv3d_zeros_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8576754Z   test_nn_ConvTranspose1d (__main__.TestJitGeneratedModule) ... ok (0.101s)
2023-01-11T21:25:38.8577074Z   test_nn_ConvTranspose1d_dilated (__main__.TestJitGeneratedModule) ... ok (0.103s)
2023-01-11T21:25:38.8577402Z   test_nn_ConvTranspose1d_groups (__main__.TestJitGeneratedModule) ... ok (0.106s)
2023-01-11T21:25:38.8577714Z   test_nn_ConvTranspose1d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.104s)
2023-01-11T21:25:38.8578063Z   test_nn_ConvTranspose2d (__main__.TestJitGeneratedModule) ... ok (0.120s)
2023-01-11T21:25:38.8578380Z   test_nn_ConvTranspose2d_dilated (__main__.TestJitGeneratedModule) ... ok (0.113s)
2023-01-11T21:25:38.8578704Z   test_nn_ConvTranspose2d_groups (__main__.TestJitGeneratedModule) ... ok (0.109s)
2023-01-11T21:25:38.8579017Z   test_nn_ConvTranspose2d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.106s)
2023-01-11T21:25:38.8579331Z   test_nn_ConvTranspose3d (__main__.TestJitGeneratedModule) ... ok (0.105s)
2023-01-11T21:25:38.8579646Z   test_nn_ConvTranspose3d_dilated (__main__.TestJitGeneratedModule) ... ok (0.104s)
2023-01-11T21:25:38.8579958Z   test_nn_CosineEmbeddingLoss (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8580290Z   test_nn_CosineEmbeddingLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8580643Z   test_nn_CosineEmbeddingLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8581037Z   test_nn_CosineEmbeddingLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8581389Z   test_nn_CosineEmbeddingLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8581725Z   test_nn_CrossEntropyLoss (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8582046Z   test_nn_CrossEntropyLoss_2d (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8582515Z   test_nn_CrossEntropyLoss_2d_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8582912Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8583302Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8583704Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8584092Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing_weight (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8584460Z   test_nn_CrossEntropyLoss_2d_prob_target (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8584818Z   test_nn_CrossEntropyLoss_2d_prob_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8585195Z   test_nn_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8585571Z   test_nn_CrossEntropyLoss_2d_prob_target_smoothing_weight (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8585943Z   test_nn_CrossEntropyLoss_2d_prob_target_weights (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8586291Z   test_nn_CrossEntropyLoss_2d_weights (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8586650Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8587027Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8587425Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8587837Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8588205Z   test_nn_CrossEntropyLoss_3d_prob_target (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8588564Z   test_nn_CrossEntropyLoss_3d_prob_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8588940Z   test_nn_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8589319Z   test_nn_CrossEntropyLoss_3d_prob_target_weights (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8589660Z   test_nn_CrossEntropyLoss_4d_prob_target (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8590079Z   test_nn_CrossEntropyLoss_4d_prob_target_weights (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8590421Z   test_nn_CrossEntropyLoss_dim_is_3 (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8590813Z   test_nn_CrossEntropyLoss_higher_dim (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8591145Z   test_nn_CrossEntropyLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8591469Z   test_nn_CrossMapLRN2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8591763Z   test_nn_ELU (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8592044Z   test_nn_ELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8592343Z   test_nn_ELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8592637Z   test_nn_Embedding (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8592999Z   test_nn_EmbeddingBag_discontiguous (__main__.TestJitGeneratedModule) ... ok (0.063s)
2023-01-11T21:25:38.8593312Z   test_nn_EmbeddingBag_max (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8593637Z   test_nn_EmbeddingBag_max_padding_idx (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8593965Z   test_nn_EmbeddingBag_mean (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:38.8594278Z   test_nn_EmbeddingBag_mean_padding_idx (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:38.8594605Z   test_nn_EmbeddingBag_sparse (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:38.8594916Z   test_nn_EmbeddingBag_sum (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:38.8595240Z   test_nn_EmbeddingBag_sum_padding_idx (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8595561Z   test_nn_Embedding_discontiguous (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:38.8595885Z   test_nn_Embedding_sparse (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8596183Z   test_nn_Flatten (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8596471Z   test_nn_Flatten_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8596768Z   test_nn_Fold (__main__.TestJitGeneratedModule) ... ok (0.050s)
2023-01-11T21:25:38.8597056Z   test_nn_Fold_int_input (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8597366Z   test_nn_Fold_no_batch_dim_input (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:38.8597676Z   test_nn_Fold_no_batch_dim_int_input (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:38.8598012Z   test_nn_FractionalMaxPool2d_ratio (__main__.TestJitGeneratedModule) ... ok (0.093s)
2023-01-11T21:25:38.8598367Z   test_nn_FractionalMaxPool2d_ratio_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.078s)
2023-01-11T21:25:38.8598743Z   test_nn_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.081s)
2023-01-11T21:25:38.8599129Z   test_nn_FractionalMaxPool2d_ratio_return_indices (__main__.TestJitGeneratedModule) ... ok (0.074s)
2023-01-11T21:25:38.8599479Z   test_nn_FractionalMaxPool2d_size (__main__.TestJitGeneratedModule) ... ok (0.075s)
2023-01-11T21:25:38.8599833Z   test_nn_FractionalMaxPool2d_size_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.075s)
2023-01-11T21:25:38.8600203Z   test_nn_FractionalMaxPool2d_size_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.078s)
2023-01-11T21:25:38.8600575Z   test_nn_FractionalMaxPool3d_asymsize (__main__.TestJitGeneratedModule) ... ok (0.090s)
2023-01-11T21:25:38.8600920Z   test_nn_FractionalMaxPool3d_ratio (__main__.TestJitGeneratedModule) ... ok (0.077s)
2023-01-11T21:25:38.8601258Z   test_nn_FractionalMaxPool3d_ratio_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.089s)
2023-01-11T21:25:38.8601677Z   test_nn_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.088s)
2023-01-11T21:25:38.8602062Z   test_nn_FractionalMaxPool3d_ratio_return_indices (__main__.TestJitGeneratedModule) ... ok (0.073s)
2023-01-11T21:25:38.8602411Z   test_nn_FractionalMaxPool3d_size (__main__.TestJitGeneratedModule) ... ok (0.074s)
2023-01-11T21:25:38.8602747Z   test_nn_FractionalMaxPool3d_size_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.075s)
2023-01-11T21:25:38.8603126Z   test_nn_FractionalMaxPool3d_size_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.078s)
2023-01-11T21:25:38.8603462Z   test_nn_GELU (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8603756Z   test_nn_GELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8604042Z   test_nn_GELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8604329Z   test_nn_GLU (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8604643Z   test_nn_GLU_dim (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8604930Z   test_nn_GLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8605225Z   test_nn_GRUCell (__main__.TestJitGeneratedModule) ... ok (0.071s)
2023-01-11T21:25:38.8605525Z   test_nn_GroupNorm_1d_affine (__main__.TestJitGeneratedModule) ... ok (0.074s)
2023-01-11T21:25:38.8605840Z   test_nn_GroupNorm_1d_affine_GN (__main__.TestJitGeneratedModule) ... ok (0.068s)
2023-01-11T21:25:38.8606154Z   test_nn_GroupNorm_1d_affine_large_batch (__main__.TestJitGeneratedModule) ... ok (0.069s)
2023-01-11T21:25:38.8606482Z   test_nn_GroupNorm_1d_no_affine_IN (__main__.TestJitGeneratedModule) ... ok (0.067s)
2023-01-11T21:25:38.8606802Z   test_nn_GroupNorm_1d_no_affine_LN (__main__.TestJitGeneratedModule) ... ok (0.067s)
2023-01-11T21:25:38.8607103Z   test_nn_GroupNorm_2d_affine (__main__.TestJitGeneratedModule) ... ok (0.069s)
2023-01-11T21:25:38.8607436Z   test_nn_GroupNorm_2d_affine_large_feature (__main__.TestJitGeneratedModule) ... ok (0.075s)
2023-01-11T21:25:38.8607767Z   test_nn_GroupNorm_2d_no_affine_IN (__main__.TestJitGeneratedModule) ... ok (0.068s)
2023-01-11T21:25:38.8608085Z   test_nn_GroupNorm_2d_no_affine_LN (__main__.TestJitGeneratedModule) ... ok (0.067s)
2023-01-11T21:25:38.8608403Z   test_nn_GroupNorm_2d_no_affine_large_feature (__main__.TestJitGeneratedModule) ... ok (0.080s)
2023-01-11T21:25:38.8608722Z   test_nn_Hardshrink (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8609056Z   test_nn_Hardshrink_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8609363Z   test_nn_Hardshrink_scalar (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8609713Z   test_nn_Hardsigmoid_no_batch_dim (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.002s)
2023-01-11T21:25:38.8610099Z   test_nn_Hardswish_no_batch_dim (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.002s)
2023-01-11T21:25:38.8610437Z   test_nn_Hardtanh (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8610734Z   test_nn_Hardtanh_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8611042Z   test_nn_Hardtanh_scalar (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:38.8611355Z   test_nn_HingeEmbeddingLoss (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8611674Z   test_nn_HingeEmbeddingLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:38.8612024Z   test_nn_HingeEmbeddingLoss_margin_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8612387Z   test_nn_HingeEmbeddingLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8612750Z   test_nn_HingeEmbeddingLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8613095Z   test_nn_HingeEmbeddingLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8613472Z   test_nn_HingeEmbeddingLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8613825Z   test_nn_HingeEmbeddingLoss_scalar_margin (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8614135Z   test_nn_HuberLoss (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8614436Z   test_nn_HuberLoss_delta (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8614753Z   test_nn_HuberLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.024s)
2023-01-11T21:25:38.8615081Z   test_nn_HuberLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.024s)
2023-01-11T21:25:38.8615397Z   test_nn_HuberLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.023s)
2023-01-11T21:25:38.8615718Z   test_nn_InstanceNorm1d (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:38.8616121Z   test_nn_InstanceNorm1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:38.8616463Z   test_nn_InstanceNorm1d_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.102s)
2023-01-11T21:25:38.8616806Z   test_nn_InstanceNorm1d_tracking_stats_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.103s)
2023-01-11T21:25:38.8617142Z   test_nn_InstanceNorm2d (__main__.TestJitGeneratedModule) ... ok (0.109s)
2023-01-11T21:25:38.8617465Z   test_nn_InstanceNorm2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.094s)
2023-01-11T21:25:38.8617788Z   test_nn_InstanceNorm2d_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.100s)
2023-01-11T21:25:38.8618145Z   test_nn_InstanceNorm2d_tracking_stats_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.100s)
2023-01-11T21:25:38.8618481Z   test_nn_InstanceNorm3d (__main__.TestJitGeneratedModule) ... ok (0.098s)
2023-01-11T21:25:38.8618806Z   test_nn_InstanceNorm3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:38.8619127Z   test_nn_InstanceNorm3d_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.102s)
2023-01-11T21:25:38.8619479Z   test_nn_InstanceNorm3d_tracking_stats_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.101s)
2023-01-11T21:25:38.8619807Z   test_nn_KLDivLoss (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:38.8620101Z   test_nn_KLDivLoss_log_target (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8620430Z   test_nn_KLDivLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8620758Z   test_nn_KLDivLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8621089Z   test_nn_KLDivLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8621394Z   test_nn_KLDivLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8621720Z   test_nn_KLDivLoss_no_reduce_log_target (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8622052Z   test_nn_KLDivLoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8622573Z   test_nn_KLDivLoss_no_reduce_scalar_log_target (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8622931Z   test_nn_KLDivLoss_scalar (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8623256Z   test_nn_KLDivLoss_scalar_log_target (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8623597Z   test_nn_KLDivLoss_with_log_target_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8623928Z   test_nn_KLDivLoss_with_target_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8624238Z   test_nn_L1Loss (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8624544Z   test_nn_L1Loss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8624853Z   test_nn_L1Loss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8625233Z   test_nn_L1Loss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8625546Z   test_nn_L1Loss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8625862Z   test_nn_L1Loss_no_reduce_complex (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8626169Z   test_nn_L1Loss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8626478Z   test_nn_L1Loss_scalar (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8626778Z   test_nn_LPPool1d (__main__.TestJitGeneratedModule) ... ok (0.109s)
2023-01-11T21:25:38.8627072Z   test_nn_LPPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.109s)
2023-01-11T21:25:38.8627385Z   test_nn_LPPool1d_norm (__main__.TestJitGeneratedModule) ... ok (0.113s)
2023-01-11T21:25:38.8627817Z   test_nn_LPPool2d (__main__.TestJitGeneratedModule) ... ok (0.136s)
2023-01-11T21:25:38.8628116Z   test_nn_LPPool2d_norm (__main__.TestJitGeneratedModule) ... ok (0.142s)
2023-01-11T21:25:38.8628442Z   test_nn_LSTMCell (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:38.8628763Z   test_nn_LayerNorm_1d_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.050s)
2023-01-11T21:25:38.8629113Z   test_nn_LayerNorm_1d_empty_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8629453Z   test_nn_LayerNorm_1d_no_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8629794Z   test_nn_LayerNorm_3d_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:38.8630137Z   test_nn_LayerNorm_3d_no_affine_large_feature (__main__.TestJitGeneratedModule) ... ok (0.156s)
2023-01-11T21:25:38.8630480Z   test_nn_LayerNorm_3d_no_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:38.8630863Z   test_nn_LeakyReLU (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8631180Z   test_nn_LeakyReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8631507Z   test_nn_LeakyReLU_with_negval (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8631823Z   test_nn_LeakyReLU_with_negval_scalar (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8632151Z   test_nn_LeakyReLU_with_zero_negval (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8632459Z   test_nn_Linear (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8632757Z   test_nn_Linear_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8633051Z   test_nn_Linear_no_bias (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8633406Z   test_nn_LocalResponseNorm_1d (__main__.TestJitGeneratedModule) ... ok (0.166s)
2023-01-11T21:25:38.8633744Z   test_nn_LocalResponseNorm_2d_uneven_pad (__main__.TestJitGeneratedModule) ... ok (0.182s)
2023-01-11T21:25:38.8634084Z   test_nn_LocalResponseNorm_3d_custom_params (__main__.TestJitGeneratedModule) ... ok (0.203s)
2023-01-11T21:25:38.8634410Z   test_nn_LogSigmoid (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8634723Z   test_nn_LogSigmoid_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8635042Z   test_nn_LogSigmoid_scalar (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8635331Z   test_nn_LogSoftmax (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:38.8635640Z   test_nn_LogSoftmax_multiparam (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:38.8635974Z   test_nn_LogSoftmax_multiparam_scalar (__main__.TestJitGeneratedModule) ... ok (0.050s)
2023-01-11T21:25:38.8636302Z   test_nn_LogSoftmax_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:38.8636594Z   test_nn_MSELoss (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8636904Z   test_nn_MSELoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8637266Z   test_nn_MSELoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8637573Z   test_nn_MSELoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8637890Z   test_nn_MSELoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8638204Z   test_nn_MSELoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8638517Z   test_nn_MSELoss_prec (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:38.8638804Z   test_nn_MSELoss_scalar (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8639115Z   test_nn_MarginRankingLoss (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8639446Z   test_nn_MarginRankingLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8639780Z   test_nn_MarginRankingLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8640165Z   test_nn_MarginRankingLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8640523Z   test_nn_MarginRankingLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8640845Z   test_nn_MaxPool1d (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:38.8641147Z   test_nn_MaxPool1d_return_indices (__main__.TestJitGeneratedModule) ... ok (0.061s)
2023-01-11T21:25:38.8641469Z   test_nn_MaxPool1d_stride (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8641779Z   test_nn_MaxPool2d_3d_input (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8642076Z   test_nn_MaxPool2d_4d_input (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:38.8642396Z   test_nn_MaxPool2d_return_indices (__main__.TestJitGeneratedModule) ... ok (0.061s)
2023-01-11T21:25:38.8642705Z   test_nn_MaxPool3d (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8643022Z   test_nn_MaxPool3d_return_indices (__main__.TestJitGeneratedModule) ... ok (0.061s)
2023-01-11T21:25:38.8643328Z   test_nn_MaxPool3d_stride (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8643644Z   test_nn_MaxPool3d_stride_padding (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8643944Z   test_nn_Mish (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8644225Z   test_nn_Mish_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8644526Z   test_nn_Mish_scalar (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8644839Z   test_nn_MultiLabelMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8645183Z   test_nn_MultiLabelMarginLoss_0d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8645514Z   test_nn_MultiLabelMarginLoss_1d (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:38.8645860Z   test_nn_MultiLabelMarginLoss_1d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8646223Z   test_nn_MultiLabelMarginLoss_index_neg (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8646569Z   test_nn_MultiLabelMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:38.8646935Z   test_nn_MultiLabelMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8647299Z   test_nn_MultiLabelMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8647656Z   test_nn_MultiLabelMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8647993Z   test_nn_MultiLabelSoftMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.085s)
2023-01-11T21:25:38.8648357Z   test_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.064s)
2023-01-11T21:25:38.8648735Z   test_nn_MultiLabelSoftMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.064s)
2023-01-11T21:25:38.8649152Z   test_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.065s)
2023-01-11T21:25:38.8649508Z   test_nn_MultiLabelSoftMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:38.8649874Z   test_nn_MultiLabelSoftMarginLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.081s)
2023-01-11T21:25:38.8650248Z   test_nn_MultiLabelSoftMarginLoss_weights_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:38.8650579Z   test_nn_MultiMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8650899Z   test_nn_MultiMarginLoss_1d (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8651229Z   test_nn_MultiMarginLoss_1d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8651561Z   test_nn_MultiMarginLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8651909Z   test_nn_MultiMarginLoss_margin_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8652253Z   test_nn_MultiMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8652578Z   test_nn_MultiMarginLoss_p (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8652892Z   test_nn_MultiMarginLoss_p_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8653224Z   test_nn_MultiMarginLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8653564Z   test_nn_MultiMarginLoss_weights_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8653974Z   test_nn_MultiheadAttention (__main__.TestJitGeneratedModule) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:25:38.8654332Z   test_nn_NLLLoss (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8654639Z   test_nn_NLLLoss2d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8654972Z   test_nn_NLLLoss2d_no_reduce_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8655307Z   test_nn_NLLLoss2d_no_reduce_weights (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8655620Z   test_nn_NLLLossNd_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8655948Z   test_nn_NLLLossNd_no_reduce_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8656284Z   test_nn_NLLLossNd_no_reduce_weights (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8656581Z   test_nn_NLLLoss_2d (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8656894Z   test_nn_NLLLoss_2d_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8657210Z   test_nn_NLLLoss_2d_weights (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8657515Z   test_nn_NLLLoss_dim_is_3 (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8657810Z   test_nn_NLLLoss_higher_dim (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8658255Z   test_nn_NLLLoss_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8658583Z   test_nn_NLLLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8658893Z   test_nn_NLLLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8659275Z   test_nn_NLLLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8659674Z   test_nn_NLLLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8660001Z   test_nn_NLLLoss_no_reduce_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8660320Z   test_nn_NLLLoss_no_reduce_weights (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8660660Z   test_nn_NLLLoss_no_reduce_weights_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8661019Z   test_nn_NLLLoss_no_reduce_weights_ignore_index_neg (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8661383Z   test_nn_NLLLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8661707Z   test_nn_NLLLoss_weights_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8662047Z   test_nn_NLLLoss_weights_ignore_index_neg (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8662478Z   test_nn_PReLU_1d (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8662774Z   test_nn_PReLU_1d_multiparam (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8663081Z   test_nn_PReLU_2d (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8663388Z   test_nn_PReLU_2d_multiparam (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8663679Z   test_nn_PReLU_3d (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8664039Z   test_nn_PReLU_3d_multiparam (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8664362Z   test_nn_PReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8664674Z   test_nn_PReLU_scalar (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8664980Z   test_nn_Padding122112_3dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8665309Z   test_nn_Padding1221_2dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8665633Z   test_nn_Padding12_1dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8665940Z   test_nn_Padding2322_2dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8666264Z   test_nn_Padding31_1dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8666588Z   test_nn_Padding322112_3dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8666914Z   test_nn_Padding332122_3dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8667229Z   test_nn_Padding3331_2dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8667545Z   test_nn_Padding33_1dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8667863Z   test_nn_PairwiseDistance (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8668183Z   test_nn_PairwiseDistance_broadcast_lhs (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8668530Z   test_nn_PairwiseDistance_broadcast_rhs (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8668873Z   test_nn_PairwiseDistance_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8669224Z   test_nn_PairwiseDistance_with_non_default_args (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8669545Z   test_nn_PixelShuffle (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8669853Z   test_nn_PixelUnshuffle (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8670179Z   test_nn_PoissonNLLLoss_full_loss (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8670507Z   test_nn_PoissonNLLLoss_full_loss_no_log_input (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8670913Z   test_nn_PoissonNLLLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8671264Z   test_nn_PoissonNLLLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8671614Z   test_nn_PoissonNLLLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8671937Z   test_nn_PoissonNLLLoss_no_full_loss (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8672288Z   test_nn_PoissonNLLLoss_no_full_loss_no_log_input (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8672636Z   test_nn_PoissonNLLLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8672934Z   test_nn_RNNCell (__main__.TestJitGeneratedModule) ... ok (0.071s)
2023-01-11T21:25:38.8673270Z   test_nn_RReLU (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8673566Z   test_nn_RReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:38.8673876Z   test_nn_RReLU_with_up_down (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:38.8674185Z   test_nn_RReLU_with_up_down_scalar (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8674485Z   test_nn_ReLU (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8674766Z   test_nn_ReLU6 (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8675051Z   test_nn_ReLU6_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8675352Z   test_nn_ReLU6_scalar (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8675654Z   test_nn_ReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8675986Z   test_nn_ReLU_scalar (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8676283Z   test_nn_ReflectionPad1d (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8676606Z   test_nn_ReflectionPad1d_batch (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8676941Z   test_nn_ReflectionPad1d_complex (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8677251Z   test_nn_ReflectionPad2d (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8677571Z   test_nn_ReflectionPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8677906Z   test_nn_ReflectionPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8678229Z   test_nn_ReflectionPad3d (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:38.8678541Z   test_nn_ReflectionPad3d_complex (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8678876Z   test_nn_ReflectionPad3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8679203Z   test_nn_ReplicationPad1d (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8679518Z   test_nn_ReplicationPad1d_batch (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8679849Z   test_nn_ReplicationPad1d_complex (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:38.8680176Z   test_nn_ReplicationPad2d (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8680503Z   test_nn_ReplicationPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8680833Z   test_nn_ReplicationPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8681167Z   test_nn_ReplicationPad3d (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8681495Z   test_nn_ReplicationPad3d_complex (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8681817Z   test_nn_ReplicationPad3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8682132Z   test_nn_SELU (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8682274Z   test_nn_SELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8682412Z   test_nn_SELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:38.8682542Z   test_nn_SiLU (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8682685Z   test_nn_SiLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8682812Z   test_nn_SiLU_scalar (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8682945Z   test_nn_Sigmoid (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8683090Z   test_nn_Sigmoid_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8683231Z   test_nn_Sigmoid_scalar (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8683369Z   test_nn_SmoothL1Loss (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:38.8683554Z   test_nn_SmoothL1Loss_beta (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8683715Z   test_nn_SmoothL1Loss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8683872Z   test_nn_SmoothL1Loss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8684014Z   test_nn_SmoothL1Loss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8684165Z   test_nn_SmoothL1Loss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8684325Z   test_nn_SmoothL1Loss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8684473Z   test_nn_SmoothL1Loss_scalar (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8684619Z   test_nn_SmoothL1Loss_zero_beta (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8684762Z   test_nn_SoftMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:38.8684958Z   test_nn_SoftMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8685122Z   test_nn_SoftMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8685282Z   test_nn_SoftMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:38.8685423Z   test_nn_SoftMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8685556Z   test_nn_Softmax (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:38.8685695Z   test_nn_Softmax2d (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:38.8685843Z   test_nn_Softmax2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8685988Z   test_nn_Softmax_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:38.8686127Z   test_nn_Softmax_scalar (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:38.8686265Z   test_nn_Softmin (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:38.8686405Z   test_nn_Softmin_multidim (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:38.8686535Z   test_nn_Softmin_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:38.8686674Z   test_nn_Softmin_scalar (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:38.8686810Z   test_nn_Softplus (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8686951Z   test_nn_Softplus_beta (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8687105Z   test_nn_Softplus_beta_threshold (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8687265Z   test_nn_Softplus_beta_threshold_scalar (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8687413Z   test_nn_Softplus_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8687549Z   test_nn_Softshrink (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8687684Z   test_nn_Softshrink_lambda (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8687835Z   test_nn_Softshrink_lambda_scalar (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8687986Z   test_nn_Softshrink_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:38.8688119Z   test_nn_Softsign (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:38.8688265Z   test_nn_Softsign_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.073s)
2023-01-11T21:25:38.8688409Z   test_nn_Softsign_scalar (__main__.TestJitGeneratedModule) ... ok (0.071s)
2023-01-11T21:25:38.8688539Z   test_nn_Tanh (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:38.8688682Z   test_nn_Tanh_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8688806Z   test_nn_Tanh_scalar (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:38.8688947Z   test_nn_Tanhshrink (__main__.TestJitGeneratedModule) ... ok (0.124s)
2023-01-11T21:25:38.8689133Z   test_nn_Tanhshrink_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.078s)
2023-01-11T21:25:38.8689277Z   test_nn_Tanhshrink_scalar (__main__.TestJitGeneratedModule) ... ok (0.068s)
2023-01-11T21:25:38.8689425Z   test_nn_Threshold_large_value (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8689575Z   test_nn_Threshold_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8689730Z   test_nn_Threshold_threshold_value (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:38.8689892Z   test_nn_Threshold_threshold_value_scalar (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:38.8690091Z   test_nn_Transformer (__main__.TestJitGeneratedModule) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:25:38.8690274Z   test_nn_TransformerDecoderLayer_gelu_activation (__main__.TestJitGeneratedModule) ... ok (1.953s)
2023-01-11T21:25:38.8690496Z   test_nn_TransformerDecoderLayer_relu_activation (__main__.TestJitGeneratedModule) ... ok (1.854s)
2023-01-11T21:25:38.8690670Z   test_nn_TransformerEncoderLayer_gelu_activation (__main__.TestJitGeneratedModule) ... ok (1.717s)
2023-01-11T21:25:38.8690842Z   test_nn_TransformerEncoderLayer_relu_activation (__main__.TestJitGeneratedModule) ... ok (1.710s)
2023-01-11T21:25:38.8690999Z   test_nn_Transformer_multilayer_coder (__main__.TestJitGeneratedModule) ... ok (5.719s)
2023-01-11T21:25:38.8691170Z   test_nn_TripletMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:38.8691338Z   test_nn_TripletMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:38.8691504Z   test_nn_TripletMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:38.8691640Z   test_nn_Unflatten_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:38.8691772Z   test_nn_Unfold (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:38.8691917Z   test_nn_Unfold_int_input (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:38.8692055Z   test_nn_ZeroPad2d (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8692202Z   test_nn_ZeroPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8692354Z   test_nn_ZeroPad2d_negative_dims (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8692505Z   test_nn_ZeroPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:38.8692654Z   test_nn_interpolate_bicubic_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8692801Z   test_nn_interpolate_bicubic_2d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8692956Z   test_nn_interpolate_bicubic_scale_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8693131Z   test_nn_interpolate_bicubic_scale_tuple_shared_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8693303Z   test_nn_interpolate_bicubic_scale_tuple_skewed_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8693493Z   test_nn_interpolate_bicubic_scale_tuple_skewed_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8693649Z   test_nn_interpolate_bicubic_tuple_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8693822Z   test_nn_interpolate_bicubic_tuple_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8693974Z   test_nn_interpolate_bilinear_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8694133Z   test_nn_interpolate_bilinear_2d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8694276Z   test_nn_interpolate_bilinear_scale_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8694452Z   test_nn_interpolate_bilinear_scale_tuple_shared_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8694654Z   test_nn_interpolate_bilinear_scale_tuple_skewed_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8694845Z   test_nn_interpolate_bilinear_scale_tuple_skewed_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8695001Z   test_nn_interpolate_bilinear_tuple_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8695176Z   test_nn_interpolate_bilinear_tuple_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8695326Z   test_nn_interpolate_linear_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8695492Z   test_nn_interpolate_linear_1d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8695638Z   test_nn_interpolate_linear_1d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8695792Z   test_nn_interpolate_linear_scale_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8695994Z   test_nn_interpolate_linear_scale_1d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8696148Z   test_nn_interpolate_linear_tuple_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8696298Z   test_nn_interpolate_nearest_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8696455Z   test_nn_interpolate_nearest_1d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8696608Z   test_nn_interpolate_nearest_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8696783Z   test_nn_interpolate_nearest_2d_launch_configs (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8696942Z   test_nn_interpolate_nearest_2d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8697078Z   test_nn_interpolate_nearest_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8697237Z   test_nn_interpolate_nearest_3d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8697394Z   test_nn_interpolate_nearest_scale_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8697550Z   test_nn_interpolate_nearest_scale_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8697701Z   test_nn_interpolate_nearest_scale_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8697860Z   test_nn_interpolate_nearest_tuple_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8698015Z   test_nn_interpolate_nearest_tuple_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8698169Z   test_nn_interpolate_nearest_tuple_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8698309Z   test_nn_interpolate_trilinear_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8698473Z   test_nn_interpolate_trilinear_3d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8698635Z   test_nn_interpolate_trilinear_scale_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8698812Z   test_nn_interpolate_trilinear_scale_3d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8698971Z   test_nn_interpolate_trilinear_tuple_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8699148Z   test_nn_interpolate_trilinear_tuple_3d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8699292Z   test_nn_log_softmax_dim0 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8699438Z   test_nn_log_softmax_dim3 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8699582Z   test_nn_log_softmax_lastdim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8699716Z   test_nn_log_softmax_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8699866Z   test_nn_log_softmax_spatial (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8700056Z   test_nn_log_softmax_spatial_special (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8700231Z   test_nn_multimarginloss_1d_input_0d_target_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8700380Z   test_nn_softmax_functional_dim0 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8700529Z   test_nn_softmax_functional_dim3 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8700682Z   test_nn_softmax_functional_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8700823Z   test_nn_softmax_lastdim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8700959Z   test_nn_softmax_lastdim_dtype (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8701101Z   test_nn_softmax_spatial (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8701252Z   test_nn_softmax_spatial_dtype (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8701438Z   test_nn_softmax_spatial_special (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:38.8701606Z   test_checkscriptassertraisesregex (jit.test_jit_utils.TestJitUtils) ... ok (0.002s)
2023-01-11T21:25:38.8701766Z   test_get_callable_argument_names_hybrid (jit.test_jit_utils.TestJitUtils) ... ok (0.004s)
2023-01-11T21:25:38.8701931Z   test_get_callable_argument_names_keyword_only (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:38.8702109Z   test_get_callable_argument_names_positional_only (jit.test_jit_utils.TestJitUtils) ... ok (0.003s)
2023-01-11T21:25:38.8702272Z   test_get_callable_argument_names_positional_or_keyword (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:38.8702557Z   test_get_callable_argument_names_var_keyword (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:38.8702732Z   test_get_callable_argument_names_var_positional (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:38.8702894Z   test_no_tracer_warn_context_manager (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:38.8703035Z   test_comprehension_iterable (jit.test_list_dict.TestList) ... ok (0.018s)
2023-01-11T21:25:38.8703187Z   test_comprehension_out_type_not_in_type (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:38.8703328Z   test_comprehensions_basic (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:38.8703473Z   test_comprehensions_basic_float (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:38.8703615Z   test_comprehensions_two_comps (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8703734Z   test_copy_list_immutable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:38.8703867Z   test_copy_list_mutable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:38.8703985Z   test_del (jit.test_list_dict.TestList) ... ok (0.010s)
2023-01-11T21:25:38.8704138Z   test_dict_keyword_is_correctly_typed (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8704299Z   test_dict_keyword_with_dict_comprehension (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8704472Z   test_dict_keyword_with_dict_comprehension_and_kwargs (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8704636Z   test_dict_keyword_with_empty_dict_comprehension (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:38.8704785Z   test_dict_keyword_with_empty_iterable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:38.8704939Z   test_dict_keyword_with_internal_aggregate_function (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8705081Z   test_dict_keyword_with_iterable (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8705219Z   test_dict_keyword_with_kwargs (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8705386Z   test_dict_keyword_with_kwargs_using_container_values (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:38.8705533Z   test_dict_keyword_with_mapping (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8705771Z   test_dict_keyword_with_mapping_and_kwargs (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8705931Z   test_dict_keyword_with_mismatched_annotations (jit.test_list_dict.TestList) ... ok (0.002s)
2023-01-11T21:25:38.8706079Z   test_dict_keyword_with_nested_call (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8706235Z   test_dict_keyword_with_previously_declared_variable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:38.8706415Z   test_dict_keyword_with_previously_declared_variable_and_kwargs (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8706555Z   test_extend_list_immutable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:38.8706688Z   test_extend_list_mutable (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8706813Z   test_in_check (jit.test_list_dict.TestList) ... ok (0.013s)
2023-01-11T21:25:38.8706988Z   test_list_bool_conversion (jit.test_list_dict.TestList) ... ok (0.019s)
2023-01-11T21:25:38.8707114Z   test_list_count (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:38.8707252Z   test_list_count_not_existing (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8707363Z   test_list_gather (jit.test_list_dict.TestList) ... ok (0.011s)
2023-01-11T21:25:38.8707487Z   test_list_index (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8707627Z   test_list_index_not_existing (jit.test_list_dict.TestList) ... ok (0.017s)
2023-01-11T21:25:38.8707753Z   test_list_keyword (jit.test_list_dict.TestList) ... ok (0.014s)
2023-01-11T21:25:38.8707874Z   test_list_len (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8707998Z   test_list_literal (jit.test_list_dict.TestList) ... ok (0.012s)
2023-01-11T21:25:38.8708118Z   test_list_none (jit.test_list_dict.TestList) ... ok (0.001s)
2023-01-11T21:25:38.8708241Z   test_list_ops (jit.test_list_dict.TestList) ... ok (0.056s)
2023-01-11T21:25:38.8708354Z   test_list_slice (jit.test_list_dict.TestList) ... ok (0.021s)
2023-01-11T21:25:38.8708473Z   test_list_sort (jit.test_list_dict.TestList) ... ok (0.035s)
2023-01-11T21:25:38.8708609Z   test_list_unification_hint (jit.test_list_dict.TestList) ... ok (0.001s)
2023-01-11T21:25:38.8708719Z   test_list_variance (jit.test_list_dict.TestList)
2023-01-11T21:25:38.8708849Z `List[T1]` is not a subtype of `List[T2]`, even if `T1` is a ... ok (0.009s)
2023-01-11T21:25:38.8708975Z   test_min_bool_list (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8709096Z   test_min_max_list (jit.test_list_dict.TestList) ... ok (0.059s)
2023-01-11T21:25:38.8709230Z   test_min_max_single_list (jit.test_list_dict.TestList) ... ok (0.041s)
2023-01-11T21:25:38.8709350Z   test_mutable_list_append (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8709484Z   test_mutable_list_append_2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8709622Z   test_mutable_list_append_if (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8709770Z   test_mutable_list_append_if_else (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8709910Z   test_mutable_list_append_loop (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8710055Z   test_mutable_list_append_loop_if (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:38.8710186Z   test_mutable_list_clear (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8710324Z   test_mutable_list_clear_empty (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8710455Z   test_mutable_list_function_inline (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8710589Z   test_mutable_list_insert (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8710797Z   test_mutable_list_insert_neg_out_of_bounds (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8710943Z   test_mutable_list_insert_negative (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8711227Z   test_mutable_list_insert_out_of_bounds (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8711371Z   test_mutable_list_nested_loop (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8711500Z   test_mutable_list_pop (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8711633Z   test_mutable_list_pop2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8711752Z   test_mutable_list_pop_at (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8711890Z   test_mutable_list_pop_at2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8712038Z   test_mutable_list_pop_at_negative (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8712182Z   test_mutable_list_pop_at_negative2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8712321Z   test_mutable_list_pop_empty (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8712489Z   test_mutable_list_pop_slice (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8712628Z   test_mutable_list_remove (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:38.8712761Z   test_mutable_list_remove2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8712895Z   test_mutable_list_remove_not_existing (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8713037Z   test_mutable_list_remove_tensor (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8713169Z   test_mutable_list_reverse (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8713310Z   test_mutable_list_reverse_empty (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:38.8713453Z   test_mutable_tensor_list_reverse (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:38.8713595Z   test_no_element_type_annotation (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:38.8713718Z   test_slice_index (jit.test_list_dict.TestList) ... ok (0.028s)
2023-01-11T21:25:38.8713854Z   test_tensor_list_count (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8713987Z   test_tensor_list_count_not_existing (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8714114Z   test_tensor_list_index (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:38.8714255Z   test_tensor_list_index_not_existing (jit.test_list_dict.TestList) ... ok (0.018s)
2023-01-11T21:25:38.8714360Z   test_to_list (jit.test_list_dict.TestList)
2023-01-11T21:25:38.8714479Z Unit tests for Tensor.tolist() function. ... ok (0.229s)
2023-01-11T21:25:38.8714589Z   test_to_list_gpu (jit.test_list_dict.TestList)
2023-01-11T21:25:38.8714734Z GPU tests for Tensor.tolist() function. ... skip: CUDA is not available (0.001s)
2023-01-11T21:25:38.8714874Z   test_bump_numeric_counter (jit.test_logging.TestLogging) ... ok (0.008s)
2023-01-11T21:25:38.8715003Z   test_counter_aggregation (jit.test_logging.TestLogging) ... ok (0.007s)
2023-01-11T21:25:38.8715144Z   test_logging_levels_set (jit.test_logging.TestLogging) ... ok (0.001s)
2023-01-11T21:25:38.8715291Z   test_time_measurement_counter (jit.test_logging.TestLogging) ... ok (0.002s)
2023-01-11T21:25:38.8715443Z   test_time_measurement_counter_script (jit.test_logging.TestLogging) ... ok (0.006s)
2023-01-11T21:25:38.8715583Z   test_trace_numeric_counter (jit.test_logging.TestLogging) ... ok (0.006s)
2023-01-11T21:25:38.8715747Z   test_always_alive_values (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.020s)
2023-01-11T21:25:38.8715903Z   test_merge_liveness (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.011s)
2023-01-11T21:25:38.8716054Z   test_successful (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.012s)
2023-01-11T21:25:38.8716206Z   test_switch_inputs_to_inplace (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.011s)
2023-01-11T21:25:38.8716320Z   test_broadcasting_list (jit.test_misc.TestMisc)
2023-01-11T21:25:38.8716452Z Test BroadcastingList and torch.nn._size_N_t alias ... ok (0.006s)
2023-01-11T21:25:38.8716588Z   test_export_opnames_interface (jit.test_misc.TestMisc) ... ok (0.024s)
2023-01-11T21:25:38.8716749Z   test_future_isinstance (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8716869Z   test_hacked_twin (jit.test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:25:38.8716982Z   test_if_returning_any (jit.test_misc.TestMisc)
2023-01-11T21:25:38.8717093Z Check that an if statement can return different ... ok (0.005s)
2023-01-11T21:25:38.8717211Z   test_joined_str (jit.test_misc.TestMisc) ... ok (0.004s)
2023-01-11T21:25:38.8717335Z   test_kwarg_support (jit.test_misc.TestMisc) ... ok (0.008s)
2023-01-11T21:25:38.8717470Z   test_legacy_tensor_constructor (jit.test_misc.TestMisc) ... ok (0.016s)
2023-01-11T21:25:38.8717594Z   test_list_literal_infer (jit.test_misc.TestMisc) ... ok (0.008s)
2023-01-11T21:25:38.8717709Z   test_math_inf (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8717832Z   test_parse_ir_annotate (jit.test_misc.TestMisc) ... ok (0.001s)
2023-01-11T21:25:38.8718015Z   test_parse_ir_single_element_tensor_negative (jit.test_misc.TestMisc) ... ok (0.001s)
2023-01-11T21:25:38.8718157Z   test_parse_ir_single_element_tensor_positive (jit.test_misc.TestMisc) ... ok (0.001s)
2023-01-11T21:25:38.8718291Z   test_script_many_decorators (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8718412Z   test_str_refine_any (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8718551Z   test_subexpression_Dict_int_Future (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8718691Z   test_subexpression_Future_annotate (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8718826Z   test_subexpression_List_Future (jit.test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:25:38.8718958Z   test_subexpression_Optional (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8719105Z   test_subexpression_Tuple_int_int_Future (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:38.8719228Z   test_tuple_subscripted_assign (jit.test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:25:38.8719415Z   test_call_script_fn_from_traced_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.011s)
2023-01-11T21:25:38.8719601Z   test_call_script_module_from_traced_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.016s)
2023-01-11T21:25:38.8719775Z   test_call_traced_fn_from_script_fn (jit.test_tracer.TestMixTracingScripting) ... ok (0.006s)
2023-01-11T21:25:38.8719948Z   test_call_traced_mod_from_script_fn (jit.test_tracer.TestMixTracingScripting) ... ok (0.010s)
2023-01-11T21:25:38.8720126Z   test_call_tracing_fn_from_script_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:38.8720310Z   test_call_tracing_mod_from_script_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.012s)
2023-01-11T21:25:38.8720489Z   test_script_inline_trace_multiple_args (jit.test_tracer.TestMixTracingScripting) ... ok (0.011s)
2023-01-11T21:25:38.8720652Z   test_trace_dict_mix_script (jit.test_tracer.TestMixTracingScripting) ... ok (0.043s)
2023-01-11T21:25:38.8720804Z   test_trace_hierarchy (jit.test_tracer.TestMixTracingScripting) ... ok (0.024s)
2023-01-11T21:25:38.8720960Z   test_trace_linear (jit.test_tracer.TestMixTracingScripting) ... ok (0.020s)
2023-01-11T21:25:38.8721146Z   test_trace_mixed_by_script_with_dict_output (jit.test_tracer.TestMixTracingScripting) ... ok (0.011s)
2023-01-11T21:25:38.8721303Z   test_trace_of_script (jit.test_tracer.TestMixTracingScripting) ... ok (0.011s)
2023-01-11T21:25:38.8721463Z   test_trace_parameter (jit.test_tracer.TestMixTracingScripting) ... ok (0.022s)
2023-01-11T21:25:38.8721635Z   test_trace_returning_dict_with_tensor_tuples (jit.test_tracer.TestMixTracingScripting)
2023-01-11T21:25:38.8721798Z Tracing over a module returning a dictionary whose values are tuples of tensors ... ok (0.012s)
2023-01-11T21:25:38.8721950Z   test_trace_script (jit.test_tracer.TestMixTracingScripting) ... ok (0.153s)
2023-01-11T21:25:38.8722108Z   test_trace_script_returning_complex_dict (jit.test_tracer.TestMixTracingScripting)
2023-01-11T21:25:38.8722296Z Tracing over a script function returning a dictionary should work. ... ok (0.025s)
2023-01-11T21:25:38.8722450Z   test_trace_with_size (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:38.8722651Z   test_traced_module_contains_scripted_interface_types (jit.test_tracer.TestMixTracingScripting) ... ok (0.067s)
2023-01-11T21:25:38.8722834Z   test_traced_module_implements_interface (jit.test_tracer.TestMixTracingScripting) ... ok (0.186s)
2023-01-11T21:25:38.8722994Z   test_tracing_indexing (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:38.8723149Z   test_tracing_slicing (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:38.8723270Z   test_alexnet (jit.test_models.TestModels) ... ok (0.579s)
2023-01-11T21:25:38.8723385Z   test_dcgan_models (jit.test_models.TestModels) ... ok (0.304s)
2023-01-11T21:25:38.8723530Z   test_dcgan_models_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8723682Z   test_mnist (jit.test_models.TestModels) ... ok (0.340s)
2023-01-11T21:25:38.8723818Z   test_mnist_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8723985Z   test_mnist_training_leaks_no_memory_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8724185Z   test_neural_style (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:38.8724330Z   test_neural_style_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:38.8724471Z   test_reinforcement_learning (jit.test_models.TestModels) ... ok (0.039s)
2023-01-11T21:25:38.8724629Z   test_reinforcement_learning_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:38.8724831Z   test_script_module_script_resnet (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.003s)
2023-01-11T21:25:38.8725048Z   test_script_module_trace_resnet18 (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:38.8725237Z   test_snli (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:25:38.8725378Z   test_snli_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:38.8725511Z   test_snli_quantized (jit.test_models.TestModels) ... ok (0.663s)
2023-01-11T21:25:38.8725713Z   test_super_resolution (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:38.8725865Z   test_super_resolution_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:38.8726008Z   test_time_sequence_prediction (jit.test_models.TestModels) ... ok (0.131s)
2023-01-11T21:25:38.8726115Z   test_vae (jit.test_models.TestModels) ... ok (0.172s)
2023-01-11T21:25:38.8726250Z   test_vae_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8726381Z   test_vae_quantized (jit.test_models.TestModels) ... ok (0.090s)
2023-01-11T21:25:38.8726530Z   test_customized_state_dict_methods (jit.test_module_apis.TestModuleAPIs)
2023-01-11T21:25:38.8726665Z Tests that customized state dict methods are in effect ... ok (0.032s)
2023-01-11T21:25:38.8726812Z   test_default_state_dict_methods (jit.test_module_apis.TestModuleAPIs)
2023-01-11T21:25:38.8726961Z Tests that default state dict methods are automatically available ... ok (0.025s)
2023-01-11T21:25:38.8727122Z   test_submodule_customized_state_dict_methods (jit.test_module_apis.TestModuleAPIs)
2023-01-11T21:25:38.8727256Z Tests that customized state dict methods on submodules are in effect ... ok (0.040s)
2023-01-11T21:25:38.8727438Z   test_custom_container_forward (jit.test_module_containers.TestModuleContainers) ... ok (0.070s)
2023-01-11T21:25:38.8727625Z   test_empty_dict_override_contains (jit.test_module_containers.TestModuleContainers) ... ok (0.020s)
2023-01-11T21:25:38.8727842Z   test_module_inplace_construct (jit.test_module_containers.TestModuleContainers) ... ok (0.018s)
2023-01-11T21:25:38.8728033Z   test_module_interface_special_methods (jit.test_module_containers.TestModuleContainers) ... ok (0.051s)
2023-01-11T21:25:38.8728208Z   test_module_properties (jit.test_module_containers.TestModuleContainers) ... ok (0.030s)
2023-01-11T21:25:38.8728372Z   test_moduledict (jit.test_module_containers.TestModuleContainers) ... ok (0.188s)
2023-01-11T21:25:38.8728545Z   test_moduledict_getitem (jit.test_module_containers.TestModuleContainers) ... ok (0.033s)
2023-01-11T21:25:38.8728721Z   test_moduledict_keyerror (jit.test_module_containers.TestModuleContainers) ... ok (0.011s)
2023-01-11T21:25:38.8728892Z   test_normal_list_attribute_with_modules_error (jit.test_module_containers.TestModuleContainers)
2023-01-11T21:25:38.8729045Z Test that an attempt to script a module with a regular list attribute ... ok (0.003s)
2023-01-11T21:25:38.8729274Z   test_parameterdict_script_getitem (jit.test_module_containers.TestModuleContainers) ... ok (0.014s)
2023-01-11T21:25:38.8729459Z   test_parameterlist_script_getitem (jit.test_module_containers.TestModuleContainers) ... ok (0.042s)
2023-01-11T21:25:38.8729642Z   test_parameterlist_script_iter (jit.test_module_containers.TestModuleContainers) ... ok (0.060s)
2023-01-11T21:25:38.8729826Z   test_script_module_list_sequential (jit.test_module_containers.TestModuleContainers) ... ok (0.023s)
2023-01-11T21:25:38.8730006Z   test_script_modulelist_index (jit.test_module_containers.TestModuleContainers) ... ok (0.103s)
2023-01-11T21:25:38.8730193Z   test_sequential_intermediary_types (jit.test_module_containers.TestModuleContainers) ... ok (0.026s)
2023-01-11T21:25:38.8730378Z   test_special_method_with_override (jit.test_module_containers.TestModuleContainers) ... ok (0.023s)
2023-01-11T21:25:38.8730525Z   test_typed_module_dict (jit.test_module_containers.TestModuleContainers)
2023-01-11T21:25:38.8730682Z Test that a type annotation can be provided for a ModuleDict that allows ... ok (0.060s)
2023-01-11T21:25:38.8730843Z   test_typed_module_list (jit.test_module_containers.TestModuleContainers)
2023-01-11T21:25:38.8731000Z Test that a type annotation can be provided for a ModuleList that allows ... ok (0.051s)
2023-01-11T21:25:38.8731202Z   test_freeze_module_with_inplace_mutation_in_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.022s)
2023-01-11T21:25:38.8731382Z   test_freeze_module_with_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.019s)
2023-01-11T21:25:38.8731573Z   test_freeze_module_with_interface_and_fork (jit.test_module_interface.TestModuleInterface) ... ok (0.034s)
2023-01-11T21:25:38.8731761Z   test_freeze_module_with_mutated_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.027s)
2023-01-11T21:25:38.8731949Z   test_freeze_module_with_setattr_in_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.020s)
2023-01-11T21:25:38.8732109Z   test_module_apis_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.015s)
2023-01-11T21:25:38.8732276Z   test_module_doc_string (jit.test_module_interface.TestModuleInterface) ... ok (0.022s)
2023-01-11T21:25:38.8732444Z   test_module_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.094s)
2023-01-11T21:25:38.8732624Z   test_module_interface_inheritance (jit.test_module_interface.TestModuleInterface) ... ok (0.001s)
2023-01-11T21:25:38.8732801Z   test_module_interface_subtype (jit.test_module_interface.TestModuleInterface) ... ok (0.051s)
2023-01-11T21:25:38.8732961Z   test_module_swap (jit.test_module_interface.TestModuleInterface) ... ok (0.022s)
2023-01-11T21:25:38.8733141Z   test_module_swap_no_lazy_compile (jit.test_module_interface.TestModuleInterface) ... ok (0.025s)
2023-01-11T21:25:38.8733323Z   test_module_swap_no_module_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.016s)
2023-01-11T21:25:38.8733483Z   test_module_swap_wrong_module (jit.test_module_interface.TestModuleInterface) ... ok (0.019s)
2023-01-11T21:25:38.8733697Z   test_not_submodule_interface_call (jit.test_module_interface.TestModuleInterface) ... ok (0.011s)
2023-01-11T21:25:38.8733878Z   test_script_module_as_interface_swap (jit.test_module_interface.TestModuleInterface) ... ok (0.023s)
2023-01-11T21:25:38.8734024Z   test_script_module_with_constants_list (jit.test_modules.TestModules)
2023-01-11T21:25:38.8734159Z Test that a module that has __constants__ set to something ... ok (0.009s)
2023-01-11T21:25:38.8734299Z   test_namedtuple (jit.test_list_dict.TestNamedTuple) ... ok (0.004s)
2023-01-11T21:25:38.8734446Z   test_namedtuple_as_attr (jit.test_list_dict.TestNamedTuple) ... ok (0.005s)
2023-01-11T21:25:38.8734595Z   test_namedtuple_constant (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:38.8734736Z   test_namedtuple_kwarg_construct (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:38.8734877Z   test_namedtuple_lower (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:38.8735060Z   test_namedtuple_resolution (jit.test_list_dict.TestNamedTuple) ... ok (0.004s)
2023-01-11T21:25:38.8735260Z   test_namedtuple_serialization (jit.test_list_dict.TestNamedTuple) ... skip: broken while these tests were not in CI (0.001s)
2023-01-11T21:25:38.8735413Z   test_namedtuple_slice_unpack (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:38.8735569Z   test_namedtuple_type_annotation (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:38.8735717Z   test_namedtuple_wrong_types (jit.test_list_dict.TestNamedTuple) ... ok (0.002s)
2023-01-11T21:25:38.8735861Z   test_return_named_tuple (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:38.8736266Z   test_adaptive_avg_pool2d (jit.test_backend_nnapi.TestNnapiBackend) ... /var/lib/jenkins/workspace/test/test_nnapi.py:14: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:25:38.8736348Z   t = torch.tensor(t)
2023-01-11T21:25:38.8736403Z ok (0.332s)
2023-01-11T21:25:38.8736549Z   test_avg_pool2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (1.069s)
2023-01-11T21:25:38.8736686Z   test_cat (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.047s)
2023-01-11T21:25:38.8736841Z   test_compile_spec_santiy (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.008s)
2023-01-11T21:25:38.8736980Z   test_conv2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (3.351s)
2023-01-11T21:25:38.8737131Z   test_conv2d_transpose (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.616s)
2023-01-11T21:25:38.8737276Z   test_dequantize (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.011s)
2023-01-11T21:25:38.8737401Z   test_detach (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.022s)
2023-01-11T21:25:38.8737544Z   test_flatten (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.096s)
2023-01-11T21:25:38.8737689Z   test_hardtanh (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.026s)
2023-01-11T21:25:38.8737831Z   test_linear (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.024s)
2023-01-11T21:25:38.8738450Z   test_log_softmax (jit.test_backend_nnapi.TestNnapiBackend) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1482: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:38.8738546Z   return forward_call(*args, **kwargs)
2023-01-11T21:25:38.8738946Z /opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py:460: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:38.8739058Z   outs = wrap_retval(mod(*_clone_inputs(inputs)))
2023-01-11T21:25:38.8739127Z ok (0.022s)
2023-01-11T21:25:38.8739259Z   test_max_pool2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.484s)
2023-01-11T21:25:38.8739398Z   test_mean (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.067s)
2023-01-11T21:25:38.8739617Z   test_multi_output (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.013s)
2023-01-11T21:25:38.8739766Z   test_pointwise_binary (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.101s)
2023-01-11T21:25:38.8739922Z   test_pointwise_binary_const (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.043s)
2023-01-11T21:25:38.8740075Z   test_pointwise_unary (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.041s)
2023-01-11T21:25:38.8740214Z   test_prelu (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.032s)
2023-01-11T21:25:38.8740353Z   test_qadd (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.130s)
2023-01-11T21:25:38.8740478Z   test_qlinear (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.093s)
2023-01-11T21:25:38.8740620Z   test_quantize (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.016s)
2023-01-11T21:25:38.8740766Z   test_reshape (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.029s)
2023-01-11T21:25:38.8740945Z   test_seblock_mul (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.011s)
2023-01-11T21:25:38.8741088Z   test_slice (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.040s)
2023-01-11T21:25:38.8741595Z   test_softmax (jit.test_backend_nnapi.TestNnapiBackend) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1482: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:38.8741690Z   return forward_call(*args, **kwargs)
2023-01-11T21:25:38.8742078Z /opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py:460: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:38.8742189Z   outs = wrap_retval(mod(*_clone_inputs(inputs)))
2023-01-11T21:25:38.8742244Z ok (0.032s)
2023-01-11T21:25:38.8742513Z   test_tensor_input (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.018s)
2023-01-11T21:25:38.8742657Z   test_to (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.029s)
2023-01-11T21:25:38.8742806Z   test_unsqueeze (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.050s)
2023-01-11T21:25:38.8742963Z   test_upsample_nearest2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.367s)
2023-01-11T21:25:38.8743143Z   test_op_decomposition (jit.test_op_decompositions.TestOpDecompositions) ... ok (0.072s)
2023-01-11T21:25:38.8743337Z   test_registered_decomposition (jit.test_op_decompositions.TestOpDecompositions) ... ok (0.006s)
2023-01-11T21:25:38.8743601Z   test_fuse_activation_with_pack_ops_linear_conv2d_1 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.026s)
2023-01-11T21:25:38.8743853Z   test_fuse_activation_with_pack_ops_linear_conv2d_2 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.026s)
2023-01-11T21:25:38.8744113Z   test_fuse_activation_with_pack_ops_linear_conv2d_3 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.025s)
2023-01-11T21:25:38.8744374Z   test_fuse_activation_with_pack_ops_linear_conv2d_4 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.031s)
2023-01-11T21:25:38.8744642Z   test_insert_pre_packed_linear_before_inline_and_conv_2d_op (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.016s)
2023-01-11T21:25:38.8744893Z   test_insert_pre_packed_linear_op (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.010s)
2023-01-11T21:25:38.8745137Z   test_replace_conv1d_with_conv2d (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.006s)
2023-01-11T21:25:38.8745247Z   test_any (jit.test_pdt.TestPDT) ... ok (0.011s)
2023-01-11T21:25:38.8745381Z   test_class_as_profiled_types (jit.test_pdt.TestPDT) ... ok (0.017s)
2023-01-11T21:25:38.8745503Z   test_class_methods (jit.test_pdt.TestPDT) ... ok (0.008s)
2023-01-11T21:25:38.8745712Z   test_class_with_args_as_profiled_types (jit.test_pdt.TestPDT) ... ok (0.015s)
2023-01-11T21:25:38.8745834Z   test_class_with_multiple_methods (jit.test_pdt.TestPDT) ... ok (0.010s)
2023-01-11T21:25:38.8745973Z   test_fx_tracing_with_typing (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:38.8746115Z   test_multiple_class_with_same_method (jit.test_pdt.TestPDT) ... ok (0.015s)
2023-01-11T21:25:38.8746250Z   test_nested_function_in_forward (jit.test_pdt.TestPDT) ... ok (0.008s)
2023-01-11T21:25:38.8746380Z   test_nested_list_and_tuple (jit.test_pdt.TestPDT) ... ok (0.015s)
2023-01-11T21:25:38.8746510Z   test_nested_nn_module_class (jit.test_pdt.TestPDT) ... ok (0.010s)
2023-01-11T21:25:38.8746652Z   test_nested_nn_module_class_with_args (jit.test_pdt.TestPDT) ... ok (0.011s)
2023-01-11T21:25:38.8746766Z   test_nn_module (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:38.8746891Z   test_nn_module_with_export_function (jit.test_pdt.TestPDT) ... ok (0.009s)
2023-01-11T21:25:38.8747055Z   test_nn_parameter_as_arg (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:38.8747197Z   test_nonetype_as_optional_of_type (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:38.8747307Z   test_pdt (jit.test_pdt.TestPDT) ... ok (0.017s)
2023-01-11T21:25:38.8747421Z   test_pdt_dict (jit.test_pdt.TestPDT) ... ok (0.006s)
2023-01-11T21:25:38.8747546Z   test_pdt_list_and_tuple (jit.test_pdt.TestPDT) ... ok (0.008s)
2023-01-11T21:25:38.8747715Z   test_scriptable (jit.test_parametrization.TestParametrization) ... ok (0.030s)
2023-01-11T21:25:38.8747855Z   test_traceable (jit.test_parametrization.TestParametrization)
2023-01-11T21:25:38.8747997Z Test the jit scripting and tracing of a parametrized model. ... ok (0.030s)
2023-01-11T21:25:38.8748139Z   test_conv_dim_folding (jit.test_peephole.TestPeephole) ... ok (0.061s)
2023-01-11T21:25:38.8748286Z   test_integer_refinement (jit.test_peephole.TestPeephole) ... ok (0.017s)
2023-01-11T21:25:38.8748425Z   test_noop_peephole (jit.test_peephole.TestPeephole) ... ok (0.015s)
2023-01-11T21:25:38.8748573Z   test_normalized_is_op (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:38.8748718Z   test_normalized_isnot_op (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:38.8748858Z   test_normalized_rsub (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:38.8749009Z   test_optimize_out_comparison_same_value (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:38.8749143Z   test_peephole (jit.test_peephole.TestPeephole) ... ok (0.009s)
2023-01-11T21:25:38.8749286Z   test_peephole_add_zero (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8749426Z   test_peephole_arith (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:38.8749598Z   test_peephole_cuda (jit.test_peephole.TestPeephole) ... skip: cpp tests require CUDA (0.001s)
2023-01-11T21:25:38.8749786Z   test_peephole_dict_getitem_no_optimization_dict_modified (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8749979Z   test_peephole_dict_getitem_no_optimization_get_input_arg (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8750172Z   test_peephole_dict_getitem_no_optimization_keys_might_overlap (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:38.8750356Z   test_peephole_dict_getitem_no_optimization_missing_key (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:38.8750532Z   test_peephole_dict_getitem_no_optimization_overlapping_keys (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:38.8750773Z   test_peephole_dict_getitem_no_optimization_unsupported_type (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8750933Z   test_peephole_dict_getitem_simple (jit.test_peephole.TestPeephole) ... ok (0.008s)
2023-01-11T21:25:38.8751078Z   test_peephole_dict_len (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:38.8751266Z   test_peephole_dict_len_no_optimization_keys_might_overlap (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:38.8751490Z   test_peephole_dict_len_no_optimization_overlapping_keys (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8751679Z   test_peephole_dict_len_no_optimization_unsupported_type (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8751824Z   test_peephole_dynamic (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:38.8751948Z   test_peephole_int (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:38.8752090Z   test_peephole_len_list (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:38.8752233Z   test_peephole_list_len (jit.test_peephole.TestPeephole) ... ok (0.039s)
2023-01-11T21:25:38.8752374Z   test_peephole_list_ops (jit.test_peephole.TestPeephole) ... ok (0.012s)
2023-01-11T21:25:38.8752531Z   test_peephole_no_output_aliasing (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:38.8752685Z   test_peephole_optional_refine (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8752878Z   test_peephole_slice_all_three_args (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:38.8753039Z   test_peephole_slice_one_empty_arg (jit.test_peephole.TestPeephole) ... ok (0.011s)
2023-01-11T21:25:38.8753214Z   test_peephole_slice_optimization_not_applied_list_modified (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8753403Z   test_peephole_slice_optimization_not_applied_non_const_args (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8753558Z   test_peephole_slice_two_empty_args (jit.test_peephole.TestPeephole) ... ok (0.011s)
2023-01-11T21:25:38.8753712Z   test_peephole_type_refinements (jit.test_peephole.TestPeephole) ... ok (0.010s)
2023-01-11T21:25:38.8753870Z   test_peephole_with_non_output_writes (jit.test_peephole.TestPeephole) ... ok (0.006s)
2023-01-11T21:25:38.8754016Z   test_peephole_with_writes (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:38.8754166Z   test_refine_integer_values (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:38.8754326Z   test_short_circuit_optimization (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:38.8754454Z   test_version (__main__.TestProducerVersion) ... ok (0.000s)
2023-01-11T21:25:38.8754580Z   test_aliasing_merge (jit.test_profiler.TestProfiler) ... ok (0.045s)
2023-01-11T21:25:38.8754728Z   test_autograd_fallback_graph (jit.test_profiler.TestProfiler) ... ok (0.040s)
2023-01-11T21:25:38.8754888Z   test_fallback_graph_not_specialized (jit.test_profiler.TestProfiler) ... ok (0.024s)
2023-01-11T21:25:38.8755028Z   test_iterative_fusion (jit.test_profiler.TestProfiler) ... ok (0.044s)
2023-01-11T21:25:38.8755173Z   test_local_fusion_strategy (jit.test_profiler.TestProfiler) ... ok (0.025s)
2023-01-11T21:25:38.8755317Z   test_not_fusing_scalar_ops (jit.test_profiler.TestProfiler) ... ok (0.002s)
2023-01-11T21:25:38.8755466Z   test_not_optimizing_property (jit.test_profiler.TestProfiler) ... ok (0.024s)
2023-01-11T21:25:38.8755616Z   test_specialize_backward (jit.test_profiler.TestProfiler) ... ok (0.094s)
2023-01-11T21:25:38.8755750Z   test_specialized_types (jit.test_profiler.TestProfiler) ... ok (0.023s)
2023-01-11T21:25:38.8755887Z   test_tensor_constant (jit.test_profiler.TestProfiler) ... ok (0.024s)
2023-01-11T21:25:38.8756055Z   test_tensor_type_not_determined_by_inputs (jit.test_profiler.TestProfiler) ... ok (0.052s)
2023-01-11T21:25:38.8756194Z   test_use_not_profiled (jit.test_profiler.TestProfiler) ... ok (0.025s)
2023-01-11T21:25:38.8756349Z   test_add_input (jit.test_python_bindings.TestPythonBindings) ... ok (0.001s)
2023-01-11T21:25:38.8756500Z   test_aliasdb (jit.test_python_bindings.TestPythonBindings) ... ok (0.003s)
2023-01-11T21:25:38.8756658Z   test_canonicalize (jit.test_python_bindings.TestPythonBindings) ... ok (0.001s)
2023-01-11T21:25:38.8756821Z   test_cu_create_function (jit.test_python_bindings.TestPythonBindings) ... ok (0.004s)
2023-01-11T21:25:38.8756967Z   test_cu_get_functions (jit.test_python_bindings.TestPythonBindings) ... ok (0.004s)
2023-01-11T21:25:38.8757160Z   test_graph_create (jit.test_python_bindings.TestPythonBindings) ... ok (0.002s)
2023-01-11T21:25:38.8757331Z   test_graph_iterator_keepalive (jit.test_python_bindings.TestPythonBindings) ... ok (0.002s)
2023-01-11T21:25:38.8757486Z   test_invalidation (jit.test_python_bindings.TestPythonBindings) ... ok (0.003s)
2023-01-11T21:25:38.8757634Z   test_add (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.007s)
2023-01-11T21:25:38.8757799Z   test_adv_indexing_list (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.022s)
2023-01-11T21:25:38.8757961Z   test_advancedindex (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.022s)
2023-01-11T21:25:38.8758113Z   test_gather (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.004s)
2023-01-11T21:25:38.8758249Z   test_index (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.027s)
2023-01-11T21:25:38.8758410Z   test_index_ellipses (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.093s)
2023-01-11T21:25:38.8758590Z   test_inf (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.006s)
2023-01-11T21:25:38.8758748Z   test_matmul_py3 (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.008s)
2023-01-11T21:25:38.8758893Z   test_mul (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.006s)
2023-01-11T21:25:38.8759038Z   test_pow (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.033s)
2023-01-11T21:25:38.8759191Z   test_random (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.003s)
2023-01-11T21:25:38.8759339Z   test_slice (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.014s)
2023-01-11T21:25:38.8759495Z   test_stepped_tuple_slicing (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.006s)
2023-01-11T21:25:38.8759652Z   test_str_to_float (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.006s)
2023-01-11T21:25:38.8759804Z   test_triple (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.005s)
2023-01-11T21:25:38.8759939Z   test_param_strides (jit.test_python_ir.TestPythonIr) ... ok (0.004s)
2023-01-11T21:25:38.8760098Z   test_attributes (jit.test_recursive_script.TestRecursiveScript) ... ok (0.044s)
2023-01-11T21:25:38.8760257Z   test_class_compile (jit.test_recursive_script.TestRecursiveScript) ... ok (0.018s)
2023-01-11T21:25:38.8760423Z   test_constants_with_final (jit.test_recursive_script.TestRecursiveScript) ... ok (0.018s)
2023-01-11T21:25:38.8760568Z   test_dir (jit.test_recursive_script.TestRecursiveScript) ... ok (0.044s)
2023-01-11T21:25:38.8760708Z   test_error_stack (jit.test_recursive_script.TestRecursiveScript) ... ok (0.004s)
2023-01-11T21:25:38.8760875Z   test_error_stack_annotation (jit.test_recursive_script.TestRecursiveScript) ... ok (0.009s)
2023-01-11T21:25:38.8761035Z   test_error_stack_class (jit.test_recursive_script.TestRecursiveScript) ... ok (0.008s)
2023-01-11T21:25:38.8761197Z   test_error_stack_module (jit.test_recursive_script.TestRecursiveScript) ... ok (0.007s)
2023-01-11T21:25:38.8761378Z   test_failed_function_compilation (jit.test_recursive_script.TestRecursiveScript) ... ok (0.003s)
2023-01-11T21:25:38.8761561Z   test_function_attribute_in_submodule (jit.test_recursive_script.TestRecursiveScript) ... ok (0.019s)
2023-01-11T21:25:38.8761719Z   test_ignore_class (jit.test_recursive_script.TestRecursiveScript) ... ok (0.009s)
2023-01-11T21:25:38.8761881Z   test_inferred_nonetype (jit.test_recursive_script.TestRecursiveScript) ... ok (0.007s)
2023-01-11T21:25:38.8762034Z   test_init_error (jit.test_recursive_script.TestRecursiveScript) ... ok (0.001s)
2023-01-11T21:25:38.8762190Z   test_inner_traced_module (jit.test_recursive_script.TestRecursiveScript) ... ok (0.017s)
2023-01-11T21:25:38.8762352Z   test_iterable_modules (jit.test_recursive_script.TestRecursiveScript) ... ok (0.047s)
2023-01-11T21:25:38.8762507Z   test_method_call (jit.test_recursive_script.TestRecursiveScript) ... ok (0.009s)
2023-01-11T21:25:38.8762663Z   test_module_basic (jit.test_recursive_script.TestRecursiveScript) ... ok (0.014s)
2023-01-11T21:25:38.8762867Z   test_module_function_export (jit.test_recursive_script.TestRecursiveScript) ... ok (0.015s)
2023-01-11T21:25:38.8763025Z   test_module_name (jit.test_recursive_script.TestRecursiveScript) ... ok (0.004s)
2023-01-11T21:25:38.8763178Z   test_module_repr (jit.test_recursive_script.TestRecursiveScript) ... ok (0.020s)
2023-01-11T21:25:38.8763339Z   test_optional_module (jit.test_recursive_script.TestRecursiveScript) ... ok (0.019s)
2023-01-11T21:25:38.8763505Z   test_override_instance_method_ignore (jit.test_recursive_script.TestRecursiveScript) ... ok (0.003s)
2023-01-11T21:25:38.8763680Z   test_prepare_scriptable_basic (jit.test_recursive_script.TestRecursiveScript) ... ok (0.005s)
2023-01-11T21:25:38.8763853Z   test_prepare_scriptable_cycle (jit.test_recursive_script.TestRecursiveScript) ... ok (0.003s)
2023-01-11T21:25:38.8764041Z   test_prepare_scriptable_iterable_modules (jit.test_recursive_script.TestRecursiveScript) ... ok (0.049s)
2023-01-11T21:25:38.8764241Z   test_python_function_attribute (jit.test_recursive_script.TestRecursiveScript) ... ok (0.006s)
2023-01-11T21:25:38.8764410Z   test_repeated_error_stack (jit.test_recursive_script.TestRecursiveScript) ... ok (0.005s)
2023-01-11T21:25:38.8764575Z   test_script_after_eval (jit.test_recursive_script.TestRecursiveScript) ... ok (0.008s)
2023-01-11T21:25:38.8764731Z   test_script_basic (jit.test_recursive_script.TestRecursiveScript) ... ok (0.005s)
2023-01-11T21:25:38.8764902Z   test_script_function_attribute (jit.test_recursive_script.TestRecursiveScript) ... ok (0.016s)
2023-01-11T21:25:38.8765044Z   test_script_loaded_module (jit.test_recursive_script.TestRecursiveScript)
2023-01-11T21:25:38.8765184Z Test that we can hold a loaded ScriptModule as a submodule. ... ok (0.012s)
2023-01-11T21:25:38.8765339Z   test_aten_inplace (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.015s)
2023-01-11T21:25:38.8765505Z   test_common_pytorch_list_ops (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.027s)
2023-01-11T21:25:38.8765657Z   test_if_output (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.004s)
2023-01-11T21:25:38.8765814Z   test_if_output_fail (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.006s)
2023-01-11T21:25:38.8765980Z   test_list_indexing_removal (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.014s)
2023-01-11T21:25:38.8766138Z   test_lists_append (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.005s)
2023-01-11T21:25:38.8766274Z   test_lists_insert (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.003s)
2023-01-11T21:25:38.8766439Z   test_special_mapped_op (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.009s)
2023-01-11T21:25:38.8766569Z   test_different_functions (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:38.8766708Z Exercise the situation where we have the same qualified name ... ok (0.022s)
2023-01-11T21:25:38.8766840Z   test_different_interfaces (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:38.8766978Z Exercise the situation where we have the same qualified name ... ok (0.047s)
2023-01-11T21:25:38.8767108Z   test_different_modules (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:38.8767245Z Exercise the situation where we have the same qualified name ... ok (0.034s)
2023-01-11T21:25:38.8767368Z   test_many_collisions (jit.test_save_load.TestSaveLoad) ... ok (0.183s)
2023-01-11T21:25:38.8767496Z   test_save_load_meta_tensors (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:38.8767653Z Check that parameters, buffers, and submodules are the same after loading ... ok (0.019s)
2023-01-11T21:25:38.8767802Z   test_save_load_params_buffers_submodules (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:38.8767959Z Check that parameters, buffers, and submodules are the same after loading. ... ok (0.008s)
2023-01-11T21:25:38.8768104Z   test_save_load_using_pathlib (jit.test_save_load.TestSaveLoad) ... ok (0.006s)
2023-01-11T21:25:38.8768252Z   test_save_load_with_extra_files (jit.test_save_load.TestSaveLoad) ... ok (0.012s)
2023-01-11T21:25:38.8768393Z   test_save_namedtuple_input_only (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:38.8768569Z Even if a NamedTuple is only used as an input argument, saving and ... ok (0.007s)
2023-01-11T21:25:38.8768705Z   test_save_namedtuple_output_only (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:38.8768852Z Even if a NamedTuple is only used as an output argument, saving and ... ok (0.007s)
2023-01-11T21:25:38.8768989Z   test_save_nonexit_file (jit.test_save_load.TestSaveLoad) ... ok (0.008s)
2023-01-11T21:25:38.8769138Z   test_different_functions (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:38.8769337Z Exercise the situation where we have the same qualified name ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:38.8769488Z   test_different_interfaces (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:38.8769677Z Exercise the situation where we have the same qualified name ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:38.8769853Z   test_different_modules (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:38.8770052Z Exercise the situation where we have the same qualified name ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:38.8770267Z   test_many_collisions (jit.test_save_load.TestSaveLoadFlatbuffer) ... skip: Need to enable flatbuffer to run the below tests (0.002s)
2023-01-11T21:25:38.8770489Z   test_module_info_flatbuffer (jit.test_save_load.TestSaveLoadFlatbuffer) ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:38.8770656Z   test_save_load_params_buffers_submodules (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:38.8770875Z Check that parameters, buffers, and submodules are the same after loading. ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:38.8771095Z   test_save_load_using_pathlib (jit.test_save_load.TestSaveLoadFlatbuffer) ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:38.8771255Z   test_save_load_with_extra_files (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:38.8771466Z Check that parameters, buffers, and submodules are the same after loading. ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:38.8771622Z   test_save_namedtuple_input_only (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:38.8771809Z Even if a NamedTuple is only used as an input argument, saving and ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:38.8771969Z   test_save_namedtuple_output_only (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:38.8772166Z Even if a NamedTuple is only used as an output argument, saving and ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:38.8772408Z   test_versioned_div_scalar (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... Falsifying explicit example: test_versioned_div_scalar(
2023-01-11T21:25:38.8772608Z     self=<jit.test_save_load_for_op_version.TestSaveLoadForOpVersion testMethod=test_versioned_div_scalar>,
2023-01-11T21:25:38.8772697Z     sample_input=(2, 3, 2.0, 3.0),
2023-01-11T21:25:38.8772758Z )
2023-01-11T21:25:38.8772854Z skip: Failed to load fixture! (0.121s)
2023-01-11T21:25:38.8773041Z   test_versioned_div_scalar_inplace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.418s)
2023-01-11T21:25:38.8773244Z   test_versioned_div_scalar_reciprocal (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.102s)
2023-01-11T21:25:38.8773440Z   test_versioned_div_scalar_scalar (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.006s)
2023-01-11T21:25:38.8773625Z   test_versioned_div_tensor (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.089s)
2023-01-11T21:25:38.8773822Z   test_versioned_div_tensor_inplace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.536s)
2023-01-11T21:25:38.8774016Z   test_versioned_div_tensor_out (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (1.165s)
2023-01-11T21:25:38.8774232Z   test_versioned_linspace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.014s)
2023-01-11T21:25:38.8774424Z   test_versioned_linspace_out (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.011s)
2023-01-11T21:25:38.8774611Z   test_versioned_logspace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.013s)
2023-01-11T21:25:38.8774788Z   test_versioned_logspace_out (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.010s)
2023-01-11T21:25:38.8774899Z   test_add_out (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8775033Z   test_add_tuple_different_types (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8775160Z   test_add_tuple_non_optional (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8775282Z   test_add_tuple_optional (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8775444Z   test_add_tuple_same_types (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8775543Z   test_addmm_grad (__main__.TestScript)
2023-01-11T21:25:38.8775650Z This test checks several things: ... ok (0.005s)
2023-01-11T21:25:38.8775777Z   test_alias_covariant_type_containers (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8775882Z   test_all (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8776005Z   test_annot_ast_mypy_fn (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:38.8776131Z   test_annot_ast_mypy_method (__main__.TestScript) ... ok (0.034s)
2023-01-11T21:25:38.8776249Z   test_annot_ast_py3_fn (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:38.8776372Z   test_annot_ast_py3_method (__main__.TestScript) ... ok (0.036s)
2023-01-11T21:25:38.8776493Z   test_annot_string_mypy_fn (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8776620Z   test_annot_string_mypy_method (__main__.TestScript) ... ok (0.024s)
2023-01-11T21:25:38.8776727Z   test_annot_string_py3_fn (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8776859Z   test_annot_string_py3_method (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:38.8776981Z   test_annotated_script_fn (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8777121Z   test_annotated_script_fn_arg_mismatch (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8777267Z   test_annotated_script_fn_return_mismatch (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8777395Z   test_annotated_script_method (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8777515Z   test_annoying_doubles (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8777603Z   test_any (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:38.8777743Z   test_assert_is_scripting_metacompile (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8777879Z   test_assertion_optional_refinement (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8778005Z   test_attr_module_constants (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:38.8778130Z   test_attr_qscheme_script (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8778248Z   test_attribute_in_init (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8778383Z   test_attribute_serialization (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8778506Z   test_attribute_unpickling (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8778612Z   test_augmented_assign (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8778746Z   test_autodiff_complex (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8778872Z   test_backend_cudnn_enabled (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8779002Z   test_bad_multiline_annotations (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8779147Z   test_bailout_loop_carried_deps_name_clash (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8779285Z   test_bailout_loop_counter_transition (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8779478Z   test_batch_norm_inference_backward_cuda (__main__.TestScript) ... skip: running tests on cuda to verify cudnn fix (0.002s)
2023-01-11T21:25:38.8779643Z   test_batchnorm_fuser_cpu (__main__.TestScript) ... ok (0.303s)
2023-01-11T21:25:38.8779752Z   test_big_float_literals (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8779871Z   test_big_int_literals (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8779992Z   test_binary_op_shape (__main__.TestScript) ... ok (0.218s)
2023-01-11T21:25:38.8780105Z   test_bitwise_ops (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:38.8780234Z   test_block_input_grad_in_loop (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8780364Z   test_bool_augassign_bitwise_and (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8780492Z   test_bool_augassign_bitwise_or (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8780620Z   test_bool_augassign_bitwise_xor (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8780726Z   test_bool_dispatch (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:38.8780909Z   test_boolean_literal_constant_metacompile (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8781031Z   test_break_continue_error (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8781149Z   test_breaks_continues (__main__.TestScript) ... ok (0.117s)
2023-01-11T21:25:38.8781261Z   test_builtin_args (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8781421Z   test_builtin_args_fails (__main__.TestScript) ... You have not run this instance of FileCheck!
2023-01-11T21:25:38.8781495Z FileCheck checks:
2023-01-11T21:25:38.8781844Z [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[List[int], int]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:38.8781961Z   File "/var/lib/jenkins/workspace/test/test_jit.py", line 10812
2023-01-11T21:25:38.8782044Z             @torch.jit.script
2023-01-11T21:25:38.8782122Z             def f6(a):
2023-01-11T21:25:38.8782210Z                 a.expand(size=[3, [4]])
2023-01-11T21:25:38.8782532Z                               ~~~~~~ <--- HERE
2023-01-11T21:25:38.8782623Z  (function emitListLiteral)
2023-01-11T21:25:38.8782693Z ok (0.006s)
2023-01-11T21:25:38.8782816Z   test_builtin_function_attributes (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8782938Z   test_builtin_use_as_value (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8783045Z   test_call_ge (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8783181Z   test_call_python_fn_from_script_fn (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8783323Z   test_call_python_fn_from_script_module (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8783460Z   test_call_python_fn_from_traced_module (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8783595Z   test_call_python_fn_from_tracing_fn (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8783735Z   test_call_python_mod_from_script_fn (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8783862Z   test_call_python_mod_from_script_module (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8784002Z   test_call_python_mod_from_traced_module (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:38.8784141Z   test_call_python_mod_from_tracing_fn (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8784276Z   test_call_script_fn_from_script_fn (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8784414Z   test_call_script_fn_from_script_module (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8784548Z   test_call_script_fn_from_tracing_fn (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8784681Z   test_call_script_mod_from_script_fn (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8784819Z   test_call_script_mod_from_script_module (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8784976Z   test_call_script_mod_from_tracing_fn (__main__.TestScript) ... skip: error in first class mode (0.001s)
2023-01-11T21:25:38.8785181Z   test_call_traced_fn_from_tracing_fn (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8785349Z   test_call_traced_mod_from_tracing_fn (__main__.TestScript) ... skip: error in first class mode (0.001s)
2023-01-11T21:25:38.8785644Z   test_calls_in_type_annotations (__main__.TestScript) ... /opt/conda/lib/python3.10/site-packages/torch/__init__.py
2023-01-11T21:25:38.8785713Z ok (0.001s)
2023-01-11T21:25:38.8785850Z   test_canonicalize_control_outputs (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8786047Z   test_cast (__main__.TestScript) ... skip: RuntimeError: VariableType::ID() not implemented (0.000s)
2023-01-11T21:25:38.8786151Z   test_cat (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:38.8786248Z   test_cat_lifts (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8786349Z   test_chr (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8786459Z   test_circular_dependency (__main__.TestScript)
2023-01-11T21:25:38.8786649Z https://github.com/pytorch/pytorch/issues/25871 ... ok (0.028s)
2023-01-11T21:25:38.8786773Z   test_class_as_attribute (__main__.TestScript) ... ok (0.321s)
2023-01-11T21:25:38.8786893Z   test_class_attribute (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8787024Z   test_class_attribute_in_script (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8787170Z   test_class_with_comment_at_lower_indentation (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8787265Z   test_code_with_constants (__main__.TestScript)
2023-01-11T21:25:38.8787437Z Check that the `code_with_constants` property correctly returns graph CONSTANTS in the ... ok (0.006s)
2023-01-11T21:25:38.8787554Z   test_code_with_constants_restore (__main__.TestScript)
2023-01-11T21:25:38.8787734Z Check that the `code_with_constants` property correctly works on restoration after save() + load() ... ok (0.011s)
2023-01-11T21:25:38.8787860Z   test_comment_ignore_indent (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8787991Z   test_compare_two_bool_inputs (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8788192Z   test_compile_module_with_constant (__main__.TestScript) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:25:38.8788317Z   test_conditional_casting (__main__.TestScript) ... ok (0.043s)
2023-01-11T21:25:38.8788422Z   test_constant_as_attr (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8788567Z   test_constant_pooling_introduce_aliasing (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8788692Z   test_constant_pooling_none (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8788828Z   test_constant_pooling_same_identity (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8788950Z   test_context_manager (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:38.8789064Z   test_conv_error (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8789179Z   test_convert_base (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:38.8789311Z   test_cpp_function_tensor_str (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8789419Z   test_cpp_module_iterator (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:38.8789537Z   test_desugar_module (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8789651Z   test_device_kwarg (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8789765Z   test_device_type (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8789904Z   test_device_type_cuda (__main__.TestScript) ... skip: Requires CUDA (0.000s)
2023-01-11T21:25:38.8790008Z   test_dir (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8790115Z   test_divmod (__main__.TestScript) ... ok (0.029s)
2023-01-11T21:25:38.8790263Z   test_dominated_bailout (__main__.TestScript) ... skip: bailouts are being deprecated (0.002s)
2023-01-11T21:25:38.8790378Z   test_dropout_eval (__main__.TestScript) ... ok (0.238s)
2023-01-11T21:25:38.8790488Z   test_dtype_attr (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8790643Z   test_dtype_op_shape (__main__.TestScript) ... ok (0.036s)
2023-01-11T21:25:38.8790825Z   test_dtype_op_shape2 (__main__.TestScript) ... ok (0.048s)
2023-01-11T21:25:38.8790953Z   test_early_return_closure (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8791080Z   test_early_return_fork_join (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8791203Z   test_early_return_rewrite (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:38.8791322Z   test_early_return_type_refinement (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8791445Z   test_early_returns_loops (__main__.TestScript) ... ok (0.035s)
2023-01-11T21:25:38.8791567Z   test_ellipsis_const_end (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8791689Z   test_ellipsis_const_mid (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8791822Z   test_ellipsis_const_mid_select (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8791984Z   test_ellipsis_const_start (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8792103Z   test_ellipsis_end (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8792216Z   test_ellipsis_mid (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8792323Z   test_ellipsis_mid_select (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8792441Z   test_ellipsis_start (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8792576Z   test_embedding_renorm_grad_error (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:38.8792706Z   test_empty_like_memory_format_bc (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8792825Z   test_empty_tuple_str (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8792952Z   test_enumerate_modlist_range (__main__.TestScript) ... ok (0.032s)
2023-01-11T21:25:38.8793077Z   test_erase_number_types (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8793168Z   test_error (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:38.8793286Z   test_error_stacktrace (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:38.8793422Z   test_error_stacktrace_interface (__main__.TestScript) ... ok (0.729s)
2023-01-11T21:25:38.8793534Z   test_eval_python (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8793662Z   test_exception_exits_closure (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8793797Z   test_exceptions_with_control_flow (__main__.TestScript) ... ok (0.035s)
2023-01-11T21:25:38.8793902Z   test_expand (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8794007Z   test_fibb (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8794114Z   test_fibb_totally_better (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8794246Z   test_file_format_serialization (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8794362Z   test_file_line_error (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8794488Z   test_file_line_error_class_defn (__main__.TestScript) ... ok (0.698s)
2023-01-11T21:25:38.8794605Z   test_file_line_graph (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8794728Z   test_file_line_save_load (__main__.TestScript) ... ok (0.268s)
2023-01-11T21:25:38.8794845Z   test_file_line_string (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8794961Z   test_file_line_trace (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8795058Z   test_filecheck (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8795176Z   test_filecheck_parse (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8795295Z   test_first_class_calls (__main__.TestScript) ... ok (0.400s)
2023-01-11T21:25:38.8795416Z   test_first_class_module (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8795525Z   test_floor_div (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8795635Z   test_floordiv (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:38.8795742Z   test_for_else (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8795839Z   test_for_in_dict (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8795989Z   test_for_in_enumerate (__main__.TestScript) ... ok (0.029s)
2023-01-11T21:25:38.8796100Z   test_for_in_range (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8796214Z   test_for_in_range_ast (__main__.TestScript) ... ok (0.018s)
2023-01-11T21:25:38.8796336Z   test_for_in_range_dynamic (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:38.8796455Z   test_for_in_range_if_ast (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8796576Z   test_for_in_range_start_end (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8796710Z   test_for_in_range_start_end_step (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:38.8796820Z   test_for_in_range_zero_step (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8796933Z   test_for_in_string (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:38.8797046Z   test_for_in_tensors (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8797172Z   test_for_in_tensors_fail_scalar (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8797330Z   test_for_in_tensors_nested (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8797450Z   test_for_in_tensors_rank0 (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8797561Z   test_for_in_zip (__main__.TestScript) ... ok (0.022s)
2023-01-11T21:25:38.8797670Z   test_for_in_zip_enumerate (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:38.8797788Z   test_for_tuple_assign (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8797904Z   test_for_tuple_unpack (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:38.8798009Z   test_format (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8798117Z   test_func_call (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:38.8798250Z   test_function_compilation_caching (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8798379Z   test_function_overload_misuse (__main__.TestScript) ... ok (0.797s)
2023-01-11T21:25:38.8798519Z   test_function_overloading_isinstance (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8798630Z   test_function_overloads (__main__.TestScript) ... ok (0.050s)
2023-01-11T21:25:38.8798834Z   test_fuser_double_float_codegen (__main__.TestScript) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:38.8798969Z   test_fuser_double_literal_precision (__main__.TestScript) ... ok (0.240s)
2023-01-11T21:25:38.8799094Z   test_fuser_multiple_blocks (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8799217Z   test_gather_dynamic_index (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8799337Z   test_generic_list_errors (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8799452Z   test_get_set_state (__main__.TestScript) ... ok (0.028s)
2023-01-11T21:25:38.8799581Z   test_get_set_state_with_tensors (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8799687Z   test_grad_from_script (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8799791Z   test_hash (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8799907Z   test_hex_literals (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8800010Z   test_id (__main__.TestScript) ... ok (0.363s)
2023-01-11T21:25:38.8800111Z   test_if (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8800220Z   test_if_define (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8800341Z   test_if_different_type (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8800443Z   test_if_for_in_range (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8800565Z   test_if_is_none_dispatch (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:38.8800675Z   test_if_list_cat (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8800789Z   test_if_nest_while (__main__.TestScript) ... ok (0.054s)
2023-01-11T21:25:38.8800896Z   test_if_noelse (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8801017Z   test_if_not_defined_error (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8801130Z   test_if_supertype (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8801281Z   test_ignore_decorator (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8801385Z   test_ignored_as_value (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8801511Z   test_ignored_method_binding (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8801626Z   test_ignored_props (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8801765Z   test_import_constants_not_specialized (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:38.8801889Z   test_in_for_and_comp_expr (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:38.8802020Z   test_in_operator_with_two_strings (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8802125Z   test_index (__main__.TestScript) ... ok (0.081s)
2023-01-11T21:25:38.8802254Z   test_index_select_shape_prop (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8802358Z   test_index_with_tuple (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8802476Z   test_indexing_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8802617Z   test_infer_size (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8802727Z   test_inferred_error_msg (__main__.TestScript)
2023-01-11T21:25:38.8802875Z Test that when we get a type mismatch on a function where we inferred ... ok (0.002s)
2023-01-11T21:25:38.8802994Z   test_inherit_method (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8803200Z   test_inline_and_run_annotated_script_fn (__main__.TestScript) ... skip: https://github.com/pytorch/pytorch/issues/9595 (0.000s)
2023-01-11T21:25:38.8803303Z   test_inlined_graph (__main__.TestScript)
2023-01-11T21:25:38.8803443Z Check that the `inlined_graph` property correctly returns an inlined ... ok (0.010s)
2023-01-11T21:25:38.8803563Z   test_inlining_cleanup (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8803676Z   test_inplace_add (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8803801Z   test_inplace_copy_script (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8803930Z   test_input_keyword_in_schema (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8804042Z   test_int_cast (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8804173Z   test_integral_shape_inference (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8804278Z   test_interpret_graph (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8804398Z   test_interpreter_fuzz (__main__.TestScript) ... ok (0.289s)
2023-01-11T21:25:38.8804510Z   test_intlist_args (__main__.TestScript) ... ok (0.127s)
2023-01-11T21:25:38.8804636Z   test_invalid_call_arguments (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8804759Z   test_invalid_lhs_assignment (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8804890Z   test_invalid_prefix_annotation (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8805001Z   test_irparser (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8805112Z   test_is_after_use (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:38.8805271Z   test_is_isnot (__main__.TestScript) ... <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8805405Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8805528Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8805649Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8805775Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8805895Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806016Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806136Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806242Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806362Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806482Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806641Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806763Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8806884Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807003Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807109Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807229Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807348Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807473Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807597Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807718Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8807904Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808027Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808133Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808252Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808373Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808492Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808610Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808730Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808848Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8808965Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809069Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809196Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809316Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809435Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809556Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809676Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809801Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8809921Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810029Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810150Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810270Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810391Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810517Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810638Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810756Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810866Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8810986Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:38.8811117Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8811246Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8811375Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8811503Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8811631Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8811761Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8811907Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812037Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812166Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812292Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812418Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812543Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812670Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812796Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8812909Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813061Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813189Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813319Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813442Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813567Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813692Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813818Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8813930Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814056Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814182Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814306Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814442Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814568Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814693Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814819Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8814932Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815059Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815184Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815309Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815433Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815559Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815690Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815814Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8815928Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816053Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816177Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816305Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816430Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816556Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816682Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816795Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8816923Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8817080Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:38.8817147Z ok (0.045s)
2023-01-11T21:25:38.8817261Z   test_is_optional (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8817377Z   test_is_scripting (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8817510Z   test_is_scripting_metacompile (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8817620Z   test_isinstance (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8817729Z   test_isinstance_dynamic (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8817858Z   test_isinstance_metacompile (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8817983Z   test_isinstance_refinement (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8818092Z   test_jitter_bug (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8818201Z   test_keyword (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8818358Z   test_kwarg_expansion_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8818483Z   test_kwargs_error_msg (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8818582Z   test_lazy_script (__main__.TestScript) ... ok (0.018s)
2023-01-11T21:25:38.8818722Z   test_lhs_advanced_indexing_assignment (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8818878Z   test_lhs_advanced_indexing_augmented_assignment (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8818995Z   test_lhs_indexing (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8819120Z   test_lhs_indexing_increment (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8819252Z   test_lhs_indexing_increment_list (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8819389Z   test_lhs_indexing_increment_list_prim (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8819507Z   test_lhs_indexing_list (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8819616Z   test_lhs_indexing_multi (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8819733Z   test_linear_grad (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8819870Z   test_list_comprehension_modulelist (__main__.TestScript) ... ok (0.045s)
2023-01-11T21:25:38.8820011Z   test_list_comprehension_variable_write (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8820133Z   test_list_iterables (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8820249Z   test_list_python_op (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8820358Z   test_list_unify (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8820466Z   test_literal (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:38.8820562Z   test_literals (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8820686Z   test_logical_short_circuit (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:38.8820800Z   test_loop_liveness (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8820922Z   test_loop_unroll_negative (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8821057Z   test_loop_unroll_unused_counter (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8821174Z   test_loop_unrolling (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8821297Z   test_loop_unrolling_const (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8821421Z   test_loop_unrolling_nested (__main__.TestScript) ... ok (0.052s)
2023-01-11T21:25:38.8821530Z   test_lower_nested_tuples (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8821639Z   test_math_ops (__main__.TestScript) ... ok (0.208s)
2023-01-11T21:25:38.8821812Z   test_maxpool_guard_elimination (__main__.TestScript) ... skip: bailouts are being deprecated (0.001s)
2023-01-11T21:25:38.8822018Z   test_meshgrid (__main__.TestScript) ... skip: Profiling executor fails to recognize that tensors in a list require gradients (0.001s)
2023-01-11T21:25:38.8822141Z   test_method_casts_script (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8822256Z   test_method_no_self (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8822538Z   test_method_overloading (__main__.TestScript) ... ok (7.707s)
2023-01-11T21:25:38.8822662Z   test_missing_getstate (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8822760Z   test_mm_batching (__main__.TestScript) ... ok (0.519s)
2023-01-11T21:25:38.8822873Z   test_module_apis (__main__.TestScript) ... ok (0.049s)
2023-01-11T21:25:38.8822988Z   test_module_attrs (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8823122Z   test_module_copy_with_attributes (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8823240Z   test_module_copying (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:38.8823354Z   test_module_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8823488Z   test_module_method_reassignment (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8823596Z   test_module_none_attrs (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8823821Z   test_module_parameters_and_buffers (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:38.8823937Z   test_module_str (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8824074Z   test_module_with_params_called_fails (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8824192Z   test_multi_reduction (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8824319Z   test_multi_starred_expr_lhs (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8824448Z   test_multiline_annot_ast_py3_fn (__main__.TestScript) ... ok (0.022s)
2023-01-11T21:25:38.8824591Z   test_multiline_optional_future_refinement (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8824708Z   test_multiline_string_dedents (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8824831Z   test_multiple_assign (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8824956Z   test_multiple_assignment (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8825069Z   test_mutable_dce (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8825195Z   test_mutable_dce_block (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8825325Z   test_mutable_dce_graph_input (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8825469Z   test_mutable_dce_indirect_wildcard_write (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8825605Z   test_mutable_dce_indirect_wildcards (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8825711Z   test_mutable_dce_list (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8825831Z   test_mutable_dce_loop (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8825956Z   test_mutable_dce_wildcards (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8826076Z   test_mutate_constant (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8826194Z   test_mypy_type_ignore (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8826329Z   test_named_buffers_are_iterable (__main__.TestScript) ... ok (0.043s)
2023-01-11T21:25:38.8826449Z   test_namedtuple_attr (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8826586Z   test_namedtuple_default_values_Tensor_type (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8826734Z   test_namedtuple_default_values_container_type (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8826872Z   test_namedtuple_default_values_missing (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8827015Z   test_namedtuple_default_values_simple_type (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8827183Z   test_namedtuple_default_values_using_factory_constructor (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8827307Z   test_namedtuple_python (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8827437Z   test_namedtuple_type_inference (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8827549Z   test_narrow_copy (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8827656Z   test_nested_aug_assign (__main__.TestScript) ... ok (1.097s)
2023-01-11T21:25:38.8827775Z   test_nested_bailouts (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8827926Z   test_nested_breaks (__main__.TestScript) ... ok (0.026s)
2023-01-11T21:25:38.8828049Z   test_nested_list_construct (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8828170Z   test_nested_select_assign (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:38.8828276Z   test_nn_GRU (__main__.TestScript) ... ok (0.168s)
2023-01-11T21:25:38.8828385Z   test_nn_LSTM (__main__.TestScript) ... ok (0.110s)
2023-01-11T21:25:38.8828503Z   test_nn_LSTM_with_layers (__main__.TestScript) ... ok (0.109s)
2023-01-11T21:25:38.8828596Z   test_nn_init (__main__.TestScript) ... ok (0.032s)
2023-01-11T21:25:38.8828711Z   test_no_dtype_shape (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8828841Z   test_no_self_arg_ignore_function (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8828956Z   test_non_final_return (__main__.TestScript) ... ok (0.079s)
2023-01-11T21:25:38.8829068Z   test_none_type_str (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8829201Z   test_not (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8829323Z   test_not_initialized_err (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8829430Z   test_ntuple_builtins (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8829538Z   test_number_abs (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8829656Z   test_number_augassign (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8829793Z   test_number_augassign_bitwise_lshift (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8829925Z   test_number_augassign_bitwise_pow (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8830059Z   test_number_augassign_bitwise_rshift (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8830170Z   test_number_div (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8830282Z   test_number_math (__main__.TestScript) ... ok (1.892s)
2023-01-11T21:25:38.8830377Z   test_number_neg (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8830548Z   test_old_models_bc (__main__.TestScript) ... skip: PyTorch is build without Caffe2 support (0.001s)
2023-01-11T21:25:38.8830665Z   test_oneline_func (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8830831Z   test_op_dtype (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8830957Z   test_operator_precedence (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8831305Z   test_optional_list (__main__.TestScript) ... skip: the current version of Profiler doesn't profile/specialize Optionals (0.001s)
2023-01-11T21:25:38.8831604Z   test_optional_tensor (__main__.TestScript) ... skip: the current version of Profiler doesn't profile/specialize Optionals (0.001s)
2023-01-11T21:25:38.8831709Z   test_ord (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8831814Z   test_override_magic (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:38.8831941Z   test_pack_tuple_into_non_var (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8832063Z   test_pack_unpack_nested (__main__.TestScript) ... ok (0.027s)
2023-01-11T21:25:38.8832191Z   test_pack_unpack_state (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8832346Z   test_parameter_order (__main__.TestScript) ... tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
2023-01-11T21:25:38.8832445Z         14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
2023-01-11T21:25:38.8832544Z         28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
2023-01-11T21:25:38.8832633Z         42., 43., 44., 45., 46., 47., 48., 49., 50., 51.],
2023-01-11T21:25:38.8832705Z        grad_fn=<CatBackward0>)
2023-01-11T21:25:38.8832811Z tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
2023-01-11T21:25:38.8832911Z         14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
2023-01-11T21:25:38.8833009Z         28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
2023-01-11T21:25:38.8833099Z         42., 43., 44., 45., 46., 47., 48., 49., 50., 51.],
2023-01-11T21:25:38.8833222Z        grad_fn=<CatBackward0>)
2023-01-11T21:25:38.8833288Z ok (0.010s)
2023-01-11T21:25:38.8833412Z   test_parse_empty_tuple_annotation (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8833562Z   test_parse_empty_tuple_annotation_element_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8833683Z   test_parse_nested_names (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8833813Z   test_parse_none_type_annotation (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8833939Z   test_parse_tensor_constants (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8834058Z   test_parser_kwargonly (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8834188Z   test_parser_type_annotations (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8834325Z   test_parser_type_annotations_comment (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8834469Z   test_parser_type_annotations_incompatible_expression (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8834655Z   test_parser_type_annotations_subscript_non_ident (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8834805Z   test_parser_type_annotations_subscript_tensor (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8834947Z   test_parser_type_annotations_unknown_type (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8835066Z   test_partial_returns (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8835168Z   test_pass (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8835290Z   test_pickle_checkpoint (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8835432Z   test_pickle_checkpoint_cuda (__main__.TestScript) ... skip: no CUDA (0.000s)
2023-01-11T21:25:38.8835543Z   test_pickle_checkpoint_tup (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8835708Z   test_pow_scalar_backward_cuda (__main__.TestScript) ... skip: device tests require CUDA (0.001s)
2023-01-11T21:25:38.8835833Z   test_pretty_print_function (__main__.TestScript) ... ok (0.028s)
2023-01-11T21:25:38.8836011Z   test_prim_grad_undefined (__main__.TestScript) ... skip: shape analysis is only enabled in Legacy (0.001s)
2023-01-11T21:25:38.8836116Z   test_print (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8836228Z   test_print_kwargs (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8836398Z   test_profiling_graph_executor (__main__.TestScript) ... skip: bailouts are being deprecated (0.001s)
2023-01-11T21:25:38.8836518Z   test_profiling_merge (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8836636Z   test_pybind_type_comparisons (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8836747Z   test_python_call (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8836872Z   test_python_call_annotation (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8837007Z   test_python_call_annoytation_failure (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8837130Z   test_python_call_failure (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8837259Z   test_python_call_non_tensor (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8837390Z   test_python_call_non_tensor_wrong (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8837507Z   test_python_frontend (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8837616Z   test_python_frontend_py3 (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8837747Z   test_python_frontend_source_range (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8837869Z   test_python_op_builtins (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8837984Z   test_python_op_name (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8838113Z   test_python_val_doesnt_have_attr (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8838260Z   test_rand (__main__.TestScript) ... skip: the original version of test_rand (0.001s)
2023-01-11T21:25:38.8838377Z   test_rand_profiling (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8838487Z   test_range_args (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8838626Z   test_reassign_module_lhs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8838748Z   test_reassign_module_rhs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8838872Z   test_refine_tuple_types (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8838988Z   test_remove_dropout (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8839120Z   test_repeated_script_on_function (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8839235Z   test_request_bailout (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8839388Z   test_requires_grad_loop (__main__.TestScript) ... skip: Peeling is now disabled (0.001s)
2023-01-11T21:25:38.8839507Z   test_rescripting_loaded_modules (__main__.TestScript) ... ok (0.026s)
2023-01-11T21:25:38.8839627Z   test_resize_input_ops (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8839733Z   test_return (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:38.8839887Z   test_return_stmt_not_at_end (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8840001Z   test_return_tuple (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8840126Z   test_robust_op_resolution (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8840230Z   test_round (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8840351Z   test_save_load_attr_error (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8840459Z   test_script_annotation (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8840580Z   test_script_bool_constant (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8840691Z   test_script_chunk (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8840809Z   test_script_clamp_none (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:38.8840921Z   test_script_copy (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8841030Z   test_script_cu (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8841155Z   test_script_define_order (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8841296Z   test_script_define_order_recursive_fail (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8841403Z   test_script_docstring (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8841542Z   test_script_forward_method_replacement (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8841689Z   test_script_get_device_cuda (__main__.TestScript) ... skip: requires CUDA (0.000s)
2023-01-11T21:25:38.8841816Z   test_script_get_tracing_state (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8841933Z   test_script_is_tracing (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8842057Z   test_script_kwargs_fn_call (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8842187Z   test_script_method_docstring (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8842319Z   test_script_method_torch_function_overload (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8842434Z   test_script_module (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:38.8842568Z   test_script_module_call_noscript (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8842688Z   test_script_module_const (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8842829Z   test_script_module_const_submodule_fail (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:38.8842961Z   test_script_module_export_blocks (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8843441Z   test_script_module_export_shared_storage (__main__.TestScript) ... /var/lib/jenkins/workspace/test/test_jit.py:10624: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:38.8843623Z   self.assertTrue(m_import.param1.storage().data_ptr() == m_import.param2.storage().data_ptr())
2023-01-11T21:25:38.8844034Z /var/lib/jenkins/workspace/test/test_jit.py:10625: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:38.8844243Z   self.assertTrue(m_import.param1.storage().data_ptr() != m_import.param3.storage().data_ptr())
2023-01-11T21:25:38.8844311Z ok (0.008s)
2023-01-11T21:25:38.8844434Z   test_script_module_export_submodule (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:38.8844613Z   test_script_module_export_tensor_cuda (__main__.TestScript) ... skip: testing cuda tensors require CUDA (0.001s)
2023-01-11T21:25:38.8845121Z   test_script_module_export_tensor_type (__main__.TestScript) ... /var/lib/jenkins/workspace/test/test_jit.py:10560: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:38.8845250Z   self.assertTrue(m_orig.param.storage().size() == 25)
2023-01-11T21:25:38.8845317Z ok (0.010s)
2023-01-11T21:25:38.8845446Z   test_script_module_fail_exist (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8845567Z   test_script_module_for (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8845688Z   test_script_module_for2 (__main__.TestScript) ... ok (0.032s)
2023-01-11T21:25:38.8845820Z   test_script_module_invalid_consts (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8845946Z   test_script_module_nochange_submodule (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8846300Z   test_script_module_none_exist_fail (__main__.TestScript) ... skip: [module dedupe] currently NoneType refinement on optional attributes doesn't work. (0.001s)
2023-01-11T21:25:38.8846428Z   test_script_module_not_tuple (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8846574Z   test_script_module_param_buffer_mutation (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8846704Z   test_script_module_star_assign2 (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8846843Z   test_script_module_star_assign2_inplace (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:38.8846986Z   test_script_module_star_assign_fail_builtin (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8847134Z   test_script_module_star_assign_fail_pythonop (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8847263Z   test_script_module_tensor_subclass_argument (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8847388Z   test_script_nested_mod_list (__main__.TestScript) ... ok (0.035s)
2023-01-11T21:25:38.8847522Z   test_script_non_tensor_args_outputs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8847643Z   test_script_optional_none (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8847761Z   test_script_outputs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8847897Z   test_script_pack_padded_sequence (__main__.TestScript) ... ok (0.036s)
2023-01-11T21:25:38.8848039Z   test_script_pad_sequence_pack_sequence (__main__.TestScript) ... ok (0.040s)
2023-01-11T21:25:38.8848153Z   test_script_scope (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8848265Z   test_script_sequential_for (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:38.8848399Z   test_script_sequential_in_mod_list (__main__.TestScript) ... ok (0.041s)
2023-01-11T21:25:38.8848539Z   test_script_sequential_multi_output_fail (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:38.8848670Z   test_script_sequential_orderdict (__main__.TestScript) ... ok (0.029s)
2023-01-11T21:25:38.8848811Z   test_script_sequential_sliced_iteration (__main__.TestScript) ... ok (0.030s)
2023-01-11T21:25:38.8848932Z   test_script_star_assign (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8849053Z   test_script_star_expr (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:38.8849214Z   test_script_star_expr_string (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:38.8849326Z   test_scriptable_fn_as_attr (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8849478Z   test_scriptmodule_multi_head_attn_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8849635Z   test_scriptmodule_releases_tensors_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8849784Z   test_scriptmodule_transformer_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8849906Z   test_select_after_chunk (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8850026Z   test_sequence_parsing (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8850163Z   test_sequential_intermediary_types (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:38.8850289Z   test_serialization_big_ints (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8850431Z   test_serialization_sharing (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8850556Z   test_serialize_long_lines (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8850685Z   test_serialized_source_ranges (__main__.TestScript) ... ok (0.277s)
2023-01-11T21:25:38.8850816Z   test_serialized_source_ranges2 (__main__.TestScript) ... ok (0.404s)
2023-01-11T21:25:38.8850958Z   test_serialized_source_ranges_dont_jitter (__main__.TestScript) ... ok (0.069s)
2023-01-11T21:25:38.8851093Z   test_serialized_source_ranges_graph (__main__.TestScript) ... ok (0.275s)
2023-01-11T21:25:38.8851231Z   test_serialized_source_ranges_no_dups (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:38.8851353Z   test_set_attribute_through_optional (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8851486Z   test_shape_analysis_grad_property (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8851609Z   test_shape_analysis_loop (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8851746Z   test_shape_prop_promote_scalar_arg (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8851872Z   test_shape_prop_promotion (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8851993Z   test_signed_float_zero (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8852126Z   test_single_starred_expr_for_loop (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8852245Z   test_single_starred_lhs (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8852359Z   test_singleton_tuple_unpack (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8852527Z   test_slice_guard_elimination (__main__.TestScript) ... skip: bailouts are being deprecated (0.000s)
2023-01-11T21:25:38.8852633Z   test_split (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8852734Z   test_stack (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8852852Z   test_static_if_prop (__main__.TestScript) ... ok (2.003s)
2023-01-11T21:25:38.8852965Z   test_static_method_on_module (__main__.TestScript)
2023-01-11T21:25:38.8853123Z Check that the `@staticmethod` annotation on a function on a module works. ... ok (0.008s)
2023-01-11T21:25:38.8853240Z   test_static_methods (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:38.8853336Z   test_str_cast (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8853445Z   test_string_cu (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8853585Z   test_string_device_implicit_conversion (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8853707Z   test_string_frontend_elif (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8853821Z   test_string_index (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8853931Z   test_string_len (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8854043Z   test_string_list (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8854160Z   test_string_new_line (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8854258Z   test_string_ops (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8854371Z   test_string_print (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8854535Z   test_string_single_escape (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8854652Z   test_string_slicing (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8854764Z   test_string_sort (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8854878Z   test_string_sorted (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8855019Z   test_submodule_attribute_serialization (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:38.8855126Z   test_submodule_twice (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8855229Z   test_sum (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8855350Z   test_sum_list_diff_elms (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8855464Z   test_sum_list_empty (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8855580Z   test_sum_list_literal (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8855690Z   test_sum_list_one (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8855840Z   test_sum_list_wrong_type (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8855960Z   test_sys_stdout_override (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8856249Z   test_tensor_as_tensor_shape_prop (__main__.TestScript) ... skip: Simple Executor doesn't have any shapes to propagate (0.001s)
2023-01-11T21:25:38.8856363Z   test_tensor_data (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8856514Z   test_tensor_device (__main__.TestScript) ... skip: device tests require CUDA (0.001s)
2023-01-11T21:25:38.8856629Z   test_tensor_dtype (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8856743Z   test_tensor_grad (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8856864Z   test_tensor_import_export (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8856974Z   test_tensor_len (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8857092Z   test_tensor_number_math (__main__.TestScript) ... ok (0.398s)
2023-01-11T21:25:38.8857219Z   test_tensor_number_math_cuda (__main__.TestScript) ... skip: No CUDA (0.000s)
2023-01-11T21:25:38.8857376Z   test_tensor_requires_grad (__main__.TestScript) ... skip: testing legacy behavior (0.001s)
2023-01-11T21:25:38.8857489Z   test_tensor_shape (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8858059Z   test_tensor_subclasses (__main__.TestScript) ... /opt/conda/lib/python3.10/site-packages/torch/jit/annotations.py:309: UserWarning: TorchScript will treat type annotations of Tensor dtype-specific subtypes as if they are normal Tensors. dtype constraints are not enforced in compilation either.
2023-01-11T21:25:38.8858202Z   warnings.warn("TorchScript will treat type annotations of Tensor "
2023-01-11T21:25:38.8858269Z ok (0.007s)
2023-01-11T21:25:38.8858381Z   test_tensor_to (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:38.8858494Z   test_tensor_to_cpu (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8858630Z   test_tensor_to_cuda (__main__.TestScript) ... skip: device tests require CUDA (0.000s)
2023-01-11T21:25:38.8858785Z   test_tensor_to_device (__main__.TestScript) ... skip: device tests require CUDA (0.000s)
2023-01-11T21:25:38.8858893Z   test_ternary (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8859022Z   test_ternary_module_type_hint (__main__.TestScript) ... ok (0.035s)
2023-01-11T21:25:38.8859152Z   test_ternary_right_associative (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8859271Z   test_ternary_static_if (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8859379Z   test_torch_any (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:38.8859540Z   test_torch_functional (__main__.TestScript) ... skip: Skipping while landing PR stack (0.002s)
2023-01-11T21:25:38.8859663Z   test_torch_functional_tensordot_int (__main__.TestScript) ... ok (0.042s)
2023-01-11T21:25:38.8859800Z   test_torch_functional_tensordot_list (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8859938Z   test_torch_functional_tensordot_tensor (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:38.8860110Z   test_torch_functional_tensordot_tuple (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8860247Z   test_torch_ignore_conversion_to_none (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8860364Z   test_torch_manual_seed (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8860475Z   test_torch_pow (__main__.TestScript) ... ok (0.031s)
2023-01-11T21:25:38.8861041Z   test_torch_tensor_as_tensor (__main__.TestScript) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: Casting complex values to real discards the imaginary part (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Copy.cpp:276.)
2023-01-11T21:25:38.8861131Z   return callable(*args, **kwargs)
2023-01-11T21:25:38.8861185Z ok (0.513s)
2023-01-11T21:25:38.8861321Z   test_torch_tensor_as_tensor_empty_list (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8861445Z   test_torch_tensor_bad_input (__main__.TestScript) ... ok (0.027s)
2023-01-11T21:25:38.8861597Z   test_torch_tensor_dtype (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:38.8861730Z   test_torchscript_memoryformat (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8861860Z   test_torchscript_multi_head_attn (__main__.TestScript) ... ok (0.075s)
2023-01-11T21:25:38.8862006Z   test_torchscript_multi_head_attn_fast_path (__main__.TestScript) ... ok (0.155s)
2023-01-11T21:25:38.8862111Z   test_training_param (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8862233Z   test_tuple_assignments (__main__.TestScript) ... ok (0.345s)
2023-01-11T21:25:38.8862470Z   test_tuple_error_msg (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8862593Z   test_tuple_index_to_list (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8862714Z   test_tuple_indexing (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:38.8862825Z   test_tuple_len (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8862947Z   test_tuple_nested_sort (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8863064Z   test_tuple_sort (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8863172Z   test_tuple_sort_reverse (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8863289Z   test_tuple_sorted (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8863408Z   test_tuple_to_opt_list (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8863541Z   test_tuple_unsortable_diff_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8863678Z   test_tuple_unsortable_element_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8863820Z   test_tuple_unsortable_nested_diff_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8863937Z   test_type_annotate (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:38.8864049Z   test_type_annotation_module (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8864172Z   test_type_annotation_py3 (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:38.8864295Z   test_type_annotations (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:38.8864439Z   test_type_annotations_repeated_list (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8864571Z   test_type_annotations_varargs (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:38.8864693Z   test_type_call_in_script (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8864802Z   test_type_cast (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8864925Z   test_type_comments_in_body (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8865040Z   test_type_inferred_from_empty_annotation (__main__.TestScript)
2023-01-11T21:25:38.8865228Z Test that the type inferred from an empty or missing annotation is Torch.Tensor wtih `inferred=true` ... ok (0.002s)
2023-01-11T21:25:38.8865451Z   test_unbind (__main__.TestScript) ... skip: Profiling executor will be using different heuristics for constructing differentiable graphs (0.001s)
2023-01-11T21:25:38.8865568Z   test_unfold_zero_dim (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8865742Z   test_unicode_comments (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8865860Z   test_uninitialized (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8865977Z   test_union_to_number (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8866097Z   test_unknown_builtin (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8866214Z   test_unmatched_type_annotation (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8866347Z   test_unspecialized_any_binding (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8866526Z   test_unsqueeze_guard_elimination (__main__.TestScript) ... skip: bailouts are being deprecated (0.000s)
2023-01-11T21:25:38.8866660Z   test_unsupported_builtin_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8866783Z   test_unused_decorator (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8866958Z   test_unwrap_optional_builtin (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:38.8867183Z   test_var_aug_assign (__main__.TestScript) ... ok (0.940s)
2023-01-11T21:25:38.8867306Z   test_vararg_zeros (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8867429Z   test_view_listconstruct_shape_prop (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8867548Z   test_view_shape_prop (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8867659Z   test_view_write (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:38.8867782Z   test_weak_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:38.8867889Z   test_where (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8868003Z   test_where_method (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:38.8868107Z   test_while (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8868221Z   test_while_nest_if (__main__.TestScript) ... ok (0.042s)
2023-01-11T21:25:38.8868340Z   test_while_nonexistent_cond_value (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:38.8868467Z   test_while_nonexistent_value (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8868604Z   test_while_write_outer_then_read (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:38.8868725Z   test_wrong_attr_lookup (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8868851Z   test_wrong_implicit_expand (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:38.8868979Z   test_wrong_method_call_inputs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8869104Z   test_wrong_module_attr_lookup (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8869209Z   test_wrong_return_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:38.8869332Z   test_wrong_use_as_callable (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:38.8869451Z   test_wrong_use_as_tuple (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:38.8869556Z   test_zeros (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:38.8869684Z   test_zip_enumerate_modulelist (__main__.TestScript) ... ok (0.412s)
2023-01-11T21:25:38.8869800Z   test_bool (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8869928Z Test the __bool__ method. This should return True ... ok (0.002s)
2023-01-11T21:25:38.8870049Z   test_contains (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8870155Z Test membership checks (x in y, x not in y). ... ok (0.001s)
2023-01-11T21:25:38.8870270Z   test_delitem (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8870353Z Test deletion. ... ok (0.001s)
2023-01-11T21:25:38.8870469Z   test_getitem (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8870606Z Test accessing dictionary values using the [] operator. ... ok (0.001s)
2023-01-11T21:25:38.8870772Z   test_items (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8870857Z Test .items(). ... ok (0.001s)
2023-01-11T21:25:38.8870955Z   test_iter (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8871150Z Test iteration over a dictionary's keys. ... ok (0.001s)
2023-01-11T21:25:38.8871266Z   test_len (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8871371Z Test len() builtin function. ... ok (0.001s)
2023-01-11T21:25:38.8871521Z   test_nested (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8871759Z Test that reference semantics are honoured when the ScriptDict that is ... skip: Cannot pass until all dicts returned from TorchScript are ScriptDicts (0.001s)
2023-01-11T21:25:38.8871892Z   test_reference_semantics (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8872043Z Test that reference semantics are honoured; that modifications made ... ok (0.003s)
2023-01-11T21:25:38.8872141Z   test_repr (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8872238Z Test the __repr__ method. ... ok (0.001s)
2023-01-11T21:25:38.8872351Z   test_setitem (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:38.8872483Z Test setting dictionary values using the [] operator. ... ok (0.001s)
2023-01-11T21:25:38.8872596Z   test_append (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8872686Z Test append method. ... ok (0.001s)
2023-01-11T21:25:38.8872824Z   test_bool (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8872937Z Test the __bool__ method. This should return True ... ok (0.001s)
2023-01-11T21:25:38.8873051Z   test_clear (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8873129Z Test clear. ... ok (0.001s)
2023-01-11T21:25:38.8873244Z   test_contains (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8873362Z Test membership checks (x in y, x not in y). ... ok (0.001s)
2023-01-11T21:25:38.8873476Z   test_count (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8873562Z Test count method. ... ok (0.001s)
2023-01-11T21:25:38.8873664Z   test_delitem (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8873747Z Test deletion. ... ok (0.001s)
2023-01-11T21:25:38.8873858Z   test_extend (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8873939Z Test extend. ... ok (0.002s)
2023-01-11T21:25:38.8874053Z   test_getitem (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8874181Z Test accessing list elements using the [] operator. ... ok (0.003s)
2023-01-11T21:25:38.8874299Z   test_insert (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8874367Z Test insert. ... ok (0.001s)
2023-01-11T21:25:38.8874477Z   test_iter (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8874642Z Test iteration over a list's elements. ... ok (0.001s)
2023-01-11T21:25:38.8874754Z   test_len (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8874853Z Test len() builtin function. ... ok (0.001s)
2023-01-11T21:25:38.8874965Z   test_nested (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8875202Z Test that reference semantics are honoured when the ScriptList that is ... skip: Cannot pass until all list returned from TorchScript are ScriptLists (0.001s)
2023-01-11T21:25:38.8875313Z   test_pop (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8875378Z Test pop. ... ok (0.001s)
2023-01-11T21:25:38.8875509Z   test_reference_semantics (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8875665Z Test that reference semantics are honoured; that modifications made ... ok (0.003s)
2023-01-11T21:25:38.8875781Z   test_remove (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8875869Z Test remove method. ... ok (0.001s)
2023-01-11T21:25:38.8875979Z   test_repr (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8876073Z Test the __repr__ method. ... ok (0.001s)
2023-01-11T21:25:38.8876174Z   test_setitem (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:38.8876298Z Test setting list elements using the [] operator. ... ok (0.002s)
2023-01-11T21:25:38.8876571Z   test_annotated_class_level_annotation_and_init_annotation (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8876823Z   test_annotated_class_level_annotation_only (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8877077Z   test_annotated_class_level_jit_annotation (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8877346Z   test_annotated_empty_dict (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.008s)
2023-01-11T21:25:38.8877575Z   test_annotated_empty_list (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.007s)
2023-01-11T21:25:38.8877815Z   test_annotated_empty_optional (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8878051Z   test_annotated_empty_tensor (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8878291Z   test_annotated_falsy_base_type (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8878524Z   test_annotated_nonempty_container (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8878767Z   test_annotated_with_jit_attribute (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8879041Z   test_annotated_with_jit_empty_dict (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.007s)
2023-01-11T21:25:38.8879284Z   test_annotated_with_jit_empty_list (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8879530Z   test_annotated_with_jit_empty_optional (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:38.8879773Z   test_annotated_with_torch_jit_import (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.005s)
2023-01-11T21:25:38.8879919Z   test_basic (jit.test_script_profile.TestScriptProfile) ... ok (0.071s)
2023-01-11T21:25:38.8880066Z   test_empty (jit.test_script_profile.TestScriptProfile) ... ok (0.001s)
2023-01-11T21:25:38.8880211Z   test_multi (jit.test_script_profile.TestScriptProfile) ... ok (0.078s)
2023-01-11T21:25:38.8880344Z   test_script (jit.test_script_profile.TestScriptProfile) ... ok (0.209s)
2023-01-11T21:25:38.8880496Z   test_section (jit.test_script_profile.TestScriptProfile) ... ok (0.246s)
2023-01-11T21:25:38.8880629Z   test_module_list_slicing (jit.test_slice.TestSlice) ... ok (0.020s)
2023-01-11T21:25:38.8880760Z   test_slice_as_variable (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8880890Z   test_slice_dynamic_index (jit.test_slice.TestSlice) ... ok (0.005s)
2023-01-11T21:25:38.8881011Z   test_slice_kwarg (jit.test_slice.TestSlice) ... ok (0.001s)
2023-01-11T21:25:38.8881136Z   test_slice_one_none (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8881262Z   test_slice_start_stop (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8881384Z   test_slice_start_stop_step (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8881522Z   test_slice_start_stop_with_none (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8881655Z   test_slice_stop_clipped (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8881785Z   test_slice_stop_only (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8881926Z   test_slice_stop_only_with_nones (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8882048Z   test_slice_string (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8882169Z   test_slice_tensor (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8882303Z   test_slice_tensor_multidim (jit.test_slice.TestSlice) ... ok (0.005s)
2023-01-11T21:25:38.8882432Z   test_slice_tensor_multidim_with_dots (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8882558Z   test_slice_three_nones (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8882681Z   test_slice_two_nones (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:38.8882807Z   test_tuple_slicing (jit.test_slice.TestSlice) ... ok (0.005s)
2023-01-11T21:25:38.8882946Z   test_freeze_sparse_coo (jit.test_sparse.TestSparse) ... ok (0.007s)
2023-01-11T21:25:38.8883079Z   test_freeze_sparse_csr (jit.test_sparse.TestSparse) ... ok (0.008s)
2023-01-11T21:25:38.8883245Z   test_serialize_sparse_coo (jit.test_sparse.TestSparse) ... ok (0.006s)
2023-01-11T21:25:38.8883367Z   test_serialize_sparse_csr (jit.test_sparse.TestSparse) ... ok (0.006s)
2023-01-11T21:25:38.8883538Z   test_modulo_operator (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8883756Z   test_string_interpolation_with_alternate_digit_placeholder (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8883995Z   test_string_interpolation_with_capital_exponent_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8884213Z   test_string_interpolation_with_char_placeholder_and_char_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8884436Z   test_string_interpolation_with_char_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8884709Z   test_string_interpolation_with_char_placeholder_and_true_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:38.8884934Z   test_string_interpolation_with_digit_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8885157Z   test_string_interpolation_with_digit_placeholder_and_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:38.8885364Z   test_string_interpolation_with_double_percent_in_string (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8885579Z   test_string_interpolation_with_exponent_placeholder_and_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:38.8885799Z   test_string_interpolation_with_float_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8886022Z   test_string_interpolation_with_float_placeholder_and_float_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8886262Z   test_string_interpolation_with_lowercase_exponent_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8886469Z   test_string_interpolation_with_multiple_placeholders (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8886668Z   test_string_interpolation_with_percent_in_string (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:38.8886892Z   test_string_interpolation_with_string_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8887121Z   test_string_interpolation_with_string_placeholder_and_format_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8887348Z   test_string_interpolation_with_string_placeholder_and_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:38.8887542Z   test_string_interpolation_with_subscript (jit.test_string_formatting.TestStringFormatting) ... ok (0.004s)
2023-01-11T21:25:38.8887728Z   test_string_interpolation_with_too_few_arguments (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:38.8887927Z   test_string_interpolation_with_too_many_arguments (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:38.8888134Z   test_string_interpolation_with_unknown_format_specifier (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:38.8888330Z   test_adaptive_avg_pool2d (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.023s)
2023-01-11T21:25:38.8888516Z   test_arange_shape (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.017s)
2023-01-11T21:25:38.8888722Z   test_binary_shape_fns_inplace (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.005s)
2023-01-11T21:25:38.8888947Z   test_binary_shape_functions (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.004s)
2023-01-11T21:25:38.8889140Z   test_convolution_backward (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.015s)
2023-01-11T21:25:38.8889326Z   test_if_propagation (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.003s)
2023-01-11T21:25:38.8889518Z   test_partial_eval_graph_conv (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.011s)
2023-01-11T21:25:38.8889697Z   test_partial_eval_stitching (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.033s)
2023-01-11T21:25:38.8889907Z   test_refinement_through_graph_stitching (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.020s)
2023-01-11T21:25:38.8890113Z   test_register_function_error_checking (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.011s)
2023-01-11T21:25:38.8890347Z   test_returning_input_symbolic_shapes (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.008s)
2023-01-11T21:25:38.8890534Z   test_shape_analysis (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.006s)
2023-01-11T21:25:38.8890715Z   test_shape_concat (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.084s)
2023-01-11T21:25:38.8890906Z   test_shape_embedding_bag (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.015s)
2023-01-11T21:25:38.8891147Z   test_shape_function_includes (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... skip: shape functions not loaded in python (0.001s)
2023-01-11T21:25:38.8891336Z   test_shared_shape_graph (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.001s)
2023-01-11T21:25:38.8891504Z   test_size_and_sizes (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.005s)
2023-01-11T21:25:38.8891722Z   test_stitching_concat (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.021s)
2023-01-11T21:25:38.8892006Z   test_stitching_multi_output (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.017s)
2023-01-11T21:25:38.8892228Z   test_sym_ir_parsing (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.001s)
2023-01-11T21:25:38.8892424Z   test_unary_shape_fns_inplace (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.002s)
2023-01-11T21:25:38.8892614Z   test_unary_shape_functions (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.002s)
2023-01-11T21:25:38.8892790Z   test_write (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.003s)
2023-01-11T21:25:38.8892943Z   test_method_on_number (jit.test_builtins.TestTensorBuiltins) ... ok (0.002s)
2023-01-11T21:25:38.8893107Z   test_scalar_to_num_conversions (jit.test_builtins.TestTensorBuiltins) ... ok (0.015s)
2023-01-11T21:25:38.8893238Z   test_tensor_item (jit.test_builtins.TestTensorBuiltins) ... ok (0.005s)
2023-01-11T21:25:38.8893397Z   test_tensor_properties (jit.test_builtins.TestTensorBuiltins) ... ok (0.012s)
2023-01-11T21:25:38.8893561Z   test_tensor_subscript_assign (jit.test_builtins.TestTensorBuiltins) ... ok (0.027s)
2023-01-11T21:25:38.8893751Z   test_tensor_subscript_assign_device (jit.test_builtins.TestTensorBuiltins) ... skip: requires CUDA (0.000s)
2023-01-11T21:25:38.8893930Z   test_randperm_default_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:38.8894111Z   test_randperm_specifed_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:38.8894292Z   test_tril_indices_default_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:38.8894475Z   test_tril_indices_specified_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:38.8894638Z   test_triu_indices_default_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:38.8894863Z   test_triu_indices_specified_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:38.8895007Z   test_getitem (jit.test_tensor_methods.TestTensorMethods) ... ok (0.005s)
2023-01-11T21:25:38.8895162Z   test_getitem_invalid (jit.test_tensor_methods.TestTensorMethods) ... ok (0.001s)
2023-01-11T21:25:38.8895303Z   test_default_args (jit.test_torchbind.TestTorchbind) ... ok (0.009s)
2023-01-11T21:25:38.8895455Z   test_lambda_as_constructor (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:38.8895849Z   test_profiler_custom_op (jit.test_torchbind.TestTorchbind) ... STAGE:2023-01-11 21:25:35 1925:1925 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:38.8896107Z STAGE:2023-01-11 21:25:35 1925:1925 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:38.8896369Z STAGE:2023-01-11 21:25:35 1925:1925 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:38.8896466Z ok (0.003s)
2023-01-11T21:25:38.8896610Z   test_staticmethod (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8896748Z   test_torchbind (jit.test_torchbind.TestTorchbind) ... ok (0.008s)
2023-01-11T21:25:38.8896906Z   test_torchbind_attr_exception (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8897067Z   test_torchbind_class_attr_recursive (jit.test_torchbind.TestTorchbind) ... ok (0.003s)
2023-01-11T21:25:38.8897226Z   test_torchbind_class_attribute (jit.test_torchbind.TestTorchbind) ... ok (0.005s)
2023-01-11T21:25:38.8897374Z   test_torchbind_deepcopy (jit.test_torchbind.TestTorchbind) ... ok (0.003s)
2023-01-11T21:25:38.8897543Z   test_torchbind_def_property_getter_setter (jit.test_torchbind.TestTorchbind) ... ok (0.007s)
2023-01-11T21:25:38.8897693Z   test_torchbind_def_property_just_getter (jit.test_torchbind.TestTorchbind) ... ok (0.004s)
2023-01-11T21:25:38.8897858Z   test_torchbind_def_property_readwrite (jit.test_torchbind.TestTorchbind) ... ok (0.004s)
2023-01-11T21:25:38.8898009Z   test_torchbind_getattr (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8898155Z   test_torchbind_getstate (jit.test_torchbind.TestTorchbind) ... ok (0.005s)
2023-01-11T21:25:38.8898323Z   test_torchbind_instantiate_missing_class (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8898476Z   test_torchbind_lambda_method (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:38.8898621Z   test_torchbind_no_init (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8898785Z   test_torchbind_optional_explicit_attr (jit.test_torchbind.TestTorchbind) ... ok (0.005s)
2023-01-11T21:25:38.8898926Z   test_torchbind_pass_wrong_type (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:38.8899088Z   test_torchbind_pickle_serialization (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:38.8899247Z   test_torchbind_python_deepcopy (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:38.8899404Z   test_torchbind_return_instance (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8899572Z   test_torchbind_return_instance_from_method (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8899726Z   test_torchbind_return_tuple (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:38.8899874Z   test_torchbind_save_load (jit.test_torchbind.TestTorchbind) ... ok (0.003s)
2023-01-11T21:25:38.8900023Z   test_torchbind_take_as_arg (jit.test_torchbind.TestTorchbind) ... ok (0.012s)
2023-01-11T21:25:38.8900177Z   test_torchbind_take_instance_as_method_arg (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:38.8900321Z   test_torchbind_tracing (jit.test_torchbind.TestTorchbind) ... ok (0.011s)
2023-01-11T21:25:38.8900473Z   test_torchbind_tracing_nested (jit.test_torchbind.TestTorchbind) ... ok (0.012s)
2023-01-11T21:25:38.8900623Z   test_call_traced_fn_from_traced_module (jit.test_tracer.TestTracer) ... ok (0.013s)
2023-01-11T21:25:38.8900812Z   test_call_traced_module_from_traced_module (jit.test_tracer.TestTracer) ... ok (0.018s)
2023-01-11T21:25:38.8900962Z   test_canonicalize_tensor_iterator (jit.test_tracer.TestTracer) ... ok (0.010s)
2023-01-11T21:25:38.8901084Z   test_constant (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8901202Z   test_conv (jit.test_tracer.TestTracer) ... ok (0.022s)
2023-01-11T21:25:38.8901325Z   test_export_no_reorder (jit.test_tracer.TestTracer) ... ok (0.016s)
2023-01-11T21:25:38.8901465Z   test_force_outplace_check_fill (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8901603Z   test_force_outplace_check_zero (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8901717Z   test_ge (jit.test_tracer.TestTracer) ... ok (0.013s)
2023-01-11T21:25:38.8901858Z   test_ge_cuda (jit.test_tracer.TestTracer) ... skip: requires CUDA (0.000s)
2023-01-11T21:25:38.8901986Z   test_ge_optimized (jit.test_tracer.TestTracer) ... ok (0.178s)
2023-01-11T21:25:38.8902149Z   test_ge_unoptimized (jit.test_tracer.TestTracer) ... ok (0.036s)
2023-01-11T21:25:38.8902273Z   test_index_put (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8902539Z   test_index_put_trace_with_view (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8902686Z   test_index_put_trace_without_view (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8902817Z   test_inplace_check (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8902946Z   test_inplace_copy (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8903093Z   test_inplace_copy_force_outplace (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8903222Z   test_inplace_flags (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8903365Z   test_inplace_transplant (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8903493Z   test_inplace_warn (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8903624Z   test_input_dict_checkTrace_mut (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8903758Z   test_input_dict_empty (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8903897Z   test_input_dict_empty_list (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8904030Z   test_input_dict_insertion_order (jit.test_tracer.TestTracer)
2023-01-11T21:25:38.8904250Z Check that dictionary access doesn't care about insertion order ... ok (0.009s)
2023-01-11T21:25:38.8904386Z   test_input_dict_of_dicts (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8904520Z   test_input_dict_of_lists (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8904660Z   test_input_dict_recursive (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8904776Z   test_input_dict_remembers_keys (jit.test_tracer.TestTracer)
2023-01-11T21:25:38.8904920Z Check that the trace remembers which keys were in a dict input ... ok (0.011s)
2023-01-11T21:25:38.8905053Z   test_input_dict_unify (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8905171Z   test_input_flatten (jit.test_tracer.TestTracer)
2023-01-11T21:25:38.8905306Z Check that inputs to traced functions are flattened ... ok (0.011s)
2023-01-11T21:25:38.8905442Z   test_input_list_mixed_type (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:38.8905575Z   test_input_list_of_tuples (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8905718Z   test_input_list_toplevel_flatten (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8905857Z   test_input_list_toplevel_flatten_direct (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8905991Z   test_input_tuple_of_dicts (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8906126Z   test_interpolate_trace (jit.test_tracer.TestTracer) ... ok (0.028s)
2023-01-11T21:25:38.8906282Z   test_large_nbr_kernel_args (jit.test_tracer.TestTracer) ... skip: requires CUDA (0.001s)
2023-01-11T21:25:38.8906411Z   test_lhs_index_fails (jit.test_tracer.TestTracer) ... ok (0.012s)
2023-01-11T21:25:38.8906594Z   test_lhs_index_trivial (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8906717Z   test_max_pool (jit.test_tracer.TestTracer) ... ok (0.013s)
2023-01-11T21:25:38.8906846Z   test_nested_inplace (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8906964Z   test_non_tensor_tracing (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8907087Z   test_output_unflatten (jit.test_tracer.TestTracer)
2023-01-11T21:25:38.8907272Z Check that outputs of traced functions retain the original structure and nesting ... expected failure (0.007s)
2023-01-11T21:25:38.8907401Z   test_python_function (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8907540Z   test_python_function_tup (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8907672Z   test_repeated_input (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8907804Z   test_repeated_output (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8907970Z   test_shared_param (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8908078Z   test_simple (jit.test_tracer.TestTracer) ... ok (0.015s)
2023-01-11T21:25:38.8908224Z   test_tensor_with_grad_as_constant (jit.test_tracer.TestTracer) ... ok (0.001s)
2023-01-11T21:25:38.8908371Z   test_trace_aliased_parameter (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8908504Z   test_trace_annotation (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8908631Z   test_trace_arange (jit.test_tracer.TestTracer) ... ok (0.017s)
2023-01-11T21:25:38.8908769Z   test_trace_arange_with_grad (jit.test_tracer.TestTracer) ... ok (0.018s)
2023-01-11T21:25:38.8908907Z   test_trace_autograd_function (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8909078Z   test_trace_c10_ops (jit.test_tracer.TestTracer) ... skip: Skip the test since c2 ops are not registered. (0.002s)
2023-01-11T21:25:38.8909202Z   test_trace_casts (jit.test_tracer.TestTracer) ... ok (0.036s)
2023-01-11T21:25:38.8909349Z   test_trace_checker_control_flow (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8909487Z   test_trace_checker_dot_data (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8909635Z   test_trace_checker_dropout_notrain (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8909779Z   test_trace_checker_dropout_train (jit.test_tracer.TestTracer) ... ok (0.017s)
2023-01-11T21:25:38.8909927Z   test_trace_checker_inplace_on_view (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8910072Z   test_trace_checker_memoization (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8910212Z   test_trace_checker_slice_lhs (jit.test_tracer.TestTracer) ... ok (0.011s)
2023-01-11T21:25:38.8910349Z   test_trace_checking_with_global_name (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8910880Z   test_trace_contiguous (jit.test_tracer.TestTracer) ... /var/lib/jenkins/workspace/test/jit/test_tracer.py:1673: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:38.8911029Z   self.assertNotEqual(x.storage().data_ptr(), y.storage().data_ptr())
2023-01-11T21:25:38.8911097Z ok (0.008s)
2023-01-11T21:25:38.8911247Z   test_trace_contiguous_short_circuit (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8911376Z   test_trace_detach (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8911516Z   test_trace_detach_inplace (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8911670Z   test_trace_detach_inplace_redispatch (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8911813Z   test_trace_detach_redispatch (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:38.8911936Z   test_trace_dict_input (jit.test_tracer.TestTracer) ... ok (0.010s)
2023-01-11T21:25:38.8912137Z   test_trace_dict_output (jit.test_tracer.TestTracer) ... ok (0.016s)
2023-01-11T21:25:38.8912267Z   test_trace_export_fns (jit.test_tracer.TestTracer) ... ok (0.018s)
2023-01-11T21:25:38.8912410Z   test_trace_export_fns_recursive (jit.test_tracer.TestTracer) ... ok (0.041s)
2023-01-11T21:25:38.8912553Z   test_trace_fork_join_and_module (jit.test_tracer.TestTracer) ... ok (0.030s)
2023-01-11T21:25:38.8912697Z   test_trace_full_dynamic_shape (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8912851Z   test_trace_func_argument_names_captured (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8912978Z   test_trace_index (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8913101Z   test_trace_index_constant (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8913244Z   test_trace_indexed_assignment (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8913408Z   test_trace_inline_shape (jit.test_tracer.TestTracer) ... ok (0.014s)
2023-01-11T21:25:38.8913541Z   test_trace_inverse (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8913689Z   test_trace_invert_module_hierarchy (jit.test_tracer.TestTracer) ... ok (0.019s)
2023-01-11T21:25:38.8913822Z   test_trace_legacy_ctor (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:38.8913980Z   test_trace_module_argument_names_captured (jit.test_tracer.TestTracer) ... ok (0.036s)
2023-01-11T21:25:38.8914114Z   test_trace_modulelist (jit.test_tracer.TestTracer) ... ok (0.024s)
2023-01-11T21:25:38.8914281Z   test_trace_multi_output_function (jit.test_tracer.TestTracer) ... graph(%self : __torch__.jit.test_tracer.Bar,
2023-01-11T21:25:38.8914403Z       %x : Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu),
2023-01-11T21:25:38.8914526Z       %y : Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu)):
2023-01-11T21:25:38.8914753Z   %5 : Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu) = aten::relu(%x) # /var/lib/jenkins/workspace/test/jit/test_tracer.py:1429:0
2023-01-11T21:25:38.8914955Z   %6 : Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu) = aten::relu(%y) # /var/lib/jenkins/workspace/test/jit/test_tracer.py:1430:0
2023-01-11T21:25:38.8915474Z   %9 : (Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu), Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu)) = ^Foo[inplace=0, module="jit.test_tracer", Subgraph=<Graph>]()(%5, %6) # /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py:429:0
2023-01-11T21:25:38.8915680Z   %10 : Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu), %11 : Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu) = prim::TupleUnpack(%9)
2023-01-11T21:25:38.8915890Z   %12 : (Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu), Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu)) = prim::TupleConstruct(%10, %11)
2023-01-11T21:25:38.8915962Z   return (%12)
2023-01-11T21:25:38.8915973Z 
2023-01-11T21:25:38.8916039Z ok (0.007s)
2023-01-11T21:25:38.8916180Z   test_trace_namedtuple (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8916307Z   test_trace_nested_datatypes (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:38.8916442Z   test_trace_nested_fn (jit.test_tracer.TestTracer) ... ok (0.015s)
2023-01-11T21:25:38.8916567Z   test_trace_numel (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8916704Z   test_trace_optioanl_dtype (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8916837Z   test_trace_optional (jit.test_tracer.TestTracer) ... ok (0.014s)
2023-01-11T21:25:38.8917000Z   test_trace_partial_func_argument_names_captured (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8917126Z   test_trace_random (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:38.8917246Z   test_trace_records_names (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:38.8917370Z   test_trace_save (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8917538Z   test_trace_save_load_copy (jit.test_tracer.TestTracer) ... ok (0.031s)
2023-01-11T21:25:38.8917675Z   test_trace_single_tuple (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:38.8917797Z   test_trace_size (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8917932Z   test_trace_size_with_grad (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8918074Z   test_trace_skip_none_submodule (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8918197Z   test_trace_slice (jit.test_tracer.TestTracer) ... ok (0.042s)
2023-01-11T21:25:38.8918332Z   test_trace_slice_expr_complete_type (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8918465Z   test_trace_slice_full_dim (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8918617Z   test_trace_slice_setitem_dynamic_shape (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:38.8918751Z   test_trace_slice_with_grad (jit.test_tracer.TestTracer) ... ok (0.045s)
2023-01-11T21:25:38.8918921Z   test_trace_tensor_factory (jit.test_tracer.TestTracer) ... ok (0.035s)
2023-01-11T21:25:38.8919046Z   test_trace_topk (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:38.8919172Z   test_trace_tuple (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:38.8919320Z   test_trace_variable_instantiation (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8919429Z   test_trace_warn (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:38.8919582Z   test_trace_with_conditional_property (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8919737Z   test_trace_with_nested_tensor_list_output (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:38.8919880Z   test_trace_with_number_list_output (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:38.8920025Z   test_trace_with_tensor_list_output (jit.test_tracer.TestTracer) ... ok (0.015s)
2023-01-11T21:25:38.8920179Z   test_traced_module_cuda (jit.test_tracer.TestTracer) ... skip: calls .cuda() (0.001s)
2023-01-11T21:25:38.8920328Z   test_tracing_backward_hook_error (jit.test_tracer.TestTracer) ... ok (0.001s)
2023-01-11T21:25:38.8920454Z   test_tracing_hooks (jit.test_tracer.TestTracer) ... ok (0.040s)
2023-01-11T21:25:38.8920584Z   test_tracing_multiple_methods (jit.test_tracer.TestTracer) ... ok (0.128s)
2023-01-11T21:25:38.8920717Z   test_typeas_trace_check (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:38.8920848Z   test_wrapped_number (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:38.8920988Z   test_assign_python_attr (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8921214Z Assigning a new (python-only) attribute should not change type sharing ... ok (0.031s)
2023-01-11T21:25:38.8921355Z   test_basic (jit.test_type_sharing.TestTypeSharing) ... ok (0.006s)
2023-01-11T21:25:38.8921516Z   test_builtin_function_different (jit.test_type_sharing.TestTypeSharing) ... ok (0.007s)
2023-01-11T21:25:38.8921673Z   test_builtin_function_same (jit.test_type_sharing.TestTypeSharing) ... ok (0.005s)
2023-01-11T21:25:38.8921793Z   test_constants (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8921979Z Types should be shared for identical constant values, and different for different constant values ... ok (0.010s)
2023-01-11T21:25:38.8922117Z   test_diff_attr_values (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8922252Z Types should be shared even if attribute values differ ... ok (0.005s)
2023-01-11T21:25:38.8922403Z   test_failed_attribute_compilation (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8922559Z Attributes whose type cannot be inferred should fail cleanly with nice hints ... ok (0.002s)
2023-01-11T21:25:38.8922705Z   test_ignored_fns (jit.test_type_sharing.TestTypeSharing) ... ok (0.005s)
2023-01-11T21:25:38.8922831Z   test_linear (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8922929Z Simple example with a real nn Module ... ok (0.011s)
2023-01-11T21:25:38.8923084Z   test_loaded_modules_work (jit.test_type_sharing.TestTypeSharing) ... ok (0.015s)
2023-01-11T21:25:38.8923275Z   test_module_dict_same_type_different_name (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8923428Z We should be able to differentiate between two ModuleDict instances ... ok (0.030s)
2023-01-11T21:25:38.8923563Z   test_mutate_attr_value (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8923710Z Mutating the value of an attribute should not change type sharing ... ok (0.017s)
2023-01-11T21:25:38.8923847Z   test_param_vs_attribute (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8924067Z The same module with an `foo` as a parameter vs. attribute shouldn't ... ok (0.007s)
2023-01-11T21:25:38.8924216Z   test_python_function_attribute_different (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8924360Z Different functions passed in should lead to different types ... ok (0.010s)
2023-01-11T21:25:38.8924510Z   test_python_function_attribute_same (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8924680Z Same functions passed in should lead to same types ... ok (0.007s)
2023-01-11T21:25:38.8924828Z   test_same_but_different_classes (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8924980Z Even if everything about the module is the same, different originating ... ok (0.021s)
2023-01-11T21:25:38.8925138Z   test_script_function_attribute_different (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8925281Z Different functions passed in should lead to different types ... ok (0.010s)
2023-01-11T21:25:38.8925419Z   test_script_function_attribute_same (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8925549Z Same functions passed in should lead to same types ... ok (0.007s)
2023-01-11T21:25:38.8925730Z   test_script_module_containing_traced_module (jit.test_type_sharing.TestTypeSharing) ... ok (0.020s)
2023-01-11T21:25:38.8925861Z   test_submodules (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8925985Z If submodules differ, the types should differ. ... ok (0.042s)
2023-01-11T21:25:38.8926142Z   test_tracing_gives_different_types (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8926359Z Since we can't guarantee that methods are the same between different ... ok (0.012s)
2023-01-11T21:25:38.8926519Z   test_type_not_shared_ignored_attributes (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8926640Z Test that types are not shared if the exclusion of their ... ok (0.007s)
2023-01-11T21:25:38.8926792Z   test_type_shared_ignored_attributes (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8926922Z Test that types are shared if the exclusion of their ... ok (0.005s)
2023-01-11T21:25:38.8927070Z   test_type_sharing_define_in_init (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8927203Z Tests that types between instances of a ScriptModule ... ok (0.006s)
2023-01-11T21:25:38.8927344Z   test_type_sharing_disabled (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:38.8927459Z Test that type sharing can be disabled. ... ok (0.019s)
2023-01-11T21:25:38.8927626Z   test_annotate_outside_init (jit.test_types.TestTypesAndAnnotation) ... ok (0.015s)
2023-01-11T21:25:38.8927761Z   test_bad_types (jit.test_types.TestTypesAndAnnotation) ... ok (0.002s)
2023-01-11T21:25:38.8927917Z   test_ignore_with_types (jit.test_types.TestTypesAndAnnotation) ... ok (0.004s)
2023-01-11T21:25:38.8928071Z   test_ignoring_module_attributes (jit.test_types.TestTypesAndAnnotation)
2023-01-11T21:25:38.8928192Z Test that module attributes can be ignored. ... ok (0.007s)
2023-01-11T21:25:38.8928360Z   test_inferred_type_error_message (jit.test_types.TestTypesAndAnnotation) ... ok (0.003s)
2023-01-11T21:25:38.8928523Z   test_mismatched_annotation (jit.test_types.TestTypesAndAnnotation) ... ok (0.001s)
2023-01-11T21:25:38.8928687Z   test_optional_no_element_type_annotation (jit.test_types.TestTypesAndAnnotation)
2023-01-11T21:25:38.8928839Z Test that using an optional with no contained types produces an error. ... ok (0.002s)
2023-01-11T21:25:38.8928975Z   test_parser_bug (jit.test_types.TestTypesAndAnnotation) ... ok (0.000s)
2023-01-11T21:25:38.8929158Z   test_pep585_type (jit.test_types.TestTypesAndAnnotation) ... ok (0.008s)
2023-01-11T21:25:38.8929311Z   test_python_callable (jit.test_types.TestTypesAndAnnotation) ... ok (0.002s)
2023-01-11T21:25:38.8929461Z   test_reannotate (jit.test_types.TestTypesAndAnnotation) ... ok (0.001s)
2023-01-11T21:25:38.8929622Z   test_tuple_no_element_type_annotation (jit.test_types.TestTypesAndAnnotation)
2023-01-11T21:25:38.8929770Z Test that using a tuple with no contained types produces an error. ... ok (0.002s)
2023-01-11T21:25:38.8929927Z   test_type_annotate_py3 (jit.test_types.TestTypesAndAnnotation) ... ok (0.010s)
2023-01-11T21:25:38.8930082Z   test_types_as_values (jit.test_types.TestTypesAndAnnotation) ... ok (0.010s)
2023-01-11T21:25:38.8930234Z   test_unimported_type_resolution (jit.test_types.TestTypesAndAnnotation) ... ok (0.002s)
2023-01-11T21:25:38.8930362Z   test_bool_list_io (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:38.8930531Z   test_dict_comprehension (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:38.8930677Z   test_dict_comprehension_scope (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:38.8930836Z   test_dict_comprehension_with_type_annotation (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:38.8930963Z   test_dict_in_not_in (jit.test_typing.TestTyping) ... ok (0.021s)
2023-01-11T21:25:38.8931105Z   test_dict_invalid_annotations (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:38.8931536Z   test_dict_type_refinement_annotation_key_mismatch (jit.test_typing.TestTyping) ... [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[int, str]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:38.8931672Z   File "/var/lib/jenkins/workspace/test/jit/test_typing.py", line 90
2023-01-11T21:25:38.8931731Z         def fn():
2023-01-11T21:25:38.8931810Z             l1 = [1, 2, "foo", 3]
2023-01-11T21:25:38.8931937Z                  ~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:38.8932029Z             l2 = ["foo", "bar", "baz", "qux"]
2023-01-11T21:25:38.8932143Z             d: Dict[int, str] = {k : v for k, v in zip(l1, l2)}
2023-01-11T21:25:38.8932233Z  (function emitListLiteral)
2023-01-11T21:25:38.8932303Z ok (0.002s)
2023-01-11T21:25:38.8932731Z   test_dict_type_refinement_annotation_value_mismatch (jit.test_typing.TestTyping) ... [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[int, str]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:38.8932855Z   File "/var/lib/jenkins/workspace/test/jit/test_typing.py", line 104
2023-01-11T21:25:38.8932924Z         def fn():
2023-01-11T21:25:38.8933017Z             l1 = ["foo", "bar", "baz", "qux"]
2023-01-11T21:25:38.8933092Z             l2 = [1, 2, "foo", 3]
2023-01-11T21:25:38.8933216Z                  ~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:38.8933331Z             d: Dict[str, int] = {k : v for k, v in zip(l1, l2)}
2023-01-11T21:25:38.8933400Z             return d
2023-01-11T21:25:38.8933477Z  (function emitListLiteral)
2023-01-11T21:25:38.8933543Z ok (0.002s)
2023-01-11T21:25:38.8933668Z   test_for_in_dict (jit.test_typing.TestTyping) ... ok (0.009s)
2023-01-11T21:25:38.8933796Z   test_for_in_string (jit.test_typing.TestTyping) ... ok (0.014s)
2023-01-11T21:25:38.8933930Z   test_for_tuple_assign (jit.test_typing.TestTyping) ... ok (0.008s)
2023-01-11T21:25:38.8934061Z   test_for_tuple_unpack (jit.test_typing.TestTyping) ... ok (0.015s)
2023-01-11T21:25:38.8934181Z   test_list_io (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:38.8934312Z   test_list_iterables (jit.test_typing.TestTyping) ... ok (0.001s)
2023-01-11T21:25:38.8934454Z   test_list_sum (jit.test_typing.TestTyping) ... ok (0.010s)
2023-01-11T21:25:38.8934630Z   test_list_type_refinement_annotation_element_mismatch (jit.test_typing.TestTyping) ... ok (0.001s)
2023-01-11T21:25:38.8934765Z   test_list_unification (jit.test_typing.TestTyping) ... ok (0.007s)
2023-01-11T21:25:38.8934895Z   test_multiple_assign (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:38.8935034Z   test_namedtuple_good_error (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:38.8935163Z   test_namedtuple_py2 (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:38.8935300Z   test_namedtuple_redefine (jit.test_typing.TestTyping) ... ok (0.007s)
2023-01-11T21:25:38.8935425Z   test_nested_list (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:38.8935548Z   test_opt_opt_refinement (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:38.8935682Z   test_optional_conversion (jit.test_typing.TestTyping) ... ok (0.012s)
2023-01-11T21:25:38.8935847Z   test_optional_refinement (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:38.8935984Z   test_optional_tuple (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:38.8936124Z   test_singleton_tuple_unpack (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:38.8936256Z   test_sum_list_diff_elms (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:38.8936382Z   test_sum_list_empty (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:38.8936514Z   test_sum_list_literal (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:38.8936625Z   test_sum_list_one (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:38.8936757Z   test_sum_list_wrong_type (jit.test_typing.TestTyping) ... ok (0.001s)
2023-01-11T21:25:38.8936893Z   test_tuple_assignments (jit.test_typing.TestTyping) ... ok (0.020s)
2023-01-11T21:25:38.8937025Z   test_tuple_create_return (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:38.8937149Z   test_tuple_io (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:38.8937284Z   test_tuple_keyword (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:38.8937422Z   test_tuple_specialization (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:38.8937544Z   test_check_union_annotation (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:38.8937698Z   test_union_T_None_is_equivalent_to_optional_T (jit.test_union.TestUnion) ... ok (0.010s)
2023-01-11T21:25:38.8937843Z   test_union_argument_order_is_ignored (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:38.8938000Z   test_union_argument_order_is_ignored_container (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:38.8938132Z   test_union_as_annotation (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8938283Z   test_union_as_annotation_in_typed_container (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:38.8938419Z   test_union_as_annotation_py2 (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8938548Z   test_union_as_dict_key (jit.test_union.TestUnion) ... ok (0.008s)
2023-01-11T21:25:38.8938664Z   test_union_as_dict_value (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:38.8938803Z   test_union_as_internal_tuple_type (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:38.8938974Z   test_union_branching_does_not_autoinfer_undeclared_union (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8939145Z   test_union_branching_does_not_widen_existing_inferred_type (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8939315Z   test_union_branching_with_union_return_and_homogenous_types (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:38.8939477Z   test_union_does_not_replace_existing_annotated_type (jit.test_union.TestUnion) ... ok (0.001s)
2023-01-11T21:25:38.8939662Z   test_union_does_not_replace_existing_annotated_type_empty_container (jit.test_union.TestUnion) ... ok (0.001s)
2023-01-11T21:25:38.8939834Z   test_union_does_not_replace_existing_annotated_type_union (jit.test_union.TestUnion) ... ok (0.001s)
2023-01-11T21:25:38.8940006Z   test_union_in_class_constructor (jit.test_union.TestUnion) ... ok (0.009s)
2023-01-11T21:25:38.8940618Z   test_union_memory_aliasing (jit.test_union.TestUnion) ... /opt/conda/lib/python3.10/site-packages/torch/_jit_internal.py:1282: UserWarning: The inner type of a container is lost when calling torch.jit.isinstance in eager mode. For example, List[int] would become list and therefore falsely return True for List[float] or List[str].
2023-01-11T21:25:38.8940682Z   warnings.warn(
2023-01-11T21:25:38.8940750Z ok (0.009s)
2023-01-11T21:25:38.8940900Z   test_union_module_with_union_class_variable (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:38.8941054Z   test_union_module_with_union_instance_variable (jit.test_union.TestUnion) ... ok (0.009s)
2023-01-11T21:25:38.8941204Z   test_union_optional_of_union_is_flattened (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:38.8941386Z   test_union_redundant_arguments_are_skipped (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8941557Z   test_union_redundant_arguments_are_skipped_container (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8941727Z   test_union_redundant_arguments_are_skipped_optional (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8941879Z   test_union_redundant_arguments_are_skipped_subtyping (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8942010Z   test_union_return_type (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8942164Z   test_union_schema_matching_on_internal_type (jit.test_union.TestUnion) ... ok (0.008s)
2023-01-11T21:25:38.8942460Z   test_union_serialization_preserves_type_annotations (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:38.8942605Z   test_union_subclasses_larger_union (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8942749Z   test_union_subtractive_refinement (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:38.8942912Z   test_union_subtractive_refinement_with_container (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:38.8943050Z   test_union_type_refinement (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:38.8943198Z   test_union_type_refinement_internal_declaration (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:38.8943376Z   test_union_type_refinement_partial_static_refinement_tuple_rhs (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:38.8943551Z   test_union_type_refinement_partial_static_refinement_union_rhs (jit.test_union.TestUnion) ... ok (0.006s)
2023-01-11T21:25:38.8943706Z   test_union_type_refinement_statically_false (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8943858Z   test_union_type_refinement_statically_true (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8944002Z   test_union_type_refinement_tuple_rhs (jit.test_union.TestUnion) ... ok (0.010s)
2023-01-11T21:25:38.8944171Z   test_union_type_refinement_tuple_rhs_noncontained_type (jit.test_union.TestUnion) ... ok (0.008s)
2023-01-11T21:25:38.8944328Z   test_union_type_refinement_tuple_rhs_union (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8944474Z   test_union_type_refinement_union_rhs (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:38.8944608Z   test_union_variable_can_be_reassigned (jit.test_union.TestUnion) ... ok (0.008s)
2023-01-11T21:25:38.8944743Z   test_union_with_collections (jit.test_union.TestUnion) ... ok (0.006s)
2023-01-11T21:25:38.8944884Z   test_union_with_dict_assignment (jit.test_union.TestUnion) ... ok (0.027s)
2023-01-11T21:25:38.8945011Z   test_union_with_enum (jit.test_union.TestUnion) ... ok (0.011s)
2023-01-11T21:25:38.8945417Z   test_union_with_list_assignment (jit.test_union.TestUnion) ... [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[Tensor, int]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:38.8945560Z   File "<string>", line 3
2023-01-11T21:25:38.8945569Z 
2023-01-11T21:25:38.8945636Z def fn():
2023-01-11T21:25:38.8945791Z     x: Union[List[str], List[torch.Tensor]] = [torch.add(1, x) for x in [torch.arange(5), 1]]
2023-01-11T21:25:38.8945991Z                                                                         ~~~~~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:38.8946095Z     if torch.jit.isinstance(x, List[torch.Tensor]):
2023-01-11T21:25:38.8946185Z         x.append(torch.tensor(3))
2023-01-11T21:25:38.8946274Z  (function emitListLiteral)
2023-01-11T21:25:38.8946622Z [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[Tensor, int]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:38.8946701Z   File "<string>", line 3
2023-01-11T21:25:38.8946706Z 
2023-01-11T21:25:38.8946776Z def fn():
2023-01-11T21:25:38.8946964Z     x: Union[List[torch.Tensor], int] = [torch.add(1, x) for x in [torch.arange(5), 1]]
2023-01-11T21:25:38.8947150Z                                                                   ~~~~~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:38.8947255Z     if torch.jit.isinstance(x, List[torch.Tensor]):
2023-01-11T21:25:38.8947342Z         x.append(torch.tensor(3))
2023-01-11T21:25:38.8947430Z  (function emitListLiteral)
2023-01-11T21:25:38.8947496Z ok (0.021s)
2023-01-11T21:25:38.8947637Z   test_union_with_scalar_values (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:38.8947786Z   test_unions_of_a_single_argument_vanish (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8947929Z   test_unions_of_unions_are_flattened (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:38.8948107Z   test_factory_ops_requires_grad_fail (jit.test_unsupported_ops.TestUnsupportedOps) ... ok (0.003s)
2023-01-11T21:25:38.8948246Z   test_init_ops (jit.test_unsupported_ops.TestUnsupportedOps) ... ok (0.016s)
2023-01-11T21:25:38.8948404Z   test_add_value_to_version_map (jit.test_upgraders.TestUpgraders) ... ok (0.001s)
2023-01-11T21:25:38.8948554Z   test_aten_div_scalar_at_3 (jit.test_upgraders.TestUpgraders) ... ok (0.004s)
2023-01-11T21:25:38.8948701Z   test_aten_div_tensor_at_3 (jit.test_upgraders.TestUpgraders) ... ok (0.004s)
2023-01-11T21:25:38.8948852Z   test_aten_div_tensor_out_at_3 (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:38.8948990Z   test_aten_full_at_4 (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:38.8949143Z   test_aten_full_other_variants (jit.test_upgraders.TestUpgraders) ... ok (0.005s)
2023-01-11T21:25:38.8949287Z   test_aten_full_out_at_4 (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:38.8949415Z   test_aten_linspace (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:38.8949560Z   test_aten_linspace_out (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:38.8949707Z   test_aten_logspace (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:38.8949855Z   test_aten_logspace_out (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:38.8950007Z   test_aten_test_serialization (jit.test_upgraders.TestUpgraders) ... ok (0.006s)
2023-01-11T21:25:38.8950171Z   test_populated_test_upgrader_graph (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:38.8960386Z   test_populated_upgrader_graph (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:38.8960598Z   test_warn (jit.test_warn.TestWarn) ... ok (0.002s)
2023-01-11T21:25:38.8960761Z   test_warn_multiple_calls_multiple_warnings (jit.test_warn.TestWarn) ... ok (0.002s)
2023-01-11T21:25:38.8960918Z   test_warn_multiple_calls_same_func_diff_stack (jit.test_warn.TestWarn) ... ok (0.007s)
2023-01-11T21:25:38.8961048Z   test_warn_once_per_func (jit.test_warn.TestWarn) ... ok (0.006s)
2023-01-11T21:25:38.8961187Z   test_warn_once_per_func_in_loop (jit.test_warn.TestWarn) ... ok (0.006s)
2023-01-11T21:25:38.8961429Z   test_warn_only_once (jit.test_warn.TestWarn) ... ok (0.003s)
2023-01-11T21:25:38.8961566Z   test_warn_only_once_in_loop_func (jit.test_warn.TestWarn) ... ok (0.004s)
2023-01-11T21:25:38.8961668Z   test_with_as (jit.test_with.TestWith)
2023-01-11T21:25:38.8961936Z Check that with statements that use the 'as' keyword to bind expressions ... ok (0.065s)
2023-01-11T21:25:38.8962042Z   test_with_errors (jit.test_with.TestWith)
2023-01-11T21:25:38.8962300Z Check that errors related to with-statements are detected and reported correctly. ... ok (0.027s)
2023-01-11T21:25:38.8962414Z   test_with_exceptions (jit.test_with.TestWith)
2023-01-11T21:25:38.8962633Z Check that exceptions thrown in the bodies of with-statements are ... ok (0.025s)
2023-01-11T21:25:38.8962736Z   test_with_no_as (jit.test_with.TestWith)
2023-01-11T21:25:38.8962974Z Check that with statements that do not use the 'as' keyword to bind expressions ... ok (0.067s)
2023-01-11T21:25:38.8963120Z   test_with_no_grad (jit.test_with.TestWith)
2023-01-11T21:25:38.8963258Z Check that torch.no_grad() works. Most of these are adapted from ... ok (0.017s)
2023-01-11T21:25:38.8963373Z   test_with_record_function (jit.test_with.TestWith)
2023-01-11T21:25:38.8963761Z Check that torch.autograd.profiler.record_function context manager is ... STAGE:2023-01-11 21:25:37 1925:1925 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:38.8964016Z STAGE:2023-01-11 21:25:37 1925:1925 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:38.8964276Z STAGE:2023-01-11 21:25:37 1925:1925 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:38.8964343Z ok (0.025s)
2023-01-11T21:25:38.8964350Z 
2023-01-11T21:25:38.8964545Z ----------------------------------------------------------------------
2023-01-11T21:25:38.8964615Z Ran 2544 tests in 130.977s
2023-01-11T21:25:38.8964621Z 
2023-01-11T21:25:38.8964703Z OK (skipped=114, expected failures=2)
2023-01-11T21:25:38.8964715Z 
2023-01-11T21:25:38.8964789Z Generating XML reports...
2023-01-11T21:25:38.8965112Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_alias_analysis.TestAliasAnalysis-20230111212326.xml
2023-01-11T21:25:38.8965397Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_async.TestAsync-20230111212326.xml
2023-01-11T21:25:38.8965691Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_aten_pow.TestAtenPow-20230111212326.xml
2023-01-11T21:25:38.8966000Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_autodiff.TestAutodiffJit-20230111212326.xml
2023-01-11T21:25:38.8966374Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing-20230111212326.xml
2023-01-11T21:25:38.8966673Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackends-20230111212326.xml
2023-01-11T21:25:38.8967015Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackendsWithCompiler-20230111212326.xml
2023-01-11T21:25:38.8967302Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_batch_mm.TestBatchMM-20230111212326.xml
2023-01-11T21:25:38.8967585Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_builtins.TestBuiltins-20230111212326.xml
2023-01-11T21:25:38.8967879Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_class_type.TestClassType-20230111212326.xml
2023-01-11T21:25:38.8968166Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_complex.TestComplex-20230111212326.xml
2023-01-11T21:25:38.8968499Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_custom_operators.TestCustomOperators-20230111212326.xml
2023-01-11T21:25:38.8968767Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_dce.TestDCE-20230111212326.xml
2023-01-11T21:25:38.8969084Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_dataclasses.TestDataclasses-20230111212326.xml
2023-01-11T21:25:38.8969442Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_device_analysis.TestDeviceAnalysis-20230111212326.xml
2023-01-11T21:25:38.8969720Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestDict-20230111212326.xml
2023-01-11T21:25:38.8970047Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_dtype_analysis.TestDtypeAnalysis-20230111212326.xml
2023-01-11T21:25:38.8970307Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_enum.TestEnum-20230111212326.xml
2023-01-11T21:25:38.8970602Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_freezing.TestFreezing-20230111212326.xml
2023-01-11T21:25:38.8970861Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestFrontend-20230111212326.xml
2023-01-11T21:25:38.8971222Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_freezing.TestFrozenOptimizations-20230111212326.xml
2023-01-11T21:25:38.8971559Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_functional_blocks.TestFunctionalBlocks-20230111212326.xml
2023-01-11T21:25:38.8971945Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_convert_activation.TestFunctionalToInplaceActivation-20230111212326.xml
2023-01-11T21:25:38.8972246Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_attr.TestGetDefaultAttr-20230111212326.xml
2023-01-11T21:25:38.8972593Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_graph_rewrite_passes.TestGraphRewritePasses-20230111212326.xml
2023-01-11T21:25:38.8972868Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_hash.TestHash-20230111212326.xml
2023-01-11T21:25:38.8973148Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_hooks.TestHooks-20230111212326.xml
2023-01-11T21:25:38.8973454Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_ignorable_args.TestIgnorableArgs-20230111212326.xml
2023-01-11T21:25:38.8973811Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_ignore_context_manager.TestIgnoreContextManager-20230111212326.xml
2023-01-11T21:25:38.8974200Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_convert_activation.TestInplaceToFunctionalActivation-20230111212326.xml
2023-01-11T21:25:38.8974507Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_isinstance.TestIsinstance-20230111212326.xml
2023-01-11T21:25:38.8974755Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestJit-20230111212326.xml
2023-01-11T21:25:38.8975048Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestJitGeneratedModule-20230111212326.xml
2023-01-11T21:25:38.8975341Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_jit_utils.TestJitUtils-20230111212326.xml
2023-01-11T21:25:38.8975625Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestList-20230111212326.xml
2023-01-11T21:25:38.8975914Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_logging.TestLogging-20230111212326.xml
2023-01-11T21:25:38.8976242Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_freezing.TestMKLDNNReinplacing-20230111212326.xml
2023-01-11T21:25:38.8976498Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_misc.TestMisc-20230111212326.xml
2023-01-11T21:25:38.8976822Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tracer.TestMixTracingScripting-20230111212326.xml
2023-01-11T21:25:38.8977103Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_models.TestModels-20230111212326.xml
2023-01-11T21:25:38.8977402Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_module_apis.TestModuleAPIs-20230111212326.xml
2023-01-11T21:25:38.8977776Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_module_containers.TestModuleContainers-20230111212326.xml
2023-01-11T21:25:38.8978107Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_module_interface.TestModuleInterface-20230111212326.xml
2023-01-11T21:25:38.8978399Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_modules.TestModules-20230111212326.xml
2023-01-11T21:25:38.8978698Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestNamedTuple-20230111212326.xml
2023-01-11T21:25:38.8979007Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_backend_nnapi.TestNnapiBackend-20230111212326.xml
2023-01-11T21:25:38.8979343Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_op_decompositions.TestOpDecompositions-20230111212326.xml
2023-01-11T21:25:38.8979818Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo-20230111212326.xml
2023-01-11T21:25:38.8980089Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_pdt.TestPDT-20230111212326.xml
2023-01-11T21:25:38.8980435Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_parametrization.TestParametrization-20230111212326.xml
2023-01-11T21:25:38.8980734Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_peephole.TestPeephole-20230111212326.xml
2023-01-11T21:25:38.8981016Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestProducerVersion-20230111212326.xml
2023-01-11T21:25:38.8981311Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_profiler.TestProfiler-20230111212326.xml
2023-01-11T21:25:38.8981637Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_python_bindings.TestPythonBindings-20230111212326.xml
2023-01-11T21:25:38.8981970Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_python_builtins.TestPythonBuiltinOP-20230111212326.xml
2023-01-11T21:25:38.8982261Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_python_ir.TestPythonIr-20230111212326.xml
2023-01-11T21:25:38.8982720Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_recursive_script.TestRecursiveScript-20230111212326.xml
2023-01-11T21:25:38.8983045Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_remove_mutation.TestRemoveMutation-20230111212326.xml
2023-01-11T21:25:38.8983334Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_save_load.TestSaveLoad-20230111212326.xml
2023-01-11T21:25:38.8983692Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_save_load_for_op_version.TestSaveLoadForOpVersion-20230111212326.xml
2023-01-11T21:25:38.8983950Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestScript-20230111212326.xml
2023-01-11T21:25:38.8984255Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestScriptDict-20230111212326.xml
2023-01-11T21:25:38.8984550Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestScriptList-20230111212326.xml
2023-01-11T21:25:38.8984975Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation-20230111212326.xml
2023-01-11T21:25:38.8985296Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_script_profile.TestScriptProfile-20230111212326.xml
2023-01-11T21:25:38.8985575Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_slice.TestSlice-20230111212326.xml
2023-01-11T21:25:38.8985846Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_sparse.TestSparse-20230111212326.xml
2023-01-11T21:25:38.8986188Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_string_formatting.TestStringFormatting-20230111212326.xml
2023-01-11T21:25:38.8986608Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis-20230111212326.xml
2023-01-11T21:25:38.8986924Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_builtins.TestTensorBuiltins-20230111212326.xml
2023-01-11T21:25:38.8987259Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tensor_creation_ops.TestTensorCreationOps-20230111212326.xml
2023-01-11T21:25:38.8987577Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tensor_methods.TestTensorMethods-20230111212326.xml
2023-01-11T21:25:38.8987882Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_torchbind.TestTorchbind-20230111212326.xml
2023-01-11T21:25:38.8988167Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tracer.TestTracer-20230111212326.xml
2023-01-11T21:25:38.8988513Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_type_sharing.TestTypeSharing-20230111212326.xml
2023-01-11T21:25:38.8988834Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_types.TestTypesAndAnnotation-20230111212326.xml
2023-01-11T21:25:38.8989110Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_typing.TestTyping-20230111212326.xml
2023-01-11T21:25:38.8989390Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_union.TestUnion-20230111212326.xml
2023-01-11T21:25:38.8989714Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_unsupported_ops.TestUnsupportedOps-20230111212326.xml
2023-01-11T21:25:38.8990019Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_upgraders.TestUpgraders-20230111212326.xml
2023-01-11T21:25:38.8990291Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_warn.TestWarn-20230111212326.xml
2023-01-11T21:25:38.8990563Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_with.TestWith-20230111212326.xml
2023-01-11T21:25:38.8990942Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_data_parallel.TestDataParallel-20230111212326.xml
2023-01-11T21:25:38.8991267Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_save_load.TestSaveLoadFlatbuffer-20230111212326.xml
2023-01-11T21:25:38.8991273Z 
2023-01-11T21:25:38.8991653Z ##[endgroup]
2023-01-11T21:25:38.8991912Z FINISHED PRINTING LOG FILE of test_jit (/var/lib/jenkins/workspace/test/test-reports/test_jit_k9iebijm)
2023-01-11T21:25:38.8991917Z 
2023-01-11T21:25:40.7216433Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:25:40.8154894Z Ignoring disabled issues:  []
2023-01-11T21:25:40.8963235Z Running test_masked ... [2023-01-11 21:25:40.895935]
2023-01-11T21:25:40.8964463Z Executing ['/opt/conda/bin/python', '-bb', 'test_masked.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:25:40.896214]
2023-01-11T21:25:43.5545772Z 
2023-01-11T21:25:43.5547321Z Expand the folded group to see the log file of test_masked
2023-01-11T21:25:43.5548117Z ##[group]PRINTING LOG FILE of test_masked (/var/lib/jenkins/workspace/test/test-reports/test_masked_2slvrldx)
2023-01-11T21:25:43.5548357Z 
2023-01-11T21:25:43.5548433Z Running tests...
2023-01-11T21:25:43.5548834Z ----------------------------------------------------------------------
2023-01-11T21:25:43.5549009Z 
2023-01-11T21:25:43.5549205Z ----------------------------------------------------------------------
2023-01-11T21:25:43.5549434Z Ran 0 tests in 0.000s
2023-01-11T21:25:43.5549548Z 
2023-01-11T21:25:43.5549610Z OK
2023-01-11T21:25:43.5549704Z 
2023-01-11T21:25:43.5549791Z Generating XML reports...
2023-01-11T21:25:43.5550105Z Test results will be stored in test-reports/python-unittest/test_masked
2023-01-11T21:25:43.5550286Z 
2023-01-11T21:25:43.5550512Z ##[endgroup]
2023-01-11T21:25:43.5550962Z FINISHED PRINTING LOG FILE of test_masked (/var/lib/jenkins/workspace/test/test-reports/test_masked_2slvrldx)
2023-01-11T21:25:43.5551380Z 
2023-01-11T21:25:45.4233039Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:25:45.4894640Z Ignoring disabled issues:  []
2023-01-11T21:25:45.5035239Z Running test_meta ... [2023-01-11 21:25:45.503200]
2023-01-11T21:25:45.5036534Z Executing ['/opt/conda/bin/python', '-bb', 'test_meta.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:25:45.503467]
2023-01-11T21:25:48.4139399Z 
2023-01-11T21:25:48.4139958Z Expand the folded group to see the log file of test_meta
2023-01-11T21:25:48.4140939Z ##[group]PRINTING LOG FILE of test_meta (/var/lib/jenkins/workspace/test/test-reports/test_meta_wkf1cvw6)
2023-01-11T21:25:48.4141335Z 
2023-01-11T21:25:48.4141462Z Running tests...
2023-01-11T21:25:48.4142060Z ----------------------------------------------------------------------
2023-01-11T21:25:48.4142827Z Test results will be stored in test-reports/python-unittest/test_meta
2023-01-11T21:25:48.4143701Z   test_channels_last (__main__.TestMetaConverter) ... ok (0.114s)
2023-01-11T21:25:48.4144214Z   test_channels_last_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4144673Z   test_channels_last_non_leaf (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:48.4145780Z   test_complex_noncontiguous_bug (__main__.TestMetaConverter) ... /var/lib/jenkins/workspace/test/test_meta.py:233: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:25:48.4146289Z   x = torch.randn((2, 2, 4, 9), dtype=torch.complex32)[:, 0, :, :]
2023-01-11T21:25:48.4146556Z ok (0.002s)
2023-01-11T21:25:48.4146786Z   test_empty_strided_non_dense_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4147071Z   test_imag (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4147379Z   test_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4147651Z   test_non_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4148301Z   test_non_leaf_torture (__main__.TestMetaConverter) ... /var/lib/jenkins/workspace/test/test_meta.py:212: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:48.4148896Z   x.set_(x.storage(), 10, (2,), (2,))
2023-01-11T21:25:48.4149082Z ok (0.001s)
2023-01-11T21:25:48.4149299Z   test_requires_grad_false (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4149646Z   test_tensor_outlives_converter (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4149940Z   test_view_as_complex (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4150248Z   test_view_as_real (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4150543Z   test_view_dtype (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:48.4150879Z   test_view_of_leaf (__main__.TestMetaConverter) ... ok (0.003s)
2023-01-11T21:25:48.4151205Z   test_view_of_non_leaf (__main__.TestMetaConverter) ... ok (0.003s)
2023-01-11T21:25:48.4151495Z   test_view_of_view_of_leaf (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:48.4151792Z   test_weakref (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:48.4151953Z 
2023-01-11T21:25:48.4152170Z ----------------------------------------------------------------------
2023-01-11T21:25:48.4152417Z Ran 18 tests in 0.141s
2023-01-11T21:25:48.4152567Z 
2023-01-11T21:25:48.4152626Z OK
2023-01-11T21:25:48.4152718Z 
2023-01-11T21:25:48.4152805Z Generating XML reports...
2023-01-11T21:25:48.4153221Z Generated XML report: test-reports/python-unittest/test_meta/TEST-TestMetaConverter-20230111212547.xml
2023-01-11T21:25:48.4153500Z 
2023-01-11T21:25:48.4153744Z ##[endgroup]
2023-01-11T21:25:48.4154297Z FINISHED PRINTING LOG FILE of test_meta (/var/lib/jenkins/workspace/test/test-reports/test_meta_wkf1cvw6)
2023-01-11T21:25:48.4154505Z 
2023-01-11T21:25:50.3845377Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:25:50.4675445Z Ignoring disabled issues:  []
2023-01-11T21:25:50.4814659Z Running test_proxy_tensor ... [2023-01-11 21:25:50.481213]
2023-01-11T21:25:50.4817673Z Executing ['/opt/conda/bin/python', '-bb', 'test_proxy_tensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:25:50.481490]
2023-01-11T21:26:30.4011104Z 
2023-01-11T21:26:30.4012393Z Expand the folded group to see the log file of test_proxy_tensor
2023-01-11T21:26:30.4015931Z ##[group]PRINTING LOG FILE of test_proxy_tensor (/var/lib/jenkins/workspace/test/test-reports/test_proxy_tensor_5ujmqt7b)
2023-01-11T21:26:30.4016593Z 
2023-01-11T21:26:30.4016974Z Running tests...
2023-01-11T21:26:30.4018128Z ----------------------------------------------------------------------
2023-01-11T21:26:30.4019356Z Test results will be stored in test-reports/python-unittest/test_proxy_tensor
2023-01-11T21:26:30.4020104Z   test_alias (__main__.TestFakeProxyTensor) ... ok (0.100s)
2023-01-11T21:26:30.4020748Z   test_issue82547 (__main__.TestFakeProxyTensor) ... ok (0.005s)
2023-01-11T21:26:30.4021402Z   test_meta (__main__.TestFakeProxyTensor) ... ok (0.006s)
2023-01-11T21:26:30.4067536Z   test_use_fake_and_tensor (__main__.TestFakeProxyTensor) ... ok (0.006s)
2023-01-11T21:26:30.4068040Z   test_allclose (__main__.TestGenericProxyTensorFake) ... ok (0.003s)
2023-01-11T21:26:30.4068707Z   test_amp_cache (__main__.TestGenericProxyTensorFake) ... skip: CUDA-only test (0.001s)
2023-01-11T21:26:30.4069146Z   test_constant_blowup (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:30.4069564Z   test_constant_proxy_tensor_mut (__main__.TestGenericProxyTensorFake) ... ok (0.010s)
2023-01-11T21:26:30.4069989Z   test_constant_random (__main__.TestGenericProxyTensorFake) ... ok (0.003s)
2023-01-11T21:26:30.4070426Z   test_constant_unbind (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:30.4070839Z   test_decomp_of_capture (__main__.TestGenericProxyTensorFake) ... ok (0.031s)
2023-01-11T21:26:30.4071391Z   test_decomposition_interpreter (__main__.TestGenericProxyTensorFake) ... ok (0.024s)
2023-01-11T21:26:30.4071830Z   test_inplace_metadata (__main__.TestGenericProxyTensorFake) ... ok (0.006s)
2023-01-11T21:26:30.4074626Z   test_isolated_graphmodule (__main__.TestGenericProxyTensorFake) ... ok (0.232s)
2023-01-11T21:26:30.4075126Z   test_make_fx_model_double_param (__main__.TestGenericProxyTensorFake) ... ok (0.057s)
2023-01-11T21:26:30.4075566Z   test_make_fx_model_fwd_bwd (__main__.TestGenericProxyTensorFake) ... ok (0.039s)
2023-01-11T21:26:30.4076010Z   test_make_fx_model_fwd_bwd_wgtupdate (__main__.TestGenericProxyTensorFake) ... ok (0.042s)
2023-01-11T21:26:30.4076436Z   test_make_fx_overloads (__main__.TestGenericProxyTensorFake) ... ok (0.010s)
2023-01-11T21:26:30.4076890Z   test_make_fx_reentrant_dispatch (__main__.TestGenericProxyTensorFake) ... ok (0.006s)
2023-01-11T21:26:30.4077317Z   test_make_fx_simple (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:30.4077755Z   test_mode_tracing_factory_function (__main__.TestGenericProxyTensorFake) ... ok (0.009s)
2023-01-11T21:26:30.4078169Z   test_partial_decomp (__main__.TestGenericProxyTensorFake) ... ok (0.088s)
2023-01-11T21:26:30.4078581Z   test_pickle_issue89626 (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:30.4081983Z   test_pr_86917 (__main__.TestGenericProxyTensorFake) ... ok (0.022s)
2023-01-11T21:26:30.4082648Z   test_proxy_tensor (__main__.TestGenericProxyTensorFake) ... ok (0.031s)
2023-01-11T21:26:30.4083307Z   test_proxy_tensor_mode_with_decomp_table_preserves_proxy (__main__.TestGenericProxyTensorFake) ... ok (0.020s)
2023-01-11T21:26:30.4083819Z   test_resnet18_backward_trace (__main__.TestGenericProxyTensorFake) ... ok (2.519s)
2023-01-11T21:26:30.4084700Z   test_scalar_device (__main__.TestGenericProxyTensorFake) ... ok (0.006s)
2023-01-11T21:26:30.4085127Z   test_strides (__main__.TestGenericProxyTensorFake) ... ok (0.064s)
2023-01-11T21:26:30.4085645Z   test_tensor_constants (__main__.TestGenericProxyTensorFake) ... ok (0.009s)
2023-01-11T21:26:30.4086178Z   test_trace_subclasses (__main__.TestGenericProxyTensorFake) ... ok (0.009s)
2023-01-11T21:26:30.4087739Z   test_val_metadata_mutation (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:30.4088331Z   test_varargs (__main__.TestGenericProxyTensorFake) ... ok (0.005s)
2023-01-11T21:26:30.4088869Z   test_allclose (__main__.TestGenericProxyTensorReal) ... ok (0.009s)
2023-01-11T21:26:30.4089607Z   test_amp_cache (__main__.TestGenericProxyTensorReal) ... skip: CUDA-only test (0.001s)
2023-01-11T21:26:30.4089933Z   test_constant_blowup (__main__.TestGenericProxyTensorReal) ... ok (0.020s)
2023-01-11T21:26:30.4090263Z   test_constant_proxy_tensor_mut (__main__.TestGenericProxyTensorReal) ... ok (0.020s)
2023-01-11T21:26:30.4090711Z   test_constant_random (__main__.TestGenericProxyTensorReal) ... ok (0.015s)
2023-01-11T21:26:30.4091022Z   test_constant_unbind (__main__.TestGenericProxyTensorReal) ... ok (0.016s)
2023-01-11T21:26:30.4091343Z   test_decomp_of_capture (__main__.TestGenericProxyTensorReal) ... ok (0.031s)
2023-01-11T21:26:30.4091684Z   test_decomposition_interpreter (__main__.TestGenericProxyTensorReal) ... ok (0.031s)
2023-01-11T21:26:30.4092020Z   test_inplace_metadata (__main__.TestGenericProxyTensorReal) ... ok (0.019s)
2023-01-11T21:26:30.4092338Z   test_isolated_graphmodule (__main__.TestGenericProxyTensorReal) ... ok (0.235s)
2023-01-11T21:26:30.4092680Z   test_make_fx_model_double_param (__main__.TestGenericProxyTensorReal) ... ok (0.057s)
2023-01-11T21:26:30.4093016Z   test_make_fx_model_fwd_bwd (__main__.TestGenericProxyTensorReal) ... ok (0.254s)
2023-01-11T21:26:30.4093345Z   test_make_fx_model_fwd_bwd_wgtupdate (__main__.TestGenericProxyTensorReal) ... ok (0.328s)
2023-01-11T21:26:30.4093686Z   test_make_fx_overloads (__main__.TestGenericProxyTensorReal) ... ok (0.033s)
2023-01-11T21:26:30.4094020Z   test_make_fx_reentrant_dispatch (__main__.TestGenericProxyTensorReal) ... ok (0.042s)
2023-01-11T21:26:30.4094352Z   test_make_fx_simple (__main__.TestGenericProxyTensorReal) ... ok (0.020s)
2023-01-11T21:26:30.4094675Z   test_mode_tracing_factory_function (__main__.TestGenericProxyTensorReal) ... ok (0.027s)
2023-01-11T21:26:30.4095006Z   test_partial_decomp (__main__.TestGenericProxyTensorReal) ... ok (0.120s)
2023-01-11T21:26:30.4095325Z   test_pickle_issue89626 (__main__.TestGenericProxyTensorReal) ... ok (0.017s)
2023-01-11T21:26:30.4095620Z   test_pr_86917 (__main__.TestGenericProxyTensorReal) ... ok (0.029s)
2023-01-11T21:26:30.4095921Z   test_proxy_tensor (__main__.TestGenericProxyTensorReal) ... ok (0.249s)
2023-01-11T21:26:30.4096278Z   test_proxy_tensor_mode_with_decomp_table_preserves_proxy (__main__.TestGenericProxyTensorReal) ... ok (0.031s)
2023-01-11T21:26:30.4096648Z   test_resnet18_backward_trace (__main__.TestGenericProxyTensorReal) ... ok (6.637s)
2023-01-11T21:26:30.4096960Z   test_scalar_device (__main__.TestGenericProxyTensorReal) ... ok (0.019s)
2023-01-11T21:26:30.4097271Z   test_strides (__main__.TestGenericProxyTensorReal) ... ok (0.064s)
2023-01-11T21:26:30.4097581Z   test_tensor_constants (__main__.TestGenericProxyTensorReal) ... ok (0.013s)
2023-01-11T21:26:30.4097889Z   test_trace_subclasses (__main__.TestGenericProxyTensorReal) ... ok (0.043s)
2023-01-11T21:26:30.4098215Z   test_val_metadata_mutation (__main__.TestGenericProxyTensorReal) ... ok (0.020s)
2023-01-11T21:26:30.4098532Z   test_varargs (__main__.TestGenericProxyTensorReal) ... ok (0.023s)
2023-01-11T21:26:30.4098853Z   test_allclose (__main__.TestGenericProxyTensorSymbolic) ... ok (0.039s)
2023-01-11T21:26:30.4099277Z   test_amp_cache (__main__.TestGenericProxyTensorSymbolic) ... skip: CUDA-only test (0.001s)
2023-01-11T21:26:30.4099633Z   test_constant_blowup (__main__.TestGenericProxyTensorSymbolic) ... ok (0.010s)
2023-01-11T21:26:30.4100040Z   test_constant_proxy_tensor_mut (__main__.TestGenericProxyTensorSymbolic) ... ok (0.013s)
2023-01-11T21:26:30.4100511Z   test_constant_random (__main__.TestGenericProxyTensorSymbolic) ... ok (0.008s)
2023-01-11T21:26:30.4100850Z   test_constant_unbind (__main__.TestGenericProxyTensorSymbolic) ... ok (0.005s)
2023-01-11T21:26:30.4101195Z   test_decomp_of_capture (__main__.TestGenericProxyTensorSymbolic) ... ok (0.032s)
2023-01-11T21:26:30.4101551Z   test_decomposition_interpreter (__main__.TestGenericProxyTensorSymbolic) ... ok (0.069s)
2023-01-11T21:26:30.4101895Z   test_inplace_metadata (__main__.TestGenericProxyTensorSymbolic) ... ok (0.035s)
2023-01-11T21:26:30.4102255Z   test_isolated_graphmodule (__main__.TestGenericProxyTensorSymbolic) ... ok (0.241s)
2023-01-11T21:26:30.4102931Z   test_make_fx_model_double_param (__main__.TestGenericProxyTensorSymbolic) ... ok (0.058s)
2023-01-11T21:26:30.4103491Z   test_make_fx_model_fwd_bwd (__main__.TestGenericProxyTensorSymbolic) ... ok (0.243s)
2023-01-11T21:26:30.4105034Z   test_make_fx_model_fwd_bwd_wgtupdate (__main__.TestGenericProxyTensorSymbolic) ... ok (0.198s)
2023-01-11T21:26:30.4105734Z   test_make_fx_overloads (__main__.TestGenericProxyTensorSymbolic) ... expected failure (0.031s)
2023-01-11T21:26:30.4106315Z   test_make_fx_reentrant_dispatch (__main__.TestGenericProxyTensorSymbolic) ... ok (0.024s)
2023-01-11T21:26:30.4108224Z   test_make_fx_simple (__main__.TestGenericProxyTensorSymbolic) ... ok (0.016s)
2023-01-11T21:26:30.4108847Z   test_mode_tracing_factory_function (__main__.TestGenericProxyTensorSymbolic) ... ok (0.022s)
2023-01-11T21:26:30.4109205Z   test_partial_decomp (__main__.TestGenericProxyTensorSymbolic) ... ok (0.086s)
2023-01-11T21:26:30.4109549Z   test_pickle_issue89626 (__main__.TestGenericProxyTensorSymbolic) ... ok (0.015s)
2023-01-11T21:26:30.4109861Z   test_pr_86917 (__main__.TestGenericProxyTensorSymbolic) ... ok (0.068s)
2023-01-11T21:26:30.4110192Z   test_proxy_tensor (__main__.TestGenericProxyTensorSymbolic) ... ok (0.167s)
2023-01-11T21:26:30.4110577Z   test_proxy_tensor_mode_with_decomp_table_preserves_proxy (__main__.TestGenericProxyTensorSymbolic) ... ok (0.021s)
2023-01-11T21:26:30.4111014Z   test_resnet18_backward_trace (__main__.TestGenericProxyTensorSymbolic) ... ok (21.911s)
2023-01-11T21:26:30.4111365Z   test_scalar_device (__main__.TestGenericProxyTensorSymbolic) ... ok (0.024s)
2023-01-11T21:26:30.4111714Z   test_strides (__main__.TestGenericProxyTensorSymbolic) ... ok (0.063s)
2023-01-11T21:26:30.4112048Z   test_tensor_constants (__main__.TestGenericProxyTensorSymbolic) ... ok (0.011s)
2023-01-11T21:26:30.4112394Z   test_trace_subclasses (__main__.TestGenericProxyTensorSymbolic) ... expected failure (0.009s)
2023-01-11T21:26:30.4112758Z   test_val_metadata_mutation (__main__.TestGenericProxyTensorSymbolic) ... ok (0.022s)
2023-01-11T21:26:30.4113094Z   test_varargs (__main__.TestGenericProxyTensorSymbolic) ... ok (0.027s)
2023-01-11T21:26:30.4113403Z   test_binary_broadcast (__main__.TestSymbolicTracing) ... ok (0.033s)
2023-01-11T21:26:30.4113669Z   test_cat (__main__.TestSymbolicTracing) ... ok (0.066s)
2023-01-11T21:26:30.4113959Z   test_debug_interpreter (__main__.TestSymbolicTracing) ... ok (0.025s)
2023-01-11T21:26:30.4114271Z   test_elementwise_meta_with_sym_numbers (__main__.TestSymbolicTracing) ... ok (0.049s)
2023-01-11T21:26:30.4114552Z   test_expand (__main__.TestSymbolicTracing) ... ok (0.036s)
2023-01-11T21:26:30.4114826Z   test_guards_equal (__main__.TestSymbolicTracing) ... ok (0.504s)
2023-01-11T21:26:30.4115101Z   test_item (__main__.TestSymbolicTracing) ... ok (0.012s)
2023-01-11T21:26:30.4115356Z   test_mega_guard (__main__.TestSymbolicTracing) ... ok (0.029s)
2023-01-11T21:26:30.4115629Z   test_metadata (__main__.TestSymbolicTracing) ... ok (0.025s)
2023-01-11T21:26:30.4115912Z   test_metadata_fresh (__main__.TestSymbolicTracing) ... ok (0.018s)
2023-01-11T21:26:30.4116200Z   test_multiply_shape (__main__.TestSymbolicTracing) ... ok (0.017s)
2023-01-11T21:26:30.4116562Z   test_neg_shape (__main__.TestSymbolicTracing) ... ok (0.030s)
2023-01-11T21:26:30.4116835Z   test_new_empty (__main__.TestSymbolicTracing) ... ok (0.034s)
2023-01-11T21:26:30.4117138Z   test_nonidentity_transitive_guards (__main__.TestSymbolicTracing) ... ok (0.149s)
2023-01-11T21:26:30.4117432Z   test_resize_from_zero (__main__.TestSymbolicTracing) ... ok (0.012s)
2023-01-11T21:26:30.4117714Z   test_return_symint (__main__.TestSymbolicTracing) ... ok (0.027s)
2023-01-11T21:26:30.4117992Z   test_rmethod (__main__.TestSymbolicTracing) ... ok (0.015s)
2023-01-11T21:26:30.4118269Z   test_size_with_tensor (__main__.TestSymbolicTracing) ... ok (0.065s)
2023-01-11T21:26:30.4118536Z   test_sqrt_size (__main__.TestSymbolicTracing) ... ok (0.015s)
2023-01-11T21:26:30.4118818Z   test_sym_storage_offset (__main__.TestSymbolicTracing) ... ok (0.020s)
2023-01-11T21:26:30.4119107Z   test_symint_to_tensor (__main__.TestSymbolicTracing) ... ok (0.029s)
2023-01-11T21:26:30.4119414Z   test_unary (__main__.TestSymbolicTracing) ... ok (0.024s)
2023-01-11T21:26:30.4119568Z 
2023-01-11T21:26:30.4119804Z ----------------------------------------------------------------------
2023-01-11T21:26:30.4120050Z Ran 113 tests in 36.517s
2023-01-11T21:26:30.4120166Z 
2023-01-11T21:26:30.4120249Z OK (skipped=3, expected failures=2)
2023-01-11T21:26:30.4120376Z 
2023-01-11T21:26:30.4120463Z Generating XML reports...
2023-01-11T21:26:30.4120907Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestFakeProxyTensor-20230111212552.xml
2023-01-11T21:26:30.4124754Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestGenericProxyTensorFake-20230111212552.xml
2023-01-11T21:26:30.4125760Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestGenericProxyTensorReal-20230111212552.xml
2023-01-11T21:26:30.4126787Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestGenericProxyTensorSymbolic-20230111212552.xml
2023-01-11T21:26:30.4127751Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestSymbolicTracing-20230111212552.xml
2023-01-11T21:26:30.4128137Z 
2023-01-11T21:26:30.4128703Z ##[endgroup]
2023-01-11T21:26:30.4129325Z FINISHED PRINTING LOG FILE of test_proxy_tensor (/var/lib/jenkins/workspace/test/test-reports/test_proxy_tensor_5ujmqt7b)
2023-01-11T21:26:30.4129652Z 
2023-01-11T21:26:32.5022898Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:32.5896581Z Ignoring disabled issues:  []
2023-01-11T21:26:32.6040629Z Running test_public_bindings ... [2023-01-11 21:26:32.603722]
2023-01-11T21:26:32.6042717Z Executing ['/opt/conda/bin/python', '-bb', 'test_public_bindings.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:32.604002]
2023-01-11T21:26:36.7910702Z 
2023-01-11T21:26:36.7916939Z Expand the folded group to see the log file of test_public_bindings
2023-01-11T21:26:36.7917962Z ##[group]PRINTING LOG FILE of test_public_bindings (/var/lib/jenkins/workspace/test/test-reports/test_public_bindings_jnijx7zv)
2023-01-11T21:26:36.7918335Z 
2023-01-11T21:26:36.7918437Z Running tests...
2023-01-11T21:26:36.7918981Z ----------------------------------------------------------------------
2023-01-11T21:26:36.7919511Z Test results will be stored in test-reports/python-unittest/test_public_bindings
2023-01-11T21:26:36.7919929Z   test_correct_module_names (__main__.TestPublicBindings)
2023-01-11T21:26:36.7920554Z An API is considered public, if  its  `__module__` starts with `torch.` ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:36.7920911Z ok (2.075s)
2023-01-11T21:26:36.7921203Z   test_no_new_bindings (__main__.TestPublicBindings)
2023-01-11T21:26:36.7921608Z This test aims to stop the introduction of new JIT bindings into torch._C ... ok (0.003s)
2023-01-11T21:26:36.7921857Z 
2023-01-11T21:26:36.7922135Z ----------------------------------------------------------------------
2023-01-11T21:26:36.7922447Z Ran 2 tests in 2.078s
2023-01-11T21:26:36.7922875Z 
2023-01-11T21:26:36.7922962Z OK
2023-01-11T21:26:36.7923089Z 
2023-01-11T21:26:36.7923205Z Generating XML reports...
2023-01-11T21:26:36.7923806Z Generated XML report: test-reports/python-unittest/test_public_bindings/TEST-TestPublicBindings-20230111212634.xml
2023-01-11T21:26:36.7924141Z 
2023-01-11T21:26:36.7924483Z ##[endgroup]
2023-01-11T21:26:36.7925048Z FINISHED PRINTING LOG FILE of test_public_bindings (/var/lib/jenkins/workspace/test/test-reports/test_public_bindings_jnijx7zv)
2023-01-11T21:26:36.7925359Z 
2023-01-11T21:26:38.9053638Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:38.9935650Z Ignoring disabled issues:  []
2023-01-11T21:26:39.0078985Z Running test_python_dispatch ... [2023-01-11 21:26:39.007592]
2023-01-11T21:26:39.0081031Z Executing ['/opt/conda/bin/python', '-bb', 'test_python_dispatch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:39.007874]
2023-01-11T21:26:41.1319238Z 
2023-01-11T21:26:41.1320068Z Expand the folded group to see the log file of test_python_dispatch
2023-01-11T21:26:41.1321171Z ##[group]PRINTING LOG FILE of test_python_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_python_dispatch_hr9qzlvq)
2023-01-11T21:26:41.1321491Z 
2023-01-11T21:26:41.1321593Z Running tests...
2023-01-11T21:26:41.1322146Z ----------------------------------------------------------------------
2023-01-11T21:26:41.1322836Z Test results will be stored in test-reports/python-unittest/test_python_dispatch
2023-01-11T21:26:41.1323393Z   test_all_same_mode (__main__.TestPythonDispatch) ... ok (0.235s)
2023-01-11T21:26:41.1323898Z   test_autograd_in_attr (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1324417Z   test_basic (__main__.TestPythonDispatch) ... ok (0.006s)
2023-01-11T21:26:41.1324919Z   test_capture_logs_with_torch_dispatch_mode (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1325426Z   test_construct_int_tensor (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1325855Z   test_custom_autograd (__main__.TestPythonDispatch) ... ok (0.004s)
2023-01-11T21:26:41.1326286Z   test_deepcopy_non_wrapper_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1326764Z   test_deepcopy_wrapper_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1327442Z   test_deepcopy_wrapper_subclass_with_clone_returning_different_type (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1328007Z   test_detach_appears_twice_when_called_once (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1328594Z   test_device_slowpath (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1329272Z   test_dim_slowpath (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1329981Z   test_dispatch_super_call (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1330707Z   test_dispatch_super_call_list_arg (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1331493Z   test_dispatch_super_dont_autograd (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1332277Z   test_error_using_class_method_on_mode (__main__.TestPythonDispatch) ... ok (0.005s)
2023-01-11T21:26:41.1333035Z   test_exception_handling (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1333723Z   test_fancy_strides (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1334378Z   test_format (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1335096Z   test_get_cur_mode (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1335772Z   test_get_mode_stack (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1336495Z   test_index_put_where_only_index_is_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1337565Z   test_invalid_ret (__main__.TestPythonDispatch) ... /var/lib/jenkins/workspace/test/test_python_dispatch.py:447: DeprecationWarning: Please use assertRaisesRegex instead.
2023-01-11T21:26:41.1338462Z   self.assertRaisesRegexp(
2023-01-11T21:26:41.1339125Z ok (0.001s)
2023-01-11T21:26:41.1339663Z   test_is_contiguous_slow_path (__main__.TestPythonDispatch) ... ok (0.003s)
2023-01-11T21:26:41.1340361Z   test_kwarg_only (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1341104Z   test_kwarg_only_and_positional_default (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1341808Z   test_layout_slow_path (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1342677Z   test_like (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1343291Z   test_list_ret (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1343988Z   test_make_subclass_with_modes (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1344744Z   test_make_wrapper_subclass_noalloc (__main__.TestPythonDispatch) ... ok (0.000s)
2023-01-11T21:26:41.1345574Z   test_make_wrapper_subclass_propagates_metadata (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1346467Z   test_maybe_tuple_bug (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1347173Z   test_mode_with_make_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1347893Z   test_multiple_ops_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1348671Z   test_nested_push_logging_tensor_mode (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1349383Z   test_nesting_same_mode (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1350038Z   test_new_ones (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1350704Z   test_none_wrapping (__main__.TestPythonDispatch) ... ok (0.008s)
2023-01-11T21:26:41.1351500Z   test_notimplemented_mode (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1352200Z   test_optional_tensor_list (__main__.TestPythonDispatch) ... woof
2023-01-11T21:26:41.1352738Z ok (0.002s)
2023-01-11T21:26:41.1353238Z   test_out (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1353889Z   test_produce_real_type (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1354518Z   test_set_data (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1355208Z   test_shallow_copy_and_detach (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1355880Z   test_sizes_slow_path (__main__.TestPythonDispatch) ... ok (0.003s)
2023-01-11T21:26:41.1356588Z   test_standard_is_not_subclass (__main__.TestPythonDispatch) ... ok (0.000s)
2023-01-11T21:26:41.1358255Z   test_storage (__main__.TestPythonDispatch) ... /var/lib/jenkins/workspace/test/test_python_dispatch.py:469: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:26:41.1359832Z   self.assertRaises(RuntimeError, lambda: x.storage())
2023-01-11T21:26:41.1360341Z ok (0.002s)
2023-01-11T21:26:41.1361852Z   test_storage_can_be_converted_to_python_object (__main__.TestPythonDispatch) ... /var/lib/jenkins/workspace/test/test_python_dispatch.py:1197: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:26:41.1363322Z   s = torch.Storage()
2023-01-11T21:26:41.1363723Z ok (0.001s)
2023-01-11T21:26:41.1364266Z   test_strides_slow_path (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1364985Z   test_subclass_autograd_device_check (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1365697Z   test_subclass_creation (__main__.TestPythonDispatch) ... ok (0.005s)
2023-01-11T21:26:41.1366394Z   test_subclass_priority (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1367145Z   test_tolist_numpy_with_torch_dispatch_mode (__main__.TestPythonDispatch) ... ok (0.003s)
2023-01-11T21:26:41.1368075Z   test_torch_dispatch_mode_basic (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1368876Z   test_torch_dispatch_mode_respects_no_dispatch (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1369704Z   test_torch_dispatch_mode_subclass_priority (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1370504Z   test_torch_dispatch_mode_unrelated_tensors (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1371228Z   test_version (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1371937Z   test_with_mode_created_separately (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1372655Z   test_with_nested_modes (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:41.1373390Z   test_wrapper_subclass_serializes (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:41.1374098Z   test_basic (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T21:26:41.1374816Z   test_lstsq (__main__.TestPythonDispatcher) ... ok (0.006s)
2023-01-11T21:26:41.1375537Z   test_alias_analysis (__main__.TestPythonRegistration) ... ok (0.006s)
2023-01-11T21:26:41.1376268Z   test_create_new_library (__main__.TestPythonRegistration) ... ok (0.002s)
2023-01-11T21:26:41.1377048Z   test_error_for_unsupported_ns_or_kind (__main__.TestPythonRegistration) ... ok (0.001s)
2023-01-11T21:26:41.1377822Z   test_error_if_fn_not_callable (__main__.TestPythonRegistration) ... ok (0.001s)
2023-01-11T21:26:41.1378629Z   test_extend_library_with_dispatch_key_arg (__main__.TestPythonRegistration) ... ok (0.002s)
2023-01-11T21:26:41.1380450Z   test_override_aten_ops_with_multiple_libraries (__main__.TestPythonRegistration) ... /opt/conda/lib/python3.10/site-packages/torch/library.py:126: UserWarning: Overriding a previously registered kernel for the same operator and the same dispatch key
2023-01-11T21:26:41.1381821Z   operator: aten::mul.Tensor(Tensor self, Tensor other) -> Tensor
2023-01-11T21:26:41.1382761Z     registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6
2023-01-11T21:26:41.1383387Z   dispatch key: ZeroTensor
2023-01-11T21:26:41.1384153Z   previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1070
2023-01-11T21:26:41.1385239Z        new kernel: registered at /dev/null:549 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:159.)
2023-01-11T21:26:41.1386232Z   self.m.impl(name, dispatch_key if dispatch_key != "" else "CompositeImplicitAutograd", fn)
2023-01-11T21:26:41.1386852Z ok (0.004s)
2023-01-11T21:26:41.1387407Z   test_override_cpu_sum (__main__.TestPythonRegistration) ... ok (0.001s)
2023-01-11T21:26:41.1388181Z   test_override_cuda_with_jiterator (__main__.TestPythonRegistration) ... ok (0.002s)
2023-01-11T21:26:41.1388626Z 
2023-01-11T21:26:41.1389123Z ----------------------------------------------------------------------
2023-01-11T21:26:41.1389707Z Ran 72 tests in 0.365s
2023-01-11T21:26:41.1389975Z 
2023-01-11T21:26:41.1390101Z OK
2023-01-11T21:26:41.1390320Z 
2023-01-11T21:26:41.1390517Z Generating XML reports...
2023-01-11T21:26:41.1391624Z Generated XML report: test-reports/python-unittest/test_python_dispatch/TEST-TestPythonDispatch-20230111212640.xml
2023-01-11T21:26:41.1393001Z Generated XML report: test-reports/python-unittest/test_python_dispatch/TEST-TestPythonDispatcher-20230111212640.xml
2023-01-11T21:26:41.1394422Z Generated XML report: test-reports/python-unittest/test_python_dispatch/TEST-TestPythonRegistration-20230111212640.xml
2023-01-11T21:26:41.1395068Z 
2023-01-11T21:26:41.1395647Z ##[endgroup]
2023-01-11T21:26:41.1396610Z FINISHED PRINTING LOG FILE of test_python_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_python_dispatch_hr9qzlvq)
2023-01-11T21:26:41.1397165Z 
2023-01-11T21:26:43.0285581Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:43.0932533Z Ignoring disabled issues:  []
2023-01-11T21:26:43.1074103Z Running test_scatter_gather_ops ... [2023-01-11 21:26:43.107077]
2023-01-11T21:26:43.1075474Z Executing ['/opt/conda/bin/python', '-bb', 'test_scatter_gather_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:43.107336]
2023-01-11T21:26:45.0588315Z 
2023-01-11T21:26:45.0588913Z Expand the folded group to see the log file of test_scatter_gather_ops
2023-01-11T21:26:45.0589883Z ##[group]PRINTING LOG FILE of test_scatter_gather_ops (/var/lib/jenkins/workspace/test/test-reports/test_scatter_gather_ops_3lbxs6ip)
2023-01-11T21:26:45.0590123Z 
2023-01-11T21:26:45.0590199Z Running tests...
2023-01-11T21:26:45.0590600Z ----------------------------------------------------------------------
2023-01-11T21:26:45.0590771Z 
2023-01-11T21:26:45.0591019Z ----------------------------------------------------------------------
2023-01-11T21:26:45.0591243Z Ran 0 tests in 0.000s
2023-01-11T21:26:45.0591361Z 
2023-01-11T21:26:45.0591422Z OK
2023-01-11T21:26:45.0591534Z 
2023-01-11T21:26:45.0591824Z Generating XML reports...
2023-01-11T21:26:45.0592175Z Test results will be stored in test-reports/python-unittest/test_scatter_gather_ops
2023-01-11T21:26:45.0592352Z 
2023-01-11T21:26:45.0592578Z ##[endgroup]
2023-01-11T21:26:45.0592993Z FINISHED PRINTING LOG FILE of test_scatter_gather_ops (/var/lib/jenkins/workspace/test/test-reports/test_scatter_gather_ops_3lbxs6ip)
2023-01-11T21:26:45.0593221Z 
2023-01-11T21:26:46.9903727Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:47.0754566Z Ignoring disabled issues:  []
2023-01-11T21:26:47.0894884Z Running test_sort_and_select ... [2023-01-11 21:26:47.089149]
2023-01-11T21:26:47.0896020Z Executing ['/opt/conda/bin/python', '-bb', 'test_sort_and_select.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:47.089401]
2023-01-11T21:26:49.0010072Z 
2023-01-11T21:26:49.0010638Z Expand the folded group to see the log file of test_sort_and_select
2023-01-11T21:26:49.0011996Z ##[group]PRINTING LOG FILE of test_sort_and_select (/var/lib/jenkins/workspace/test/test-reports/test_sort_and_select_r6czj8ih)
2023-01-11T21:26:49.0012516Z 
2023-01-11T21:26:49.0012654Z Running tests...
2023-01-11T21:26:49.0013341Z ----------------------------------------------------------------------
2023-01-11T21:26:49.0013592Z 
2023-01-11T21:26:49.0013930Z ----------------------------------------------------------------------
2023-01-11T21:26:49.0014305Z Ran 0 tests in 0.000s
2023-01-11T21:26:49.0014482Z 
2023-01-11T21:26:49.0014571Z OK
2023-01-11T21:26:49.0014712Z 
2023-01-11T21:26:49.0014833Z Generating XML reports...
2023-01-11T21:26:49.0015425Z Test results will be stored in test-reports/python-unittest/test_sort_and_select
2023-01-11T21:26:49.0015728Z 
2023-01-11T21:26:49.0016130Z ##[endgroup]
2023-01-11T21:26:49.0016780Z FINISHED PRINTING LOG FILE of test_sort_and_select (/var/lib/jenkins/workspace/test/test-reports/test_sort_and_select_r6czj8ih)
2023-01-11T21:26:49.0017161Z 
2023-01-11T21:26:50.8836181Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:50.9517895Z Ignoring disabled issues:  []
2023-01-11T21:26:50.9659788Z Running test_sparse ... [2023-01-11 21:26:50.965688]
2023-01-11T21:26:50.9662069Z Executing ['/opt/conda/bin/python', '-bb', 'test_sparse.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:50.965969]
2023-01-11T21:26:53.7607721Z 
2023-01-11T21:26:53.7608251Z Expand the folded group to see the log file of test_sparse
2023-01-11T21:26:53.7609413Z ##[group]PRINTING LOG FILE of test_sparse (/var/lib/jenkins/workspace/test/test-reports/test_sparse_5v068md3)
2023-01-11T21:26:53.7609814Z 
2023-01-11T21:26:53.7609940Z Running tests...
2023-01-11T21:26:53.7610573Z ----------------------------------------------------------------------
2023-01-11T21:26:53.7611219Z Test results will be stored in test-reports/python-unittest/test_sparse
2023-01-11T21:26:53.7611716Z   test_basic (__main__.TestSparseMeta) ... ok (0.004s)
2023-01-11T21:26:53.7612486Z   test_cuda_from_cpu (__main__.TestSparseOneOff) ... skip: CUDA not available (0.001s)
2023-01-11T21:26:53.7613043Z   test_cuda_sparse_cpu_dense_add (__main__.TestSparseOneOff) ... skip: CUDA not available (0.001s)
2023-01-11T21:26:53.7613351Z 
2023-01-11T21:26:53.7613702Z ----------------------------------------------------------------------
2023-01-11T21:26:53.7614084Z Ran 3 tests in 0.005s
2023-01-11T21:26:53.7614261Z 
2023-01-11T21:26:53.7614379Z OK (skipped=2)
2023-01-11T21:26:53.7614552Z 
2023-01-11T21:26:53.7614686Z Generating XML reports...
2023-01-11T21:26:53.7615366Z Generated XML report: test-reports/python-unittest/test_sparse/TEST-TestSparseMeta-20230111212653.xml
2023-01-11T21:26:53.7616258Z Generated XML report: test-reports/python-unittest/test_sparse/TEST-TestSparseOneOff-20230111212653.xml
2023-01-11T21:26:53.7616650Z 
2023-01-11T21:26:53.7617067Z ##[endgroup]
2023-01-11T21:26:53.7617715Z FINISHED PRINTING LOG FILE of test_sparse (/var/lib/jenkins/workspace/test/test-reports/test_sparse_5v068md3)
2023-01-11T21:26:53.7618266Z 
2023-01-11T21:26:55.7672448Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:55.8532978Z Ignoring disabled issues:  []
2023-01-11T21:26:55.8679553Z Running test_stateless ... [2023-01-11 21:26:55.867560]
2023-01-11T21:26:55.8680774Z Executing ['/opt/conda/bin/python', '-bb', 'test_stateless.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:55.867863]
2023-01-11T21:27:03.7789386Z 
2023-01-11T21:27:03.7790224Z Expand the folded group to see the log file of test_stateless
2023-01-11T21:27:03.7791486Z ##[group]PRINTING LOG FILE of test_stateless (/var/lib/jenkins/workspace/test/test-reports/test_stateless_s5o9c9bb)
2023-01-11T21:27:03.7791891Z 
2023-01-11T21:27:03.7792009Z Running tests...
2023-01-11T21:27:03.7792429Z ----------------------------------------------------------------------
2023-01-11T21:27:03.7792802Z Test results will be stored in test-reports/python-unittest/test_stateless
2023-01-11T21:27:03.7793173Z   test_runs_with_optimize_flag (__main__.TestPythonOptimizeMode) ... ok (4.227s)
2023-01-11T21:27:03.7793505Z   test_private_stateless_warns (__main__.TestStatelessDeprecation) ... ok (1.497s)
2023-01-11T21:27:03.7793839Z   test_circular_references (__main__.TestStatelessFunctionalAPI) ... ok (0.003s)
2023-01-11T21:27:03.7794157Z   test_functional_batch_norm (__main__.TestStatelessFunctionalAPI) ... ok (0.002s)
2023-01-11T21:27:03.7794483Z   test_functional_call (__main__.TestStatelessFunctionalAPI) ... ok (0.002s)
2023-01-11T21:27:03.7794955Z   test_functional_call_with_data_parallel (__main__.TestStatelessFunctionalAPI) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:27:03.7795337Z   test_functional_call_with_gradient (__main__.TestStatelessFunctionalAPI) ... ok (0.002s)
2023-01-11T21:27:03.7795665Z   test_functional_call_with_jit (__main__.TestStatelessFunctionalAPI) ... ok (0.056s)
2023-01-11T21:27:03.7796029Z   test_reparamertize_module_fail_reset_to_original (__main__.TestStatelessFunctionalAPI) ... ok (0.034s)
2023-01-11T21:27:03.7796432Z   test_reparametrized_module_change_parametrization_original (__main__.TestStatelessFunctionalAPI) ... ok (0.004s)
2023-01-11T21:27:03.7796789Z   test_setattr (__main__.TestStatelessFunctionalAPI) ... ok (0.001s)
2023-01-11T21:27:03.7797095Z   test_tied_weights_warns (__main__.TestStatelessFunctionalAPI) ... ok (0.002s)
2023-01-11T21:27:03.7797278Z 
2023-01-11T21:27:03.7797477Z ----------------------------------------------------------------------
2023-01-11T21:27:03.7797727Z Ran 12 tests in 5.831s
2023-01-11T21:27:03.7797841Z 
2023-01-11T21:27:03.7797899Z OK (skipped=1)
2023-01-11T21:27:03.7798004Z 
2023-01-11T21:27:03.7798090Z Generating XML reports...
2023-01-11T21:27:03.7798528Z Generated XML report: test-reports/python-unittest/test_stateless/TEST-TestPythonOptimizeMode-20230111212657.xml
2023-01-11T21:27:03.7799085Z Generated XML report: test-reports/python-unittest/test_stateless/TEST-TestStatelessDeprecation-20230111212657.xml
2023-01-11T21:27:03.7799853Z Generated XML report: test-reports/python-unittest/test_stateless/TEST-TestStatelessFunctionalAPI-20230111212657.xml
2023-01-11T21:27:03.7800109Z 
2023-01-11T21:27:03.7800357Z ##[endgroup]
2023-01-11T21:27:03.7800742Z FINISHED PRINTING LOG FILE of test_stateless (/var/lib/jenkins/workspace/test/test-reports/test_stateless_s5o9c9bb)
2023-01-11T21:27:03.7800945Z 
2023-01-11T21:27:05.8309177Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:05.9163369Z Ignoring disabled issues:  []
2023-01-11T21:27:05.9306786Z Running test_testing ... [2023-01-11 21:27:05.930371]
2023-01-11T21:27:05.9308593Z Executing ['/opt/conda/bin/python', '-bb', 'test_testing.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:05.930650]
2023-01-11T21:27:30.6993419Z 
2023-01-11T21:27:30.6993872Z Expand the folded group to see the log file of test_testing
2023-01-11T21:27:30.7025561Z ##[group]PRINTING LOG FILE of test_testing (/var/lib/jenkins/workspace/test/test-reports/test_testing_hmmsnhea)
2023-01-11T21:27:30.7026123Z 
2023-01-11T21:27:30.7026298Z Running tests...
2023-01-11T21:27:30.7027257Z ----------------------------------------------------------------------
2023-01-11T21:27:30.7028126Z Test results will be stored in test-reports/python-unittest/test_testing
2023-01-11T21:27:30.7028802Z   test_bool (__main__.TestAssertClose) ... ok (0.003s)
2023-01-11T21:27:30.7029535Z   test_default_tolerance_selection_mismatching_dtypes (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7030317Z   test_docstring_examples (__main__.TestAssertClose) ... ok (0.032s)
2023-01-11T21:27:30.7030966Z   test_matching (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7031688Z   test_matching_atol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7032334Z   test_matching_conjugate_bit (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7032994Z   test_matching_nan (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7033658Z   test_matching_nan_with_equal_nan (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7034364Z   test_matching_rtol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7034955Z   test_meta (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7035602Z   test_mismatching_dtype (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7036329Z   test_mismatching_dtype_no_check (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7037972Z   test_mismatching_layout (__main__.TestAssertClose) ... /var/lib/jenkins/workspace/test/test_testing.py:619: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:27:30.7039498Z   sparse_csr = strided.to_sparse_csr()
2023-01-11T21:27:30.7039970Z ok (0.002s)
2023-01-11T21:27:30.7040561Z   test_mismatching_layout_no_check (__main__.TestAssertClose) ... ok (0.005s)
2023-01-11T21:27:30.7041280Z   test_mismatching_shape (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7041946Z   test_mismatching_stride (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7042669Z   test_mismatching_stride_no_check (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7043397Z   test_mismatching_types (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7044079Z   test_mismatching_types_subclasses (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7044846Z   test_mismatching_types_type_equality (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7045578Z   test_mismatching_values (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7046265Z   test_mismatching_values_atol (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7046979Z   test_mismatching_values_rtol (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7047851Z   test_none (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7048463Z   test_none_mismatch (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7049076Z   test_numpy (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7049667Z   test_only_atol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7050270Z   test_only_rtol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7050866Z   test_scalar (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7051528Z   test_unexpected_error_compare (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:30.7052281Z   test_unexpected_error_originate (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7052965Z   test_unknown_layout (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:30.7053613Z   test_unknown_type (__main__.TestAssertClose) ... ok (0.011s)
2023-01-11T21:27:30.7054353Z   test_mapping_mismatching_keys (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:30.7055334Z   test_mapping_mismatching_values_msg (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:30.7056165Z   test_sequence_mismatching_len (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:30.7056996Z   test_sequence_mismatching_values_msg (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:30.7057791Z   test_abs_diff (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:30.7058538Z   test_abs_diff_scalar (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:30.7059299Z   test_atol (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:30.7060074Z   test_identifier_scalars (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:30.7060891Z   test_identifier_tensor_likes (__main__.TestAssertCloseErrorMessage) ... ok (0.002s)
2023-01-11T21:27:30.7061717Z   test_mismatched_elements (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:30.7062769Z   test_msg_callable (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:30.7063531Z   test_msg_str (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:30.7064258Z   test_not_close (__main__.TestAssertCloseErrorMessage) ... ok (0.007s)
2023-01-11T21:27:30.7065012Z   test_not_equal (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:30.7065736Z   test_rel_diff (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:30.7066489Z   test_rel_diff_scalar (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:30.7067212Z   test_rtol (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:30.7067967Z   test_zero_div_zero (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:30.7068750Z   test_matching_per_channel (__main__.TestAssertCloseQuantized) ... ok (0.002s)
2023-01-11T21:27:30.7069500Z   test_matching_per_tensor (__main__.TestAssertCloseQuantized) ... ok (0.002s)
2023-01-11T21:27:30.7070265Z   test_mismatching_is_quantized (__main__.TestAssertCloseQuantized) ... ok (0.001s)
2023-01-11T21:27:30.7071139Z   test_mismatching_qscheme (__main__.TestAssertCloseQuantized) ... ok (0.001s)
2023-01-11T21:27:30.7071876Z   test_matching (__main__.TestAssertCloseSparseBSC) ... ok (0.002s)
2023-01-11T21:27:30.7072626Z   test_mismatching_ccol_indices_msg (__main__.TestAssertCloseSparseBSC) ... ok (0.003s)
2023-01-11T21:27:30.7073448Z   test_mismatching_row_indices_msg (__main__.TestAssertCloseSparseBSC) ... ok (0.003s)
2023-01-11T21:27:30.7074222Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseBSC) ... ok (0.003s)
2023-01-11T21:27:30.7074901Z   test_matching (__main__.TestAssertCloseSparseBSR) ... ok (0.002s)
2023-01-11T21:27:30.7075618Z   test_mismatching_col_indices_msg (__main__.TestAssertCloseSparseBSR) ... ok (0.003s)
2023-01-11T21:27:30.7076389Z   test_mismatching_crow_indices_msg (__main__.TestAssertCloseSparseBSR) ... ok (0.003s)
2023-01-11T21:27:30.7077156Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseBSR) ... ok (0.003s)
2023-01-11T21:27:30.7078088Z   test_matching_coalesced (__main__.TestAssertCloseSparseCOO) ... ok (0.002s)
2023-01-11T21:27:30.7078871Z   test_matching_uncoalesced (__main__.TestAssertCloseSparseCOO) ... ok (0.001s)
2023-01-11T21:27:30.7079668Z   test_mismatching_indices_msg (__main__.TestAssertCloseSparseCOO) ... ok (0.003s)
2023-01-11T21:27:30.7080467Z   test_mismatching_nnz (__main__.TestAssertCloseSparseCOO) ... ok (0.002s)
2023-01-11T21:27:30.7081269Z   test_mismatching_sparse_dims (__main__.TestAssertCloseSparseCOO) ... ok (0.002s)
2023-01-11T21:27:30.7082076Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseCOO) ... ok (0.003s)
2023-01-11T21:27:30.7082825Z   test_matching (__main__.TestAssertCloseSparseCSC) ... ok (0.002s)
2023-01-11T21:27:30.7083584Z   test_mismatching_ccol_indices_msg (__main__.TestAssertCloseSparseCSC) ... ok (0.003s)
2023-01-11T21:27:30.7084413Z   test_mismatching_row_indices_msg (__main__.TestAssertCloseSparseCSC) ... ok (0.003s)
2023-01-11T21:27:30.7085373Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseCSC) ... ok (0.003s)
2023-01-11T21:27:30.7085810Z   test_hybrid_support (__main__.TestAssertCloseSparseCSR) ... expected failure (0.007s)
2023-01-11T21:27:30.7086256Z   test_matching (__main__.TestAssertCloseSparseCSR) ... ok (0.002s)
2023-01-11T21:27:30.7086718Z   test_mismatching_col_indices_msg (__main__.TestAssertCloseSparseCSR) ... ok (0.003s)
2023-01-11T21:27:30.7087209Z   test_mismatching_crow_indices_msg (__main__.TestAssertCloseSparseCSR) ... ok (0.003s)
2023-01-11T21:27:30.7087680Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseCSR) ... ok (0.003s)
2023-01-11T21:27:30.7088140Z   test_filtering_env_var (__main__.TestFrameworkUtils) ... ok (8.213s)
2023-01-11T21:27:30.7088540Z   test_circular_dependencies (__main__.TestImports)
2023-01-11T21:27:30.7089250Z Checks that all modules inside torch can be imported ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:30.7090011Z 2023-01-11 21:27:24,141 - torch.distributed.nn.jit.instantiator - INFO - Created a temporary directory at /tmp/tmpeo02lgi9
2023-01-11T21:27:30.7090790Z 2023-01-11 21:27:24,142 - torch.distributed.nn.jit.instantiator - INFO - Writing /tmp/tmpeo02lgi9/_remote_module_non_scriptable.py
2023-01-11T21:27:30.7091225Z ok (8.694s)
2023-01-11T21:27:30.7091578Z   test_no_mutate_global_logging_on_import_path_functorch (__main__.TestImports) ... ok (1.512s)
2023-01-11T21:27:30.7092049Z   test_no_mutate_global_logging_on_import_path_torch (__main__.TestImports) ... ok (1.507s)
2023-01-11T21:27:30.7092654Z   test_no_warning_on_import (__main__.TestImports) ... /var/lib/jenkins/workspace/test/test_testing.py:1836: DeprecationWarning: Please use assertEqual instead.
2023-01-11T21:27:30.7093134Z   self.assertEquals(out, "")
2023-01-11T21:27:30.7093373Z ok (1.528s)
2023-01-11T21:27:30.7093685Z   test_sample_input (__main__.TestOpInfos) ... ok (0.002s)
2023-01-11T21:27:30.7094066Z   test_sample_input_metadata (__main__.TestOpInfos) ... ok (0.001s)
2023-01-11T21:27:30.7094481Z   test_default_names (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7094961Z   test_modules_decorator_misuse_error (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7095451Z   test_multiple_handling_of_same_param_error (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7095898Z   test_name_fn (__main__.TestTestParametrization) ... ok (0.002s)
2023-01-11T21:27:30.7096330Z   test_ops_decorator_misuse_error (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7096797Z   test_subtest_expected_failure_x_1 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7097290Z   test_subtest_expected_failure_x_2 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:30.7097797Z   test_subtest_expected_failure_x_3 (__main__.TestTestParametrization) ... ok (0.000s)
2023-01-11T21:27:30.7098254Z   test_subtest_names (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7098882Z   test_two_things_subtest_expected_failure_x_1_y_4 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:30.7099422Z   test_two_things_subtest_expected_failure_x_1_y_5 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:30.7099985Z   test_two_things_subtest_expected_failure_x_1_y_6 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:30.7100512Z   test_two_things_subtest_expected_failure_x_2_y_4 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7101051Z   test_two_things_subtest_expected_failure_x_2_y_5 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7101564Z   test_two_things_subtest_expected_failure_x_2_y_6 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:30.7102089Z   test_two_things_subtest_expected_failure_x_3_y_4 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7102872Z   test_two_things_subtest_expected_failure_x_3_y_5 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:30.7103430Z   test_two_things_subtest_expected_failure_x_3_y_6 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:30.7103731Z 
2023-01-11T21:27:30.7104061Z ----------------------------------------------------------------------
2023-01-11T21:27:30.7104393Z Ran 103 tests in 21.677s
2023-01-11T21:27:30.7104558Z 
2023-01-11T21:27:30.7104683Z OK (expected failures=7)
2023-01-11T21:27:30.7104844Z 
2023-01-11T21:27:30.7104953Z Generating XML reports...
2023-01-11T21:27:30.7105562Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertClose-20230111212708.xml
2023-01-11T21:27:30.7106359Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseContainer-20230111212708.xml
2023-01-11T21:27:30.7107165Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseErrorMessage-20230111212708.xml
2023-01-11T21:27:30.7107968Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseQuantized-20230111212708.xml
2023-01-11T21:27:30.7108717Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseBSC-20230111212708.xml
2023-01-11T21:27:30.7109478Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseBSR-20230111212708.xml
2023-01-11T21:27:30.7110263Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseCOO-20230111212708.xml
2023-01-11T21:27:30.7111141Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseCSC-20230111212708.xml
2023-01-11T21:27:30.7111954Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseCSR-20230111212708.xml
2023-01-11T21:27:30.7112729Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestFrameworkUtils-20230111212708.xml
2023-01-11T21:27:30.7113426Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestImports-20230111212708.xml
2023-01-11T21:27:30.7114100Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestOpInfos-20230111212708.xml
2023-01-11T21:27:30.7114844Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestTestParametrization-20230111212708.xml
2023-01-11T21:27:30.7115196Z 
2023-01-11T21:27:30.7115623Z ##[endgroup]
2023-01-11T21:27:30.7116167Z FINISHED PRINTING LOG FILE of test_testing (/var/lib/jenkins/workspace/test/test-reports/test_testing_hmmsnhea)
2023-01-11T21:27:30.7116473Z 
2023-01-11T21:27:32.7437301Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:32.8280251Z Ignoring disabled issues:  []
2023-01-11T21:27:32.8423556Z Running test_transformers ... [2023-01-11 21:27:32.842040]
2023-01-11T21:27:32.8425523Z Executing ['/opt/conda/bin/python', '-bb', 'test_transformers.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:32.842338]
2023-01-11T21:27:41.9101071Z 
2023-01-11T21:27:41.9101620Z Expand the folded group to see the log file of test_transformers
2023-01-11T21:27:41.9102722Z ##[group]PRINTING LOG FILE of test_transformers (/var/lib/jenkins/workspace/test/test-reports/test_transformers_ohr3p3nk)
2023-01-11T21:27:41.9103069Z 
2023-01-11T21:27:41.9103379Z Running tests...
2023-01-11T21:27:41.9103945Z ----------------------------------------------------------------------
2023-01-11T21:27:41.9104685Z Test results will be stored in test-reports/python-unittest/test_transformers
2023-01-11T21:27:41.9105413Z   test_bias_is_none (__main__.TestTransformers) ... ok (0.003s)
2023-01-11T21:27:41.9106837Z   test_decoder_only_layer (__main__.TestTransformers) ... skip: Fairseq not found (0.005s)
2023-01-11T21:27:41.9107544Z   test_flash_autocast_fp32_bfloat16 (__main__.TestTransformers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:27:41.9108192Z   test_flash_autocast_fp32_float16 (__main__.TestTransformers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:27:41.9108709Z   test_flash_fail_fp32t (__main__.TestTransformers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:27:41.9109040Z   test_fused_sdp_choice_type_dense (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9109338Z   test_fused_sdp_choice_type_nested (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9109628Z   test_mask_check_fastpath (__main__.TestTransformers)
2023-01-11T21:27:41.9112979Z Test that fastpath is executed independently of the masks that are passed. ... /var/lib/jenkins/workspace/test/test_transformers.py:916: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:27:41.9114442Z   nested_tensor_return_value = torch.nested.nested_tensor([torch.ones((2, 2), dtype=torch.float)])
2023-01-11T21:27:41.9115059Z ok (0.011s)
2023-01-11T21:27:41.9115748Z   test_scaled_dot_product_attention_3D_input_dim_2D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.003s)
2023-01-11T21:27:41.9116741Z   test_scaled_dot_product_attention_3D_input_dim_2D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9117742Z   test_scaled_dot_product_attention_3D_input_dim_2D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9118711Z   test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9119716Z   test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9120728Z   test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9121751Z   test_scaled_dot_product_attention_3D_input_dim_3D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9122705Z   test_scaled_dot_product_attention_3D_input_dim_3D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9123662Z   test_scaled_dot_product_attention_3D_input_dim_3D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9143516Z   test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:41.9144068Z   test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9144584Z   test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9145222Z   test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.020s)
2023-01-11T21:27:41.9146042Z   test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.015s)
2023-01-11T21:27:41.9146433Z   test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.017s)
2023-01-11T21:27:41.9146830Z   test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9147232Z   test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9147633Z   test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9148027Z   test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:41.9148694Z   test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9149435Z   test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9150031Z   test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9150765Z   test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9151386Z   test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9151797Z   test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:41.9152223Z   test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9152759Z   test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:41.9153143Z   test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.014s)
2023-01-11T21:27:41.9153569Z   test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.016s)
2023-01-11T21:27:41.9154218Z   test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.016s)
2023-01-11T21:27:41.9154990Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_dense_fused_kernel_flash (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:41.9155959Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_dense_fused_kernel_mem_efficient (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:41.9156493Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_nested_fused_kernel_flash (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:41.9157027Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_nested_fused_kernel_mem_efficient (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:41.9157554Z   test_scaled_dot_product_attention_fused_kernels_packed_type_dense_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9158063Z   test_scaled_dot_product_attention_fused_kernels_packed_type_dense_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9158654Z   test_scaled_dot_product_attention_fused_kernels_packed_type_nested_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9159612Z   test_scaled_dot_product_attention_fused_kernels_packed_type_nested_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9160229Z   test_scaled_dot_product_attention_fused_kernels_type_dense_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9161086Z   test_scaled_dot_product_attention_fused_kernels_type_dense_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9161575Z   test_scaled_dot_product_attention_fused_kernels_type_nested_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9162109Z   test_scaled_dot_product_attention_fused_kernels_type_nested_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9162573Z   test_sdp_fused_grad_against_math_contiguous_inputs_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9163017Z   test_sdp_fused_grad_against_math_contiguous_inputs_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9163453Z   test_sdp_math_gradcheck_contiguous_inputs_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9163882Z   test_sdp_math_gradcheck_contiguous_inputs_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:41.9164247Z   test_sdp_runtime_dispatch (__main__.TestTransformers) ... skip: CUDA unavailable (0.002s)
2023-01-11T21:27:41.9164894Z   test_self_attn_TxT_attn_mask (__main__.TestTransformers) ... skip: 4D mask not supported yet - activate when 4D mask supported (0.001s)
2023-01-11T21:27:41.9165329Z   test_train_with_is_causal_device_cpu (__main__.TestTransformers) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:27:41.9165775Z   test_train_with_pad_and_catch_error_device_cpu (__main__.TestTransformers) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:27:41.9166213Z   test_transformerencoder_batch_first_False_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.031s)
2023-01-11T21:27:41.9166658Z   test_transformerencoder_batch_first_False_training_False_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.030s)
2023-01-11T21:27:41.9167093Z   test_transformerencoder_batch_first_False_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.033s)
2023-01-11T21:27:41.9167530Z   test_transformerencoder_batch_first_False_training_True_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.033s)
2023-01-11T21:27:41.9167949Z   test_transformerencoder_batch_first_True_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.027s)
2023-01-11T21:27:41.9168385Z   test_transformerencoder_batch_first_True_training_False_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.027s)
2023-01-11T21:27:41.9168812Z   test_transformerencoder_batch_first_True_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.033s)
2023-01-11T21:27:41.9169239Z   test_transformerencoder_batch_first_True_training_True_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.033s)
2023-01-11T21:27:41.9169679Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_False_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:41.9170374Z Test TransformerEncoder fastpath output matches slowpath output ... /var/lib/jenkins/workspace/test/test_transformers.py:215: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:27:41.9170989Z   torch.tensor(pair[0], device=device, dtype=torch.get_default_dtype()),  # float input
2023-01-11T21:27:41.9171237Z ok (0.565s)
2023-01-11T21:27:41.9171569Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_False_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:41.9171961Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.807s)
2023-01-11T21:27:41.9172374Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_True_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:41.9173129Z Test TransformerEncoder fastpath output matches slowpath output ... /opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
2023-01-11T21:27:41.9173697Z   warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
2023-01-11T21:27:41.9173940Z ok (0.543s)
2023-01-11T21:27:41.9174268Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_True_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:41.9174669Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.835s)
2023-01-11T21:27:41.9175074Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_False_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:41.9175452Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.600s)
2023-01-11T21:27:41.9175858Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_False_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:41.9176256Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.932s)
2023-01-11T21:27:41.9176639Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_True_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:41.9177028Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.532s)
2023-01-11T21:27:41.9177431Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_True_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:41.9177822Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.898s)
2023-01-11T21:27:41.9178199Z   test_transformerencoder_square_input_with_no_grad_False_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:41.9178610Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.007s)
2023-01-11T21:27:41.9179026Z   test_transformerencoder_square_input_with_no_grad_False_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:41.9179435Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.006s)
2023-01-11T21:27:41.9179832Z   test_transformerencoder_square_input_with_no_grad_True_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:41.9180238Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.005s)
2023-01-11T21:27:41.9180645Z   test_transformerencoder_square_input_with_no_grad_True_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:41.9181049Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.005s)
2023-01-11T21:27:41.9181447Z   test_transformerencoderlayer_src_mask_device_cpu_nhead_1 (__main__.TestTransformers) ... ok (0.003s)
2023-01-11T21:27:41.9181824Z   test_transformerencoderlayer_src_mask_device_cpu_nhead_4 (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9182193Z   test_transformerencoderlayer_src_mask_device_cpu_nhead_8 (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:41.9182723Z   test_unaligned_tensors (__main__.TestTransformers) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:27:41.9182897Z 
2023-01-11T21:27:41.9183120Z ----------------------------------------------------------------------
2023-01-11T21:27:41.9183367Z Ran 82 tests in 6.285s
2023-01-11T21:27:41.9183486Z 
2023-01-11T21:27:41.9183562Z OK (skipped=25)
2023-01-11T21:27:41.9183674Z 
2023-01-11T21:27:41.9183746Z Generating XML reports...
2023-01-11T21:27:41.9184169Z Generated XML report: test-reports/python-unittest/test_transformers/TEST-TestTransformers-20230111212735.xml
2023-01-11T21:27:41.9184403Z 
2023-01-11T21:27:41.9184765Z ##[endgroup]
2023-01-11T21:27:41.9185233Z FINISHED PRINTING LOG FILE of test_transformers (/var/lib/jenkins/workspace/test/test-reports/test_transformers_ohr3p3nk)
2023-01-11T21:27:41.9185461Z 
2023-01-11T21:27:43.9281997Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:44.0159733Z Ignoring disabled issues:  []
2023-01-11T21:27:44.0301828Z Running test_utils ... [2023-01-11 21:27:44.029901]
2023-01-11T21:27:44.0304332Z Executing ['/opt/conda/bin/python', '-bb', 'test_utils.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:44.030161]
2023-01-11T21:27:48.6727735Z 
2023-01-11T21:27:48.6728269Z Expand the folded group to see the log file of test_utils
2023-01-11T21:27:48.6729399Z ##[group]PRINTING LOG FILE of test_utils (/var/lib/jenkins/workspace/test/test-reports/test_utils_3ng1pffq)
2023-01-11T21:27:48.6730255Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:48.6730534Z 
2023-01-11T21:27:48.6730661Z Running tests...
2023-01-11T21:27:48.6731249Z ----------------------------------------------------------------------
2023-01-11T21:27:48.6731833Z Test results will be stored in test-reports/python-unittest/test_utils
2023-01-11T21:27:48.6732244Z   test_assert_scriptable (__main__.TestAssert) ... ok (0.243s)
2023-01-11T21:27:48.6732627Z   test_assert_true (__main__.TestAssert) ... ok (0.001s)
2023-01-11T21:27:48.6733470Z   test_bottleneck_cpu_only (__main__.TestBottleneck) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/68433 for allplatform(s) . If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:27:48.6734050Z   test_bottleneck_cuda (__main__.TestBottleneck) ... skip: No CUDA (0.000s)
2023-01-11T21:27:48.6734321Z   test_checkpoint (__main__.TestCheckpoint) ... ok (0.011s)
2023-01-11T21:27:48.6734603Z   test_checkpoint_module_list (__main__.TestCheckpoint) ... ok (0.010s)
2023-01-11T21:27:48.6735187Z   test_checkpoint_no_tensors (__main__.TestCheckpoint) ... /opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
2023-01-11T21:27:48.6735634Z   warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
2023-01-11T21:27:48.6735861Z ok (0.003s)
2023-01-11T21:27:48.6736088Z   test_checkpoint_non_tensor (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:48.6736396Z   test_checkpoint_non_tensor_inputs_outputs (__main__.TestCheckpoint) ... ok (0.003s)
2023-01-11T21:27:48.6736741Z   test_checkpoint_not_preserve_rng_state_and_without_reentrant (__main__.TestCheckpoint) ... skip: No CUDA (0.000s)
2023-01-11T21:27:48.6737078Z   test_checkpoint_partial_grad (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:48.6737366Z   test_checkpoint_rng_cpu (__main__.TestCheckpoint) ... ok (0.011s)
2023-01-11T21:27:48.6737878Z   test_checkpoint_rng_cuda (__main__.TestCheckpoint) ... skip: No CUDA (0.001s)
2023-01-11T21:27:48.6738192Z   test_checkpoint_sequential_deprecated_multiple_args (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:48.6738527Z   test_checkpoint_sequential_deprecated_no_args (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:48.6738835Z   test_checkpoint_trigger (__main__.TestCheckpoint) ... ok (0.004s)
2023-01-11T21:27:48.6739099Z   test_checkpoint_valid (__main__.TestCheckpoint) ... ok (0.003s)
2023-01-11T21:27:48.6739428Z   test_checkpointing_without_reentrant_early_free (__main__.TestCheckpoint) ... skip: Test requires CUDA (0.001s)
2023-01-11T21:27:48.6740266Z   test_smoke (__main__.TestCollectEnv) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/77345 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:27:48.6740900Z   test_cc_compiler_is_ok (__main__.TestCppExtensionUtils) ... ok (0.010s)
2023-01-11T21:27:48.6741208Z   test_cpp_compiler_is_ok (__main__.TestCppExtensionUtils) ... ok (0.009s)
2023-01-11T21:27:48.6742014Z   test_multi_drop (__main__.TestDataLoaderUtils) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82865 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:27:48.6743058Z   test_multi_keep (__main__.TestDataLoaderUtils) ... skip: FIXME: Intermittent CUDA out-of-memory error on Windows and time-out under ASAN (0.000s)
2023-01-11T21:27:48.6743429Z   test_random_seed (__main__.TestDataLoaderUtils) ... ok (0.133s)
2023-01-11T21:27:48.6743709Z   test_single_drop (__main__.TestDataLoaderUtils) ... ok (0.002s)
2023-01-11T21:27:48.6743973Z   test_single_keep (__main__.TestDataLoaderUtils) ... ok (0.001s)
2023-01-11T21:27:48.6744267Z   test_external_module_register (__main__.TestExtensionUtils) ... ok (0.002s)
2023-01-11T21:27:48.6744549Z   test_import_hipify (__main__.TestHipify) ... ok (0.000s)
2023-01-11T21:27:48.6744806Z   test_check_onnx_broadcast (__main__.TestONNXUtils) ... ok (0.001s)
2023-01-11T21:27:48.6745092Z   test_prepare_onnx_paddings (__main__.TestONNXUtils) ... ok (0.001s)
2023-01-11T21:27:48.6745384Z   test_load_standalone (__main__.TestStandaloneCPPJIT) ... ok (2.070s)
2023-01-11T21:27:48.6745554Z 
2023-01-11T21:27:48.6745756Z ----------------------------------------------------------------------
2023-01-11T21:27:48.6745981Z Ran 31 tests in 2.528s
2023-01-11T21:27:48.6746093Z 
2023-01-11T21:27:48.6746167Z OK (skipped=8)
2023-01-11T21:27:48.6746273Z 
2023-01-11T21:27:48.6746357Z Generating XML reports...
2023-01-11T21:27:48.6746729Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestAssert-20230111212745.xml
2023-01-11T21:27:48.6747224Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestCheckpoint-20230111212745.xml
2023-01-11T21:27:48.6747742Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestCppExtensionUtils-20230111212745.xml
2023-01-11T21:27:48.6748266Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestDataLoaderUtils-20230111212745.xml
2023-01-11T21:27:48.6748756Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestExtensionUtils-20230111212745.xml
2023-01-11T21:27:48.6749237Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestHipify-20230111212745.xml
2023-01-11T21:27:48.6749711Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestONNXUtils-20230111212745.xml
2023-01-11T21:27:48.6750213Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestStandaloneCPPJIT-20230111212745.xml
2023-01-11T21:27:48.6750696Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestBottleneck-20230111212745.xml
2023-01-11T21:27:48.6751310Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestCollectEnv-20230111212745.xml
2023-01-11T21:27:48.6751527Z 
2023-01-11T21:27:48.6751829Z ##[endgroup]
2023-01-11T21:27:48.6752186Z FINISHED PRINTING LOG FILE of test_utils (/var/lib/jenkins/workspace/test/test-reports/test_utils_3ng1pffq)
2023-01-11T21:27:48.6752392Z 
2023-01-11T21:27:50.5671561Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:50.6343857Z Ignoring disabled issues:  []
2023-01-11T21:27:50.6484852Z Running backends/xeon/test_launch ... [2023-01-11 21:27:50.648244]
2023-01-11T21:27:50.6487589Z Executing ['/opt/conda/bin/python', '-bb', 'backends/xeon/test_launch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:50.648486]
2023-01-11T21:27:54.0805991Z 
2023-01-11T21:27:54.0806562Z Expand the folded group to see the log file of backends/xeon/test_launch
2023-01-11T21:27:54.0808050Z ##[group]PRINTING LOG FILE of backends/xeon/test_launch (/var/lib/jenkins/workspace/test/test-reports/backends-xeon-test_launch_vgmgy27h)
2023-01-11T21:27:54.0808526Z 
2023-01-11T21:27:54.0808654Z Running tests...
2023-01-11T21:27:54.0809319Z ----------------------------------------------------------------------
2023-01-11T21:27:54.0810054Z Test results will be stored in test-reports/python-unittest/backends.xeon.test_launch
2023-01-11T21:27:54.0811570Z   test_cpu_info (__main__.TestTorchrun) ... 2023-01-11 21:27:52,117 - torch.backends.xeon.run_cpu - WARNING - Numa Aware: cores:[2, 3, 4, 5] on different NUMA nodes:[0, 1]. To avoid this behavior, please use --ncores_per_instance knob to make sure number of cores is divisible by --ncores_per_instance. Alternatively, please use --skip_cross_node_cores knob.
2023-01-11T21:27:54.0812418Z ok (0.137s)
2023-01-11T21:27:54.0812794Z   test_multi_threads (__main__.TestTorchrun) ... ok (1.699s)
2023-01-11T21:27:54.0813015Z 
2023-01-11T21:27:54.0813226Z ----------------------------------------------------------------------
2023-01-11T21:27:54.0813585Z Ran 2 tests in 1.836s
2023-01-11T21:27:54.0813712Z 
2023-01-11T21:27:54.0813775Z OK
2023-01-11T21:27:54.0813868Z 
2023-01-11T21:27:54.0813953Z Generating XML reports...
2023-01-11T21:27:54.0814391Z Generated XML report: test-reports/python-unittest/backends.xeon.test_launch/TEST-TestTorchrun-20230111212751.xml
2023-01-11T21:27:54.0814623Z 
2023-01-11T21:27:54.0814874Z ##[endgroup]
2023-01-11T21:27:54.0815347Z FINISHED PRINTING LOG FILE of backends/xeon/test_launch (/var/lib/jenkins/workspace/test/test-reports/backends-xeon-test_launch_vgmgy27h)
2023-01-11T21:27:54.0815590Z 
2023-01-11T21:27:55.9220311Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:55.9862309Z Ignoring disabled issues:  []
2023-01-11T21:27:56.0003333Z Running benchmark_utils/test_benchmark_utils ... [2023-01-11 21:27:56.000005]
2023-01-11T21:27:56.0004797Z Executing ['/opt/conda/bin/python', '-bb', 'benchmark_utils/test_benchmark_utils.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:56.000251]
2023-01-11T21:27:58.5770561Z 
2023-01-11T21:27:58.5771484Z Expand the folded group to see the log file of benchmark_utils/test_benchmark_utils
2023-01-11T21:27:58.5773138Z ##[group]PRINTING LOG FILE of benchmark_utils/test_benchmark_utils (/var/lib/jenkins/workspace/test/test-reports/benchmark_utils-test_benchmark_utils__beoo97f)
2023-01-11T21:27:58.5774294Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:58.5774684Z 
2023-01-11T21:27:58.5774843Z Running tests...
2023-01-11T21:27:58.5775552Z ----------------------------------------------------------------------
2023-01-11T21:27:58.5776551Z Test results will be stored in test-reports/python-unittest/benchmark_utils.test_benchmark_utils
2023-01-11T21:27:58.5777361Z   test_adaptive_timer (__main__.TestBenchmarkUtils) ... ok (0.312s)
2023-01-11T21:27:58.5778237Z   test_collect_callgrind (__main__.TestBenchmarkUtils) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:27:58.5779474Z   test_collect_cpp_callgrind (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.001s)
2023-01-11T21:27:58.5780197Z   test_compare (__main__.TestBenchmarkUtils) ... ok (0.108s)
2023-01-11T21:27:58.5780907Z   test_cpp_timer (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.000s)
2023-01-11T21:27:58.5781597Z   test_fuzzer (__main__.TestBenchmarkUtils) ... ok (0.002s)
2023-01-11T21:27:58.5782281Z   test_manipulate_callgrind_stats (__main__.TestBenchmarkUtils) ... ok (0.037s)
2023-01-11T21:27:58.5783164Z   test_timer (__main__.TestBenchmarkUtils) ... ok (0.029s)
2023-01-11T21:27:58.5783911Z   test_timer_tiny_fast_snippet (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.000s)
2023-01-11T21:27:58.5784378Z 
2023-01-11T21:27:58.5784860Z ----------------------------------------------------------------------
2023-01-11T21:27:58.5785403Z Ran 9 tests in 0.492s
2023-01-11T21:27:58.5785652Z 
2023-01-11T21:27:58.5785823Z OK (skipped=4)
2023-01-11T21:27:58.5786244Z 
2023-01-11T21:27:58.5786443Z Generating XML reports...
2023-01-11T21:27:58.5787566Z Generated XML report: test-reports/python-unittest/benchmark_utils.test_benchmark_utils/TEST-TestBenchmarkUtils-20230111212757.xml
2023-01-11T21:27:58.5788214Z 
2023-01-11T21:27:58.5788742Z ##[endgroup]
2023-01-11T21:27:58.5789842Z FINISHED PRINTING LOG FILE of benchmark_utils/test_benchmark_utils (/var/lib/jenkins/workspace/test/test-reports/benchmark_utils-test_benchmark_utils__beoo97f)
2023-01-11T21:27:58.5790500Z 
2023-01-11T21:28:00.4830597Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:00.5668814Z Ignoring disabled issues:  []
2023-01-11T21:28:00.5811606Z Running dynamo/test_aot_autograd ... [2023-01-11 21:28:00.580867]
2023-01-11T21:28:00.5813323Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:00.581117]
2023-01-11T21:28:06.5918470Z 
2023-01-11T21:28:06.5919088Z Expand the folded group to see the log file of dynamo/test_aot_autograd
2023-01-11T21:28:06.5920157Z ##[group]PRINTING LOG FILE of dynamo/test_aot_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_autograd_0yukwqll)
2023-01-11T21:28:06.5920543Z 
2023-01-11T21:28:06.5920661Z Running tests...
2023-01-11T21:28:06.5921300Z ----------------------------------------------------------------------
2023-01-11T21:28:06.5921954Z Test results will be stored in test-reports/python-unittest/dynamo.test_aot_autograd
2023-01-11T21:28:06.5922958Z   test_LSTM (__main__.AotAutogradFallbackTests) ... [2023-01-11 21:28:05,125] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of LSTM
2023-01-11T21:28:06.5923525Z ok (3.184s)
2023-01-11T21:28:06.5924177Z   test_arg_dupe_via_dynamo_recompiles (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:06.5924928Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:06.5925424Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:06.5925942Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:06.5926513Z aot_autograd [('total', 3), ('ok', 3)]
2023-01-11T21:28:06.5926840Z ok (0.094s)
2023-01-11T21:28:06.5927490Z   test_arg_dupe_via_dynamo_recompiles_many_args (__main__.AotAutogradFallbackTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:06.5928220Z stats [('calls_captured', 28), ('fusions_possible', 24), ('unique_graphs', 4)]
2023-01-11T21:28:06.5928691Z aot_autograd [('total', 4), ('ok', 4)]
2023-01-11T21:28:06.5929026Z ok (0.169s)
2023-01-11T21:28:06.5929946Z   test_call_fn_with_non_const_inputs_aot_safe (__main__.AotAutogradFallbackTests) ... inline_call [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1)]
2023-01-11T21:28:06.5930760Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:06.5931535Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:06.5931903Z unimplemented []
2023-01-11T21:28:06.5932952Z graph_break [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1), ('call_function args: ListVariable() UserDefinedObjectVariable(LeafSpec) ', 1)]
2023-01-11T21:28:06.5933544Z ok (0.045s)
2023-01-11T21:28:06.5934520Z   test_call_fn_with_non_const_inputs_aot_unsafe (__main__.AotAutogradFallbackTests) ... inline_call [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1)]
2023-01-11T21:28:06.5935449Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:06.5935912Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:06.5936184Z unimplemented []
2023-01-11T21:28:06.5937094Z graph_break [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1), ('call_function args: ListVariable() UserDefinedObjectVariable(LeafSpec) ', 1)]
2023-01-11T21:28:06.5937906Z ok (0.021s)
2023-01-11T21:28:06.5938584Z   test_call_fn_with_non_const_inputs_aot_unsafe_control_flow (__main__.AotAutogradFallbackTests) ... frames [('total', 8), ('ok', 8)]
2023-01-11T21:28:06.5939245Z inline_call [('generic_jump TensorVariable()', 2)]
2023-01-11T21:28:06.5939634Z unimplemented []
2023-01-11T21:28:06.5940076Z graph_break [('generic_jump TensorVariable()', 2)]
2023-01-11T21:28:06.5940664Z stats [('calls_captured', 14), ('unique_graphs', 8), ('fusions_possible', 6)]
2023-01-11T21:28:06.5941046Z ok (0.045s)
2023-01-11T21:28:06.5941652Z   test_mutation (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:06.5942591Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:06.5942977Z ok (0.006s)
2023-01-11T21:28:06.5943576Z   test_mutation1 (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:06.5944200Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:06.5944580Z ok (0.264s)
2023-01-11T21:28:06.5945186Z   test_negative_testing (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:06.5945816Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:06.5946188Z ok (0.006s)
2023-01-11T21:28:06.5946778Z   test_negative_testing_mutation (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:06.5947500Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:06.5947926Z ok (0.271s)
2023-01-11T21:28:06.5948608Z   test_requires_grad_fake_via_dynamo_recompiles (__main__.AotAutogradFallbackTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:06.5949320Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:06.5949797Z aot_autograd [('total', 3), ('ok', 3)]
2023-01-11T21:28:06.5950151Z ok (0.043s)
2023-01-11T21:28:06.5950354Z 
2023-01-11T21:28:06.5950725Z ----------------------------------------------------------------------
2023-01-11T21:28:06.5951193Z Ran 11 tests in 4.147s
2023-01-11T21:28:06.5951387Z 
2023-01-11T21:28:06.5951490Z OK
2023-01-11T21:28:06.5951638Z 
2023-01-11T21:28:06.5969149Z Generating XML reports...
2023-01-11T21:28:06.5970176Z Generated XML report: test-reports/python-unittest/dynamo.test_aot_autograd/TEST-AotAutogradFallbackTests-20230111212802.xml
2023-01-11T21:28:06.5970639Z 
2023-01-11T21:28:06.5971096Z ##[endgroup]
2023-01-11T21:28:06.5971827Z FINISHED PRINTING LOG FILE of dynamo/test_aot_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_autograd_0yukwqll)
2023-01-11T21:28:06.5972216Z 
2023-01-11T21:28:08.4945090Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:08.5595619Z Ignoring disabled issues:  []
2023-01-11T21:28:08.5737573Z Running dynamo/test_aot_cudagraphs ... [2023-01-11 21:28:08.573472]
2023-01-11T21:28:08.5739665Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_aot_cudagraphs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:08.573731]
2023-01-11T21:28:10.3179681Z 
2023-01-11T21:28:10.3180298Z Expand the folded group to see the log file of dynamo/test_aot_cudagraphs
2023-01-11T21:28:10.3181463Z ##[group]PRINTING LOG FILE of dynamo/test_aot_cudagraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_cudagraphs_0b9tux2z)
2023-01-11T21:28:10.3181912Z 
2023-01-11T21:28:10.3182044Z Running tests...
2023-01-11T21:28:10.3182855Z ----------------------------------------------------------------------
2023-01-11T21:28:10.3183680Z   test_basic (__main__.TestAotCudagraphs) ... Test results will be stored in test-reports/python-unittest/dynamo.test_aot_cudagraphs
2023-01-11T21:28:10.3184203Z skip: these tests require cuda (0.000s)
2023-01-11T21:28:10.3184690Z   test_dead_fill (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T21:28:10.3185501Z   test_dtoh (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:28:10.3186096Z   test_factory (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:28:10.3186635Z   test_htod (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:28:10.3187194Z   test_mutate_constant (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:28:10.3187789Z   test_mutate_input (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:28:10.3188337Z   test_mutated_metadata (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:28:10.3188672Z 
2023-01-11T21:28:10.3189076Z ----------------------------------------------------------------------
2023-01-11T21:28:10.3189496Z Ran 8 tests in 0.004s
2023-01-11T21:28:10.3189691Z 
2023-01-11T21:28:10.3189815Z OK (skipped=8)
2023-01-11T21:28:10.3189992Z 
2023-01-11T21:28:10.3190121Z Generating XML reports...
2023-01-11T21:28:10.3190905Z Generated XML report: test-reports/python-unittest/dynamo.test_aot_cudagraphs/TEST-TestAotCudagraphs-20230111212810.xml
2023-01-11T21:28:10.3191406Z 
2023-01-11T21:28:10.3191837Z ##[endgroup]
2023-01-11T21:28:10.3192517Z FINISHED PRINTING LOG FILE of dynamo/test_aot_cudagraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_cudagraphs_0b9tux2z)
2023-01-11T21:28:10.3192882Z 
2023-01-11T21:28:12.2309348Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:12.2990711Z Ignoring disabled issues:  []
2023-01-11T21:28:12.3135907Z Running dynamo/test_comptime ... [2023-01-11 21:28:12.313260]
2023-01-11T21:28:12.3137342Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_comptime.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:12.313510]
2023-01-11T21:28:14.5121754Z 
2023-01-11T21:28:14.5122303Z Expand the folded group to see the log file of dynamo/test_comptime
2023-01-11T21:28:14.5123311Z ##[group]PRINTING LOG FILE of dynamo/test_comptime (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_comptime_pfy01_f9)
2023-01-11T21:28:14.5123725Z 
2023-01-11T21:28:14.5123830Z Running tests...
2023-01-11T21:28:14.5124428Z ----------------------------------------------------------------------
2023-01-11T21:28:14.5125088Z Test results will be stored in test-reports/python-unittest/dynamo.test_comptime
2023-01-11T21:28:14.5125587Z   test_get_local (__main__.ComptimeTests) ... ok (0.250s)
2023-01-11T21:28:14.5126183Z   test_graph_break (__main__.ComptimeTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:14.5127807Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:14.5144614Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:28:14.5145186Z stats [('calls_captured', 5), ('unique_graphs', 4), ('fusions_possible', 1)]
2023-01-11T21:28:14.5145608Z unimplemented []
2023-01-11T21:28:14.5146108Z graph_break [('ComptimeContext.graph_break', 2)]
2023-01-11T21:28:14.5146645Z inline_call [('ComptimeContext.graph_break', 1)]
2023-01-11T21:28:14.5147236Z ok (0.019s)
2023-01-11T21:28:14.5147734Z   test_print_bt (__main__.ComptimeTests) ...   File "/var/lib/jenkins/workspace/test/dynamo/test_comptime.py", line 152, in f
2023-01-11T21:28:14.5148280Z     y = g(y)
2023-01-11T21:28:14.5148702Z   File "/var/lib/jenkins/workspace/test/dynamo/test_comptime.py", line 145, in g
2023-01-11T21:28:14.5149137Z     comptime.print_bt()
2023-01-11T21:28:14.5149331Z 
2023-01-11T21:28:14.5149538Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:14.5149842Z inline_call []
2023-01-11T21:28:14.5150367Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:14.5150758Z ok (0.089s)
2023-01-11T21:28:14.5151242Z   test_print_disas (__main__.ComptimeTests) ...  54           0 LOAD_FAST                0 (x)
2023-01-11T21:28:14.5151663Z               2 LOAD_CONST               1 (2)
2023-01-11T21:28:14.5151999Z               4 BINARY_MULTIPLY
2023-01-11T21:28:14.5152335Z               6 STORE_FAST               1 (y)
2023-01-11T21:28:14.5152711Z 
2023-01-11T21:28:14.5152862Z  56           8 LOAD_GLOBAL              0 (comptime)
2023-01-11T21:28:14.5153081Z 
2023-01-11T21:28:14.5153406Z  57          10 LOAD_CONST               2 (<code object _ at 0x7f92c36f2810, file "/var/lib/jenkins/workspace/test/dynamo/test_comptime.py", line 56>)
2023-01-11T21:28:14.5154224Z              12 LOAD_CONST               3 ('ComptimeTests.test_print_disas.<locals>.f.<locals>._')
2023-01-11T21:28:14.5154698Z              14 MAKE_FUNCTION            0
2023-01-11T21:28:14.5155028Z              16 CALL_FUNCTION            1
2023-01-11T21:28:14.5155370Z              18 STORE_FAST               2 (_)
2023-01-11T21:28:14.5155576Z 
2023-01-11T21:28:14.5155727Z  60          20 LOAD_GLOBAL              0 (comptime)
2023-01-11T21:28:14.5156081Z              22 LOAD_METHOD              1 (print_disas)
2023-01-11T21:28:14.5156424Z              24 CALL_METHOD              0
2023-01-11T21:28:14.5156791Z     -->      26 POP_TOP
2023-01-11T21:28:14.5156983Z 
2023-01-11T21:28:14.5157134Z  62          28 LOAD_FAST                1 (y)
2023-01-11T21:28:14.5157456Z              30 LOAD_CONST               4 (3)
2023-01-11T21:28:14.5157764Z              32 BINARY_ADD
2023-01-11T21:28:14.5158062Z              34 RETURN_VALUE
2023-01-11T21:28:14.5158253Z 
2023-01-11T21:28:14.5158450Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:14.5158751Z inline_call []
2023-01-11T21:28:14.5159286Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:14.5159658Z ok (0.007s)
2023-01-11T21:28:14.5160002Z   test_print_graph (__main__.ComptimeTests) ... 
2023-01-11T21:28:14.5160247Z 
2023-01-11T21:28:14.5160255Z 
2023-01-11T21:28:14.5160424Z def forward(self, x : torch.Tensor):
2023-01-11T21:28:14.5160894Z     # File: /var/lib/jenkins/workspace/test/dynamo/test_comptime.py:26, code: y = x * 2
2023-01-11T21:28:14.5161323Z     mul = x * 2;  x = None
2023-01-11T21:28:14.5161605Z     
2023-01-11T21:28:14.5161970Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:14.5162273Z inline_call []
2023-01-11T21:28:14.5162807Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:14.5163195Z ok (0.006s)
2023-01-11T21:28:14.5163542Z   test_print_guards (__main__.ComptimeTests) ... -
2023-01-11T21:28:14.5164000Z             local 'x' TENSOR_MATCH
2023-01-11T21:28:14.5164303Z             {
2023-01-11T21:28:14.5164657Z                 'guard_types': None,
2023-01-11T21:28:14.5165035Z                 'code': None,
2023-01-11T21:28:14.5165411Z                 'obj_weakref': None
2023-01-11T21:28:14.5165805Z                 'guarded_class': None
2023-01-11T21:28:14.5166097Z             }
2023-01-11T21:28:14.5166355Z             
2023-01-11T21:28:14.5166713Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:14.5167016Z inline_call []
2023-01-11T21:28:14.5167536Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:14.5167894Z ok (0.006s)
2023-01-11T21:28:14.5168439Z   test_print_locals (__main__.ComptimeTests) ... x = TensorVariable()
2023-01-11T21:28:14.5168854Z y = TensorVariable()
2023-01-11T21:28:14.5169187Z _ = ConstantVariable(NoneType)
2023-01-11T21:28:14.5169619Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:14.5169928Z inline_call []
2023-01-11T21:28:14.5170423Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:14.5170773Z ok (0.006s)
2023-01-11T21:28:14.5171275Z   test_print_value_stack (__main__.ComptimeTests) ... - TensorVariable()
2023-01-11T21:28:14.5171679Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:14.5171941Z inline_call []
2023-01-11T21:28:14.5172413Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:14.5172765Z ok (0.007s)
2023-01-11T21:28:14.5172911Z 
2023-01-11T21:28:14.5173252Z ----------------------------------------------------------------------
2023-01-11T21:28:14.5173647Z Ran 8 tests in 0.391s
2023-01-11T21:28:14.5173832Z 
2023-01-11T21:28:14.5173931Z OK
2023-01-11T21:28:14.5174194Z 
2023-01-11T21:28:14.5174322Z Generating XML reports...
2023-01-11T21:28:14.5175036Z Generated XML report: test-reports/python-unittest/dynamo.test_comptime/TEST-ComptimeTests-20230111212813.xml
2023-01-11T21:28:14.5175444Z 
2023-01-11T21:28:14.5175901Z ##[endgroup]
2023-01-11T21:28:14.5176609Z FINISHED PRINTING LOG FILE of dynamo/test_comptime (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_comptime_pfy01_f9)
2023-01-11T21:28:14.5177000Z 
2023-01-11T21:28:16.4527504Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:16.5413309Z Ignoring disabled issues:  []
2023-01-11T21:28:16.5556819Z Running dynamo/test_dynamic_shapes ... [2023-01-11 21:28:16.555371]
2023-01-11T21:28:16.5558762Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_dynamic_shapes.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:16.555650]
2023-01-11T21:28:59.0910521Z 
2023-01-11T21:28:59.0911097Z Expand the folded group to see the log file of dynamo/test_dynamic_shapes
2023-01-11T21:28:59.0915404Z ##[group]PRINTING LOG FILE of dynamo/test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_dynamic_shapes_kfuvrwhx)
2023-01-11T21:28:59.0915943Z 
2023-01-11T21:28:59.0916154Z Running tests...
2023-01-11T21:28:59.0916768Z ----------------------------------------------------------------------
2023-01-11T21:28:59.0917387Z Test results will be stored in test-reports/python-unittest/dynamo.test_dynamic_shapes
2023-01-11T21:28:59.0918197Z   test_dict_return_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.390s)
2023-01-11T21:28:59.0919116Z   test_dict_return_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.0919752Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.0920000Z ok (0.072s)
2023-01-11T21:28:59.0920637Z   test_dupes_2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0921108Z ok (0.027s)
2023-01-11T21:28:59.0921827Z   test_dupes_2_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0922293Z ok (0.039s)
2023-01-11T21:28:59.0922976Z   test_dupes_and_bypass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0923354Z ok (0.030s)
2023-01-11T21:28:59.0924089Z   test_dupes_and_bypass_reorder_with_non_tensor_arg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0924862Z ok (0.030s)
2023-01-11T21:28:59.0925666Z   test_dupes_and_bypass_reorder_with_non_tensor_arg_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0926164Z ok (0.044s)
2023-01-11T21:28:59.0926818Z   test_dupes_and_bypass_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0927222Z ok (0.043s)
2023-01-11T21:28:59.0927954Z   test_dupes_and_bypass_with_non_tensor_arg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0928349Z ok (0.031s)
2023-01-11T21:28:59.0929215Z   test_dupes_and_bypass_with_non_tensor_arg_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0929777Z ok (0.044s)
2023-01-11T21:28:59.0930566Z   test_dupes_and_bypass_with_non_tensor_output_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.0931073Z ok (0.036s)
2023-01-11T21:28:59.0931876Z   test_dupes_and_bypass_with_non_tensor_output_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.0932422Z ok (0.034s)
2023-01-11T21:28:59.0932976Z   test_dupes_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0933358Z ok (0.027s)
2023-01-11T21:28:59.0933914Z   test_dupes_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0934259Z ok (0.039s)
2023-01-11T21:28:59.0934830Z   test_export_compare_optimize_with_make_fx_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:59.0935204Z ok (0.328s)
2023-01-11T21:28:59.0935550Z   test_export_decomp_asserts_bad_args_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:59.0936106Z   test_export_decomp_asserts_bad_args_mode_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:59.0937019Z   test_export_decomp_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.0937516Z ok (0.093s)
2023-01-11T21:28:59.0937936Z   test_export_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.0938531Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:28:59.0938875Z ok (0.115s)
2023-01-11T21:28:59.0939555Z   test_export_graph_bypass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0939896Z ok (0.034s)
2023-01-11T21:28:59.0940396Z   test_export_graph_bypass_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0940920Z ok (0.045s)
2023-01-11T21:28:59.0941690Z   test_export_graph_with_complex_reorder_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.0942219Z ok (0.054s)
2023-01-11T21:28:59.0943182Z   test_export_graph_with_complex_reorder_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.0943734Z ok (0.079s)
2023-01-11T21:28:59.0944425Z   test_export_graph_with_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0944751Z ok (0.038s)
2023-01-11T21:28:59.0945353Z   test_export_graph_with_list_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0945717Z ok (0.049s)
2023-01-11T21:28:59.0946315Z   test_export_meta_val_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.0946777Z ok (0.072s)
2023-01-11T21:28:59.0947501Z   test_export_mismatched_out_2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0948008Z ok (0.034s)
2023-01-11T21:28:59.0948792Z   test_export_mismatched_out_2_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0949287Z ok (0.046s)
2023-01-11T21:28:59.0949807Z   test_export_mismatched_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0950156Z ok (0.035s)
2023-01-11T21:28:59.0950670Z   test_export_mismatched_out_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0951014Z ok (0.046s)
2023-01-11T21:28:59.0951515Z   test_export_shape_control_flow_1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.0951965Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:59.0952181Z ok (0.048s)
2023-01-11T21:28:59.0952493Z   test_export_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.0952932Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:28:59.0953163Z ok (0.361s)
2023-01-11T21:28:59.0953781Z   test_export_with_constant_dict_values_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0954267Z ok (0.030s)
2023-01-11T21:28:59.0955031Z   test_export_with_constant_free_function_and_class_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.0955565Z ok (0.048s)
2023-01-11T21:28:59.0956363Z   test_export_with_constant_free_function_and_class_method_multiarg_diff_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.0956876Z ok (0.023s)
2023-01-11T21:28:59.0957539Z   test_export_with_constant_free_function_and_class_method_multiarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0957919Z ok (0.062s)
2023-01-11T21:28:59.0958421Z   test_export_with_constant_free_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0958773Z ok (0.060s)
2023-01-11T21:28:59.0959351Z   test_export_with_constant_list_nonzero_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('call_function BuiltinVariable(iter) [TensorVariable()] {}', 1)]
2023-01-11T21:28:59.0959902Z expected failure (0.014s)
2023-01-11T21:28:59.0960781Z   test_export_with_constant_list_nonzero_free_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('call_function BuiltinVariable(iter) [TensorVariable()] {}', 1)]
2023-01-11T21:28:59.0961375Z expected failure (0.011s)
2023-01-11T21:28:59.0962170Z   test_export_with_constant_method_on_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.0962636Z ok (0.046s)
2023-01-11T21:28:59.0963177Z   test_export_with_constant_method_on_module_invoke_twice_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0963547Z ok (0.058s)
2023-01-11T21:28:59.0964065Z   test_export_with_constant_none_control_flow_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.0964410Z ok (0.008s)
2023-01-11T21:28:59.0964944Z   test_export_with_constant_none_control_flow_free_func_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.0965471Z ok (0.007s)
2023-01-11T21:28:59.0966230Z   test_export_with_constant_not_none_control_flow_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.0966721Z ok (0.020s)
2023-01-11T21:28:59.0967496Z   test_export_with_constant_not_none_control_flow_free_func_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.0968031Z ok (0.020s)
2023-01-11T21:28:59.0968700Z   test_export_with_constant_not_none_control_flow_pos_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.0969056Z ok (0.021s)
2023-01-11T21:28:59.0969570Z   test_export_with_constant_not_return_const_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.0969923Z ok (0.008s)
2023-01-11T21:28:59.0970452Z   test_export_with_constant_tuple_nonzero_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('call_function BuiltinVariable(iter) [TensorVariable()] {}', 1)]
2023-01-11T21:28:59.0970830Z expected failure (0.094s)
2023-01-11T21:28:59.0971155Z   test_export_with_module_layer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.0971581Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.0971855Z ok (0.075s)
2023-01-11T21:28:59.0972354Z   test_export_with_stack_trace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:59.0972693Z ok (0.160s)
2023-01-11T21:28:59.0972995Z   test_func_return_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.0973613Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.0973936Z ok (0.063s)
2023-01-11T21:28:59.0974402Z   test_func_return_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.0975001Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.0975358Z ok (0.098s)
2023-01-11T21:28:59.0976171Z   test_input_container_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.0976607Z ok (0.100s)
2023-01-11T21:28:59.0977087Z   test_list_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0977418Z ok (0.036s)
2023-01-11T21:28:59.0977924Z   test_list_unpack_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.0978267Z ok (0.051s)
2023-01-11T21:28:59.0978776Z   test_zeroes_in_and_out_different_shape_on_test_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.0979145Z ok (0.047s)
2023-01-11T21:28:59.0979694Z   test_zeroes_in_and_out_different_shape_on_test_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.0980068Z ok (0.079s)
2023-01-11T21:28:59.0980564Z   test_zeroes_in_new_shape_scalar_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 16), ('fusions_possible', 14), ('unique_graphs', 2)]
2023-01-11T21:28:59.0980913Z ok (0.057s)
2023-01-11T21:28:59.0981456Z   test_zeroes_in_new_shape_scalar_out_permute_dupe_and_bypass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:28:59.0981818Z ok (0.072s)
2023-01-11T21:28:59.0982511Z   test_zeroes_in_new_shape_scalar_out_permute_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:28:59.0983052Z ok (0.072s)
2023-01-11T21:28:59.0983826Z   test_T_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.0984141Z ok (0.033s)
2023-01-11T21:28:59.0984678Z   test_add__dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:73: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:59.0985201Z   a_copy = torch.tensor(a)
2023-01-11T21:28:59.0985836Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:59.0986388Z   return node.target(*args, **kwargs)
2023-01-11T21:28:59.0986793Z <eval_with_key>.331:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:59.0987188Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:28:59.0987521Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0987735Z ok (0.041s)
2023-01-11T21:28:59.0988208Z   test_add_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.0988537Z ok (0.021s)
2023-01-11T21:28:59.0989122Z   test_addcdiv__dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:84: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:59.0989642Z   a_copy = torch.tensor(a)
2023-01-11T21:28:59.0990267Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:59.0990707Z   return node.target(*args, **kwargs)
2023-01-11T21:28:59.0991120Z <eval_with_key>.336:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:59.0991561Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:28:59.0991979Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.0992342Z ok (0.050s)
2023-01-11T21:28:59.0993039Z   test_addcdiv_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0993494Z ok (0.039s)
2023-01-11T21:28:59.0993955Z   test_build_list_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.0994572Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.0994914Z ok (0.065s)
2023-01-11T21:28:59.0995375Z   test_chunks1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.0996281Z   test_const_tuple_add1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.0996750Z ok (0.041s)
2023-01-11T21:28:59.0997219Z   test_const_tuple_add2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.0997554Z ok (0.041s)
2023-01-11T21:28:59.0998227Z   test_constant1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0998693Z ok (0.038s)
2023-01-11T21:28:59.0999363Z   test_constant2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.0999838Z ok (0.036s)
2023-01-11T21:28:59.1000526Z   test_constant3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1001103Z ok (0.018s)
2023-01-11T21:28:59.1001615Z   test_constant4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1002102Z ok (0.020s)
2023-01-11T21:28:59.1002808Z   test_default_dict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1003255Z ok (0.032s)
2023-01-11T21:28:59.1003950Z   test_del_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1004428Z ok (0.035s)
2023-01-11T21:28:59.1005122Z   test_device_constant_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1005449Z ok (0.028s)
2023-01-11T21:28:59.1005928Z   test_device_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1006255Z ok (0.019s)
2023-01-11T21:28:59.1006715Z   test_dict_copy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1007043Z ok (0.019s)
2023-01-11T21:28:59.1007560Z   test_dict_ops_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:59.1008065Z ok (0.073s)
2023-01-11T21:28:59.1008764Z   test_dict_param_keys_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1009242Z ok (0.020s)
2023-01-11T21:28:59.1009984Z   test_distributed_is_available_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1010483Z ok (0.019s)
2023-01-11T21:28:59.1011082Z   test_distributed_is_initialized_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1011433Z ok (0.019s)
2023-01-11T21:28:59.1011914Z   test_dtype_compare_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1012247Z ok (0.028s)
2023-01-11T21:28:59.1012703Z   test_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1013026Z ok (0.018s)
2023-01-11T21:28:59.1013491Z   test_finfo_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1013801Z ok (0.023s)
2023-01-11T21:28:59.1014256Z   test_float_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1014577Z ok (0.019s)
2023-01-11T21:28:59.1015057Z   test_fn_with_self_set_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1015373Z ok (0.103s)
2023-01-11T21:28:59.1015840Z   test_fstrings1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1016235Z ok (0.021s)
2023-01-11T21:28:59.1016541Z   test_fstrings2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1017165Z   test_fstrings3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1017499Z ok (0.019s)
2023-01-11T21:28:59.1017801Z   test_funcdef_closure_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1018208Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:28:59.1018435Z ok (0.086s)
2023-01-11T21:28:59.1018963Z   test_get_default_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1019305Z ok (0.020s)
2023-01-11T21:28:59.1019762Z   test_globalfn_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1020088Z ok (0.021s)
2023-01-11T21:28:59.1020563Z   test_globalmodule_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1020895Z ok (0.033s)
2023-01-11T21:28:59.1021495Z   test_globalvar_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1021951Z ok (0.031s)
2023-01-11T21:28:59.1022784Z   test_import1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1023235Z ok (0.030s)
2023-01-11T21:28:59.1023934Z   test_indirect1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1024403Z ok (0.021s)
2023-01-11T21:28:59.1025033Z   test_indirect2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1025342Z ok (0.021s)
2023-01-11T21:28:59.1025815Z   test_indirect3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1026146Z ok (0.021s)
2023-01-11T21:28:59.1026443Z   test_inline_jit_annotations_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1027097Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1027415Z ok (0.034s)
2023-01-11T21:28:59.1028123Z   test_inline_softmax_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1028584Z ok (0.073s)
2023-01-11T21:28:59.1029034Z   test_inline_with_default_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1029656Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1029949Z ok (0.031s)
2023-01-11T21:28:59.1030237Z   test_inner_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1030767Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1030995Z ok (0.019s)
2023-01-11T21:28:59.1031382Z   test_is_contiguous_memory_format_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1032033Z   test_is_fx_tracing_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1032365Z ok (0.021s)
2023-01-11T21:28:59.1032849Z   test_is_in_onnx_export_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1033172Z ok (0.020s)
2023-01-11T21:28:59.1033699Z   test_is_not_null_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1034033Z ok (0.021s)
2023-01-11T21:28:59.1034496Z   test_is_quantized_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1034825Z ok (0.020s)
2023-01-11T21:28:59.1035294Z   test_is_sparse_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1035620Z ok (0.018s)
2023-01-11T21:28:59.1036082Z   test_islice_chain_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1036410Z ok (0.058s)
2023-01-11T21:28:59.1037077Z   test_jit_annotate_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1037546Z ok (0.027s)
2023-01-11T21:28:59.1038242Z   test_len_constant_dict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1038724Z ok (0.020s)
2023-01-11T21:28:59.1039430Z   test_len_constant_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1039905Z ok (0.020s)
2023-01-11T21:28:59.1040403Z   test_len_constant_misc_iterables_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1040747Z ok (0.020s)
2023-01-11T21:28:59.1041057Z   test_len_tensor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.008s)
2023-01-11T21:28:59.1041649Z   test_list_add_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1041977Z ok (0.021s)
2023-01-11T21:28:59.1042450Z   test_list_clear_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1042775Z ok (0.037s)
2023-01-11T21:28:59.1043236Z   test_list_convert_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1043566Z ok (0.043s)
2023-01-11T21:28:59.1044044Z   test_list_reversed_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1044437Z ok (0.052s)
2023-01-11T21:28:59.1044916Z   test_list_slice_assignment_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1045261Z ok (0.021s)
2023-01-11T21:28:59.1045738Z   test_list_truth_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1046066Z ok (0.021s)
2023-01-11T21:28:59.1046524Z   test_listarg1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1046856Z ok (0.030s)
2023-01-11T21:28:59.1047385Z   test_listarg2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1047700Z ok (0.030s)
2023-01-11T21:28:59.1048172Z   test_listarg3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1048498Z ok (0.031s)
2023-01-11T21:28:59.1048959Z   test_listarg4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1049265Z ok (0.031s)
2023-01-11T21:28:59.1049727Z   test_listarg5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1050057Z ok (0.030s)
2023-01-11T21:28:59.1050536Z   test_load_global_bool_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1050856Z ok (0.019s)
2023-01-11T21:28:59.1051145Z   test_map_sum_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1051556Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:59.1051767Z ok (0.077s)
2023-01-11T21:28:59.1052063Z   test_methodcall1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1052477Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1052692Z ok (0.039s)
2023-01-11T21:28:59.1052987Z   test_methodcall2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1053398Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1053620Z ok (0.035s)
2023-01-11T21:28:59.1054045Z   test_methodcall3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1054638Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1054980Z ok (0.036s)
2023-01-11T21:28:59.1055645Z   test_min_max_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:28:59.1056120Z ok (0.051s)
2023-01-11T21:28:59.1056806Z   test_module_constant_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1057265Z ok (0.045s)
2023-01-11T21:28:59.1057718Z   test_ndim_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1058106Z ok (0.019s)
2023-01-11T21:28:59.1058576Z   test_pop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1058907Z ok (0.045s)
2023-01-11T21:28:59.1059378Z   test_range1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1059862Z ok (0.015s)
2023-01-11T21:28:59.1061853Z   test_range2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 15), ('fusions_possible', 14), ('unique_graphs', 1)]
2023-01-11T21:28:59.1062463Z ok (0.114s)
2023-01-11T21:28:59.1063136Z   test_reduce_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1063633Z ok (0.040s)
2023-01-11T21:28:59.1064354Z   test_return_dict2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1064823Z ok (0.023s)
2023-01-11T21:28:59.1065520Z   test_return_dict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1065983Z ok (0.023s)
2023-01-11T21:28:59.1066679Z   test_return_tuple1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1067037Z ok (0.030s)
2023-01-11T21:28:59.1067626Z   test_return_tuple2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1068105Z ok (0.021s)
2023-01-11T21:28:59.1068552Z   test_shape1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1069196Z   test_shape2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1070074Z   test_slice1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1070550Z ok (0.016s)
2023-01-11T21:28:59.1071015Z   test_slice2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1071404Z ok (0.014s)
2023-01-11T21:28:59.1071881Z   test_slice3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1072209Z ok (0.043s)
2023-01-11T21:28:59.1072663Z   test_slice4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1072988Z ok (0.018s)
2023-01-11T21:28:59.1073456Z   test_slice5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1073785Z ok (0.033s)
2023-01-11T21:28:59.1074236Z   test_slice6_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1074560Z ok (0.040s)
2023-01-11T21:28:59.1075031Z   test_startswith_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1075436Z ok (0.029s)
2023-01-11T21:28:59.1075748Z   test_tensor_len_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.019s)
2023-01-11T21:28:59.1076214Z   test_tensor_new_with_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1076694Z   test_tensor_new_with_size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1077145Z   test_tensor_type2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.000s)
2023-01-11T21:28:59.1077797Z   test_tensor_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1078134Z ok (0.053s)
2023-01-11T21:28:59.1078627Z   test_transpose_for_scores_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1078955Z ok (0.036s)
2023-01-11T21:28:59.1079423Z   test_tuple1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1079751Z ok (0.021s)
2023-01-11T21:28:59.1080222Z   test_tuple2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1080533Z ok (0.021s)
2023-01-11T21:28:59.1081013Z   test_tuple_contains_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1081354Z ok (0.021s)
2023-01-11T21:28:59.1081827Z   test_tuple_iadd_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1082140Z ok (0.029s)
2023-01-11T21:28:59.1082610Z   test_unpack1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1082934Z ok (0.036s)
2023-01-11T21:28:59.1083386Z   test_unpack2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1083756Z ok (0.034s)
2023-01-11T21:28:59.1084440Z   test_unpack3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1084907Z ok (0.033s)
2023-01-11T21:28:59.1085578Z   test_unpack_ex1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1086050Z ok (0.048s)
2023-01-11T21:28:59.1086733Z   test_unpack_ex2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1087191Z ok (0.049s)
2023-01-11T21:28:59.1087723Z   test_unpack_ex3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1088056Z ok (0.049s)
2023-01-11T21:28:59.1088530Z   test_viamethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1088900Z ok (0.021s)
2023-01-11T21:28:59.1089487Z   test_viatorch_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1089958Z ok (0.020s)
2023-01-11T21:28:59.1090580Z   test_allow_in_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1091196Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1091553Z ok (0.042s)
2023-01-11T21:28:59.1092256Z   test_autocast_cpu_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1092682Z ok (0.034s)
2023-01-11T21:28:59.1093032Z   test_autocast_device_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1093667Z   test_autocast_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1094301Z   test_autocast_float64_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1094944Z   test_autograd_function_equivalence_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1095593Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:28:59.1095948Z ok (0.084s)
2023-01-11T21:28:59.1096669Z   test_autograd_profiler_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... STAGE:2023-01-11 21:28:25 3597:3597 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:28:59.1097219Z STAGE:2023-01-11 21:28:25 3597:3597 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:28:59.1097699Z STAGE:2023-01-11 21:28:25 3597:3597 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:28:59.1098326Z [2023-01-11 21:28:25,422] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:28:59.1098783Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1099091Z unimplemented []
2023-01-11T21:28:59.1099459Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:59.1099997Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1100332Z ok (0.065s)
2023-01-11T21:28:59.1101089Z   test_autograd_profiler_enabled_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... STAGE:2023-01-11 21:28:25 3597:3597 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:28:59.1101658Z STAGE:2023-01-11 21:28:25 3597:3597 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:28:59.1102109Z STAGE:2023-01-11 21:28:25 3597:3597 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:28:59.1102582Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1102779Z unimplemented []
2023-01-11T21:28:59.1103092Z graph_break [('torch.autograd._profiler_enabled not supported yet', 1)]
2023-01-11T21:28:59.1103448Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.1103679Z ok (0.044s)
2023-01-11T21:28:59.1104155Z   test_boolarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:59.1104468Z ok (0.054s)
2023-01-11T21:28:59.1104776Z   test_build_tuple_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1105204Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1105546Z ok (0.059s)
2023-01-11T21:28:59.1105980Z   test_builder_for_class_with_metaclass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1106421Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1106650Z ok (0.019s)
2023-01-11T21:28:59.1107125Z   test_builtin_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1107466Z ok (0.018s)
2023-01-11T21:28:59.1107792Z   test_builtin_subclasses_as_method_on_class_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.003s)
2023-01-11T21:28:59.1108298Z   test_builtin_subclasses_as_method_on_var_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.004s)
2023-01-11T21:28:59.1108749Z   test_call_parent_non_class_methods_from_child_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1109195Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1109422Z ok (0.031s)
2023-01-11T21:28:59.1109898Z   test_callpacked_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1110211Z ok (0.059s)
2023-01-11T21:28:59.1110626Z   test_cell_output1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1111053Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1111336Z ok (0.018s)
2023-01-11T21:28:59.1111742Z   test_cell_output2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1112050Z unimplemented []
2023-01-11T21:28:59.1112444Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1112848Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1113073Z ok (0.028s)
2023-01-11T21:28:59.1113911Z   test_change_backends_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:28:59.1114582Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:28:59.1114928Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:59.1115211Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1115390Z ok (0.064s)
2023-01-11T21:28:59.1115774Z   test_cond_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1116066Z inline_call []
2023-01-11T21:28:59.1116377Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1116725Z ok (0.026s)
2023-01-11T21:28:59.1117150Z   test_cond_export_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1117758Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1118102Z ok (0.045s)
2023-01-11T21:28:59.1119326Z   test_cond_export_single_arg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91143 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:28:59.1120544Z   test_cond_nested_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1120992Z inline_call []
2023-01-11T21:28:59.1121460Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1121797Z ok (0.043s)
2023-01-11T21:28:59.1122264Z   test_cond_side_effects_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.001s)
2023-01-11T21:28:59.1123088Z   test_config_getattr_default_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1123742Z stats [('calls_captured', 21), ('fusions_possible', 18), ('unique_graphs', 3)]
2023-01-11T21:28:59.1123960Z ok (0.158s)
2023-01-11T21:28:59.1124510Z   test_config_log_level_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1125146Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1125501Z ok (0.016s)
2023-01-11T21:28:59.1126109Z   test_config_obj_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1126719Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:28:59.1127091Z ok (0.074s)
2023-01-11T21:28:59.1127499Z   test_const_dict_variable_python_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:59.1128437Z   test_cross_entropy_loss_fancy_ctor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:28:59.1129132Z   warnings.warn(warning.format(ret))
2023-01-11T21:28:59.1129453Z ok (0.002s)
2023-01-11T21:28:59.1129890Z   test_cross_entropy_loss_simple_ctor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:59.1130712Z   test_dataclass_fields_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1131158Z inline_call []
2023-01-11T21:28:59.1131468Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1131698Z ok (0.047s)
2023-01-11T21:28:59.1132248Z   test_dict_mutation_side_effect_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1132903Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1133250Z ok (0.017s)
2023-01-11T21:28:59.1133931Z   test_dict_reconstruct_keeps_original_order_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 13), ('ok', 12)]
2023-01-11T21:28:59.1134705Z unimplemented [("Guard setup for uninitialized class <class 'torch.nn.modules.container.ModuleDict'>", 1)]
2023-01-11T21:28:59.1135795Z graph_break [('UnspecializedNNModuleVariable missing add_module', 3), ('construct nn.Module: ReLU', 1), ('call_function in skip_files /opt/conda/lib/python3.10/collections/__init__.py', 1), ('construct nn.Module: ModuleDict', 1), ('Patched init cannot be inlined.', 1), ('construct nn.Module: Linear', 1), ('construct nn.Module: Sigmoid', 1), ('call_method ConstDictVariable() update [TupleVariable()] {}', 1)]
2023-01-11T21:28:59.1136770Z inline_call [('inline __setitem__', 2), ('Patched init cannot be inlined.', 1)]
2023-01-11T21:28:59.1137157Z ok (0.040s)
2023-01-11T21:28:59.1137782Z   test_dictcomp_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1138227Z inline_call []
2023-01-11T21:28:59.1138671Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1139020Z ok (0.022s)
2023-01-11T21:28:59.1139338Z   test_disable_flag_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:59.1139742Z   test_disable_optimize_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:59.1140289Z   test_disallow_in_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1140596Z unimplemented []
2023-01-11T21:28:59.1141036Z graph_break [('call_function UserDefinedObjectVariable(sub) [TensorVariable(), ConstantVariable(int)] {}', 1)]
2023-01-11T21:28:59.1141669Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1142003Z ok (0.044s)
2023-01-11T21:28:59.1142619Z   test_dunder_methods_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1143242Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1143595Z ok (0.060s)
2023-01-11T21:28:59.1144050Z   test_duplicate_graph_break_warning_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... break
2023-01-11T21:28:59.1144479Z break
2023-01-11T21:28:59.1144698Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:28:59.1145038Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:28:59.1145300Z unimplemented []
2023-01-11T21:28:59.1145625Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 4)]
2023-01-11T21:28:59.1146001Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:28:59.1146309Z ok (0.118s)
2023-01-11T21:28:59.1147049Z   test_dynamo_min_operator_with_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1147569Z ok (0.012s)
2023-01-11T21:28:59.1148180Z   test_empty_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1148799Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.1149147Z ok (0.040s)
2023-01-11T21:28:59.1149714Z   test_enum_no_graphbreaks_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1150059Z ok (0.032s)
2023-01-11T21:28:59.1150475Z   test_error_on_nested_fx_trace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1150915Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1151137Z ok (0.022s)
2023-01-11T21:28:59.1151667Z   test_fold_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1151981Z ok (0.019s)
2023-01-11T21:28:59.1152487Z   test_frozenset_torch_func_contains_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1152836Z ok (0.030s)
2023-01-11T21:28:59.1153326Z   test_function_annotation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.1153761Z ok (0.030s)
2023-01-11T21:28:59.1154238Z   test_generate_tensor_from_list_of_numpy_primitive_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1154589Z unimplemented []
2023-01-11T21:28:59.1154945Z graph_break [('numpy', 1)]
2023-01-11T21:28:59.1155414Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1155771Z ok (0.009s)
2023-01-11T21:28:59.1156218Z   test_get_device_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1156978Z   test_grad_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1157505Z unimplemented []
2023-01-11T21:28:59.1157894Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:28:59.1158220Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1158459Z ok (0.068s)
2023-01-11T21:28:59.1158876Z   test_grad_mode_guard_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1159165Z unimplemented []
2023-01-11T21:28:59.1159403Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:59.1159724Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1159990Z ok (0.049s)
2023-01-11T21:28:59.1160612Z   test_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1161070Z unimplemented []
2023-01-11T21:28:59.1161642Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 2)]
2023-01-11T21:28:59.1162240Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:28:59.1162581Z ok (0.085s)
2023-01-11T21:28:59.1163268Z   test_guard_failure_fn2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1163608Z ok (0.030s)
2023-01-11T21:28:59.1164088Z   test_guard_failure_fn_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:59.1164422Z ok (0.071s)
2023-01-11T21:28:59.1164899Z   test_id_of_nn_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1165220Z ok (0.031s)
2023-01-11T21:28:59.1165704Z   test_if_cond_nn_mod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1166040Z ok (0.045s)
2023-01-11T21:28:59.1166458Z   test_inference_mode_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1166875Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1167101Z ok (0.024s)
2023-01-11T21:28:59.1167526Z   test_inline_dict_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1167822Z inline_call []
2023-01-11T21:28:59.1168120Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1168344Z ok (0.018s)
2023-01-11T21:28:59.1168792Z   test_inline_func_jump_on_tensor_condition_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1169261Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:59.1169482Z unimplemented []
2023-01-11T21:28:59.1169749Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:59.1170077Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:59.1170305Z ok (0.042s)
2023-01-11T21:28:59.1170819Z   test_inline_list_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1171246Z inline_call []
2023-01-11T21:28:59.1171682Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1172031Z ok (0.016s)
2023-01-11T21:28:59.1172697Z   test_inplace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1173200Z ok (0.019s)
2023-01-11T21:28:59.1173906Z   test_inplace_param_update_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1174314Z ok (0.011s)
2023-01-11T21:28:59.1174714Z   test_is_compiling_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1175143Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1175370Z ok (0.010s)
2023-01-11T21:28:59.1175857Z   test_is_floating_point2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1176178Z ok (0.036s)
2023-01-11T21:28:59.1176671Z   test_is_floating_point_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1177017Z ok (0.035s)
2023-01-11T21:28:59.1177427Z   test_is_tensor2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1177837Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.1178062Z ok (0.037s)
2023-01-11T21:28:59.1178530Z   test_is_tensor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1178845Z ok (0.034s)
2023-01-11T21:28:59.1179386Z   test_is_tensor_like2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1179822Z unimplemented []
2023-01-11T21:28:59.1180333Z graph_break [('call_function args: UserDefinedObjectVariable(MyTensor) ', 1)]
2023-01-11T21:28:59.1180878Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.1181240Z ok (0.017s)
2023-01-11T21:28:59.1181938Z   test_is_tensor_like_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1182525Z ok (0.024s)
2023-01-11T21:28:59.1183033Z   test_item_changes_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1183367Z ok (0.041s)
2023-01-11T21:28:59.1184083Z   test_item_changes_new_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1184570Z ok (0.044s)
2023-01-11T21:28:59.1185275Z   test_item_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1185882Z ok (0.023s)
2023-01-11T21:28:59.1186315Z   test_large_reduction_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.013s)
2023-01-11T21:28:59.1186877Z   test_linetable_writer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:59.1187439Z   test_list_append_return_none_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1187875Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1188088Z ok (0.012s)
2023-01-11T21:28:59.1188641Z   test_list_mul_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1189149Z ok (0.003s)
2023-01-11T21:28:59.1189779Z   test_listcomp_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1190210Z inline_call []
2023-01-11T21:28:59.1190677Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1191041Z ok (0.042s)
2023-01-11T21:28:59.1191636Z   test_lnotab_writer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: use lnotab when python < 3.10 (0.000s)
2023-01-11T21:28:59.1192272Z   test_manual_seed_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1192784Z ok (0.030s)
2023-01-11T21:28:59.1193483Z   test_matmul1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1193942Z ok (0.012s)
2023-01-11T21:28:59.1194394Z   test_module_complex_iter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.011s)
2023-01-11T21:28:59.1195190Z   test_module_deepcopy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:28:59.1195607Z unimplemented []
2023-01-11T21:28:59.1195938Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 2)]
2023-01-11T21:28:59.1196180Z inline_call []
2023-01-11T21:28:59.1196481Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1196747Z ok (0.064s)
2023-01-11T21:28:59.1197217Z   test_named_parameters_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.024s)
2023-01-11T21:28:59.1198010Z   test_namedtuple1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1198628Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1198980Z ok (0.021s)
2023-01-11T21:28:59.1199595Z   test_namedtuple2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1200133Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1200348Z ok (0.028s)
2023-01-11T21:28:59.1200758Z   test_namedtuple3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1201179Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1201391Z ok (0.023s)
2023-01-11T21:28:59.1201789Z   test_nan_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1202486Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1202820Z ok (0.021s)
2023-01-11T21:28:59.1203268Z   test_nested_closure_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1203890Z stats [('calls_captured', 9), ('fusions_possible', 7), ('unique_graphs', 2)]
2023-01-11T21:28:59.1204228Z ok (0.071s)
2023-01-11T21:28:59.1204644Z   test_nested_closure_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1205232Z stats [('calls_captured', 11), ('fusions_possible', 9), ('unique_graphs', 2)]
2023-01-11T21:28:59.1205460Z ok (0.039s)
2023-01-11T21:28:59.1206355Z   test_nested_disable_decorator_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... [2023-01-11 21:28:28,088] torch._dynamo.convert_frame: [ERROR] WON'T CONVERT fn3 /var/lib/jenkins/workspace/test/dynamo/test_misc.py line 1197 
2023-01-11T21:28:59.1206935Z due to: 
2023-01-11T21:28:59.1207231Z Traceback (most recent call last):
2023-01-11T21:28:59.1207813Z   File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 67, in unimplemented
2023-01-11T21:28:59.1208200Z     raise Unsupported(msg)
2023-01-11T21:28:59.1208739Z torch._dynamo.exc.Unsupported: call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f8c4c165000>
2023-01-11T21:28:59.1209085Z 
2023-01-11T21:28:59.1209159Z from user code:
2023-01-11T21:28:59.1209403Z    File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1199, in fn3
2023-01-11T21:28:59.1209665Z     return fn2(x)
2023-01-11T21:28:59.1210051Z   File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1192, in fn2
2023-01-11T21:28:59.1210413Z     x = fn1(x)  # graph break
2023-01-11T21:28:59.1210571Z 
2023-01-11T21:28:59.1210783Z Set torch._dynamo.config.verbose=True for more information
2023-01-11T21:28:59.1211026Z 
2023-01-11T21:28:59.1211032Z 
2023-01-11T21:28:59.1211224Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1211529Z unimplemented []
2023-01-11T21:28:59.1212146Z graph_break [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f8c4c165000>', 1)]
2023-01-11T21:28:59.1212790Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1213303Z inline_call [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f8c4c165000>', 1)]
2023-01-11T21:28:59.1213766Z ok (0.081s)
2023-01-11T21:28:59.1214219Z   test_nested_optimize_decorator_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1214871Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1215214Z ok (0.052s)
2023-01-11T21:28:59.1215955Z   test_nested_optimize_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:59.1216432Z ok (0.086s)
2023-01-11T21:28:59.1216999Z   test_nested_optimize_run_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:59.1217336Z ok (0.089s)
2023-01-11T21:28:59.1218013Z   test_nn_functional_reduction_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1218517Z ok (0.026s)
2023-01-11T21:28:59.1218981Z   test_nn_sequential_invocation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1219628Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1220089Z ok (0.174s)
2023-01-11T21:28:59.1220597Z   test_nn_sequential_invocation_reposition_indices_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1221085Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1221307Z ok (0.052s)
2023-01-11T21:28:59.1221962Z   test_no_error_on_nested_fx_trace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1222746Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1223085Z ok (0.022s)
2023-01-11T21:28:59.1223774Z   test_no_grad_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 40), ('fusions_possible', 32), ('unique_graphs', 8)]
2023-01-11T21:28:59.1224266Z ok (0.275s)
2023-01-11T21:28:59.1224955Z   test_not_dynamic_scope_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1225272Z inline_call []
2023-01-11T21:28:59.1225563Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1225797Z ok (0.012s)
2023-01-11T21:28:59.1226263Z   test_numel_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1226577Z ok (0.035s)
2023-01-11T21:28:59.1226997Z   test_numpy_int_constant_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1227429Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1227640Z ok (0.022s)
2023-01-11T21:28:59.1228077Z   test_numpy_variable_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1228513Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1228736Z ok (0.012s)
2023-01-11T21:28:59.1229034Z   test_object_classmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1229455Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1229681Z ok (0.012s)
2023-01-11T21:28:59.1229977Z   test_object_staticmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1230402Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1230628Z ok (0.011s)
2023-01-11T21:28:59.1231128Z   test_onnx_shape_as_tensor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 15), ('fusions_possible', 10), ('unique_graphs', 5)]
2023-01-11T21:28:59.1231521Z ok (0.049s)
2023-01-11T21:28:59.1232013Z   test_optimize_on_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1232350Z ok (0.022s)
2023-01-11T21:28:59.1233016Z   test_pair_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:59.1233461Z ok (0.044s)
2023-01-11T21:28:59.1234067Z   test_python_slice_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1234692Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1235002Z ok (0.039s)
2023-01-11T21:28:59.1235620Z   test_raise_on_backend_error_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1)]
2023-01-11T21:28:59.1236179Z stats [('calls_captured', 3), ('fusions_possible', 2)]
2023-01-11T21:28:59.1236390Z ok (0.028s)
2023-01-11T21:28:59.1236781Z   test_raises_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1237084Z unimplemented []
2023-01-11T21:28:59.1237517Z graph_break [('call_function BuiltinVariable(str) [TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1238043Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1238407Z ok (0.041s)
2023-01-11T21:28:59.1238859Z   test_rand_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1239452Z   test_range_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1240146Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1240486Z ok (0.035s)
2023-01-11T21:28:59.1241143Z   test_recursive_inline_list_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1241592Z inline_call []
2023-01-11T21:28:59.1242069Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1242429Z ok (0.016s)
2023-01-11T21:28:59.1243039Z   test_release_input_memory_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1243707Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1243951Z ok (0.014s)
2023-01-11T21:28:59.1244501Z   test_release_module_memory_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1245124Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1245490Z ok (0.033s)
2023-01-11T21:28:59.1246248Z   test_repro_graph_breaks_in__get_item_by_idx_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1246740Z ok (0.034s)
2023-01-11T21:28:59.1247398Z   test_restore_graphstate_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1247802Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:59.1248019Z unimplemented []
2023-01-11T21:28:59.1248328Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:59.1248856Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:28:59.1249214Z ok (0.075s)
2023-01-11T21:28:59.1249957Z   test_restore_graphstate_internals_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1250473Z ok (0.031s)
2023-01-11T21:28:59.1251114Z   test_return_nested_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1251663Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 2)]
2023-01-11T21:28:59.1251876Z ok (0.059s)
2023-01-11T21:28:59.1252170Z   test_sample_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.620s)
2023-01-11T21:28:59.1252908Z   test_setattr_mutation1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1253644Z unimplemented [('call_method UserDefinedObjectVariable(member_descriptor) __mul__ [ConstantVariable(int)] {}', 1)]
2023-01-11T21:28:59.1254520Z graph_break [("isinstance called on UserDefinedClass UserDefinedObjectVariable(member_descriptor) <class 'torch.Tensor'>", 1)]
2023-01-11T21:28:59.1255056Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1255550Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:28:59.1255773Z ok (0.097s)
2023-01-11T21:28:59.1256372Z   test_setattr_mutation2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1256819Z inline_call []
2023-01-11T21:28:59.1257284Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:59.1257617Z ok (0.074s)
2023-01-11T21:28:59.1258261Z   test_setattr_mutation3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1258723Z inline_call []
2023-01-11T21:28:59.1259213Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:59.1259444Z ok (0.073s)
2023-01-11T21:28:59.1259984Z   test_shape_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1260595Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1260967Z ok (0.021s)
2023-01-11T21:28:59.1261642Z   test_side_effects_codegen_update_mutated_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:28:59.1262113Z unimplemented []
2023-01-11T21:28:59.1262585Z graph_break [('Tensor.item', 4)]
2023-01-11T21:28:59.1263083Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:28:59.1263313Z ok (0.132s)
2023-01-11T21:28:59.1263717Z   test_size_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1264310Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.1264643Z ok (0.040s)
2023-01-11T21:28:59.1265324Z   test_slice_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:59.1265817Z ok (0.033s)
2023-01-11T21:28:59.1266309Z   test_tensor_build_list_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1267188Z   test_tensor_data_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1267508Z ok (0.020s)
2023-01-11T21:28:59.1267925Z   test_tensor_dict1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1268455Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1268815Z ok (0.019s)
2023-01-11T21:28:59.1269413Z   test_tensor_dict2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1270030Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:59.1270398Z ok (0.039s)
2023-01-11T21:28:59.1271024Z   test_tensor_dot_grad_no_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1271533Z unimplemented []
2023-01-11T21:28:59.1271784Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:28:59.1272284Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1272615Z ok (0.052s)
2023-01-11T21:28:59.1273432Z   test_tensor_is_contiguous_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1274077Z stats [('calls_captured', 10), ('fusions_possible', 8), ('unique_graphs', 2)]
2023-01-11T21:28:59.1274388Z ok (0.077s)
2023-01-11T21:28:59.1275060Z   test_tensor_item_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1275506Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1275719Z ok (0.036s)
2023-01-11T21:28:59.1276267Z   test_tensor_item_no_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1276715Z unimplemented []
2023-01-11T21:28:59.1277105Z graph_break [('Tensor.item', 1)]
2023-01-11T21:28:59.1277562Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1278050Z ok (0.029s)
2023-01-11T21:28:59.1278757Z   test_tensor_layout_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1279209Z ok (0.021s)
2023-01-11T21:28:59.1279780Z   test_tensor_types_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 10), ('ok', 10)]
2023-01-11T21:28:59.1280426Z stats [('calls_captured', 10), ('unique_graphs', 10), ('fusions_possible', 0)]
2023-01-11T21:28:59.1280787Z ok (0.087s)
2023-01-11T21:28:59.1281493Z   test_top_package_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1281995Z ok (0.022s)
2023-01-11T21:28:59.1282690Z   test_torch_cuda_is_available_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1283024Z ok (0.015s)
2023-01-11T21:28:59.1283361Z   test_torch_cudnn_is_acceptable_bad_inputs_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1283844Z   test_torch_cudnn_is_acceptable_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.000s)
2023-01-11T21:28:59.1284562Z   test_torch_nn_parameter_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1285194Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1285555Z ok (0.021s)
2023-01-11T21:28:59.1286343Z   test_torch_profiler_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... STAGE:2023-01-11 21:28:31 3597:3597 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:28:59.1287148Z STAGE:2023-01-11 21:28:31 3597:3597 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:28:59.1287670Z STAGE:2023-01-11 21:28:31 3597:3597 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:28:59.1288153Z [2023-01-11 21:28:31,040] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:28:59.1288753Z [2023-01-11 21:28:31,049] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:28:59.1289212Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1289486Z unimplemented []
2023-01-11T21:28:59.1289854Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:59.1290357Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1290684Z ok (0.067s)
2023-01-11T21:28:59.1291373Z   test_torch_seed_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1291790Z ok (0.011s)
2023-01-11T21:28:59.1292257Z   test_torch_size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1292586Z ok (0.024s)
2023-01-11T21:28:59.1293200Z   test_type_copy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1293831Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1294149Z ok (0.061s)
2023-01-11T21:28:59.1294804Z   test_typing_variable_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1295453Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1295833Z ok (0.023s)
2023-01-11T21:28:59.1296310Z   test_unpack4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:59.1296635Z ok (0.051s)
2023-01-11T21:28:59.1297176Z   test_unpack5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:59.1297637Z ok (0.049s)
2023-01-11T21:28:59.1298317Z   test_update_locals_and_stack_uses_shared_cache_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1298795Z inline_call []
2023-01-11T21:28:59.1299081Z unimplemented []
2023-01-11T21:28:59.1299577Z graph_break [('call_method ListVariable() extend [ListIteratorVariable()] {}', 1)]
2023-01-11T21:28:59.1299967Z ok (0.021s)
2023-01-11T21:28:59.1300539Z   test_user_defined_class_name_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1300864Z ok (0.034s)
2023-01-11T21:28:59.1301194Z   test_user_function_variable_supports_enum_argument_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1301554Z ok (0.020s)
2023-01-11T21:28:59.1302031Z   test_user_function_variable_supports_function_argument_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1302821Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1303169Z ok (0.021s)
2023-01-11T21:28:59.1303663Z   test_user_function_variable_supports_type_abcmeta_argument_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1304302Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1304666Z ok (0.022s)
2023-01-11T21:28:59.1305175Z   test_user_getattr1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1305476Z inline_call []
2023-01-11T21:28:59.1305931Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1306265Z ok (0.024s)
2023-01-11T21:28:59.1306885Z   test_user_getattr2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1307308Z inline_call []
2023-01-11T21:28:59.1307783Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1308129Z ok (0.029s)
2023-01-11T21:28:59.1308755Z   test_user_property_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1309172Z inline_call []
2023-01-11T21:28:59.1309474Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1309687Z ok (0.022s)
2023-01-11T21:28:59.1310037Z   test_usr_cls_classmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1310656Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1311004Z ok (0.030s)
2023-01-11T21:28:59.1311511Z   test_usr_cls_staticmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1312154Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1312479Z ok (0.030s)
2023-01-11T21:28:59.1312908Z   test_version_ci_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:59.1313546Z   test_write_to_closures_in_inlining_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1313990Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1314216Z ok (0.040s)
2023-01-11T21:28:59.1314509Z   test_access_by_keys_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1314929Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1315154Z ok (0.117s)
2023-01-11T21:28:59.1315620Z   test_basicmodule1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1315950Z ok (0.059s)
2023-01-11T21:28:59.1316435Z   test_basicmodule2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1316761Z ok (0.057s)
2023-01-11T21:28:59.1317061Z   test_call_fn_with_non_const_inputs_safe_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1317493Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1317719Z ok (0.212s)
2023-01-11T21:28:59.1318189Z   test_cfgmod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1318501Z ok (0.096s)
2023-01-11T21:28:59.1318969Z   test_children_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1319298Z ok (0.079s)
2023-01-11T21:28:59.1319759Z   test_constloop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1320085Z ok (0.148s)
2023-01-11T21:28:59.1320381Z   test_densenet_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1320791Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1321002Z ok (0.116s)
2023-01-11T21:28:59.1321299Z   test_enumvalues_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1321710Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1321918Z ok (0.113s)
2023-01-11T21:28:59.1322392Z   test_fnmember_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1322769Z ok (0.048s)
2023-01-11T21:28:59.1323242Z   test_fnmembercmp1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1323563Z ok (0.049s)
2023-01-11T21:28:59.1324038Z   test_fnmembercmp2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1324371Z ok (0.060s)
2023-01-11T21:28:59.1324657Z   test_forward_directly_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1325079Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1325305Z ok (0.077s)
2023-01-11T21:28:59.1325604Z   test_generation_tag_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:59.1326218Z   test_hasattr_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1326550Z ok (0.037s)
2023-01-11T21:28:59.1327017Z   test_intarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1327343Z ok (0.052s)
2023-01-11T21:28:59.1327793Z   test_iseval1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1328123Z ok (0.046s)
2023-01-11T21:28:59.1328592Z   test_iseval2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1328901Z ok (0.047s)
2023-01-11T21:28:59.1329382Z   test_isnonelayer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1329716Z ok (0.034s)
2023-01-11T21:28:59.1330188Z   test_istraining1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1330503Z ok (0.047s)
2023-01-11T21:28:59.1330972Z   test_istraining2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1331299Z ok (0.046s)
2023-01-11T21:28:59.1331776Z   test_layerlist_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1332093Z ok (0.065s)
2023-01-11T21:28:59.1332786Z   test_lazy_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:59.1333396Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:59.1334065Z [2023-01-11 21:28:33,313] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7f8c57632950> [SizeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:28:59.1334799Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:59.1335289Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:59.1335922Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:59.1336400Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:59.1337048Z [2023-01-11 21:28:33,442] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7f8c57632950> [SizeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:28:59.1337791Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:59.1338273Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:59.1338598Z frames [('total', 16), ('ok', 14)]
2023-01-11T21:28:59.1339353Z inline_call [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2), ('call_function UserDefinedObjectVariable(_infer_parameters) [NNModuleVariable(), TupleVariable()] {}', 1), ('call_function UserDefinedObjectVariable(_infer_parameters) [UnspecializedNNModuleVariable(LazyModule), TupleVariable()] {}', 1)]
2023-01-11T21:28:59.1340017Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.LazyModule'>", 2)]
2023-01-11T21:28:59.1340420Z graph_break [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2)]
2023-01-11T21:28:59.1340787Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:59.1341003Z ok (0.305s)
2023-01-11T21:28:59.1341327Z   test_module_attribute_precedence_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1341765Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1341994Z ok (0.045s)
2023-01-11T21:28:59.1342288Z   test_module_class_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1342862Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:59.1343091Z ok (0.128s)
2023-01-11T21:28:59.1343520Z   test_module_forward_has_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 2)]
2023-01-11T21:28:59.1343838Z inline_call []
2023-01-11T21:28:59.1344124Z unimplemented [('reconstruct: ConstantVariable(dict)', 2)]
2023-01-11T21:28:59.1344589Z graph_break [('call_function BuiltinVariable(dict) [ListIteratorVariable()] {}', 1), ('call_method NNModuleVariable() buffers [] {}', 1)]
2023-01-11T21:28:59.1345001Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1345231Z ok (0.184s)
2023-01-11T21:28:59.1345728Z   test_module_name_string_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1346055Z ok (0.055s)
2023-01-11T21:28:59.1346364Z   test_module_property_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1346784Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1347010Z ok (0.027s)
2023-01-11T21:28:59.1347302Z   test_module_static_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1347723Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:59.1347948Z ok (0.126s)
2023-01-11T21:28:59.1348419Z   test_moduledict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1348813Z ok (0.032s)
2023-01-11T21:28:59.1349292Z   test_modulelist_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 40), ('fusions_possible', 39), ('unique_graphs', 1)]
2023-01-11T21:28:59.1349625Z ok (0.522s)
2023-01-11T21:28:59.1349916Z   test_modulemethod1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1350339Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:59.1350569Z ok (0.128s)
2023-01-11T21:28:59.1350856Z   test_modulemethod2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1351335Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:59.1351567Z ok (0.127s)
2023-01-11T21:28:59.1352117Z   test_nn_moduledict_contains_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:59.1352503Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:59.1352782Z inline_call [('Patched init cannot be inlined.', 1)]
2023-01-11T21:28:59.1353243Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.NNModuleTests.test_nn_moduledict_contains.<locals>.M'>", 1)]
2023-01-11T21:28:59.1353626Z graph_break [('Patched init cannot be inlined.', 1)]
2023-01-11T21:28:59.1353836Z ok (0.046s)
2023-01-11T21:28:59.1354137Z   test_parameters1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1354555Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1354773Z ok (0.023s)
2023-01-11T21:28:59.1355072Z   test_parameters2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1355487Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1355697Z ok (0.024s)
2023-01-11T21:28:59.1356174Z   test_parameters3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1356508Z ok (0.073s)
2023-01-11T21:28:59.1356987Z   test_self_mutating1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:59.1357308Z ok (0.068s)
2023-01-11T21:28:59.1357769Z   test_seq_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1358090Z ok (0.078s)
2023-01-11T21:28:59.1358384Z   test_simple_torch_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1358811Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1359036Z ok (0.109s)
2023-01-11T21:28:59.1359515Z   test_stringmember_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1359831Z ok (0.048s)
2023-01-11T21:28:59.1360127Z   test_submodules1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1360540Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1360752Z ok (0.111s)
2023-01-11T21:28:59.1361047Z   test_submodules2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1361503Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1361726Z ok (0.111s)
2023-01-11T21:28:59.1362003Z   test_super1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1362412Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1362638Z ok (0.066s)
2023-01-11T21:28:59.1362916Z   test_super2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1363324Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1363553Z ok (0.057s)
2023-01-11T21:28:59.1363856Z   test_super_class_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1364263Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1364490Z ok (0.021s)
2023-01-11T21:28:59.1364994Z   test_tensorlist_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1365308Z ok (0.059s)
2023-01-11T21:28:59.1365811Z   test_torch_function_with_closure_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1366159Z ok (0.102s)
2023-01-11T21:28:59.1366588Z   test_unsupportedmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1367099Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1367395Z unimplemented []
2023-01-11T21:28:59.1367787Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1368192Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:59.1368421Z ok (0.095s)
2023-01-11T21:28:59.1368850Z   test_unsupportedmodule_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1369367Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1369647Z unimplemented []
2023-01-11T21:28:59.1370028Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1370440Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:28:59.1370651Z ok (0.097s)
2023-01-11T21:28:59.1370956Z   test_viamodulecall_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1371383Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1371609Z ok (0.072s)
2023-01-11T21:28:59.1371993Z   test_Size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1372469Z inline_call [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:59.1372742Z unimplemented []
2023-01-11T21:28:59.1373084Z graph_break [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:59.1373476Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1373702Z ok (0.028s)
2023-01-11T21:28:59.1374113Z   test_abc_setattr_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1374406Z unimplemented []
2023-01-11T21:28:59.1374839Z graph_break [('setattr(UserDefinedObjectVariable) <function ReproTests.test_abc_setattr.<locals>.Derived.__setattr__ at 0x7f8c1d39be20>', 1)]
2023-01-11T21:28:59.1375186Z inline_call []
2023-01-11T21:28:59.1375473Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1375700Z ok (0.025s)
2023-01-11T21:28:59.1376141Z   test_avoid_dupe_specialization_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1376588Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1376863Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1377049Z ok (0.155s)
2023-01-11T21:28:59.1377352Z   test_batch_norm_act_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1377754Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1377978Z ok (0.172s)
2023-01-11T21:28:59.1378480Z   test_batchnorm_e2e_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:59.1378848Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1379036Z inline_call []
2023-01-11T21:28:59.1379332Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1379618Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1379788Z ok (2.052s)
2023-01-11T21:28:59.1380224Z   test_bigbird_unsqueeze_inplace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1380664Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1380938Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1381122Z ok (0.438s)
2023-01-11T21:28:59.1381421Z   test_boxes_len_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1381825Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1382046Z ok (0.027s)
2023-01-11T21:28:59.1382494Z   test_chunk_reformer_ff_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1383129Z   test_class_member_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1383451Z ok (0.045s)
2023-01-11T21:28:59.1383898Z   test_convert_boxes_to_pooler_format_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1383971Z inline_call []
2023-01-11T21:28:59.1384046Z unimplemented []
2023-01-11T21:28:59.1384259Z graph_break [('dynamic shape operator: aten.repeat_interleave.Tensor', 2)]
2023-01-11T21:28:59.1384464Z stats [('calls_captured', 18), ('fusions_possible', 14), ('unique_graphs', 4)]
2023-01-11T21:28:59.1384533Z expected failure (0.180s)
2023-01-11T21:28:59.1384777Z   test_create_rand_mask_from_inputs_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:59.1384974Z   test_dict_iter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.005s)
2023-01-11T21:28:59.1385298Z   test_dict_list_values_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:59.1385378Z unimplemented []
2023-01-11T21:28:59.1385723Z graph_break [('call_function in skip_files Builtin count', 2), ('call_function BuiltinVariable(zip) [UserDefinedObjectVariable(count), ListVariable()] {}', 2)]
2023-01-11T21:28:59.1385791Z ok (0.059s)
2023-01-11T21:28:59.1386102Z   test_do_paste_mask_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1)]
2023-01-11T21:28:59.1386227Z expected failure (0.032s)
2023-01-11T21:28:59.1386635Z   test_dynamic_shapes_right_side_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1386705Z ok (0.050s)
2023-01-11T21:28:59.1387085Z   test_ellipsis_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1387153Z ok (0.143s)
2023-01-11T21:28:59.1387472Z   test_exec_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:59.1387693Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1387773Z unimplemented []
2023-01-11T21:28:59.1388045Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1388099Z ok (0.004s)
2023-01-11T21:28:59.1388432Z   test_exec_wildcard_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:59.1388647Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1388728Z unimplemented []
2023-01-11T21:28:59.1388941Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1389138Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1389205Z ok (0.016s)
2023-01-11T21:28:59.1389547Z   test_for_loop_graph_break_before_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1389610Z unimplemented []
2023-01-11T21:28:59.1389887Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:28:59.1389959Z inline_call []
2023-01-11T21:28:59.1390163Z stats [('calls_captured', 100), ('fusions_possible', 99), ('unique_graphs', 1)]
2023-01-11T21:28:59.1390230Z ok (0.720s)
2023-01-11T21:28:59.1390559Z   test_for_loop_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1390634Z inline_call []
2023-01-11T21:28:59.1390896Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:28:59.1391094Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1391160Z ok (0.032s)
2023-01-11T21:28:59.1391434Z   test_get_parameter_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1391641Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1391709Z ok (0.030s)
2023-01-11T21:28:59.1391945Z   test_grad_mode_carrying_correct_state_after_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... Break
2023-01-11T21:28:59.1392066Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1392130Z unimplemented []
2023-01-11T21:28:59.1392354Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1392549Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1392615Z ok (0.033s)
2023-01-11T21:28:59.1392951Z   test_guard_fail_nested_tuple_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1393149Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1393215Z ok (0.037s)
2023-01-11T21:28:59.1393600Z   test_guard_fail_tensor_bool_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 12), ('ok', 12)]
2023-01-11T21:28:59.1393791Z unimplemented [('FOR_ITER UserDefinedObjectVariable(product)', 1)]
2023-01-11T21:28:59.1394232Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_guard_fail_tensor_bool.<locals>.fn.<locals>.get_expected at 0x7f8c8f784670>', 5), ('data dependent operator: aten.allclose.default', 5)]
2023-01-11T21:28:59.1394429Z stats [('calls_captured', 5), ('unique_graphs', 5), ('fusions_possible', 0)]
2023-01-11T21:28:59.1394495Z ok (0.211s)
2023-01-11T21:28:59.1394707Z   test_guard_ordering_shape_fail_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:59.1394915Z   test_hf_model_output_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1395147Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:28:59.1395213Z ok (0.104s)
2023-01-11T21:28:59.1395415Z   test_hf_t5_forward_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1395606Z stats [('calls_captured', 14), ('fusions_possible', 13), ('unique_graphs', 1)]
2023-01-11T21:28:59.1395688Z expected failure (0.663s)
2023-01-11T21:28:59.1396016Z   test_indexing_with_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1396144Z inline_call [('Tensor.numpy', 1)]
2023-01-11T21:28:59.1396351Z unimplemented [('COMPARE_OP ConstantVariable(tuple) == SizeVariable()', 1)]
2023-01-11T21:28:59.1396479Z graph_break [('Tensor.numpy', 1)]
2023-01-11T21:28:59.1396545Z ok (0.036s)
2023-01-11T21:28:59.1396760Z   test_is_symbolic_tracing_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1396945Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1397010Z ok (0.016s)
2023-01-11T21:28:59.1397213Z   test_isinstance_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.007s)
2023-01-11T21:28:59.1397758Z   test_isinstance_storage_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:28:59.1397880Z   bools = torch.BoolStorage.from_buffer(f, "big")
2023-01-11T21:28:59.1398000Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:28:59.1398079Z unimplemented []
2023-01-11T21:28:59.1398454Z graph_break [('call_function BuiltinVariable(bytearray) [ListVariable()] {}', 1), ('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:28:59.1398718Z inline_call [('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:28:59.1398900Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1398983Z expected failure (0.025s)
2023-01-11T21:28:59.1399191Z   test_issue1466_size_aot_autograd_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... arf
2023-01-11T21:28:59.1399255Z arf
2023-01-11T21:28:59.1399373Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1399447Z unimplemented []
2023-01-11T21:28:59.1399671Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1399864Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1400020Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1400087Z ok (0.172s)
2023-01-11T21:28:59.1400286Z   test_issue175_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1400489Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:28:59.1400557Z ok (0.467s)
2023-01-11T21:28:59.1400948Z   test_longformer_chunk_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 38), ('fusions_possible', 36), ('unique_graphs', 2)]
2023-01-11T21:28:59.1401014Z ok (1.390s)
2023-01-11T21:28:59.1401233Z   test_maml_item_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.002s)
2023-01-11T21:28:59.1401579Z   test_maml_no_item_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:59.1401782Z inline_call [('inlining disallowed: <function grad at 0x7f8c57559480>', 1)]
2023-01-11T21:28:59.1401859Z unimplemented []
2023-01-11T21:28:59.1402196Z graph_break [('Tensor.item', 2), ('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 1), ('inlining disallowed: <function grad at 0x7f8c57559480>', 1)]
2023-01-11T21:28:59.1402397Z stats [('calls_captured', 36), ('fusions_possible', 31), ('unique_graphs', 5)]
2023-01-11T21:28:59.1402464Z ok (0.681s)
2023-01-11T21:28:59.1402839Z   test_modules_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1402905Z ok (0.069s)
2023-01-11T21:28:59.1403214Z   test_multi_dot_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1403507Z inline_call [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:28:59.1403585Z unimplemented []
2023-01-11T21:28:59.1403870Z graph_break [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:28:59.1404068Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1404135Z ok (0.027s)
2023-01-11T21:28:59.1404361Z   test_multi_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires detectron2 (0.000s)
2023-01-11T21:28:59.1404746Z   test_named_buffers_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1404812Z ok (0.051s)
2023-01-11T21:28:59.1405120Z   test_nn_parameter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1405362Z inline_call [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:59.1405438Z unimplemented []
2023-01-11T21:28:59.1405673Z graph_break [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:59.1405869Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1405935Z ok (0.016s)
2023-01-11T21:28:59.1406152Z   test_norm_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1406525Z   test_not_rewrite_assert_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:59.1406578Z ok (0.021s)
2023-01-11T21:28:59.1406933Z   test_not_rewrite_assert_for_other_errors_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1407185Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1407251Z ok (0.025s)
2023-01-11T21:28:59.1407565Z   test_numpy_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:59.1407640Z unimplemented []
2023-01-11T21:28:59.1407951Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_numpy_list.<locals>.rand_gen at 0x7f8c876a2f80>', 1)]
2023-01-11T21:28:59.1408035Z expected failure (0.032s)
2023-01-11T21:28:59.1408352Z   test_optimized_deepcopy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1408551Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1408618Z ok (0.037s)
2023-01-11T21:28:59.1408960Z   test_primtorch_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1409241Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:28:59.1409320Z unimplemented []
2023-01-11T21:28:59.1409600Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:28:59.1409665Z ok (0.013s)
2023-01-11T21:28:59.1410143Z   test_primtorch_no_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:28:59.1410213Z expected failure (0.006s)
2023-01-11T21:28:59.1410537Z   test_recursive_map_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1410614Z inline_call []
2023-01-11T21:28:59.1410812Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1410879Z ok (0.044s)
2023-01-11T21:28:59.1411084Z   test_reformer_eval_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1411284Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:28:59.1411350Z ok (0.426s)
2023-01-11T21:28:59.1411548Z   test_reformer_min_chunk_len_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1411748Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1411813Z ok (0.018s)
2023-01-11T21:28:59.1412025Z   test_reformer_remove_unused_args_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... foo
2023-01-11T21:28:59.1412087Z foo
2023-01-11T21:28:59.1412212Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1412434Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1412496Z unimplemented []
2023-01-11T21:28:59.1412720Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:28:59.1412917Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1413049Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1413113Z ok (0.168s)
2023-01-11T21:28:59.1413321Z   test_reformer_sorting_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:59.1413521Z stats [('calls_captured', 28), ('fusions_possible', 27), ('unique_graphs', 1)]
2023-01-11T21:28:59.1413586Z ok (0.098s)
2023-01-11T21:28:59.1413893Z   test_reformer_train_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1414220Z inline_call [('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:28:59.1414296Z unimplemented []
2023-01-11T21:28:59.1414661Z graph_break [('autograd.Function with requires_grad', 1), ('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:28:59.1414860Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:28:59.1414927Z ok (0.429s)
2023-01-11T21:28:59.1415248Z   test_reinplacing_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1415445Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1415559Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1415624Z ok (0.488s)
2023-01-11T21:28:59.1416045Z   test_relative_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1416114Z ok (0.034s)
2023-01-11T21:28:59.1416523Z   test_relative_import_no_modulename_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1416592Z ok (0.033s)
2023-01-11T21:28:59.1416989Z   test_rewrite_assert_noop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:59.1417055Z ok (0.091s)
2023-01-11T21:28:59.1417448Z   test_rewrite_assert_with_fstring_msg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:59.1417500Z ok (0.022s)
2023-01-11T21:28:59.1417905Z   test_rewrite_assert_with_msg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 18), ('fusions_possible', 15), ('unique_graphs', 3)]
2023-01-11T21:28:59.1417973Z ok (0.113s)
2023-01-11T21:28:59.1418382Z   test_rewrite_assert_without_msg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 12), ('fusions_possible', 10), ('unique_graphs', 2)]
2023-01-11T21:28:59.1418448Z ok (0.076s)
2023-01-11T21:28:59.1418767Z   test_rng_state_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1419012Z unimplemented [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 1)]
2023-01-11T21:28:59.1419245Z graph_break [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 2)]
2023-01-11T21:28:59.1419442Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1419499Z ok (0.039s)
2023-01-11T21:28:59.1419887Z   test_seq_append_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1419956Z ok (0.115s)
2023-01-11T21:28:59.1420632Z   test_sigmoid_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:59.1436199Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:28:59.1437447Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:28:59.1437531Z   warnings.warn(msg)
2023-01-11T21:28:59.1438149Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:59.1438244Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:28:59.1438354Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:59.1438555Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:28:59.1438628Z ok (0.035s)
2023-01-11T21:28:59.1439036Z   test_slice_into_list_mutable_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 30), ('fusions_possible', 29), ('unique_graphs', 1)]
2023-01-11T21:28:59.1439102Z ok (0.091s)
2023-01-11T21:28:59.1439443Z   test_slicing_dynamic_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1439613Z unimplemented [('Dynamic slicing not supported', 2)]
2023-01-11T21:28:59.1439814Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1439867Z ok (0.028s)
2023-01-11T21:28:59.1440219Z   test_slicing_dynamic_shape_setitem_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:59.1440385Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:28:59.1440550Z graph_break [('Dynamic slicing not supported', 1)]
2023-01-11T21:28:59.1440748Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1440873Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1440937Z ok (0.040s)
2023-01-11T21:28:59.1441608Z   test_sort_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:59.1441717Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:28:59.1442596Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:59.1442691Z   return node.target(*args, **kwargs)
2023-01-11T21:28:59.1443251Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:59.1443396Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:28:59.1443956Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:59.1444090Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:28:59.1444203Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:59.1444401Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:28:59.1444468Z ok (0.046s)
2023-01-11T21:28:59.1444802Z   test_specialized_stride_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1445000Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1445068Z ok (0.012s)
2023-01-11T21:28:59.1445401Z   test_swin_base_tensor_attr_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1445601Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1445653Z ok (0.090s)
2023-01-11T21:28:59.1445993Z   test_tensor_isinstance_tuple_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1446195Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1446262Z ok (0.011s)
2023-01-11T21:28:59.1446581Z   test_tokenization_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1446821Z inline_call [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:28:59.1446898Z unimplemented []
2023-01-11T21:28:59.1447141Z graph_break [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:28:59.1447193Z ok (0.019s)
2023-01-11T21:28:59.1447579Z   test_torch_ops_aten_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1447646Z ok (0.022s)
2023-01-11T21:28:59.1448044Z   test_vdd_duplicate_error_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1448109Z ok (0.071s)
2023-01-11T21:28:59.1448444Z   test_while_loop_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1448514Z inline_call []
2023-01-11T21:28:59.1448788Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:28:59.1448974Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1449038Z ok (0.029s)
2023-01-11T21:28:59.1449248Z   test_with_on_graph_break_inst_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... Hello world
2023-01-11T21:28:59.1449313Z Hello world
2023-01-11T21:28:59.1449435Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:28:59.1449689Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:59.1449764Z unimplemented []
2023-01-11T21:28:59.1450011Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2), ('Tensor.backward', 1)]
2023-01-11T21:28:59.1450214Z stats [('calls_captured', 11), ('fusions_possible', 7), ('unique_graphs', 4)]
2023-01-11T21:28:59.1450280Z ok (0.118s)
2023-01-11T21:28:59.1450597Z   test_capi_call1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1450673Z unimplemented []
2023-01-11T21:28:59.1450955Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1451152Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1451218Z ok (0.023s)
2023-01-11T21:28:59.1451544Z   test_capi_call2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1451623Z unimplemented []
2023-01-11T21:28:59.1451899Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1452098Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1452163Z ok (0.046s)
2023-01-11T21:28:59.1452473Z   test_capi_call3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1452548Z unimplemented []
2023-01-11T21:28:59.1452820Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1453000Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1453065Z ok (0.024s)
2023-01-11T21:28:59.1453384Z   test_control_flow1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1453582Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:59.1453650Z ok (0.047s)
2023-01-11T21:28:59.1453967Z   test_control_flow2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1454166Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1454230Z ok (0.024s)
2023-01-11T21:28:59.1454527Z   test_control_flow3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1454720Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:59.1454783Z ok (0.077s)
2023-01-11T21:28:59.1455104Z   test_control_flow4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:59.1455303Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:28:59.1455369Z ok (0.041s)
2023-01-11T21:28:59.1455677Z   test_control_flow5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:59.1455876Z stats [('calls_captured', 13), ('fusions_possible', 7), ('unique_graphs', 6)]
2023-01-11T21:28:59.1455927Z ok (0.101s)
2023-01-11T21:28:59.1456249Z   test_dynamic_duck_size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1456443Z stats [('calls_captured', 10), ('fusions_possible', 8), ('unique_graphs', 2)]
2023-01-11T21:28:59.1456508Z ok (0.042s)
2023-01-11T21:28:59.1456820Z   test_dynamic_kwarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1457053Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1457118Z ok (0.023s)
2023-01-11T21:28:59.1457458Z   test_dynamic_order_dependence_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1457641Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:59.1457707Z ok (0.069s)
2023-01-11T21:28:59.1458034Z   test_dynamic_shapes_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 11), ('ok', 11)]
2023-01-11T21:28:59.1458235Z stats [('calls_captured', 22), ('fusions_possible', 11), ('unique_graphs', 11)]
2023-01-11T21:28:59.1458301Z ok (0.062s)
2023-01-11T21:28:59.1458637Z   test_dynamic_zero_inference_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1458871Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1458938Z ok (0.025s)
2023-01-11T21:28:59.1459276Z   test_enumerate_not_break_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1459460Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1459542Z expected failure (0.027s)
2023-01-11T21:28:59.1459863Z   test_extended_args_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1460073Z stats [('calls_captured', 1026), ('fusions_possible', 1023), ('unique_graphs', 3)]
2023-01-11T21:28:59.1460138Z ok (7.169s)
2023-01-11T21:28:59.1460463Z   test_graph_break_on_item_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1460542Z unimplemented []
2023-01-11T21:28:59.1460671Z graph_break [('Tensor.item', 1)]
2023-01-11T21:28:59.1460858Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:59.1460923Z ok (0.052s)
2023-01-11T21:28:59.1461257Z   test_indirect_unsupported1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1461537Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1461613Z unimplemented []
2023-01-11T21:28:59.1461892Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1462087Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1462139Z ok (0.045s)
2023-01-11T21:28:59.1462616Z   test_indirect_unsupported2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1462906Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1462982Z unimplemented []
2023-01-11T21:28:59.1463261Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1463457Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:28:59.1463523Z ok (0.070s)
2023-01-11T21:28:59.1463857Z   test_indirect_unsupported3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1464136Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1464199Z unimplemented []
2023-01-11T21:28:59.1464479Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1464734Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:59.1464801Z ok (0.045s)
2023-01-11T21:28:59.1465116Z   test_multigraph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1465315Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:59.1465379Z ok (0.044s)
2023-01-11T21:28:59.1465715Z   test_no_graph_break_on_item_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1465897Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:59.1465964Z ok (0.048s)
2023-01-11T21:28:59.1466285Z   test_pop_after_resume_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1466418Z unimplemented []
2023-01-11T21:28:59.1466701Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1466899Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1466976Z ok (0.044s)
2023-01-11T21:28:59.1467412Z   test_restore_range_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1467535Z unimplemented []
2023-01-11T21:28:59.1468018Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1468387Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1468544Z ok (0.044s)
2023-01-11T21:28:59.1469052Z   test_restore_range_iter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1469219Z unimplemented []
2023-01-11T21:28:59.1469647Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1469995Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:59.1470119Z ok (0.025s)
2023-01-11T21:28:59.1470638Z   test_restore_state_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:59.1470816Z unimplemented []
2023-01-11T21:28:59.1471348Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1471716Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1471881Z expected failure (0.037s)
2023-01-11T21:28:59.1472364Z   test_resume1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1472532Z unimplemented []
2023-01-11T21:28:59.1472941Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1473308Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1473465Z ok (0.057s)
2023-01-11T21:28:59.1473984Z   test_resume2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1474454Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1474628Z unimplemented []
2023-01-11T21:28:59.1475063Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1475390Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:59.1475500Z ok (0.079s)
2023-01-11T21:28:59.1475994Z   test_resume3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1476600Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1476777Z unimplemented []
2023-01-11T21:28:59.1477252Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1477626Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:59.1477734Z ok (0.078s)
2023-01-11T21:28:59.1478166Z   test_resume4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1478431Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1478509Z unimplemented []
2023-01-11T21:28:59.1478829Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1479152Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:59.1479276Z ok (0.082s)
2023-01-11T21:28:59.1479594Z   test_resume5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:59.1479701Z         1.5000])
2023-01-11T21:28:59.1479857Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:59.1479949Z         1.5000])
2023-01-11T21:28:59.1480115Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:59.1480236Z         1.5000])
2023-01-11T21:28:59.1480410Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:59.1480527Z         1.5000])
2023-01-11T21:28:59.1480737Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1480881Z unimplemented []
2023-01-11T21:28:59.1481165Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1481454Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1481574Z ok (0.062s)
2023-01-11T21:28:59.1482046Z   test_resume_freevars_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1482163Z unimplemented []
2023-01-11T21:28:59.1482563Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1482868Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:59.1482984Z ok (0.061s)
2023-01-11T21:28:59.1483386Z   test_resume_paths_join_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:59.1483593Z stats [('calls_captured', 10), ('unique_graphs', 7), ('fusions_possible', 3)]
2023-01-11T21:28:59.1483661Z ok (0.135s)
2023-01-11T21:28:59.1484114Z   test_resume_tuple_iterator_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1484244Z unimplemented []
2023-01-11T21:28:59.1484642Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1484959Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:59.1485073Z ok (0.081s)
2023-01-11T21:28:59.1485524Z   test_resume_with_no_grad1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1485667Z unimplemented []
2023-01-11T21:28:59.1485894Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:28:59.1486201Z stats [('calls_captured', 18), ('fusions_possible', 14), ('unique_graphs', 4)]
2023-01-11T21:28:59.1486398Z ok (0.108s)
2023-01-11T21:28:59.1486889Z   test_resume_with_no_grad2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1487007Z unimplemented []
2023-01-11T21:28:59.1487134Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:28:59.1487337Z stats [('calls_captured', 13), ('fusions_possible', 10), ('unique_graphs', 3)]
2023-01-11T21:28:59.1487402Z ok (0.080s)
2023-01-11T21:28:59.1487730Z   test_resume_with_no_grad3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1487807Z unimplemented []
2023-01-11T21:28:59.1487937Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:59.1488136Z stats [('calls_captured', 19), ('fusions_possible', 17), ('unique_graphs', 2)]
2023-01-11T21:28:59.1488224Z ok (0.056s)
2023-01-11T21:28:59.1488746Z   test_stack_state1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1488867Z unimplemented []
2023-01-11T21:28:59.1489289Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1489592Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:59.1489693Z ok (0.064s)
2023-01-11T21:28:59.1490164Z   test_stack_state2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1490570Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1490685Z unimplemented []
2023-01-11T21:28:59.1491080Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1491385Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:59.1491457Z ok (0.087s)
2023-01-11T21:28:59.1491667Z   test_start1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:28:59.1491831Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:28:59.1491922Z tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:28:59.1492080Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:28:59.1492200Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1492266Z unimplemented []
2023-01-11T21:28:59.1492492Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1492813Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1492935Z ok (0.041s)
2023-01-11T21:28:59.1493394Z   test_start2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1493822Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1493939Z unimplemented []
2023-01-11T21:28:59.1494318Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1494636Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:59.1494739Z ok (0.070s)
2023-01-11T21:28:59.1495186Z   test_start3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:59.1495323Z unimplemented []
2023-01-11T21:28:59.1495723Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:59.1495927Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1495993Z ok (0.037s)
2023-01-11T21:28:59.1496354Z   test_start4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1496552Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:59.1496617Z ok (0.053s)
2023-01-11T21:28:59.1496851Z   test_tuple_iterator_mutate_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: not working yet (0.001s)
2023-01-11T21:28:59.1497319Z   test_tuple_iterator_return_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1497448Z unimplemented []
2023-01-11T21:28:59.1497852Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:59.1498170Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:28:59.1498265Z ok (0.073s)
2023-01-11T21:28:59.1498665Z   test_builtin_functions_on_cuda_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:59.1499139Z   test_builtin_getitem_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1)]
2023-01-11T21:28:59.1499264Z expected failure (0.015s)
2023-01-11T21:28:59.1499744Z   test_builtin_max_min_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1500065Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:59.1500137Z ok (0.024s)
2023-01-11T21:28:59.1500494Z   test_feed_random_values_into_graph_only_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1500689Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1500747Z ok (0.020s)
2023-01-11T21:28:59.1501138Z   test_multiple_consecutive_random_calls_before_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1501452Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1501571Z ok (0.030s)
2023-01-11T21:28:59.1502044Z   test_no_recompilations_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1502469Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:59.1502582Z ok (0.021s)
2023-01-11T21:28:59.1503054Z   test_numpy_correctness_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:59.1503339Z unimplemented [('reconstruct: ConstantVariable(float64)', 1)]
2023-01-11T21:28:59.1503578Z graph_break [('Tensor.numpy', 2), ('numpy', 2)]
2023-01-11T21:28:59.1503885Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:59.1504001Z ok (0.080s)
2023-01-11T21:28:59.1504513Z   test_random_call_with_while_loop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1504713Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:59.1504779Z ok (0.018s)
2023-01-11T21:28:59.1505122Z   test_random_values_with_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:59.1505185Z unimplemented []
2023-01-11T21:28:59.1505310Z graph_break [('Tensor.item', 2)]
2023-01-11T21:28:59.1505590Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:59.1505704Z ok (0.071s)
2023-01-11T21:28:59.1506194Z   test_unspec_float_precision_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:59.1506641Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:59.1506762Z ok (0.716s)
2023-01-11T21:28:59.1506774Z 
2023-01-11T21:28:59.1507071Z ----------------------------------------------------------------------
2023-01-11T21:28:59.1507194Z Ran 510 tests in 39.763s
2023-01-11T21:28:59.1507202Z 
2023-01-11T21:28:59.1507366Z OK (skipped=24, expected failures=16)
2023-01-11T21:28:59.1507376Z 
2023-01-11T21:28:59.1507530Z Generating XML reports...
2023-01-11T21:28:59.1508087Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesExportTests-20230111212818.xml
2023-01-11T21:28:59.1508659Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesFunctionTests-20230111212818.xml
2023-01-11T21:28:59.1509099Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesMiscTests-20230111212818.xml
2023-01-11T21:28:59.1509617Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesNNModuleTests-20230111212818.xml
2023-01-11T21:28:59.1510168Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesReproTests-20230111212818.xml
2023-01-11T21:28:59.1510715Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesSubGraphTests-20230111212818.xml
2023-01-11T21:28:59.1511314Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesUnspecTests-20230111212818.xml
2023-01-11T21:28:59.1511327Z 
2023-01-11T21:28:59.1511805Z ##[endgroup]
2023-01-11T21:28:59.1512294Z FINISHED PRINTING LOG FILE of dynamo/test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_dynamic_shapes_kfuvrwhx)
2023-01-11T21:28:59.1512306Z 
2023-01-11T21:29:01.0842591Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:01.1717196Z Ignoring disabled issues:  []
2023-01-11T21:29:01.1864945Z Running dynamo/test_export_mutations ... [2023-01-11 21:29:01.186193]
2023-01-11T21:29:01.1867093Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_export_mutations.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:01.186478]
2023-01-11T21:29:03.3440742Z 
2023-01-11T21:29:03.3441282Z Expand the folded group to see the log file of dynamo/test_export_mutations
2023-01-11T21:29:03.3442219Z ##[group]PRINTING LOG FILE of dynamo/test_export_mutations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_mutations_u16fd47j)
2023-01-11T21:29:03.3442908Z 
2023-01-11T21:29:03.3443154Z Running tests...
2023-01-11T21:29:03.3443853Z ----------------------------------------------------------------------
2023-01-11T21:29:03.3444497Z Test results will be stored in test-reports/python-unittest/dynamo.test_export_mutations
2023-01-11T21:29:03.3445765Z   test_module_attribute_mutation_violation_negative_1 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88468 for platform(s) linux, macos, mac. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.235s)
2023-01-11T21:29:03.3447643Z   test_module_attribute_mutation_violation_negative_2 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88475 for platform(s) linux, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:29:03.3450078Z   test_module_attribute_mutation_violation_negative_3 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88466 for platform(s) linux, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:29:03.3452737Z   test_module_attribute_mutation_violation_negative_4 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88467 for platform(s) linux, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:29:03.3454115Z   test_module_attribute_mutation_violation_positive_1 (__main__.MutationExportTests) ... ok (0.027s)
2023-01-11T21:29:03.3455040Z   test_module_attribute_mutation_violation_positive_2 (__main__.MutationExportTests) ... ok (0.003s)
2023-01-11T21:29:03.3455834Z   test_module_attribute_mutation_violation_positive_3 (__main__.MutationExportTests) ... ok (0.002s)
2023-01-11T21:29:03.3456631Z   test_module_attribute_mutation_violation_positive_4 (__main__.MutationExportTests) ... inline_call []
2023-01-11T21:29:03.3457173Z ok (0.004s)
2023-01-11T21:29:03.3457394Z 
2023-01-11T21:29:03.3457837Z ----------------------------------------------------------------------
2023-01-11T21:29:03.3458349Z Ran 8 tests in 0.273s
2023-01-11T21:29:03.3501037Z 
2023-01-11T21:29:03.3501360Z OK (skipped=4)
2023-01-11T21:29:03.3501560Z 
2023-01-11T21:29:03.3501723Z Generating XML reports...
2023-01-11T21:29:03.3504375Z Generated XML report: test-reports/python-unittest/dynamo.test_export_mutations/TEST-MutationExportTests-20230111212902.xml
2023-01-11T21:29:03.3504840Z 
2023-01-11T21:29:03.3505366Z ##[endgroup]
2023-01-11T21:29:03.3506197Z FINISHED PRINTING LOG FILE of dynamo/test_export_mutations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_mutations_u16fd47j)
2023-01-11T21:29:03.3506585Z 
2023-01-11T21:29:05.3034573Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:05.3880994Z Ignoring disabled issues:  []
2023-01-11T21:29:05.4025802Z Running dynamo/test_functions ... [2023-01-11 21:29:05.402287]
2023-01-11T21:29:05.4027374Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_functions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:05.402536]
2023-01-11T21:29:08.5404848Z 
2023-01-11T21:29:08.5405593Z Expand the folded group to see the log file of dynamo/test_functions
2023-01-11T21:29:08.5406694Z ##[group]PRINTING LOG FILE of dynamo/test_functions (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_functions_lnsbaz_r)
2023-01-11T21:29:08.5407122Z 
2023-01-11T21:29:08.5407269Z Running tests...
2023-01-11T21:29:08.5407958Z ----------------------------------------------------------------------
2023-01-11T21:29:08.5408682Z Test results will be stored in test-reports/python-unittest/dynamo.test_functions
2023-01-11T21:29:08.5409047Z   test_T (__main__.FunctionTests) ... ok (0.348s)
2023-01-11T21:29:08.5409457Z   test_add (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5409852Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5410068Z ok (0.007s)
2023-01-11T21:29:08.5410535Z   test_add_ (__main__.FunctionTests) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:73: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:29:08.5410998Z   a_copy = torch.tensor(a)
2023-01-11T21:29:08.5464450Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:29:08.5465598Z   return node.target(*args, **kwargs)
2023-01-11T21:29:08.5466341Z <eval_with_key>.7:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:29:08.5466750Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:29:08.5467095Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5467328Z ok (0.016s)
2023-01-11T21:29:08.5467683Z   test_addcdiv (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5467987Z ok (0.009s)
2023-01-11T21:29:08.5468736Z   test_addcdiv_ (__main__.FunctionTests) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:84: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:29:08.5469555Z   a_copy = torch.tensor(a)
2023-01-11T21:29:08.5470620Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:29:08.5471160Z   return node.target(*args, **kwargs)
2023-01-11T21:29:08.5472156Z <eval_with_key>.12:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:29:08.5472558Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:29:08.5473081Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:08.5473481Z ok (0.016s)
2023-01-11T21:29:08.5473889Z   test_build_list_unpack (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5474390Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:29:08.5474713Z ok (0.013s)
2023-01-11T21:29:08.5475077Z   test_chunks1 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5475325Z ok (0.008s)
2023-01-11T21:29:08.5475700Z   test_const_tuple_add1 (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:08.5475970Z ok (0.008s)
2023-01-11T21:29:08.5476331Z   test_const_tuple_add2 (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:08.5476598Z ok (0.008s)
2023-01-11T21:29:08.5477047Z   test_constant1 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5477415Z ok (0.007s)
2023-01-11T21:29:08.5477771Z   test_constant2 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5478066Z ok (0.007s)
2023-01-11T21:29:08.5478428Z   test_constant3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5478686Z ok (0.006s)
2023-01-11T21:29:08.5479031Z   test_constant4 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5479287Z ok (0.005s)
2023-01-11T21:29:08.5479652Z   test_default_dict (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5479916Z ok (0.010s)
2023-01-11T21:29:08.5480256Z   test_del (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5480626Z ok (0.007s)
2023-01-11T21:29:08.5481439Z   test_device (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5481752Z ok (0.005s)
2023-01-11T21:29:08.5482127Z   test_device_constant (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5482394Z ok (0.011s)
2023-01-11T21:29:08.5482742Z   test_dict_copy (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5483001Z ok (0.005s)
2023-01-11T21:29:08.5483365Z   test_dict_ops (__main__.FunctionTests) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:29:08.5483621Z ok (0.014s)
2023-01-11T21:29:08.5483982Z   test_dict_param_keys (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5484247Z ok (0.006s)
2023-01-11T21:29:08.5484682Z   test_distributed_is_available (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5484954Z ok (0.005s)
2023-01-11T21:29:08.5485347Z   test_distributed_is_initialized (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5485621Z ok (0.005s)
2023-01-11T21:29:08.5485980Z   test_dtype (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5486225Z ok (0.005s)
2023-01-11T21:29:08.5486591Z   test_dtype_compare (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5486851Z ok (0.007s)
2023-01-11T21:29:08.5487193Z   test_finfo (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5487446Z ok (0.008s)
2023-01-11T21:29:08.5487807Z   test_float (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5488057Z ok (0.006s)
2023-01-11T21:29:08.5488426Z   test_fn_with_self_set (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:08.5488691Z ok (0.010s)
2023-01-11T21:29:08.5489057Z   test_fstrings1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5489303Z ok (0.006s)
2023-01-11T21:29:08.5489661Z   test_fstrings2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5489921Z ok (0.006s)
2023-01-11T21:29:08.5490270Z   test_fstrings3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5490528Z ok (0.005s)
2023-01-11T21:29:08.5490749Z   test_funcdef_closure (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5491104Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:29:08.5491320Z ok (0.015s)
2023-01-11T21:29:08.5491696Z   test_get_default_dtype (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5491960Z ok (0.005s)
2023-01-11T21:29:08.5492314Z   test_globalfn (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5492574Z ok (0.006s)
2023-01-11T21:29:08.5492941Z   test_globalmodule (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5493195Z ok (0.011s)
2023-01-11T21:29:08.5493554Z   test_globalvar (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5493813Z ok (0.007s)
2023-01-11T21:29:08.5494168Z   test_import1 (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5494458Z ok (0.007s)
2023-01-11T21:29:08.5494821Z   test_indirect1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5495082Z ok (0.006s)
2023-01-11T21:29:08.5495434Z   test_indirect2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5495691Z ok (0.006s)
2023-01-11T21:29:08.5496047Z   test_indirect3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5496288Z ok (0.006s)
2023-01-11T21:29:08.5496518Z   test_inline_jit_annotations (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5496880Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5497107Z ok (0.008s)
2023-01-11T21:29:08.5497467Z   test_inline_softmax (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5497772Z ok (0.011s)
2023-01-11T21:29:08.5497996Z   test_inline_with_default (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5498482Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5498707Z ok (0.008s)
2023-01-11T21:29:08.5498927Z   test_inner_function (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5499265Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5499489Z ok (0.006s)
2023-01-11T21:29:08.5499883Z   test_is_contiguous_memory_format (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5500160Z ok (0.007s)
2023-01-11T21:29:08.5500514Z   test_is_fx_tracing (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5500776Z ok (0.006s)
2023-01-11T21:29:08.5501150Z   test_is_in_onnx_export (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5501409Z ok (0.006s)
2023-01-11T21:29:08.5501774Z   test_is_not_null (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5502031Z ok (0.006s)
2023-01-11T21:29:08.5504285Z   test_is_quantized (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5504696Z ok (0.005s)
2023-01-11T21:29:08.5505275Z   test_is_sparse (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5505648Z ok (0.005s)
2023-01-11T21:29:08.5506177Z   test_islice_chain (__main__.FunctionTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:29:08.5506573Z ok (0.012s)
2023-01-11T21:29:08.5507117Z   test_jit_annotate (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5507513Z ok (0.007s)
2023-01-11T21:29:08.5508037Z   test_len_constant_dict (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5508416Z ok (0.006s)
2023-01-11T21:29:08.5508957Z   test_len_constant_list (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5509348Z ok (0.006s)
2023-01-11T21:29:08.5509937Z   test_len_constant_misc_iterables (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5510341Z ok (0.006s)
2023-01-11T21:29:08.5510869Z   test_len_tensor (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5511225Z ok (0.005s)
2023-01-11T21:29:08.5511839Z   test_list_add (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5512419Z ok (0.006s)
2023-01-11T21:29:08.5512951Z   test_list_clear (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5513329Z ok (0.008s)
2023-01-11T21:29:08.5513855Z   test_list_convert (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:08.5514221Z ok (0.009s)
2023-01-11T21:29:08.5514755Z   test_list_reversed (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:29:08.5515143Z ok (0.010s)
2023-01-11T21:29:08.5515684Z   test_list_slice_assignment (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5516071Z ok (0.007s)
2023-01-11T21:29:08.5516606Z   test_list_truth (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5516985Z ok (0.006s)
2023-01-11T21:29:08.5517655Z   test_listarg1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5518036Z ok (0.006s)
2023-01-11T21:29:08.5518566Z   test_listarg2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5518934Z ok (0.006s)
2023-01-11T21:29:08.5519512Z   test_listarg3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5519837Z ok (0.007s)
2023-01-11T21:29:08.5520351Z   test_listarg4 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5520685Z ok (0.007s)
2023-01-11T21:29:08.5521191Z   test_listarg5 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5521573Z ok (0.007s)
2023-01-11T21:29:08.5522143Z   test_load_global_bool (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5522535Z ok (0.006s)
2023-01-11T21:29:08.5522854Z   test_map_sum (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5523363Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:29:08.5523695Z ok (0.015s)
2023-01-11T21:29:08.5523991Z   test_methodcall1 (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5524470Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5524786Z ok (0.009s)
2023-01-11T21:29:08.5525099Z   test_methodcall2 (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5525597Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5525919Z ok (0.008s)
2023-01-11T21:29:08.5526223Z   test_methodcall3 (__main__.FunctionTests) ... inline_call []
2023-01-11T21:29:08.5526717Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5527070Z ok (0.008s)
2023-01-11T21:29:08.5527601Z   test_min_max (__main__.FunctionTests) ... stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:29:08.5527982Z ok (0.019s)
2023-01-11T21:29:08.5528525Z   test_module_constant (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:08.5528893Z ok (0.011s)
2023-01-11T21:29:08.5529358Z   test_ndim (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5529709Z ok (0.007s)
2023-01-11T21:29:08.5530237Z   test_pop (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:08.5530607Z ok (0.012s)
2023-01-11T21:29:08.5531154Z   test_range1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5531548Z ok (0.007s)
2023-01-11T21:29:08.5532210Z   test_range2 (__main__.FunctionTests) ... stats [('calls_captured', 13), ('fusions_possible', 12), ('unique_graphs', 1)]
2023-01-11T21:29:08.5532583Z ok (0.018s)
2023-01-11T21:29:08.5533119Z   test_reduce (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5533498Z ok (0.010s)
2023-01-11T21:29:08.5534042Z   test_return_dict (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5534427Z ok (0.007s)
2023-01-11T21:29:08.5534976Z   test_return_dict2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5535351Z ok (0.007s)
2023-01-11T21:29:08.5535896Z   test_return_tuple1 (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5536278Z ok (0.007s)
2023-01-11T21:29:08.5536895Z   test_return_tuple2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5537280Z ok (0.007s)
2023-01-11T21:29:08.5537819Z   test_shape1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5538219Z ok (0.006s)
2023-01-11T21:29:08.5538777Z   test_shape2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5539171Z ok (0.006s)
2023-01-11T21:29:08.5539699Z   test_slice1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5540048Z ok (0.005s)
2023-01-11T21:29:08.5540574Z   test_slice2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5540948Z ok (0.005s)
2023-01-11T21:29:08.5541471Z   test_slice3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5541820Z ok (0.006s)
2023-01-11T21:29:08.5542530Z   test_slice4 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5542900Z ok (0.005s)
2023-01-11T21:29:08.5543407Z   test_slice5 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5543764Z ok (0.006s)
2023-01-11T21:29:08.5544310Z   test_slice6 (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5544690Z ok (0.007s)
2023-01-11T21:29:08.5545206Z   test_startswith (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5545581Z ok (0.007s)
2023-01-11T21:29:08.5546107Z   test_tensor_len (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5546463Z ok (0.008s)
2023-01-11T21:29:08.5547028Z   test_tensor_new_with_shape (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5547405Z ok (0.018s)
2023-01-11T21:29:08.5547950Z   test_tensor_new_with_size (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5548362Z ok (0.018s)
2023-01-11T21:29:08.5548915Z   test_tensor_type (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5549307Z ok (0.040s)
2023-01-11T21:29:08.5549641Z   test_tensor_type2 (__main__.FunctionTests) ... skip: requires cuda (0.000s)
2023-01-11T21:29:08.5550286Z   test_transpose_for_scores (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5550676Z ok (0.007s)
2023-01-11T21:29:08.5551206Z   test_tuple1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5551875Z ok (0.006s)
2023-01-11T21:29:08.5552519Z   test_tuple2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5552915Z ok (0.006s)
2023-01-11T21:29:08.5553420Z   test_tuple_contains (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5553801Z ok (0.006s)
2023-01-11T21:29:08.5554350Z   test_tuple_iadd (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:08.5554729Z ok (0.007s)
2023-01-11T21:29:08.5555297Z   test_unpack1 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5555715Z ok (0.007s)
2023-01-11T21:29:08.5556279Z   test_unpack2 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5556692Z ok (0.007s)
2023-01-11T21:29:08.5557407Z   test_unpack3 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:08.5557802Z ok (0.007s)
2023-01-11T21:29:08.5558327Z   test_unpack_ex1 (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:29:08.5558708Z ok (0.009s)
2023-01-11T21:29:08.5559237Z   test_unpack_ex2 (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:29:08.5559615Z ok (0.009s)
2023-01-11T21:29:08.5560140Z   test_unpack_ex3 (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:29:08.5560537Z ok (0.009s)
2023-01-11T21:29:08.5561082Z   test_viamethod (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5561456Z ok (0.005s)
2023-01-11T21:29:08.5562001Z   test_viatorch (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:08.5562385Z ok (0.005s)
2023-01-11T21:29:08.5562550Z 
2023-01-11T21:29:08.5562836Z ----------------------------------------------------------------------
2023-01-11T21:29:08.5563173Z Ran 109 tests in 1.228s
2023-01-11T21:29:08.5563350Z 
2023-01-11T21:29:08.5563453Z OK (skipped=1)
2023-01-11T21:29:08.5563606Z 
2023-01-11T21:29:08.5563735Z Generating XML reports...
2023-01-11T21:29:08.5564347Z Generated XML report: test-reports/python-unittest/dynamo.test_functions/TEST-FunctionTests-20230111212906.xml
2023-01-11T21:29:08.5564646Z 
2023-01-11T21:29:08.5565174Z ##[endgroup]
2023-01-11T21:29:08.5565788Z FINISHED PRINTING LOG FILE of dynamo/test_functions (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_functions_lnsbaz_r)
2023-01-11T21:29:08.5566128Z 
2023-01-11T21:29:10.4886553Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:10.5708567Z Ignoring disabled issues:  []
2023-01-11T21:29:10.5854089Z Running dynamo/test_global ... [2023-01-11 21:29:10.585118]
2023-01-11T21:29:10.5856179Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_global.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:10.585365]
2023-01-11T21:29:12.8665219Z 
2023-01-11T21:29:12.8665780Z Expand the folded group to see the log file of dynamo/test_global
2023-01-11T21:29:12.8666853Z ##[group]PRINTING LOG FILE of dynamo/test_global (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_sbipu6f9)
2023-01-11T21:29:12.8667221Z 
2023-01-11T21:29:12.8667316Z Running tests...
2023-01-11T21:29:12.8667968Z ----------------------------------------------------------------------
2023-01-11T21:29:12.8668688Z Test results will be stored in test-reports/python-unittest/dynamo.test_global
2023-01-11T21:29:12.8669221Z   test_store_global_1 (__main__.TestGlobals) ... ok (0.333s)
2023-01-11T21:29:12.8669854Z   test_store_global_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8670655Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8671169Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:29:12.8671785Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:29:12.8672167Z ok (0.010s)
2023-01-11T21:29:12.8672603Z   test_store_global_cross_file (__main__.TestGlobals) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:29:12.8672837Z unimplemented []
2023-01-11T21:29:12.8673250Z graph_break [('call_function BuiltinVariable(setattr) [PythonModuleVariable(), ConstantVariable(str), TensorVariable()] {}', 1)]
2023-01-11T21:29:12.8673675Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:12.8673887Z ok (0.008s)
2023-01-11T21:29:12.8674187Z   test_store_global_dict (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8674541Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8674854Z ok (0.005s)
2023-01-11T21:29:12.8675149Z   test_store_global_dict_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8675386Z inline_call []
2023-01-11T21:29:12.8675686Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8675900Z ok (0.008s)
2023-01-11T21:29:12.8676208Z   test_store_global_inline_1 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8676444Z inline_call []
2023-01-11T21:29:12.8676727Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8676953Z ok (0.009s)
2023-01-11T21:29:12.8677258Z   test_store_global_inline_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8677479Z inline_call []
2023-01-11T21:29:12.8677770Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8678015Z ok (0.010s)
2023-01-11T21:29:12.8678321Z   test_store_global_list (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8678667Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8678888Z ok (0.005s)
2023-01-11T21:29:12.8679191Z   test_store_global_list_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8679406Z inline_call []
2023-01-11T21:29:12.8679698Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8679924Z ok (0.007s)
2023-01-11T21:29:12.8680214Z   test_store_global_new (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8680569Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:12.8680790Z ok (0.005s)
2023-01-11T21:29:12.8681091Z   test_store_global_object (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:12.8681436Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:12.8681663Z ok (0.005s)
2023-01-11T21:29:12.8681763Z 
2023-01-11T21:29:12.8681962Z ----------------------------------------------------------------------
2023-01-11T21:29:12.8682192Z Ran 11 tests in 0.406s
2023-01-11T21:29:12.8682304Z 
2023-01-11T21:29:12.8682364Z OK
2023-01-11T21:29:12.8682454Z 
2023-01-11T21:29:12.8682539Z Generating XML reports...
2023-01-11T21:29:12.8682935Z Generated XML report: test-reports/python-unittest/dynamo.test_global/TEST-TestGlobals-20230111212912.xml
2023-01-11T21:29:12.8683164Z 
2023-01-11T21:29:12.8683442Z ##[endgroup]
2023-01-11T21:29:12.8683844Z FINISHED PRINTING LOG FILE of dynamo/test_global (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_sbipu6f9)
2023-01-11T21:29:12.8684067Z 
2023-01-11T21:29:14.8543199Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:14.9451715Z Ignoring disabled issues:  []
2023-01-11T21:29:14.9597535Z Running dynamo/test_global_declaration ... [2023-01-11 21:29:14.959432]
2023-01-11T21:29:14.9599305Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_global_declaration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:14.959693]
2023-01-11T21:29:16.4133400Z 
2023-01-11T21:29:16.4134206Z Expand the folded group to see the log file of dynamo/test_global_declaration
2023-01-11T21:29:16.4134966Z ##[group]PRINTING LOG FILE of dynamo/test_global_declaration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_declaration_mfubw6qh)
2023-01-11T21:29:16.4135277Z 
2023-01-11T21:29:16.4135505Z ##[endgroup]
2023-01-11T21:29:16.4136103Z FINISHED PRINTING LOG FILE of dynamo/test_global_declaration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_declaration_mfubw6qh)
2023-01-11T21:29:16.4136358Z 
2023-01-11T21:29:18.3589194Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:18.4244923Z Ignoring disabled issues:  []
2023-01-11T21:29:18.4395137Z Running dynamo/test_minifier ... [2023-01-11 21:29:18.439150]
2023-01-11T21:29:18.4396937Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_minifier.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:18.439450]
2023-01-11T21:30:01.7141150Z 
2023-01-11T21:30:01.7141600Z Expand the folded group to see the log file of dynamo/test_minifier
2023-01-11T21:30:01.7142653Z ##[group]PRINTING LOG FILE of dynamo/test_minifier (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_minifier_m_7uzj__)
2023-01-11T21:30:01.7144470Z 
2023-01-11T21:30:01.7145848Z Running tests...
2023-01-11T21:30:01.7146373Z ----------------------------------------------------------------------
2023-01-11T21:30:01.7146789Z Test results will be stored in test-reports/python-unittest/dynamo.test_minifier
2023-01-11T21:30:01.7147241Z   test_after_dynamo_cpu_accuracy_backend_passes (__main__.MinifierTests) ... ok (1.935s)
2023-01-11T21:30:01.7147837Z   test_after_dynamo_cpu_accuracy_error (__main__.MinifierTests) ... ok (5.241s)
2023-01-11T21:30:01.7148238Z   test_after_dynamo_cpu_compile_backend_passes (__main__.MinifierTests) ... ok (1.687s)
2023-01-11T21:30:01.7148568Z   test_after_dynamo_cpu_compile_error (__main__.MinifierTests) ... ok (5.160s)
2023-01-11T21:30:01.7152798Z   test_after_dynamo_cpu_runtime_backend_passes (__main__.MinifierTests) ... ok (1.655s)
2023-01-11T21:30:01.7153437Z   test_after_dynamo_cpu_runtime_error (__main__.MinifierTests) ... ok (5.125s)
2023-01-11T21:30:01.7154015Z   test_after_dynamo_cuda_accuracy_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:30:01.7154656Z   test_after_dynamo_cuda_accuracy_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:30:01.7155246Z   test_after_dynamo_cuda_compile_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:30:01.7155862Z   test_after_dynamo_cuda_compile_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:30:01.7156469Z   test_after_dynamo_cuda_runtime_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:30:01.7224321Z   test_after_dynamo_cuda_runtime_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:30:01.7225139Z   test_after_dynamo_custom_backend (__main__.MinifierTests) ... ok (3.316s)
2023-01-11T21:30:01.7225884Z   test_after_dynamo_with_modified_config_cpu_accuracy_error (__main__.MinifierTests) ... ok (5.271s)
2023-01-11T21:30:01.7226663Z   test_after_dynamo_with_modified_config_cpu_compile_error (__main__.MinifierTests) ... ok (5.134s)
2023-01-11T21:30:01.7227423Z   test_cpu_cuda_module_after_dynamo (__main__.MinifierTests) ... skip: requires cuda (0.001s)
2023-01-11T21:30:01.7228112Z   test_dynamo_config_serialization (__main__.MinifierTests) ... ok (1.568s)
2023-01-11T21:30:01.7228786Z   test_if_graph_minified (__main__.MinifierTests) ... ok (5.261s)
2023-01-11T21:30:01.7229167Z 
2023-01-11T21:30:01.7229710Z ----------------------------------------------------------------------
2023-01-11T21:30:01.7230278Z Ran 18 tests in 41.360s
2023-01-11T21:30:01.7230792Z 
2023-01-11T21:30:01.7230967Z OK (skipped=7)
2023-01-11T21:30:01.7231221Z 
2023-01-11T21:30:01.7231426Z Generating XML reports...
2023-01-11T21:30:01.7232522Z Generated XML report: test-reports/python-unittest/dynamo.test_minifier/TEST-MinifierTests-20230111212919.xml
2023-01-11T21:30:01.7233082Z 
2023-01-11T21:30:01.7233763Z ##[endgroup]
2023-01-11T21:30:01.7234740Z FINISHED PRINTING LOG FILE of dynamo/test_minifier (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_minifier_m_7uzj__)
2023-01-11T21:30:01.7235304Z 
2023-01-11T21:30:03.6771632Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:03.7638242Z Ignoring disabled issues:  []
2023-01-11T21:30:03.7783119Z Running dynamo/test_model_output ... [2023-01-11 21:30:03.777987]
2023-01-11T21:30:03.7785627Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_model_output.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:03.778253]
2023-01-11T21:30:05.5479500Z 
2023-01-11T21:30:05.5480364Z Expand the folded group to see the log file of dynamo/test_model_output
2023-01-11T21:30:05.5481343Z ##[group]PRINTING LOG FILE of dynamo/test_model_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_model_output_3tbp2nt7)
2023-01-11T21:30:05.5481595Z 
2023-01-11T21:30:05.5481668Z Running tests...
2023-01-11T21:30:05.5482088Z ----------------------------------------------------------------------
2023-01-11T21:30:05.5482476Z Test results will be stored in test-reports/python-unittest/dynamo.test_model_output
2023-01-11T21:30:05.5482902Z   test_pretrained (__main__.TestHFPretrained) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:30:05.5483436Z   test_mo_assign (__main__.TestModelOutput) ... skip: requires HuggingFace (0.001s)
2023-01-11T21:30:05.5483950Z   test_mo_create (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:30:05.5484517Z   test_mo_getattr (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:30:05.5485118Z   test_mo_getitem (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:30:05.5485703Z   test_mo_index (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:30:05.5486261Z   test_mo_init (__main__.TestModelOutput) ... skip: requires HuggingFace (0.001s)
2023-01-11T21:30:05.5486819Z   test_mo_tuple (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:30:05.5486995Z 
2023-01-11T21:30:05.5487197Z ----------------------------------------------------------------------
2023-01-11T21:30:05.5487439Z Ran 8 tests in 0.003s
2023-01-11T21:30:05.5487551Z 
2023-01-11T21:30:05.5487609Z OK (skipped=8)
2023-01-11T21:30:05.5487715Z 
2023-01-11T21:30:05.5487798Z Generating XML reports...
2023-01-11T21:30:05.5488234Z Generated XML report: test-reports/python-unittest/dynamo.test_model_output/TEST-TestHFPretrained-20230111213005.xml
2023-01-11T21:30:05.5488776Z Generated XML report: test-reports/python-unittest/dynamo.test_model_output/TEST-TestModelOutput-20230111213005.xml
2023-01-11T21:30:05.5489020Z 
2023-01-11T21:30:05.5489235Z ##[endgroup]
2023-01-11T21:30:05.5489650Z FINISHED PRINTING LOG FILE of dynamo/test_model_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_model_output_3tbp2nt7)
2023-01-11T21:30:05.5489882Z 
2023-01-11T21:30:07.4863529Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:07.5772889Z Ignoring disabled issues:  []
2023-01-11T21:30:07.5921944Z Running dynamo/test_modules ... [2023-01-11 21:30:07.591827]
2023-01-11T21:30:07.5923522Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_modules.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:07.592134]
2023-01-11T21:30:11.1065156Z 
2023-01-11T21:30:11.1065872Z Expand the folded group to see the log file of dynamo/test_modules
2023-01-11T21:30:11.1066851Z ##[group]PRINTING LOG FILE of dynamo/test_modules (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_modules_bv9g5cs9)
2023-01-11T21:30:11.1067364Z 
2023-01-11T21:30:11.1067447Z Running tests...
2023-01-11T21:30:11.1067967Z ----------------------------------------------------------------------
2023-01-11T21:30:11.1068467Z Test results will be stored in test-reports/python-unittest/dynamo.test_modules
2023-01-11T21:30:11.1068825Z   test_access_by_keys (__main__.NNModuleTests) ... ok (0.360s)
2023-01-11T21:30:11.1069085Z   test_basicmodule1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1069501Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:30:11.1069867Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1070087Z ok (0.016s)
2023-01-11T21:30:11.1070502Z   test_basicmodule2 (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1070785Z ok (0.013s)
2023-01-11T21:30:11.1071080Z   test_call_fn_with_non_const_inputs_safe (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1071606Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:11.1071900Z ok (0.023s)
2023-01-11T21:30:11.1072411Z   test_cfgmod (__main__.NNModuleTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:30:11.1072879Z ok (0.028s)
2023-01-11T21:30:11.1073442Z   test_children (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1073892Z ok (0.019s)
2023-01-11T21:30:11.1074559Z   test_constloop (__main__.NNModuleTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:30:11.1074866Z ok (0.026s)
2023-01-11T21:30:11.1075085Z   test_densenet (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1075444Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:30:11.1075724Z ok (0.017s)
2023-01-11T21:30:11.1075929Z   test_enumvalues (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1076326Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:30:11.1076686Z ok (0.016s)
2023-01-11T21:30:11.1077090Z   test_fnmember (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1077376Z ok (0.012s)
2023-01-11T21:30:11.1077746Z   test_fnmembercmp1 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1078060Z ok (0.012s)
2023-01-11T21:30:11.1078431Z   test_fnmembercmp2 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1078740Z ok (0.012s)
2023-01-11T21:30:11.1078963Z   test_forward_directly (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1079307Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1079587Z ok (0.020s)
2023-01-11T21:30:11.1079817Z   test_generation_tag (__main__.NNModuleTests) ... ok (0.002s)
2023-01-11T21:30:11.1080270Z   test_hasattr (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1080535Z ok (0.009s)
2023-01-11T21:30:11.1080923Z   test_intarg (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1081184Z ok (0.013s)
2023-01-11T21:30:11.1081544Z   test_iseval1 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1081855Z ok (0.012s)
2023-01-11T21:30:11.1082216Z   test_iseval2 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1082522Z ok (0.012s)
2023-01-11T21:30:11.1082892Z   test_isnonelayer (__main__.NNModuleTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:11.1083172Z ok (0.010s)
2023-01-11T21:30:11.1083630Z   test_istraining1 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1083901Z ok (0.012s)
2023-01-11T21:30:11.1084312Z   test_istraining2 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1084573Z ok (0.012s)
2023-01-11T21:30:11.1084977Z   test_layerlist (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1085239Z ok (0.016s)
2023-01-11T21:30:11.1085880Z   test_lazy_module (__main__.NNModuleTests) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:30:11.1086466Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:30:11.1087228Z [2023-01-11 21:30:09,810] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7f0a6225ee60> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:30:11.1088032Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:30:11.1088574Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:30:11.1089147Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:30:11.1089679Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:30:11.1090403Z [2023-01-11 21:30:09,882] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7f0a6225ee60> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:30:11.1091205Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:30:11.1091746Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:30:11.1092034Z frames [('total', 16), ('ok', 14)]
2023-01-11T21:30:11.1106416Z inline_call [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2), ('call_function UserDefinedObjectVariable(_infer_parameters) [NNModuleVariable(), TupleVariable()] {}', 1), ('call_function UserDefinedObjectVariable(_infer_parameters) [UnspecializedNNModuleVariable(LazyModule), TupleVariable()] {}', 1)]
2023-01-11T21:30:11.1107560Z unimplemented [("Guard setup for uninitialized class <class '__main__.LazyModule'>", 2)]
2023-01-11T21:30:11.1108249Z graph_break [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2)]
2023-01-11T21:30:11.1108923Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1109336Z ok (0.206s)
2023-01-11T21:30:11.1109764Z   test_module_attribute_precedence (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1110382Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1110753Z ok (0.011s)
2023-01-11T21:30:11.1111177Z   test_module_class_method (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1111910Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:30:11.1112290Z ok (0.030s)
2023-01-11T21:30:11.1112853Z   test_module_forward_has_graph_break (__main__.NNModuleTests) ... frames [('total', 3), ('ok', 2)]
2023-01-11T21:30:11.1113297Z inline_call []
2023-01-11T21:30:11.1113776Z unimplemented [('reconstruct: ConstantVariable(dict)', 2)]
2023-01-11T21:30:11.1114720Z graph_break [('call_function BuiltinVariable(dict) [ListIteratorVariable()] {}', 1), ('call_method NNModuleVariable() buffers [] {}', 1)]
2023-01-11T21:30:11.1115448Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:11.1115855Z ok (0.077s)
2023-01-11T21:30:11.1116273Z   test_module_name_string (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1116544Z ok (0.014s)
2023-01-11T21:30:11.1116755Z   test_module_property (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1117106Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:11.1117331Z ok (0.007s)
2023-01-11T21:30:11.1117545Z   test_module_static_method (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1117942Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:30:11.1118164Z ok (0.029s)
2023-01-11T21:30:11.1118986Z   test_moduledict (__main__.NNModuleTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:11.1119419Z ok (0.010s)
2023-01-11T21:30:11.1119777Z   test_modulelist (__main__.NNModuleTests) ... stats [('calls_captured', 40), ('fusions_possible', 39), ('unique_graphs', 1)]
2023-01-11T21:30:11.1120041Z ok (0.106s)
2023-01-11T21:30:11.1120263Z   test_modulemethod1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1120597Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:30:11.1120819Z ok (0.030s)
2023-01-11T21:30:11.1121038Z   test_modulemethod2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1121369Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:30:11.1121590Z ok (0.030s)
2023-01-11T21:30:11.1121978Z   test_nn_moduledict_contains (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:30:11.1122314Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:30:11.1122581Z inline_call [('Patched init cannot be inlined.', 1)]
2023-01-11T21:30:11.1123019Z unimplemented [("Guard setup for uninitialized class <class '__main__.NNModuleTests.test_nn_moduledict_contains.<locals>.M'>", 1)]
2023-01-11T21:30:11.1123401Z graph_break [('Patched init cannot be inlined.', 1)]
2023-01-11T21:30:11.1123593Z ok (0.020s)
2023-01-11T21:30:11.1123811Z   test_parameters1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1124157Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:11.1124368Z ok (0.010s)
2023-01-11T21:30:11.1124584Z   test_parameters2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1124922Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:11.1125141Z ok (0.009s)
2023-01-11T21:30:11.1125499Z   test_parameters3 (__main__.NNModuleTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:30:11.1125767Z ok (0.024s)
2023-01-11T21:30:11.1126133Z   test_self_mutating1 (__main__.NNModuleTests) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:30:11.1126386Z ok (0.039s)
2023-01-11T21:30:11.1126733Z   test_seq (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1126986Z ok (0.018s)
2023-01-11T21:30:11.1127199Z   test_simple_torch_function (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1127552Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1127772Z ok (0.013s)
2023-01-11T21:30:11.1128138Z   test_stringmember (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:11.1128404Z ok (0.013s)
2023-01-11T21:30:11.1128669Z   test_submodules1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1129016Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:30:11.1129287Z ok (0.026s)
2023-01-11T21:30:11.1129507Z   test_submodules2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1129846Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:30:11.1130052Z ok (0.026s)
2023-01-11T21:30:11.1130262Z   test_super1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1130598Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1130803Z ok (0.016s)
2023-01-11T21:30:11.1131008Z   test_super2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1131337Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1131554Z ok (0.015s)
2023-01-11T21:30:11.1131767Z   test_super_class_method (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1132115Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:11.1132376Z ok (0.008s)
2023-01-11T21:30:11.1132726Z   test_tensorlist (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1132989Z ok (0.011s)
2023-01-11T21:30:11.1133376Z   test_torch_function_with_closure (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1133639Z ok (0.009s)
2023-01-11T21:30:11.1133948Z   test_unsupportedmethod (__main__.NNModuleTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:11.1134396Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:11.1134689Z unimplemented []
2023-01-11T21:30:11.1135053Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:11.1135463Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:30:11.1135689Z ok (0.029s)
2023-01-11T21:30:11.1135990Z   test_unsupportedmodule (__main__.NNModuleTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:11.1136433Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:11.1136720Z unimplemented []
2023-01-11T21:30:11.1137083Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:11.1137489Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:30:11.1137711Z ok (0.031s)
2023-01-11T21:30:11.1137931Z   test_viamodulecall (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:30:11.1138267Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:11.1138486Z ok (0.015s)
2023-01-11T21:30:11.1138699Z   test_attr (__main__.OptimizedModuleTest) ... ok (0.002s)
2023-01-11T21:30:11.1139051Z   test_composition (__main__.OptimizedModuleTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:11.1139293Z inline_call []
2023-01-11T21:30:11.1139587Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1139798Z ok (0.009s)
2023-01-11T21:30:11.1140132Z   test_composition_with_opt_mod (__main__.OptimizedModuleTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:11.1140511Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:30:11.1140943Z inline_call [('inline in skipfiles: forward /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:30:11.1141213Z unimplemented []
2023-01-11T21:30:11.1141591Z graph_break [('inline in skipfiles: forward /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:30:11.1141858Z ok (0.010s)
2023-01-11T21:30:11.1142156Z   test_nn_module (__main__.OptimizedModuleTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:11.1142715Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1143020Z ok (0.012s)
2023-01-11T21:30:11.1143312Z   test_recursion (__main__.OptimizedModuleTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:11.1143674Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:11.1143900Z ok (0.013s)
2023-01-11T21:30:11.1144193Z   test_to (__main__.OptimizedModuleTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:11.1144535Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:30:11.1144755Z ok (0.033s)
2023-01-11T21:30:11.1144856Z 
2023-01-11T21:30:11.1145058Z ----------------------------------------------------------------------
2023-01-11T21:30:11.1145289Z Ran 57 tests in 1.634s
2023-01-11T21:30:11.1145399Z 
2023-01-11T21:30:11.1145463Z OK
2023-01-11T21:30:11.1145553Z 
2023-01-11T21:30:11.1145638Z Generating XML reports...
2023-01-11T21:30:11.1146087Z Generated XML report: test-reports/python-unittest/dynamo.test_modules/TEST-NNModuleTests-20230111213009.xml
2023-01-11T21:30:11.1146634Z Generated XML report: test-reports/python-unittest/dynamo.test_modules/TEST-OptimizedModuleTest-20230111213009.xml
2023-01-11T21:30:11.1146880Z 
2023-01-11T21:30:11.1147213Z ##[endgroup]
2023-01-11T21:30:11.1147611Z FINISHED PRINTING LOG FILE of dynamo/test_modules (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_modules_bv9g5cs9)
2023-01-11T21:30:11.1147822Z 
2023-01-11T21:30:13.0267209Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:13.0939208Z Ignoring disabled issues:  []
2023-01-11T21:30:13.1087528Z Running dynamo/test_nops ... [2023-01-11 21:30:13.108388]
2023-01-11T21:30:13.1088368Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_nops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:13.108638]
2023-01-11T21:30:15.2979648Z 
2023-01-11T21:30:15.2980225Z Expand the folded group to see the log file of dynamo/test_nops
2023-01-11T21:30:15.2981220Z ##[group]PRINTING LOG FILE of dynamo/test_nops (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_nops_z393rkaq)
2023-01-11T21:30:15.2981469Z 
2023-01-11T21:30:15.2981532Z Running tests...
2023-01-11T21:30:15.2981947Z ----------------------------------------------------------------------
2023-01-11T21:30:15.2982558Z Test results will be stored in test-reports/python-unittest/dynamo.test_nops
2023-01-11T21:30:15.2982829Z   test1 (__main__.NopTests) ... ok (0.241s)
2023-01-11T21:30:15.2983048Z   test2 (__main__.NopTests) ... ok (0.002s)
2023-01-11T21:30:15.2983265Z   test3 (__main__.NopTests) ... ok (0.002s)
2023-01-11T21:30:15.2983498Z   test_extended_args (__main__.NopTests) ... ok (0.032s)
2023-01-11T21:30:15.2983629Z 
2023-01-11T21:30:15.2983831Z ----------------------------------------------------------------------
2023-01-11T21:30:15.2984073Z Ran 4 tests in 0.277s
2023-01-11T21:30:15.2984185Z 
2023-01-11T21:30:15.2984244Z OK
2023-01-11T21:30:15.2984334Z 
2023-01-11T21:30:15.2984405Z Generating XML reports...
2023-01-11T21:30:15.2984813Z Generated XML report: test-reports/python-unittest/dynamo.test_nops/TEST-NopTests-20230111213014.xml
2023-01-11T21:30:15.2985033Z 
2023-01-11T21:30:15.2985267Z ##[endgroup]
2023-01-11T21:30:15.2985646Z FINISHED PRINTING LOG FILE of dynamo/test_nops (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_nops_z393rkaq)
2023-01-11T21:30:15.2985862Z 
2023-01-11T21:30:17.2531390Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:17.3411631Z Ignoring disabled issues:  []
2023-01-11T21:30:17.3561093Z Running dynamo/test_optimizers ... [2023-01-11 21:30:17.355766]
2023-01-11T21:30:17.3562807Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_optimizers.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:17.356057]
2023-01-11T21:30:20.9131824Z 
2023-01-11T21:30:20.9132405Z Expand the folded group to see the log file of dynamo/test_optimizers
2023-01-11T21:30:20.9133524Z ##[group]PRINTING LOG FILE of dynamo/test_optimizers (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizers_0uizc92k)
2023-01-11T21:30:20.9134023Z 
2023-01-11T21:30:20.9134087Z Running tests...
2023-01-11T21:30:20.9134489Z ----------------------------------------------------------------------
2023-01-11T21:30:20.9134890Z Test results will be stored in test-reports/python-unittest/dynamo.test_optimizers
2023-01-11T21:30:20.9135212Z   test_optimizing_over_tensor_with_requires_grad (__main__.End2EndTests) ... ok (0.445s)
2023-01-11T21:30:20.9135513Z frames [('total', 8), ('ok', 8)]
2023-01-11T21:30:20.9135954Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 4), ('inline with __closure__', 1)]
2023-01-11T21:30:20.9136261Z unimplemented []
2023-01-11T21:30:20.9136701Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2), ('Tensor.backward', 1), ('inline with __closure__', 1)]
2023-01-11T21:30:20.9137215Z stats [('calls_captured', 24), ('fusions_possible', 22), ('unique_graphs', 2)]
2023-01-11T21:30:20.9137587Z   test_adadelta (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9138012Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9138412Z stats [('calls_captured', 48), ('fusions_possible', 47), ('unique_graphs', 1)]
2023-01-11T21:30:20.9138639Z ok (0.088s)
2023-01-11T21:30:20.9138935Z   test_adagrad (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9139344Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9139745Z stats [('calls_captured', 48), ('fusions_possible', 47), ('unique_graphs', 1)]
2023-01-11T21:30:20.9139968Z ok (0.090s)
2023-01-11T21:30:20.9140242Z   test_adam (__main__.OptimizerTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:20.9140664Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 3)]
2023-01-11T21:30:20.9140944Z unimplemented []
2023-01-11T21:30:20.9141315Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:30:20.9141702Z stats [('calls_captured', 80), ('fusions_possible', 79), ('unique_graphs', 1)]
2023-01-11T21:30:20.9141925Z ok (0.141s)
2023-01-11T21:30:20.9142218Z   test_adamax (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9142825Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9143231Z stats [('calls_captured', 72), ('fusions_possible', 71), ('unique_graphs', 1)]
2023-01-11T21:30:20.9143456Z ok (0.118s)
2023-01-11T21:30:20.9143741Z   test_adamw (__main__.OptimizerTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:20.9144146Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 3)]
2023-01-11T21:30:20.9144426Z unimplemented []
2023-01-11T21:30:20.9144796Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:30:20.9145181Z stats [('calls_captured', 80), ('fusions_possible', 79), ('unique_graphs', 1)]
2023-01-11T21:30:20.9145407Z ok (0.130s)
2023-01-11T21:30:20.9145694Z   test_asgd (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9146100Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9146507Z stats [('calls_captured', 76), ('fusions_possible', 75), ('unique_graphs', 1)]
2023-01-11T21:30:20.9146732Z ok (0.125s)
2023-01-11T21:30:20.9147017Z   test_nadam (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9147426Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9147917Z stats [('calls_captured', 152), ('fusions_possible', 151), ('unique_graphs', 1)]
2023-01-11T21:30:20.9148147Z ok (0.194s)
2023-01-11T21:30:20.9148526Z   test_radam (__main__.OptimizerTests) ... [2023-01-11 21:30:20,189] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:30:20.9148860Z frames [('total', 8), ('ok', 8)]
2023-01-11T21:30:20.9149299Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 3), ('generic_jump TensorVariable()', 1)]
2023-01-11T21:30:20.9149612Z unimplemented []
2023-01-11T21:30:20.9149861Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:30:20.9150068Z ok (0.105s)
2023-01-11T21:30:20.9150361Z   test_rmsprop (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9150772Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9151225Z stats [('calls_captured', 28), ('fusions_possible', 27), ('unique_graphs', 1)]
2023-01-11T21:30:20.9151453Z ok (0.063s)
2023-01-11T21:30:20.9151797Z   test_rprop (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9152220Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9152621Z stats [('calls_captured', 64), ('fusions_possible', 63), ('unique_graphs', 1)]
2023-01-11T21:30:20.9152845Z ok (0.094s)
2023-01-11T21:30:20.9153115Z   test_sgd (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:20.9153533Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:30:20.9153929Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:30:20.9154143Z ok (0.039s)
2023-01-11T21:30:20.9154245Z 
2023-01-11T21:30:20.9154444Z ----------------------------------------------------------------------
2023-01-11T21:30:20.9154693Z Ran 12 tests in 1.633s
2023-01-11T21:30:20.9154806Z 
2023-01-11T21:30:20.9154867Z OK
2023-01-11T21:30:20.9154943Z 
2023-01-11T21:30:20.9155027Z Generating XML reports...
2023-01-11T21:30:20.9155439Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizers/TEST-End2EndTests-20230111213018.xml
2023-01-11T21:30:20.9155965Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizers/TEST-OptimizerTests-20230111213018.xml
2023-01-11T21:30:20.9156201Z 
2023-01-11T21:30:20.9156470Z ##[endgroup]
2023-01-11T21:30:20.9156879Z FINISHED PRINTING LOG FILE of dynamo/test_optimizers (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizers_0uizc92k)
2023-01-11T21:30:20.9157111Z 
2023-01-11T21:30:22.9773595Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:23.0634878Z Ignoring disabled issues:  []
2023-01-11T21:30:23.0786486Z Running dynamo/test_python_autograd ... [2023-01-11 21:30:23.078311]
2023-01-11T21:30:23.0789174Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:23.078663]
2023-01-11T21:30:25.6664147Z 
2023-01-11T21:30:25.6664723Z Expand the folded group to see the log file of dynamo/test_python_autograd
2023-01-11T21:30:25.6665903Z ##[group]PRINTING LOG FILE of dynamo/test_python_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_python_autograd_s19r7hj1)
2023-01-11T21:30:25.6666287Z 
2023-01-11T21:30:25.6666389Z Running tests...
2023-01-11T21:30:25.6666921Z ----------------------------------------------------------------------
2023-01-11T21:30:25.6667624Z Test results will be stored in test-reports/python-unittest/dynamo.test_python_autograd
2023-01-11T21:30:25.6668039Z   test_backwards1 (__main__.TestPythonAutograd) ... ok (0.437s)
2023-01-11T21:30:25.6668329Z   test_backwards2 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:30:25.6668690Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:30:25.6669138Z inline_call []
2023-01-11T21:30:25.6669444Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:30:25.6669661Z ok (0.103s)
2023-01-11T21:30:25.6669892Z   test_forwards1 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:30:25.6670249Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:25.6670479Z ok (0.031s)
2023-01-11T21:30:25.6670693Z   test_forwards2 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:30:25.6671050Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:25.6671274Z ok (0.030s)
2023-01-11T21:30:25.6671483Z   test_split (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:30:25.6671903Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:30:25.6672146Z ok (0.097s)
2023-01-11T21:30:25.6672248Z 
2023-01-11T21:30:25.6672516Z ----------------------------------------------------------------------
2023-01-11T21:30:25.6672769Z Ran 5 tests in 0.699s
2023-01-11T21:30:25.6672882Z 
2023-01-11T21:30:25.6672944Z OK
2023-01-11T21:30:25.6673037Z 
2023-01-11T21:30:25.6673124Z Generating XML reports...
2023-01-11T21:30:25.6673564Z Generated XML report: test-reports/python-unittest/dynamo.test_python_autograd/TEST-TestPythonAutograd-20230111213024.xml
2023-01-11T21:30:25.6673818Z 
2023-01-11T21:30:25.6674081Z ##[endgroup]
2023-01-11T21:30:25.6674512Z FINISHED PRINTING LOG FILE of dynamo/test_python_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_python_autograd_s19r7hj1)
2023-01-11T21:30:25.6674742Z 
2023-01-11T21:30:27.7137208Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:27.8006273Z Ignoring disabled issues:  []
2023-01-11T21:30:27.8156706Z Running dynamo/test_recompile_ux ... [2023-01-11 21:30:27.815249]
2023-01-11T21:30:27.8157603Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:27.815519]
2023-01-11T21:30:29.5979671Z 
2023-01-11T21:30:29.5980275Z Expand the folded group to see the log file of dynamo/test_recompile_ux
2023-01-11T21:30:29.5981705Z ##[group]PRINTING LOG FILE of dynamo/test_recompile_ux (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_recompile_ux_xiwn7yge)
2023-01-11T21:30:29.5982324Z 
2023-01-11T21:30:29.5982995Z ##[endgroup]
2023-01-11T21:30:29.5984143Z FINISHED PRINTING LOG FILE of dynamo/test_recompile_ux (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_recompile_ux_xiwn7yge)
2023-01-11T21:30:29.5984744Z 
2023-01-11T21:30:31.6068264Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:31.6963570Z Ignoring disabled issues:  []
2023-01-11T21:30:31.7115568Z Running dynamo/test_replay_record ... [2023-01-11 21:30:31.711184]
2023-01-11T21:30:31.7116557Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_replay_record.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:31.711451]
2023-01-11T21:30:33.5365220Z 
2023-01-11T21:30:33.5365867Z Expand the folded group to see the log file of dynamo/test_replay_record
2023-01-11T21:30:33.5367418Z ##[group]PRINTING LOG FILE of dynamo/test_replay_record (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_replay_record_kxslac6b)
2023-01-11T21:30:33.5368041Z 
2023-01-11T21:30:33.5368208Z Running tests...
2023-01-11T21:30:33.5368928Z ----------------------------------------------------------------------
2023-01-11T21:30:33.5369889Z Test results will be stored in test-reports/python-unittest/dynamo.test_replay_record
2023-01-11T21:30:33.5370707Z   test_fn_call_args (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:33.5371480Z   test_local_module (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:33.5372261Z   test_nonlocal_fn_call (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:33.5373077Z   test_nonlocal_module_class (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:33.5374171Z   test_nonlocal_module_fn_call (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:33.5374947Z   test_successful_inline (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:33.5375761Z   test_unsuccessful_inline (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:33.5376196Z 
2023-01-11T21:30:33.5376690Z ----------------------------------------------------------------------
2023-01-11T21:30:33.5377266Z Ran 7 tests in 0.003s
2023-01-11T21:30:33.5377541Z 
2023-01-11T21:30:33.5377687Z OK (skipped=7)
2023-01-11T21:30:33.5377933Z 
2023-01-11T21:30:33.5378134Z Generating XML reports...
2023-01-11T21:30:33.5379194Z Generated XML report: test-reports/python-unittest/dynamo.test_replay_record/TEST-ReplayRecordTests-20230111213033.xml
2023-01-11T21:30:33.5379791Z 
2023-01-11T21:30:33.5380333Z ##[endgroup]
2023-01-11T21:30:33.5381473Z FINISHED PRINTING LOG FILE of dynamo/test_replay_record (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_replay_record_kxslac6b)
2023-01-11T21:30:33.5382078Z 
2023-01-11T21:30:35.5192969Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:35.6033121Z Ignoring disabled issues:  []
2023-01-11T21:30:35.6180501Z Running dynamo/test_skip_non_tensor ... [2023-01-11 21:30:35.617665]
2023-01-11T21:30:35.6181723Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:35.617940]
2023-01-11T21:30:37.9647896Z 
2023-01-11T21:30:37.9648468Z Expand the folded group to see the log file of dynamo/test_skip_non_tensor
2023-01-11T21:30:37.9649372Z ##[group]PRINTING LOG FILE of dynamo/test_skip_non_tensor (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_skip_non_tensor_gn0673yk)
2023-01-11T21:30:37.9649640Z 
2023-01-11T21:30:37.9649718Z Running tests...
2023-01-11T21:30:37.9650158Z ----------------------------------------------------------------------
2023-01-11T21:30:37.9650568Z Test results will be stored in test-reports/python-unittest/dynamo.test_skip_non_tensor
2023-01-11T21:30:37.9650892Z   test_add_skip (__main__.SkipNonTensorTests) ... ok (0.269s)
2023-01-11T21:30:37.9651342Z   test_add_tensor1 (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:37.9651605Z ok (0.086s)
2023-01-11T21:30:37.9651991Z   test_add_tensor2 (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:37.9652267Z ok (0.005s)
2023-01-11T21:30:37.9652653Z   test_add_tensor_dict (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:37.9652921Z ok (0.005s)
2023-01-11T21:30:37.9653300Z   test_add_tensor_list (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:37.9653584Z ok (0.005s)
2023-01-11T21:30:37.9653795Z   test_custom_list (__main__.SkipNonTensorTests) ... ok (0.001s)
2023-01-11T21:30:37.9654078Z   test_recursive_list (__main__.SkipNonTensorTests) ... ok (0.001s)
2023-01-11T21:30:37.9654240Z 
2023-01-11T21:30:37.9654440Z ----------------------------------------------------------------------
2023-01-11T21:30:37.9654673Z Ran 7 tests in 0.374s
2023-01-11T21:30:37.9654788Z 
2023-01-11T21:30:37.9654850Z OK
2023-01-11T21:30:37.9654942Z 
2023-01-11T21:30:37.9655030Z Generating XML reports...
2023-01-11T21:30:37.9655471Z Generated XML report: test-reports/python-unittest/dynamo.test_skip_non_tensor/TEST-SkipNonTensorTests-20230111213037.xml
2023-01-11T21:30:37.9655722Z 
2023-01-11T21:30:37.9655928Z ##[endgroup]
2023-01-11T21:30:37.9656356Z FINISHED PRINTING LOG FILE of dynamo/test_skip_non_tensor (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_skip_non_tensor_gn0673yk)
2023-01-11T21:30:37.9656602Z 
2023-01-11T21:30:39.9706231Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:40.0573451Z Ignoring disabled issues:  []
2023-01-11T21:30:40.0723874Z Running dynamo/test_subgraphs ... [2023-01-11 21:30:40.072065]
2023-01-11T21:30:40.0726860Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_subgraphs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:40.072361]
2023-01-11T21:30:44.0662055Z 
2023-01-11T21:30:44.0662729Z Expand the folded group to see the log file of dynamo/test_subgraphs
2023-01-11T21:30:44.0663815Z ##[group]PRINTING LOG FILE of dynamo/test_subgraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_subgraphs_v9l8y_qo)
2023-01-11T21:30:44.0664265Z 
2023-01-11T21:30:44.0664396Z Running tests...
2023-01-11T21:30:44.0665076Z ----------------------------------------------------------------------
2023-01-11T21:30:44.0665771Z Test results will be stored in test-reports/python-unittest/dynamo.test_subgraphs
2023-01-11T21:30:44.0666319Z   test_capi_call1 (__main__.SubGraphTests) ... ok (0.354s)
2023-01-11T21:30:44.0667151Z   test_capi_call2 (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:44.0667583Z unimplemented []
2023-01-11T21:30:44.0668294Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0669104Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:44.0669603Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0669934Z unimplemented []
2023-01-11T21:30:44.0670552Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0671109Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:44.0671378Z ok (0.013s)
2023-01-11T21:30:44.0671987Z   test_capi_call3 (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:44.0672372Z unimplemented []
2023-01-11T21:30:44.0673033Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0673795Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:44.0674209Z ok (0.007s)
2023-01-11T21:30:44.0674763Z   test_control_flow1 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0675314Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:30:44.0675692Z ok (0.012s)
2023-01-11T21:30:44.0676243Z   test_control_flow2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0676941Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:44.0677325Z ok (0.007s)
2023-01-11T21:30:44.0677756Z   test_control_flow3 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0678119Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:44.0678332Z ok (0.016s)
2023-01-11T21:30:44.0678641Z   test_control_flow4 (__main__.SubGraphTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:30:44.0679001Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:30:44.0679227Z ok (0.014s)
2023-01-11T21:30:44.0679517Z   test_control_flow5 (__main__.SubGraphTests) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:30:44.0679876Z stats [('calls_captured', 13), ('fusions_possible', 7), ('unique_graphs', 6)]
2023-01-11T21:30:44.0680101Z ok (0.038s)
2023-01-11T21:30:44.0680395Z   test_dynamic_duck_size (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0680762Z stats [('calls_captured', 10), ('fusions_possible', 8), ('unique_graphs', 2)]
2023-01-11T21:30:44.0680991Z ok (0.073s)
2023-01-11T21:30:44.0681280Z   test_dynamic_kwarg (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:44.0681642Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:44.0682008Z ok (0.023s)
2023-01-11T21:30:44.0682326Z   test_dynamic_order_dependence (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0682708Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:30:44.0682937Z ok (0.068s)
2023-01-11T21:30:44.0683247Z   test_dynamic_shapes (__main__.SubGraphTests) ... frames [('total', 11), ('ok', 11)]
2023-01-11T21:30:44.0683604Z stats [('calls_captured', 22), ('fusions_possible', 11), ('unique_graphs', 11)]
2023-01-11T21:30:44.0683840Z ok (0.062s)
2023-01-11T21:30:44.0684160Z   test_dynamic_zero_inference (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0684519Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:44.0684747Z ok (0.026s)
2023-01-11T21:30:44.0685078Z   test_enumerate_not_break_graph (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:44.0685484Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:44.0685717Z ok (0.008s)
2023-01-11T21:30:44.0686020Z   test_extended_args (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0686395Z stats [('calls_captured', 1026), ('fusions_possible', 1023), ('unique_graphs', 3)]
2023-01-11T21:30:44.0686615Z ok (0.832s)
2023-01-11T21:30:44.0686928Z   test_graph_break_on_item (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0687172Z unimplemented []
2023-01-11T21:30:44.0687401Z graph_break [('Tensor.item', 1)]
2023-01-11T21:30:44.0687723Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:30:44.0687954Z ok (0.014s)
2023-01-11T21:30:44.0688257Z   test_indirect_unsupported1 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0688716Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0689014Z unimplemented []
2023-01-11T21:30:44.0689407Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0689810Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:30:44.0690041Z ok (0.011s)
2023-01-11T21:30:44.0690359Z   test_indirect_unsupported2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0690798Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0691095Z unimplemented []
2023-01-11T21:30:44.0691484Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0691885Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:30:44.0692116Z ok (0.016s)
2023-01-11T21:30:44.0692435Z   test_indirect_unsupported3 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0695026Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0715488Z unimplemented []
2023-01-11T21:30:44.0716294Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0717111Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:30:44.0717531Z ok (0.011s)
2023-01-11T21:30:44.0718063Z   test_multigraph (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0718681Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:30:44.0718903Z ok (0.011s)
2023-01-11T21:30:44.0719203Z   test_no_graph_break_on_item (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:44.0719664Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:30:44.0720009Z ok (0.009s)
2023-01-11T21:30:44.0720713Z   test_pop_after_resume (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0721514Z unimplemented []
2023-01-11T21:30:44.0722248Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0723022Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:44.0723368Z ok (0.016s)
2023-01-11T21:30:44.0723788Z   test_restore_range (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0724190Z unimplemented []
2023-01-11T21:30:44.0724784Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0725200Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:44.0725425Z ok (0.012s)
2023-01-11T21:30:44.0725731Z   test_restore_range_iter (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0725959Z unimplemented []
2023-01-11T21:30:44.0726442Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0726867Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:30:44.0727078Z ok (0.011s)
2023-01-11T21:30:44.0727382Z   test_restore_state (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0727616Z unimplemented []
2023-01-11T21:30:44.0727986Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0728398Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:44.0728619Z ok (0.011s)
2023-01-11T21:30:44.0728905Z   test_resume1 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0729115Z unimplemented []
2023-01-11T21:30:44.0729496Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0729911Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:44.0730124Z ok (0.011s)
2023-01-11T21:30:44.0730410Z   test_resume2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0730840Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0731115Z unimplemented []
2023-01-11T21:30:44.0731497Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0731907Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:44.0732130Z ok (0.016s)
2023-01-11T21:30:44.0732405Z   test_resume3 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0732829Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0733120Z unimplemented []
2023-01-11T21:30:44.0733494Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0733899Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:44.0734126Z ok (0.016s)
2023-01-11T21:30:44.0734414Z   test_resume4 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0734830Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0735117Z unimplemented []
2023-01-11T21:30:44.0735493Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0735885Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:44.0736103Z ok (0.016s)
2023-01-11T21:30:44.0736346Z   test_resume5 (__main__.SubGraphTests) ... tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:44.0736612Z         1.5000])
2023-01-11T21:30:44.0736808Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:44.0736993Z         1.5000])
2023-01-11T21:30:44.0737189Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:44.0737366Z         1.5000])
2023-01-11T21:30:44.0737560Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:44.0737757Z         1.5000])
2023-01-11T21:30:44.0737961Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:44.0738150Z unimplemented []
2023-01-11T21:30:44.0738469Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0738819Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:44.0739042Z ok (0.013s)
2023-01-11T21:30:44.0739344Z   test_resume_freevars (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0739570Z unimplemented []
2023-01-11T21:30:44.0740023Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0740434Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:30:44.0740661Z ok (0.012s)
2023-01-11T21:30:44.0740956Z   test_resume_paths_join (__main__.SubGraphTests) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:30:44.0741320Z stats [('calls_captured', 10), ('unique_graphs', 7), ('fusions_possible', 3)]
2023-01-11T21:30:44.0741546Z ok (0.033s)
2023-01-11T21:30:44.0741847Z   test_resume_tuple_iterator (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0742090Z unimplemented []
2023-01-11T21:30:44.0742701Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0743131Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:30:44.0743346Z ok (0.018s)
2023-01-11T21:30:44.0743668Z   test_resume_with_no_grad1 (__main__.SubGraphTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:44.0743973Z unimplemented []
2023-01-11T21:30:44.0744200Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:30:44.0744594Z stats [('calls_captured', 18), ('fusions_possible', 14), ('unique_graphs', 4)]
2023-01-11T21:30:44.0744940Z ok (0.025s)
2023-01-11T21:30:44.0745487Z   test_resume_with_no_grad2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0745923Z unimplemented []
2023-01-11T21:30:44.0746351Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:30:44.0746937Z stats [('calls_captured', 13), ('fusions_possible', 10), ('unique_graphs', 3)]
2023-01-11T21:30:44.0747350Z ok (0.020s)
2023-01-11T21:30:44.0747919Z   test_resume_with_no_grad3 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0748313Z unimplemented []
2023-01-11T21:30:44.0748535Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:30:44.0748861Z stats [('calls_captured', 19), ('fusions_possible', 17), ('unique_graphs', 2)]
2023-01-11T21:30:44.0749096Z ok (0.018s)
2023-01-11T21:30:44.0749384Z   test_stack_state1 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0749619Z unimplemented []
2023-01-11T21:30:44.0750006Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0750408Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:44.0750632Z ok (0.012s)
2023-01-11T21:30:44.0750931Z   test_stack_state2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0751368Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0751746Z unimplemented []
2023-01-11T21:30:44.0752138Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0752651Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:44.0752866Z ok (0.017s)
2023-01-11T21:30:44.0753106Z   test_start1 (__main__.SubGraphTests) ... tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:30:44.0753564Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:30:44.0753882Z tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:30:44.0754275Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:30:44.0754633Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:44.0754967Z unimplemented []
2023-01-11T21:30:44.0755416Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0755925Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:44.0756262Z ok (0.010s)
2023-01-11T21:30:44.0756732Z   test_start2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0757619Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0758145Z unimplemented []
2023-01-11T21:30:44.0758765Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0759419Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:44.0759672Z ok (0.013s)
2023-01-11T21:30:44.0759960Z   test_start3 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:44.0760178Z unimplemented []
2023-01-11T21:30:44.0760567Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:44.0760983Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:44.0761196Z ok (0.008s)
2023-01-11T21:30:44.0761483Z   test_start4 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0761838Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:30:44.0762067Z ok (0.012s)
2023-01-11T21:30:44.0762300Z   test_tuple_iterator_mutate (__main__.SubGraphTests) ... skip: not working yet (0.001s)
2023-01-11T21:30:44.0762689Z   test_tuple_iterator_return (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:44.0762936Z unimplemented []
2023-01-11T21:30:44.0763307Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:44.0763723Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:30:44.0763949Z ok (0.023s)
2023-01-11T21:30:44.0764050Z 
2023-01-11T21:30:44.0764237Z ----------------------------------------------------------------------
2023-01-11T21:30:44.0764479Z Ran 44 tests in 1.972s
2023-01-11T21:30:44.0764591Z 
2023-01-11T21:30:44.0764662Z OK (skipped=1)
2023-01-11T21:30:44.0764768Z 
2023-01-11T21:30:44.0764851Z Generating XML reports...
2023-01-11T21:30:44.0765262Z Generated XML report: test-reports/python-unittest/dynamo.test_subgraphs/TEST-SubGraphTests-20230111213041.xml
2023-01-11T21:30:44.0765500Z 
2023-01-11T21:30:44.0765890Z ##[endgroup]
2023-01-11T21:30:44.0766302Z FINISHED PRINTING LOG FILE of dynamo/test_subgraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_subgraphs_v9l8y_qo)
2023-01-11T21:30:44.0766534Z 
2023-01-11T21:30:46.1606493Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:46.2493224Z Ignoring disabled issues:  []
2023-01-11T21:30:46.2647993Z Running dynamo/test_torchxla_num_output ... [2023-01-11 21:30:46.264414]
2023-01-11T21:30:46.2649323Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_torchxla_num_output.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:46.264697]
2023-01-11T21:30:47.9090135Z 
2023-01-11T21:30:47.9090735Z Expand the folded group to see the log file of dynamo/test_torchxla_num_output
2023-01-11T21:30:47.9091768Z ##[group]PRINTING LOG FILE of dynamo/test_torchxla_num_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_num_output_7jmnji7l)
2023-01-11T21:30:47.9092234Z 
2023-01-11T21:30:47.9092483Z ##[endgroup]
2023-01-11T21:30:47.9093080Z FINISHED PRINTING LOG FILE of dynamo/test_torchxla_num_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_num_output_7jmnji7l)
2023-01-11T21:30:47.9093437Z 
2023-01-11T21:30:49.9425457Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:50.0386112Z Ignoring disabled issues:  []
2023-01-11T21:30:50.0536013Z Running dynamo/test_torchxla_util ... [2023-01-11 21:30:50.053200]
2023-01-11T21:30:50.0537136Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_torchxla_util.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:50.053494]
2023-01-11T21:30:50.0900389Z 
2023-01-11T21:30:50.0900835Z Expand the folded group to see the log file of dynamo/test_torchxla_util
2023-01-11T21:30:50.0902221Z ##[group]PRINTING LOG FILE of dynamo/test_torchxla_util (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_util_i3q0g9n8)
2023-01-11T21:30:50.0902700Z 
2023-01-11T21:30:50.0902947Z ##[endgroup]
2023-01-11T21:30:50.0903472Z FINISHED PRINTING LOG FILE of dynamo/test_torchxla_util (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_util_i3q0g9n8)
2023-01-11T21:30:50.0903708Z 
2023-01-11T21:30:52.1241873Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:52.2190672Z Ignoring disabled issues:  []
2023-01-11T21:30:52.2336919Z Running dynamo/test_verify_correctness ... [2023-01-11 21:30:52.233351]
2023-01-11T21:30:52.2338405Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:52.233627]
2023-01-11T21:30:54.5654324Z 
2023-01-11T21:30:54.5654918Z Expand the folded group to see the log file of dynamo/test_verify_correctness
2023-01-11T21:30:54.5656009Z ##[group]PRINTING LOG FILE of dynamo/test_verify_correctness (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_verify_correctness_8fofd13v)
2023-01-11T21:30:54.5656430Z 
2023-01-11T21:30:54.5656594Z Running tests...
2023-01-11T21:30:54.5657218Z ----------------------------------------------------------------------
2023-01-11T21:30:54.5657944Z Test results will be stored in test-reports/python-unittest/dynamo.test_verify_correctness
2023-01-11T21:30:54.5658522Z   test_example_inputs (__main__.TestVerifyCorrectness) ... ok (0.337s)
2023-01-11T21:30:54.5659039Z   test_incorrect_verify_false (__main__.TestVerifyCorrectness)
2023-01-11T21:30:54.5659764Z The bad optimization return a graph that ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:54.5660315Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:54.5660877Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 2)]
2023-01-11T21:30:54.5661276Z ok (0.018s)
2023-01-11T21:30:54.5661698Z   test_incorrect_verify_true (__main__.TestVerifyCorrectness)
2023-01-11T21:30:54.5662717Z If a bad optimization return a graph that ... [2023-01-11 21:30:54,072] torch._dynamo.output_graph: [ERROR] error in verify_correctness
2023-01-11T21:30:54.5663212Z Traceback (most recent call last):
2023-01-11T21:30:54.5663850Z   File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 173, in __call__
2023-01-11T21:30:54.5664375Z     raise RuntimeError(f"incorrect results of backend {self}")
2023-01-11T21:30:54.5664970Z RuntimeError: incorrect results of backend <torch._dynamo.output_graph.WrapperBackend object at 0x7f0ac08ea9b0>
2023-01-11T21:30:54.5665539Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:30:54.5666089Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:30:54.5666437Z ok (0.016s)
2023-01-11T21:30:54.5666851Z   test_ipex_fp32 (__main__.TestVerifyCorrectness) ... skip: requires ipex (0.001s)
2023-01-11T21:30:54.5667503Z   test_nnc (__main__.TestVerifyCorrectness) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:54.5668404Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:54.5668791Z ok (0.089s)
2023-01-11T21:30:54.5668970Z 
2023-01-11T21:30:54.5669330Z ----------------------------------------------------------------------
2023-01-11T21:30:54.5669718Z Ran 5 tests in 0.461s
2023-01-11T21:30:54.5669908Z 
2023-01-11T21:30:54.5670023Z OK (skipped=1)
2023-01-11T21:30:54.5670201Z 
2023-01-11T21:30:54.5670353Z Generating XML reports...
2023-01-11T21:30:54.5671086Z Generated XML report: test-reports/python-unittest/dynamo.test_verify_correctness/TEST-TestVerifyCorrectness-20230111213053.xml
2023-01-11T21:30:54.5671488Z 
2023-01-11T21:30:54.5671986Z ##[endgroup]
2023-01-11T21:30:54.5672755Z FINISHED PRINTING LOG FILE of dynamo/test_verify_correctness (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_verify_correctness_8fofd13v)
2023-01-11T21:30:54.5673186Z 
2023-01-11T21:30:56.5820463Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:56.6732205Z Ignoring disabled issues:  []
2023-01-11T21:30:56.6882013Z Running inductor/test_minifier ... [2023-01-11 21:30:56.687793]
2023-01-11T21:30:56.6883353Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_minifier.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:56.688069]
2023-01-11T21:32:34.0706431Z 
2023-01-11T21:32:34.0707009Z Expand the folded group to see the log file of inductor/test_minifier
2023-01-11T21:32:34.0708146Z ##[group]PRINTING LOG FILE of inductor/test_minifier (/var/lib/jenkins/workspace/test/test-reports/inductor-test_minifier_3y8_71cj)
2023-01-11T21:32:34.0708573Z 
2023-01-11T21:32:34.0744367Z Running tests...
2023-01-11T21:32:34.0745208Z ----------------------------------------------------------------------
2023-01-11T21:32:34.0746168Z Test results will be stored in test-reports/python-unittest/inductor.test_minifier
2023-01-11T21:32:34.0747010Z   test_after_aot_cpu_accuracy_backend_passes (__main__.MinifierTests) ... ok (10.074s)
2023-01-11T21:32:34.0747937Z   test_after_aot_cpu_accuracy_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:34.0748843Z   test_after_aot_cpu_compile_backend_passes (__main__.MinifierTests) ... ok (8.301s)
2023-01-11T21:32:34.0749736Z   test_after_aot_cpu_compile_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:34.0750622Z   test_after_aot_cpu_runtime_backend_passes (__main__.MinifierTests) ... ok (8.333s)
2023-01-11T21:32:34.0751519Z   test_after_aot_cpu_runtime_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:34.0762106Z   test_after_aot_cuda_accuracy_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:34.0762675Z   test_after_aot_cuda_accuracy_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:34.0763243Z   test_after_aot_cuda_compile_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:34.0763746Z   test_after_aot_cuda_compile_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:34.0764255Z   test_after_aot_cuda_runtime_backend_passes (__main__.MinifierTests) ... skip:  (0.000s)
2023-01-11T21:32:34.0764711Z   test_after_aot_cuda_runtime_error (__main__.MinifierTests) ... skip:  (0.000s)
2023-01-11T21:32:34.0765353Z   test_after_aot_with_modified_config_accuracy_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:34.0766077Z   test_after_aot_with_modified_config_compile_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:34.0766667Z   test_inductor_config_serialization (__main__.MinifierTests) ... ok (1.569s)
2023-01-11T21:32:34.0767330Z   test_torch_compile_after_aot_accuracy_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:34.0768290Z   test_torch_compile_after_aot_compile_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:34.0768934Z   test_torch_compile_after_dynamo_accuracy_error (__main__.MinifierTests) ... ok (29.803s)
2023-01-11T21:32:34.0769464Z   test_torch_compile_after_dynamo_compile_error (__main__.MinifierTests) ... ok (37.338s)
2023-01-11T21:32:34.0769745Z 
2023-01-11T21:32:34.0770145Z ----------------------------------------------------------------------
2023-01-11T21:32:34.0770541Z Ran 19 tests in 95.423s
2023-01-11T21:32:34.0805824Z 
2023-01-11T21:32:34.0806054Z OK (skipped=13)
2023-01-11T21:32:34.0806239Z 
2023-01-11T21:32:34.0806381Z Generating XML reports...
2023-01-11T21:32:34.0807254Z Generated XML report: test-reports/python-unittest/inductor.test_minifier/TEST-MinifierTests-20230111213058.xml
2023-01-11T21:32:34.0807860Z 
2023-01-11T21:32:34.0808370Z ##[endgroup]
2023-01-11T21:32:34.0808841Z FINISHED PRINTING LOG FILE of inductor/test_minifier (/var/lib/jenkins/workspace/test/test-reports/inductor-test_minifier_3y8_71cj)
2023-01-11T21:32:34.0809977Z 
2023-01-11T21:32:35.9825660Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:36.0713605Z Ignoring disabled issues:  []
2023-01-11T21:32:36.0859658Z Running inductor/test_perf ... [2023-01-11 21:32:36.085622]
2023-01-11T21:32:36.0861058Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_perf.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:36.085868]
2023-01-11T21:32:44.3424199Z 
2023-01-11T21:32:44.3424773Z Expand the folded group to see the log file of inductor/test_perf
2023-01-11T21:32:44.3425696Z ##[group]PRINTING LOG FILE of inductor/test_perf (/var/lib/jenkins/workspace/test/test-reports/inductor-test_perf_wvrgf29e)
2023-01-11T21:32:44.3426443Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:44.3426686Z 
2023-01-11T21:32:44.3426994Z ##[endgroup]
2023-01-11T21:32:44.3427576Z FINISHED PRINTING LOG FILE of inductor/test_perf (/var/lib/jenkins/workspace/test/test-reports/inductor-test_perf_wvrgf29e)
2023-01-11T21:32:44.3427911Z 
2023-01-11T21:32:46.2521993Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:46.3346083Z Ignoring disabled issues:  []
2023-01-11T21:32:46.3492487Z Running inductor/test_smoke ... [2023-01-11 21:32:46.348843]
2023-01-11T21:32:46.3493676Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_smoke.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:46.349096]
2023-01-11T21:32:48.1734116Z 
2023-01-11T21:32:48.1734882Z Expand the folded group to see the log file of inductor/test_smoke
2023-01-11T21:32:48.1735718Z ##[group]PRINTING LOG FILE of inductor/test_smoke (/var/lib/jenkins/workspace/test/test-reports/inductor-test_smoke_q262m6v1)
2023-01-11T21:32:48.1735986Z 
2023-01-11T21:32:48.1736211Z ##[endgroup]
2023-01-11T21:32:48.1736716Z FINISHED PRINTING LOG FILE of inductor/test_smoke (/var/lib/jenkins/workspace/test/test-reports/inductor-test_smoke_q262m6v1)
2023-01-11T21:32:48.1736944Z 
2023-01-11T21:32:50.0678806Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:50.1564468Z Ignoring disabled issues:  []
2023-01-11T21:32:50.1713968Z Running lazy/test_bindings ... [2023-01-11 21:32:50.171078]
2023-01-11T21:32:50.1715684Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_bindings.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:50.171359]
2023-01-11T21:32:51.5790229Z 
2023-01-11T21:32:51.5790817Z Expand the folded group to see the log file of lazy/test_bindings
2023-01-11T21:32:51.5791709Z ##[group]PRINTING LOG FILE of lazy/test_bindings (/var/lib/jenkins/workspace/test/test-reports/lazy-test_bindings_jgkdvxws)
2023-01-11T21:32:51.5791941Z 
2023-01-11T21:32:51.5792237Z ##[endgroup]
2023-01-11T21:32:51.5792954Z FINISHED PRINTING LOG FILE of lazy/test_bindings (/var/lib/jenkins/workspace/test/test-reports/lazy-test_bindings_jgkdvxws)
2023-01-11T21:32:51.5793173Z 
2023-01-11T21:32:53.5205952Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:53.5852280Z Ignoring disabled issues:  []
2023-01-11T21:32:53.5999096Z Running lazy/test_debug_util ... [2023-01-11 21:32:53.599624]
2023-01-11T21:32:53.6001581Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_debug_util.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:53.599869]
2023-01-11T21:32:55.6364309Z 
2023-01-11T21:32:55.6364926Z Expand the folded group to see the log file of lazy/test_debug_util
2023-01-11T21:32:55.6366019Z ##[group]PRINTING LOG FILE of lazy/test_debug_util (/var/lib/jenkins/workspace/test/test-reports/lazy-test_debug_util_f1fxs1su)
2023-01-11T21:32:55.6366434Z 
2023-01-11T21:32:55.6366581Z Running tests...
2023-01-11T21:32:55.6367522Z ----------------------------------------------------------------------
2023-01-11T21:32:55.6368242Z Test results will be stored in test-reports/python-unittest/lazy.test_debug_util
2023-01-11T21:32:55.6368794Z   test_get_python_frames (__main__.DebugUtilTest) ... ok (0.286s)
2023-01-11T21:32:55.6369078Z 
2023-01-11T21:32:55.6369290Z ----------------------------------------------------------------------
2023-01-11T21:32:55.6369532Z Ran 1 test in 0.286s
2023-01-11T21:32:55.6369646Z 
2023-01-11T21:32:55.6369707Z OK
2023-01-11T21:32:55.6369798Z 
2023-01-11T21:32:55.6369882Z Generating XML reports...
2023-01-11T21:32:55.6370290Z Generated XML report: test-reports/python-unittest/lazy.test_debug_util/TEST-DebugUtilTest-20230111213254.xml
2023-01-11T21:32:55.6370524Z 
2023-01-11T21:32:55.6370752Z ##[endgroup]
2023-01-11T21:32:55.6371148Z FINISHED PRINTING LOG FILE of lazy/test_debug_util (/var/lib/jenkins/workspace/test/test-reports/lazy-test_debug_util_f1fxs1su)
2023-01-11T21:32:55.6371355Z 
2023-01-11T21:32:57.5716275Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:57.6547816Z Ignoring disabled issues:  []
2023-01-11T21:32:57.6695595Z Running lazy/test_extract_compiled_graph ... [2023-01-11 21:32:57.669183]
2023-01-11T21:32:57.6696656Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:57.669452]
2023-01-11T21:32:59.1016211Z 
2023-01-11T21:32:59.1018612Z Expand the folded group to see the log file of lazy/test_extract_compiled_graph
2023-01-11T21:32:59.1019381Z ##[group]PRINTING LOG FILE of lazy/test_extract_compiled_graph (/var/lib/jenkins/workspace/test/test-reports/lazy-test_extract_compiled_graph_ix5ue00z)
2023-01-11T21:32:59.1019640Z 
2023-01-11T21:32:59.1019844Z ##[endgroup]
2023-01-11T21:32:59.1020519Z FINISHED PRINTING LOG FILE of lazy/test_extract_compiled_graph (/var/lib/jenkins/workspace/test/test-reports/lazy-test_extract_compiled_graph_ix5ue00z)
2023-01-11T21:32:59.1020774Z 
2023-01-11T21:33:01.0637953Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:01.1461654Z Ignoring disabled issues:  []
2023-01-11T21:33:01.1608317Z Running lazy/test_meta_kernel ... [2023-01-11 21:33:01.160510]
2023-01-11T21:33:01.1610066Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_meta_kernel.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:01.160764]
2023-01-11T21:33:02.8503123Z 
2023-01-11T21:33:02.8503615Z Expand the folded group to see the log file of lazy/test_meta_kernel
2023-01-11T21:33:02.8504390Z ##[group]PRINTING LOG FILE of lazy/test_meta_kernel (/var/lib/jenkins/workspace/test/test-reports/lazy-test_meta_kernel_kd0mxbk5)
2023-01-11T21:33:02.8504633Z 
2023-01-11T21:33:02.8504888Z ##[endgroup]
2023-01-11T21:33:02.8505409Z FINISHED PRINTING LOG FILE of lazy/test_meta_kernel (/var/lib/jenkins/workspace/test/test-reports/lazy-test_meta_kernel_kd0mxbk5)
2023-01-11T21:33:02.8505685Z 
2023-01-11T21:33:04.7812573Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:04.8662896Z Ignoring disabled issues:  []
2023-01-11T21:33:04.8809938Z Running lazy/test_reuse_ir ... [2023-01-11 21:33:04.880649]
2023-01-11T21:33:04.8811170Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_reuse_ir.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:04.880917]
2023-01-11T21:33:07.2339078Z 
2023-01-11T21:33:07.2339674Z Expand the folded group to see the log file of lazy/test_reuse_ir
2023-01-11T21:33:07.2340562Z ##[group]PRINTING LOG FILE of lazy/test_reuse_ir (/var/lib/jenkins/workspace/test/test-reports/lazy-test_reuse_ir_i_vjtzrn)
2023-01-11T21:33:07.2340788Z 
2023-01-11T21:33:07.2340861Z Running tests...
2023-01-11T21:33:07.2341255Z ----------------------------------------------------------------------
2023-01-11T21:33:07.2341641Z Test results will be stored in test-reports/python-unittest/lazy.test_reuse_ir
2023-01-11T21:33:07.2341933Z   testAdd (__main__.TestLazyReuseIr) ... ok (0.268s)
2023-01-11T21:33:07.2342531Z   testAddSub (__main__.TestLazyReuseIr) ... ok (0.068s)
2023-01-11T21:33:07.2342825Z   testAddSubFallback (__main__.TestLazyReuseIr) ... ok (0.003s)
2023-01-11T21:33:07.2343101Z   testBatchNorm (__main__.TestLazyReuseIr) ... ok (0.289s)
2023-01-11T21:33:07.2343248Z 
2023-01-11T21:33:07.2343455Z ----------------------------------------------------------------------
2023-01-11T21:33:07.2343683Z Ran 4 tests in 0.628s
2023-01-11T21:33:07.2343796Z 
2023-01-11T21:33:07.2343857Z OK
2023-01-11T21:33:07.2343946Z 
2023-01-11T21:33:07.2344031Z Generating XML reports...
2023-01-11T21:33:07.2344460Z Generated XML report: test-reports/python-unittest/lazy.test_reuse_ir/TEST-TestLazyReuseIr-20230111213306.xml
2023-01-11T21:33:07.2344681Z 
2023-01-11T21:33:07.2344918Z ##[endgroup]
2023-01-11T21:33:07.2345305Z FINISHED PRINTING LOG FILE of lazy/test_reuse_ir (/var/lib/jenkins/workspace/test/test-reports/lazy-test_reuse_ir_i_vjtzrn)
2023-01-11T21:33:07.2345522Z 
2023-01-11T21:33:09.1813114Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:09.2674203Z Ignoring disabled issues:  []
2023-01-11T21:33:09.2822214Z Running lazy/test_step_closures ... [2023-01-11 21:33:09.281885]
2023-01-11T21:33:09.2823566Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_step_closures.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:09.282138]
2023-01-11T21:33:13.3077768Z 
2023-01-11T21:33:13.3078394Z Expand the folded group to see the log file of lazy/test_step_closures
2023-01-11T21:33:13.3079328Z ##[group]PRINTING LOG FILE of lazy/test_step_closures (/var/lib/jenkins/workspace/test/test-reports/lazy-test_step_closures_75rxie_y)
2023-01-11T21:33:13.3079588Z 
2023-01-11T21:33:13.3079649Z Running tests...
2023-01-11T21:33:13.3080113Z ----------------------------------------------------------------------
2023-01-11T21:33:13.3080550Z Test results will be stored in test-reports/python-unittest/lazy.test_step_closures
2023-01-11T21:33:13.3080868Z   test_asynchronous (__main__.ClosuresTest) ... ok (0.247s)
2023-01-11T21:33:13.3081153Z   test_asynchronous_exception (__main__.ClosuresTest) ... ok (1.001s)
2023-01-11T21:33:13.3081482Z   test_synchronous (__main__.ClosuresTest) ... ok (1.002s)
2023-01-11T21:33:13.3081752Z   test_synchronous_exception (__main__.ClosuresTest) ... ok (0.001s)
2023-01-11T21:33:13.3081911Z 
2023-01-11T21:33:13.3082152Z ----------------------------------------------------------------------
2023-01-11T21:33:13.3082395Z Ran 4 tests in 2.252s
2023-01-11T21:33:13.3082509Z 
2023-01-11T21:33:13.3082569Z OK
2023-01-11T21:33:13.3082658Z 
2023-01-11T21:33:13.3082746Z Generating XML reports...
2023-01-11T21:33:13.3083205Z Generated XML report: test-reports/python-unittest/lazy.test_step_closures/TEST-ClosuresTest-20230111213310.xml
2023-01-11T21:33:13.3083439Z 
2023-01-11T21:33:13.3083710Z ##[endgroup]
2023-01-11T21:33:13.3084109Z FINISHED PRINTING LOG FILE of lazy/test_step_closures (/var/lib/jenkins/workspace/test/test-reports/lazy-test_step_closures_75rxie_y)
2023-01-11T21:33:13.3084555Z 
2023-01-11T21:33:15.1965916Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:15.2872230Z Ignoring disabled issues:  []
2023-01-11T21:33:15.3019890Z Running lazy/test_ts_opinfo ... [2023-01-11 21:33:15.301686]
2023-01-11T21:33:15.3022608Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:15.301955]
2023-01-11T21:33:18.1714354Z 
2023-01-11T21:33:18.1714927Z Expand the folded group to see the log file of lazy/test_ts_opinfo
2023-01-11T21:33:18.1715995Z ##[group]PRINTING LOG FILE of lazy/test_ts_opinfo (/var/lib/jenkins/workspace/test/test-reports/lazy-test_ts_opinfo_tbq39ol5)
2023-01-11T21:33:18.1716311Z 
2023-01-11T21:33:18.1716375Z Running tests...
2023-01-11T21:33:18.1716935Z ----------------------------------------------------------------------
2023-01-11T21:33:18.1717423Z Test results will be stored in test-reports/python-unittest/lazy.test_ts_opinfo
2023-01-11T21:33:18.1717928Z   test_nonzero_dynamic (__main__.TestLazyDynamicOps) ... ok (0.167s)
2023-01-11T21:33:18.1718340Z   testConvolutionBackward (__main__.TestLazyTensor) ... skip: Disable until autograd supports symints (0.001s)
2023-01-11T21:33:18.1718667Z   test_tensor_ctr (__main__.TestLazyTensor) ... ok (0.002s)
2023-01-11T21:33:18.1719018Z   test_view_mark_step_preserved (__main__.TestLazyTensor) ... ok (0.003s)
2023-01-11T21:33:18.1719186Z 
2023-01-11T21:33:18.1719378Z ----------------------------------------------------------------------
2023-01-11T21:33:18.1719679Z Ran 4 tests in 0.173s
2023-01-11T21:33:18.1719796Z 
2023-01-11T21:33:18.1719908Z OK (skipped=1)
2023-01-11T21:33:18.1720080Z 
2023-01-11T21:33:18.1720170Z Generating XML reports...
2023-01-11T21:33:18.1720660Z Generated XML report: test-reports/python-unittest/lazy.test_ts_opinfo/TEST-TestLazyDynamicOps-20230111213317.xml
2023-01-11T21:33:18.1721357Z Generated XML report: test-reports/python-unittest/lazy.test_ts_opinfo/TEST-TestLazyTensor-20230111213317.xml
2023-01-11T21:33:18.1721777Z 
2023-01-11T21:33:18.1722212Z ##[endgroup]
2023-01-11T21:33:18.1722609Z FINISHED PRINTING LOG FILE of lazy/test_ts_opinfo (/var/lib/jenkins/workspace/test/test-reports/lazy-test_ts_opinfo_tbq39ol5)
2023-01-11T21:33:18.1722831Z 
2023-01-11T21:33:20.0861164Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:20.1732868Z Ignoring disabled issues:  []
2023-01-11T21:33:20.1886608Z Running nn/test_dropout ... [2023-01-11 21:33:20.188211]
2023-01-11T21:33:20.1887159Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_dropout.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:20.188482]
2023-01-11T21:33:22.3667910Z 
2023-01-11T21:33:22.3672352Z Expand the folded group to see the log file of nn/test_dropout
2023-01-11T21:33:22.3677062Z ##[group]PRINTING LOG FILE of nn/test_dropout (/var/lib/jenkins/workspace/test/test-reports/nn-test_dropout_bmle1pyu)
2023-01-11T21:33:22.3677315Z 
2023-01-11T21:33:22.3677391Z Running tests...
2023-01-11T21:33:22.3677868Z ----------------------------------------------------------------------
2023-01-11T21:33:22.3678241Z Test results will be stored in test-reports/python-unittest/nn.test_dropout
2023-01-11T21:33:22.3678602Z   test_AlphaDropout (__main__.TestDropoutNN) ... ok (0.003s)
2023-01-11T21:33:22.3678895Z   test_FeatureAlphaDropout (__main__.TestDropoutNN) ... ok (0.124s)
2023-01-11T21:33:22.3679219Z   test_invalid_dropout_p (__main__.TestDropoutNN) ... ok (0.001s)
2023-01-11T21:33:22.3679534Z   test_native_dropout_corner_case (__main__.TestDropoutNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:33:22.3679723Z 
2023-01-11T21:33:22.3679973Z ----------------------------------------------------------------------
2023-01-11T21:33:22.3680215Z Ran 4 tests in 0.129s
2023-01-11T21:33:22.3680326Z 
2023-01-11T21:33:22.3680384Z OK (skipped=1)
2023-01-11T21:33:22.3680489Z 
2023-01-11T21:33:22.3680575Z Generating XML reports...
2023-01-11T21:33:22.3681052Z Generated XML report: test-reports/python-unittest/nn.test_dropout/TEST-TestDropoutNN-20230111213321.xml
2023-01-11T21:33:22.3681519Z 
2023-01-11T21:33:22.3812256Z ##[endgroup]
2023-01-11T21:33:22.3812805Z FINISHED PRINTING LOG FILE of nn/test_dropout (/var/lib/jenkins/workspace/test/test-reports/nn-test_dropout_bmle1pyu)
2023-01-11T21:33:22.3813028Z 
2023-01-11T21:33:24.2658406Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:24.3349618Z Ignoring disabled issues:  []
2023-01-11T21:33:24.3501638Z Running nn/test_embedding ... [2023-01-11 21:33:24.349812]
2023-01-11T21:33:24.3503316Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_embedding.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:24.350098]
2023-01-11T21:33:26.3802534Z 
2023-01-11T21:33:26.3803242Z Expand the folded group to see the log file of nn/test_embedding
2023-01-11T21:33:26.3804293Z ##[group]PRINTING LOG FILE of nn/test_embedding (/var/lib/jenkins/workspace/test/test-reports/nn-test_embedding_3n1jpjm3)
2023-01-11T21:33:26.3804689Z 
2023-01-11T21:33:26.3805057Z Running tests...
2023-01-11T21:33:26.3805634Z ----------------------------------------------------------------------
2023-01-11T21:33:26.3806213Z Test results will be stored in test-reports/python-unittest/nn.test_embedding
2023-01-11T21:33:26.3806668Z   test_embedding_bag_from_pretrained (__main__.TestEmbeddingNN) ... ok (0.003s)
2023-01-11T21:33:26.3807154Z   test_embedding_bag_from_pretrained_padding_idx (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3807618Z   test_embedding_bag_functional (__main__.TestEmbeddingNN) ... ok (0.002s)
2023-01-11T21:33:26.3808073Z   test_embedding_bag_padding_idx_error (__main__.TestEmbeddingNN) ... ok (0.011s)
2023-01-11T21:33:26.3808529Z   test_embedding_from_pretrained_float32 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3808989Z   test_embedding_from_pretrained_float64 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3809449Z   test_embedding_from_pretrained_int16 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3809898Z   test_embedding_from_pretrained_int32 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3810351Z   test_embedding_from_pretrained_int64 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3810808Z   test_embedding_from_pretrained_int8 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3811268Z   test_embedding_from_pretrained_options (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3811728Z   test_embedding_from_pretrained_padding_idx (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3812200Z   test_embedding_from_pretrained_uint8 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3812637Z   test_embedding_functional (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3813032Z   test_embedding_max_norm (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3813534Z   test_embedding_max_norm_unsorted_repeating_indices (__main__.TestEmbeddingNN) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:33:26.3814053Z   test_embedding_sparse_basic (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3814487Z   test_embedding_sparse_empty_tensor (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3814918Z   test_embeddingbag_from_pretrained (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3815389Z   test_embeddingbag_from_pretrained_options (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3815868Z   test_embeddingbag_include_last_offset (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3816311Z   test_move_sparse_half_embedding (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:33:26.3816556Z 
2023-01-11T21:33:26.3816921Z ----------------------------------------------------------------------
2023-01-11T21:33:26.3817276Z Ran 22 tests in 0.038s
2023-01-11T21:33:26.3817436Z 
2023-01-11T21:33:26.3817537Z OK (skipped=1)
2023-01-11T21:33:26.3817670Z 
2023-01-11T21:33:26.3817792Z Generating XML reports...
2023-01-11T21:33:26.3818646Z Generated XML report: test-reports/python-unittest/nn.test_embedding/TEST-TestEmbeddingNN-20230111213325.xml
2023-01-11T21:33:26.3819011Z 
2023-01-11T21:33:26.3819386Z ##[endgroup]
2023-01-11T21:33:26.3819948Z FINISHED PRINTING LOG FILE of nn/test_embedding (/var/lib/jenkins/workspace/test/test-reports/nn-test_embedding_3n1jpjm3)
2023-01-11T21:33:26.3820272Z 
2023-01-11T21:33:28.3173429Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:28.4052196Z Ignoring disabled issues:  []
2023-01-11T21:33:28.4203685Z Running nn/test_init ... [2023-01-11 21:33:28.420020]
2023-01-11T21:33:28.4205086Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_init.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:28.420300]
2023-01-11T21:33:30.3130045Z 
2023-01-11T21:33:30.3130602Z Expand the folded group to see the log file of nn/test_init
2023-01-11T21:33:30.3131622Z ##[group]PRINTING LOG FILE of nn/test_init (/var/lib/jenkins/workspace/test/test-reports/nn-test_init_8shnnm85)
2023-01-11T21:33:30.3132165Z 
2023-01-11T21:33:30.3132388Z ##[endgroup]
2023-01-11T21:33:30.3132924Z FINISHED PRINTING LOG FILE of nn/test_init (/var/lib/jenkins/workspace/test/test-reports/nn-test_init_8shnnm85)
2023-01-11T21:33:30.3133133Z 
2023-01-11T21:33:32.3075176Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:32.3910817Z Ignoring disabled issues:  []
2023-01-11T21:33:32.4060347Z Running nn/test_lazy_modules ... [2023-01-11 21:33:32.405721]
2023-01-11T21:33:32.4061727Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_lazy_modules.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:32.405957]
2023-01-11T21:33:34.8905214Z 
2023-01-11T21:33:34.8905722Z Expand the folded group to see the log file of nn/test_lazy_modules
2023-01-11T21:33:34.8906767Z ##[group]PRINTING LOG FILE of nn/test_lazy_modules (/var/lib/jenkins/workspace/test/test-reports/nn-test_lazy_modules_0hh1l97v)
2023-01-11T21:33:34.8907178Z 
2023-01-11T21:33:34.8907285Z Running tests...
2023-01-11T21:33:34.8907788Z ----------------------------------------------------------------------
2023-01-11T21:33:34.8908189Z Test results will be stored in test-reports/python-unittest/nn.test_lazy_modules
2023-01-11T21:33:34.8908630Z   test_chained_initialization (__main__.TestLazyModules) ... ok (0.010s)
2023-01-11T21:33:34.8909153Z   test_invalid_functions (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8910281Z   test_lazy_batchnorm1d (__main__.TestLazyModules) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:33:34.8910974Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:33:34.8911207Z ok (0.009s)
2023-01-11T21:33:34.8911502Z   test_lazy_batchnorm1d_pickle (__main__.TestLazyModules) ... ok (0.008s)
2023-01-11T21:33:34.8912037Z   test_lazy_batchnorm1d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8912600Z   test_lazy_batchnorm2d (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8913057Z   test_lazy_batchnorm2d_pickle (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8913348Z   test_lazy_batchnorm2d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8913621Z   test_lazy_batchnorm3d (__main__.TestLazyModules) ... ok (0.006s)
2023-01-11T21:33:34.8914011Z   test_lazy_batchnorm3d_pickle (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8914300Z   test_lazy_batchnorm3d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8914561Z   test_lazy_conv1d (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:33:34.8914833Z   test_lazy_conv1d_pickle (__main__.TestLazyModules) ... ok (0.003s)
2023-01-11T21:33:34.8915110Z   test_lazy_conv1d_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8915377Z   test_lazy_conv2d (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8915910Z   test_lazy_conv2d_pickle (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8916189Z   test_lazy_conv2d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8916460Z   test_lazy_conv3d (__main__.TestLazyModules) ... ok (0.016s)
2023-01-11T21:33:34.8916716Z   test_lazy_conv3d_pickle (__main__.TestLazyModules) ... ok (0.008s)
2023-01-11T21:33:34.8916992Z   test_lazy_conv3d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8917281Z   test_lazy_conv_transpose1d_pickle (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8917572Z   test_lazy_conv_transpose1d_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8917865Z   test_lazy_conv_transpose2d (__main__.TestLazyModules) ... ok (0.014s)
2023-01-11T21:33:34.8918158Z   test_lazy_conv_transpose2d_pickle (__main__.TestLazyModules) ... ok (0.006s)
2023-01-11T21:33:34.8918464Z   test_lazy_conv_transpose2d_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8918808Z   test_lazy_conv_transpose3d (__main__.TestLazyModules) ... ok (0.175s)
2023-01-11T21:33:34.8919110Z   test_lazy_conv_transpose3d_pickle (__main__.TestLazyModules) ... ok (0.060s)
2023-01-11T21:33:34.8919410Z   test_lazy_conv_transpose3d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8919693Z   test_lazy_conv_transposed1d (__main__.TestLazyModules) ... ok (0.008s)
2023-01-11T21:33:34.8919963Z   test_lazy_forward_hook (__main__.TestLazyModules)
2023-01-11T21:33:34.8920664Z This test is to test whether lazymodule can register other forward hook ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:33:34.8921225Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:33:34.8921446Z ok (0.001s)
2023-01-11T21:33:34.8921674Z   test_lazy_instancenorm1d (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8921975Z   test_lazy_instancenorm1d_pickle (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8925309Z   test_lazy_instancenorm1d_state (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8925786Z   test_lazy_instancenorm2d (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8926308Z   test_lazy_instancenorm2d_pickle (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8926863Z   test_lazy_instancenorm2d_state (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8927222Z   test_lazy_instancenorm3d (__main__.TestLazyModules) ... ok (0.006s)
2023-01-11T21:33:34.8927682Z   test_lazy_instancenorm3d_pickle (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8928182Z   test_lazy_instancenorm3d_state (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:33:34.8928702Z   test_lazy_linear_pickle (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8929192Z   test_lazy_module_buffer (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:33:34.8929719Z   test_lazy_module_jit_buffer (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8930159Z   test_lazy_module_jit_param (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8930429Z   test_lazy_module_parameter (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8930702Z   test_lazy_pre_forward_hook (__main__.TestLazyModules)
2023-01-11T21:33:34.8931423Z This test is to test whether lazymodule can register other pre-forward hook ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:33:34.8932469Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:33:34.8932704Z ok (0.001s)
2023-01-11T21:33:34.8932937Z   test_lazy_share_memory_buffer (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8933234Z   test_lazy_share_memory_param (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8933586Z   test_linear (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8933833Z   test_linear_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8934133Z   test_materialize_device (__main__.TestLazyModules) ... skip: CUDA not available (0.000s)
2023-01-11T21:33:34.8934442Z   test_materialize_dtype (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8934719Z   test_optimizer_pass (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:33:34.8934975Z   test_spectral_norm (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8935243Z   test_weight_norm (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:33:34.8935394Z 
2023-01-11T21:33:34.8935597Z ----------------------------------------------------------------------
2023-01-11T21:33:34.8935826Z Ran 54 tests in 0.428s
2023-01-11T21:33:34.8935942Z 
2023-01-11T21:33:34.8936014Z OK (skipped=1)
2023-01-11T21:33:34.8936123Z 
2023-01-11T21:33:34.8936251Z Generating XML reports...
2023-01-11T21:33:34.8936673Z Generated XML report: test-reports/python-unittest/nn.test_lazy_modules/TEST-TestLazyModules-20230111213334.xml
2023-01-11T21:33:34.8936899Z 
2023-01-11T21:33:34.8937203Z ##[endgroup]
2023-01-11T21:33:34.8937602Z FINISHED PRINTING LOG FILE of nn/test_lazy_modules (/var/lib/jenkins/workspace/test/test-reports/nn-test_lazy_modules_0hh1l97v)
2023-01-11T21:33:34.8937824Z 
2023-01-11T21:33:36.7843193Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:36.8513917Z Ignoring disabled issues:  []
2023-01-11T21:33:36.8660777Z Running nn/test_module_hooks ... [2023-01-11 21:33:36.865828]
2023-01-11T21:33:36.8662975Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_module_hooks.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:36.866074]
2023-01-11T21:33:39.0303899Z 
2023-01-11T21:33:39.0304457Z Expand the folded group to see the log file of nn/test_module_hooks
2023-01-11T21:33:39.0305552Z ##[group]PRINTING LOG FILE of nn/test_module_hooks (/var/lib/jenkins/workspace/test/test-reports/nn-test_module_hooks_l0byf1fd)
2023-01-11T21:33:39.0306036Z 
2023-01-11T21:33:39.0306168Z Running tests...
2023-01-11T21:33:39.0306836Z ----------------------------------------------------------------------
2023-01-11T21:33:39.0307569Z Test results will be stored in test-reports/python-unittest/nn.test_module_hooks
2023-01-11T21:33:39.0309283Z   test_global_and_local_hooks_order (__main__.TestModuleGlobalHooks) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1331: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T21:33:39.0310676Z   warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes "
2023-01-11T21:33:39.0311159Z ok (0.011s)
2023-01-11T21:33:39.0311640Z   test_module_backward_global_hook_writeable (__main__.TestModuleGlobalHooks) ... ok (0.002s)
2023-01-11T21:33:39.0312326Z   test_module_forward_forward_hook_removable (__main__.TestModuleGlobalHooks)
2023-01-11T21:33:39.0312912Z This test is to test when multiple forward hook functions can be registered ... ok (0.002s)
2023-01-11T21:33:39.0313537Z   test_module_forward_preforward_hook_removable (__main__.TestModuleGlobalHooks)
2023-01-11T21:33:39.0314317Z This test is to test when multiple pre-forward hook functions can be ... ok (0.001s)
2023-01-11T21:33:39.0314936Z   test_module_global_forward_preforward_hook_writeable (__main__.TestModuleGlobalHooks) ... ok (0.001s)
2023-01-11T21:33:39.0315593Z   test_module_global_hook_invalid_outputs (__main__.TestModuleGlobalHooks) ... ok (0.001s)
2023-01-11T21:33:39.0316183Z   test_module_global_hooks (__main__.TestModuleGlobalHooks) ... ok (0.009s)
2023-01-11T21:33:39.0316751Z   test_backward_hooks_interaction (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0317570Z   test_hook_backward_size (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:39.0318106Z   test_hook_backward_writeable (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0318666Z   test_hook_buffer_registration (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0319172Z   test_hook_cpp (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:39.0319663Z   test_hook_extra_input (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0320205Z   test_hook_forward_preforward_writable (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0320731Z   test_hook_inplace (__main__.TestModuleHookNN) ... ok (0.024s)
2023-01-11T21:33:39.0321254Z   test_hook_invalid_outputs (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:39.0321797Z   test_hook_last_arg_requires_grad (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0322335Z   test_hook_no_requires_grad (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:39.0323030Z   test_hook_non_full_warning (__main__.TestModuleHookNN) ... ok (0.010s)
2023-01-11T21:33:39.0323600Z   test_hook_parameter_registration (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0324158Z   test_hook_requires_grad (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0324684Z   test_hook_submodule_registration (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:39.0326314Z   test_hooks (__main__.TestModuleHookNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1331: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T21:33:39.0327646Z   warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes "
2023-01-11T21:33:39.0328122Z ok (0.014s)
2023-01-11T21:33:39.0328501Z   test_forward_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:39.0329004Z   test_forward_pre_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:39.0329514Z   test_full_backward_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:39.0330035Z   test_full_backward_pre_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:39.0330529Z   test_kwarg_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:39.0331005Z   test_mixed_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:39.0331485Z   test_remove_kwarg_hooks (__main__.TestModuleHooks) ... ok (0.003s)
2023-01-11T21:33:39.0332010Z   test_load_state_dict_module_pre_hook (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T21:33:39.0332586Z   test_load_state_dict_post_hook (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T21:33:39.0333204Z   test_load_state_dict_post_hook_backward_compatibility (__main__.TestStateDictHooks) ... ok (0.011s)
2023-01-11T21:33:39.0333796Z   test_load_state_dict_pre_hook (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T21:33:39.0334316Z   test_no_extra_ref_to_module (__main__.TestStateDictHooks) ... ok (0.001s)
2023-01-11T21:33:39.0334818Z   test_pickled_hook (__main__.TestStateDictHooks) ... ok (0.001s)
2023-01-11T21:33:39.0335102Z 
2023-01-11T21:33:39.0335486Z ----------------------------------------------------------------------
2023-01-11T21:33:39.0335894Z Ran 36 tests in 0.142s
2023-01-11T21:33:39.0336091Z 
2023-01-11T21:33:39.0336196Z OK
2023-01-11T21:33:39.0336359Z 
2023-01-11T21:33:39.0336506Z Generating XML reports...
2023-01-11T21:33:39.0337310Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleGlobalHooks-20230111213338.xml
2023-01-11T21:33:39.0338271Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHookNN-20230111213338.xml
2023-01-11T21:33:39.0339232Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHooks-20230111213338.xml
2023-01-11T21:33:39.0340339Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestStateDictHooks-20230111213338.xml
2023-01-11T21:33:39.0340778Z 
2023-01-11T21:33:39.0341199Z ##[endgroup]
2023-01-11T21:33:39.0341909Z FINISHED PRINTING LOG FILE of nn/test_module_hooks (/var/lib/jenkins/workspace/test/test-reports/nn-test_module_hooks_l0byf1fd)
2023-01-11T21:33:39.0342311Z 
2023-01-11T21:33:40.9411234Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:41.0059645Z Ignoring disabled issues:  []
2023-01-11T21:33:41.0206473Z Running nn/test_multihead_attention ... [2023-01-11 21:33:41.020370]
2023-01-11T21:33:41.0208261Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_multihead_attention.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:41.020615]
2023-01-11T21:33:47.4481271Z 
2023-01-11T21:33:47.4481788Z Expand the folded group to see the log file of nn/test_multihead_attention
2023-01-11T21:33:47.4483609Z ##[group]PRINTING LOG FILE of nn/test_multihead_attention (/var/lib/jenkins/workspace/test/test-reports/nn-test_multihead_attention_lx0axkkl)
2023-01-11T21:33:47.4484085Z 
2023-01-11T21:33:47.4484264Z Running tests...
2023-01-11T21:33:47.4484954Z ----------------------------------------------------------------------
2023-01-11T21:33:47.4485772Z Test results will be stored in test-reports/python-unittest/nn.test_multihead_attention
2023-01-11T21:33:47.4486712Z   test_multihead_attention_average_attn_weights_False (__main__.TestMultiheadAttentionNN) ... ok (2.246s)
2023-01-11T21:33:47.4487655Z   test_multihead_attention_average_attn_weights_True (__main__.TestMultiheadAttentionNN) ... ok (2.223s)
2023-01-11T21:33:47.4488552Z   test_multihead_attn_3d_attn_mask (__main__.TestMultiheadAttentionNN) ... ok (0.007s)
2023-01-11T21:33:47.4489392Z   test_multihead_attn_fast_path_invalid_shape (__main__.TestMultiheadAttentionNN) ... ok (0.004s)
2023-01-11T21:33:47.4490256Z   test_multihead_attn_invalid_shape (__main__.TestMultiheadAttentionNN) ... ok (0.002s)
2023-01-11T21:33:47.4491897Z   test_multihead_attn_nested_tensor_outside_fast_path (__main__.TestMultiheadAttentionNN) ... /var/lib/jenkins/workspace/test/nn/test_multihead_attention.py:494: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:33:47.4493300Z   nt = torch.nested.nested_tensor([torch.randn(4, 4)])
2023-01-11T21:33:47.4493767Z ok (0.003s)
2023-01-11T21:33:47.4494373Z   test_multihead_attn_no_bias (__main__.TestMultiheadAttentionNN) ... ok (0.001s)
2023-01-11T21:33:47.4494798Z 
2023-01-11T21:33:47.4495306Z ----------------------------------------------------------------------
2023-01-11T21:33:47.4495884Z Ran 7 tests in 4.486s
2023-01-11T21:33:47.4496156Z 
2023-01-11T21:33:47.4496299Z OK
2023-01-11T21:33:47.4496511Z 
2023-01-11T21:33:47.4496712Z Generating XML reports...
2023-01-11T21:33:47.4497863Z Generated XML report: test-reports/python-unittest/nn.test_multihead_attention/TEST-TestMultiheadAttentionNN-20230111213342.xml
2023-01-11T21:33:47.4498559Z 
2023-01-11T21:33:47.4499085Z ##[endgroup]
2023-01-11T21:33:47.4500127Z FINISHED PRINTING LOG FILE of nn/test_multihead_attention (/var/lib/jenkins/workspace/test/test-reports/nn-test_multihead_attention_lx0axkkl)
2023-01-11T21:33:47.4500713Z 
2023-01-11T21:33:49.3814652Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:49.4669143Z Ignoring disabled issues:  []
2023-01-11T21:33:49.4817109Z Running nn/test_packed_sequence ... [2023-01-11 21:33:49.481366]
2023-01-11T21:33:49.4818237Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_packed_sequence.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:49.481623]
2023-01-11T21:33:51.6588995Z 
2023-01-11T21:33:51.6590580Z Expand the folded group to see the log file of nn/test_packed_sequence
2023-01-11T21:33:51.6591794Z ##[group]PRINTING LOG FILE of nn/test_packed_sequence (/var/lib/jenkins/workspace/test/test-reports/nn-test_packed_sequence_i3w_vdz0)
2023-01-11T21:33:51.6592501Z 
2023-01-11T21:33:51.6592621Z Running tests...
2023-01-11T21:33:51.6593221Z ----------------------------------------------------------------------
2023-01-11T21:33:51.6593868Z Test results will be stored in test-reports/python-unittest/nn.test_packed_sequence
2023-01-11T21:33:51.6594405Z   test_pack_padded_sequence (__main__.PackedSequenceTest) ... ok (0.264s)
2023-01-11T21:33:51.6594883Z   test_pack_sequence (__main__.PackedSequenceTest) ... ok (0.089s)
2023-01-11T21:33:51.6595487Z   test_pad_sequence (__main__.PackedSequenceTest) ... ok (0.017s)
2023-01-11T21:33:51.6596012Z   test_pad_sequence_with_non_iterable_sequences (__main__.PackedSequenceTest) ... ok (0.001s)
2023-01-11T21:33:51.6596547Z   test_pad_sequence_with_tensor_sequences (__main__.PackedSequenceTest) ... ok (0.001s)
2023-01-11T21:33:51.6597027Z   test_to (__main__.PackedSequenceTest) ... ok (0.002s)
2023-01-11T21:33:51.6642207Z   test_to_memory_format (__main__.PackedSequenceTest) ... ok (0.001s)
2023-01-11T21:33:51.6642703Z   test_total_length (__main__.PackedSequenceTest) ... ok (0.003s)
2023-01-11T21:33:51.6643138Z   test_type_casts (__main__.PackedSequenceTest)
2023-01-11T21:33:51.6643605Z Test type casting of `PackedSequence` against type casting of tensor ... ok (0.024s)
2023-01-11T21:33:51.6644096Z   test_unpack_sequence (__main__.PackedSequenceTest) ... ok (0.012s)
2023-01-11T21:33:51.6644559Z   test_unpad_sequence (__main__.PackedSequenceTest) ... ok (0.009s)
2023-01-11T21:33:51.6644996Z   test_wrong_order (__main__.PackedSequenceTest) ... ok (0.004s)
2023-01-11T21:33:51.6645253Z 
2023-01-11T21:33:51.6645648Z ----------------------------------------------------------------------
2023-01-11T21:33:51.6646045Z Ran 12 tests in 0.428s
2023-01-11T21:33:51.6646221Z 
2023-01-11T21:33:51.6646302Z OK
2023-01-11T21:33:51.6646444Z 
2023-01-11T21:33:51.6646577Z Generating XML reports...
2023-01-11T21:33:51.6647304Z Generated XML report: test-reports/python-unittest/nn.test_packed_sequence/TEST-PackedSequenceTest-20230111213350.xml
2023-01-11T21:33:51.6647708Z 
2023-01-11T21:33:51.6648107Z ##[endgroup]
2023-01-11T21:33:51.6648811Z FINISHED PRINTING LOG FILE of nn/test_packed_sequence (/var/lib/jenkins/workspace/test/test-reports/nn-test_packed_sequence_i3w_vdz0)
2023-01-11T21:33:51.6649204Z 
2023-01-11T21:33:53.5959178Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:53.6785400Z Ignoring disabled issues:  []
2023-01-11T21:33:53.6934449Z Running nn/test_parametrization ... [2023-01-11 21:33:53.693082]
2023-01-11T21:33:53.6935663Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_parametrization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:53.693350]
2023-01-11T21:33:56.0521401Z 
2023-01-11T21:33:56.0521951Z Expand the folded group to see the log file of nn/test_parametrization
2023-01-11T21:33:56.0523347Z ##[group]PRINTING LOG FILE of nn/test_parametrization (/var/lib/jenkins/workspace/test/test-reports/nn-test_parametrization_y1d91i7c)
2023-01-11T21:33:56.0524036Z 
2023-01-11T21:33:56.0524202Z Running tests...
2023-01-11T21:33:56.0525007Z ----------------------------------------------------------------------
2023-01-11T21:33:56.0525954Z Test results will be stored in test-reports/python-unittest/nn.test_parametrization
2023-01-11T21:33:56.0526743Z   test_caching_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:56.0527417Z Test the caching system of a parametrization ... ok (0.012s)
2023-01-11T21:33:56.0528264Z   test_caching_parametrization_with_transfer_parametrizations_and_params (__main__.TestNNParametrization)
2023-01-11T21:33:56.0529354Z Test that transferring parametrizations doesn't cause issues with caching ... ok (0.003s)
2023-01-11T21:33:56.0530167Z   test_deepcopy_after_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:56.0531132Z Test that we are able to create a deepcopy of the module when it's parametrized. ... ok (0.008s)
2023-01-11T21:33:56.0532298Z   test_errors_parametrized_tensor_parametrization (__main__.TestNNParametrization) ... ok (0.004s)
2023-01-11T21:33:56.0533229Z   test_errors_unparametrized_tensor_parametrization (__main__.TestNNParametrization) ... ok (0.005s)
2023-01-11T21:33:56.0534067Z   test_initialization_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:56.0534843Z Test that it is possible to initialize a parametrization when it ... ok (0.005s)
2023-01-11T21:33:56.0535631Z   test_multiple_inputs_parametrization (__main__.TestNNParametrization) ... ok (0.008s)
2023-01-11T21:33:56.0536413Z   test_new_spectral_norm (__main__.TestNNParametrization) ... ok (0.041s)
2023-01-11T21:33:56.0537113Z   test_new_spectral_norm_dim (__main__.TestNNParametrization) ... ok (0.003s)
2023-01-11T21:33:56.0537874Z   test_new_spectral_norm_forward (__main__.TestNNParametrization) ... ok (0.003s)
2023-01-11T21:33:56.0538673Z   test_new_spectral_norm_load_state_dict (__main__.TestNNParametrization) ... ok (0.016s)
2023-01-11T21:33:56.0539613Z   test_orthogonal_errors (__main__.TestNNParametrization) ... ok (0.005s)
2023-01-11T21:33:56.0540359Z   test_orthogonal_parametrization (__main__.TestNNParametrization) ... ok (0.181s)
2023-01-11T21:33:56.0541156Z   test_parametrization_same_training_mode (__main__.TestNNParametrization)
2023-01-11T21:33:56.0541928Z Test training mode updated on parametrization registration ... ok (0.001s)
2023-01-11T21:33:56.0542946Z   test_register_and_remove_buffer_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:56.0543769Z Test that it is possible to add and remove parametrizations on buffers ... ok (0.002s)
2023-01-11T21:33:56.0544573Z   test_register_and_remove_nested_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:56.0545338Z Test that it is possible to nest the parametrizations ... ok (0.002s)
2023-01-11T21:33:56.0546077Z   test_register_and_remove_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:56.0546852Z Test that it is possible to add a few parametrizations ... ok (0.018s)
2023-01-11T21:33:56.0547579Z   test_serialization_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:56.0548301Z Test that it is possible to serialize a parametrized model via state_dict ... ok (0.005s)
2023-01-11T21:33:56.0549103Z   test_transfer_parametrizations_and_params (__main__.TestNNParametrization)
2023-01-11T21:33:56.0549969Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.004s)
2023-01-11T21:33:56.0550900Z   test_transfer_parametrizations_and_params_many_to_one (__main__.TestNNParametrization) ... ok (0.005s)
2023-01-11T21:33:56.0551828Z   test_transfer_parametrizations_and_params_right_inverse (__main__.TestNNParametrization)
2023-01-11T21:33:56.0552784Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.002s)
2023-01-11T21:33:56.0553678Z   test_transfer_parametrizations_and_params_single_param (__main__.TestNNParametrization)
2023-01-11T21:33:56.0554587Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.003s)
2023-01-11T21:33:56.0555406Z   test_type_before_parametrizations (__main__.TestNNParametrization)
2023-01-11T21:33:56.0556211Z Test that type_before_parametrizations always retrieves original type ... ok (0.001s)
2023-01-11T21:33:56.0556654Z 
2023-01-11T21:33:56.0557171Z ----------------------------------------------------------------------
2023-01-11T21:33:56.0557718Z Ran 23 tests in 0.337s
2023-01-11T21:33:56.0557987Z 
2023-01-11T21:33:56.0558122Z OK
2023-01-11T21:33:56.0558335Z 
2023-01-11T21:33:56.0558522Z Generating XML reports...
2023-01-11T21:33:56.0559563Z Generated XML report: test-reports/python-unittest/nn.test_parametrization/TEST-TestNNParametrization-20230111213355.xml
2023-01-11T21:33:56.0560196Z 
2023-01-11T21:33:56.0560781Z ##[endgroup]
2023-01-11T21:33:56.0561799Z FINISHED PRINTING LOG FILE of nn/test_parametrization (/var/lib/jenkins/workspace/test/test-reports/nn-test_parametrization_y1d91i7c)
2023-01-11T21:33:56.0562569Z 
2023-01-11T21:33:57.9911437Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:58.0562310Z Ignoring disabled issues:  []
2023-01-11T21:33:58.0710566Z Running nn/test_pruning ... [2023-01-11 21:33:58.070744]
2023-01-11T21:33:58.0712134Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_pruning.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:58.070998]
2023-01-11T21:34:00.1352270Z 
2023-01-11T21:34:00.1352821Z Expand the folded group to see the log file of nn/test_pruning
2023-01-11T21:34:00.1353911Z ##[group]PRINTING LOG FILE of nn/test_pruning (/var/lib/jenkins/workspace/test/test-reports/nn-test_pruning_doeazfs5)
2023-01-11T21:34:00.1354289Z 
2023-01-11T21:34:00.1354416Z Running tests...
2023-01-11T21:34:00.1355069Z ----------------------------------------------------------------------
2023-01-11T21:34:00.1355742Z Test results will be stored in test-reports/python-unittest/nn.test_pruning
2023-01-11T21:34:00.1356514Z   test_compute_nparams_to_prune (__main__.TestPruningNN)
2023-01-11T21:34:00.1357044Z Test that requested pruning `amount` gets translated into the ... ok (0.010s)
2023-01-11T21:34:00.1357524Z   test_custom_from_mask_pruning (__main__.TestPruningNN)
2023-01-11T21:34:00.1357980Z Test that the CustomFromMask is capable of receiving ... ok (0.001s)
2023-01-11T21:34:00.1358444Z   test_global_pruning (__main__.TestPruningNN)
2023-01-11T21:34:00.1358930Z Test that global l1 unstructured pruning over 2 parameters removes ... ok (0.002s)
2023-01-11T21:34:00.1359462Z   test_global_pruning_importance_scores (__main__.TestPruningNN)
2023-01-11T21:34:00.1359988Z Test that global l1 unstructured pruning over 2 parameters removes ... ok (0.002s)
2023-01-11T21:34:00.1360479Z   test_identity_pruning (__main__.TestPruningNN)
2023-01-11T21:34:00.1360941Z Test that a mask of 1s does not change forward or backward. ... ok (0.003s)
2023-01-11T21:34:00.1361404Z   test_l1_unstructured_pruning (__main__.TestPruningNN)
2023-01-11T21:34:00.1361990Z Test that l1 unstructured pruning actually removes the lowest ... ok (0.002s)
2023-01-11T21:34:00.1362540Z   test_l1_unstructured_pruning_with_importance_scores (__main__.TestPruningNN)
2023-01-11T21:34:00.1363072Z Test that l1 unstructured pruning actually removes the lowest ... ok (0.002s)
2023-01-11T21:34:00.1363565Z   test_ln_structured_pruning (__main__.TestPruningNN)
2023-01-11T21:34:00.1363983Z Check Ln structured pruning by hand. ... ok (0.002s)
2023-01-11T21:34:00.1364471Z   test_ln_structured_pruning_importance_scores (__main__.TestPruningNN)
2023-01-11T21:34:00.1364936Z Check Ln structured pruning by hand. ... ok (0.002s)
2023-01-11T21:34:00.1365391Z   test_multiple_pruning_calls (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T21:34:00.1365851Z   test_prune (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:34:00.1366301Z   test_prune_importance_scores (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:34:00.1366842Z   test_prune_importance_scores_mimic_default (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:34:00.1367379Z   test_pruning_container (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:34:00.1367859Z   test_pruning_container_compute_mask (__main__.TestPruningNN)
2023-01-11T21:34:00.1368351Z Test `compute_mask` of pruning container with a known `t` and ... ok (0.002s)
2023-01-11T21:34:00.1368846Z   test_pruning_id_consistency (__main__.TestPruningNN)
2023-01-11T21:34:00.1369538Z Test that pruning doesn't change the id of the parameters, which ... ok (0.001s)
2023-01-11T21:34:00.1370016Z   test_pruning_rollback (__main__.TestPruningNN)
2023-01-11T21:34:00.1370502Z Test that if something fails when the we try to compute the mask, ... ok (0.002s)
2023-01-11T21:34:00.1371043Z   test_pruning_serialization_model (__main__.TestPruningNN) ... ok (0.003s)
2023-01-11T21:34:00.1371563Z   test_pruning_serialization_state_dict (__main__.TestPruningNN) ... ok (0.003s)
2023-01-11T21:34:00.1372069Z   test_random_pruning (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T21:34:00.1372733Z   test_random_pruning_0perc (__main__.TestPruningNN)
2023-01-11T21:34:00.1373219Z Test that a mask of 1s does not change forward or backward. ... ok (0.003s)
2023-01-11T21:34:00.1373695Z   test_random_pruning_forward (__main__.TestPruningNN)
2023-01-11T21:34:00.1374129Z check forward with mask (by hand). ... ok (0.002s)
2023-01-11T21:34:00.1374577Z   test_random_pruning_new_weight (__main__.TestPruningNN)
2023-01-11T21:34:00.1375050Z Test that module.name now contains a pruned version of ... ok (0.002s)
2023-01-11T21:34:00.1375534Z   test_random_pruning_orig (__main__.TestPruningNN)
2023-01-11T21:34:00.1376154Z Test that original tensor is correctly stored in 'orig' ... ok (0.002s)
2023-01-11T21:34:00.1376647Z   test_random_pruning_pickle (__main__.TestPruningNN) ... ok (0.004s)
2023-01-11T21:34:00.1377122Z   test_random_pruning_sizes (__main__.TestPruningNN)
2023-01-11T21:34:00.1377625Z Test that the new parameters and buffers created by the pruning ... ok (0.002s)
2023-01-11T21:34:00.1378281Z   test_random_structured_pruning_amount (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:34:00.1378762Z   test_remove_pruning (__main__.TestPruningNN)
2023-01-11T21:34:00.1379253Z `prune.remove` removes the hook and the reparametrization ... ok (0.002s)
2023-01-11T21:34:00.1379769Z   test_remove_pruning_exception (__main__.TestPruningNN)
2023-01-11T21:34:00.1380247Z Removing from an unpruned tensor throws an assertion error ... ok (0.001s)
2023-01-11T21:34:00.1380751Z   test_remove_pruning_forward (__main__.TestPruningNN)
2023-01-11T21:34:00.1381248Z Remove pruning and check forward is unchanged from previous ... ok (0.001s)
2023-01-11T21:34:00.1381732Z   test_rnn_pruning (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T21:34:00.1382219Z   test_unstructured_pruning_same_magnitude (__main__.TestPruningNN)
2023-01-11T21:34:00.1383033Z Since it may happen that the tensor to prune has entries with the ... ok (0.001s)
2023-01-11T21:34:00.1383545Z   test_validate_pruning_amount (__main__.TestPruningNN)
2023-01-11T21:34:00.1384037Z Tests the second util function that validates the pruning ... ok (0.001s)
2023-01-11T21:34:00.1384543Z   test_validate_pruning_amount_init (__main__.TestPruningNN)
2023-01-11T21:34:00.1385037Z Test the first util function that validates the pruning ... ok (0.001s)
2023-01-11T21:34:00.1385335Z 
2023-01-11T21:34:00.1385711Z ----------------------------------------------------------------------
2023-01-11T21:34:00.1386141Z Ran 34 tests in 0.073s
2023-01-11T21:34:00.1386337Z 
2023-01-11T21:34:00.1386446Z OK
2023-01-11T21:34:00.1386604Z 
2023-01-11T21:34:00.1386748Z Generating XML reports...
2023-01-11T21:34:00.1387473Z Generated XML report: test-reports/python-unittest/nn.test_pruning/TEST-TestPruningNN-20230111213359.xml
2023-01-11T21:34:00.1387888Z 
2023-01-11T21:34:00.1388334Z ##[endgroup]
2023-01-11T21:34:00.1389017Z FINISHED PRINTING LOG FILE of nn/test_pruning (/var/lib/jenkins/workspace/test/test-reports/nn-test_pruning_doeazfs5)
2023-01-11T21:34:00.1389400Z 
2023-01-11T21:34:02.1262904Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:02.2118938Z Ignoring disabled issues:  []
2023-01-11T21:34:02.2270557Z Running profiler/test_memory_profiler ... [2023-01-11 21:34:02.226700]
2023-01-11T21:34:02.2271496Z Executing ['/opt/conda/bin/python', '-bb', 'profiler/test_memory_profiler.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:02.226966]
2023-01-11T21:34:07.2513401Z 
2023-01-11T21:34:07.2514125Z Expand the folded group to see the log file of profiler/test_memory_profiler
2023-01-11T21:34:07.2515094Z ##[group]PRINTING LOG FILE of profiler/test_memory_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_memory_profiler_5mscg61c)
2023-01-11T21:34:07.2515577Z 
2023-01-11T21:34:07.2515694Z Running tests...
2023-01-11T21:34:07.2516213Z ----------------------------------------------------------------------
2023-01-11T21:34:07.2516629Z Test results will be stored in test-reports/python-unittest/profiler.test_memory_profiler
2023-01-11T21:34:07.2517385Z   test_data_flow_graph_complicated (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2517871Z STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2518325Z STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2519106Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:336: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2520144Z   lines.append(f"{name + ':':<8} T{storage_to_id[t.storage().data_ptr()]}")
2023-01-11T21:34:07.2520619Z STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2521061Z STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2521509Z STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2521768Z ok (0.298s)
2023-01-11T21:34:07.2522205Z   test_data_flow_graph_non_op_allocations (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2522702Z STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2523148Z STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2523396Z ok (0.061s)
2023-01-11T21:34:07.2523828Z   test_data_flow_graph_simple (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:03 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2524315Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2524757Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2525942Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:337: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:34:07.2526678Z   if t.grad is not None:
2023-01-11T21:34:07.2527024Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2527457Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2527905Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2528165Z ok (0.110s)
2023-01-11T21:34:07.2528598Z   test_data_flow_graph_simple_backward (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2543809Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2544551Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2545348Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:338: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2546532Z   grad_id = storage_to_id[t.grad.storage().data_ptr()]
2023-01-11T21:34:07.2546748Z ok (0.061s)
2023-01-11T21:34:07.2547266Z   test_data_flow_graph_simple_inplace (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2547780Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2548553Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2549230Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2549981Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2550920Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2551400Z ok (0.126s)
2023-01-11T21:34:07.2552052Z   test_data_flow_graph_stacked (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2552598Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2553048Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2553484Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2553919Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2554350Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2554815Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2555403Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2555848Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2556096Z ok (0.204s)
2023-01-11T21:34:07.2556544Z   test_data_flow_graph_with_annotations (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2557035Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2557465Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2557721Z ok (0.087s)
2023-01-11T21:34:07.2558139Z   test_match_schemas (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2558637Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2559068Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2559326Z ok (0.007s)
2023-01-11T21:34:07.2559758Z   test_match_schemas_backward (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2560237Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2560664Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2560923Z ok (0.005s)
2023-01-11T21:34:07.2561354Z   test_match_schemas_tensorlist (__main__.TestDataFlow) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2561918Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2562348Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2562611Z ok (0.002s)
2023-01-11T21:34:07.2563073Z   test_extract_gradients_from_module (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2563564Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2564010Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2564679Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:117: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2565236Z   return tensor.storage().data_ptr() == key.storage.ptr
2023-01-11T21:34:07.2565807Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:147: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2566337Z   allowed_set = {t.storage().data_ptr() for t in tensors}
2023-01-11T21:34:07.2566724Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2567160Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2567606Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2568031Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2568461Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2568899Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2569334Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2569747Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2570187Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2570445Z ok (0.037s)
2023-01-11T21:34:07.2570918Z   test_extract_gradients_from_module_and_optimizer (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2571436Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2571880Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2572138Z ok (0.008s)
2023-01-11T21:34:07.2572590Z   test_extract_gradients_from_optimizer (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2573092Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2573534Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2573955Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2574388Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2574866Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2575301Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2585244Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2586383Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2586919Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2587383Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2588129Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2588540Z ok (0.040s)
2023-01-11T21:34:07.2589535Z   test_extract_gradients_from_optimizer_set_to_none (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2590501Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2591323Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2592101Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2592970Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2593787Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2594233Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2594654Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2595101Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2595534Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2595947Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2596387Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2596646Z ok (0.047s)
2023-01-11T21:34:07.2597106Z   test_extract_gradients_low_level (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2597590Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2598030Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2598460Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2598870Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2599309Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2599564Z ok (0.011s)
2023-01-11T21:34:07.2599991Z   test_config_check (__main__.TestMemoryProfiler) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2600452Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2600890Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2601322Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2601830Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2602259Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2602694Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2603122Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2603549Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2603806Z ok (0.004s)
2023-01-11T21:34:07.2604274Z   test_categories_e2e_sequential_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2605006Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:81: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2605601Z   if isinstance(t, torch.Tensor) and t.storage()
2023-01-11T21:34:07.2606165Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:78: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2606651Z   return tuple(
2023-01-11T21:34:07.2607174Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:79: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2607689Z   (t._cdata, t.storage().data_ptr())
2023-01-11T21:34:07.2608070Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2608504Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2608764Z ok (0.072s)
2023-01-11T21:34:07.2609233Z   test_categories_e2e_sequential_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2609851Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2624426Z STAGE:2023-01-11 21:34:04 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2624722Z ok (0.322s)
2023-01-11T21:34:07.2625197Z   test_categories_e2e_simple_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2625693Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2626139Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2626395Z ok (0.037s)
2023-01-11T21:34:07.2626849Z   test_categories_e2e_simple_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2627348Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2627788Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2628165Z ok (0.191s)
2023-01-11T21:34:07.2628624Z   test_categories_e2e_simple_fwd_bwd_step (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2629132Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2629574Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2629836Z ok (0.231s)
2023-01-11T21:34:07.2630336Z   test_categories_e2e_simple_module_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2630827Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2631266Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2631524Z ok (0.029s)
2023-01-11T21:34:07.2632045Z   test_categories_e2e_simple_module_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2632628Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2633077Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2633337Z ok (0.126s)
2023-01-11T21:34:07.2633804Z   test_categories_e2e_simple_module_fwd_bwd_step (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2634315Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2634760Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2635021Z ok (0.203s)
2023-01-11T21:34:07.2635441Z   test_inputs_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2635922Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2636362Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2636986Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:828: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2637486Z   storage = t.storage()
2023-01-11T21:34:07.2638020Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:836: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:07.2638589Z   if key.storage.ptr == storage.data_ptr() and key.device == storage.device
2023-01-11T21:34:07.2638822Z ok (0.025s)
2023-01-11T21:34:07.2639249Z   test_inputs_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2639734Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2640179Z STAGE:2023-01-11 21:34:05 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2640439Z ok (0.148s)
2023-01-11T21:34:07.2640867Z   test_inputs_fwd_lazy (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2641397Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2641834Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2642077Z ok (0.030s)
2023-01-11T21:34:07.2642516Z   test_lazily_initialized (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2643006Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2643442Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2643687Z ok (0.077s)
2023-01-11T21:34:07.2644132Z   test_manual_optimizer_step (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2644674Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2645116Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2645355Z ok (0.040s)
2023-01-11T21:34:07.2645790Z   test_memory_timeline (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2646273Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2646700Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2646957Z ok (0.183s)
2023-01-11T21:34:07.2647410Z   test_parameters_and_gradients (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2647909Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2648332Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2648760Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2649187Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2649615Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2650042Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2650467Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2650902Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2651322Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2651748Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2652183Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2652435Z ok (0.154s)
2023-01-11T21:34:07.2652897Z   test_parameters_and_gradients_set_to_none (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2653393Z [W CPUAllocator.cpp:231] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
2023-01-11T21:34:07.2653879Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2654318Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2654768Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:07.2655194Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:07.2655629Z STAGE:2023-01-11 21:34:06 5825:5825 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:07.2655871Z ok (0.236s)
2023-01-11T21:34:07.2655975Z 
2023-01-11T21:34:07.2656172Z ----------------------------------------------------------------------
2023-01-11T21:34:07.2656414Z Ran 32 tests in 3.214s
2023-01-11T21:34:07.2656528Z 
2023-01-11T21:34:07.2656589Z OK
2023-01-11T21:34:07.2656667Z 
2023-01-11T21:34:07.2656752Z Generating XML reports...
2023-01-11T21:34:07.2657172Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestDataFlow-20230111213403.xml
2023-01-11T21:34:07.2657788Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestIdentifyGradients-20230111213403.xml
2023-01-11T21:34:07.2658347Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestMemoryProfiler-20230111213403.xml
2023-01-11T21:34:07.2658919Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestMemoryProfilerE2E-20230111213403.xml
2023-01-11T21:34:07.2659171Z 
2023-01-11T21:34:07.2659511Z ##[endgroup]
2023-01-11T21:34:07.2659948Z FINISHED PRINTING LOG FILE of profiler/test_memory_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_memory_profiler_5mscg61c)
2023-01-11T21:34:07.2660181Z 
2023-01-11T21:34:09.1586697Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:09.2426580Z Ignoring disabled issues:  []
2023-01-11T21:34:09.2577735Z Running profiler/test_profiler ... [2023-01-11 21:34:09.257474]
2023-01-11T21:34:09.2579062Z Executing ['/opt/conda/bin/python', '-bb', 'profiler/test_profiler.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:09.257719]
2023-01-11T21:34:18.2347886Z 
2023-01-11T21:34:18.2348456Z Expand the folded group to see the log file of profiler/test_profiler
2023-01-11T21:34:18.2350658Z ##[group]PRINTING LOG FILE of profiler/test_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_fqkfqero)
2023-01-11T21:34:18.2351549Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:18.2351807Z 
2023-01-11T21:34:18.2351897Z Running tests...
2023-01-11T21:34:18.2352476Z ----------------------------------------------------------------------
2023-01-11T21:34:18.2353146Z Test results will be stored in test-reports/python-unittest/profiler.test_profiler
2023-01-11T21:34:18.2353697Z   test_execution_graph_alone (__main__.TestExecutionGraph) ... ok (0.010s)
2023-01-11T21:34:18.2354187Z   test_execution_graph_no_capture (__main__.TestExecutionGraph) ... ok (0.001s)
2023-01-11T21:34:18.2354741Z   test_execution_graph_repeat_in_loop (__main__.TestExecutionGraph) ... ok (0.012s)
2023-01-11T21:34:18.2355305Z   test_execution_graph_start_stop (__main__.TestExecutionGraph) ... ok (0.010s)
2023-01-11T21:34:18.2356006Z   test_execution_graph_with_kineto (__main__.TestExecutionGraph) ... [W kineto_shim.cpp:330] Profiler is not initialized: skipping step() invocation
2023-01-11T21:34:18.2356700Z [W kineto_shim.cpp:330] Profiler is not initialized: skipping step() invocation
2023-01-11T21:34:18.2357275Z [W kineto_shim.cpp:330] Profiler is not initialized: skipping step() invocation
2023-01-11T21:34:18.2358062Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2358847Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2359374Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2359814Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2360463Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2360902Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2361167Z ok (0.030s)
2023-01-11T21:34:18.2361395Z   test_bfs (__main__.TestExperimentalUtils) ... ok (0.001s)
2023-01-11T21:34:18.2361655Z   test_dfs (__main__.TestExperimentalUtils) ... ok (0.001s)
2023-01-11T21:34:18.2362223Z   test_profiler_conv2d_bias_followed_by_batchnorm2d_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2362752Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2363199Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2363711Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2364149Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2364589Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2365028Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2365446Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2365890Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2366149Z ok (0.008s)
2023-01-11T21:34:18.2366420Z   test_profiler_extra_cuda_copy_pattern (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.001s)
2023-01-11T21:34:18.2366816Z   test_profiler_extra_cuda_copy_pattern_benchmark (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.000s)
2023-01-11T21:34:18.2367408Z   test_profiler_for_loop_indexing_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2367922Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2368504Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2368945Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2369378Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2369823Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2370247Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2370681Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2371126Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2371567Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2371982Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2372428Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2372867Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2373286Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2373732Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2374034Z ok (0.084s)
2023-01-11T21:34:18.2374313Z   test_profiler_fp32_matmul_pattern (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.000s)
2023-01-11T21:34:18.2374871Z   test_profiler_grad_not_set_to_none_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2375380Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2375827Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2376260Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2376681Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2377158Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2377593Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2378005Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2378449Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2378890Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2379320Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2379748Z STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2380011Z ok (0.045s)
2023-01-11T21:34:18.2380294Z   test_profiler_matmul_dim_fp16_pattern (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.001s)
2023-01-11T21:34:18.2380839Z   test_profiler_name_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:11 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2381333Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2381779Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2382038Z ok (2.164s)
2023-01-11T21:34:18.2382764Z   test_profiler_optimizer_single_tensor_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2383311Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2383767Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2384222Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2384650Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2385092Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2385529Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2385956Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2386384Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2386821Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2387251Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2387688Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2388232Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2388685Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2389135Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2389561Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2389989Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2390436Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2390697Z ok (0.167s)
2023-01-11T21:34:18.2391196Z   test_profiler_pattern_match_helper (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2391709Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2392154Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2392463Z ok (0.004s)
2023-01-11T21:34:18.2392943Z   test_profiler_pattern_matcher_json_report (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2393461Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2393911Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2394156Z ok (0.025s)
2023-01-11T21:34:18.2394643Z   test_profiler_synchronized_dataloader_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2395158Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2395605Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2395851Z ok (0.090s)
2023-01-11T21:34:18.2396094Z   test_utils_compute_idle_time (__main__.TestExperimentalUtils) ... ok (0.002s)
2023-01-11T21:34:18.2396412Z   test_utils_compute_queue_depth (__main__.TestExperimentalUtils) ... ok (0.001s)
2023-01-11T21:34:18.2396963Z   test_utils_compute_queue_depth_when_no_cuda_events (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2397488Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2397934Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2398202Z ok (0.044s)
2023-01-11T21:34:18.2398642Z   test_utils_compute_self_time (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2399136Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2399581Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2399825Z ok (0.007s)
2023-01-11T21:34:18.2400081Z   test_utils_get_optimizable_events (__main__.TestExperimentalUtils) ... ok (0.007s)
2023-01-11T21:34:18.2400398Z   test_utils_intervals_overlap (__main__.TestExperimentalUtils) ... 5
2023-01-11T21:34:18.2400630Z ok (0.001s)
2023-01-11T21:34:18.2401035Z   test_export_stacks (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2401576Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2402024Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2402270Z ok (0.005s)
2023-01-11T21:34:18.2402675Z   test_flops (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2403141Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2403593Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2403838Z ok (0.080s)
2023-01-11T21:34:18.2404045Z   test_high_level_trace (__main__.TestProfiler)
2023-01-11T21:34:18.2404522Z Checks that python side high level events are recorded. ... STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2405032Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2405476Z STAGE:2023-01-11 21:34:13 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2405911Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2406342Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2406772Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2407200Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2407630Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2408075Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2408322Z ok (0.121s)
2023-01-11T21:34:18.2409082Z   test_kineto (__main__.TestProfiler) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88377 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:34:18.2409667Z   test_kineto_multigpu (__main__.TestProfiler) ... skip: Multiple GPUs needed (0.001s)
2023-01-11T21:34:18.2410173Z   test_kineto_profiler_api (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2410638Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2411082Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2411524Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2411954Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2412388Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2412824Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2413253Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2413681Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2414112Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2414542Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2415029Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2415271Z ok (0.010s)
2023-01-11T21:34:18.2415725Z   test_kineto_profiler_multiple_steppers (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2416219Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2416658Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2417073Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2417498Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2417936Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2418388Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2418814Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2419254Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2419512Z ok (0.031s)
2023-01-11T21:34:18.2420260Z   test_memory_profiler (__main__.TestProfiler) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/72280 for platform(s) linux, mac. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.002s)
2023-01-11T21:34:18.2421013Z   test_module_hierarchy (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2421492Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2421932Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2422493Z ERROR:2023-01-11 21:34:14 5872:5872 CudaDeviceProperties.cpp:27] cudaGetDeviceCount failed with code 35
2023-01-11T21:34:18.2422758Z ok (0.019s)
2023-01-11T21:34:18.2423265Z   test_nested_tensor_with_shapes (__main__.TestProfiler) ... /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1318: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:34:18.2423780Z   inp = torch.nested.nested_tensor([a, b])
2023-01-11T21:34:18.2424150Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2424587Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2425037Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2425298Z ok (0.003s)
2023-01-11T21:34:18.2425500Z   test_oom_tracing (__main__.TestProfiler) ... ok (0.002s)
2023-01-11T21:34:18.2425760Z   test_profiler_correlation_id (__main__.TestProfiler)
2023-01-11T21:34:18.2426296Z We expect the correlation_id to be unique across multiple invokation of the profiler, ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2426797Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2427239Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2427669Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2428166Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2428601Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2429045Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2429475Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2429919Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2430338Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2430767Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2431213Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2431678Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2432107Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2432630Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2432898Z ok (0.159s)
2023-01-11T21:34:18.2433280Z   test_profiler_fwd_bwd_link (__main__.TestProfiler) ... skip: Disable forward->backward link to workaround profiler crash (0.002s)
2023-01-11T21:34:18.2433830Z   test_profiler_metadata (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2434314Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2434759Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2435011Z ok (0.003s)
2023-01-11T21:34:18.2435434Z   test_profiler_tracing (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2435909Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2436340Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2436772Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2437196Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2437639Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2438056Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2438485Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2438926Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2439341Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2439767Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2440206Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2440462Z ok (0.008s)
2023-01-11T21:34:18.2440867Z   test_profiler_type (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2441342Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2441831Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2442089Z ok (0.002s)
2023-01-11T21:34:18.2442269Z   test_source (__main__.TestProfiler)
2023-01-11T21:34:18.2442765Z Checks that source code attribution works for eager, TS and autograd mode ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2443266Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2443697Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2443954Z ok (0.057s)
2023-01-11T21:34:18.2444393Z   test_tensorboard_trace_handler (__main__.TestProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2444879Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2445346Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2445791Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2446220Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2446647Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2447077Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2447510Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2447951Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2448370Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2448801Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2449240Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2449672Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2450085Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2450522Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2450953Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2451379Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2451807Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2452239Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2452669Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2453095Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2453353Z ok (0.018s)
2023-01-11T21:34:18.2453614Z   test_custom_module_input_op_ids (__main__.TestProfilerCUDA) ... skip: CUDA is required (0.001s)
2023-01-11T21:34:18.2453901Z   test_mem_leak (__main__.TestProfilerCUDA)
2023-01-11T21:34:18.2454265Z Checks that there's no memory leak when using profiler with CUDA ... skip: CUDA is required (0.001s)
2023-01-11T21:34:18.2454590Z   test_custom_module_input_op_ids (__main__.TestProfilerITT) ... ok (0.001s)
2023-01-11T21:34:18.2454909Z   test_datapipe_delegation_with_profiler (__main__.TestRecordFunction) ... ok (0.002s)
2023-01-11T21:34:18.2455481Z   test_datapipe_with_record_function (__main__.TestRecordFunction) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2455987Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2456432Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2456691Z ok (0.004s)
2023-01-11T21:34:18.2457139Z   test_datapipe_with_record_function_fork (__main__.TestRecordFunction) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2457640Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2458084Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2458331Z ok (0.004s)
2023-01-11T21:34:18.2458808Z   test_record_function (__main__.TestRecordFunction) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2459298Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2459743Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2459988Z ok (0.002s)
2023-01-11T21:34:18.2460445Z   test_allocation_id_uniqueness (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2460944Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2461386Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2461633Z ok (0.246s)
2023-01-11T21:34:18.2462077Z   test_allocation_ids (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2462698Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2463128Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2463386Z ok (0.180s)
2023-01-11T21:34:18.2463853Z   test_allocation_ids_with_other_ops (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2464357Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2464788Z STAGE:2023-01-11 21:34:14 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2465055Z ok (0.160s)
2023-01-11T21:34:18.2465494Z   test_allocations (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2465986Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2466415Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2466848Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2467277Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2467700Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2467960Z ok (0.187s)
2023-01-11T21:34:18.2468393Z   test_extra_fields (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2468880Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2469425Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2469684Z ok (0.002s)
2023-01-11T21:34:18.2470116Z   test_impl_reuse (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2470584Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2471025Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2471284Z ok (0.237s)
2023-01-11T21:34:18.2471722Z   test_mkldnn_tensors (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2472297Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2472761Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2473023Z ok (0.003s)
2023-01-11T21:34:18.2473475Z   test_module_and_optimizer_ids (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2473955Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2474395Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2474828Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2475243Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2475699Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2475958Z ok (0.014s)
2023-01-11T21:34:18.2476400Z   test_nnmodule_params (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2476872Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2477313Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2477940Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1985: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2478580Z   expected = [(name, val.storage().data_ptr(), val.grad.storage().data_ptr()) for name, val in net.fc1._parameters.items()]
2023-01-11T21:34:18.2479213Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1986: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2479807Z   expected += [(name, val.storage().data_ptr(), val.grad.storage().data_ptr()) for name, val in net.fc2._parameters.items()]
2023-01-11T21:34:18.2480082Z ok (0.004s)
2023-01-11T21:34:18.2480512Z   test_optimizer (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2480998Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2481432Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2481754Z ok (0.012s)
2023-01-11T21:34:18.2482216Z   test_optimizer_parameters_adam (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2482706Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2483152Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2483766Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:2009: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2484319Z   [(v.storage().data_ptr()) for v in group.get("params", [])],
2023-01-11T21:34:18.2484923Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:2022: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2485477Z   {name: value.storage().data_ptr() for name, value in parameter_state.items()},
2023-01-11T21:34:18.2486063Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:2023: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2486600Z   observed_state.get(parameter.storage().data_ptr(), [])
2023-01-11T21:34:18.2486816Z ok (0.037s)
2023-01-11T21:34:18.2487266Z   test_optimizer_parameters_sgd (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2487772Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2488220Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2488480Z ok (0.020s)
2023-01-11T21:34:18.2489047Z   test_pointers_and_ids (__main__.TestTorchTidyProfiler) ... /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1366: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2489635Z   a_initial_storage_data = a.storage().data_ptr()
2023-01-11T21:34:18.2490188Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1371: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2490719Z   c_initial_storage_data = c.storage().data_ptr()
2023-01-11T21:34:18.2491101Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2491508Z [W CPUAllocator.cpp:231] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
2023-01-11T21:34:18.2492170Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1384: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2492694Z   c.set_(d.storage())
2023-01-11T21:34:18.2493054Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2493486Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2493746Z ok (0.005s)
2023-01-11T21:34:18.2494176Z   test_scalar_ins (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2494665Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2495095Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2495354Z ok (0.002s)
2023-01-11T21:34:18.2495824Z   test_sparse_tensors (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2496304Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2496746Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2497004Z ok (0.004s)
2023-01-11T21:34:18.2497435Z   test_tensor_lists (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2497907Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2498350Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2498971Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1958: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2499539Z   self.assertEqual(x.storage().data_ptr(), inputs[0][0].storage_data_ptr)
2023-01-11T21:34:18.2500107Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2500668Z   self.assertEqual(y.storage().data_ptr(), inputs[0][1].storage_data_ptr)
2023-01-11T21:34:18.2500892Z ok (0.002s)
2023-01-11T21:34:18.2501343Z   test_tensor_properties (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2501829Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2502280Z STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2502673Z ok (0.003s)
2023-01-11T21:34:18.2503137Z   test_tensorimpl_invalidation_full (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:15 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2503809Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1546: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2504312Z   x_storages = [x.storage()]
2023-01-11T21:34:18.2504848Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1549: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2505417Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:34:18.2505952Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1550: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2506454Z   x_storages.append(x.storage())
2023-01-11T21:34:18.2507037Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1559: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2507546Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:34:18.2508077Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1563: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2508577Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:34:18.2508939Z STAGE:2023-01-11 21:34:16 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2509386Z STAGE:2023-01-11 21:34:16 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2509642Z ok (0.508s)
2023-01-11T21:34:18.2510106Z   test_tensorimpl_invalidation_keep_alive (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:16 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2510789Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1478: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2511286Z   x_storages = [x.storage()]
2023-01-11T21:34:18.2511819Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1481: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2512414Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:34:18.2512938Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1485: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2513448Z   x_storages.append(x.storage())
2023-01-11T21:34:18.2513981Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1499: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2514481Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:34:18.2514856Z STAGE:2023-01-11 21:34:16 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2515293Z STAGE:2023-01-11 21:34:16 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2515772Z STAGE:2023-01-11 21:34:16 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2516204Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2516634Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2516891Z ok (1.172s)
2023-01-11T21:34:18.2517364Z   test_tensorimpl_invalidation_scalar_args (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2517873Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2518302Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2518560Z ok (0.181s)
2023-01-11T21:34:18.2519056Z   test_tensorimpl_invalidation_set (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2519737Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1454: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:18.2520235Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:34:18.2520605Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2521050Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2521481Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:34:18.2521905Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:34:18.2522347Z STAGE:2023-01-11 21:34:17 5872:5872 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:34:18.2522606Z ok (0.312s)
2023-01-11T21:34:18.2522712Z 
2023-01-11T21:34:18.2522896Z ----------------------------------------------------------------------
2023-01-11T21:34:18.2523142Z Ran 71 tests in 6.548s
2023-01-11T21:34:18.2523257Z 
2023-01-11T21:34:18.2523329Z OK (skipped=10)
2023-01-11T21:34:18.2523436Z 
2023-01-11T21:34:18.2523519Z Generating XML reports...
2023-01-11T21:34:18.2523938Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestExecutionGraph-20230111213411.xml
2023-01-11T21:34:18.2524507Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestExperimentalUtils-20230111213411.xml
2023-01-11T21:34:18.2525047Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestProfiler-20230111213411.xml
2023-01-11T21:34:18.2525564Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestProfilerITT-20230111213411.xml
2023-01-11T21:34:18.2526109Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestRecordFunction-20230111213411.xml
2023-01-11T21:34:18.2526665Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestTorchTidyProfiler-20230111213411.xml
2023-01-11T21:34:18.2527210Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestProfilerCUDA-20230111213411.xml
2023-01-11T21:34:18.2527449Z 
2023-01-11T21:34:18.2527780Z ##[endgroup]
2023-01-11T21:34:18.2528198Z FINISHED PRINTING LOG FILE of profiler/test_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_fqkfqero)
2023-01-11T21:34:18.2528433Z 
2023-01-11T21:34:20.1434642Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:20.2104021Z Ignoring disabled issues:  []
2023-01-11T21:34:20.2256994Z Running test_autocast ... [2023-01-11 21:34:20.225285]
2023-01-11T21:34:20.2258204Z Executing ['/opt/conda/bin/python', '-bb', 'test_autocast.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:20.225546]
2023-01-11T21:34:22.3162397Z 
2023-01-11T21:34:22.3162962Z Expand the folded group to see the log file of test_autocast
2023-01-11T21:34:22.3164027Z ##[group]PRINTING LOG FILE of test_autocast (/var/lib/jenkins/workspace/test/test-reports/test_autocast_wlibco_t)
2023-01-11T21:34:22.3164274Z 
2023-01-11T21:34:22.3164340Z Running tests...
2023-01-11T21:34:22.3164750Z ----------------------------------------------------------------------
2023-01-11T21:34:22.3165127Z Test results will be stored in test-reports/python-unittest/test_autocast
2023-01-11T21:34:22.3165449Z   test_autocast_methods_expect_builtin_promote (__main__.TestAutocastCPU) ... ok (0.236s)
2023-01-11T21:34:22.3165755Z   test_autocast_nn_bf16 (__main__.TestAutocastCPU) ... ok (0.007s)
2023-01-11T21:34:22.3166241Z   test_autocast_nn_fp32 (__main__.TestAutocastCPU) ... ok (0.009s)
2023-01-11T21:34:22.3166527Z   test_autocast_torch_bf16 (__main__.TestAutocastCPU) ... ok (0.019s)
2023-01-11T21:34:22.3166819Z   test_autocast_torch_expect_builtin_promote (__main__.TestAutocastCPU) ... ok (0.008s)
2023-01-11T21:34:22.3167121Z   test_autocast_torch_fp32 (__main__.TestAutocastCPU) ... ok (0.082s)
2023-01-11T21:34:22.3167425Z   test_autocast_torch_need_autocast_promote (__main__.TestAutocastCPU) ... ok (0.007s)
2023-01-11T21:34:22.3167704Z   test_cast_cache_is_global (__main__.TestAutocastGPU)
2023-01-11T21:34:22.3168012Z Verifies that the autocast cache is global. This is done by ... skip: requires cuda (0.001s)
2023-01-11T21:34:22.3168328Z   test_autocast_fast_dtype (__main__.TestTorchAutocast) ... ok (0.001s)
2023-01-11T21:34:22.3168494Z 
2023-01-11T21:34:22.3168702Z ----------------------------------------------------------------------
2023-01-11T21:34:22.3168933Z Ran 9 tests in 0.370s
2023-01-11T21:34:22.3169053Z 
2023-01-11T21:34:22.3169127Z OK (skipped=1)
2023-01-11T21:34:22.3169234Z 
2023-01-11T21:34:22.3169318Z Generating XML reports...
2023-01-11T21:34:22.3169725Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestAutocastCPU-20230111213421.xml
2023-01-11T21:34:22.3170244Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestTorchAutocast-20230111213421.xml
2023-01-11T21:34:22.3170756Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestAutocastGPU-20230111213421.xml
2023-01-11T21:34:22.3170983Z 
2023-01-11T21:34:22.3171211Z ##[endgroup]
2023-01-11T21:34:22.3171584Z FINISHED PRINTING LOG FILE of test_autocast (/var/lib/jenkins/workspace/test/test-reports/test_autocast_wlibco_t)
2023-01-11T21:34:22.3171795Z 
2023-01-11T21:34:24.2462862Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:24.3128515Z Ignoring disabled issues:  []
2023-01-11T21:34:24.3287108Z Running test_binary_ufuncs ... [2023-01-11 21:34:24.328393]
2023-01-11T21:34:24.3290263Z Executing ['/opt/conda/bin/python', '-bb', 'test_binary_ufuncs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:24.328729]
2023-01-11T21:34:26.9987863Z 
2023-01-11T21:34:26.9988415Z Expand the folded group to see the log file of test_binary_ufuncs
2023-01-11T21:34:26.9989435Z ##[group]PRINTING LOG FILE of test_binary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_binary_ufuncs_gnv1g3qt)
2023-01-11T21:34:26.9989678Z 
2023-01-11T21:34:26.9989755Z Running tests...
2023-01-11T21:34:26.9990156Z ----------------------------------------------------------------------
2023-01-11T21:34:26.9990332Z 
2023-01-11T21:34:26.9990532Z ----------------------------------------------------------------------
2023-01-11T21:34:26.9990773Z Ran 0 tests in 0.000s
2023-01-11T21:34:26.9990891Z 
2023-01-11T21:34:26.9990940Z OK
2023-01-11T21:34:26.9991032Z 
2023-01-11T21:34:26.9991119Z Generating XML reports...
2023-01-11T21:34:26.9991707Z Test results will be stored in test-reports/python-unittest/test_binary_ufuncs
2023-01-11T21:34:26.9991893Z 
2023-01-11T21:34:26.9992115Z ##[endgroup]
2023-01-11T21:34:26.9992585Z FINISHED PRINTING LOG FILE of test_binary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_binary_ufuncs_gnv1g3qt)
2023-01-11T21:34:26.9992809Z 
2023-01-11T21:34:28.9150372Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:28.9994317Z Ignoring disabled issues:  []
2023-01-11T21:34:29.0146420Z Running test_bundled_inputs ... [2023-01-11 21:34:29.014319]
2023-01-11T21:34:29.0147937Z Executing ['/opt/conda/bin/python', '-bb', 'test_bundled_inputs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:29.014594]
2023-01-11T21:34:32.4159309Z 
2023-01-11T21:34:32.4159767Z Expand the folded group to see the log file of test_bundled_inputs
2023-01-11T21:34:32.4161143Z ##[group]PRINTING LOG FILE of test_bundled_inputs (/var/lib/jenkins/workspace/test/test-reports/test_bundled_inputs_6051al9e)
2023-01-11T21:34:32.4162015Z 
2023-01-11T21:34:32.4162184Z Running tests...
2023-01-11T21:34:32.4163011Z ----------------------------------------------------------------------
2023-01-11T21:34:32.4163921Z Test results will be stored in test-reports/python-unittest/test_bundled_inputs
2023-01-11T21:34:32.4164668Z   test_bad_inputs (__main__.TestBundledInputs) ... ok (0.258s)
2023-01-11T21:34:32.4166227Z   test_dict_args (__main__.TestBundledInputs) ... /var/lib/jenkins/workspace/test/test_bundled_inputs.py:361: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:32.4167653Z   assert ret.storage().size() == 1
2023-01-11T21:34:32.4169624Z /opt/conda/lib/python3.10/site-packages/torch/utils/bundled_inputs.py:394: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:32.4171068Z   if arg._typed_storage().size() <= MAX_RAW_TENSOR_SIZE or skip_size_check:
2023-01-11T21:34:32.4171622Z ok (1.201s)
2023-01-11T21:34:32.4172179Z   test_double_augment_fail (__main__.TestBundledInputs) ... ok (0.007s)
2023-01-11T21:34:32.4172870Z   test_double_augment_non_mutator (__main__.TestBundledInputs) ... ok (0.007s)
2023-01-11T21:34:32.4173606Z   test_double_augment_success (__main__.TestBundledInputs) ... ok (0.009s)
2023-01-11T21:34:32.4174326Z   test_large_tensor_with_inflation (__main__.TestBundledInputs) ... ok (0.012s)
2023-01-11T21:34:32.4175057Z   test_multiple_methods_with_inputs (__main__.TestBundledInputs) ... ok (0.048s)
2023-01-11T21:34:32.4175873Z   test_multiple_methods_with_inputs_both_defined_failure (__main__.TestBundledInputs) ... ok (0.005s)
2023-01-11T21:34:32.4176744Z   test_multiple_methods_with_inputs_neither_defined_failure (__main__.TestBundledInputs) ... ok (0.004s)
2023-01-11T21:34:32.4177521Z   test_non_tensors (__main__.TestBundledInputs) ... ok (0.008s)
2023-01-11T21:34:32.4179869Z   test_rejected_tensors (__main__.TestBundledInputs) ... /opt/conda/lib/python3.10/site-packages/torch/utils/bundled_inputs.py:410: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:34:32.4181445Z   f"a tensor with storage size {arg._typed_storage().size()}. "
2023-01-11T21:34:32.4181967Z ok (0.010s)
2023-01-11T21:34:32.4182715Z   test_single_tensors (__main__.TestBundledInputs) ... ok (0.050s)
2023-01-11T21:34:32.4183102Z 
2023-01-11T21:34:32.4183584Z ----------------------------------------------------------------------
2023-01-11T21:34:32.4184306Z Ran 12 tests in 1.619s
2023-01-11T21:34:32.4184563Z 
2023-01-11T21:34:32.4184702Z OK
2023-01-11T21:34:32.4184921Z 
2023-01-11T21:34:32.4185102Z Generating XML reports...
2023-01-11T21:34:32.4186064Z Generated XML report: test-reports/python-unittest/test_bundled_inputs/TEST-TestBundledInputs-20230111213430.xml
2023-01-11T21:34:32.4186652Z 
2023-01-11T21:34:32.4187205Z ##[endgroup]
2023-01-11T21:34:32.4188180Z FINISHED PRINTING LOG FILE of test_bundled_inputs (/var/lib/jenkins/workspace/test/test-reports/test_bundled_inputs_6051al9e)
2023-01-11T21:34:32.4188729Z 
2023-01-11T21:34:34.4071938Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:34.4949774Z Ignoring disabled issues:  []
2023-01-11T21:34:34.5102784Z Running test_comparison_utils ... [2023-01-11 21:34:34.509916]
2023-01-11T21:34:34.5104311Z Executing ['/opt/conda/bin/python', '-bb', 'test_comparison_utils.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:34.510190]
2023-01-11T21:34:36.1893769Z 
2023-01-11T21:34:36.1894381Z Expand the folded group to see the log file of test_comparison_utils
2023-01-11T21:34:36.1895484Z ##[group]PRINTING LOG FILE of test_comparison_utils (/var/lib/jenkins/workspace/test/test-reports/test_comparison_utils_2qqxnon_)
2023-01-11T21:34:36.1895923Z 
2023-01-11T21:34:36.1897016Z ##[endgroup]
2023-01-11T21:34:36.1897857Z FINISHED PRINTING LOG FILE of test_comparison_utils (/var/lib/jenkins/workspace/test/test-reports/test_comparison_utils_2qqxnon_)
2023-01-11T21:34:36.1898278Z 
2023-01-11T21:34:38.1049243Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:38.1901941Z Ignoring disabled issues:  []
2023-01-11T21:34:38.2051694Z Running test_complex ... [2023-01-11 21:34:38.204884]
2023-01-11T21:34:38.2053547Z Executing ['/opt/conda/bin/python', '-bb', 'test_complex.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:38.205137]
2023-01-11T21:34:40.2088986Z 
2023-01-11T21:34:40.2089552Z Expand the folded group to see the log file of test_complex
2023-01-11T21:34:40.2090578Z ##[group]PRINTING LOG FILE of test_complex (/var/lib/jenkins/workspace/test/test-reports/test_complex_9sm1gm2r)
2023-01-11T21:34:40.2090963Z 
2023-01-11T21:34:40.2091091Z Running tests...
2023-01-11T21:34:40.2091735Z ----------------------------------------------------------------------
2023-01-11T21:34:40.2092041Z 
2023-01-11T21:34:40.2092386Z ----------------------------------------------------------------------
2023-01-11T21:34:40.2092784Z Ran 0 tests in 0.000s
2023-01-11T21:34:40.2092979Z 
2023-01-11T21:34:40.2093080Z OK
2023-01-11T21:34:40.2093236Z 
2023-01-11T21:34:40.2093373Z Generating XML reports...
2023-01-11T21:34:40.2093936Z Test results will be stored in test-reports/python-unittest/test_complex
2023-01-11T21:34:40.2094252Z 
2023-01-11T21:34:40.2094630Z ##[endgroup]
2023-01-11T21:34:40.2095318Z FINISHED PRINTING LOG FILE of test_complex (/var/lib/jenkins/workspace/test/test-reports/test_complex_9sm1gm2r)
2023-01-11T21:34:40.2095703Z 
2023-01-11T21:34:42.1722531Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:42.2586794Z Ignoring disabled issues:  []
2023-01-11T21:34:42.2735617Z Running test_cuda_sanitizer ... [2023-01-11 21:34:42.273278]
2023-01-11T21:34:42.2737922Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_sanitizer.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:42.273532]
2023-01-11T21:34:43.9708273Z 
2023-01-11T21:34:43.9708843Z Expand the folded group to see the log file of test_cuda_sanitizer
2023-01-11T21:34:43.9709866Z ##[group]PRINTING LOG FILE of test_cuda_sanitizer (/var/lib/jenkins/workspace/test/test-reports/test_cuda_sanitizer_6g1nc35w)
2023-01-11T21:34:43.9710424Z CUDA not available, skipping tests
2023-01-11T21:34:43.9710649Z 
2023-01-11T21:34:43.9710761Z Running tests...
2023-01-11T21:34:43.9711383Z ----------------------------------------------------------------------
2023-01-11T21:34:43.9711936Z 
2023-01-11T21:34:43.9712372Z ----------------------------------------------------------------------
2023-01-11T21:34:43.9712780Z Ran 0 tests in 0.000s
2023-01-11T21:34:43.9712979Z 
2023-01-11T21:34:43.9713086Z OK
2023-01-11T21:34:43.9713245Z 
2023-01-11T21:34:43.9713396Z Generating XML reports...
2023-01-11T21:34:43.9713992Z Test results will be stored in test-reports/python-unittest/test_cuda_sanitizer
2023-01-11T21:34:43.9714322Z 
2023-01-11T21:34:43.9714718Z ##[endgroup]
2023-01-11T21:34:43.9715444Z FINISHED PRINTING LOG FILE of test_cuda_sanitizer (/var/lib/jenkins/workspace/test/test-reports/test_cuda_sanitizer_6g1nc35w)
2023-01-11T21:34:43.9715849Z 
2023-01-11T21:34:45.9824850Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:46.1015218Z Ignoring disabled issues:  []
2023-01-11T21:34:46.1247515Z Running test_dataloader ... [2023-01-11 21:34:46.124401]
2023-01-11T21:34:46.1249308Z Executing ['/opt/conda/bin/python', '-bb', 'test_dataloader.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:46.124695]
2023-01-11T21:36:14.9700212Z 
2023-01-11T21:36:14.9701340Z Expand the folded group to see the log file of test_dataloader
2023-01-11T21:36:14.9707178Z ##[group]PRINTING LOG FILE of test_dataloader (/var/lib/jenkins/workspace/test/test-reports/test_dataloader_itutkeds)
2023-01-11T21:36:14.9707675Z 
2023-01-11T21:36:14.9708100Z Running tests...
2023-01-11T21:36:14.9708724Z ----------------------------------------------------------------------
2023-01-11T21:36:14.9709171Z Test results will be stored in test-reports/python-unittest/test_dataloader
2023-01-11T21:36:14.9709499Z   test_shuffler_iterdatapipe (__main__.IntegrationTestDataLoaderDataPipe)
2023-01-11T21:36:14.9709952Z Verify ``IterDataPipe.shuffle`` is controlled by ``DataLoader`` ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:36:14.9767422Z   test_add_dataset (__main__.TestConcatDataset) ... ok (0.224s)
2023-01-11T21:36:14.9767934Z   test_concat_raises_index_error (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:36:14.9768397Z   test_concat_two_non_singletons (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:36:14.9768858Z   test_concat_two_non_singletons_with_empty (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:36:14.9769293Z   test_concat_two_singletons (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:36:14.9769848Z   test_iterable_dataset_err (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:36:14.9771606Z   test_conv_after_fork (__main__.TestConvAfterFork) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/75492 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:36:14.9772635Z   test_custom_batch_pin (__main__.TestCustomPinFn) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:36:14.9773254Z   test_custom_batch_pin_worker (__main__.TestCustomPinFn) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:36:14.9774395Z   test_batch_sampler (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9775021Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9775458Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9775806Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9776293Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9781142Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9782004Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9783021Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9783776Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9784361Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9784788Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9785138Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9785775Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9786292Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9787133Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9788056Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9788539Z ok (2.433s)
2023-01-11T21:36:14.9789029Z   test_builtin_collection_conversion (__main__.TestDataLoader) ... ok (0.274s)
2023-01-11T21:36:14.9789681Z   test_bulk_loading_nobatch (__main__.TestDataLoader) ... ok (0.066s)
2023-01-11T21:36:14.9790321Z   test_chain_iterable_style_dataset (__main__.TestDataLoader) ... ok (0.171s)
2023-01-11T21:36:14.9790905Z   test_default_collate_bad_numpy_types (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:36:14.9791491Z   test_default_collate_bad_sequence_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9792040Z   test_default_collate_dtype (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:36:14.9792588Z   test_default_collate_mapping_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9794926Z   test_default_collate_numpy_memmap (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py:172: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:210.)
2023-01-11T21:36:14.9796646Z   return collate([torch.as_tensor(b) for b in batch], collate_fn_map=collate_fn_map)
2023-01-11T21:36:14.9797158Z ok (0.003s)
2023-01-11T21:36:14.9797677Z   test_default_collate_sequence_dont_keep_type (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:36:14.9798364Z   test_default_collate_sequence_keep_type (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:36:14.9799026Z   test_default_collate_shared_tensor (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:36:14.9799695Z   test_default_convert_mapping_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9800376Z   test_default_convert_sequence_dont_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9801062Z   test_default_convert_sequence_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9801744Z   test_distributed_sampler_invalid_rank (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9802403Z   test_duplicating_data_with_drop_last (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:36:14.9802993Z   test_error (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:36:14.9803537Z   test_error_in_init (__main__.TestDataLoader) ... ok (0.083s)
2023-01-11T21:36:14.9804100Z   test_error_workers (__main__.TestDataLoader) ... ok (0.068s)
2023-01-11T21:36:14.9804706Z   test_excessive_thread_creation_warning (__main__.TestDataLoader) ... ok (0.008s)
2023-01-11T21:36:14.9805330Z   test_fd_limit_exceeded (__main__.TestDataLoader) ... ok (1.554s)
2023-01-11T21:36:14.9806485Z   test_get_worker_info (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9807446Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9808395Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9809154Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9809632Z ok (3.467s)
2023-01-11T21:36:14.9810082Z   test_growing_dataset (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9810690Z   test_invalid_assign_after_init (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9811344Z   test_invalid_ctor_args_combinations (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:36:14.9811960Z   test_iterable_style_dataset (__main__.TestDataLoader) ... ok (0.900s)
2023-01-11T21:36:14.9812575Z   test_iterabledataset_len (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:36:14.9813826Z   test_large_sampler_indices (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9814669Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9815613Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9816372Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9816861Z ok (3.214s)
2023-01-11T21:36:14.9817273Z   test_len (__main__.TestDataLoader) ... ok (0.007s)
2023-01-11T21:36:14.9817866Z   test_multi_epochs_reproducibility (__main__.TestDataLoader) ... ok (0.132s)
2023-01-11T21:36:14.9819066Z   test_multiple_dataloaders (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9819897Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9820839Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9821600Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9822726Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9823419Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9824375Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9825121Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9826090Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9826793Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9827751Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9828496Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9829433Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9830144Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9831102Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9831855Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9832314Z ok (3.432s)
2023-01-11T21:36:14.9833450Z   test_multiprocessing_contexts (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9834442Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9835401Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9836130Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9837084Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9837795Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9838753Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9839506Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9840468Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9841178Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9842201Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9842960Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9848793Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9850334Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9851083Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9851642Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9852451Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9853031Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9853795Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9854424Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9855233Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9855808Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9856531Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9857142Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9857565Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9857887Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9858326Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9858666Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9859085Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9859412Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9859838Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9860165Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9860590Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9860921Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9861349Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9861775Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9862216Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9862720Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9863153Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9863478Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9863910Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9864235Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9864652Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9865068Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9865513Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9865834Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9866251Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9866590Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9866813Z ok (7.001s)
2023-01-11T21:36:14.9867511Z   test_multiprocessing_iterdatapipe (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/graph_settings.py:88: UserWarning: `shuffle=True` was set, but the datapipe does not contain a `Shuffler`. Adding one at the end. Be aware that the default buffer size might not be sufficient for your task.
2023-01-11T21:36:14.9868035Z   warnings.warn(
2023-01-11T21:36:14.9868425Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9868766Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9869194Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9869531Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9869957Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9870283Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9870712Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9871040Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9871474Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9871802Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9872214Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9872554Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9875141Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9875705Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9876485Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9877067Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9877818Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9878577Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9879375Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9880006Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9880772Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9881381Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9881817Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9882160Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9882591Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9882974Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9883394Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9883738Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9884169Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9884479Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9884907Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9885246Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9885467Z ok (6.605s)
2023-01-11T21:36:14.9885907Z   test_no_segfault (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9886285Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9886714Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9887041Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9887263Z ok (1.712s)
2023-01-11T21:36:14.9887473Z   test_numpy (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:36:14.9887733Z   test_numpy_gen_state (__main__.TestDataLoader) ... ok (0.006s)
2023-01-11T21:36:14.9887987Z   test_numpy_scalars (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:36:14.9888249Z   test_partial_workers (__main__.TestDataLoader)
2023-01-11T21:36:14.9888528Z Check that workers exit even if the iterator is not exhausted. ... ok (0.086s)
2023-01-11T21:36:14.9888783Z   test_proper_exit (__main__.TestDataLoader)
2023-01-11T21:36:14.9889201Z There might be ConnectionResetError or leaked semaphore warning (due to dirty process exit), but they are all safe to ignore ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.003s)
2023-01-11T21:36:14.9889619Z   test_random_sampler (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:36:14.9889918Z   test_random_sampler_len_with_replacement (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9890225Z   test_random_sampler_len_without_replacement (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9890749Z   test_sampler (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9891119Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9891542Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9891894Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9892380Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9892709Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9893131Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9893476Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9893910Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9894378Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9894797Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9895139Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9895619Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9895939Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9896367Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9896715Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9896935Z ok (2.465s)
2023-01-11T21:36:14.9897159Z   test_sampler_reproducibility (__main__.TestDataLoader) ... ok (0.028s)
2023-01-11T21:36:14.9897674Z   test_segfault (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9898043Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9898466Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9898828Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9899054Z ok (5.407s)
2023-01-11T21:36:14.9899282Z   test_seqential_batch_workers (__main__.TestDataLoader) ... ok (0.110s)
2023-01-11T21:36:14.9899577Z   test_seqential_batch_workers_prefetch (__main__.TestDataLoader) ... ok (0.111s)
2023-01-11T21:36:14.9899874Z   test_sequential_batch (__main__.TestDataLoader) ... ok (0.041s)
2023-01-11T21:36:14.9900152Z   test_sequential_nonbatch (__main__.TestDataLoader) ... ok (0.022s)
2023-01-11T21:36:14.9900449Z   test_sequential_pin_memory (__main__.TestDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:36:14.9900756Z   test_sequential_workers (__main__.TestDataLoader) ... ok (0.174s)
2023-01-11T21:36:14.9901020Z   test_shuffle (__main__.TestDataLoader) ... ok (0.062s)
2023-01-11T21:36:14.9901278Z   test_shuffle_batch (__main__.TestDataLoader) ... ok (0.054s)
2023-01-11T21:36:14.9901534Z   test_shuffle_batch_none (__main__.TestDataLoader) ... ok (0.056s)
2023-01-11T21:36:14.9901819Z   test_shuffle_batch_workers (__main__.TestDataLoader) ... ok (0.158s)
2023-01-11T21:36:14.9902117Z   test_shuffle_batch_workers_prefetch (__main__.TestDataLoader) ... ok (0.144s)
2023-01-11T21:36:14.9904378Z   test_shuffle_pin_memory (__main__.TestDataLoader) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:36:14.9904709Z   test_shuffle_reproducibility (__main__.TestDataLoader) ... ok (0.309s)
2023-01-11T21:36:14.9904999Z   test_shuffle_workers (__main__.TestDataLoader) ... ok (0.210s)
2023-01-11T21:36:14.9905506Z   test_timeout (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9905880Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9906317Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9906669Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9906976Z ok (2.683s)
2023-01-11T21:36:14.9907189Z   test_typing (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:36:14.9907450Z   test_worker_init_fn (__main__.TestDataLoader) ... ok (0.045s)
2023-01-11T21:36:14.9907703Z   test_worker_seed (__main__.TestDataLoader) ... ok (0.092s)
2023-01-11T21:36:14.9907986Z   test_worker_seed_reproducibility (__main__.TestDataLoader) ... ok (0.167s)
2023-01-11T21:36:14.9908566Z   test_batch_sampler (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9908974Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9909394Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9909738Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9910225Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9910556Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9910978Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9911320Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9911758Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9912071Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9912497Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9912910Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9913349Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9913665Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9914095Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9914436Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9914645Z ok (2.448s)
2023-01-11T21:36:14.9914926Z   test_builtin_collection_conversion (__main__.TestDataLoaderPersistentWorkers) ... ok (0.227s)
2023-01-11T21:36:14.9915291Z   test_bulk_loading_nobatch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.068s)
2023-01-11T21:36:14.9915654Z   test_chain_iterable_style_dataset (__main__.TestDataLoaderPersistentWorkers) ... ok (0.102s)
2023-01-11T21:36:14.9916002Z   test_dataset_not_reset (__main__.TestDataLoaderPersistentWorkers) ... ok (0.093s)
2023-01-11T21:36:14.9916374Z   test_default_collate_bad_numpy_types (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9916754Z   test_default_collate_bad_sequence_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9917109Z   test_default_collate_dtype (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9917480Z   test_default_collate_mapping_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9917851Z   test_default_collate_numpy_memmap (__main__.TestDataLoaderPersistentWorkers) ... ok (0.003s)
2023-01-11T21:36:14.9918230Z   test_default_collate_sequence_dont_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9918602Z   test_default_collate_sequence_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9918977Z   test_default_collate_shared_tensor (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9919352Z   test_default_convert_mapping_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9919787Z   test_default_convert_sequence_dont_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9920164Z   test_default_convert_sequence_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9920551Z   test_distributed_sampler_invalid_rank (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9920931Z   test_duplicating_data_with_drop_last (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9921271Z   test_early_exit (__main__.TestDataLoaderPersistentWorkers) ... ok (1.518s)
2023-01-11T21:36:14.9921597Z   test_error (__main__.TestDataLoaderPersistentWorkers) ... ok (0.003s)
2023-01-11T21:36:14.9921922Z   test_error_in_init (__main__.TestDataLoaderPersistentWorkers) ... ok (0.080s)
2023-01-11T21:36:14.9922251Z   test_error_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.058s)
2023-01-11T21:36:14.9922656Z   test_excessive_thread_creation_warning (__main__.TestDataLoaderPersistentWorkers) ... ok (0.013s)
2023-01-11T21:36:14.9923027Z   test_fd_limit_exceeded (__main__.TestDataLoaderPersistentWorkers) ... ok (1.589s)
2023-01-11T21:36:14.9923630Z   test_get_worker_info (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9924042Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9924464Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9924815Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9925041Z ok (3.527s)
2023-01-11T21:36:14.9925285Z   test_growing_dataset (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9925641Z   test_invalid_assign_after_init (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9926015Z   test_invalid_ctor_args_combinations (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9926487Z   test_iterable_style_dataset (__main__.TestDataLoaderPersistentWorkers) ... Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f6daf1d0b80>
2023-01-11T21:36:14.9926856Z Traceback (most recent call last):
2023-01-11T21:36:14.9927240Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1480, in __del__
2023-01-11T21:36:14.9927606Z Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f6daf1d0b80>
2023-01-11T21:36:14.9927876Z     self._shutdown_workers()
2023-01-11T21:36:14.9928258Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1463, in _shutdown_workers
2023-01-11T21:36:14.9928549Z Traceback (most recent call last):
2023-01-11T21:36:14.9928919Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1480, in __del__
2023-01-11T21:36:14.9929169Z     if w.is_alive():
2023-01-11T21:36:14.9929424Z   File "/opt/conda/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
2023-01-11T21:36:14.9929803Z     assert self._parent_pid == os.getpid(), 'can only test a child process'
2023-01-11T21:36:14.9930069Z AssertionError: can only test a child process
2023-01-11T21:36:14.9930288Z     self._shutdown_workers()
2023-01-11T21:36:14.9930668Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1463, in _shutdown_workers
2023-01-11T21:36:14.9931028Z Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f6daf1d0b80>
2023-01-11T21:36:14.9931320Z Traceback (most recent call last):
2023-01-11T21:36:14.9931690Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1480, in __del__
2023-01-11T21:36:14.9931950Z     if w.is_alive():
2023-01-11T21:36:14.9932193Z   File "/opt/conda/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
2023-01-11T21:36:14.9932611Z     assert self._parent_pid == os.getpid(), 'can only test a child process'
2023-01-11T21:36:14.9932886Z AssertionError: can only test a child process
2023-01-11T21:36:14.9933095Z     self._shutdown_workers()
2023-01-11T21:36:14.9933475Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1463, in _shutdown_workers
2023-01-11T21:36:14.9933744Z     if w.is_alive():
2023-01-11T21:36:14.9933987Z   File "/opt/conda/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
2023-01-11T21:36:14.9934361Z     assert self._parent_pid == os.getpid(), 'can only test a child process'
2023-01-11T21:36:14.9934631Z AssertionError: can only test a child process
2023-01-11T21:36:14.9934831Z ok (0.794s)
2023-01-11T21:36:14.9935086Z   test_iterabledataset_len (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9935740Z   test_large_sampler_indices (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9936159Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9936580Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9936931Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9937154Z ok (3.232s)
2023-01-11T21:36:14.9937398Z   test_len (__main__.TestDataLoaderPersistentWorkers) ... ok (0.007s)
2023-01-11T21:36:14.9937738Z   test_multi_epochs_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.050s)
2023-01-11T21:36:14.9938367Z   test_multiple_dataloaders (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9938784Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9939213Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9939561Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9939998Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9940326Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9940741Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9941087Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9941519Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9941843Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9942258Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9942749Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9943185Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9943494Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9943918Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9944256Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9944480Z ok (3.431s)
2023-01-11T21:36:14.9944990Z   test_multiprocessing_contexts (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9945414Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9945925Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9946270Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9946689Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9947013Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9947439Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9947766Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9948196Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9948520Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9948996Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9949327Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9949759Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9950083Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9950496Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9950835Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9951265Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9951589Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9952007Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9952352Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9952839Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9953162Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9953579Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9953923Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9954345Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9954657Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9955085Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9955429Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9955857Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9956167Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9956584Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9956909Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9957326Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9957671Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9958108Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9958491Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9958910Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9959239Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9959665Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9960002Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9960416Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9960744Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9961167Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9961559Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9961996Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9962322Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9962750Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9963075Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9963298Z ok (6.832s)
2023-01-11T21:36:14.9964053Z   test_multiprocessing_iterdatapipe (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/graph_settings.py:88: UserWarning: `shuffle=True` was set, but the datapipe does not contain a `Shuffler`. Adding one at the end. Be aware that the default buffer size might not be sufficient for your task.
2023-01-11T21:36:14.9964583Z   warnings.warn(
2023-01-11T21:36:14.9964962Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9965290Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9965718Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9966048Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9966481Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9966807Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9967234Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9967557Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9967997Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9968325Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9968738Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9969076Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9969504Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9969826Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9970239Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9970580Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9971015Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9971382Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9971795Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9972134Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9972562Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9972871Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9973297Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9973635Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9974062Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9974407Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9974838Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9975174Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9975587Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9975909Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9976337Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9976674Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9976884Z ok (6.618s)
2023-01-11T21:36:14.9977383Z   test_no_segfault (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9977784Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9978210Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9978538Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9978764Z ok (1.745s)
2023-01-11T21:36:14.9979009Z   test_numpy (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9979329Z   test_numpy_gen_state (__main__.TestDataLoaderPersistentWorkers) ... ok (0.003s)
2023-01-11T21:36:14.9979665Z   test_numpy_scalars (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9979998Z   test_partial_workers (__main__.TestDataLoaderPersistentWorkers)
2023-01-11T21:36:14.9980296Z Check that workers exit even if the iterator is not exhausted. ... ok (0.070s)
2023-01-11T21:36:14.9980607Z   test_proper_exit (__main__.TestDataLoaderPersistentWorkers)
2023-01-11T21:36:14.9981057Z There might be ConnectionResetError or leaked semaphore warning (due to dirty process exit), but they are all safe to ignore ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.005s)
2023-01-11T21:36:14.9981516Z   test_random_sampler (__main__.TestDataLoaderPersistentWorkers) ... ok (0.004s)
2023-01-11T21:36:14.9981873Z   test_random_sampler_len_with_replacement (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:36:14.9982267Z   test_random_sampler_len_without_replacement (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9983054Z   test_sampler (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9983459Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9983888Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9984298Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9984734Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9985064Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9985481Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9985821Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9986252Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9986564Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9986992Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9987383Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9987816Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9988131Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9988557Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9988896Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9989104Z ok (2.546s)
2023-01-11T21:36:14.9989377Z   test_sampler_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.015s)
2023-01-11T21:36:14.9989969Z   test_segfault (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9990371Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9990789Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9991132Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9991353Z ok (3.328s)
2023-01-11T21:36:14.9991607Z   test_seqential_batch_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.118s)
2023-01-11T21:36:14.9991978Z   test_seqential_batch_workers_prefetch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.116s)
2023-01-11T21:36:14.9992339Z   test_sequential_batch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.039s)
2023-01-11T21:36:14.9992741Z   test_sequential_nonbatch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.021s)
2023-01-11T21:36:14.9993119Z   test_sequential_pin_memory (__main__.TestDataLoaderPersistentWorkers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:36:14.9993500Z   test_sequential_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.180s)
2023-01-11T21:36:14.9993841Z   test_shuffle (__main__.TestDataLoaderPersistentWorkers) ... ok (0.062s)
2023-01-11T21:36:14.9994160Z   test_shuffle_batch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.053s)
2023-01-11T21:36:14.9994504Z   test_shuffle_batch_none (__main__.TestDataLoaderPersistentWorkers) ... ok (0.055s)
2023-01-11T21:36:14.9994852Z   test_shuffle_batch_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.158s)
2023-01-11T21:36:14.9995213Z   test_shuffle_batch_workers_prefetch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.154s)
2023-01-11T21:36:14.9995585Z   test_shuffle_pin_memory (__main__.TestDataLoaderPersistentWorkers) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:36:14.9995966Z   test_shuffle_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.271s)
2023-01-11T21:36:14.9996314Z   test_shuffle_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.214s)
2023-01-11T21:36:14.9996899Z   test_timeout (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:36:14.9997334Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:36:14.9997770Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:36:14.9998116Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:36:14.9998339Z ok (2.721s)
2023-01-11T21:36:14.9998658Z   test_typing (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:36:14.9998989Z   test_worker_init_fn (__main__.TestDataLoaderPersistentWorkers) ... ok (0.050s)
2023-01-11T21:36:14.9999325Z   test_worker_seed (__main__.TestDataLoaderPersistentWorkers) ... ok (0.099s)
2023-01-11T21:36:14.9999669Z   test_worker_seed_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.186s)
2023-01-11T21:36:15.0000069Z   test_incomplete_fractional_splits (__main__.TestDatasetRandomSplit) ... ok (0.001s)
2023-01-11T21:36:15.0000410Z   test_lengths_must_equal_dataset_size (__main__.TestDatasetRandomSplit) ... ok (0.001s)
2023-01-11T21:36:15.0000724Z   test_slicing_of_subset_of_dataset (__main__.TestDatasetRandomSplit) ... ok (0.002s)
2023-01-11T21:36:15.0001049Z   test_slicing_of_subset_of_subset (__main__.TestDatasetRandomSplit) ... ok (0.002s)
2023-01-11T21:36:15.0001374Z   test_splits_are_mutually_exclusive (__main__.TestDatasetRandomSplit) ... ok (0.001s)
2023-01-11T21:36:15.0001691Z   test_splits_generator (__main__.TestDatasetRandomSplit) ... ok (0.002s)
2023-01-11T21:36:15.0001993Z   test_splits_have_correct_size (__main__.TestDatasetRandomSplit) ... ok (0.002s)
2023-01-11T21:36:15.0002300Z   test_splits_indexing_type (__main__.TestDatasetRandomSplit)
2023-01-11T21:36:15.0002567Z Indices generated by random_split ... ok (0.003s)
2023-01-11T21:36:15.0002843Z   test_splits_reproducibility (__main__.TestDatasetRandomSplit) ... ok (0.007s)
2023-01-11T21:36:15.0003174Z   test_pin_memory (__main__.TestDictDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:36:15.0003501Z   test_pin_memory_device (__main__.TestDictDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:36:15.0003846Z   test_pin_memory_with_only_device (__main__.TestDictDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:36:15.0004159Z   test_sequential_batch (__main__.TestDictDataLoader) ... ok (0.037s)
2023-01-11T21:36:15.0005051Z   test_ind_worker_queue (__main__.TestIndividualWorkerQueue) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/68643 for platform(s) macos, linux, win. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:36:15.0005686Z   test_dataloader_with_namedtuple (__main__.TestNamedTupleDataLoader) ... ok (0.002s)
2023-01-11T21:36:15.0006015Z   test_set_affinity_in_worker_init (__main__.TestSetAffinity) ... ok (0.053s)
2023-01-11T21:36:15.0006332Z   test_shuffle_pin_memory (__main__.TestStringDataLoader) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:36:15.0006637Z   test_getitem (__main__.TestTensorDataset) ... ok (0.005s)
2023-01-11T21:36:15.0006903Z   test_getitem_1d (__main__.TestTensorDataset) ... ok (0.004s)
2023-01-11T21:36:15.0007151Z   test_len (__main__.TestTensorDataset) ... ok (0.001s)
2023-01-11T21:36:15.0007419Z   test_many_tensors (__main__.TestTensorDataset) ... ok (0.004s)
2023-01-11T21:36:15.0007694Z   test_single_tensor (__main__.TestTensorDataset) ... ok (0.001s)
2023-01-11T21:36:15.0007851Z 
2023-01-11T21:36:15.0008059Z ----------------------------------------------------------------------
2023-01-11T21:36:15.0008291Z Ran 164 tests in 86.915s
2023-01-11T21:36:15.0008405Z 
2023-01-11T21:36:15.0008479Z OK (skipped=15)
2023-01-11T21:36:15.0008585Z 
2023-01-11T21:36:15.0008674Z Generating XML reports...
2023-01-11T21:36:15.0009129Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestConcatDataset-20230111213447.xml
2023-01-11T21:36:15.0009650Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDataLoader-20230111213447.xml
2023-01-11T21:36:15.0010217Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDataLoaderPersistentWorkers-20230111213447.xml
2023-01-11T21:36:15.0010792Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDatasetRandomSplit-20230111213447.xml
2023-01-11T21:36:15.0011314Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDictDataLoader-20230111213447.xml
2023-01-11T21:36:15.0011858Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestNamedTupleDataLoader-20230111213447.xml
2023-01-11T21:36:15.0012381Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestSetAffinity-20230111213447.xml
2023-01-11T21:36:15.0012996Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestTensorDataset-20230111213447.xml
2023-01-11T21:36:15.0013559Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-IntegrationTestDataLoaderDataPipe-20230111213447.xml
2023-01-11T21:36:15.0014130Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestConvAfterFork-20230111213447.xml
2023-01-11T21:36:15.0014641Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestCustomPinFn-20230111213447.xml
2023-01-11T21:36:15.0015180Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestIndividualWorkerQueue-20230111213447.xml
2023-01-11T21:36:15.0015712Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestStringDataLoader-20230111213447.xml
2023-01-11T21:36:15.0015950Z 
2023-01-11T21:36:15.0016323Z ##[endgroup]
2023-01-11T21:36:15.0016711Z FINISHED PRINTING LOG FILE of test_dataloader (/var/lib/jenkins/workspace/test/test-reports/test_dataloader_itutkeds)
2023-01-11T21:36:15.0016932Z 
2023-01-11T21:36:16.9499687Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:17.0352570Z Ignoring disabled issues:  []
2023-01-11T21:36:17.0504339Z Running test_datapipe ... [2023-01-11 21:36:17.050121]
2023-01-11T21:36:17.0506426Z Executing ['/opt/conda/bin/python', '-bb', 'test_datapipe.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:17.050435]
2023-01-11T21:36:19.4561049Z 
2023-01-11T21:36:19.4561606Z Expand the folded group to see the log file of test_datapipe
2023-01-11T21:36:19.4562677Z ##[group]PRINTING LOG FILE of test_datapipe (/var/lib/jenkins/workspace/test/test-reports/test_datapipe_fixg6u32)
2023-01-11T21:36:19.4563026Z 
2023-01-11T21:36:19.4563268Z Running tests...
2023-01-11T21:36:19.4563954Z ----------------------------------------------------------------------
2023-01-11T21:36:19.4564807Z   test_basic_capture (__main__.TestCaptureDataFrame) ... Test results will be stored in test-reports/python-unittest/test_datapipe
2023-01-11T21:36:19.4565430Z skip: no dataframes (pandas) (0.000s)
2023-01-11T21:36:19.4565938Z   test_circular_serialization_with_dill (__main__.TestCircularSerialization) ... skip: no dill (0.001s)
2023-01-11T21:36:19.4567126Z   test_circular_serialization_with_pickle (__main__.TestCircularSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:36:19.4567718Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:36:19.4567974Z ok (0.248s)
2023-01-11T21:36:19.4568187Z   test_as_string (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:36:19.4568441Z   test_getitem (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:36:19.4568672Z   test_iter (__main__.TestDataChunk) ... ok (0.000s)
2023-01-11T21:36:19.4569097Z   test_len (__main__.TestDataChunk) ... ok (0.000s)
2023-01-11T21:36:19.4569352Z   test_random_shuffle (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:36:19.4569594Z   test_reverse (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:36:19.4569840Z   test_sort (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:36:19.4570132Z   test_batch (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:36:19.4570456Z   test_capture (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:36:19.4570795Z   test_collate (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.001s)
2023-01-11T21:36:19.4571126Z   test_filter (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:36:19.4571451Z   test_shuffle (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:36:19.4571767Z   test_unbatch (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:36:19.4572168Z   test_batch_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.003s)
2023-01-11T21:36:19.4572908Z   test_collate_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4573364Z   warnings.warn(
2023-01-11T21:36:19.4573526Z ok (0.004s)
2023-01-11T21:36:19.4573781Z   test_concat_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.003s)
2023-01-11T21:36:19.4574510Z   test_demux_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4574943Z   warnings.warn(
2023-01-11T21:36:19.4575475Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4575849Z   warnings.warn(
2023-01-11T21:36:19.4576372Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4576724Z   warnings.warn(
2023-01-11T21:36:19.4577244Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4577615Z   warnings.warn(
2023-01-11T21:36:19.4577787Z ok (0.008s)
2023-01-11T21:36:19.4578022Z   test_filter_datapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.005s)
2023-01-11T21:36:19.4578351Z   test_fork_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.010s)
2023-01-11T21:36:19.4578697Z   test_iterable_wrapper_datapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T21:36:19.4579039Z   test_map_dict_with_col_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.016s)
2023-01-11T21:36:19.4579770Z   test_map_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4580216Z   warnings.warn(
2023-01-11T21:36:19.4580385Z ok (0.005s)
2023-01-11T21:36:19.4580645Z   test_map_tuple_list_with_col_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.031s)
2023-01-11T21:36:19.4580992Z   test_mux_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T21:36:19.4581328Z   test_sampler_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.001s)
2023-01-11T21:36:19.4582088Z   test_serializable (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:36:19.4582898Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:36:19.4583575Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:36:19.4584077Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:36:19.4584332Z ok (0.038s)
2023-01-11T21:36:19.4584564Z   test_serializable_with_dill (__main__.TestFunctionalIterDataPipe)
2023-01-11T21:36:19.4584924Z Only for DataPipes that take in a function as argument ... ok (0.020s)
2023-01-11T21:36:19.4585248Z   test_shuffler_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.005s)
2023-01-11T21:36:19.4585582Z   test_stream_reader_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.001s)
2023-01-11T21:36:19.4585931Z   test_unbatch_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.004s)
2023-01-11T21:36:19.4586264Z   test_zip_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T21:36:19.4586600Z   test_batch_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:36:19.4586913Z   test_concat_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:36:19.4587635Z   test_map_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4588087Z   warnings.warn(
2023-01-11T21:36:19.4588247Z ok (0.003s)
2023-01-11T21:36:19.4588506Z   test_sequence_wrapper_datapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:36:19.4588836Z   test_serializable (__main__.TestFunctionalMapDataPipe) ... ok (0.014s)
2023-01-11T21:36:19.4589148Z   test_serializable_with_dill (__main__.TestFunctionalMapDataPipe)
2023-01-11T21:36:19.4589437Z Only for DataPipes that take in a function as argument ... ok (0.009s)
2023-01-11T21:36:19.4589752Z   test_shuffler_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.006s)
2023-01-11T21:36:19.4590083Z   test_zip_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:36:19.4590741Z   test_simple_traverse (__main__.TestGraph) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4591159Z   warnings.warn(
2023-01-11T21:36:19.4591332Z ok (0.002s)
2023-01-11T21:36:19.4591562Z   test_traverse_circular_datapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:36:19.4591824Z   test_traverse_forked (__main__.TestGraph) ... ok (0.002s)
2023-01-11T21:36:19.4592093Z   test_traverse_mapdatapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:36:19.4592366Z   test_traverse_mixdatapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:36:19.4592700Z   test_traverse_unhashable_datapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:36:19.4593075Z   test_iterdatapipe_sample_yielded_generator_function (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:36:19.4593529Z   test_iterdatapipe_sample_yielded_generator_function_exception (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.002s)
2023-01-11T21:36:19.4593959Z   test_iterdatapipe_sample_yielded_next (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:36:19.4594363Z   test_iterdatapipe_sample_yielded_next_exception (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:36:19.4594847Z   test_iterdatapipe_sample_yielded_return_self (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:36:19.4595254Z   test_simple_snapshot_custom_non_generator (__main__.TestIterDataPipeGraphFastForward) ... ok (0.001s)
2023-01-11T21:36:19.4595645Z   test_simple_snapshot_custom_self_next (__main__.TestIterDataPipeGraphFastForward) ... ok (0.001s)
2023-01-11T21:36:19.4596006Z   test_simple_snapshot_graph (__main__.TestIterDataPipeGraphFastForward) ... ok (0.012s)
2023-01-11T21:36:19.4596846Z   test_simple_snapshot_graph_repeated (__main__.TestIterDataPipeGraphFastForward) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:36:19.4597488Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:36:19.4597748Z ok (0.005s)
2023-01-11T21:36:19.4598032Z   test_simple_snapshot_graph_with_serialization (__main__.TestIterDataPipeGraphFastForward) ... ok (0.010s)
2023-01-11T21:36:19.4598423Z   test_iterdatapipe_singleton_buggy (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:36:19.4598873Z Buggy test case case where IterDataPipe's `__iter__` returns a new object, but also has ... ok (0.002s)
2023-01-11T21:36:19.4599247Z   test_iterdatapipe_singleton_constraint_multiple_outputs (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:36:19.4599635Z Testing for the case where IterDataPipe has multiple child DataPipes as outputs. ... ok (0.006s)
2023-01-11T21:36:19.4600003Z   test_iterdatapipe_singleton_generator (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:36:19.4600429Z Testing for the case where IterDataPipe's `__iter__` is a generator function. ... ok (0.003s)
2023-01-11T21:36:19.4600798Z   test_iterdatapipe_singleton_new_object (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:36:19.4601237Z Testing for the case where IterDataPipe's `__iter__` isn't a generator nor returns `self`, ... ok (0.002s)
2023-01-11T21:36:19.4601603Z   test_iterdatapipe_singleton_self_next (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:36:19.4602062Z Testing for the case where IterDataPipe's `__iter__` returns `self` and there is a `__next__` method ... ok (0.003s)
2023-01-11T21:36:19.4602801Z   test_demux_mux_datapipe (__main__.TestIterableDataPipeBasic) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:36:19.4603229Z   warnings.warn(
2023-01-11T21:36:19.4603400Z ok (0.004s)
2023-01-11T21:36:19.4603660Z   test_groupby_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.006s)
2023-01-11T21:36:19.4604011Z   test_listdirfiles_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.003s)
2023-01-11T21:36:19.4604384Z   test_listdirfilesdeterministic_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.002s)
2023-01-11T21:36:19.4604762Z   test_map_with_col_file_handle_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.003s)
2023-01-11T21:36:19.4605123Z   test_openfilesfromdisk_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.004s)
2023-01-11T21:36:19.4605474Z   test_routeddecoder_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.004s)
2023-01-11T21:36:19.4605806Z   test_spawn_lambdas_iter (__main__.TestSerialization) ... skip: no dill (0.000s)
2023-01-11T21:36:19.4606117Z   test_spawn_lambdas_map (__main__.TestSerialization) ... skip: no dill (0.000s)
2023-01-11T21:36:19.4606406Z   test_multi_sharding (__main__.TestSharding) ... ok (0.003s)
2023-01-11T21:36:19.4606658Z   test_old_dataloader (__main__.TestSharding) ... ok (0.051s)
2023-01-11T21:36:19.4606970Z   test_sharding_groups (__main__.TestSharding) ... ok (0.002s)
2023-01-11T21:36:19.4607234Z   test_sharding_length (__main__.TestSharding) ... ok (0.002s)
2023-01-11T21:36:19.4607484Z   test_simple_sharding (__main__.TestSharding) ... ok (0.003s)
2023-01-11T21:36:19.4607743Z   test_api (__main__.TestStreamWrapper) ... ok (0.001s)
2023-01-11T21:36:19.4608001Z   test_dir (__main__.TestStreamWrapper) ... ok (0.001s)
2023-01-11T21:36:19.4608246Z   test_pickle (__main__.TestStreamWrapper) ... ok (0.002s)
2023-01-11T21:36:19.4608508Z   test_repr (__main__.TestStreamWrapper) ... ok (0.002s)
2023-01-11T21:36:19.4608794Z   test_compile_time (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.002s)
2023-01-11T21:36:19.4609104Z   test_construct_time (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:36:19.4609371Z   test_isinstance (__main__.TestTyping) ... ok (0.001s)
2023-01-11T21:36:19.4609652Z   test_issubinstance (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:36:19.4610007Z   test_protocol (__main__.TestTyping) ... ok (0.001s)
2023-01-11T21:36:19.4610269Z   test_reinforce (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:36:19.4610562Z   test_runtime (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:36:19.4610853Z   test_subtype (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:36:19.4611021Z 
2023-01-11T21:36:19.4611229Z ----------------------------------------------------------------------
2023-01-11T21:36:19.4611461Z Ran 89 tests in 0.622s
2023-01-11T21:36:19.4611575Z 
2023-01-11T21:36:19.4611650Z OK (skipped=16)
2023-01-11T21:36:19.4611758Z 
2023-01-11T21:36:19.4611843Z Generating XML reports...
2023-01-11T21:36:19.4612275Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestCircularSerialization-20230111213618.xml
2023-01-11T21:36:19.4612809Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestDataChunk-20230111213618.xml
2023-01-11T21:36:19.4613350Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestFunctionalIterDataPipe-20230111213618.xml
2023-01-11T21:36:19.4613921Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestFunctionalMapDataPipe-20230111213618.xml
2023-01-11T21:36:19.4614426Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestGraph-20230111213618.xml
2023-01-11T21:36:19.4614980Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeCountSampleYielded-20230111213618.xml
2023-01-11T21:36:19.4615596Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeGraphFastForward-20230111213618.xml
2023-01-11T21:36:19.4616215Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeSingletonConstraint-20230111213618.xml
2023-01-11T21:36:19.4616795Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterableDataPipeBasic-20230111213618.xml
2023-01-11T21:36:19.4617317Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestSharding-20230111213618.xml
2023-01-11T21:36:19.4617816Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestStreamWrapper-20230111213618.xml
2023-01-11T21:36:19.4618308Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestTyping-20230111213618.xml
2023-01-11T21:36:19.4618796Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestCaptureDataFrame-20230111213618.xml
2023-01-11T21:36:19.4619327Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestDataFramesPipes-20230111213618.xml
2023-01-11T21:36:19.4619845Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestSerialization-20230111213618.xml
2023-01-11T21:36:19.4620073Z 
2023-01-11T21:36:19.4620378Z ##[endgroup]
2023-01-11T21:36:19.4620765Z FINISHED PRINTING LOG FILE of test_datapipe (/var/lib/jenkins/workspace/test/test-reports/test_datapipe_fixg6u32)
2023-01-11T21:36:19.4621028Z 
2023-01-11T21:36:21.3856081Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:21.4712349Z Ignoring disabled issues:  []
2023-01-11T21:36:21.4865106Z Running test_decomp ... [2023-01-11 21:36:21.486156]
2023-01-11T21:36:21.4866934Z Executing ['/opt/conda/bin/python', '-bb', 'test_decomp.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:21.486434]
2023-01-11T21:36:24.1080777Z 
2023-01-11T21:36:24.1081325Z Expand the folded group to see the log file of test_decomp
2023-01-11T21:36:24.1082192Z ##[group]PRINTING LOG FILE of test_decomp (/var/lib/jenkins/workspace/test/test-reports/test_decomp_ovlbxq8p)
2023-01-11T21:36:24.1082426Z 
2023-01-11T21:36:24.1082502Z Running tests...
2023-01-11T21:36:24.1082885Z ----------------------------------------------------------------------
2023-01-11T21:36:24.1083057Z 
2023-01-11T21:36:24.1083251Z ----------------------------------------------------------------------
2023-01-11T21:36:24.1083488Z Ran 0 tests in 0.000s
2023-01-11T21:36:24.1083830Z 
2023-01-11T21:36:24.1083879Z OK
2023-01-11T21:36:24.1083968Z 
2023-01-11T21:36:24.1084053Z Generating XML reports...
2023-01-11T21:36:24.1084380Z Test results will be stored in test-reports/python-unittest/test_decomp
2023-01-11T21:36:24.1084556Z 
2023-01-11T21:36:24.1084774Z ##[endgroup]
2023-01-11T21:36:24.1085151Z FINISHED PRINTING LOG FILE of test_decomp (/var/lib/jenkins/workspace/test/test-reports/test_decomp_ovlbxq8p)
2023-01-11T21:36:24.1085361Z 
2023-01-11T21:36:26.0309828Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:26.1143667Z Ignoring disabled issues:  []
2023-01-11T21:36:26.1295554Z Running test_deploy ... [2023-01-11 21:36:26.129209]
2023-01-11T21:36:26.1297620Z Executing ['/opt/conda/bin/python', '-bb', '-m', 'pytest', 'test_deploy.py', '-v'] ... [2023-01-11 21:36:26.129483]
2023-01-11T21:36:28.9085575Z 
2023-01-11T21:36:28.9086387Z Expand the folded group to see the log file of test_deploy
2023-01-11T21:36:28.9087202Z ##[group]PRINTING LOG FILE of test_deploy (/var/lib/jenkins/workspace/test/test-reports/test_deploy_r5le7pbk)
2023-01-11T21:36:28.9087522Z ============================= test session starts ==============================
2023-01-11T21:36:28.9088005Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:36:28.9088276Z cachedir: .pytest_cache
2023-01-11T21:36:28.9088715Z hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/var/lib/jenkins/workspace/test/.hypothesis/examples')
2023-01-11T21:36:28.9089077Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:36:28.9089509Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:36:28.9089809Z collecting ... collected 1 item
2023-01-11T21:36:28.9090087Z Running 1 items in this shard: test/test_deploy.py::TestFreezer::test_compile_string
2023-01-11T21:36:28.9090256Z 
2023-01-11T21:36:28.9090403Z test_deploy.py::TestFreezer::test_compile_string PASSED                  [100%]
2023-01-11T21:36:28.9090574Z 
2023-01-11T21:36:28.9090682Z ============================== 1 passed in 1.48s ===============================
2023-01-11T21:36:28.9090816Z 
2023-01-11T21:36:28.9091033Z ##[endgroup]
2023-01-11T21:36:28.9091391Z FINISHED PRINTING LOG FILE of test_deploy (/var/lib/jenkins/workspace/test/test-reports/test_deploy_r5le7pbk)
2023-01-11T21:36:28.9091599Z 
2023-01-11T21:36:30.8563793Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:30.9613623Z Ignoring disabled issues:  []
2023-01-11T21:36:30.9804408Z Running test_dlpack ... [2023-01-11 21:36:30.979941]
2023-01-11T21:36:30.9805896Z Executing ['/opt/conda/bin/python', '-bb', 'test_dlpack.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:30.980290]
2023-01-11T21:36:32.9305133Z 
2023-01-11T21:36:32.9305789Z Expand the folded group to see the log file of test_dlpack
2023-01-11T21:36:32.9307191Z ##[group]PRINTING LOG FILE of test_dlpack (/var/lib/jenkins/workspace/test/test-reports/test_dlpack_lm9sk8oi)
2023-01-11T21:36:32.9307921Z 
2023-01-11T21:36:32.9308039Z Running tests...
2023-01-11T21:36:32.9308645Z ----------------------------------------------------------------------
2023-01-11T21:36:32.9308915Z 
2023-01-11T21:36:32.9309210Z ----------------------------------------------------------------------
2023-01-11T21:36:32.9309580Z Ran 0 tests in 0.000s
2023-01-11T21:36:32.9309744Z 
2023-01-11T21:36:32.9309834Z OK
2023-01-11T21:36:32.9309970Z 
2023-01-11T21:36:32.9310100Z Generating XML reports...
2023-01-11T21:36:32.9310615Z Test results will be stored in test-reports/python-unittest/test_dlpack
2023-01-11T21:36:32.9310884Z 
2023-01-11T21:36:32.9311273Z ##[endgroup]
2023-01-11T21:36:32.9311885Z FINISHED PRINTING LOG FILE of test_dlpack (/var/lib/jenkins/workspace/test/test-reports/test_dlpack_lm9sk8oi)
2023-01-11T21:36:32.9312191Z 
2023-01-11T21:36:34.8349765Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:34.9253499Z Ignoring disabled issues:  []
2023-01-11T21:36:34.9407622Z Running test_dynamic_shapes ... [2023-01-11 21:36:34.940370]
2023-01-11T21:36:34.9409228Z Executing ['/opt/conda/bin/python', '-bb', 'test_dynamic_shapes.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:34.940677]
2023-01-11T21:36:37.8049131Z 
2023-01-11T21:36:37.8049699Z Expand the folded group to see the log file of test_dynamic_shapes
2023-01-11T21:36:37.8050789Z ##[group]PRINTING LOG FILE of test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/test_dynamic_shapes_ezsjxa5o)
2023-01-11T21:36:37.8051217Z 
2023-01-11T21:36:37.8051920Z Running tests...
2023-01-11T21:36:37.8052557Z ----------------------------------------------------------------------
2023-01-11T21:36:37.8053211Z Test results will be stored in test-reports/python-unittest/test_dynamic_shapes
2023-01-11T21:36:37.8053699Z   test_arith_ops (__main__.TestPySymInt) ... ok (0.508s)
2023-01-11T21:36:37.8054125Z   test_aten_ops (__main__.TestPySymInt) ... ok (0.021s)
2023-01-11T21:36:37.8054545Z   test_binary (__main__.TestPySymInt) ... ok (0.025s)
2023-01-11T21:36:37.8054968Z   test_fx_trace_intlist (__main__.TestPySymInt) ... ok (0.010s)
2023-01-11T21:36:37.8055368Z   test_guard_int (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:36:37.8055780Z   test_int_conversion (__main__.TestPySymInt) ... ok (0.001s)
2023-01-11T21:36:37.8056224Z   test_int_to_float (__main__.TestPySymInt) ... ok (0.002s)
2023-01-11T21:36:37.8056667Z   test_meta_symint (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:36:37.8057065Z   test_numel (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:36:37.8057498Z   test_print_readable_with_symints (__main__.TestPySymInt) ... ok (0.090s)
2023-01-11T21:36:37.8057984Z   test_reverse_arith_ops (__main__.TestPySymInt) ... ok (0.065s)
2023-01-11T21:36:37.8058385Z   test_roundtrip (__main__.TestPySymInt) ... ok (0.025s)
2023-01-11T21:36:37.8058817Z   test_size_expressions (__main__.TestPySymInt) ... ok (0.007s)
2023-01-11T21:36:37.8059283Z   test_stride (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:36:37.8059708Z   test_sym_floor (__main__.TestPySymInt) ... ok (0.069s)
2023-01-11T21:36:37.8060147Z   test_sym_int (__main__.TestPySymInt) ... ok (0.020s)
2023-01-11T21:36:37.8060585Z   test_sym_sqrt (__main__.TestPySymInt) ... ok (0.040s)
2023-01-11T21:36:37.8060977Z   test_symint_args (__main__.TestPySymInt) ... ok (0.031s)
2023-01-11T21:36:37.8061415Z   test_symint_as_scalar (__main__.TestPySymInt) ... ok (0.001s)
2023-01-11T21:36:37.8061835Z   test_symint_vargs (__main__.TestPySymInt) ... ok (0.024s)
2023-01-11T21:36:37.8062657Z   test_method_fn_add_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: add is not a float magic method (0.001s)
2023-01-11T21:36:37.8063390Z   test_method_fn_add_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: add is not a float magic method (0.001s)
2023-01-11T21:36:37.8064061Z   test_method_fn_add_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.002s)
2023-01-11T21:36:37.8064931Z   test_method_fn_add_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8065664Z   test_method_fn_ceil_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: ceil is not a float magic method (0.001s)
2023-01-11T21:36:37.8066372Z   test_method_fn_ceil_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: ceil is not a float magic method (0.001s)
2023-01-11T21:36:37.8067131Z   test_method_fn_ceil_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: ceil is unary and already tested (0.001s)
2023-01-11T21:36:37.8067689Z   test_method_fn_ceil_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8068095Z   test_method_fn_eq_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: eq is not a float magic method (0.001s)
2023-01-11T21:36:37.8068628Z   test_method_fn_eq_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: eq is not a float magic method (0.001s)
2023-01-11T21:36:37.8069015Z   test_method_fn_eq_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8069387Z   test_method_fn_eq_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8069796Z   test_method_fn_floor_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: floor is not a float magic method (0.001s)
2023-01-11T21:36:37.8070242Z   test_method_fn_floor_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: floor is not a float magic method (0.001s)
2023-01-11T21:36:37.8070673Z   test_method_fn_floor_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: floor is unary and already tested (0.001s)
2023-01-11T21:36:37.8071084Z   test_method_fn_floor_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8071505Z   test_method_fn_floordiv_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: floordiv is not a float magic method (0.001s)
2023-01-11T21:36:37.8071960Z   test_method_fn_floordiv_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: floordiv is not a float magic method (0.001s)
2023-01-11T21:36:37.8072371Z   test_method_fn_floordiv_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.003s)
2023-01-11T21:36:37.8072823Z   test_method_fn_floordiv_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8073235Z   test_method_fn_ge_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: ge is not a float magic method (0.001s)
2023-01-11T21:36:37.8073678Z   test_method_fn_ge_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: ge is not a float magic method (0.001s)
2023-01-11T21:36:37.8074069Z   test_method_fn_ge_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8074444Z   test_method_fn_ge_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8074846Z   test_method_fn_gt_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: gt is not a float magic method (0.001s)
2023-01-11T21:36:37.8075278Z   test_method_fn_gt_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: gt is not a float magic method (0.001s)
2023-01-11T21:36:37.8075660Z   test_method_fn_gt_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8076030Z   test_method_fn_gt_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8076440Z   test_method_fn_le_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: le is not a float magic method (0.001s)
2023-01-11T21:36:37.8076915Z   test_method_fn_le_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: le is not a float magic method (0.001s)
2023-01-11T21:36:37.8077295Z   test_method_fn_le_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8077665Z   test_method_fn_le_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8078065Z   test_method_fn_lt_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: lt is not a float magic method (0.001s)
2023-01-11T21:36:37.8078501Z   test_method_fn_lt_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: lt is not a float magic method (0.001s)
2023-01-11T21:36:37.8078884Z   test_method_fn_lt_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8079285Z   test_method_fn_lt_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8079696Z   test_method_fn_max_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: max is not a float magic method (0.001s)
2023-01-11T21:36:37.8080130Z   test_method_fn_max_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: max is not a float magic method (0.001s)
2023-01-11T21:36:37.8080526Z   test_method_fn_max_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8080898Z   test_method_fn_max_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8081301Z   test_method_fn_min_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: min is not a float magic method (0.001s)
2023-01-11T21:36:37.8081740Z   test_method_fn_min_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: min is not a float magic method (0.001s)
2023-01-11T21:36:37.8082136Z   test_method_fn_min_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8082514Z   test_method_fn_min_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8082924Z   test_method_fn_mod_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: mod is not a float magic method (0.001s)
2023-01-11T21:36:37.8083358Z   test_method_fn_mod_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: mod is not a float magic method (0.001s)
2023-01-11T21:36:37.8083751Z   test_method_fn_mod_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8084121Z   test_method_fn_mod_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8084532Z   test_method_fn_mul_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: mul is not a float magic method (0.001s)
2023-01-11T21:36:37.8084958Z   test_method_fn_mul_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: mul is not a float magic method (0.001s)
2023-01-11T21:36:37.8085360Z   test_method_fn_mul_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8085732Z   test_method_fn_mul_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8086138Z   test_method_fn_neg_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: neg is not a float magic method (0.001s)
2023-01-11T21:36:37.8086558Z   test_method_fn_neg_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: neg is not a float magic method (0.001s)
2023-01-11T21:36:37.8086993Z   test_method_fn_neg_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: neg is unary and already tested (0.001s)
2023-01-11T21:36:37.8087433Z   test_method_fn_neg_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8087841Z   test_method_fn_pow_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: pow is not a float magic method (0.001s)
2023-01-11T21:36:37.8088267Z   test_method_fn_pow_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: pow is not a float magic method (0.001s)
2023-01-11T21:36:37.8088669Z   test_method_fn_pow_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8089051Z   test_method_fn_pow_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8089455Z   test_method_fn_sub_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sub is not a float magic method (0.001s)
2023-01-11T21:36:37.8089900Z   test_method_fn_sub_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: sub is not a float magic method (0.001s)
2023-01-11T21:36:37.8090301Z   test_method_fn_sub_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8090669Z   test_method_fn_sub_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8091088Z   test_method_fn_sym_float_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_float is not a float magic method (0.001s)
2023-01-11T21:36:37.8091535Z   test_method_fn_sym_float_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: sym_float is not a float magic method (0.001s)
2023-01-11T21:36:37.8091992Z   test_method_fn_sym_float_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_float is unary and already tested (0.001s)
2023-01-11T21:36:37.8092408Z   test_method_fn_sym_float_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8092832Z   test_method_fn_sym_sqrt_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_sqrt is not a float magic method (0.001s)
2023-01-11T21:36:37.8093285Z   test_method_fn_sym_sqrt_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: sym_sqrt is not a float magic method (0.001s)
2023-01-11T21:36:37.8093722Z   test_method_fn_sym_sqrt_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_sqrt is unary and already tested (0.001s)
2023-01-11T21:36:37.8094130Z   test_method_fn_sym_sqrt_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8094549Z   test_method_fn_truediv_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: truediv is not a float magic method (0.001s)
2023-01-11T21:36:37.8095007Z   test_method_fn_truediv_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: truediv is not a float magic method (0.001s)
2023-01-11T21:36:37.8095418Z   test_method_fn_truediv_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8095800Z   test_method_fn_truediv_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:36:37.8096001Z 
2023-01-11T21:36:37.8096279Z ----------------------------------------------------------------------
2023-01-11T21:36:37.8096512Z Ran 96 tests in 1.029s
2023-01-11T21:36:37.8096627Z 
2023-01-11T21:36:37.8096699Z OK (skipped=43)
2023-01-11T21:36:37.8096807Z 
2023-01-11T21:36:37.8096894Z Generating XML reports...
2023-01-11T21:36:37.8097317Z Generated XML report: test-reports/python-unittest/test_dynamic_shapes/TEST-TestPySymInt-20230111213636.xml
2023-01-11T21:36:37.8097861Z Generated XML report: test-reports/python-unittest/test_dynamic_shapes/TEST-TestSymNumberMagicMethods-20230111213636.xml
2023-01-11T21:36:37.8098121Z 
2023-01-11T21:36:37.8098442Z ##[endgroup]
2023-01-11T21:36:37.8098843Z FINISHED PRINTING LOG FILE of test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/test_dynamic_shapes_ezsjxa5o)
2023-01-11T21:36:37.8099115Z 
2023-01-11T21:36:39.7052091Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:39.7733273Z Ignoring disabled issues:  []
2023-01-11T21:36:39.7887253Z Running test_expanded_weights ... [2023-01-11 21:36:39.788452]
2023-01-11T21:36:39.7889421Z Executing ['/opt/conda/bin/python', '-bb', 'test_expanded_weights.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:39.788734]
2023-01-11T21:36:42.7764927Z 
2023-01-11T21:36:42.7765461Z Expand the folded group to see the log file of test_expanded_weights
2023-01-11T21:36:42.7766649Z ##[group]PRINTING LOG FILE of test_expanded_weights (/var/lib/jenkins/workspace/test/test-reports/test_expanded_weights_1qdkv6ra)
2023-01-11T21:36:42.7767107Z 
2023-01-11T21:36:42.7767240Z Running tests...
2023-01-11T21:36:42.7767912Z ----------------------------------------------------------------------
2023-01-11T21:36:42.7768900Z Test results will be stored in test-reports/python-unittest/test_expanded_weights
2023-01-11T21:36:42.7769395Z   test_Conv1d (__main__.TestExpandedWeightModule) ... ok (0.005s)
2023-01-11T21:36:42.7769956Z   test_Conv1d_circular_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7770620Z   test_Conv1d_circular_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.042s)
2023-01-11T21:36:42.7771248Z   test_Conv1d_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7771761Z   test_Conv1d_pad1 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7772307Z   test_Conv1d_pad1_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.007s)
2023-01-11T21:36:42.7772855Z   test_Conv1d_pad1size1 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7773444Z   test_Conv1d_pad1size1_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7774038Z   test_Conv1d_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7774616Z   test_Conv1d_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7775136Z   test_Conv1d_pad2size1 (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7775852Z   test_Conv1d_pad2size1_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7776477Z   test_Conv1d_reflect_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7777131Z   test_Conv1d_reflect_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.011s)
2023-01-11T21:36:42.7777732Z   test_Conv1d_replicate_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7779320Z   test_Conv1d_replicate_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.006s)
2023-01-11T21:36:42.7779706Z   test_Conv1d_stride (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7780038Z   test_Conv1d_stride_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7780576Z   test_Conv1d_zero_batch (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7781163Z   test_Conv1d_zero_batch_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7781565Z   test_Conv1d_zeros_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7781919Z   test_Conv1d_zeros_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7782230Z   test_Conv2d (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7782652Z   test_Conv2d_circular_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7783015Z   test_Conv2d_circular_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7783491Z   test_Conv2d_dilated (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7783806Z   test_Conv2d_dilated_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7784140Z   test_Conv2d_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7784459Z   test_Conv2d_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7784766Z   test_Conv2d_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7785085Z   test_Conv2d_padding (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7785418Z   test_Conv2d_padding_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7785758Z   test_Conv2d_reflect_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7786150Z   test_Conv2d_reflect_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7786511Z   test_Conv2d_replicate_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7786867Z   test_Conv2d_replicate_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7787194Z   test_Conv2d_strided (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7787517Z   test_Conv2d_strided_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7788031Z   test_Conv2d_zero_batch (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7788614Z   test_Conv2d_zero_batch_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7789009Z   test_Conv2d_zeros_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7789364Z   test_Conv2d_zeros_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7789692Z   test_Conv3d (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7790000Z   test_Conv3d_1x1x1_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7790318Z   test_Conv3d_1x1x1_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7790659Z   test_Conv3d_circular_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7791017Z   test_Conv3d_circular_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.006s)
2023-01-11T21:36:42.7791348Z   test_Conv3d_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7791660Z   test_Conv3d_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7791982Z   test_Conv3d_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7792328Z   test_Conv3d_replicate_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7792675Z   test_Conv3d_replicate_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.006s)
2023-01-11T21:36:42.7793073Z   test_Conv3d_stride (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7793400Z   test_Conv3d_stride_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7793720Z   test_Conv3d_stride_padding (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7794068Z   test_Conv3d_stride_padding_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:36:42.7794594Z   test_Conv3d_zero_batch (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7795186Z   test_Conv3d_zero_batch_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7795618Z   test_Conv3d_zeros_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7795971Z   test_Conv3d_zeros_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7796304Z   test_Embedding (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7796623Z   test_Embedding_discontiguous (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7796964Z   test_Embedding_discontiguous_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7797312Z   test_Embedding_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.001s)
2023-01-11T21:36:42.7797638Z   test_GroupNorm_1d_affine (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7797944Z   test_GroupNorm_1d_affine_GN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7798321Z   test_GroupNorm_1d_affine_GN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7798682Z   test_GroupNorm_1d_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7799022Z   test_GroupNorm_1d_no_affine_IN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7799356Z   test_GroupNorm_1d_no_affine_IN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7799699Z   test_GroupNorm_1d_no_affine_LN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7800044Z   test_GroupNorm_1d_no_affine_LN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7800383Z   test_GroupNorm_2d_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7800703Z   test_GroupNorm_2d_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7801040Z   test_GroupNorm_2d_no_affine_IN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7801393Z   test_GroupNorm_2d_no_affine_IN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7801723Z   test_GroupNorm_2d_no_affine_LN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7802067Z   test_GroupNorm_2d_no_affine_LN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7802415Z   test_LayerNorm_1d_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7802781Z   test_LayerNorm_1d_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7803334Z   test_LayerNorm_1d_empty_elementwise_affine (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7803973Z   test_LayerNorm_1d_empty_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:36:42.7804412Z   test_LayerNorm_1d_no_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7804785Z   test_LayerNorm_1d_no_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7805137Z   test_LayerNorm_3d_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7805502Z   test_LayerNorm_3d_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7805868Z   test_LayerNorm_3d_no_affine_large_feature (__main__.TestExpandedWeightModule) ... ok (0.040s)
2023-01-11T21:36:42.7806242Z   test_LayerNorm_3d_no_affine_large_feature_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.020s)
2023-01-11T21:36:42.7806594Z   test_LayerNorm_3d_no_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7806961Z   test_LayerNorm_3d_no_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7807334Z   test_Linear (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7807632Z   test_Linear_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7808114Z   test_Linear_no_batch_dim (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients for input of rank 1 (0.001s)
2023-01-11T21:36:42.7808671Z   test_Linear_no_batch_dim_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients for input of rank 1 (0.001s)
2023-01-11T21:36:42.7809051Z   test_Linear_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7809366Z   test_Linear_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7809712Z   test_per_sample_api_compute_batch_size (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:36:42.7810113Z   test_per_sample_api_compute_batch_size_not_pytreeable (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7810473Z   test_per_sample_api_failing (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:36:42.7810642Z 
2023-01-11T21:36:42.7810848Z ----------------------------------------------------------------------
2023-01-11T21:36:42.7811092Z Ran 99 tests in 0.370s
2023-01-11T21:36:42.7811209Z 
2023-01-11T21:36:42.7811283Z OK (skipped=10)
2023-01-11T21:36:42.7811391Z 
2023-01-11T21:36:42.7811462Z Generating XML reports...
2023-01-11T21:36:42.7811912Z Generated XML report: test-reports/python-unittest/test_expanded_weights/TEST-TestExpandedWeightModule-20230111213641.xml
2023-01-11T21:36:42.7812179Z 
2023-01-11T21:36:42.7812477Z ##[endgroup]
2023-01-11T21:36:42.7812870Z FINISHED PRINTING LOG FILE of test_expanded_weights (/var/lib/jenkins/workspace/test/test-reports/test_expanded_weights_1qdkv6ra)
2023-01-11T21:36:42.7813099Z 
2023-01-11T21:36:44.6663411Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:44.7317018Z Ignoring disabled issues:  []
2023-01-11T21:36:44.7471237Z Running test_functional_autograd_benchmark ... [2023-01-11 21:36:44.746795]
2023-01-11T21:36:44.7473432Z Executing ['/opt/conda/bin/python', '-bb', 'test_functional_autograd_benchmark.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:44.747072]
2023-01-11T21:37:11.4601642Z 
2023-01-11T21:37:11.4602148Z Expand the folded group to see the log file of test_functional_autograd_benchmark
2023-01-11T21:37:11.4602964Z ##[group]PRINTING LOG FILE of test_functional_autograd_benchmark (/var/lib/jenkins/workspace/test/test-reports/test_functional_autograd_benchmark__eoex8y2)
2023-01-11T21:37:11.4603560Z 
2023-01-11T21:37:11.4603925Z Running tests...
2023-01-11T21:37:11.4604585Z ----------------------------------------------------------------------
2023-01-11T21:37:11.4605051Z Test results will be stored in test-reports/python-unittest/test_functional_autograd_benchmark
2023-01-11T21:37:11.4605699Z   test_fast_tasks (__main__.TestFunctionalAutogradBenchmark) ... Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:37:11.4606852Z Results for model resnet18 on task vjp: nans (var: nan)
2023-01-11T21:37:11.4607349Z Results for model resnet18 on task vjp using Functorch: nans (var: nan)
2023-01-11T21:37:11.4607831Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:37:11.4608085Z Results for model ppl_simple_reg on task vjp: nans (var: nan)
2023-01-11T21:37:11.4608365Z Results for model ppl_simple_reg on task vjp using Functorch: nans (var: nan)
2023-01-11T21:37:11.4608623Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:37:11.4608865Z Results for model ppl_robust_reg on task vjp: nans (var: nan)
2023-01-11T21:37:11.4609143Z Results for model ppl_robust_reg on task vjp using Functorch: nans (var: nan)
2023-01-11T21:37:11.4609398Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:37:11.4609633Z Results for model wav2letter on task vjp: nans (var: nan)
2023-01-11T21:37:11.4609913Z Results for model wav2letter on task vjp using Functorch: nans (var: nan)
2023-01-11T21:37:11.4610318Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:37:11.4610556Z Results for model transformer on task vjp: nans (var: nan)
2023-01-11T21:37:11.4610842Z Results for model transformer on task vjp using Functorch: nans (var: nan)
2023-01-11T21:37:11.4611083Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:37:11.4611326Z Results for model multiheadattn on task vjp: nans (var: nan)
2023-01-11T21:37:11.4611618Z Results for model multiheadattn on task vjp using Functorch: nans (var: nan)
2023-01-11T21:37:11.4611839Z ok (24.940s)
2023-01-11T21:37:11.4612160Z   test_slow_tasks (__main__.TestFunctionalAutogradBenchmark) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:37:11.4612488Z 
2023-01-11T21:37:11.4612721Z ----------------------------------------------------------------------
2023-01-11T21:37:11.4612964Z Ran 2 tests in 24.940s
2023-01-11T21:37:11.4613066Z 
2023-01-11T21:37:11.4613135Z OK (skipped=1)
2023-01-11T21:37:11.4613247Z 
2023-01-11T21:37:11.4613399Z Generating XML reports...
2023-01-11T21:37:11.4613913Z Generated XML report: test-reports/python-unittest/test_functional_autograd_benchmark/TEST-TestFunctionalAutogradBenchmark-20230111213646.xml
2023-01-11T21:37:11.4614210Z 
2023-01-11T21:37:11.4614546Z ##[endgroup]
2023-01-11T21:37:11.4614999Z FINISHED PRINTING LOG FILE of test_functional_autograd_benchmark (/var/lib/jenkins/workspace/test/test-reports/test_functional_autograd_benchmark__eoex8y2)
2023-01-11T21:37:11.4615254Z 
2023-01-11T21:37:13.3604509Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:13.4250890Z Ignoring disabled issues:  []
2023-01-11T21:37:13.4403837Z Running test_functional_optim ... [2023-01-11 21:37:13.440036]
2023-01-11T21:37:13.4405242Z Executing ['/opt/conda/bin/python', '-bb', 'test_functional_optim.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:13.440308]
2023-01-11T21:37:15.6697583Z 
2023-01-11T21:37:15.6698239Z Expand the folded group to see the log file of test_functional_optim
2023-01-11T21:37:15.6699063Z ##[group]PRINTING LOG FILE of test_functional_optim (/var/lib/jenkins/workspace/test/test-reports/test_functional_optim_mrovt3mh)
2023-01-11T21:37:15.6699321Z 
2023-01-11T21:37:15.6699393Z Running tests...
2023-01-11T21:37:15.6699789Z ----------------------------------------------------------------------
2023-01-11T21:37:15.6700184Z Test results will be stored in test-reports/python-unittest/test_functional_optim
2023-01-11T21:37:15.6700517Z   test_functional_optim_parity_adam (__main__.TestFunctionalOptimParity) ... ok (0.259s)
2023-01-11T21:37:15.6700867Z   test_functional_optim_parity_adam_w (__main__.TestFunctionalOptimParity) ... ok (0.025s)
2023-01-11T21:37:15.6701213Z   test_functional_optim_parity_sgd (__main__.TestFunctionalOptimParity) ... ok (0.022s)
2023-01-11T21:37:15.6701558Z   test_functional_optim_registration (__main__.TestFunctionalOptimParity) ... ok (0.001s)
2023-01-11T21:37:15.6701741Z 
2023-01-11T21:37:15.6701943Z ----------------------------------------------------------------------
2023-01-11T21:37:15.6702183Z Ran 4 tests in 0.307s
2023-01-11T21:37:15.6702295Z 
2023-01-11T21:37:15.6702523Z OK
2023-01-11T21:37:15.6702619Z 
2023-01-11T21:37:15.6702691Z Generating XML reports...
2023-01-11T21:37:15.6703160Z Generated XML report: test-reports/python-unittest/test_functional_optim/TEST-TestFunctionalOptimParity-20230111213714.xml
2023-01-11T21:37:15.6703424Z 
2023-01-11T21:37:15.6703651Z ##[endgroup]
2023-01-11T21:37:15.6704042Z FINISHED PRINTING LOG FILE of test_functional_optim (/var/lib/jenkins/workspace/test/test-reports/test_functional_optim_mrovt3mh)
2023-01-11T21:37:15.6704275Z 
2023-01-11T21:37:17.5870075Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:17.6706785Z Ignoring disabled issues:  []
2023-01-11T21:37:17.6860394Z Running test_functionalization ... [2023-01-11 21:37:17.685719]
2023-01-11T21:37:17.6863107Z Executing ['/opt/conda/bin/python', '-bb', 'test_functionalization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:17.685987]
2023-01-11T21:37:35.6202313Z 
2023-01-11T21:37:35.6203150Z Expand the folded group to see the log file of test_functionalization
2023-01-11T21:37:35.6204330Z ##[group]PRINTING LOG FILE of test_functionalization (/var/lib/jenkins/workspace/test/test-reports/test_functionalization_rkizb6ok)
2023-01-11T21:37:35.6204881Z 
2023-01-11T21:37:35.6204997Z Running tests...
2023-01-11T21:37:35.6205651Z ----------------------------------------------------------------------
2023-01-11T21:37:35.6206400Z Test results will be stored in test-reports/python-unittest/test_functionalization
2023-01-11T21:37:35.6207044Z   test_advanced_indexing (__main__.TestCrossRefFunctionalization) ... ok (0.464s)
2023-01-11T21:37:35.6207712Z   test_advanced_indexing_correct_strides (__main__.TestCrossRefFunctionalization) ... ok (0.140s)
2023-01-11T21:37:35.6209298Z   test_aliases_maintained_after_pass_when_reapplying_views (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:19: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:37:35.6210385Z   x_storage = StorageWeakRef(x.storage())
2023-01-11T21:37:35.6211418Z /var/lib/jenkins/workspace/test/test_functionalization.py:20: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:37:35.6212380Z   y_storage = StorageWeakRef(y.storage())
2023-01-11T21:37:35.6212715Z ok (0.001s)
2023-01-11T21:37:35.6213183Z   test_as_strided (__main__.TestCrossRefFunctionalization) ... expected failure (0.006s)
2023-01-11T21:37:35.6213813Z   test_batch_norm (__main__.TestCrossRefFunctionalization) ... ok (1.059s)
2023-01-11T21:37:35.6214409Z   test_cat (__main__.TestCrossRefFunctionalization) ... ok (0.121s)
2023-01-11T21:37:35.6215011Z   test_copy_ (__main__.TestCrossRefFunctionalization) ... expected failure (0.035s)
2023-01-11T21:37:35.6215650Z   test_copy_stride_mismatch (__main__.TestCrossRefFunctionalization) ... ok (0.010s)
2023-01-11T21:37:35.6216311Z   test_diagonal (__main__.TestCrossRefFunctionalization) ... expected failure (0.010s)
2023-01-11T21:37:35.6216996Z   test_diagonal_mutated_input (__main__.TestCrossRefFunctionalization) ... expected failure (0.009s)
2023-01-11T21:37:35.6217674Z   test_everything (__main__.TestCrossRefFunctionalization) ... expected failure (0.014s)
2023-01-11T21:37:35.6218310Z   test_expand_symint (__main__.TestCrossRefFunctionalization) ... ok (0.043s)
2023-01-11T21:37:35.6218935Z   test_fill_ (__main__.TestCrossRefFunctionalization) ... expected failure (0.006s)
2023-01-11T21:37:35.6219547Z   test_freeze (__main__.TestCrossRefFunctionalization) ... ok (0.021s)
2023-01-11T21:37:35.6220181Z   test_index_mutation_on_non_input (__main__.TestCrossRefFunctionalization) ... ok (0.205s)
2023-01-11T21:37:35.6220829Z   test_inplace_on_non_view (__main__.TestCrossRefFunctionalization) ... ok (0.214s)
2023-01-11T21:37:35.6221402Z   test_instance_norm (__main__.TestCrossRefFunctionalization) ... ok (1.435s)
2023-01-11T21:37:35.6221993Z   test_metadata_change (__main__.TestCrossRefFunctionalization) ... ok (0.138s)
2023-01-11T21:37:35.6222862Z   test_metadata_change_out_op (__main__.TestCrossRefFunctionalization) ... ok (0.001s)
2023-01-11T21:37:35.6223520Z   test_mixed_wrappers_invalid (__main__.TestCrossRefFunctionalization) ... ok (0.004s)
2023-01-11T21:37:35.6224138Z   test_mixed_wrappers_valid (__main__.TestCrossRefFunctionalization) ... ok (0.001s)
2023-01-11T21:37:35.6225750Z   test_multi_out (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6227328Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:37:35.6228770Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6230096Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:37:35.6231427Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6232667Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:37:35.6234027Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6235298Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:37:35.6236661Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6237903Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:37:35.6238276Z ok (0.202s)
2023-01-11T21:37:35.6238763Z   test_multiple_views_of_same_base (__main__.TestCrossRefFunctionalization) ... ok (0.099s)
2023-01-11T21:37:35.6239419Z   test_mutable_op_not_inplace_or_other (__main__.TestCrossRefFunctionalization) ... ok (0.079s)
2023-01-11T21:37:35.6240128Z   test_nested_functions_propagate_updates (__main__.TestCrossRefFunctionalization) ... ok (0.077s)
2023-01-11T21:37:35.6240785Z   test_only_one_view (__main__.TestCrossRefFunctionalization) ... ok (0.019s)
2023-01-11T21:37:35.6241399Z   test_optional_tensor_list (__main__.TestCrossRefFunctionalization) ... ok (0.236s)
2023-01-11T21:37:35.6242040Z   test_reapply_views_simple (__main__.TestCrossRefFunctionalization) ... ok (0.188s)
2023-01-11T21:37:35.6242675Z   test_resize_larger_invalid (__main__.TestCrossRefFunctionalization) ... ok (0.014s)
2023-01-11T21:37:35.6243310Z   test_resize_larger_valid (__main__.TestCrossRefFunctionalization) ... ok (0.252s)
2023-01-11T21:37:35.6243946Z   test_resize_smaller (__main__.TestCrossRefFunctionalization) ... ok (0.556s)
2023-01-11T21:37:35.6244689Z   test_save_for_backwards_segfault (__main__.TestCrossRefFunctionalization) ... ok (0.001s)
2023-01-11T21:37:35.6245334Z   test_scalars (__main__.TestCrossRefFunctionalization) ... ok (0.203s)
2023-01-11T21:37:35.6246580Z   test_set_ (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:149: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:37:35.6247642Z   y.set_(x.storage())
2023-01-11T21:37:35.6247949Z ok (0.001s)
2023-01-11T21:37:35.6248388Z   test_simple (__main__.TestCrossRefFunctionalization) ... ok (0.275s)
2023-01-11T21:37:35.6249982Z   test_simple_out (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:266: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [4, 2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6251327Z   torch.add(y, tmp, out=z)
2023-01-11T21:37:35.6253168Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:37:35.6254302Z   warnings.warn(msg)
2023-01-11T21:37:35.6256030Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:37:35.6257166Z   warnings.warn(msg)
2023-01-11T21:37:35.6258762Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:37:35.6259853Z   warnings.warn(msg)
2023-01-11T21:37:35.6261600Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:37:35.6262893Z   warnings.warn(msg)
2023-01-11T21:37:35.6263202Z ok (0.245s)
2023-01-11T21:37:35.6263660Z   test_split (__main__.TestCrossRefFunctionalization) ... expected failure (0.013s)
2023-01-11T21:37:35.6264276Z   test_tensor_ctr (__main__.TestCrossRefFunctionalization) ... ok (0.227s)
2023-01-11T21:37:35.6264887Z   test_tensor_list_composite (__main__.TestCrossRefFunctionalization) ... ok (0.042s)
2023-01-11T21:37:35.6265718Z   test_tensor_list_mixed_functional_nonfunctional (__main__.TestCrossRefFunctionalization) ... ok (0.017s)
2023-01-11T21:37:35.6266747Z   test_view_clone_view_inplace (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:185: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
2023-01-11T21:37:35.6267632Z   with torch.autograd.detect_anomaly(check_nan=False):
2023-01-11T21:37:35.6268583Z /opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:197: UserWarning: Error detected in SumBackward0. Traceback of forward call that caused the error:
2023-01-11T21:37:35.6269347Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 1487, in <module>
2023-01-11T21:37:35.6269779Z     run_tests()
2023-01-11T21:37:35.6270451Z   File "/opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 787, in run_tests
2023-01-11T21:37:35.6271163Z     unittest.main(argv=argv, testRunner=xmlrunner.XMLTestRunner(
2023-01-11T21:37:35.6271698Z   File "/opt/conda/lib/python3.10/unittest/main.py", line 101, in __init__
2023-01-11T21:37:35.6272115Z     self.runTests()
2023-01-11T21:37:35.6272546Z   File "/opt/conda/lib/python3.10/unittest/main.py", line 271, in runTests
2023-01-11T21:37:35.6273064Z     self.result = testRunner.run(self.test)
2023-01-11T21:37:35.6273719Z   File "/opt/conda/lib/python3.10/site-packages/xmlrunner/runner.py", line 67, in run
2023-01-11T21:37:35.6274169Z     test(result)
2023-01-11T21:37:35.6274594Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 84, in __call__
2023-01-11T21:37:35.6275116Z     return self.run(*args, **kwds)
2023-01-11T21:37:35.6275550Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 122, in run
2023-01-11T21:37:35.6275945Z     test(result)
2023-01-11T21:37:35.6276372Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 84, in __call__
2023-01-11T21:37:35.6276805Z     return self.run(*args, **kwds)
2023-01-11T21:37:35.6277273Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 122, in run
2023-01-11T21:37:35.6277680Z     test(result)
2023-01-11T21:37:35.6278229Z   File "/opt/conda/lib/python3.10/unittest/case.py", line 650, in __call__
2023-01-11T21:37:35.6278654Z     return self.run(*args, **kwds)
2023-01-11T21:37:35.6279378Z   File "/opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 2225, in run
2023-01-11T21:37:35.6279877Z     self._run_with_retry(
2023-01-11T21:37:35.6280564Z   File "/opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 2154, in _run_with_retry
2023-01-11T21:37:35.6281084Z     super().run(result=result)
2023-01-11T21:37:35.6281515Z   File "/opt/conda/lib/python3.10/unittest/case.py", line 591, in run
2023-01-11T21:37:35.6281949Z     self._callTestMethod(testMethod)
2023-01-11T21:37:35.6282442Z   File "/opt/conda/lib/python3.10/unittest/case.py", line 549, in _callTestMethod
2023-01-11T21:37:35.6282877Z     method()
2023-01-11T21:37:35.6283358Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 186, in test_view_clone_view_inplace
2023-01-11T21:37:35.6283923Z     logs = self.get_logs(g, torch.ones(16, 64, 128, 128, requires_grad=True))
2023-01-11T21:37:35.6284506Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 66, in get_logs
2023-01-11T21:37:35.6285138Z     traced_f = make_fx(_functionalize(func, reapply_views=reapply_views, crossref=self.crossref))(*inpts)
2023-01-11T21:37:35.6285966Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 692, in wrapped
2023-01-11T21:37:35.6286648Z     t = dispatch_trace(wrap_key(func, args, fx_tracer), tracer=fx_tracer, concrete_args=tuple(phs))
2023-01-11T21:37:35.6287517Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 431, in dispatch_trace
2023-01-11T21:37:35.6288098Z     graph = tracer.trace(root, concrete_args)
2023-01-11T21:37:35.6288872Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 739, in trace
2023-01-11T21:37:35.6289383Z     (self.create_arg(fn(*args)),),
2023-01-11T21:37:35.6290090Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 447, in wrapped
2023-01-11T21:37:35.6290558Z     out = f(*tensors)
2023-01-11T21:37:35.6290900Z   File "<string>", line 1, in <lambda>
2023-01-11T21:37:35.6291417Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 40, in wrapped
2023-01-11T21:37:35.6291889Z     out = f(*inputs_functional)
2023-01-11T21:37:35.6292380Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 177, in g
2023-01-11T21:37:35.6292836Z     loss = f(x).sum()
2023-01-11T21:37:35.6293362Z  (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/autograd/python_anomaly_mode.cpp:119.)
2023-01-11T21:37:35.6294019Z   Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
2023-01-11T21:37:35.6294577Z expected failure (0.308s)
2023-01-11T21:37:35.6295109Z   test_view_inplace (__main__.TestCrossRefFunctionalization) ... expected failure (0.021s)
2023-01-11T21:37:35.6295723Z   test_advanced_indexing (__main__.TestFunctionalization) ... ok (0.088s)
2023-01-11T21:37:35.6296317Z   test_advanced_indexing_correct_strides (__main__.TestFunctionalization) ... ok (0.110s)
2023-01-11T21:37:35.6297684Z   test_aliases_maintained_after_pass_when_reapplying_views (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:19: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:37:35.6298806Z   x_storage = StorageWeakRef(x.storage())
2023-01-11T21:37:35.6299881Z /var/lib/jenkins/workspace/test/test_functionalization.py:20: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:37:35.6300901Z   y_storage = StorageWeakRef(y.storage())
2023-01-11T21:37:35.6301270Z ok (0.001s)
2023-01-11T21:37:35.6301671Z   test_as_strided (__main__.TestFunctionalization) ... ok (0.104s)
2023-01-11T21:37:35.6302197Z   test_batch_norm (__main__.TestFunctionalization) ... ok (0.759s)
2023-01-11T21:37:35.6302854Z   test_cat (__main__.TestFunctionalization) ... ok (0.067s)
2023-01-11T21:37:35.6303342Z   test_copy_ (__main__.TestFunctionalization) ... ok (0.942s)
2023-01-11T21:37:35.6303876Z   test_copy_stride_mismatch (__main__.TestFunctionalization) ... ok (0.002s)
2023-01-11T21:37:35.6304405Z   test_diagonal (__main__.TestFunctionalization) ... ok (0.245s)
2023-01-11T21:37:35.6304977Z   test_diagonal_mutated_input (__main__.TestFunctionalization) ... ok (0.158s)
2023-01-11T21:37:35.6305560Z   test_everything (__main__.TestFunctionalization) ... ok (1.467s)
2023-01-11T21:37:35.6306062Z   test_expand_symint (__main__.TestFunctionalization) ... ok (0.034s)
2023-01-11T21:37:35.6306594Z   test_fill_ (__main__.TestFunctionalization) ... ok (0.161s)
2023-01-11T21:37:35.6307115Z   test_freeze (__main__.TestFunctionalization) ... ok (0.009s)
2023-01-11T21:37:35.6307629Z   test_index_mutation_on_non_input (__main__.TestFunctionalization) ... ok (0.158s)
2023-01-11T21:37:35.6308197Z   test_inplace_on_non_view (__main__.TestFunctionalization) ... ok (0.152s)
2023-01-11T21:37:35.6308745Z   test_instance_norm (__main__.TestFunctionalization) ... ok (1.406s)
2023-01-11T21:37:35.6309286Z   test_metadata_change (__main__.TestFunctionalization) ... ok (0.107s)
2023-01-11T21:37:35.6309829Z   test_metadata_change_out_op (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:37:35.6310418Z   test_mixed_wrappers_invalid (__main__.TestFunctionalization) ... ok (0.004s)
2023-01-11T21:37:35.6311100Z   test_mixed_wrappers_valid (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:37:35.6312612Z   test_multi_out (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6314042Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:37:35.6314391Z ok (0.124s)
2023-01-11T21:37:35.6314840Z   test_multiple_views_of_same_base (__main__.TestFunctionalization) ... ok (0.082s)
2023-01-11T21:37:35.6315518Z   test_mutable_op_not_inplace_or_other (__main__.TestFunctionalization) ... ok (0.063s)
2023-01-11T21:37:35.6316141Z   test_nested_functions_propagate_updates (__main__.TestFunctionalization) ... ok (0.064s)
2023-01-11T21:37:35.6316731Z   test_only_one_view (__main__.TestFunctionalization) ... ok (0.016s)
2023-01-11T21:37:35.6317286Z   test_optional_tensor_list (__main__.TestFunctionalization) ... ok (0.144s)
2023-01-11T21:37:35.6317839Z   test_reapply_views_simple (__main__.TestFunctionalization) ... ok (0.137s)
2023-01-11T21:37:35.6318407Z   test_resize_larger_invalid (__main__.TestFunctionalization) ... ok (0.004s)
2023-01-11T21:37:35.6318970Z   test_resize_larger_valid (__main__.TestFunctionalization) ... ok (0.204s)
2023-01-11T21:37:35.6319520Z   test_resize_smaller (__main__.TestFunctionalization) ... ok (0.520s)
2023-01-11T21:37:35.6320086Z   test_save_for_backwards_segfault (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:37:35.6320637Z   test_scalars (__main__.TestFunctionalization) ... ok (0.154s)
2023-01-11T21:37:35.6321830Z   test_set_ (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:149: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:37:35.6322874Z   y.set_(x.storage())
2023-01-11T21:37:35.6323172Z ok (0.001s)
2023-01-11T21:37:35.6323580Z   test_simple (__main__.TestFunctionalization) ... ok (0.206s)
2023-01-11T21:37:35.6325022Z   test_simple_out (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:266: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [4, 2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:37:35.6326334Z   torch.add(y, tmp, out=z)
2023-01-11T21:37:35.6326637Z ok (0.151s)
2023-01-11T21:37:35.6327052Z   test_split (__main__.TestFunctionalization) ... ok (0.275s)
2023-01-11T21:37:35.6327588Z   test_tensor_ctr (__main__.TestFunctionalization) ... ok (0.158s)
2023-01-11T21:37:35.6328114Z   test_tensor_list_composite (__main__.TestFunctionalization) ... ok (0.028s)
2023-01-11T21:37:35.6328741Z   test_tensor_list_mixed_functional_nonfunctional (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:37:35.6329716Z   test_view_clone_view_inplace (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:185: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
2023-01-11T21:37:35.6330644Z   with torch.autograd.detect_anomaly(check_nan=False):
2023-01-11T21:37:35.6331018Z ok (0.667s)
2023-01-11T21:37:35.6331438Z   test_view_inplace (__main__.TestFunctionalization) ... ok (0.204s)
2023-01-11T21:37:35.6331744Z 
2023-01-11T21:37:35.6332158Z ----------------------------------------------------------------------
2023-01-11T21:37:35.6332596Z Ran 84 tests in 16.201s
2023-01-11T21:37:35.6332785Z 
2023-01-11T21:37:35.6332929Z OK (expected failures=9)
2023-01-11T21:37:35.6333149Z 
2023-01-11T21:37:35.6333299Z Generating XML reports...
2023-01-11T21:37:35.6334193Z Generated XML report: test-reports/python-unittest/test_functionalization/TEST-TestCrossRefFunctionalization-20230111213719.xml
2023-01-11T21:37:35.6335314Z Generated XML report: test-reports/python-unittest/test_functionalization/TEST-TestFunctionalization-20230111213719.xml
2023-01-11T21:37:35.6335797Z 
2023-01-11T21:37:35.6336258Z ##[endgroup]
2023-01-11T21:37:35.6337101Z FINISHED PRINTING LOG FILE of test_functionalization (/var/lib/jenkins/workspace/test/test-reports/test_functionalization_rkizb6ok)
2023-01-11T21:37:35.6337556Z 
2023-01-11T21:37:37.5224273Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:37.5868266Z Ignoring disabled issues:  []
2023-01-11T21:37:37.6021269Z Running test_futures ... [2023-01-11 21:37:37.601786]
2023-01-11T21:37:37.6022905Z Executing ['/opt/conda/bin/python', '-bb', 'test_futures.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:37.602037]
2023-01-11T21:37:40.1746480Z 
2023-01-11T21:37:40.1747179Z Expand the folded group to see the log file of test_futures
2023-01-11T21:37:40.1748148Z ##[group]PRINTING LOG FILE of test_futures (/var/lib/jenkins/workspace/test/test-reports/test_futures_7d97sleu)
2023-01-11T21:37:40.1748408Z 
2023-01-11T21:37:40.1748484Z Running tests...
2023-01-11T21:37:40.1748887Z ----------------------------------------------------------------------
2023-01-11T21:37:40.1749269Z Test results will be stored in test-reports/python-unittest/test_futures
2023-01-11T21:37:40.1749703Z   test_add_done_callback_error_is_ignored (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: ValueError: Expected error
2023-01-11T21:37:40.1749955Z 
2023-01-11T21:37:40.1750016Z At:
2023-01-11T21:37:40.1750231Z   /var/lib/jenkins/workspace/test/test_futures.py(236): raise_value_error
2023-01-11T21:37:40.1750621Z   /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py(244): set_result
2023-01-11T21:37:40.1750951Z   /var/lib/jenkins/workspace/test/test_futures.py(229): _test_add_done_callback_error_ignored
2023-01-11T21:37:40.1751284Z   /var/lib/jenkins/workspace/test/test_futures.py(238): test_add_done_callback_error_is_ignored
2023-01-11T21:37:40.1751579Z   /opt/conda/lib/python3.10/unittest/case.py(549): _callTestMethod
2023-01-11T21:37:40.1751849Z   /opt/conda/lib/python3.10/unittest/case.py(591): run
2023-01-11T21:37:40.1752245Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2154): _run_with_retry
2023-01-11T21:37:40.1752658Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2225): run
2023-01-11T21:37:40.1753013Z   /opt/conda/lib/python3.10/unittest/case.py(650): __call__
2023-01-11T21:37:40.1753281Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:37:40.1753528Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:37:40.1753784Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:37:40.1754042Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:37:40.1754383Z   /opt/conda/lib/python3.10/site-packages/xmlrunner/runner.py(67): run
2023-01-11T21:37:40.1754656Z   /opt/conda/lib/python3.10/unittest/main.py(271): runTests
2023-01-11T21:37:40.1754917Z   /opt/conda/lib/python3.10/unittest/main.py(101): __init__
2023-01-11T21:37:40.1755295Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(787): run_tests
2023-01-11T21:37:40.1755791Z   /var/lib/jenkins/workspace/test/test_futures.py(340): <module>
2023-01-11T21:37:40.1755948Z 
2023-01-11T21:37:40.1756014Z ok (0.229s)
2023-01-11T21:37:40.1756265Z   test_add_done_callback_maintains_callback_order (__main__.TestFuture) ... ok (0.002s)
2023-01-11T21:37:40.1756782Z   test_add_done_callback_no_arg_error_is_ignored (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: TypeError: TestFuture.test_add_done_callback_no_arg_error_is_ignored.<locals>.no_arg() takes 0 positional arguments but 1 was given
2023-01-11T21:37:40.1757199Z ok (0.001s)
2023-01-11T21:37:40.1757413Z   test_add_done_callback_simple (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1757681Z   test_chained_then (__main__.TestFuture) ... ok (0.003s)
2023-01-11T21:37:40.1757932Z   test_collect_all (__main__.TestFuture) ... ok (0.101s)
2023-01-11T21:37:40.1758156Z   test_done (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1758400Z   test_done_exception (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1758794Z   test_interleaving_then_and_add_done_callback_maintains_callback_order (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1759244Z   test_interleaving_then_and_add_done_callback_propagates_error (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: ValueError: Expected error
2023-01-11T21:37:40.1759519Z 
2023-01-11T21:37:40.1759580Z At:
2023-01-11T21:37:40.1759804Z   /var/lib/jenkins/workspace/test/test_futures.py(280): raise_value_error
2023-01-11T21:37:40.1760189Z   /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py(244): set_result
2023-01-11T21:37:40.1760538Z   /var/lib/jenkins/workspace/test/test_futures.py(285): test_interleaving_then_and_add_done_callback_propagates_error
2023-01-11T21:37:40.1760875Z   /opt/conda/lib/python3.10/unittest/case.py(549): _callTestMethod
2023-01-11T21:37:40.1761142Z   /opt/conda/lib/python3.10/unittest/case.py(591): run
2023-01-11T21:37:40.1761520Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2154): _run_with_retry
2023-01-11T21:37:40.1761949Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2225): run
2023-01-11T21:37:40.1762250Z   /opt/conda/lib/python3.10/unittest/case.py(650): __call__
2023-01-11T21:37:40.1762507Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:37:40.1762753Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:37:40.1763007Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:37:40.1763261Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:37:40.1763587Z   /opt/conda/lib/python3.10/site-packages/xmlrunner/runner.py(67): run
2023-01-11T21:37:40.1763869Z   /opt/conda/lib/python3.10/unittest/main.py(271): runTests
2023-01-11T21:37:40.1764124Z   /opt/conda/lib/python3.10/unittest/main.py(101): __init__
2023-01-11T21:37:40.1764502Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(787): run_tests
2023-01-11T21:37:40.1764804Z   /var/lib/jenkins/workspace/test/test_futures.py(340): <module>
2023-01-11T21:37:40.1764960Z 
2023-01-11T21:37:40.1765026Z ok (0.001s)
2023-01-11T21:37:40.1765238Z   test_mark_future_twice (__main__.TestFuture) ... ok (0.002s)
2023-01-11T21:37:40.1765479Z   test_pickle_future (__main__.TestFuture) ... ok (0.003s)
2023-01-11T21:37:40.1765728Z   test_set_exception (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1765998Z   test_set_exception_multithreading (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1766248Z   test_then (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1766485Z   test_then_no_arg (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1766726Z   test_then_raise (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1766971Z   test_then_wrong_arg (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1767199Z   test_wait (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:37:40.1767431Z   test_wait_all (__main__.TestFuture) ... [1, 2]
2023-01-11T21:37:40.1767674Z ok (0.001s)
2023-01-11T21:37:40.1767873Z   test_wait_multi_thread (__main__.TestFuture) ... ok (0.502s)
2023-01-11T21:37:40.1768119Z   test_wait_none (__main__.TestFuture) ... ok (0.005s)
2023-01-11T21:37:40.1768256Z 
2023-01-11T21:37:40.1768460Z ----------------------------------------------------------------------
2023-01-11T21:37:40.1768688Z Ran 22 tests in 0.861s
2023-01-11T21:37:40.1768799Z 
2023-01-11T21:37:40.1768859Z OK
2023-01-11T21:37:40.1768949Z 
2023-01-11T21:37:40.1769032Z Generating XML reports...
2023-01-11T21:37:40.1769419Z Generated XML report: test-reports/python-unittest/test_futures/TEST-TestFuture-20230111213738.xml
2023-01-11T21:37:40.1769624Z 
2023-01-11T21:37:40.1769890Z ##[endgroup]
2023-01-11T21:37:40.1770270Z FINISHED PRINTING LOG FILE of test_futures (/var/lib/jenkins/workspace/test/test-reports/test_futures_7d97sleu)
2023-01-11T21:37:40.1770479Z 
2023-01-11T21:37:42.0719233Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:42.1367946Z Ignoring disabled issues:  []
2023-01-11T21:37:42.1522114Z Running test_fx_experimental ... [2023-01-11 21:37:42.151942]
2023-01-11T21:37:42.1524178Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_experimental.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:42.152209]
2023-01-11T21:38:01.8404590Z 
2023-01-11T21:38:01.8405126Z Expand the folded group to see the log file of test_fx_experimental
2023-01-11T21:38:01.8409357Z ##[group]PRINTING LOG FILE of test_fx_experimental (/var/lib/jenkins/workspace/test/test-reports/test_fx_experimental_uqdvcu62)
2023-01-11T21:38:01.8409864Z 
2023-01-11T21:38:01.8409995Z Running tests...
2023-01-11T21:38:01.8410461Z ----------------------------------------------------------------------
2023-01-11T21:38:01.8410854Z Test results will be stored in test-reports/python-unittest/test_fx_experimental
2023-01-11T21:38:01.8411172Z   test_annotate_getitem_node (__main__.TestFXExperimental) ... ok (0.004s)
2023-01-11T21:38:01.8412042Z   test_annotate_returns_with_schema (__main__.TestFXExperimental) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:38:01.8412678Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:38:01.8412901Z ok (1.910s)
2023-01-11T21:38:01.8413135Z   test_aot_based_partition (__main__.TestFXExperimental) ... ok (0.006s)
2023-01-11T21:38:01.8413421Z   test_call_to_assert_no_msg (__main__.TestFXExperimental) ... ok (0.004s)
2023-01-11T21:38:01.8413728Z   test_call_to_assert_with_empty_msg (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:38:01.8414037Z   test_call_to_assert_with_msg (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:38:01.8414346Z   test_call_to_assert_with_multiline_message (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:38:01.8414654Z   test_conv_bn_fusion (__main__.TestFXExperimental) ... ok (1.192s)
2023-01-11T21:38:01.8414956Z   test_conv_bn_fusion_not_running_state (__main__.TestFXExperimental) ... ok (0.010s)
2023-01-11T21:38:01.8415265Z   test_cost_aware_partition (__main__.TestFXExperimental) ... ok (0.010s)
2023-01-11T21:38:01.8415531Z   test_fetch (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:38:01.8415809Z   test_find_single_partition (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:38:01.8416104Z   test_lack_of_devices (__main__.TestFXExperimental) ... ok (0.002s)
2023-01-11T21:38:01.8416418Z   test_large_node_error (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:38:01.8416736Z   test_merge_matmuls (__main__.TestFXExperimental)
2023-01-11T21:38:01.8417035Z A collection of test cases for torch.fx.experimental.merge_matmul, ... ok (0.025s)
2023-01-11T21:38:01.8417329Z   test_meta_tracer (__main__.TestFXExperimental) ... ok (0.019s)
2023-01-11T21:38:01.8417771Z   test_normalize_args (__main__.TestFXExperimental) ... ok (0.708s)
2023-01-11T21:38:01.8418521Z   test_normalize_args_perserve_type (__main__.TestFXExperimental) ... /opt/conda/lib/python3.10/site-packages/torch/fx/operator_schemas.py:207: UserWarning: Does not support nested parametric types, got typing.List[~t]. Please file a bug.
2023-01-11T21:38:01.8418927Z   warnings.warn(
2023-01-11T21:38:01.8419087Z ok (0.007s)
2023-01-11T21:38:01.8419333Z   test_normalize_args_preserve_meta (__main__.TestFXExperimental) ... ok (0.006s)
2023-01-11T21:38:01.8419649Z   test_normalize_binary_operators (__main__.TestFXExperimental) ... ok (0.054s)
2023-01-11T21:38:01.8419939Z   test_normalize_modules_exhaustive (__main__.TestFXExperimental)
2023-01-11T21:38:01.8420851Z Exhaustively test `Node.normalized_arguments` on all standard ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:38:01.8421422Z   return F.conv1d(input, weight, bias, self.stride,
2023-01-11T21:38:01.8421626Z ok (1.541s)
2023-01-11T21:38:01.8421853Z   test_optimize_for_inference_cpu (__main__.TestFXExperimental) ... ok (0.263s)
2023-01-11T21:38:01.8422179Z   test_optimize_for_inference_cpu_torchvision (__main__.TestFXExperimental) ... ok (10.326s)
2023-01-11T21:38:01.8422700Z   test_partition_device_mapping (__main__.TestFXExperimental) ... ok (0.008s)
2023-01-11T21:38:01.8423008Z   test_partition_latency (__main__.TestFXExperimental) ... ok (0.006s)
2023-01-11T21:38:01.8423301Z   test_partition_node_manipulation (__main__.TestFXExperimental) ... ok (0.004s)
2023-01-11T21:38:01.8423616Z   test_replace_target_nodes_with (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:38:01.8423915Z   test_saturate_host (__main__.TestFXExperimental) ... [0, 4]
2023-01-11T21:38:01.8424119Z [1, 2]
2023-01-11T21:38:01.8424276Z ok (0.006s)
2023-01-11T21:38:01.8424511Z   test_size_based_partition (__main__.TestFXExperimental) ... ok (0.005s)
2023-01-11T21:38:01.8424800Z   test_sparse_nn_partition (__main__.TestFXExperimental) ... ok (0.244s)
2023-01-11T21:38:01.8425110Z   test_split_module_default_arg (__main__.TestFXExperimental) ... ok (0.007s)
2023-01-11T21:38:01.8425420Z   test_split_module_kwargs_expansion (__main__.TestFXExperimental) ... ok (0.004s)
2023-01-11T21:38:01.8425730Z   test_split_qualname_mapping (__main__.TestFXExperimental) ... ok (0.005s)
2023-01-11T21:38:01.8426016Z   test_subgraph_creation (__main__.TestFXExperimental) ... ok (0.007s)
2023-01-11T21:38:01.8426316Z   test_subgraph_trivial_resnet (__main__.TestFXExperimental) ... ok (0.254s)
2023-01-11T21:38:01.8426613Z   test_subgraph_uniquename (__main__.TestFXExperimental) ... ok (0.006s)
2023-01-11T21:38:01.8427362Z   test_to_folder (__main__.TestFXExperimental) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph_module.py:476: UserWarning: Was not able to save the following children modules as reprs -saved as pickled files instead: ['seq']
2023-01-11T21:38:01.8427892Z   warnings.warn("Was not able to save the following children modules as reprs -"
2023-01-11T21:38:01.8428127Z ok (0.009s)
2023-01-11T21:38:01.8428388Z   test_traceable_function_with_nonstandard_name (__main__.TestFXExperimental) ... ok (0.002s)
2023-01-11T21:38:01.8428688Z   test_type_matches (__main__.TestFXExperimental) ... ok (0.002s)
2023-01-11T21:38:01.8428845Z 
2023-01-11T21:38:01.8429049Z ----------------------------------------------------------------------
2023-01-11T21:38:01.8429295Z Ran 39 tests in 16.681s
2023-01-11T21:38:01.8429411Z 
2023-01-11T21:38:01.8429459Z OK
2023-01-11T21:38:01.8429553Z 
2023-01-11T21:38:01.8429637Z Generating XML reports...
2023-01-11T21:38:01.8430069Z Generated XML report: test-reports/python-unittest/test_fx_experimental/TEST-TestFXExperimental-20230111213744.xml
2023-01-11T21:38:01.8430399Z 
2023-01-11T21:38:01.8430671Z ##[endgroup]
2023-01-11T21:38:01.8431080Z FINISHED PRINTING LOG FILE of test_fx_experimental (/var/lib/jenkins/workspace/test/test-reports/test_fx_experimental_uqdvcu62)
2023-01-11T21:38:01.8431308Z 
2023-01-11T21:38:03.7432653Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:38:03.8083405Z Ignoring disabled issues:  []
2023-01-11T21:38:03.8236460Z Running test_fx_passes ... [2023-01-11 21:38:03.823360]
2023-01-11T21:38:03.8238142Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_passes.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:38:03.823606]
2023-01-11T21:38:06.1105389Z 
2023-01-11T21:38:06.1105902Z Expand the folded group to see the log file of test_fx_passes
2023-01-11T21:38:06.1106970Z ##[group]PRINTING LOG FILE of test_fx_passes (/var/lib/jenkins/workspace/test/test-reports/test_fx_passes_qfw2fcdq)
2023-01-11T21:38:06.1107222Z 
2023-01-11T21:38:06.1107299Z Running tests...
2023-01-11T21:38:06.1107896Z ----------------------------------------------------------------------
2023-01-11T21:38:06.1108287Z Test results will be stored in test-reports/python-unittest/test_fx_passes
2023-01-11T21:38:06.1108591Z   test_fuser_pass_deep_model (__main__.TestFXGraphPasses) ... ok (0.283s)
2023-01-11T21:38:06.1109010Z   test_fuser_util_partition_[['add', 'add_1', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.007s)
2023-01-11T21:38:06.1109450Z   test_fuser_util_partition_[['add', 'add_1'], ['add_5', 'add_6']] (__main__.TestFXGraphPasses) ... ok (0.006s)
2023-01-11T21:38:06.1109961Z   test_fuser_util_partition_[['add', 'linear', 'add_1', 'param', 'add_2', 'add_3', 'add_4', 'linear2', 'add_5', 'add_6', 'relu']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1110496Z   test_fuser_util_partition_[['add_2', 'add_3']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1110894Z   test_fuser_util_partition_[['add_3', 'add_4']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1111325Z   test_fuser_util_partition_[['add_4', 'add_1', 'add_3', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1111772Z   test_fuser_util_partition_[['add_5', 'add_6'], ['add_1', 'add_2', 'add_3', 'add_4']] (__main__.TestFXGraphPasses) ... ok (0.006s)
2023-01-11T21:38:06.1112201Z   test_fuser_util_partition_[['add_5', 'linear2']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1112597Z   test_fuser_util_partition_[['add_6', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1112991Z   test_fuser_util_partition_[['add_6', 'relu']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1113478Z   test_fuser_util_partition_[['param', 'add_1', 'linear']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1113894Z   test_fuser_util_partition_[['param', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1114314Z   test_fuser_util_xfail_partition_[['add', 'add_1', 'add_3']] (__main__.TestFXGraphPasses) ... ok (0.003s)
2023-01-11T21:38:06.1114760Z   test_fuser_util_xfail_partition_[['add', 'add_1'], ['add_1', 'add_5', 'add_6']] (__main__.TestFXGraphPasses) ... ok (0.003s)
2023-01-11T21:38:06.1115192Z   test_fuser_util_xfail_partition_[['add_4', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.003s)
2023-01-11T21:38:06.1115608Z   test_fuser_util_xfail_partition_[['relu', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.003s)
2023-01-11T21:38:06.1116262Z   test_partitioner_fn_<function TestPartitionFunctions_forward1 at 0x7fc84dd7ca60>_expected_partition_[['add_7', 'add_6'], ['add_5', 'add_4', 'add_3'], ['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.006s)
2023-01-11T21:38:06.1117004Z   test_partitioner_fn_<function TestPartitionFunctions_forward10 at 0x7fc84dd7cf70>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.012s)
2023-01-11T21:38:06.1117686Z   test_partitioner_fn_<function TestPartitionFunctions_forward11 at 0x7fc84dd7d000>_expected_partition_[['add_1'], ['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1118491Z   test_partitioner_fn_<function TestPartitionFunctions_forward12 at 0x7fc84dd7d090>_expected_partition_[['add_2'], ['add_3', 'add_4', 'add_1'], ['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.007s)
2023-01-11T21:38:06.1119204Z   test_partitioner_fn_<function TestPartitionFunctions_forward13 at 0x7fc84dd7d120>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1119927Z   test_partitioner_fn_<function TestPartitionFunctions_forward14 at 0x7fc84dd7d1b0>_expected_partition_[['add', 'std_mean', 'getitem', 'getitem_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:38:06.1120757Z   test_partitioner_fn_<function TestPartitionFunctions_forward15 at 0x7fc84dd7d240>_expected_partition_[['add_1', 'add', 'permute_1', 'view', 'permute_2', 'permute_3', 'permute']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1121496Z   test_partitioner_fn_<function TestPartitionFunctions_forward15 at 0x7fc84dd7d240>_expected_partition_[['permute_1', 'add_1', 'add']]_bookend_non_compute_pass_True (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1122266Z   test_partitioner_fn_<function TestPartitionFunctions_forward16 at 0x7fc84dd7d2d0>_expected_partition_[['add_1', 'add', 'permute_1', 'view', 'permute_2', 'permute_3', 'permute']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1123006Z   test_partitioner_fn_<function TestPartitionFunctions_forward16 at 0x7fc84dd7d2d0>_expected_partition_[['permute_1', 'add_1', 'add']]_bookend_non_compute_pass_True (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1123724Z   test_partitioner_fn_<function TestPartitionFunctions_forward2 at 0x7fc84dd7caf0>_expected_partition_[['add_3', 'add_2'], ['add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1124423Z   test_partitioner_fn_<function TestPartitionFunctions_forward3 at 0x7fc84dd7cb80>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1125112Z   test_partitioner_fn_<function TestPartitionFunctions_forward4 at 0x7fc84dd7cc10>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1125776Z   test_partitioner_fn_<function TestPartitionFunctions_forward5 at 0x7fc84dd7cca0>_expected_partition_[['add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:38:06.1126440Z   test_partitioner_fn_<function TestPartitionFunctions_forward6 at 0x7fc84dd7cd30>_expected_partition_[['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:38:06.1127139Z   test_partitioner_fn_<function TestPartitionFunctions_forward7 at 0x7fc84dd7cdc0>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:38:06.1127843Z   test_partitioner_fn_<function TestPartitionFunctions_forward8 at 0x7fc84dd7ce50>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:38:06.1128550Z   test_partitioner_fn_<function TestPartitionFunctions_forward9 at 0x7fc84dd7cee0>_expected_partition_[['add_3', 'add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:38:06.1129107Z   test_subgraph_matcher_test_model_<class '__main___ChainRepeatedPattern'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1129627Z   test_subgraph_matcher_test_model_<class '__main___DiamondShapePatternTestCase'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1130204Z   test_subgraph_matcher_test_model_<class '__main___MultiOutputWithWithInvalidMatches'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1130756Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsHorizontalPattern'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:38:06.1131288Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsIdenticalAnchor'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:38:06.1131870Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsMultipleNonOverlappingMatches'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1132469Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsMultipleOverlappingMatches'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1133028Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsWithDependency'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:38:06.1133585Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsWithoutDependency'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1134084Z   test_subgraph_matcher_test_model_<class '__main___NoAnchorFound'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:38:06.1134569Z   test_subgraph_matcher_test_model_<class '__main___NonFullyContainedMatches'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T21:38:06.1135072Z   test_subgraph_matcher_test_model_<class '__main___QuantizationFp8Pattern'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T21:38:06.1135540Z   test_subgraph_matcher_test_model_<class '__main___QuantizationModel'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:38:06.1136032Z   test_subgraph_matcher_test_model_<class '__main___SimpleFullGraphMatching'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1136513Z   test_subgraph_matcher_test_model_<class '__main___SimplePattern'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:38:06.1136984Z   test_subgraph_matcher_test_model_<class '__main___SingleNodePattern'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:38:06.1137177Z 
2023-01-11T21:38:06.1137380Z ----------------------------------------------------------------------
2023-01-11T21:38:06.1137627Z Ran 51 tests in 0.513s
2023-01-11T21:38:06.1137742Z 
2023-01-11T21:38:06.1137804Z OK
2023-01-11T21:38:06.1137896Z 
2023-01-11T21:38:06.1137967Z Generating XML reports...
2023-01-11T21:38:06.1138383Z Generated XML report: test-reports/python-unittest/test_fx_passes/TEST-TestFXGraphPasses-20230111213805.xml
2023-01-11T21:38:06.1138914Z Generated XML report: test-reports/python-unittest/test_fx_passes/TEST-TestFXMatcherUtils-20230111213805.xml
2023-01-11T21:38:06.1139148Z 
2023-01-11T21:38:06.1139405Z ##[endgroup]
2023-01-11T21:38:06.1139786Z FINISHED PRINTING LOG FILE of test_fx_passes (/var/lib/jenkins/workspace/test/test-reports/test_fx_passes_qfw2fcdq)
2023-01-11T21:38:06.1140002Z 
2023-01-11T21:38:07.9878709Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:38:08.0526950Z Ignoring disabled issues:  []
2023-01-11T21:38:08.0680370Z Running test_fx_reinplace_pass ... [2023-01-11 21:38:08.067746]
2023-01-11T21:38:08.0682544Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_reinplace_pass.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:38:08.068003]
2023-01-11T21:38:11.0882853Z 
2023-01-11T21:38:11.0883448Z Expand the folded group to see the log file of test_fx_reinplace_pass
2023-01-11T21:38:11.0884670Z ##[group]PRINTING LOG FILE of test_fx_reinplace_pass (/var/lib/jenkins/workspace/test/test-reports/test_fx_reinplace_pass_ftuarg3r)
2023-01-11T21:38:11.0885094Z 
2023-01-11T21:38:11.0885234Z Running tests...
2023-01-11T21:38:11.0885869Z ----------------------------------------------------------------------
2023-01-11T21:38:11.0886597Z Test results will be stored in test-reports/python-unittest/test_fx_reinplace_pass
2023-01-11T21:38:11.0887385Z   test_out_node_updated (__main__.TestReinplacePass) ... ok (0.278s)
2023-01-11T21:38:11.0943857Z   test_reinplace_basic (__main__.TestReinplacePass) ... ok (0.117s)
2023-01-11T21:38:11.0944664Z   test_reinplace_different_metadata (__main__.TestReinplacePass) ... ok (0.028s)
2023-01-11T21:38:11.0945447Z   test_reinplace_index_mutation (__main__.TestReinplacePass) ... ok (0.146s)
2023-01-11T21:38:11.0946226Z   test_reinplace_overlapping_memory (__main__.TestReinplacePass) ... ok (0.029s)
2023-01-11T21:38:11.0946970Z   test_reinplace_scatter_op (__main__.TestReinplacePass) ... ok (0.249s)
2023-01-11T21:38:11.0947721Z   test_reinplace_scatter_twice (__main__.TestReinplacePass) ... ok (0.236s)
2023-01-11T21:38:11.0948545Z   test_reinplace_scatter_twice_with_different_view_op_invalid (__main__.TestReinplacePass) ... ok (0.065s)
2023-01-11T21:38:11.0949474Z   test_reinplace_scatter_twice_with_different_view_op_invalid2 (__main__.TestReinplacePass) ... ok (0.064s)
2023-01-11T21:38:11.0950602Z   test_reinplace_scatter_twice_with_different_view_op_valid (__main__.TestReinplacePass) ... ok (0.066s)
2023-01-11T21:38:11.0951442Z   test_reinplace_with_view (__main__.TestReinplacePass) ... ok (0.037s)
2023-01-11T21:38:11.0982859Z 
2023-01-11T21:38:11.0983481Z ----------------------------------------------------------------------
2023-01-11T21:38:11.0984001Z Ran 11 tests in 1.316s
2023-01-11T21:38:11.0984208Z 
2023-01-11T21:38:11.0984316Z OK
2023-01-11T21:38:11.0984481Z 
2023-01-11T21:38:11.0984621Z Generating XML reports...
2023-01-11T21:38:11.0985438Z Generated XML report: test-reports/python-unittest/test_fx_reinplace_pass/TEST-TestReinplacePass-20230111213809.xml
2023-01-11T21:38:11.0985776Z 
2023-01-11T21:38:11.0986112Z ##[endgroup]
2023-01-11T21:38:11.0986513Z FINISHED PRINTING LOG FILE of test_fx_reinplace_pass (/var/lib/jenkins/workspace/test/test-reports/test_fx_reinplace_pass_ftuarg3r)
2023-01-11T21:38:11.0986746Z 
2023-01-11T21:38:12.9814026Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:38:13.0662012Z Ignoring disabled issues:  []
2023-01-11T21:38:13.0816011Z Running test_hub ... [2023-01-11 21:38:13.081302]
2023-01-11T21:38:13.0818179Z Executing ['/opt/conda/bin/python', '-bb', 'test_hub.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:38:13.081554]
2023-01-11T21:38:14.7081903Z 
2023-01-11T21:38:14.7082406Z Expand the folded group to see the log file of test_hub
2023-01-11T21:38:14.7083667Z ##[group]PRINTING LOG FILE of test_hub (/var/lib/jenkins/workspace/test/test-reports/test_hub_nevyfmcm)
2023-01-11T21:38:14.7084031Z 
2023-01-11T21:38:14.7084392Z ##[endgroup]
2023-01-11T21:38:14.7085131Z FINISHED PRINTING LOG FILE of test_hub (/var/lib/jenkins/workspace/test/test-reports/test_hub_nevyfmcm)
2023-01-11T21:38:14.7085479Z 
2023-01-11T21:38:16.6442242Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:38:16.7097187Z Ignoring disabled issues:  []
2023-01-11T21:38:16.7253637Z Running test_import_stats ... [2023-01-11 21:38:16.724990]
2023-01-11T21:38:16.7254746Z Executing ['/opt/conda/bin/python', '-bb', 'test_import_stats.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:38:16.725258]
2023-01-11T21:38:21.4839238Z 
2023-01-11T21:38:21.4839911Z Expand the folded group to see the log file of test_import_stats
2023-01-11T21:38:21.4840929Z ##[group]PRINTING LOG FILE of test_import_stats (/var/lib/jenkins/workspace/test/test-reports/test_import_stats__ar66mzs)
2023-01-11T21:38:21.4841324Z 
2023-01-11T21:38:21.4841459Z Running tests...
2023-01-11T21:38:21.4842112Z ----------------------------------------------------------------------
2023-01-11T21:38:21.4842831Z Test results will be stored in test-reports/python-unittest/test_import_stats
2023-01-11T21:38:21.4843400Z   test_time_cuda_device_count (__main__.TestImportTime) ... ok (1.635s)
2023-01-11T21:38:21.4843882Z   test_time_import_torch (__main__.TestImportTime) ... ok (1.423s)
2023-01-11T21:38:21.4844168Z 
2023-01-11T21:38:21.4844839Z ----------------------------------------------------------------------
2023-01-11T21:38:21.4845279Z Ran 2 tests in 3.058s
2023-01-11T21:38:21.4845498Z 
2023-01-11T21:38:21.4845607Z OK
2023-01-11T21:38:21.4845747Z 
2023-01-11T21:38:21.4845858Z Generating XML reports...
2023-01-11T21:38:21.4846281Z Generated XML report: test-reports/python-unittest/test_import_stats/TEST-TestImportTime-20230111213818.xml
2023-01-11T21:38:21.4846514Z 
2023-01-11T21:38:21.4846742Z ##[endgroup]
2023-01-11T21:38:21.4847120Z FINISHED PRINTING LOG FILE of test_import_stats (/var/lib/jenkins/workspace/test/test-reports/test_import_stats__ar66mzs)
2023-01-11T21:38:21.4847335Z 
2023-01-11T21:38:23.3885868Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:38:23.4617088Z Ignoring disabled issues:  []
2023-01-11T21:38:23.4771347Z Running test_itt ... [2023-01-11 21:38:23.476741]
2023-01-11T21:38:23.4772144Z Executing ['/opt/conda/bin/python', '-bb', 'test_itt.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:38:23.476982]
2023-01-11T21:38:25.4158780Z 
2023-01-11T21:38:25.4159314Z Expand the folded group to see the log file of test_itt
2023-01-11T21:38:25.4160298Z ##[group]PRINTING LOG FILE of test_itt (/var/lib/jenkins/workspace/test/test-reports/test_itt_58t6di67)
2023-01-11T21:38:25.4160671Z 
2023-01-11T21:38:25.4160779Z Running tests...
2023-01-11T21:38:25.4161373Z ----------------------------------------------------------------------
2023-01-11T21:38:25.4162024Z Test results will be stored in test-reports/python-unittest/test_itt
2023-01-11T21:38:25.4162505Z   test_itt (__main__.TestItt) ... ok (0.230s)
2023-01-11T21:38:25.4162711Z 
2023-01-11T21:38:25.4163062Z ----------------------------------------------------------------------
2023-01-11T21:38:25.4163569Z Ran 1 test in 0.230s
2023-01-11T21:38:25.4163750Z 
2023-01-11T21:38:25.4163875Z OK
2023-01-11T21:38:25.4164055Z 
2023-01-11T21:38:25.4164182Z Generating XML reports...
2023-01-11T21:38:25.4164902Z Generated XML report: test-reports/python-unittest/test_itt/TEST-TestItt-20230111213824.xml
2023-01-11T21:38:25.4165254Z 
2023-01-11T21:38:25.4165772Z ##[endgroup]
2023-01-11T21:38:25.4166357Z FINISHED PRINTING LOG FILE of test_itt (/var/lib/jenkins/workspace/test/test-reports/test_itt_58t6di67)
2023-01-11T21:38:25.4166710Z 
2023-01-11T21:38:27.3003191Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:38:27.3657669Z Ignoring disabled issues:  []
2023-01-11T21:38:27.3812894Z Running test_jit_autocast ... [2023-01-11 21:38:27.380912]
2023-01-11T21:38:27.3813771Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_autocast.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:38:27.381172]
2023-01-11T21:38:45.5963196Z 
2023-01-11T21:38:45.5963716Z Expand the folded group to see the log file of test_jit_autocast
2023-01-11T21:38:45.5965073Z ##[group]PRINTING LOG FILE of test_jit_autocast (/var/lib/jenkins/workspace/test/test-reports/test_jit_autocast_7wc5d46q)
2023-01-11T21:38:45.5965872Z CUDA not available, skipping tests
2023-01-11T21:38:45.5966445Z 
2023-01-11T21:38:45.5966615Z Running tests...
2023-01-11T21:38:45.5967451Z ----------------------------------------------------------------------
2023-01-11T21:38:45.5968420Z Test results will be stored in test-reports/python-unittest/test_jit_autocast
2023-01-11T21:38:45.5969152Z   test_autocast_api (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5969990Z   test_autocast_api_not_supported (__main__.TestAutocast) ... skip: we need to provide dtype argument at this moment (0.000s)
2023-01-11T21:38:45.5970822Z   test_autocast_autodiff (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5971631Z   test_autocast_decorator (__main__.TestAutocast) ... skip: autocast decorators not supported (0.000s)
2023-01-11T21:38:45.5972463Z   test_autocast_decorator_outside_jit (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5973195Z   test_autocast_mixed_dtypes (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5974137Z   test_callees (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5974834Z   test_callees_with_autocast_off (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5975558Z   test_callees_with_autocast_on (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5976302Z   test_conditional_autocast (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5977120Z   test_control_flow (__main__.TestAutocast) ... skip: broken due to lack of type propagation (0.001s)
2023-01-11T21:38:45.5977889Z   test_divergent_autocast (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5978596Z   test_divergent_types (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5979296Z   test_duplicate_inputs (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5979991Z   test_eager_and_script (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5980815Z   test_explicit_casts (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5981484Z   test_fp32_policy (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5982149Z   test_fp32_policy_with_fp64 (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5983079Z   test_fp32_set_opt_dtype_policy (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5983831Z   test_fp32_set_opt_dtype_policy_fp64 (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5984516Z   test_ignore_amp (__main__.TestAutocast) ... ok (0.014s)
2023-01-11T21:38:45.5985208Z   test_implicitly_nested_autocast (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5985909Z   test_inplace (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5986566Z   test_jit_autocast_softmax_cpu (__main__.TestAutocast) ... ok (0.057s)
2023-01-11T21:38:45.5987258Z   test_jit_autocast_softmax_gpu (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5987999Z   test_jit_call_method_under_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5988772Z   test_jit_executor_under_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5989524Z   test_jit_freeze_autocast_basic (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5990213Z   test_jit_freeze_autocast_constants (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5990935Z   test_linear_bf16 (__main__.TestAutocast) ... skip: No cuda bfloat16 support (0.000s)
2023-01-11T21:38:45.5991633Z   test_minimal (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5992293Z   test_minimal_cpu (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5992947Z   test_minimal_off (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5993701Z   test_nested_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5994398Z   test_promote_policy (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5995099Z   test_promote_policy_fp64 (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5995778Z   test_reused_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.5996574Z   test_reused_autocast_expr (__main__.TestAutocast) ... skip: unsuported autocast syntax (0.001s)
2023-01-11T21:38:45.5997367Z   test_runtime_autocast_state (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5998083Z   test_runtime_autocast_state_expr (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5998832Z   test_script_and_tracing (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.5999719Z   test_script_and_tracing_with_autocast (__main__.TestAutocast) ... skip: autocast(False) is ignored inside traced functions (0.000s)
2023-01-11T21:38:45.6000544Z   test_script_module (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.6001204Z   test_tracing_and_script (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:38:45.6002251Z   test_tracing_with_autocast_and_script (__main__.TestAutocast) ... skip: scripted called from traced TorchScript is not yet working (0.000s)
2023-01-11T21:38:45.6003128Z   test_cat_promote (__main__.TestJitTraceAutocast) ... ok (0.257s)
2023-01-11T21:38:45.6003850Z   test_generate_autocast_jit_trace_model (__main__.TestJitTraceAutocast) ... ok (3.873s)
2023-01-11T21:38:45.6004666Z   test_nchw_autocast_jit_trace_model (__main__.TestJitTraceAutocast) ... ok (4.886s)
2023-01-11T21:38:45.6005477Z   test_nhwc_autocast_jit_trace_model (__main__.TestJitTraceAutocast) ... ok (5.434s)
2023-01-11T21:38:45.6022880Z   test_script_autocast_cpu (__main__.TestJitTraceAutocast) ... ok (0.149s)
2023-01-11T21:38:45.6023672Z   test_script_autocast_cuda (__main__.TestJitTraceAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:38:45.6024481Z   test_script_autocast_enable_and_check (__main__.TestJitTraceAutocast) ... ok (0.157s)
2023-01-11T21:38:45.6025415Z   test_scripted_aliasing (__main__.TestJitTraceAutocast) ... ok (0.152s)
2023-01-11T21:38:45.6025825Z 
2023-01-11T21:38:45.6026346Z ----------------------------------------------------------------------
2023-01-11T21:38:45.6026922Z Ran 53 tests in 15.007s
2023-01-11T21:38:45.6027189Z 
2023-01-11T21:38:45.6027359Z OK (skipped=44)
2023-01-11T21:38:45.6027621Z 
2023-01-11T21:38:45.6027819Z Generating XML reports...
2023-01-11T21:38:45.6028781Z Generated XML report: test-reports/python-unittest/test_jit_autocast/TEST-TestAutocast-20230111213829.xml
2023-01-11T21:38:45.6030054Z Generated XML report: test-reports/python-unittest/test_jit_autocast/TEST-TestJitTraceAutocast-20230111213829.xml
2023-01-11T21:38:45.6030662Z 
2023-01-11T21:38:45.6031243Z ##[endgroup]
2023-01-11T21:38:45.6032188Z FINISHED PRINTING LOG FILE of test_jit_autocast (/var/lib/jenkins/workspace/test/test-reports/test_jit_autocast_7wc5d46q)
2023-01-11T21:38:45.6032703Z 
2023-01-11T21:38:47.4638691Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:38:47.5293824Z Ignoring disabled issues:  []
2023-01-11T21:38:47.5449123Z Running test_jit_fuser_te ... [2023-01-11 21:38:47.544624]
2023-01-11T21:38:47.5451229Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_fuser_te.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:38:47.544875]
2023-01-11T21:40:24.0528505Z 
2023-01-11T21:40:24.0529444Z Expand the folded group to see the log file of test_jit_fuser_te
2023-01-11T21:40:24.0531015Z ##[group]PRINTING LOG FILE of test_jit_fuser_te (/var/lib/jenkins/workspace/test/test-reports/test_jit_fuser_te_bqdb0ty0)
2023-01-11T21:40:24.0531559Z CUDA not available, skipping tests
2023-01-11T21:40:24.0531782Z 
2023-01-11T21:40:24.0531918Z Running tests...
2023-01-11T21:40:24.0535194Z ----------------------------------------------------------------------
2023-01-11T21:40:24.0536248Z Test results will be stored in test-reports/python-unittest/test_jit_fuser_te
2023-01-11T21:40:24.0537079Z   test_autodiff_fallback (jit.test_fuser_common.TestFuserCommon) ... ok (0.111s)
2023-01-11T21:40:24.0537784Z   test_abs (__main__.TestTEFuserDynamic) ... ok (0.280s)
2023-01-11T21:40:24.0538458Z   test_adaptive_avg_pool2d (__main__.TestTEFuserDynamic) ... ok (0.062s)
2023-01-11T21:40:24.0539144Z   test_add_bool (__main__.TestTEFuserDynamic) ... ok (0.274s)
2023-01-11T21:40:24.0539793Z   test_addcmul (__main__.TestTEFuserDynamic) ... ok (0.089s)
2023-01-11T21:40:24.0540588Z   test_arg_configurations_smoke (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0541771Z   test_autocast_down (__main__.TestTEFuserDynamic) ... skip: half-precision NNC fusion requires CUDA (0.000s)
2023-01-11T21:40:24.0543084Z   test_autocast_up (__main__.TestTEFuserDynamic) ... skip: half-precision NNC fusion requires CUDA (0.000s)
2023-01-11T21:40:24.0543874Z   test_batch_norm (__main__.TestTEFuserDynamic) ... ok (0.660s)
2023-01-11T21:40:24.0544542Z   test_binary_div_ops (__main__.TestTEFuserDynamic) ... ok (2.402s)
2023-01-11T21:40:24.0545510Z   test_binary_ops (__main__.TestTEFuserDynamic) ... ok (8.674s)
2023-01-11T21:40:24.0546158Z   test_binary_pow (__main__.TestTEFuserDynamic) ... ok (0.803s)
2023-01-11T21:40:24.0546825Z   test_binary_scalar_ops (__main__.TestTEFuserDynamic) ... ok (1.026s)
2023-01-11T21:40:24.0547735Z   test_binary_tensor_scalar_ops (__main__.TestTEFuserDynamic) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:40:24.0548575Z   test_bitwise_ops (__main__.TestTEFuserDynamic) ... ok (1.911s)
2023-01-11T21:40:24.0549234Z   test_broadcast (__main__.TestTEFuserDynamic) ... ok (0.232s)
2023-01-11T21:40:24.0549863Z   test_cat_2k_args (__main__.TestTEFuserDynamic) ... ok (0.689s)
2023-01-11T21:40:24.0550529Z   test_cat_graph_opt (__main__.TestTEFuserDynamic) ... ok (0.763s)
2023-01-11T21:40:24.0551300Z   test_channels_last_dims_dynamic (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0552288Z   test_checks_cat_inputs (__main__.TestTEFuserDynamic) ... ok (0.456s)
2023-01-11T21:40:24.0553055Z   test_chunk (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0553933Z   test_chunk_correctness (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0554827Z   test_chunk_distributes (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0555752Z   test_chunk_motion_deduplicates_inputs (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0556628Z   test_chunk_mul_one (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0557447Z   test_chunk_multiple (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0558137Z   test_clamp (__main__.TestTEFuserDynamic) ... ok (3.515s)
2023-01-11T21:40:24.0558817Z   test_clamp_double (__main__.TestTEFuserDynamic) ... ok (0.139s)
2023-01-11T21:40:24.0559499Z   test_clamp_int (__main__.TestTEFuserDynamic) ... ok (0.133s)
2023-01-11T21:40:24.0560153Z   test_comparison_eq_ne (__main__.TestTEFuserDynamic) ... ok (0.297s)
2023-01-11T21:40:24.0560873Z   test_comparison_ge_le (__main__.TestTEFuserDynamic) ... ok (0.286s)
2023-01-11T21:40:24.0561572Z   test_comparison_gt_lt (__main__.TestTEFuserDynamic) ... ok (0.287s)
2023-01-11T21:40:24.0562220Z   test_concat (__main__.TestTEFuserDynamic) ... ok (0.567s)
2023-01-11T21:40:24.0562791Z   test_concat_invariant (__main__.TestTEFuserDynamic) ... ok (0.755s)
2023-01-11T21:40:24.0563592Z   test_constant_chunk_shapes (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:40:24.0564695Z   test_conv2d (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.001s)
2023-01-11T21:40:24.0565772Z   test_conv2d_depthwise (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.001s)
2023-01-11T21:40:24.0566626Z   test_cuda_half (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0567330Z   test_dims (__main__.TestTEFuserDynamic) ... ok (0.128s)
2023-01-11T21:40:24.0567948Z   test_disabled (__main__.TestTEFuserDynamic) ... ok (0.005s)
2023-01-11T21:40:24.0568552Z   test_div_bool (__main__.TestTEFuserDynamic) ... ok (0.135s)
2023-01-11T21:40:24.0569189Z   test_dynamic_cat (__main__.TestTEFuserDynamic) ... ok (0.009s)
2023-01-11T21:40:24.0569829Z   test_dynamic_shapes (__main__.TestTEFuserDynamic) ... ok (2.038s)
2023-01-11T21:40:24.0570539Z   test_eq_unsqueeze_type_as (__main__.TestTEFuserDynamic) ... ok (0.303s)
2023-01-11T21:40:24.0571189Z   test_erf (__main__.TestTEFuserDynamic) ... ok (0.001s)
2023-01-11T21:40:24.0571888Z   test_exhaust_specializations (__main__.TestTEFuserDynamic) ... ok (0.028s)
2023-01-11T21:40:24.0572563Z   test_exp (__main__.TestTEFuserDynamic) ... ok (0.088s)
2023-01-11T21:40:24.0573296Z   test_fusion_reuse_multi_gpu (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0574158Z   test_gelu (__main__.TestTEFuserDynamic) ... ok (0.026s)
2023-01-11T21:40:24.0574828Z   test_hardsigmoid_fwd_bwd (__main__.TestTEFuserDynamic) ... ok (0.267s)
2023-01-11T21:40:24.0575533Z   test_hardswish_fwd_bwd (__main__.TestTEFuserDynamic) ... ok (0.292s)
2023-01-11T21:40:24.0576251Z   test_inlined_optimized_graph (__main__.TestTEFuserDynamic) ... ok (0.409s)
2023-01-11T21:40:24.0576947Z   test_isnan (__main__.TestTEFuserDynamic) ... ok (0.653s)
2023-01-11T21:40:24.0577671Z   test_kernel_cache_multi_gpu (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0578404Z   test_lerp (__main__.TestTEFuserDynamic) ... ok (0.339s)
2023-01-11T21:40:24.0579572Z   test_list_ops (__main__.TestTEFuserDynamic) ... skip: FIXME: fuser doesn't include ListConstruct nodes to the group causing a failure (0.001s)
2023-01-11T21:40:24.0580416Z   test_lstm (__main__.TestTEFuserDynamic) ... ok (0.749s)
2023-01-11T21:40:24.0581156Z   test_lstm_concat (__main__.TestTEFuserDynamic) ... ok (0.242s)
2023-01-11T21:40:24.0581877Z   test_lstm_gates_permutations (__main__.TestTEFuserDynamic) ... ok (10.584s)
2023-01-11T21:40:24.0582696Z   test_lstm_traced (__main__.TestTEFuserDynamic) ... ok (0.430s)
2023-01-11T21:40:24.0583434Z   test_masked_fill (__main__.TestTEFuserDynamic) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:40:24.0584494Z   test_matmul (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.002s)
2023-01-11T21:40:24.0585513Z   test_milstm (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.001s)
2023-01-11T21:40:24.0586236Z   test_minmax (__main__.TestTEFuserDynamic) ... ok (1.150s)
2023-01-11T21:40:24.0586906Z   test_minmax_int_ops (__main__.TestTEFuserDynamic) ... ok (0.802s)
2023-01-11T21:40:24.0587562Z   test_mul_bool (__main__.TestTEFuserDynamic) ... ok (0.169s)
2023-01-11T21:40:24.0588201Z   test_neg_pow (__main__.TestTEFuserDynamic) ... ok (0.141s)
2023-01-11T21:40:24.0589146Z   test_nonzero_device_cuda (__main__.TestTEFuserDynamic) ... skip: needs non-zero device (0.001s)
2023-01-11T21:40:24.0589896Z   test_nop (__main__.TestTEFuserDynamic) ... ok (0.001s)
2023-01-11T21:40:24.0591078Z   test_profiler (__main__.TestTEFuserDynamic) ... STAGE:2023-01-11 21:39:33 12456:12456 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:40:24.0592284Z STAGE:2023-01-11 21:39:33 12456:12456 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:40:24.0593381Z STAGE:2023-01-11 21:39:33 12456:12456 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:40:24.0594084Z ok (0.076s)
2023-01-11T21:40:24.0594698Z   test_rand_broadcast_cuda (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0595541Z   test_rand_cuda (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0596324Z   test_rand_diamond (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.000s)
2023-01-11T21:40:24.0597046Z   test_relu (__main__.TestTEFuserDynamic) ... ok (0.139s)
2023-01-11T21:40:24.0597662Z   test_relu_fwd_bwd (__main__.TestTEFuserDynamic) ... ok (0.162s)
2023-01-11T21:40:24.0598384Z   test_remove_output_used_only_in_size (__main__.TestTEFuserDynamic) ... ok (0.029s)
2023-01-11T21:40:24.0599060Z   test_scalar (__main__.TestTEFuserDynamic) ... ok (0.049s)
2023-01-11T21:40:24.0599705Z   test_scalar_arg (__main__.TestTEFuserDynamic) ... ok (0.501s)
2023-01-11T21:40:24.0600373Z   test_scalar_only_inputs (__main__.TestTEFuserDynamic) ... ok (0.048s)
2023-01-11T21:40:24.0601071Z   test_skip_grad_in_check (__main__.TestTEFuserDynamic) ... ok (0.158s)
2023-01-11T21:40:24.0601758Z   test_small_constant (__main__.TestTEFuserDynamic) ... ok (0.317s)
2023-01-11T21:40:24.0602404Z   test_sub_gt_and (__main__.TestTEFuserDynamic) ... ok (0.181s)
2023-01-11T21:40:24.0603023Z   test_sum_dim (__main__.TestTEFuserDynamic) ... ok (0.271s)
2023-01-11T21:40:24.0603668Z   test_sum_keepdim_cast (__main__.TestTEFuserDynamic) ... ok (0.138s)
2023-01-11T21:40:24.0604474Z   test_sum_simple (__main__.TestTEFuserDynamic) ... ok (0.091s)
2023-01-11T21:40:24.0605131Z   test_superslomo (__main__.TestTEFuserDynamic) ... ok (1.293s)
2023-01-11T21:40:24.0605800Z   test_tensor_scalar_ops (__main__.TestTEFuserDynamic) ... ok (0.653s)
2023-01-11T21:40:24.0606470Z   test_ternary_norm_ops (__main__.TestTEFuserDynamic) ... ok (0.417s)
2023-01-11T21:40:24.0607127Z   test_ternary_ops (__main__.TestTEFuserDynamic) ... ok (0.660s)
2023-01-11T21:40:24.0607781Z   test_threshold (__main__.TestTEFuserDynamic) ... ok (0.210s)
2023-01-11T21:40:24.0608401Z   test_to_device (__main__.TestTEFuserDynamic) ... ok (0.148s)
2023-01-11T21:40:24.0608975Z   test_to_dtype (__main__.TestTEFuserDynamic) ... ok (0.381s)
2023-01-11T21:40:24.0609563Z   test_torch_to (__main__.TestTEFuserDynamic) ... ok (3.948s)
2023-01-11T21:40:24.0610157Z   test_type_as_cat (__main__.TestTEFuserDynamic) ... ok (2.110s)
2023-01-11T21:40:24.0610765Z   test_typecheck (__main__.TestTEFuserDynamic) ... ok (0.052s)
2023-01-11T21:40:24.0611688Z   test_unary_ops (__main__.TestTEFuserDynamic) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:40:24.0612526Z   test_unrolled_cat (__main__.TestTEFuserDynamic) ... ok (0.012s)
2023-01-11T21:40:24.0613264Z   test_unsqueeze_size_calculation (__main__.TestTEFuserDynamic) ... ok (0.347s)
2023-01-11T21:40:24.0613994Z   test_unsqueeze_var_dim (__main__.TestTEFuserDynamic) ... ok (0.354s)
2023-01-11T21:40:24.0615894Z   test_unsupported_dtypes (__main__.TestTEFuserDynamic) ... /var/lib/jenkins/workspace/test/test_jit_fuser_te.py:1186: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:40:24.0617128Z   return v.to(dtype)
2023-01-11T21:40:24.0617548Z ok (0.037s)
2023-01-11T21:40:24.0618097Z   test_where_and_typing (__main__.TestTEFuserDynamic) ... ok (0.314s)
2023-01-11T21:40:24.0618770Z   test_where_ops (__main__.TestTEFuserDynamic) ... ok (2.693s)
2023-01-11T21:40:24.0619594Z   test_with_strict_fusion (__main__.TestTEFuserDynamic) ... /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py:216: UserWarning: Only works in script mode
2023-01-11T21:40:24.0620107Z   warnings.warn("Only works in script mode")
2023-01-11T21:40:24.0620381Z ok (0.172s)
2023-01-11T21:40:24.0620726Z   test_zero_element_tensors (__main__.TestTEFuserDynamic) ... ok (0.093s)
2023-01-11T21:40:24.0621120Z   test_abs (__main__.TestTEFuserStatic) ... ok (0.050s)
2023-01-11T21:40:24.0621512Z   test_adaptive_avg_pool2d (__main__.TestTEFuserStatic) ... ok (0.061s)
2023-01-11T21:40:24.0621933Z   test_add_bool (__main__.TestTEFuserStatic) ... ok (0.107s)
2023-01-11T21:40:24.0622315Z   test_addcmul (__main__.TestTEFuserStatic) ... ok (0.042s)
2023-01-11T21:40:24.0622875Z   test_arg_configurations_smoke (__main__.TestTEFuserStatic) ... ok (0.041s)
2023-01-11T21:40:24.0623533Z   test_autocast_down (__main__.TestTEFuserStatic) ... skip: half-precision NNC fusion requires CUDA (0.001s)
2023-01-11T21:40:24.0624237Z   test_autocast_up (__main__.TestTEFuserStatic) ... skip: half-precision NNC fusion requires CUDA (0.000s)
2023-01-11T21:40:24.0624686Z   test_batch_norm (__main__.TestTEFuserStatic) ... ok (0.289s)
2023-01-11T21:40:24.0625074Z   test_binary_div_ops (__main__.TestTEFuserStatic) ... ok (1.112s)
2023-01-11T21:40:24.0625463Z   test_binary_ops (__main__.TestTEFuserStatic) ... ok (3.260s)
2023-01-11T21:40:24.0625868Z   test_binary_pow (__main__.TestTEFuserStatic) ... ok (0.275s)
2023-01-11T21:40:24.0626270Z   test_binary_scalar_ops (__main__.TestTEFuserStatic) ... ok (1.029s)
2023-01-11T21:40:24.0626693Z   test_binary_tensor_scalar_ops (__main__.TestTEFuserStatic) ... ok (8.356s)
2023-01-11T21:40:24.0627129Z   test_bitwise_ops (__main__.TestTEFuserStatic) ... ok (0.733s)
2023-01-11T21:40:24.0627515Z   test_broadcast (__main__.TestTEFuserStatic) ... ok (0.058s)
2023-01-11T21:40:24.0627890Z   test_cat_2k_args (__main__.TestTEFuserStatic) ... ok (0.732s)
2023-01-11T21:40:24.0628433Z   test_cat_graph_opt (__main__.TestTEFuserStatic) ... ok (0.080s)
2023-01-11T21:40:24.0628905Z   test_channels_last_dims_dynamic (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0629348Z   test_checks_cat_inputs (__main__.TestTEFuserStatic) ... ok (0.070s)
2023-01-11T21:40:24.0629731Z   test_chunk (__main__.TestTEFuserStatic) ... ok (0.102s)
2023-01-11T21:40:24.0630114Z   test_chunk_correctness (__main__.TestTEFuserStatic) ... ok (1.101s)
2023-01-11T21:40:24.0630528Z   test_chunk_distributes (__main__.TestTEFuserStatic) ... ok (0.056s)
2023-01-11T21:40:24.0630975Z   test_chunk_motion_deduplicates_inputs (__main__.TestTEFuserStatic) ... ok (0.102s)
2023-01-11T21:40:24.0631416Z   test_chunk_mul_one (__main__.TestTEFuserStatic) ... ok (0.112s)
2023-01-11T21:40:24.0631812Z   test_chunk_multiple (__main__.TestTEFuserStatic) ... ok (0.183s)
2023-01-11T21:40:24.0632173Z   test_clamp (__main__.TestTEFuserStatic) ... ok (0.900s)
2023-01-11T21:40:24.0632665Z   test_clamp_double (__main__.TestTEFuserStatic) ... ok (0.058s)
2023-01-11T21:40:24.0633061Z   test_clamp_int (__main__.TestTEFuserStatic) ... ok (0.054s)
2023-01-11T21:40:24.0633458Z   test_comparison_eq_ne (__main__.TestTEFuserStatic) ... ok (0.084s)
2023-01-11T21:40:24.0633960Z   test_comparison_ge_le (__main__.TestTEFuserStatic) ... ok (0.084s)
2023-01-11T21:40:24.0634380Z   test_comparison_gt_lt (__main__.TestTEFuserStatic) ... ok (0.084s)
2023-01-11T21:40:24.0634782Z   test_concat (__main__.TestTEFuserStatic) ... ok (0.061s)
2023-01-11T21:40:24.0635161Z   test_concat_invariant (__main__.TestTEFuserStatic) ... ok (0.079s)
2023-01-11T21:40:24.0636137Z   test_constant_chunk_shapes (__main__.TestTEFuserStatic) ... /var/lib/jenkins/workspace/test/test_jit_fuser_te.py:2443: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
2023-01-11T21:40:24.0636952Z   r = torch.tensor(4)
2023-01-11T21:40:24.0637203Z ok (0.110s)
2023-01-11T21:40:24.0637514Z   test_conv2d (__main__.TestTEFuserStatic) ... ok (0.026s)
2023-01-11T21:40:24.0637903Z   test_conv2d_depthwise (__main__.TestTEFuserStatic) ... ok (0.868s)
2023-01-11T21:40:24.0638360Z   test_cuda_half (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0638775Z   test_dims (__main__.TestTEFuserStatic) ... ok (0.067s)
2023-01-11T21:40:24.0639137Z   test_disabled (__main__.TestTEFuserStatic) ... ok (0.005s)
2023-01-11T21:40:24.0639532Z   test_div_bool (__main__.TestTEFuserStatic) ... ok (0.035s)
2023-01-11T21:40:24.0639926Z   test_dynamic_cat (__main__.TestTEFuserStatic) ... ok (0.008s)
2023-01-11T21:40:24.0640325Z   test_dynamic_shapes (__main__.TestTEFuserStatic) ... ok (2.059s)
2023-01-11T21:40:24.0640728Z   test_eq_unsqueeze_type_as (__main__.TestTEFuserStatic) ... ok (0.110s)
2023-01-11T21:40:24.0641092Z   test_erf (__main__.TestTEFuserStatic) ... ok (0.001s)
2023-01-11T21:40:24.0641485Z   test_exhaust_specializations (__main__.TestTEFuserStatic) ... ok (0.029s)
2023-01-11T21:40:24.0641847Z   test_exp (__main__.TestTEFuserStatic) ... ok (0.047s)
2023-01-11T21:40:24.0642275Z   test_fusion_reuse_multi_gpu (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0642728Z   test_gelu (__main__.TestTEFuserStatic) ... ok (0.026s)
2023-01-11T21:40:24.0643132Z   test_hardsigmoid_fwd_bwd (__main__.TestTEFuserStatic) ... ok (0.103s)
2023-01-11T21:40:24.0643572Z   test_hardswish_fwd_bwd (__main__.TestTEFuserStatic) ... ok (0.120s)
2023-01-11T21:40:24.0644029Z   test_inlined_optimized_graph (__main__.TestTEFuserStatic) ... ok (0.088s)
2023-01-11T21:40:24.0644446Z   test_isnan (__main__.TestTEFuserStatic) ... ok (0.355s)
2023-01-11T21:40:24.0644889Z   test_kernel_cache_multi_gpu (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0645408Z   test_lerp (__main__.TestTEFuserStatic) ... ok (0.068s)
2023-01-11T21:40:24.0646109Z   test_list_ops (__main__.TestTEFuserStatic) ... skip: FIXME: fuser doesn't include ListConstruct nodes to the group causing a failure (0.001s)
2023-01-11T21:40:24.0646600Z   test_lstm (__main__.TestTEFuserStatic) ... ok (0.168s)
2023-01-11T21:40:24.0646979Z   test_lstm_concat (__main__.TestTEFuserStatic) ... ok (0.164s)
2023-01-11T21:40:24.0647402Z   test_lstm_gates_permutations (__main__.TestTEFuserStatic) ... ok (1.744s)
2023-01-11T21:40:24.0647821Z   test_lstm_traced (__main__.TestTEFuserStatic) ... ok (0.140s)
2023-01-11T21:40:24.0648262Z   test_masked_fill (__main__.TestTEFuserStatic) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:40:24.0648676Z   test_matmul (__main__.TestTEFuserStatic) ... ok (0.843s)
2023-01-11T21:40:24.0649058Z   test_milstm (__main__.TestTEFuserStatic) ... ok (0.922s)
2023-01-11T21:40:24.0649412Z   test_minmax (__main__.TestTEFuserStatic) ... ok (0.289s)
2023-01-11T21:40:24.0649866Z   test_minmax_int_ops (__main__.TestTEFuserStatic) ... ok (0.309s)
2023-01-11T21:40:24.0650235Z   test_mul_bool (__main__.TestTEFuserStatic) ... ok (0.038s)
2023-01-11T21:40:24.0650574Z   test_neg_pow (__main__.TestTEFuserStatic) ... ok (0.139s)
2023-01-11T21:40:24.0651155Z   test_nonzero_device_cuda (__main__.TestTEFuserStatic) ... skip: needs non-zero device (0.001s)
2023-01-11T21:40:24.0651613Z   test_nop (__main__.TestTEFuserStatic) ... ok (0.001s)
2023-01-11T21:40:24.0652354Z   test_profiler (__main__.TestTEFuserStatic) ... STAGE:2023-01-11 21:40:17 12456:12456 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:40:24.0653088Z STAGE:2023-01-11 21:40:17 12456:12456 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:40:24.0653775Z STAGE:2023-01-11 21:40:17 12456:12456 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:40:24.0654161Z ok (0.027s)
2023-01-11T21:40:24.0654558Z   test_rand_broadcast_cuda (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0655030Z   test_rand_cuda (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:40:24.0655507Z   test_rand_diamond (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.000s)
2023-01-11T21:40:24.0655953Z   test_relu (__main__.TestTEFuserStatic) ... ok (0.047s)
2023-01-11T21:40:24.0656319Z   test_relu_fwd_bwd (__main__.TestTEFuserStatic) ... ok (0.060s)
2023-01-11T21:40:24.0656741Z   test_remove_output_used_only_in_size (__main__.TestTEFuserStatic) ... ok (0.027s)
2023-01-11T21:40:24.0657158Z   test_scalar (__main__.TestTEFuserStatic) ... ok (0.048s)
2023-01-11T21:40:24.0657525Z   test_scalar_arg (__main__.TestTEFuserStatic) ... ok (0.089s)
2023-01-11T21:40:24.0657929Z   test_scalar_only_inputs (__main__.TestTEFuserStatic) ... ok (0.047s)
2023-01-11T21:40:24.0658341Z   test_skip_grad_in_check (__main__.TestTEFuserStatic) ... ok (0.029s)
2023-01-11T21:40:24.0658796Z   test_small_constant (__main__.TestTEFuserStatic) ... ok (0.073s)
2023-01-11T21:40:24.0659183Z   test_sub_gt_and (__main__.TestTEFuserStatic) ... ok (0.059s)
2023-01-11T21:40:24.0659600Z   test_sum_dim (__main__.TestTEFuserStatic) ... ok (0.121s)
2023-01-11T21:40:24.0659995Z   test_sum_keepdim_cast (__main__.TestTEFuserStatic) ... ok (0.064s)
2023-01-11T21:40:24.0660382Z   test_sum_simple (__main__.TestTEFuserStatic) ... ok (0.058s)
2023-01-11T21:40:24.0660776Z   test_superslomo (__main__.TestTEFuserStatic) ... ok (0.620s)
2023-01-11T21:40:24.0661178Z   test_tensor_scalar_ops (__main__.TestTEFuserStatic) ... ok (0.114s)
2023-01-11T21:40:24.0661588Z   test_ternary_norm_ops (__main__.TestTEFuserStatic) ... ok (0.217s)
2023-01-11T21:40:24.0661976Z   test_ternary_ops (__main__.TestTEFuserStatic) ... ok (0.259s)
2023-01-11T21:40:24.0662496Z   test_threshold (__main__.TestTEFuserStatic) ... ok (0.062s)
2023-01-11T21:40:24.0662875Z   test_to_device (__main__.TestTEFuserStatic) ... ok (0.049s)
2023-01-11T21:40:24.0663244Z   test_to_dtype (__main__.TestTEFuserStatic) ... ok (0.103s)
2023-01-11T21:40:24.0663735Z   test_torch_to (__main__.TestTEFuserStatic) ... ok (0.850s)
2023-01-11T21:40:24.0664117Z   test_type_as_cat (__main__.TestTEFuserStatic) ... ok (1.410s)
2023-01-11T21:40:24.0664477Z   test_typecheck (__main__.TestTEFuserStatic) ... ok (0.049s)
2023-01-11T21:40:24.0664979Z   test_unary_ops (__main__.TestTEFuserStatic) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:40:24.0665473Z   test_unrolled_cat (__main__.TestTEFuserStatic) ... ok (0.028s)
2023-01-11T21:40:24.0665887Z   test_unsqueeze_size_calculation (__main__.TestTEFuserStatic) ... ok (0.084s)
2023-01-11T21:40:24.0666293Z   test_unsqueeze_var_dim (__main__.TestTEFuserStatic) ... ok (0.224s)
2023-01-11T21:40:24.0666712Z   test_unsupported_dtypes (__main__.TestTEFuserStatic) ... ok (0.036s)
2023-01-11T21:40:24.0667122Z   test_where_and_typing (__main__.TestTEFuserStatic) ... ok (0.081s)
2023-01-11T21:40:24.0667604Z   test_where_ops (__main__.TestTEFuserStatic) ... ok (0.613s)
2023-01-11T21:40:24.0668378Z   test_with_strict_fusion (__main__.TestTEFuserStatic) ... /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py:216: UserWarning: Only works in script mode
2023-01-11T21:40:24.0668906Z   warnings.warn("Only works in script mode")
2023-01-11T21:40:24.0669210Z ok (0.069s)
2023-01-11T21:40:24.0669531Z   test_zero_element_tensors (__main__.TestTEFuserStatic) ... ok (0.045s)
2023-01-11T21:40:24.0669771Z 
2023-01-11T21:40:24.0670085Z ----------------------------------------------------------------------
2023-01-11T21:40:24.0670432Z Ran 207 tests in 93.405s
2023-01-11T21:40:24.0670605Z 
2023-01-11T21:40:24.0670691Z OK (skipped=39)
2023-01-11T21:40:24.0670846Z 
2023-01-11T21:40:24.0670968Z Generating XML reports...
2023-01-11T21:40:24.0671656Z Generated XML report: test-reports/python-unittest/test_jit_fuser_te/TEST-jit.test_fuser_common.TestFuserCommon-20230111213849.xml
2023-01-11T21:40:24.0672597Z Generated XML report: test-reports/python-unittest/test_jit_fuser_te/TEST-TestTEFuserDynamic-20230111213849.xml
2023-01-11T21:40:24.0673432Z Generated XML report: test-reports/python-unittest/test_jit_fuser_te/TEST-TestTEFuserStatic-20230111213849.xml
2023-01-11T21:40:24.0673804Z 
2023-01-11T21:40:24.0674262Z ##[endgroup]
2023-01-11T21:40:24.0674866Z FINISHED PRINTING LOG FILE of test_jit_fuser_te (/var/lib/jenkins/workspace/test/test-reports/test_jit_fuser_te_bqdb0ty0)
2023-01-11T21:40:24.0675204Z 
2023-01-11T21:40:25.9534010Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:26.0199169Z Ignoring disabled issues:  []
2023-01-11T21:40:26.0355007Z Running test_jit_llga_fuser ... [2023-01-11 21:40:26.035226]
2023-01-11T21:40:26.0357282Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_llga_fuser.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:26.035478]
2023-01-11T21:40:29.8454523Z 
2023-01-11T21:40:29.8455134Z Expand the folded group to see the log file of test_jit_llga_fuser
2023-01-11T21:40:29.8456167Z ##[group]PRINTING LOG FILE of test_jit_llga_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_llga_fuser_clt8hj27)
2023-01-11T21:40:29.8456507Z 
2023-01-11T21:40:29.8456949Z Running tests...
2023-01-11T21:40:29.8457658Z ----------------------------------------------------------------------
2023-01-11T21:40:29.8458509Z   test_dynamo_aot_ts_onednn (__main__.TestDynamoAOT) ... Test results will be stored in test-reports/python-unittest/test_jit_llga_fuser
2023-01-11T21:40:29.8459152Z skip: Enable when integration with dynamo aot_autograd is more stable (0.001s)
2023-01-11T21:40:29.8464318Z   test_context_manager (__main__.TestEnableDisableLlgaFuser) ... ok (0.064s)
2023-01-11T21:40:29.8464823Z   test_vision_alexnet_bfloat16 (__main__.TestModel) ... ok (0.035s)
2023-01-11T21:40:29.8465328Z   test_vision_alexnet_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8465836Z   test_vision_densenet121_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8466559Z   test_vision_densenet121_float32 (__main__.TestModel) ... ok (0.036s)
2023-01-11T21:40:29.8467070Z   test_vision_densenet161_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8467568Z   test_vision_densenet161_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8467850Z   test_vision_densenet169_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8468116Z   test_vision_densenet169_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8468394Z   test_vision_densenet201_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8468672Z   test_vision_densenet201_float32 (__main__.TestModel) ... ok (0.029s)
2023-01-11T21:40:29.8468945Z   test_vision_efficientnet_b0_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8469239Z   test_vision_efficientnet_b0_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8469530Z   test_vision_efficientnet_b1_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8469913Z   test_vision_efficientnet_b1_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8470192Z   test_vision_efficientnet_b2_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8470487Z   test_vision_efficientnet_b2_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8470776Z   test_vision_efficientnet_b3_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8471051Z   test_vision_efficientnet_b3_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8471343Z   test_vision_efficientnet_b4_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8471633Z   test_vision_efficientnet_b4_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8471919Z   test_vision_efficientnet_b5_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8504421Z   test_vision_efficientnet_b5_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8504946Z   test_vision_efficientnet_b6_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8505466Z   test_vision_efficientnet_b6_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8506013Z   test_vision_efficientnet_b7_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8506495Z   test_vision_efficientnet_b7_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8507123Z   test_vision_googlenet_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8507618Z   test_vision_googlenet_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8508114Z   test_vision_mnasnet1_0_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8508574Z   test_vision_mnasnet1_0_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:40:29.8509030Z   test_vision_mobilenet_v2_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8509521Z   test_vision_mobilenet_v2_float32 (__main__.TestModel) ... ok (0.033s)
2023-01-11T21:40:29.8510003Z   test_vision_mobilenet_v3_large_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8510499Z   test_vision_mobilenet_v3_large_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8510985Z   test_vision_regnet_y_400mf_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8511460Z   test_vision_regnet_y_400mf_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8511930Z   test_vision_resnet50_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:40:29.8512383Z   test_vision_resnet50_float32 (__main__.TestModel) ... ok (0.040s)
2023-01-11T21:40:29.8512872Z   test_vision_resnext101_32x8d_bfloat16 (__main__.TestModel) ... ok (0.033s)
2023-01-11T21:40:29.8513361Z   test_vision_resnext101_32x8d_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8513918Z   test_vision_resnext50_32x4d_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:40:29.8514392Z   test_vision_resnext50_32x4d_float32 (__main__.TestModel) ... ok (0.035s)
2023-01-11T21:40:29.8514873Z   test_vision_shufflenet_v2_x1_0_bfloat16 (__main__.TestModel) ... ok (0.035s)
2023-01-11T21:40:29.8515572Z   test_vision_shufflenet_v2_x1_0_float32 (__main__.TestModel) ... ok (0.034s)
2023-01-11T21:40:29.8516044Z   test_vision_squeezenet1_0_bfloat16 (__main__.TestModel) ... ok (0.034s)
2023-01-11T21:40:29.8516533Z   test_vision_squeezenet1_0_float32 (__main__.TestModel) ... ok (0.035s)
2023-01-11T21:40:29.8517015Z   test_vision_vgg16_bfloat16 (__main__.TestModel) ... ok (0.035s)
2023-01-11T21:40:29.8517447Z   test_vision_vgg16_float32 (__main__.TestModel) ... ok (0.036s)
2023-01-11T21:40:29.8517925Z   test_vision_wide_resnet50_2_bfloat16 (__main__.TestModel) ... ok (0.036s)
2023-01-11T21:40:29.8518398Z   test_vision_wide_resnet50_2_float32 (__main__.TestModel) ... ok (0.033s)
2023-01-11T21:40:29.8518673Z 
2023-01-11T21:40:29.8519104Z ----------------------------------------------------------------------
2023-01-11T21:40:29.8519496Z Ran 52 tests in 1.677s
2023-01-11T21:40:29.8519693Z 
2023-01-11T21:40:29.8519821Z OK (skipped=1)
2023-01-11T21:40:29.8520040Z 
2023-01-11T21:40:29.8520385Z Generating XML reports...
2023-01-11T21:40:29.8521196Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestEnableDisableLlgaFuser-20230111214027.xml
2023-01-11T21:40:29.8522144Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestModel-20230111214027.xml
2023-01-11T21:40:29.8523033Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestDynamoAOT-20230111214027.xml
2023-01-11T21:40:29.8523419Z 
2023-01-11T21:40:29.8523855Z ##[endgroup]
2023-01-11T21:40:29.8524566Z FINISHED PRINTING LOG FILE of test_jit_llga_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_llga_fuser_clt8hj27)
2023-01-11T21:40:29.8524957Z 
2023-01-11T21:40:31.7634119Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:31.8279723Z Ignoring disabled issues:  []
2023-01-11T21:40:31.8434833Z Running test_jiterator ... [2023-01-11 21:40:31.843177]
2023-01-11T21:40:31.8436543Z Executing ['/opt/conda/bin/python', '-bb', 'test_jiterator.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:31.843428]
2023-01-11T21:40:33.7828589Z 
2023-01-11T21:40:33.7829156Z Expand the folded group to see the log file of test_jiterator
2023-01-11T21:40:33.7830344Z ##[group]PRINTING LOG FILE of test_jiterator (/var/lib/jenkins/workspace/test/test-reports/test_jiterator_ol3snmf8)
2023-01-11T21:40:33.7830916Z CUDA not available, skipping tests
2023-01-11T21:40:33.7831160Z 
2023-01-11T21:40:33.7831273Z Running tests...
2023-01-11T21:40:33.7831769Z ----------------------------------------------------------------------
2023-01-11T21:40:33.7831943Z 
2023-01-11T21:40:33.7832141Z ----------------------------------------------------------------------
2023-01-11T21:40:33.7832381Z Ran 0 tests in 0.000s
2023-01-11T21:40:33.7832481Z 
2023-01-11T21:40:33.7832542Z OK
2023-01-11T21:40:33.7832633Z 
2023-01-11T21:40:33.7832719Z Generating XML reports...
2023-01-11T21:40:33.7833046Z Test results will be stored in test-reports/python-unittest/test_jiterator
2023-01-11T21:40:33.7833244Z 
2023-01-11T21:40:33.7833454Z ##[endgroup]
2023-01-11T21:40:33.7833912Z FINISHED PRINTING LOG FILE of test_jiterator (/var/lib/jenkins/workspace/test/test-reports/test_jiterator_ol3snmf8)
2023-01-11T21:40:33.7834129Z 
2023-01-11T21:40:35.6694321Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:35.7354835Z Ignoring disabled issues:  []
2023-01-11T21:40:35.7512944Z Running test_legacy_vmap ... [2023-01-11 21:40:35.750954]
2023-01-11T21:40:35.7513819Z Executing ['/opt/conda/bin/python', '-bb', 'test_legacy_vmap.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:35.751192]
2023-01-11T21:40:39.3928791Z 
2023-01-11T21:40:39.3935011Z Expand the folded group to see the log file of test_legacy_vmap
2023-01-11T21:40:39.3936185Z ##[group]PRINTING LOG FILE of test_legacy_vmap (/var/lib/jenkins/workspace/test/test-reports/test_legacy_vmap_myjl1fm9)
2023-01-11T21:40:39.3936652Z 
2023-01-11T21:40:39.3936784Z Running tests...
2023-01-11T21:40:39.3937711Z ----------------------------------------------------------------------
2023-01-11T21:40:39.3938418Z Test results will be stored in test-reports/python-unittest/test_legacy_vmap
2023-01-11T21:40:39.3939221Z   test_accepts_nested_inputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:377: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3939704Z   out = vmap(lambda z: z[0] + z[1])((x, y))
2023-01-11T21:40:39.3940023Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:379: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3940355Z   out = vmap(lambda z: z[0] + z[1], in_dims=(0,))((x, y))
2023-01-11T21:40:39.3940696Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:381: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3941019Z   out = vmap(lambda z: z[0] + z[1], in_dims=((0, 0),))((x, y))
2023-01-11T21:40:39.3941430Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:384: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3941746Z   out = vmap(lambda z: z[0] + z[1])([x, y])
2023-01-11T21:40:39.3942071Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:386: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3942573Z   out = vmap(lambda z: z[0] + z[1], in_dims=(0,))([x, y])
2023-01-11T21:40:39.3942913Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:388: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3943242Z   out = vmap(lambda z: z[0] + z[1], in_dims=([0, 0],))([x, y])
2023-01-11T21:40:39.3943563Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:391: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3943958Z   out = vmap(lambda z: z['x'] + z['y'])({'x': x, 'y': y})
2023-01-11T21:40:39.3944299Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:393: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3944710Z   out = vmap(lambda z: z['x'] + z['y'], in_dims=(0,))({'x': x, 'y': y})
2023-01-11T21:40:39.3945045Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:395: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3945457Z   out = vmap(lambda z: z['x'] + z['y'], in_dims=({'x': 0, 'y': 0},))({'x': x, 'y': y})
2023-01-11T21:40:39.3945810Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:399: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3946215Z   out_fn = vmap(lambda z: z['x'][0] + z['x'][1][0] + z['y'][0] + z['y'][1])
2023-01-11T21:40:39.3946411Z ok (0.005s)
2023-01-11T21:40:39.3946783Z   test_backward_unsupported_interaction (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:749: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3947165Z   vmap(backward_on_vmapped_tensor)(x)
2023-01-11T21:40:39.3947483Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:755: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3947805Z   vmap(backward_with_vmapped_grad)(x, grad)
2023-01-11T21:40:39.3948138Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:761: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3948461Z   vmap(completely_unrelated_backward)(y)
2023-01-11T21:40:39.3948642Z ok (0.005s)
2023-01-11T21:40:39.3948994Z   test_batched_gradient_basic (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:793: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3949353Z   jacobian = vmap(vjp_mul)(batched_v)
2023-01-11T21:40:39.3949530Z ok (0.001s)
2023-01-11T21:40:39.3949880Z   test_constant_function (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:64: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3950326Z   output = vmap(lambda x: torch.tensor(3.14))(torch.ones(3))
2023-01-11T21:40:39.3950541Z ok (0.001s)
2023-01-11T21:40:39.3950887Z   test_different_map_dim_size_raises (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:42: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3951243Z   vmap(torch.mul)(x, y)
2023-01-11T21:40:39.3951559Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:44: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3951871Z   vmap(lambda z: z[0] + z[1], in_dims=((0, 0),))((x, y))
2023-01-11T21:40:39.3952204Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:46: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3952613Z   vmap(lambda z: z['x'] + z['y'], in_dims=({'x': 0, 'y': 0},))({'x': x, 'y': y})
2023-01-11T21:40:39.3952827Z ok (0.001s)
2023-01-11T21:40:39.3953204Z   test_fallback_atan2 (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:555: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3953552Z   result = vmap(op, (2, 0))(x, y)
2023-01-11T21:40:39.3953938Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:561: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3954242Z   result = vmap(vmap(op), (2, 0))(x, y)
2023-01-11T21:40:39.3954643Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:567: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3954959Z   result = vmap(vmap(vmap(op)))(x, y)
2023-01-11T21:40:39.3955151Z ok (0.096s)
2023-01-11T21:40:39.3955375Z   test_fallback_does_not_warn_by_default (__main__.TestVmapAPI) ... ok (0.001s)
2023-01-11T21:40:39.3955804Z   test_fallback_masked_fill (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:583: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3956207Z   result = vmap(torch.index_add, (0, None, None, 0))(x, dim, index, values)
2023-01-11T21:40:39.3956578Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:583: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3956918Z   result = vmap(torch.index_add, (0, None, None, 0))(x, dim, index, values)
2023-01-11T21:40:39.3957147Z ok (0.075s)
2023-01-11T21:40:39.3957505Z   test_fallback_multiple_returns (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:601: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3957855Z   result = vmap(torch.var_mean)(tensor)
2023-01-11T21:40:39.3958186Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:607: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3958511Z   result = vmap(vmap(torch.var_mean))(tensor)
2023-01-11T21:40:39.3958850Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:613: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3959162Z   result = vmap(vmap(vmap(torch.var_mean)))(tensor)
2023-01-11T21:40:39.3959369Z ok (0.068s)
2023-01-11T21:40:39.3959617Z   test_fallback_warns_when_warnings_are_enabled (__main__.TestVmapAPI) ... ok (0.001s)
2023-01-11T21:40:39.3959914Z   test_fallback_with_undefined_grad (__main__.TestVmapAPI) ... ok (0.003s)
2023-01-11T21:40:39.3960330Z   test_fallback_zero_dim (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:526: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3960672Z   vmap(op, (0, None))(x, y)
2023-01-11T21:40:39.3960990Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:528: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3961280Z   vmap(op, (None, 0))(y, x)
2023-01-11T21:40:39.3961643Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:530: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3961938Z   vmap(op)(x, x)
2023-01-11T21:40:39.3962233Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:535: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3962529Z   vmap(op, (0, None))(x, y)
2023-01-11T21:40:39.3962839Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:537: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3963137Z   vmap(op, (None, 0))(y, x)
2023-01-11T21:40:39.3963433Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:539: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3963732Z   vmap(op)(x, x)
2023-01-11T21:40:39.3963902Z ok (0.015s)
2023-01-11T21:40:39.3964361Z   test_func_with_no_inputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:58: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3968310Z   vmap(foo)()
2023-01-11T21:40:39.3969109Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:61: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3969847Z   vmap(bar)()
2023-01-11T21:40:39.3970246Z ok (0.001s)
2023-01-11T21:40:39.3971120Z   test_functools_partial (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:799: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3972080Z   result = vmap(functools.partial(torch.mul, x))(y)
2023-01-11T21:40:39.3972567Z ok (0.001s)
2023-01-11T21:40:39.3973439Z   test_grad_unsupported_interaction (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:774: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3974367Z   vmap(output_to_grad_is_vmapped)(input_tensor)
2023-01-11T21:40:39.3975242Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:782: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3976083Z   vmap(input_to_grad_is_vmapped)(input_tensor)
2023-01-11T21:40:39.3976538Z ok (0.003s)
2023-01-11T21:40:39.3977475Z   test_in_dim_not_in_tensor_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:464: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3978356Z   vmap(foo)(torch.randn([]))
2023-01-11T21:40:39.3979144Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:466: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3979933Z   vmap(foo, in_dims=(0,))(torch.randn([]))
2023-01-11T21:40:39.3980734Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:468: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3981648Z   vmap(foo, in_dims=(-1,))(x)
2023-01-11T21:40:39.3982663Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:470: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3983432Z   vmap(foo, in_dims=(2,))(y)
2023-01-11T21:40:39.3984192Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:472: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3984982Z   vmap(lambda z: z[0] + z[1], in_dims=([3, 0],))([x, y])
2023-01-11T21:40:39.3985774Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:474: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3986564Z   vmap(foo, in_dims=(0,))(torch.randn(2, 3))
2023-01-11T21:40:39.3987376Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:475: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3988162Z   vmap(foo, in_dims=(1,))(torch.randn(2, 3))
2023-01-11T21:40:39.3988606Z ok (0.002s)
2023-01-11T21:40:39.3989515Z   test_in_dims_wrong_type_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:408: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3990587Z   vmap(torch.mul, [0, 0])(x, y)
2023-01-11T21:40:39.3991372Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:410: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3992120Z   vmap(torch.mul, set({0, 0}))(x, y)
2023-01-11T21:40:39.3992936Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:412: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3993895Z   vmap(torch.mul, 'lol')(x, y)
2023-01-11T21:40:39.3994668Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:414: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3995448Z   vmap(lambda z: z[0] + z[1], in_dims=[0, 0])([x, y])
2023-01-11T21:40:39.3996242Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:416: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3997137Z   vmap(torch.mul, (0, 0))(x, y)
2023-01-11T21:40:39.3997558Z ok (0.001s)
2023-01-11T21:40:39.3998469Z   test_inplace_fallback_nary_different_levels (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:708: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.3999402Z   vmap(op, in_dims=(0, None))(x, y)
2023-01-11T21:40:39.4002218Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:714: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4003291Z   vmap(vmap(op, in_dims=(0, None)))(x, y)
2023-01-11T21:40:39.4004110Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:722: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4005036Z   vmap(op, in_dims=(None, 0))(x, y)
2023-01-11T21:40:39.4005837Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:727: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4006701Z   vmap(vmap(op, in_dims=(0, None)), in_dims=(None, 0))(x, y)
2023-01-11T21:40:39.4007509Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:732: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4008302Z   vmap(vmap(op, in_dims=(0, None)), in_dims=(None, 1))(x, y)
2023-01-11T21:40:39.4009103Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:737: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4009868Z   vmap(vmap(op, in_dims=(None, 0)))(x, y)
2023-01-11T21:40:39.4010296Z ok (0.013s)
2023-01-11T21:40:39.4011142Z   test_inplace_fallback_nary_same_levels (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:673: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4012008Z   vmap(op, (2, 0))(x, y)
2023-01-11T21:40:39.4012713Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:681: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4013392Z   vmap(vmap(op), (2, 0))(x, y)
2023-01-11T21:40:39.4014097Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:689: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4014810Z   result = vmap(vmap(vmap(op)))(x, y)
2023-01-11T21:40:39.4015204Z ok (0.077s)
2023-01-11T21:40:39.4016068Z   test_inplace_fallback_unary (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:632: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4016934Z   result = vmap(op)(x)
2023-01-11T21:40:39.4017708Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:639: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4018508Z   result = vmap(op, out_dims=(1,))(x)
2023-01-11T21:40:39.4019312Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:646: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4020201Z   result = vmap(vmap(op))(x)
2023-01-11T21:40:39.4020960Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:653: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4021717Z   result = vmap(vmap(vmap(op)))(x)
2023-01-11T21:40:39.4022154Z ok (0.315s)
2023-01-11T21:40:39.4023241Z   test_integer_in_dim_but_not_tensor_input_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:447: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4023831Z   vmap(torch.sum)(x, 0)
2023-01-11T21:40:39.4024243Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:449: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4024705Z   vmap(torch.sum, (0, 0))(x, 0)
2023-01-11T21:40:39.4025146Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:451: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4025742Z   vmap(lambda z: z[0] + z[1], in_dims=([0, 0],))([x, 1])
2023-01-11T21:40:39.4026266Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:453: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4026724Z   vmap(torch.sum, (0, None))(x, 0)
2023-01-11T21:40:39.4026986Z ok (0.002s)
2023-01-11T21:40:39.4027496Z   test_multiple_inputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:79: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4028012Z   output = vmap(torch.mul)(x, y)
2023-01-11T21:40:39.4028268Z ok (0.001s)
2023-01-11T21:40:39.4028768Z   test_multiple_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:221: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4029294Z   result = vmap(foo, out_dims=(0, 1))(x)
2023-01-11T21:40:39.4029749Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:224: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4030359Z   result = vmap(bar, out_dims=(-1, 0, 1, 2))(x, y)
2023-01-11T21:40:39.4030637Z ok (0.002s)
2023-01-11T21:40:39.4031139Z   test_multiple_outputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:87: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4031629Z   outputs = vmap(foo)(x)
2023-01-11T21:40:39.4031883Z ok (0.001s)
2023-01-11T21:40:39.4032411Z   test_multiple_outputs_error_cases (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:107: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4032950Z   vmap(returns_tuple_of_tensors)(x)
2023-01-11T21:40:39.4033430Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:112: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4033956Z   vmap(returns_list_of_two_tensors)(x)
2023-01-11T21:40:39.4034438Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:114: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4034862Z   vmap(returns_list_of_one_tensor)(x)
2023-01-11T21:40:39.4035148Z ok (0.001s)
2023-01-11T21:40:39.4035690Z   test_nested_non_default_in_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:338: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4036235Z   result = vmap(vmap(vmap(torch.mul), (1, 0)), (1, 2))(x, y)
2023-01-11T21:40:39.4036526Z ok (0.001s)
2023-01-11T21:40:39.4037022Z   test_nested_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:237: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4037576Z   result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y))(y)
2023-01-11T21:40:39.4038071Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:242: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4038714Z   result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y), out_dims=1)(y)
2023-01-11T21:40:39.4039225Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:247: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4039866Z   result = vmap(lambda y: vmap(lambda x: x, out_dims=-1)(y), out_dims=-1)(y)
2023-01-11T21:40:39.4040380Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:254: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4041002Z   result = vmap(lambda y: vmap(lambda x: x * y, out_dims=1)(x), out_dims=-1)(y)
2023-01-11T21:40:39.4041320Z ok (0.003s)
2023-01-11T21:40:39.4041845Z   test_nested_with_different_map_dim (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:128: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4042407Z   output = vmap(lambda x: vmap(lambda y: x * y)(y))(x)
2023-01-11T21:40:39.4043011Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:133: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4043525Z   output = vmap(lambda x: vmap(lambda y: vmap(lambda z: x * y * z)(z))(y))(x)
2023-01-11T21:40:39.4043831Z ok (0.002s)
2023-01-11T21:40:39.4044358Z   test_nested_with_same_map_dim (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:119: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4044893Z   output = vmap(vmap(torch.mul))(x, y)
2023-01-11T21:40:39.4045323Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:122: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4045754Z   output = vmap(vmap(vmap(torch.mul)))(x, y)
2023-01-11T21:40:39.4046013Z ok (0.001s)
2023-01-11T21:40:39.4046480Z   test_nn_module (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:805: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4046982Z   result = vmap(model)(tensor)
2023-01-11T21:40:39.4047248Z ok (0.001s)
2023-01-11T21:40:39.4047768Z   test_non_default_in_dims_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:345: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4048355Z   result = vmap(lambda x: x, in_dims=1, out_dims=1)(x)
2023-01-11T21:40:39.4048848Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:350: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4049332Z   result = vmap(lambda x: x, in_dims=2, out_dims=1)(x)
2023-01-11T21:40:39.4049806Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:359: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4050234Z   result = vmap(foo, in_dims=1, out_dims=1)(x)
2023-01-11T21:40:39.4050724Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:363: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4051187Z   result = vmap(foo, in_dims=2, out_dims=1)(x)
2023-01-11T21:40:39.4051681Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:368: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4052122Z   result = vmap(vmap(foo, 1, 1), 1, 1)(x)
2023-01-11T21:40:39.4052403Z ok (0.002s)
2023-01-11T21:40:39.4052912Z   test_non_tensor_output_raises (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:29: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4053455Z   output = vmap(lambda x: 3.14)(torch.ones(3))
2023-01-11T21:40:39.4053939Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:35: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4054366Z   vmap(multiple_outputs)(torch.ones(3))
2023-01-11T21:40:39.4054624Z ok (0.001s)
2023-01-11T21:40:39.4055177Z   test_non_zero_in_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:311: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4055701Z   output = vmap(lambda x: x, (1,))(tensor)
2023-01-11T21:40:39.4056193Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:317: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4056645Z   output = vmap(torch.mul, (0, 1))(x, y)
2023-01-11T21:40:39.4057141Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:319: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4057619Z   output = vmap(torch.mul, (1, 0))(x, y)
2023-01-11T21:40:39.4057892Z ok (0.002s)
2023-01-11T21:40:39.4058380Z   test_none_in_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:327: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4058905Z   output = vmap(torch.mul, (0, None))(x, y)
2023-01-11T21:40:39.4059496Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:332: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4059940Z   output = vmap(torch.mul, (0, None))(x, 2)
2023-01-11T21:40:39.4060214Z ok (0.001s)
2023-01-11T21:40:39.4060740Z   test_nonzero_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:172: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4061267Z   result = vmap(lambda x: x, out_dims=1)(tensor)
2023-01-11T21:40:39.4061744Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:178: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4062195Z   result = vmap(lambda x: x, out_dims=2)(tensor)
2023-01-11T21:40:39.4062879Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:184: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4063493Z   result = vmap(lambda x: x, out_dims=-1)(tensor)
2023-01-11T21:40:39.4063998Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:191: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4064515Z   result = vmap(lambda x, y: (x, y), out_dims=2)(tensor, other)
2023-01-11T21:40:39.4065017Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:199: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4065464Z   result = vmap(lambda x: x, out_dims=2)(tensor)
2023-01-11T21:40:39.4065932Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:207: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4066390Z   result = vmap(foo, out_dims=1)(x, y)
2023-01-11T21:40:39.4066641Z ok (0.004s)
2023-01-11T21:40:39.4067139Z   test_noop_in_inner_vmap (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:140: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4067666Z   output = vmap(lambda x: vmap(lambda y: x)(y))(x)
2023-01-11T21:40:39.4067951Z ok (0.001s)
2023-01-11T21:40:39.4068439Z   test_not_enough_in_dims_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:424: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4068940Z   vmap(torch.mul, (0,))(x, y)
2023-01-11T21:40:39.4069394Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:426: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4069860Z   vmap(torch.mul, (0, 0, 0))(x, y)
2023-01-11T21:40:39.4070310Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:428: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4070750Z   vmap(lambda z: z[0] + z[1], in_dims=([0],))([x, y])
2023-01-11T21:40:39.4071220Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:430: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4071800Z   vmap(lambda z: z[0] + z[1], in_dims=((0, 0),))([x, y])
2023-01-11T21:40:39.4072288Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:432: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4072718Z   vmap(torch.mul, (0, 0))(x, y)
2023-01-11T21:40:39.4072981Z ok (0.001s)
2023-01-11T21:40:39.4073497Z   test_out_dim_out_of_bounds_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:303: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4074112Z   vmap(lambda x: x, out_dims=3)(x)
2023-01-11T21:40:39.4074587Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:305: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4075123Z   vmap(lambda x: x, out_dims=-4)(x)
2023-01-11T21:40:39.4075385Z ok (0.005s)
2023-01-11T21:40:39.4076037Z   test_out_dims_and_num_outputs_mismatch_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:286: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4076628Z   vmap(lambda x: x, out_dims=(0, 0))(x)
2023-01-11T21:40:39.4077144Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:288: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4077722Z   vmap(lambda x: (x, x, x), out_dims=(0, 0, 0, 0))(x)
2023-01-11T21:40:39.4078222Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:292: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4078613Z   vmap(lambda x: (x, x), out_dims=(0,))(x)
2023-01-11T21:40:39.4079090Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:294: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4079567Z   vmap(lambda x: (x, x, x), out_dims=(0, 0))(x)
2023-01-11T21:40:39.4079871Z ok (0.001s)
2023-01-11T21:40:39.4080419Z   test_out_dims_edge_case (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:264: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4080995Z   expected = vmap(foo, out_dims=1)(tensor)
2023-01-11T21:40:39.4081498Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:265: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4081967Z   result = vmap(foo, out_dims=(1,))(tensor)
2023-01-11T21:40:39.4082244Z ok (0.001s)
2023-01-11T21:40:39.4082789Z   test_out_dims_must_be_int_or_tuple_of_int_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:272: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4083457Z   vmap(lambda x: x, out_dims='lol')(tensor)
2023-01-11T21:40:39.4083945Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:274: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4084500Z   vmap(lambda x: x, out_dims=('lol',))(tensor)
2023-01-11T21:40:39.4084989Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:276: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4085449Z   vmap(lambda x: x, out_dims=None)(tensor)
2023-01-11T21:40:39.4085938Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:278: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4086395Z   vmap(lambda x: x, out_dims=(None,))(tensor)
2023-01-11T21:40:39.4086694Z ok (0.001s)
2023-01-11T21:40:39.4087183Z   test_single_input (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:73: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4087687Z   output = vmap(square)(x)
2023-01-11T21:40:39.4087964Z ok (0.001s)
2023-01-11T21:40:39.4088465Z   test_unsupported_op_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:151: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4089054Z   vmap(torch.ravel)(tensor)
2023-01-11T21:40:39.4089513Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:157: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4089978Z   vmap(out_op)(tensor, tensor)
2023-01-11T21:40:39.4090445Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:162: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4090907Z   vmap(lambda t: torch.atleast_1d([t]))(tensor)
2023-01-11T21:40:39.4091409Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:167: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4091871Z   vmap(torch.Tensor.item)(tensor)
2023-01-11T21:40:39.4092115Z ok (0.010s)
2023-01-11T21:40:39.4092428Z   test_T_numpy (__main__.TestVmapOperators) ... ok (0.005s)
2023-01-11T21:40:39.4092829Z   test_as_strided (__main__.TestVmapOperators) ... ok (0.024s)
2023-01-11T21:40:39.4093306Z   test_binary_pointwise_ops (__main__.TestVmapOperators) ... ok (0.112s)
2023-01-11T21:40:39.4093716Z   test_bmm (__main__.TestVmapOperators) ... ok (0.035s)
2023-01-11T21:40:39.4094086Z   test_cat (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:40:39.4094444Z   test_chunk (__main__.TestVmapOperators) ... ok (0.017s)
2023-01-11T21:40:39.4094817Z   test_clamp (__main__.TestVmapOperators) ... ok (0.014s)
2023-01-11T21:40:39.4095183Z   test_clone (__main__.TestVmapOperators) ... ok (0.014s)
2023-01-11T21:40:39.4095550Z   test_comparison_ops (__main__.TestVmapOperators) ... ok (0.051s)
2023-01-11T21:40:39.4095943Z   test_conj (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:40:39.4096324Z   test_contiguous (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:40:39.4096730Z   test_diagonal (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:40:39.4097108Z   test_dot (__main__.TestVmapOperators) ... ok (0.012s)
2023-01-11T21:40:39.4097510Z   test_expand_as (__main__.TestVmapOperators) ... ok (0.006s)
2023-01-11T21:40:39.4097937Z   test_fill_and_zero_inplace (__main__.TestVmapOperators) ... ok (0.014s)
2023-01-11T21:40:39.4098315Z   test_imag (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:40:39.4098688Z   test_is_complex (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:40:39.4099078Z   test_is_contiguous (__main__.TestVmapOperators) ... ok (0.006s)
2023-01-11T21:40:39.4099452Z   test_is_floating_point (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:40:39.4099847Z   test_mm (__main__.TestVmapOperators) ... ok (0.013s)
2023-01-11T21:40:39.4100237Z   test_movedim (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:40:39.4100605Z   test_mv (__main__.TestVmapOperators) ... ok (0.012s)
2023-01-11T21:40:39.4100961Z   test_narrow (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:40:39.4101345Z   test_new_empty (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:40:39.4101760Z   test_new_empty_strided (__main__.TestVmapOperators) ... ok (0.008s)
2023-01-11T21:40:39.4102160Z   test_new_zeros (__main__.TestVmapOperators) ... ok (0.002s)
2023-01-11T21:40:39.4102758Z   test_no_random_op_support (__main__.TestVmapOperators) ... ok (0.098s)
2023-01-11T21:40:39.4103154Z   test_real (__main__.TestVmapOperators) ... ok (0.008s)
2023-01-11T21:40:39.4103527Z   test_reshape (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:40:39.4103922Z   test_reshape_as (__main__.TestVmapOperators) ... ok (0.005s)
2023-01-11T21:40:39.4104304Z   test_result_type (__main__.TestVmapOperators) ... ok (0.005s)
2023-01-11T21:40:39.4104698Z   test_select (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:40:39.4105051Z   test_slice (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:40:39.4105422Z   test_split (__main__.TestVmapOperators) ... ok (0.036s)
2023-01-11T21:40:39.4105796Z   test_squeeze (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:40:39.4106155Z   test_stack (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:40:39.4106532Z   test_stride (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:40:39.4107023Z   test_sum_dim (__main__.TestVmapOperators) ... ok (0.005s)
2023-01-11T21:40:39.4107372Z   test_t (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:40:39.4107760Z   test_tensor_split (__main__.TestVmapOperators) ... ok (0.085s)
2023-01-11T21:40:39.4108160Z   test_to (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:40:39.4108548Z   test_trace (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:40:39.4108936Z   test_transpose (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:40:39.4109350Z   test_unary_pointwise_ops (__main__.TestVmapOperators) ... ok (0.092s)
2023-01-11T21:40:39.4109766Z   test_unbind (__main__.TestVmapOperators) ... ok (0.122s)
2023-01-11T21:40:39.4110129Z   test_unfold (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:40:39.4110520Z   test_view (__main__.TestVmapOperators) ... ok (0.011s)
2023-01-11T21:40:39.4110906Z   test_view_as (__main__.TestVmapOperators) ... ok (0.012s)
2023-01-11T21:40:39.4111397Z   test_view_as_complex (__main__.TestVmapOperators) ... ok (0.060s)
2023-01-11T21:40:39.4111828Z   test_view_as_real (__main__.TestVmapOperators) ... ok (0.008s)
2023-01-11T21:40:39.4112236Z   test_vmap_fallback_check (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:40:39.4112862Z   test_vmap_fallback_check_ok (__main__.TestVmapOperators) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:965: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:40:39.4113408Z   vmap(op_using_fallback)(torch.rand(3))
2023-01-11T21:40:39.4113766Z ok (0.001s)
2023-01-11T21:40:39.4113923Z 
2023-01-11T21:40:39.4114257Z ----------------------------------------------------------------------
2023-01-11T21:40:39.4114597Z Ran 95 tests in 1.702s
2023-01-11T21:40:39.4114763Z 
2023-01-11T21:40:39.4114853Z OK
2023-01-11T21:40:39.4114985Z 
2023-01-11T21:40:39.4115116Z Generating XML reports...
2023-01-11T21:40:39.4115726Z Generated XML report: test-reports/python-unittest/test_legacy_vmap/TEST-TestVmapAPI-20230111214037.xml
2023-01-11T21:40:39.4116509Z Generated XML report: test-reports/python-unittest/test_legacy_vmap/TEST-TestVmapOperators-20230111214037.xml
2023-01-11T21:40:39.4116865Z 
2023-01-11T21:40:39.4117393Z ##[endgroup]
2023-01-11T21:40:39.4117966Z FINISHED PRINTING LOG FILE of test_legacy_vmap (/var/lib/jenkins/workspace/test/test-reports/test_legacy_vmap_myjl1fm9)
2023-01-11T21:40:39.4118287Z 
2023-01-11T21:40:41.2773398Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:41.3420779Z Ignoring disabled issues:  []
2023-01-11T21:40:41.3577257Z Running test_license ... [2023-01-11 21:40:41.357400]
2023-01-11T21:40:41.3578953Z Executing ['/opt/conda/bin/python', '-bb', 'test_license.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:41.357662]
2023-01-11T21:40:43.2977694Z 
2023-01-11T21:40:43.2978741Z Expand the folded group to see the log file of test_license
2023-01-11T21:40:43.2979804Z ##[group]PRINTING LOG FILE of test_license (/var/lib/jenkins/workspace/test/test-reports/test_license_x55h2e9f)
2023-01-11T21:40:43.2980193Z 
2023-01-11T21:40:43.2980316Z Running tests...
2023-01-11T21:40:43.2980947Z ----------------------------------------------------------------------
2023-01-11T21:40:43.2981604Z Test results will be stored in test-reports/python-unittest/test_license
2023-01-11T21:40:43.2982050Z   test_distinfo_license (__main__.TestLicense)
2023-01-11T21:40:43.2982696Z If run when pytorch is installed via a wheel, the license will be in ... ok (0.228s)
2023-01-11T21:40:43.2983285Z   test_license_for_wheel (__main__.TestLicense) ... skip: can only be run in a source tree (0.000s)
2023-01-11T21:40:43.2983600Z 
2023-01-11T21:40:43.2983962Z ----------------------------------------------------------------------
2023-01-11T21:40:43.2984382Z Ran 2 tests in 0.228s
2023-01-11T21:40:43.2984567Z 
2023-01-11T21:40:43.2984681Z OK (skipped=1)
2023-01-11T21:40:43.2984860Z 
2023-01-11T21:40:43.2985000Z Generating XML reports...
2023-01-11T21:40:43.2985974Z Generated XML report: test-reports/python-unittest/test_license/TEST-TestLicense-20230111214042.xml
2023-01-11T21:40:43.2986368Z 
2023-01-11T21:40:43.2986813Z ##[endgroup]
2023-01-11T21:40:43.2987486Z FINISHED PRINTING LOG FILE of test_license (/var/lib/jenkins/workspace/test/test-reports/test_license_x55h2e9f)
2023-01-11T21:40:43.2987833Z 
2023-01-11T21:40:45.2171348Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:45.2844028Z Ignoring disabled issues:  []
2023-01-11T21:40:45.3001601Z Running test_logging ... [2023-01-11 21:40:45.299870]
2023-01-11T21:40:45.3003623Z Executing ['/opt/conda/bin/python', '-bb', 'test_logging.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:45.300140]
2023-01-11T21:40:49.0386088Z 
2023-01-11T21:40:49.0388135Z Expand the folded group to see the log file of test_logging
2023-01-11T21:40:49.0389467Z ##[group]PRINTING LOG FILE of test_logging (/var/lib/jenkins/workspace/test/test-reports/test_logging_lvz_phm2)
2023-01-11T21:40:49.0389931Z 
2023-01-11T21:40:49.0390061Z Running tests...
2023-01-11T21:40:49.0390712Z ----------------------------------------------------------------------
2023-01-11T21:40:49.0391369Z Test results will be stored in test-reports/python-unittest/test_logging
2023-01-11T21:40:49.0391845Z   testApiUsage (__main__.LoggingTest)
2023-01-11T21:40:49.0392330Z This test verifies that api usage logging is not triggered via static ... ok (2.060s)
2023-01-11T21:40:49.0392643Z 
2023-01-11T21:40:49.0393008Z ----------------------------------------------------------------------
2023-01-11T21:40:49.0393410Z Ran 1 test in 2.061s
2023-01-11T21:40:49.0393686Z 
2023-01-11T21:40:49.0393793Z OK
2023-01-11T21:40:49.0393947Z 
2023-01-11T21:40:49.0394089Z Generating XML reports...
2023-01-11T21:40:49.0394808Z Generated XML report: test-reports/python-unittest/test_logging/TEST-LoggingTest-20230111214046.xml
2023-01-11T21:40:49.0395202Z 
2023-01-11T21:40:49.0395624Z ##[endgroup]
2023-01-11T21:40:49.0396318Z FINISHED PRINTING LOG FILE of test_logging (/var/lib/jenkins/workspace/test/test-reports/test_logging_lvz_phm2)
2023-01-11T21:40:49.0396682Z 
2023-01-11T21:40:50.8934542Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:50.9592444Z Ignoring disabled issues:  []
2023-01-11T21:40:50.9747585Z Running test_maskedtensor ... [2023-01-11 21:40:50.974500]
2023-01-11T21:40:50.9749541Z Executing ['/opt/conda/bin/python', '-bb', 'test_maskedtensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:50.974746]
2023-01-11T21:40:53.9568528Z 
2023-01-11T21:40:53.9569122Z Expand the folded group to see the log file of test_maskedtensor
2023-01-11T21:40:53.9570133Z ##[group]PRINTING LOG FILE of test_maskedtensor (/var/lib/jenkins/workspace/test/test-reports/test_maskedtensor_a6eyf7pr)
2023-01-11T21:40:53.9570522Z 
2023-01-11T21:40:53.9570636Z Running tests...
2023-01-11T21:40:53.9571242Z ----------------------------------------------------------------------
2023-01-11T21:40:53.9571970Z Test results will be stored in test-reports/python-unittest/test_maskedtensor
2023-01-11T21:40:53.9573563Z   test_binary_fn_aten_add (__main__.TestBinary) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:53.9578495Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:53.9578857Z ok (0.004s)
2023-01-11T21:40:53.9579082Z   test_binary_fn_aten_arctan2 (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9579405Z   test_binary_fn_aten_atan2 (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9579680Z   test_binary_fn_aten_bitwise_and (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9579966Z   test_binary_fn_aten_bitwise_left_shift (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9580456Z   test_binary_fn_aten_bitwise_or (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9580751Z   test_binary_fn_aten_bitwise_right_shift (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9581075Z   test_binary_fn_aten_bitwise_xor (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9581347Z   test_binary_fn_aten_div (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9581665Z   test_binary_fn_aten_divide (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9581931Z   test_binary_fn_aten_eq (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9582188Z   test_binary_fn_aten_floor_divide (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9582683Z   test_binary_fn_aten_fmax (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9582948Z   test_binary_fn_aten_fmin (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9583217Z   test_binary_fn_aten_fmod (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9583586Z   test_binary_fn_aten_ge (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9583854Z   test_binary_fn_aten_greater (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9584165Z   test_binary_fn_aten_greater_equal (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9584438Z   test_binary_fn_aten_gt (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9584697Z   test_binary_fn_aten_le (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9585004Z   test_binary_fn_aten_less (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9585263Z   test_binary_fn_aten_less_equal (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9585585Z   test_binary_fn_aten_logaddexp (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9585869Z   test_binary_fn_aten_logaddexp2 (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9586127Z   test_binary_fn_aten_lt (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9586440Z   test_binary_fn_aten_maximum (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9586710Z   test_binary_fn_aten_minimum (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9586962Z   test_binary_fn_aten_mul (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9587280Z   test_binary_fn_aten_multiply (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9587547Z   test_binary_fn_aten_ne (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9587862Z   test_binary_fn_aten_nextafter (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9588126Z   test_binary_fn_aten_not_equal (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9588398Z   test_binary_fn_aten_remainder (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9588707Z   test_binary_fn_aten_sub (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9588962Z   test_binary_fn_aten_subtract (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9589238Z   test_binary_fn_aten_true_divide (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9589570Z   test_inplace_binary_fn_aten_add_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9589856Z   test_inplace_binary_fn_aten_arctan2_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9590176Z   test_inplace_binary_fn_aten_atan2_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9590468Z   test_inplace_binary_fn_aten_bitwise_and_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9590782Z   test_inplace_binary_fn_aten_bitwise_left_shift_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9591109Z   test_inplace_binary_fn_aten_bitwise_or_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9591419Z   test_inplace_binary_fn_aten_bitwise_right_shift_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9591774Z   test_inplace_binary_fn_aten_bitwise_xor_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9592062Z   test_inplace_binary_fn_aten_div_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9592376Z   test_inplace_binary_fn_aten_divide_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9592744Z   test_inplace_binary_fn_aten_eq_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9593042Z   test_inplace_binary_fn_aten_floor_divide_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9593372Z   test_inplace_binary_fn_aten_fmod_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9593710Z   test_inplace_binary_fn_aten_ge_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9594046Z   test_inplace_binary_fn_aten_greater_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9594349Z   test_inplace_binary_fn_aten_greater_equal_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9594669Z   test_inplace_binary_fn_aten_gt_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9594955Z   test_inplace_binary_fn_aten_le_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9595235Z   test_inplace_binary_fn_aten_less_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9595607Z   test_inplace_binary_fn_aten_less_equal_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9595900Z   test_inplace_binary_fn_aten_lt_ (__main__.TestBinary) ... ok (0.010s)
2023-01-11T21:40:53.9596233Z   test_inplace_binary_fn_aten_mul_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9596512Z   test_inplace_binary_fn_aten_multiply_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:53.9596797Z   test_inplace_binary_fn_aten_ne_ (__main__.TestBinary) ... ok (0.009s)
2023-01-11T21:40:53.9597136Z   test_inplace_binary_fn_aten_nextafter_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9597440Z   test_inplace_binary_fn_aten_not_equal_ (__main__.TestBinary) ... ok (0.009s)
2023-01-11T21:40:53.9597766Z   test_inplace_binary_fn_aten_remainder_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9598053Z   test_inplace_binary_fn_aten_sub_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9598343Z   test_inplace_binary_fn_aten_subtract_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9598686Z   test_inplace_binary_fn_aten_true_divide_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9598975Z   test_masks_match_fn_name_add (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9599301Z   test_masks_match_fn_name_add_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:53.9599564Z   test_all (__main__.TestReductions) ... ok (0.003s)
2023-01-11T21:40:53.9599798Z   test_amax (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9600547Z   test_amax_grad (__main__.TestReductions) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:53.9601128Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:53.9601382Z ok (0.002s)
2023-01-11T21:40:53.9601624Z   test_amin (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9601885Z   test_amin_grad (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9602152Z   test_grad_dtype (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9602770Z   test_max_not_implemented (__main__.TestReductions) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:299: UserWarning: max is not implemented in __torch_dispatch__ for MaskedTensor.
2023-01-11T21:40:53.9603399Z If you would like this operator to be supported, please file an issue for a feature request at https://github.com/pytorch/maskedtensor/issues with a minimal reproducible code snippet.
2023-01-11T21:40:53.9603927Z In the case that the semantics for the operator are not trivial, it would be appreciated to also include a proposal for the semantics.
2023-01-11T21:40:53.9604230Z   warnings.warn(msg)
2023-01-11T21:40:53.9604393Z ok (0.001s)
2023-01-11T21:40:53.9604646Z   test_mean (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9604907Z   test_mean_dim_grad (__main__.TestReductions) ... ok (0.003s)
2023-01-11T21:40:53.9605187Z   test_mean_grad_case_1a (__main__.TestReductions)
2023-01-11T21:40:53.9606049Z values.requires_grad = True ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:53.9606632Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:53.9607295Z /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:53.9607771Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:53.9608012Z ok (0.007s)
2023-01-11T21:40:53.9608259Z   test_mean_grad_case_1b (__main__.TestReductions)
2023-01-11T21:40:53.9608501Z values.requires_grad = False ... ok (0.003s)
2023-01-11T21:40:53.9608726Z   test_mean_grad_case_1c (__main__.TestReductions)
2023-01-11T21:40:53.9609582Z values.requires_grad = True ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:53.9610170Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:53.9610402Z ok (0.005s)
2023-01-11T21:40:53.9610592Z   test_mean_grad_case_1d (__main__.TestReductions)
2023-01-11T21:40:53.9610831Z values.requires_grad = False ... ok (0.001s)
2023-01-11T21:40:53.9611066Z   test_mean_grad_case_1e (__main__.TestReductions)
2023-01-11T21:40:53.9611679Z values.requires_grad = True ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:53.9612176Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:53.9612432Z ok (0.006s)
2023-01-11T21:40:53.9612633Z   test_mean_grad_case_1f (__main__.TestReductions)
2023-01-11T21:40:53.9613480Z values.requires_grad = False ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:53.9614067Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:53.9614304Z ok (0.001s)
2023-01-11T21:40:53.9614511Z   test_prod (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9615142Z   test_prod_grad (__main__.TestReductions) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:53.9615657Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:53.9615910Z ok (0.002s)
2023-01-11T21:40:53.9616103Z   test_sum (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9616350Z   test_sum_grad (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:53.9616617Z   test_inplace_unary_fn_aten_abs_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9616902Z   test_inplace_unary_fn_aten_absolute_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9617172Z   test_inplace_unary_fn_aten_acos_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9617489Z   test_inplace_unary_fn_aten_acosh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9617771Z   test_inplace_unary_fn_aten_arccos_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9618041Z   test_inplace_unary_fn_aten_arccosh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9618324Z   test_inplace_unary_fn_aten_arcsin_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9618604Z   test_inplace_unary_fn_aten_arcsinh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9618887Z   test_inplace_unary_fn_aten_arctan_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9619153Z   test_inplace_unary_fn_aten_arctanh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9619432Z   test_inplace_unary_fn_aten_asin_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9619751Z   test_inplace_unary_fn_aten_asinh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9620020Z   test_inplace_unary_fn_aten_atan_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9620328Z   test_inplace_unary_fn_aten_atanh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9620617Z   test_inplace_unary_fn_aten_bitwise_not_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9620899Z   test_inplace_unary_fn_aten_ceil_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9621160Z   test_inplace_unary_fn_aten_clamp_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9621436Z   test_inplace_unary_fn_aten_clip_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9621721Z   test_inplace_unary_fn_aten_conj_physical_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9621996Z   test_inplace_unary_fn_aten_cos_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9622269Z   test_inplace_unary_fn_aten_cosh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9622720Z   test_inplace_unary_fn_aten_deg2rad_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9622999Z   test_inplace_unary_fn_aten_digamma_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9623284Z   test_inplace_unary_fn_aten_erf_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9623561Z   test_inplace_unary_fn_aten_erfc_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9623840Z   test_inplace_unary_fn_aten_erfinv_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9624103Z   test_inplace_unary_fn_aten_exp2_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9624377Z   test_inplace_unary_fn_aten_exp_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9624657Z   test_inplace_unary_fn_aten_expm1_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9624925Z   test_inplace_unary_fn_aten_fix_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9625200Z   test_inplace_unary_fn_aten_floor_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9625475Z   test_inplace_unary_fn_aten_frac_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9625747Z   test_inplace_unary_fn_aten_i0_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9626013Z   test_inplace_unary_fn_aten_lgamma_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9626289Z   test_inplace_unary_fn_aten_log10_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9626565Z   test_inplace_unary_fn_aten_log1p_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9626827Z   test_inplace_unary_fn_aten_log2_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9627101Z   test_inplace_unary_fn_aten_log_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9627377Z   test_inplace_unary_fn_aten_logit_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9627646Z   test_inplace_unary_fn_aten_nan_to_num_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9627924Z   test_inplace_unary_fn_aten_neg_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9628200Z   test_inplace_unary_fn_aten_negative_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9628480Z   test_inplace_unary_fn_aten_pow_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9628824Z   test_inplace_unary_fn_aten_rad2deg_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9629112Z   test_inplace_unary_fn_aten_reciprocal_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9629399Z   test_inplace_unary_fn_aten_round_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9629665Z   test_inplace_unary_fn_aten_rsqrt_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9629939Z   test_inplace_unary_fn_aten_sgn_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9630217Z   test_inplace_unary_fn_aten_sigmoid_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9630498Z   test_inplace_unary_fn_aten_sign_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9630762Z   test_inplace_unary_fn_aten_sin_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9631035Z   test_inplace_unary_fn_aten_sinc_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9631309Z   test_inplace_unary_fn_aten_sinh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9631618Z   test_inplace_unary_fn_aten_sqrt_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9631893Z   test_inplace_unary_fn_aten_square_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9632169Z   test_inplace_unary_fn_aten_tan_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9632438Z   test_inplace_unary_fn_aten_tanh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9632702Z   test_inplace_unary_fn_aten_trunc_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9632963Z   test_unary_fn_aten_abs (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9633297Z   test_unary_fn_aten_absolute (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9633546Z   test_unary_fn_aten_acos (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9633857Z   test_unary_fn_aten_acosh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9634120Z   test_unary_fn_aten_angle (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9634369Z   test_unary_fn_aten_arccos (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9634632Z   test_unary_fn_aten_arccosh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9634892Z   test_unary_fn_aten_arcsin (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9635152Z   test_unary_fn_aten_arcsinh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9635395Z   test_unary_fn_aten_arctan (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9635653Z   test_unary_fn_aten_arctanh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9635913Z   test_unary_fn_aten_asin (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9636163Z   test_unary_fn_aten_asinh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9636419Z   test_unary_fn_aten_atan (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9636675Z   test_unary_fn_aten_atanh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9636929Z   test_unary_fn_aten_bitwise_not (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9637196Z   test_unary_fn_aten_ceil (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9637455Z   test_unary_fn_aten_clamp (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9637711Z   test_unary_fn_aten_clip (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9637964Z   test_unary_fn_aten_conj_physical (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9638229Z   test_unary_fn_aten_cos (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9638486Z   test_unary_fn_aten_cosh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9638731Z   test_unary_fn_aten_deg2rad (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9638997Z   test_unary_fn_aten_digamma (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9639261Z   test_unary_fn_aten_erf (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9639504Z   test_unary_fn_aten_erfc (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9639762Z   test_unary_fn_aten_erfinv (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9640071Z   test_unary_fn_aten_exp (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9640328Z   test_unary_fn_aten_exp2 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9640573Z   test_unary_fn_aten_expm1 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9640833Z   test_unary_fn_aten_fix (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9641091Z   test_unary_fn_aten_floor (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9641336Z   test_unary_fn_aten_frac (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9641590Z   test_unary_fn_aten_i0 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9641845Z   test_unary_fn_aten_isnan (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9642105Z   test_unary_fn_aten_lgamma (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9642351Z   test_unary_fn_aten_log (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9642608Z   test_unary_fn_aten_log10 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9642903Z   test_unary_fn_aten_log1p (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9643148Z   test_unary_fn_aten_log2 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9643404Z   test_unary_fn_aten_logit (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9643673Z   test_unary_fn_aten_nan_to_num (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9643924Z   test_unary_fn_aten_neg (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9644183Z   test_unary_fn_aten_negative (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9644455Z   test_unary_fn_aten_positive (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9644717Z   test_unary_fn_aten_pow (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9644966Z   test_unary_fn_aten_rad2deg (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9645237Z   test_unary_fn_aten_reciprocal (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9645506Z   test_unary_fn_aten_round (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9645755Z   test_unary_fn_aten_rsqrt (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9646011Z   test_unary_fn_aten_sgn (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9646277Z   test_unary_fn_aten_sigmoid (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9646527Z   test_unary_fn_aten_sign (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9646788Z   test_unary_fn_aten_signbit (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9647047Z   test_unary_fn_aten_sin (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9647299Z   test_unary_fn_aten_sinc (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9647539Z   test_unary_fn_aten_sinh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9647787Z   test_unary_fn_aten_sqrt (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9648044Z   test_unary_fn_aten_square (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9648289Z   test_unary_fn_aten_tan (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9648548Z   test_unary_fn_aten_tanh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9648804Z   test_unary_fn_aten_trunc (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:53.9648950Z 
2023-01-11T21:40:53.9649182Z ----------------------------------------------------------------------
2023-01-11T21:40:53.9649427Z Ran 207 tests in 0.400s
2023-01-11T21:40:53.9649541Z 
2023-01-11T21:40:53.9649603Z OK
2023-01-11T21:40:53.9649698Z 
2023-01-11T21:40:53.9649785Z Generating XML reports...
2023-01-11T21:40:53.9650187Z Generated XML report: test-reports/python-unittest/test_maskedtensor/TEST-TestBinary-20230111214053.xml
2023-01-11T21:40:53.9650699Z Generated XML report: test-reports/python-unittest/test_maskedtensor/TEST-TestReductions-20230111214053.xml
2023-01-11T21:40:53.9651196Z Generated XML report: test-reports/python-unittest/test_maskedtensor/TEST-TestUnary-20230111214053.xml
2023-01-11T21:40:53.9651411Z 
2023-01-11T21:40:53.9651706Z ##[endgroup]
2023-01-11T21:40:53.9652108Z FINISHED PRINTING LOG FILE of test_maskedtensor (/var/lib/jenkins/workspace/test/test-reports/test_maskedtensor_a6eyf7pr)
2023-01-11T21:40:53.9652373Z 
2023-01-11T21:40:55.9178702Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:56.0060127Z Ignoring disabled issues:  []
2023-01-11T21:40:56.0222171Z Running test_matmul_cuda ... [2023-01-11 21:40:56.021874]
2023-01-11T21:40:56.0225219Z Executing ['/opt/conda/bin/python', '-bb', 'test_matmul_cuda.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:56.022179]
2023-01-11T21:40:58.1514036Z 
2023-01-11T21:40:58.1515679Z Expand the folded group to see the log file of test_matmul_cuda
2023-01-11T21:40:58.1516811Z ##[group]PRINTING LOG FILE of test_matmul_cuda (/var/lib/jenkins/workspace/test/test-reports/test_matmul_cuda_tbwz3bgv)
2023-01-11T21:40:58.1517198Z 
2023-01-11T21:40:58.1517336Z Running tests...
2023-01-11T21:40:58.1517872Z ----------------------------------------------------------------------
2023-01-11T21:40:58.1518049Z 
2023-01-11T21:40:58.1518450Z ----------------------------------------------------------------------
2023-01-11T21:40:58.1518697Z Ran 0 tests in 0.000s
2023-01-11T21:40:58.1518813Z 
2023-01-11T21:40:58.1518876Z OK
2023-01-11T21:40:58.1518969Z 
2023-01-11T21:40:58.1519054Z Generating XML reports...
2023-01-11T21:40:58.1519379Z Test results will be stored in test-reports/python-unittest/test_matmul_cuda
2023-01-11T21:40:58.1519563Z 
2023-01-11T21:40:58.1519792Z ##[endgroup]
2023-01-11T21:40:58.1520168Z FINISHED PRINTING LOG FILE of test_matmul_cuda (/var/lib/jenkins/workspace/test/test-reports/test_matmul_cuda_tbwz3bgv)
2023-01-11T21:40:58.1520386Z 
2023-01-11T21:41:00.1095483Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:00.1954083Z Ignoring disabled issues:  []
2023-01-11T21:41:00.2112668Z Running test_mkl_verbose ... [2023-01-11 21:41:00.210888]
2023-01-11T21:41:00.2113771Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkl_verbose.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:00.211157]
2023-01-11T21:41:05.6023407Z 
2023-01-11T21:41:05.6023922Z Expand the folded group to see the log file of test_mkl_verbose
2023-01-11T21:41:05.6026446Z ##[group]PRINTING LOG FILE of test_mkl_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkl_verbose_nkxkp4k6)
2023-01-11T21:41:05.6026830Z 
2023-01-11T21:41:05.6027001Z Running tests...
2023-01-11T21:41:05.6027947Z ----------------------------------------------------------------------
2023-01-11T21:41:05.6028856Z Test results will be stored in test-reports/python-unittest/test_mkl_verbose
2023-01-11T21:41:05.6029579Z   test_verbose_off (__main__.TestMKLVerbose) ... ok (1.634s)
2023-01-11T21:41:05.6030191Z   test_verbose_on (__main__.TestMKLVerbose) ... ok (2.041s)
2023-01-11T21:41:05.6030540Z 
2023-01-11T21:41:05.6030992Z ----------------------------------------------------------------------
2023-01-11T21:41:05.6031555Z Ran 2 tests in 3.675s
2023-01-11T21:41:05.6031810Z 
2023-01-11T21:41:05.6031944Z OK
2023-01-11T21:41:05.6032187Z 
2023-01-11T21:41:05.6032402Z Generating XML reports...
2023-01-11T21:41:05.6033383Z Generated XML report: test-reports/python-unittest/test_mkl_verbose/TEST-TestMKLVerbose-20230111214101.xml
2023-01-11T21:41:05.6034009Z 
2023-01-11T21:41:05.6034571Z ##[endgroup]
2023-01-11T21:41:05.6035507Z FINISHED PRINTING LOG FILE of test_mkl_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkl_verbose_nkxkp4k6)
2023-01-11T21:41:05.6036026Z 
2023-01-11T21:41:07.5179798Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:07.6031373Z Ignoring disabled issues:  []
2023-01-11T21:41:07.6190798Z Running test_mkldnn ... [2023-01-11 21:41:07.618799]
2023-01-11T21:41:07.6192898Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:07.619059]
2023-01-11T21:41:23.4173516Z 
2023-01-11T21:41:23.4174037Z Expand the folded group to see the log file of inductor/test_torchinductor
2023-01-11T21:41:23.4175469Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_yykynwpa)
2023-01-11T21:41:23.4206349Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:23.4206745Z 
2023-01-11T21:41:23.4206880Z Running tests...
2023-01-11T21:41:23.4207447Z ----------------------------------------------------------------------
2023-01-11T21:41:23.4208194Z Test results will be stored in test-reports/python-unittest/inductor.test_torchinductor
2023-01-11T21:41:23.4208761Z   test_auto_simd (__main__.CPUReproTests) ... ok (0.246s)
2023-01-11T21:41:23.4209258Z   test_complex_memory_overlap (__main__.CPUReproTests) ... ok (0.002s)
2023-01-11T21:41:23.4210236Z   test_conv_stride_constraints (__main__.CPUReproTests) ... [2023-01-11 21:23:39,164] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4211363Z [2023-01-11 21:23:40,756] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4212215Z [2023-01-11 21:23:40,778] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4213053Z [2023-01-11 21:23:40,793] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4213422Z 
2023-01-11T21:41:23.4213592Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4213926Z import torch
2023-01-11T21:41:23.4214228Z import random
2023-01-11T21:41:23.4214611Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4215070Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4215346Z 
2023-01-11T21:41:23.4215483Z aten = torch.ops.aten
2023-01-11T21:41:23.4215920Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4216348Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4216581Z 
2023-01-11T21:41:23.4216588Z 
2023-01-11T21:41:23.4216834Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4217435Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4218057Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4218477Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4218805Z {
2023-01-11T21:41:23.4219131Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4219455Z     {
2023-01-11T21:41:23.4219770Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.4220130Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.4220425Z         {
2023-01-11T21:41:23.4220729Z             for(long i1=0; i1<5; i1+=1)
2023-01-11T21:41:23.4221046Z             {
2023-01-11T21:41:23.4221343Z                 #pragma GCC ivdep
2023-01-11T21:41:23.4221702Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:41:23.4222022Z                 {
2023-01-11T21:41:23.4222292Z                     {
2023-01-11T21:41:23.4222753Z                         {
2023-01-11T21:41:23.4223131Z                             auto tmp0 = in_ptr0[i2 + (256*i1) + (1280*i0)];
2023-01-11T21:41:23.4223541Z                             out_ptr0[i1 + (5*i2) + (1280*i0)] = tmp0;
2023-01-11T21:41:23.4223887Z                         }
2023-01-11T21:41:23.4224183Z                     }
2023-01-11T21:41:23.4224468Z                 }
2023-01-11T21:41:23.4224733Z             }
2023-01-11T21:41:23.4225010Z         }
2023-01-11T21:41:23.4225277Z     }
2023-01-11T21:41:23.4225523Z }
2023-01-11T21:41:23.4225822Z ''')
2023-01-11T21:41:23.4225983Z 
2023-01-11T21:41:23.4225991Z 
2023-01-11T21:41:23.4226150Z async_compile.wait(globals())
2023-01-11T21:41:23.4226481Z del async_compile
2023-01-11T21:41:23.4226672Z 
2023-01-11T21:41:23.4226797Z def call(args):
2023-01-11T21:41:23.4227117Z     inp_1, weight_1 = args
2023-01-11T21:41:23.4227426Z     args.clear()
2023-01-11T21:41:23.4228006Z     buf0 = empty_strided((2, 5, 16, 16), (1280, 1, 80, 5), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4228680Z     kernel_cpp_0(c_void_p(inp_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4229069Z     del inp_1
2023-01-11T21:41:23.4229494Z     buf1 = aten.convolution(buf0, weight_1, None, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:23.4229970Z     assert_size_stride(buf1, (2, 6, 14, 14), (1176, 1, 84, 6))
2023-01-11T21:41:23.4230298Z     del buf0
2023-01-11T21:41:23.4230596Z     del weight_1
2023-01-11T21:41:23.4230909Z     return (buf1, )
2023-01-11T21:41:23.4231102Z 
2023-01-11T21:41:23.4231111Z 
2023-01-11T21:41:23.4231243Z if __name__ == "__main__":
2023-01-11T21:41:23.4231622Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4232098Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4232778Z     inp_1 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4233443Z     weight_1 = rand_strided((6, 5, 3, 3), (45, 1, 15, 5), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4234100Z     print_performance(lambda: call([inp_1, weight_1]))
2023-01-11T21:41:23.4234366Z 
2023-01-11T21:41:23.4234372Z 
2023-01-11T21:41:23.4234542Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4234894Z import torch
2023-01-11T21:41:23.4235177Z import random
2023-01-11T21:41:23.4235563Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4236036Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4236310Z 
2023-01-11T21:41:23.4236432Z aten = torch.ops.aten
2023-01-11T21:41:23.4236871Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4237322Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4237553Z 
2023-01-11T21:41:23.4237560Z 
2023-01-11T21:41:23.4237716Z async_compile.wait(globals())
2023-01-11T21:41:23.4238045Z del async_compile
2023-01-11T21:41:23.4238236Z 
2023-01-11T21:41:23.4238361Z def call(args):
2023-01-11T21:41:23.4238682Z     inp_1, weight_1 = args
2023-01-11T21:41:23.4238987Z     args.clear()
2023-01-11T21:41:23.4239416Z     buf0 = aten.convolution(inp_1, weight_1, None, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:23.4239892Z     assert_size_stride(buf0, (2, 6, 14, 14), (1176, 196, 14, 1))
2023-01-11T21:41:23.4240227Z     del inp_1
2023-01-11T21:41:23.4240519Z     del weight_1
2023-01-11T21:41:23.4240819Z     return (buf0, )
2023-01-11T21:41:23.4241011Z 
2023-01-11T21:41:23.4241019Z 
2023-01-11T21:41:23.4241134Z if __name__ == "__main__":
2023-01-11T21:41:23.4241527Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4241999Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4242674Z     inp_1 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4243344Z     weight_1 = rand_strided((6, 5, 3, 3), (45, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4243839Z     print_performance(lambda: call([inp_1, weight_1]))
2023-01-11T21:41:23.4244104Z 
2023-01-11T21:41:23.4244223Z ok (1.655s)
2023-01-11T21:41:23.4245021Z   test_cpp_kernel_profile (__main__.CPUReproTests) ... STAGE:2023-01-11 21:23:40 1454:1454 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:41:23.4245910Z [2023-01-11 21:23:40,809] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 0
2023-01-11T21:41:23.4246741Z [2023-01-11 21:23:44,849] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 0
2023-01-11T21:41:23.4247553Z STAGE:2023-01-11 21:23:44 1454:1454 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:41:23.4248359Z STAGE:2023-01-11 21:23:44 1454:1454 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:41:23.4248722Z 
2023-01-11T21:41:23.4248891Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4249244Z import torch
2023-01-11T21:41:23.4249526Z import random
2023-01-11T21:41:23.4249912Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4250391Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4250741Z 
2023-01-11T21:41:23.4250881Z aten = torch.ops.aten
2023-01-11T21:41:23.4251313Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4251761Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4251992Z 
2023-01-11T21:41:23.4252000Z 
2023-01-11T21:41:23.4252249Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4252634Z #include <ATen/record_function.h>
2023-01-11T21:41:23.4253231Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4253851Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4254293Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4254684Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4255012Z {
2023-01-11T21:41:23.4255428Z     RECORD_FUNCTION("graph_0_kernel_cpp_0", c10::ArrayRef<c10::IValue>({}));
2023-01-11T21:41:23.4257380Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4257797Z     {
2023-01-11T21:41:23.4258100Z         #pragma omp for 
2023-01-11T21:41:23.4258424Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.4258737Z         {
2023-01-11T21:41:23.4259138Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4259633Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4260057Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4260427Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4260732Z         }
2023-01-11T21:41:23.4261062Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4261434Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:23.4261742Z         {
2023-01-11T21:41:23.4262041Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4262516Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.4262869Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4263201Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.4263518Z         }
2023-01-11T21:41:23.4263789Z     }
2023-01-11T21:41:23.4264036Z }
2023-01-11T21:41:23.4264352Z ''')
2023-01-11T21:41:23.4264519Z 
2023-01-11T21:41:23.4264527Z 
2023-01-11T21:41:23.4264683Z async_compile.wait(globals())
2023-01-11T21:41:23.4265013Z del async_compile
2023-01-11T21:41:23.4265211Z 
2023-01-11T21:41:23.4265334Z def call(args):
2023-01-11T21:41:23.4265645Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.4266946Z     args.clear()
2023-01-11T21:41:23.4267491Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4268066Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4268490Z     del arg0_1
2023-01-11T21:41:23.4268784Z     del arg1_1
2023-01-11T21:41:23.4269082Z     return (buf0, )
2023-01-11T21:41:23.4269276Z 
2023-01-11T21:41:23.4269284Z 
2023-01-11T21:41:23.4269413Z if __name__ == "__main__":
2023-01-11T21:41:23.4269789Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4270265Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4270896Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4271517Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4271999Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.4272263Z 
2023-01-11T21:41:23.4272380Z ok (4.059s)
2023-01-11T21:41:23.4272756Z   test_cpu_vec_cosim (__main__.CPUReproTests) ... ok (0.001s)
2023-01-11T21:41:23.4273673Z   test_inplace_add_alpha (__main__.CPUReproTests) ... [2023-01-11 21:23:44,873] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4274656Z [2023-01-11 21:23:46,439] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4275025Z 
2023-01-11T21:41:23.4275191Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4275629Z import torch
2023-01-11T21:41:23.4275935Z import random
2023-01-11T21:41:23.4276324Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4276784Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4277060Z 
2023-01-11T21:41:23.4277200Z aten = torch.ops.aten
2023-01-11T21:41:23.4277643Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4278090Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4278301Z 
2023-01-11T21:41:23.4278308Z 
2023-01-11T21:41:23.4278554Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4279155Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4279774Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4280199Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4280606Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4280939Z {
2023-01-11T21:41:23.4281317Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4281659Z     {
2023-01-11T21:41:23.4281955Z         #pragma omp for 
2023-01-11T21:41:23.4282275Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.4282581Z         {
2023-01-11T21:41:23.4282985Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4283497Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4283997Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.55));
2023-01-11T21:41:23.4284425Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.4284788Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.4285146Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4285470Z         }
2023-01-11T21:41:23.4285803Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4286151Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.4286467Z         {
2023-01-11T21:41:23.4286782Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.4287131Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.4287516Z             auto tmp2 = static_cast<float>(0.55);
2023-01-11T21:41:23.4287894Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.4288233Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.4288576Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.4288877Z         }
2023-01-11T21:41:23.4289127Z     }
2023-01-11T21:41:23.4289386Z }
2023-01-11T21:41:23.4289676Z ''')
2023-01-11T21:41:23.4289840Z 
2023-01-11T21:41:23.4289848Z 
2023-01-11T21:41:23.4290003Z async_compile.wait(globals())
2023-01-11T21:41:23.4290330Z del async_compile
2023-01-11T21:41:23.4290520Z 
2023-01-11T21:41:23.4290641Z def call(args):
2023-01-11T21:41:23.4290931Z     x_1, y_1 = args
2023-01-11T21:41:23.4291210Z     args.clear()
2023-01-11T21:41:23.4291662Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(y_1.data_ptr()), c_void_p(x_1.data_ptr()))
2023-01-11T21:41:23.4292083Z     del y_1
2023-01-11T21:41:23.4292371Z     return (x_1, )
2023-01-11T21:41:23.4292560Z 
2023-01-11T21:41:23.4292567Z 
2023-01-11T21:41:23.4292699Z if __name__ == "__main__":
2023-01-11T21:41:23.4293086Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4293540Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4294159Z     x_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4294776Z     y_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4295232Z     print_performance(lambda: call([x_1, y_1]))
2023-01-11T21:41:23.4295466Z 
2023-01-11T21:41:23.4295577Z ok (1.585s)
2023-01-11T21:41:23.4296406Z   test_inplace_squeeze_needed (__main__.CPUReproTests) ... [2023-01-11 21:23:46,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 1
2023-01-11T21:41:23.4297322Z [2023-01-11 21:23:48,242] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 1
2023-01-11T21:41:23.4297747Z 
2023-01-11T21:41:23.4297901Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4298247Z import torch
2023-01-11T21:41:23.4298551Z import random
2023-01-11T21:41:23.4298924Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4299398Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4299677Z 
2023-01-11T21:41:23.4299817Z aten = torch.ops.aten
2023-01-11T21:41:23.4300253Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4300687Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4300914Z 
2023-01-11T21:41:23.4300921Z 
2023-01-11T21:41:23.4301163Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4301761Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4302468Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.4302908Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.4303393Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4303812Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.4304208Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:23.4304607Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.4305002Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.4305379Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.4305764Z                        bool* __restrict__ out_ptr4)
2023-01-11T21:41:23.4306084Z {
2023-01-11T21:41:23.4306362Z     auto in_ptr0 = in_out_ptr0;
2023-01-11T21:41:23.4306714Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:23.4307029Z     {
2023-01-11T21:41:23.4307512Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.4308007Z         float tmp3 = 0;
2023-01-11T21:41:23.4308400Z         auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:23.4308837Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4309169Z         {
2023-01-11T21:41:23.4309513Z             #pragma omp for reduction(+:tmp3_vec) 
2023-01-11T21:41:23.4309895Z             for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.4310193Z             {
2023-01-11T21:41:23.4310590Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4311095Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4311511Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4311859Z                 tmp3_vec += tmp2;
2023-01-11T21:41:23.4312169Z             }
2023-01-11T21:41:23.4312672Z             tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:23.4313254Z             #pragma omp for simd simdlen(4) reduction(+:tmp3) 
2023-01-11T21:41:23.4313652Z             for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.4314032Z             {
2023-01-11T21:41:23.4314335Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4314696Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.4315057Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4315381Z                 tmp3 += tmp2;
2023-01-11T21:41:23.4315683Z             }
2023-01-11T21:41:23.4315965Z         }
2023-01-11T21:41:23.4316245Z         out_ptr0[0] = tmp3;
2023-01-11T21:41:23.4316546Z     }
2023-01-11T21:41:23.4316811Z     {
2023-01-11T21:41:23.4317279Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.4317771Z         float tmp8 = 0;
2023-01-11T21:41:23.4318165Z         auto tmp8_vec = at::vec::Vectorized<float>(tmp8);
2023-01-11T21:41:23.4318531Z         float tmp9 = 0;
2023-01-11T21:41:23.4318927Z         auto tmp9_vec = at::vec::Vectorized<float>(tmp9);
2023-01-11T21:41:23.4319361Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4319791Z         {
2023-01-11T21:41:23.4320180Z             #pragma omp for reduction(+:tmp8_vec) reduction(+:tmp9_vec) 
2023-01-11T21:41:23.4320609Z             for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.4320925Z             {
2023-01-11T21:41:23.4321319Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4321832Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4322328Z                 auto tmp3 = at::vec::Vectorized<float>(out_ptr0[0]);
2023-01-11T21:41:23.4322727Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4323183Z                 auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:23.4323617Z                 auto tmp5 = tmp3 / tmp4;
2023-01-11T21:41:23.4324088Z                 auto tmp6 = tmp2 - tmp5;
2023-01-11T21:41:23.4324435Z                 auto tmp7 = tmp6.pow(2);
2023-01-11T21:41:23.4324788Z                 tmp8_vec += tmp7;
2023-01-11T21:41:23.4325185Z                 tmp9_vec += tmp2;
2023-01-11T21:41:23.4325481Z             }
2023-01-11T21:41:23.4325999Z             tmp8 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp8_vec);
2023-01-11T21:41:23.4326712Z             tmp9 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp9_vec);
2023-01-11T21:41:23.4327332Z             #pragma omp for simd simdlen(4) reduction(+:tmp8) reduction(+:tmp9) 
2023-01-11T21:41:23.4327772Z             for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.4328096Z             {
2023-01-11T21:41:23.4328415Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4328756Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.4329116Z                 auto tmp3 = out_ptr0[0];
2023-01-11T21:41:23.4329476Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4329844Z                 auto tmp4 = static_cast<float>(10);
2023-01-11T21:41:23.4330236Z                 auto tmp5 = tmp3 / tmp4;
2023-01-11T21:41:23.4330675Z                 auto tmp6 = tmp2 - tmp5;
2023-01-11T21:41:23.4331022Z                 auto tmp7 = tmp6 * tmp6;
2023-01-11T21:41:23.4331357Z                 tmp8 += tmp7;
2023-01-11T21:41:23.4331677Z                 tmp9 += tmp2;
2023-01-11T21:41:23.4331963Z             }
2023-01-11T21:41:23.4332237Z         }
2023-01-11T21:41:23.4332531Z         out_ptr1[0] = tmp8;
2023-01-11T21:41:23.4332840Z         out_ptr2[0] = tmp9;
2023-01-11T21:41:23.4333142Z     }
2023-01-11T21:41:23.4333474Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4333802Z     {
2023-01-11T21:41:23.4334093Z         #pragma omp for 
2023-01-11T21:41:23.4334430Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:23.4334730Z         {
2023-01-11T21:41:23.4335005Z             {
2023-01-11T21:41:23.4335289Z                 {
2023-01-11T21:41:23.4335609Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.4335989Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.4336367Z                     auto tmp3 = out_ptr2[0];
2023-01-11T21:41:23.4336720Z                     auto tmp7 = out_ptr1[0];
2023-01-11T21:41:23.4337089Z                     auto tmp13 = in_ptr2[i0];
2023-01-11T21:41:23.4337461Z                     auto tmp15 = in_ptr3[i0];
2023-01-11T21:41:23.4337825Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4338204Z                     auto tmp4 = static_cast<float>(10);
2023-01-11T21:41:23.4338596Z                     auto tmp5 = tmp3 / tmp4;
2023-01-11T21:41:23.4339051Z                     auto tmp6 = tmp2 - tmp5;
2023-01-11T21:41:23.4339401Z                     auto tmp8 = tmp7 / tmp4;
2023-01-11T21:41:23.4339910Z                     auto tmp9 = static_cast<float>(1e-05);
2023-01-11T21:41:23.4340307Z                     auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:23.4340685Z                     auto tmp11 = 1 / std::sqrt(tmp10);
2023-01-11T21:41:23.4341069Z                     auto tmp12 = tmp6 * tmp11;
2023-01-11T21:41:23.4341507Z                     auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:23.4341867Z                     auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:23.4342232Z                     auto tmp17 = tmp16 * (tmp16>0);
2023-01-11T21:41:23.4342754Z                     auto tmp18 = static_cast<float>(0);
2023-01-11T21:41:23.4343149Z                     auto tmp19 = tmp17 <= tmp18;
2023-01-11T21:41:23.4343505Z                     in_out_ptr0[i0] = tmp12;
2023-01-11T21:41:23.4343859Z                     out_ptr3[i0] = tmp17;
2023-01-11T21:41:23.4344211Z                     out_ptr4[i0] = tmp19;
2023-01-11T21:41:23.4344510Z                 }
2023-01-11T21:41:23.4344791Z             }
2023-01-11T21:41:23.4345073Z         }
2023-01-11T21:41:23.4345357Z         #pragma omp single
2023-01-11T21:41:23.4345663Z         {
2023-01-11T21:41:23.4345931Z             {
2023-01-11T21:41:23.4346195Z                 {
2023-01-11T21:41:23.4346520Z                     auto tmp0 = out_ptr1[0];
2023-01-11T21:41:23.4346997Z                     auto tmp1 = static_cast<float>(10);
2023-01-11T21:41:23.4347377Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.4347889Z                     auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:41:23.4348289Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.4348662Z                     auto tmp5 = 1 / std::sqrt(tmp4);
2023-01-11T21:41:23.4349039Z                     auto tmp6 = tmp5 / tmp1;
2023-01-11T21:41:23.4349397Z                     in_out_ptr1[0] = tmp6;
2023-01-11T21:41:23.4349699Z                 }
2023-01-11T21:41:23.4349975Z             }
2023-01-11T21:41:23.4350251Z         }
2023-01-11T21:41:23.4350500Z     }
2023-01-11T21:41:23.4350753Z }
2023-01-11T21:41:23.4351039Z ''')
2023-01-11T21:41:23.4351203Z 
2023-01-11T21:41:23.4351212Z 
2023-01-11T21:41:23.4351368Z async_compile.wait(globals())
2023-01-11T21:41:23.4351694Z del async_compile
2023-01-11T21:41:23.4351885Z 
2023-01-11T21:41:23.4352012Z def call(args):
2023-01-11T21:41:23.4352431Z     primals_1, primals_2, primals_3, primals_4, primals_5 = args
2023-01-11T21:41:23.4352809Z     args.clear()
2023-01-11T21:41:23.4353350Z     buf0 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4353978Z     aten.mm.out(as_strided(primals_5, (1, 10), (10, 1)), as_strided(primals_1, (10, 10), (1, 10)), out=buf0)
2023-01-11T21:41:23.4354391Z     del primals_1
2023-01-11T21:41:23.4354922Z     buf1 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4355547Z     buf2 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4356152Z     buf3 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4356616Z     buf4 = as_strided(buf0, (10, ), (1, )); del buf0  # reuse
2023-01-11T21:41:23.4357206Z     buf5 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4357818Z     buf6 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.4358232Z     buf7 = buf2; del buf2  # reuse
2023-01-11T21:41:23.4359012Z     kernel_cpp_0(c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:41:23.4359705Z     del buf1
2023-01-11T21:41:23.4359987Z     del buf3
2023-01-11T21:41:23.4360260Z     del primals_2
2023-01-11T21:41:23.4360564Z     del primals_4
2023-01-11T21:41:23.4360990Z     return (buf5, primals_3, as_strided(primals_5, (1, 10), (10, 1)), buf4, buf6, buf7, )
2023-01-11T21:41:23.4361290Z 
2023-01-11T21:41:23.4361297Z 
2023-01-11T21:41:23.4361412Z if __name__ == "__main__":
2023-01-11T21:41:23.4361803Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4362281Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4362930Z     primals_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4363682Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4364329Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4364979Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4365609Z     primals_5 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4366177Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]))
2023-01-11T21:41:23.4366515Z 
2023-01-11T21:41:23.4366631Z ok (1.914s)
2023-01-11T21:41:23.4367484Z   test_load_same_bool_tensor_twice (__main__.CPUReproTests) ... [2023-01-11 21:23:48,373] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 2
2023-01-11T21:41:23.4368410Z [2023-01-11 21:23:50,007] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 2
2023-01-11T21:41:23.4368773Z 
2023-01-11T21:41:23.4368988Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4369333Z import torch
2023-01-11T21:41:23.4369616Z import random
2023-01-11T21:41:23.4369991Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4370460Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4370737Z 
2023-01-11T21:41:23.4370862Z aten = torch.ops.aten
2023-01-11T21:41:23.4371291Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4371736Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4371966Z 
2023-01-11T21:41:23.4371973Z 
2023-01-11T21:41:23.4372215Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4372800Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4373411Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.4373847Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4374241Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.4374641Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.4374973Z {
2023-01-11T21:41:23.4375285Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4375631Z     {
2023-01-11T21:41:23.4375927Z         #pragma omp for 
2023-01-11T21:41:23.4376244Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.4376560Z         {
2023-01-11T21:41:23.4376893Z             float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:23.4377335Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:23.4377836Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:23.4378357Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4378849Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:23.4379464Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:41:23.4379979Z             auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:41:23.4380403Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4380774Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.4381085Z         }
2023-01-11T21:41:23.4381415Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4381785Z         for(long i0=32; i0<34; i0+=1)
2023-01-11T21:41:23.4382087Z         {
2023-01-11T21:41:23.4382509Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4382869Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.4383337Z             auto tmp1 = static_cast<float>(-33.0);
2023-01-11T21:41:23.4383733Z             auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:23.4384091Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.4384405Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.4384706Z         }
2023-01-11T21:41:23.4384980Z     }
2023-01-11T21:41:23.4385231Z }
2023-01-11T21:41:23.4385525Z ''')
2023-01-11T21:41:23.4385693Z 
2023-01-11T21:41:23.4385786Z 
2023-01-11T21:41:23.4385952Z async_compile.wait(globals())
2023-01-11T21:41:23.4386290Z del async_compile
2023-01-11T21:41:23.4386483Z 
2023-01-11T21:41:23.4386609Z def call(args):
2023-01-11T21:41:23.4386926Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.4387226Z     args.clear()
2023-01-11T21:41:23.4387773Z     buf0 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4388411Z     buf1 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4389030Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.4389498Z     del arg0_1
2023-01-11T21:41:23.4389790Z     del arg1_1
2023-01-11T21:41:23.4390099Z     return (buf0, buf1, )
2023-01-11T21:41:23.4390286Z 
2023-01-11T21:41:23.4390309Z 
2023-01-11T21:41:23.4390427Z if __name__ == "__main__":
2023-01-11T21:41:23.4390819Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4391359Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4391981Z     arg0_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4392616Z     arg1_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.4393088Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.4393348Z 
2023-01-11T21:41:23.4393463Z ok (1.655s)
2023-01-11T21:41:23.4394314Z   test_masked_fill_softmax (__main__.CPUReproTests) ... [2023-01-11 21:23:50,052] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 3
2023-01-11T21:41:23.4395232Z [2023-01-11 21:23:51,707] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 3
2023-01-11T21:41:23.4395593Z 
2023-01-11T21:41:23.4395762Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4396096Z import torch
2023-01-11T21:41:23.4396397Z import random
2023-01-11T21:41:23.4396784Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4397267Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4397527Z 
2023-01-11T21:41:23.4397664Z aten = torch.ops.aten
2023-01-11T21:41:23.4398101Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4398547Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4398759Z 
2023-01-11T21:41:23.4398782Z 
2023-01-11T21:41:23.4399012Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4399604Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4400220Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.4400663Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:41:23.4401095Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4401499Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.4401892Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.4402202Z {
2023-01-11T21:41:23.4402506Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:23.4402887Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4403210Z     {
2023-01-11T21:41:23.4403499Z         #pragma omp for 
2023-01-11T21:41:23.4403837Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.4404140Z         {
2023-01-11T21:41:23.4404412Z             {
2023-01-11T21:41:23.4405298Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:23.4406155Z                 float tmp5 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.4406635Z                 auto tmp5_vec = at::vec::Vectorized<float>(tmp5);
2023-01-11T21:41:23.4407041Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.4407363Z                 {
2023-01-11T21:41:23.4407705Z                     float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:23.4408234Z                     flag_to_float(in_ptr0 + (8*i1) + (17*i0), g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:23.4408766Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:23.4409298Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:23.4409726Z                     auto tmp1 = (tmp0);
2023-01-11T21:41:23.4410321Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:41:23.4410836Z                     auto tmp4 = decltype(tmp2)::blendv(tmp3, tmp2, tmp1);
2023-01-11T21:41:23.4411295Z                     tmp5_vec = at::vec::maximum(tmp5_vec, tmp4);
2023-01-11T21:41:23.4411655Z                 }
2023-01-11T21:41:23.4412208Z                 tmp5 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp5_vec);
2023-01-11T21:41:23.4412824Z                 #pragma omp simd simdlen(4)  reduction(max:tmp5)
2023-01-11T21:41:23.4413282Z                 for(long i1=16; i1<17; i1+=1)
2023-01-11T21:41:23.4413604Z                 {
2023-01-11T21:41:23.4413948Z                     auto tmp0 = in_ptr0[i1 + (17*i0)];
2023-01-11T21:41:23.4414321Z                     auto tmp3 = in_ptr1[i1 + (17*i0)];
2023-01-11T21:41:23.4414720Z                     auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:23.4415239Z                     auto tmp2 = static_cast<float>(-33.0);
2023-01-11T21:41:23.4415628Z                     auto tmp4 = tmp1 ? tmp2 : tmp3;
2023-01-11T21:41:23.4416020Z                     tmp5 = std::max(tmp5, tmp4);
2023-01-11T21:41:23.4416348Z                 }
2023-01-11T21:41:23.4416645Z                 out_ptr0[i0] = tmp5;
2023-01-11T21:41:23.4416949Z             }
2023-01-11T21:41:23.4417225Z         }
2023-01-11T21:41:23.4417503Z         #pragma omp for 
2023-01-11T21:41:23.4417833Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.4418136Z         {
2023-01-11T21:41:23.4418394Z             {
2023-01-11T21:41:23.4418899Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.4419396Z                 float tmp8 = 0;
2023-01-11T21:41:23.4419803Z                 auto tmp8_vec = at::vec::Vectorized<float>(tmp8);
2023-01-11T21:41:23.4420193Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.4420505Z                 {
2023-01-11T21:41:23.4420853Z                     float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:23.4421296Z                     flag_to_float(in_ptr0 + (8*i1) + (17*i0), g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:23.4421816Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:23.4426175Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:23.4426720Z                     auto tmp5 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:23.4427122Z                     auto tmp1 = (tmp0);
2023-01-11T21:41:23.4427735Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:41:23.4428251Z                     auto tmp4 = decltype(tmp2)::blendv(tmp3, tmp2, tmp1);
2023-01-11T21:41:23.4428741Z                     auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:23.4429114Z                     auto tmp7 = tmp6.exp();
2023-01-11T21:41:23.4429505Z                     tmp7.store(out_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:23.4429859Z                     tmp8_vec += tmp7;
2023-01-11T21:41:23.4430170Z                 }
2023-01-11T21:41:23.4430691Z                 tmp8 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp8_vec);
2023-01-11T21:41:23.4431273Z                 #pragma omp simd simdlen(4)  reduction(+:tmp8)
2023-01-11T21:41:23.4431667Z                 for(long i1=16; i1<17; i1+=1)
2023-01-11T21:41:23.4431987Z                 {
2023-01-11T21:41:23.4432440Z                     auto tmp0 = in_ptr0[i1 + (17*i0)];
2023-01-11T21:41:23.4432824Z                     auto tmp3 = in_ptr1[i1 + (17*i0)];
2023-01-11T21:41:23.4433202Z                     auto tmp5 = out_ptr0[i0];
2023-01-11T21:41:23.4433601Z                     auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:23.4434178Z                     auto tmp2 = static_cast<float>(-33.0);
2023-01-11T21:41:23.4434583Z                     auto tmp4 = tmp1 ? tmp2 : tmp3;
2023-01-11T21:41:23.4435049Z                     auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:23.4435433Z                     auto tmp7 = std::exp(tmp6);
2023-01-11T21:41:23.4435794Z                     out_ptr1[i1 + (17*i0)] = tmp7;
2023-01-11T21:41:23.4436138Z                     tmp8 += tmp7;
2023-01-11T21:41:23.4436434Z                 }
2023-01-11T21:41:23.4436729Z                 out_ptr2[i0] = tmp8;
2023-01-11T21:41:23.4437038Z             }
2023-01-11T21:41:23.4437313Z         }
2023-01-11T21:41:23.4437591Z         #pragma omp for 
2023-01-11T21:41:23.4437993Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.4438305Z         {
2023-01-11T21:41:23.4438596Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.4438908Z             {
2023-01-11T21:41:23.4439325Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:23.4439821Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:23.4440235Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.4440633Z                 tmp2.store(in_out_ptr0 + (8*i1) + (17*i0));
2023-01-11T21:41:23.4440958Z             }
2023-01-11T21:41:23.4441289Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.4441657Z             for(long i1=16; i1<17; i1+=1)
2023-01-11T21:41:23.4441971Z             {
2023-01-11T21:41:23.4442293Z                 auto tmp0 = out_ptr1[i1 + (17*i0)];
2023-01-11T21:41:23.4442669Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:23.4443029Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.4443396Z                 in_out_ptr0[i1 + (17*i0)] = tmp2;
2023-01-11T21:41:23.4443720Z             }
2023-01-11T21:41:23.4443995Z         }
2023-01-11T21:41:23.4444244Z     }
2023-01-11T21:41:23.4444503Z }
2023-01-11T21:41:23.4444793Z ''')
2023-01-11T21:41:23.4444945Z 
2023-01-11T21:41:23.4444952Z 
2023-01-11T21:41:23.4445110Z async_compile.wait(globals())
2023-01-11T21:41:23.4445459Z del async_compile
2023-01-11T21:41:23.4445656Z 
2023-01-11T21:41:23.4445781Z def call(args):
2023-01-11T21:41:23.4446076Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.4446395Z     args.clear()
2023-01-11T21:41:23.4446936Z     buf0 = empty_strided((2, 1), (1, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4447564Z     buf1 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4448196Z     buf2 = empty_strided((2, 1), (1, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4448627Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:23.4449222Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.4449721Z     del arg0_1
2023-01-11T21:41:23.4450015Z     del arg1_1
2023-01-11T21:41:23.4450310Z     return (buf3, )
2023-01-11T21:41:23.4450488Z 
2023-01-11T21:41:23.4450512Z 
2023-01-11T21:41:23.4450626Z if __name__ == "__main__":
2023-01-11T21:41:23.4451019Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4451494Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4452112Z     arg0_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4452749Z     arg1_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.uint8)
2023-01-11T21:41:23.4453231Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.4453490Z 
2023-01-11T21:41:23.4453608Z ok (1.701s)
2023-01-11T21:41:23.4454418Z   test_new_vec_op_cpu_only (__main__.CPUReproTests) ... [2023-01-11 21:23:51,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4455418Z [2023-01-11 21:23:53,889] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4455783Z 
2023-01-11T21:41:23.4455951Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4456284Z import torch
2023-01-11T21:41:23.4456587Z import random
2023-01-11T21:41:23.4456970Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4457444Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4457706Z 
2023-01-11T21:41:23.4457842Z aten = torch.ops.aten
2023-01-11T21:41:23.4458277Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4458715Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4458943Z 
2023-01-11T21:41:23.4458950Z 
2023-01-11T21:41:23.4459184Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4459835Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4460459Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4460878Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4461212Z {
2023-01-11T21:41:23.4461533Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4461872Z     {
2023-01-11T21:41:23.4462152Z         #pragma omp for 
2023-01-11T21:41:23.4462611Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.4462929Z         {
2023-01-11T21:41:23.4463312Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4463739Z             auto tmp1 = tmp0.erf();
2023-01-11T21:41:23.4464105Z             auto tmp2 = tmp1.expm1();
2023-01-11T21:41:23.4464453Z             auto tmp3 = tmp2.log1p();
2023-01-11T21:41:23.4464821Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4465149Z         }
2023-01-11T21:41:23.4465468Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4465844Z         for(long i0=16; i0<18; i0+=1)
2023-01-11T21:41:23.4466157Z         {
2023-01-11T21:41:23.4466451Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4466819Z             auto tmp1 = std::erf(tmp0);
2023-01-11T21:41:23.4467192Z             auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:23.4467551Z             auto tmp3 = std::log1p(tmp2);
2023-01-11T21:41:23.4467912Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.4468217Z         }
2023-01-11T21:41:23.4468474Z     }
2023-01-11T21:41:23.4468736Z }
2023-01-11T21:41:23.4469027Z ''')
2023-01-11T21:41:23.4469195Z 
2023-01-11T21:41:23.4469203Z 
2023-01-11T21:41:23.4469363Z async_compile.wait(globals())
2023-01-11T21:41:23.4469698Z del async_compile
2023-01-11T21:41:23.4469890Z 
2023-01-11T21:41:23.4470017Z def call(args):
2023-01-11T21:41:23.4470311Z     x_1, = args
2023-01-11T21:41:23.4470595Z     args.clear()
2023-01-11T21:41:23.4471136Z     buf0 = empty_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4471659Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4472030Z     del x_1
2023-01-11T21:41:23.4472323Z     return (buf0, )
2023-01-11T21:41:23.4472515Z 
2023-01-11T21:41:23.4472522Z 
2023-01-11T21:41:23.4472655Z if __name__ == "__main__":
2023-01-11T21:41:23.4473028Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4473498Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4474169Z     x_1 = rand_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4474633Z     print_performance(lambda: call([x_1]))
2023-01-11T21:41:23.4474862Z 
2023-01-11T21:41:23.4474973Z ok (2.181s)
2023-01-11T21:41:23.4475777Z   test_no_op_squeeze (__main__.CPUReproTests) ... [2023-01-11 21:23:53,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 4
2023-01-11T21:41:23.4476699Z [2023-01-11 21:23:53,910] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 4
2023-01-11T21:41:23.4477156Z 
2023-01-11T21:41:23.4477308Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4477650Z import torch
2023-01-11T21:41:23.4477953Z import random
2023-01-11T21:41:23.4478328Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4478801Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4479080Z 
2023-01-11T21:41:23.4479217Z aten = torch.ops.aten
2023-01-11T21:41:23.4479655Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4480084Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4480308Z 
2023-01-11T21:41:23.4480316Z 
2023-01-11T21:41:23.4480474Z async_compile.wait(globals())
2023-01-11T21:41:23.4480818Z del async_compile
2023-01-11T21:41:23.4481008Z 
2023-01-11T21:41:23.4481120Z def call(args):
2023-01-11T21:41:23.4481423Z     arg0_1, = args
2023-01-11T21:41:23.4481716Z     args.clear()
2023-01-11T21:41:23.4482008Z     return (arg0_1, )
2023-01-11T21:41:23.4482201Z 
2023-01-11T21:41:23.4482213Z 
2023-01-11T21:41:23.4482406Z if __name__ == "__main__":
2023-01-11T21:41:23.4482797Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4483268Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4483889Z     arg0_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4484356Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.4484606Z 
2023-01-11T21:41:23.4484722Z ok (0.021s)
2023-01-11T21:41:23.4485529Z   test_parallel_num_threads (__main__.CPUReproTests) ... [2023-01-11 21:23:53,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 5
2023-01-11T21:41:23.4486450Z [2023-01-11 21:23:55,578] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 5
2023-01-11T21:41:23.4486809Z 
2023-01-11T21:41:23.4486973Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4487322Z import torch
2023-01-11T21:41:23.4487605Z import random
2023-01-11T21:41:23.4487995Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4488463Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4488723Z 
2023-01-11T21:41:23.4488859Z aten = torch.ops.aten
2023-01-11T21:41:23.4489289Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4489733Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4489958Z 
2023-01-11T21:41:23.4489966Z 
2023-01-11T21:41:23.4490197Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4490794Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4491415Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4491849Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4492236Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4492561Z {
2023-01-11T21:41:23.4492850Z     for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:23.4493135Z     {
2023-01-11T21:41:23.4493538Z         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4494036Z         auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4494430Z         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4494784Z         tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4495104Z     }
2023-01-11T21:41:23.4495401Z     #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.4495753Z     for(long i0=200; i0<200; i0+=1)
2023-01-11T21:41:23.4496054Z     {
2023-01-11T21:41:23.4496336Z         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4496677Z         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.4497018Z         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4497336Z         out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.4497628Z     }
2023-01-11T21:41:23.4497890Z }
2023-01-11T21:41:23.4498163Z ''')
2023-01-11T21:41:23.4498329Z 
2023-01-11T21:41:23.4498336Z 
2023-01-11T21:41:23.4498492Z async_compile.wait(globals())
2023-01-11T21:41:23.4498831Z del async_compile
2023-01-11T21:41:23.4499084Z 
2023-01-11T21:41:23.4499215Z def call(args):
2023-01-11T21:41:23.4499517Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.4499827Z     args.clear()
2023-01-11T21:41:23.4500376Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4500929Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4501367Z     del arg0_1
2023-01-11T21:41:23.4501661Z     del arg1_1
2023-01-11T21:41:23.4501944Z     return (buf0, )
2023-01-11T21:41:23.4502134Z 
2023-01-11T21:41:23.4502142Z 
2023-01-11T21:41:23.4502275Z if __name__ == "__main__":
2023-01-11T21:41:23.4502790Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4503255Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4503903Z     arg0_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4504545Z     arg1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4505111Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.4505360Z 
2023-01-11T21:41:23.4505472Z ok (1.671s)
2023-01-11T21:41:23.4506287Z   test_sign_cpu_only (__main__.CPUReproTests) ... [2023-01-11 21:23:55,602] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4507207Z [2023-01-11 21:23:57,231] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4507575Z 
2023-01-11T21:41:23.4507740Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4508069Z import torch
2023-01-11T21:41:23.4508375Z import random
2023-01-11T21:41:23.4508761Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4509224Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4509500Z 
2023-01-11T21:41:23.4509637Z aten = torch.ops.aten
2023-01-11T21:41:23.4510069Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4510517Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4510742Z 
2023-01-11T21:41:23.4510749Z 
2023-01-11T21:41:23.4510991Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4511594Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4512214Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4512628Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4512955Z {
2023-01-11T21:41:23.4513280Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4513598Z     {
2023-01-11T21:41:23.4513943Z         #pragma omp for 
2023-01-11T21:41:23.4514281Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.4514576Z         {
2023-01-11T21:41:23.4514973Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4515554Z             auto tmp1 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), decltype(tmp0)(0) < tmp0);
2023-01-11T21:41:23.4516182Z             auto tmp2 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), tmp0 < decltype(tmp0)(0));
2023-01-11T21:41:23.4516744Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:23.4517120Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4517448Z         }
2023-01-11T21:41:23.4517767Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4518139Z         for(long i0=16; i0<18; i0+=1)
2023-01-11T21:41:23.4518451Z         {
2023-01-11T21:41:23.4518740Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4519098Z             auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:41:23.4519457Z             auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:41:23.4519870Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:23.4520218Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.4520519Z         }
2023-01-11T21:41:23.4520776Z     }
2023-01-11T21:41:23.4521040Z }
2023-01-11T21:41:23.4521327Z ''')
2023-01-11T21:41:23.4521475Z 
2023-01-11T21:41:23.4521600Z 
2023-01-11T21:41:23.4521754Z async_compile.wait(globals())
2023-01-11T21:41:23.4522096Z del async_compile
2023-01-11T21:41:23.4522289Z 
2023-01-11T21:41:23.4522416Z def call(args):
2023-01-11T21:41:23.4522701Z     x_1, = args
2023-01-11T21:41:23.4522996Z     args.clear()
2023-01-11T21:41:23.4523533Z     buf0 = empty_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4524035Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4524425Z     del x_1
2023-01-11T21:41:23.4524719Z     return (buf0, )
2023-01-11T21:41:23.4524910Z 
2023-01-11T21:41:23.4524917Z 
2023-01-11T21:41:23.4525051Z if __name__ == "__main__":
2023-01-11T21:41:23.4525430Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4525906Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4526524Z     x_1 = rand_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4526964Z     print_performance(lambda: call([x_1]))
2023-01-11T21:41:23.4527209Z 
2023-01-11T21:41:23.4527378Z ok (1.649s)
2023-01-11T21:41:23.4527771Z   test_timed_cpu_only (__main__.CPUReproTests) ... ok (0.003s)
2023-01-11T21:41:23.4528675Z   test_vec_dynamic_shapes (__main__.CPUReproTests) ... [2023-01-11 21:23:57,396] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 6
2023-01-11T21:41:23.4529596Z [2023-01-11 21:23:59,007] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 6
2023-01-11T21:41:23.4529957Z 
2023-01-11T21:41:23.4530127Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4530471Z import torch
2023-01-11T21:41:23.4530755Z import random
2023-01-11T21:41:23.4531139Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4531613Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4531888Z 
2023-01-11T21:41:23.4532011Z aten = torch.ops.aten
2023-01-11T21:41:23.4532449Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4532906Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4533136Z 
2023-01-11T21:41:23.4533143Z 
2023-01-11T21:41:23.4533387Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4533971Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4534581Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.4535024Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4535409Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.4535801Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.4536165Z                        const long ks0,
2023-01-11T21:41:23.4536490Z                        const long ks1)
2023-01-11T21:41:23.4536794Z {
2023-01-11T21:41:23.4537099Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:23.4537464Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4537802Z     {
2023-01-11T21:41:23.4538095Z         #pragma omp for 
2023-01-11T21:41:23.4538431Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:41:23.4538744Z         {
2023-01-11T21:41:23.4539013Z             {
2023-01-11T21:41:23.4539281Z                 {
2023-01-11T21:41:23.4539868Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.4540296Z                     for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:41:23.4540630Z                     {
2023-01-11T21:41:23.4540905Z                         {
2023-01-11T21:41:23.4541266Z                             auto tmp0 = in_ptr0[i1 + (i0*ks1)];
2023-01-11T21:41:23.4541679Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:23.4542010Z                         }
2023-01-11T21:41:23.4542309Z                     }
2023-01-11T21:41:23.4542748Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.4543045Z                 }
2023-01-11T21:41:23.4543326Z             }
2023-01-11T21:41:23.4543605Z         }
2023-01-11T21:41:23.4543885Z         #pragma omp for 
2023-01-11T21:41:23.4544315Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:41:23.4544636Z         {
2023-01-11T21:41:23.4544894Z             {
2023-01-11T21:41:23.4545176Z                 {
2023-01-11T21:41:23.4545489Z                     float tmp4 = 0;
2023-01-11T21:41:23.4545834Z                     for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:41:23.4546158Z                     {
2023-01-11T21:41:23.4546446Z                         {
2023-01-11T21:41:23.4546791Z                             auto tmp0 = in_ptr0[i1 + (i0*ks1)];
2023-01-11T21:41:23.4547188Z                             auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:23.4547678Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.4548076Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:23.4548453Z                             out_ptr1[i1 + (i0*ks1)] = tmp3;
2023-01-11T21:41:23.4548808Z                             tmp4 += tmp3;
2023-01-11T21:41:23.4549120Z                         }
2023-01-11T21:41:23.4549462Z                     }
2023-01-11T21:41:23.4549786Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.4550100Z                 }
2023-01-11T21:41:23.4550361Z             }
2023-01-11T21:41:23.4550637Z         }
2023-01-11T21:41:23.4550957Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.4551305Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:41:23.4551612Z         {
2023-01-11T21:41:23.4551918Z             for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:41:23.4552221Z             {
2023-01-11T21:41:23.4552496Z                 {
2023-01-11T21:41:23.4552776Z                     {
2023-01-11T21:41:23.4553106Z                         auto tmp0 = out_ptr1[i1 + (i0*ks1)];
2023-01-11T21:41:23.4553495Z                         auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:23.4553932Z                         auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.4554307Z                         in_out_ptr0[i1 + (i0*ks1)] = tmp2;
2023-01-11T21:41:23.4554646Z                     }
2023-01-11T21:41:23.4554938Z                 }
2023-01-11T21:41:23.4555210Z             }
2023-01-11T21:41:23.4555489Z         }
2023-01-11T21:41:23.4555753Z     }
2023-01-11T21:41:23.4555999Z }
2023-01-11T21:41:23.4556288Z ''')
2023-01-11T21:41:23.4556456Z 
2023-01-11T21:41:23.4556463Z 
2023-01-11T21:41:23.4556620Z async_compile.wait(globals())
2023-01-11T21:41:23.4556965Z del async_compile
2023-01-11T21:41:23.4557141Z 
2023-01-11T21:41:23.4557266Z def call(args):
2023-01-11T21:41:23.4557567Z     arg0_1, = args
2023-01-11T21:41:23.4557862Z     args.clear()
2023-01-11T21:41:23.4558164Z     arg0_1_size = arg0_1.size()
2023-01-11T21:41:23.4558482Z     s0 = arg0_1_size[0]
2023-01-11T21:41:23.4558784Z     s1 = arg0_1_size[1]
2023-01-11T21:41:23.4559315Z     buf0 = empty_strided((s0, 1), (1, s0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4559967Z     buf1 = empty_strided((s0, s1), (s1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4560614Z     buf2 = empty_strided((s0, 1), (1, s0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4561038Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:23.4561614Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_long(s0), c_long(s1))
2023-01-11T21:41:23.4562126Z     del arg0_1
2023-01-11T21:41:23.4562407Z     return (buf3, )
2023-01-11T21:41:23.4562600Z 
2023-01-11T21:41:23.4562608Z 
2023-01-11T21:41:23.4562745Z if __name__ == "__main__":
2023-01-11T21:41:23.4563140Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4563612Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4564227Z     arg0_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4564693Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.4564944Z 
2023-01-11T21:41:23.4565062Z ok (1.777s)
2023-01-11T21:41:23.4565873Z   test_vec_kernel_cpu_only (__main__.CPUReproTests) ... [2023-01-11 21:23:59,180] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4566879Z [2023-01-11 21:23:59,944] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4567716Z [2023-01-11 21:24:00,116] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4568551Z [2023-01-11 21:24:01,715] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4569369Z [2023-01-11 21:24:01,884] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4569730Z 
2023-01-11T21:41:23.4569896Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4570239Z import torch
2023-01-11T21:41:23.4570547Z import random
2023-01-11T21:41:23.4570917Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4571394Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4571672Z 
2023-01-11T21:41:23.4571814Z aten = torch.ops.aten
2023-01-11T21:41:23.4572292Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4572742Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4572965Z 
2023-01-11T21:41:23.4572972Z 
2023-01-11T21:41:23.4573220Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4573809Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4574429Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4574867Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4575272Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4575583Z {
2023-01-11T21:41:23.4575899Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4576235Z     {
2023-01-11T21:41:23.4576512Z         #pragma omp for 
2023-01-11T21:41:23.4579337Z         for(long i0=0; i0<200; i0+=1)
2023-01-11T21:41:23.4579653Z         {
2023-01-11T21:41:23.4579909Z             {
2023-01-11T21:41:23.4580202Z                 {
2023-01-11T21:41:23.4580539Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4580898Z                     auto tmp12 = in_ptr1[i0];
2023-01-11T21:41:23.4581278Z                     auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:23.4581665Z                     auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:23.4582091Z                     auto tmp3 = -tmp2;
2023-01-11T21:41:23.4582569Z                     auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:23.4583048Z                     auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:23.4583408Z                     auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:23.4583782Z                     auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:23.4584167Z                     auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:23.4584545Z                     auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:23.4584916Z                     auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:23.4585295Z                     auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:23.4585766Z                     auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:23.4586129Z                     auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:23.4586497Z                     auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:23.4586867Z                     auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:23.4587223Z                     auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:23.4587590Z                     auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:23.4587960Z                     auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:23.4588329Z                     auto tmp20 = std::log(tmp19);
2023-01-11T21:41:23.4588722Z                     auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:23.4589112Z                     auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:23.4589505Z                     auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:23.4589891Z                     auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:23.4590296Z                     auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:23.4590790Z                     auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:23.4591153Z                     auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:23.4591624Z                     auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:23.4592000Z                     auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:23.4592343Z                     out_ptr0[i0] = tmp29;
2023-01-11T21:41:23.4592659Z                 }
2023-01-11T21:41:23.4592942Z             }
2023-01-11T21:41:23.4593205Z         }
2023-01-11T21:41:23.4593476Z     }
2023-01-11T21:41:23.4593796Z }
2023-01-11T21:41:23.4594076Z ''')
2023-01-11T21:41:23.4594245Z 
2023-01-11T21:41:23.4594253Z 
2023-01-11T21:41:23.4594409Z async_compile.wait(globals())
2023-01-11T21:41:23.4594757Z del async_compile
2023-01-11T21:41:23.4594950Z 
2023-01-11T21:41:23.4595079Z def call(args):
2023-01-11T21:41:23.4595364Z     x1_1, x2_1 = args
2023-01-11T21:41:23.4595670Z     args.clear()
2023-01-11T21:41:23.4596295Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4603579Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4604894Z     del x1_1
2023-01-11T21:41:23.4605182Z     del x2_1
2023-01-11T21:41:23.4605455Z     return (buf0, )
2023-01-11T21:41:23.4605645Z 
2023-01-11T21:41:23.4605653Z 
2023-01-11T21:41:23.4605787Z if __name__ == "__main__":
2023-01-11T21:41:23.4606183Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4608924Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4609577Z     x1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4610210Z     x2_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4610675Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:23.4610914Z 
2023-01-11T21:41:23.4610936Z 
2023-01-11T21:41:23.4611086Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4611440Z import torch
2023-01-11T21:41:23.4611742Z import random
2023-01-11T21:41:23.4612115Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4612583Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4612857Z 
2023-01-11T21:41:23.4612993Z aten = torch.ops.aten
2023-01-11T21:41:23.4618669Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4619142Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4619371Z 
2023-01-11T21:41:23.4619378Z 
2023-01-11T21:41:23.4619632Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4620224Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4620826Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4621262Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4621661Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4621974Z {
2023-01-11T21:41:23.4622313Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4622775Z     {
2023-01-11T21:41:23.4623053Z         #pragma omp for 
2023-01-11T21:41:23.4623395Z         for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:23.4623707Z         {
2023-01-11T21:41:23.4624098Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4624601Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4625022Z             auto tmp1 = tmp0.abs();
2023-01-11T21:41:23.4625377Z             auto tmp2 = tmp1.sin();
2023-01-11T21:41:23.4625715Z             auto tmp3 = tmp2.neg();
2023-01-11T21:41:23.4626061Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:23.4626501Z             auto tmp5 = decltype(tmp4)(1)/(decltype(tmp4)(1) + tmp4.neg().exp());
2023-01-11T21:41:23.4626994Z             auto tmp6 = at::vec::clamp_min(tmp5, decltype(tmp5)(0));
2023-01-11T21:41:23.4627398Z             auto tmp7 = tmp6.cos();
2023-01-11T21:41:23.4627744Z             auto tmp8 = tmp7.exp();
2023-01-11T21:41:23.4628195Z             auto tmp9 = tmp8.sqrt();
2023-01-11T21:41:23.4628551Z             auto tmp10 = tmp9 + tmp0;
2023-01-11T21:41:23.4629000Z             auto tmp12 = tmp10 - tmp11;
2023-01-11T21:41:23.4629344Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:23.4629697Z             auto tmp14 = tmp13 / tmp0;
2023-01-11T21:41:23.4630048Z             auto tmp15 = tmp14 * tmp14;
2023-01-11T21:41:23.4630401Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:23.4630738Z             auto tmp17 = tmp16 * tmp14;
2023-01-11T21:41:23.4631089Z             auto tmp18 = tmp17 * tmp17;
2023-01-11T21:41:23.4631439Z             auto tmp19 = tmp18.log();
2023-01-11T21:41:23.4631780Z             auto tmp20 = tmp19.floor();
2023-01-11T21:41:23.4632137Z             auto tmp21 = tmp20.ceil();
2023-01-11T21:41:23.4632493Z             auto tmp22 = tmp21.trunc();
2023-01-11T21:41:23.4632836Z             auto tmp23 = tmp22.lgamma();
2023-01-11T21:41:23.4633281Z             auto tmp24 = tmp23.fmod(tmp11);
2023-01-11T21:41:23.4633863Z             auto tmp25 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), decltype(tmp24)(0) < tmp24);
2023-01-11T21:41:23.4634492Z             auto tmp26 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), tmp24 < decltype(tmp24)(0));
2023-01-11T21:41:23.4635067Z             auto tmp27 = tmp25 - tmp26;
2023-01-11T21:41:23.4635430Z             auto tmp28 = tmp27 + tmp11;
2023-01-11T21:41:23.4635773Z             tmp28.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4636080Z         }
2023-01-11T21:41:23.4636407Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4636774Z         for(long i0=200; i0<200; i0+=1)
2023-01-11T21:41:23.4637069Z         {
2023-01-11T21:41:23.4637375Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4637731Z             auto tmp12 = in_ptr1[i0];
2023-01-11T21:41:23.4638075Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:23.4638435Z             auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:23.4638857Z             auto tmp3 = -tmp2;
2023-01-11T21:41:23.4639187Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:23.4639636Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:23.4639996Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:23.4640334Z             auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:23.4640705Z             auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:23.4641073Z             auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:23.4641446Z             auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:23.4641795Z             auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:23.4642235Z             auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:23.4642592Z             auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:23.4642936Z             auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:23.4643294Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:23.4643647Z             auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:23.4643993Z             auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:23.4644353Z             auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:23.4644718Z             auto tmp20 = std::log(tmp19);
2023-01-11T21:41:23.4645082Z             auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:23.4645461Z             auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:23.4645845Z             auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:23.4646215Z             auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:23.4646617Z             auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:23.4647001Z             auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:23.4647363Z             auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:23.4647786Z             auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:23.4648145Z             auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:23.4648492Z             out_ptr0[i0] = tmp29;
2023-01-11T21:41:23.4648780Z         }
2023-01-11T21:41:23.4649051Z     }
2023-01-11T21:41:23.4649312Z }
2023-01-11T21:41:23.4649583Z ''')
2023-01-11T21:41:23.4649817Z 
2023-01-11T21:41:23.4649830Z 
2023-01-11T21:41:23.4649985Z async_compile.wait(globals())
2023-01-11T21:41:23.4650113Z del async_compile
2023-01-11T21:41:23.4650121Z 
2023-01-11T21:41:23.4650245Z def call(args):
2023-01-11T21:41:23.4650368Z     x1_1, x2_1 = args
2023-01-11T21:41:23.4650482Z     args.clear()
2023-01-11T21:41:23.4650852Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4651130Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4651244Z     del x1_1
2023-01-11T21:41:23.4651355Z     del x2_1
2023-01-11T21:41:23.4651480Z     return (buf0, )
2023-01-11T21:41:23.4651488Z 
2023-01-11T21:41:23.4651495Z 
2023-01-11T21:41:23.4651626Z if __name__ == "__main__":
2023-01-11T21:41:23.4651831Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4652036Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4652442Z     x1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4652807Z     x2_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4652997Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:23.4653005Z 
2023-01-11T21:41:23.4653507Z [2023-01-11 21:24:03,604] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4653993Z [2023-01-11 21:24:03,826] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.4654478Z [2023-01-11 21:24:05,569] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.4654487Z 
2023-01-11T21:41:23.4654651Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4654758Z import torch
2023-01-11T21:41:23.4654880Z import random
2023-01-11T21:41:23.4655088Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4655312Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4655330Z 
2023-01-11T21:41:23.4655468Z aten = torch.ops.aten
2023-01-11T21:41:23.4655700Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4655864Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4655873Z 
2023-01-11T21:41:23.4655880Z 
2023-01-11T21:41:23.4656126Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4656482Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4656697Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4656879Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4657055Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4657160Z {
2023-01-11T21:41:23.4657335Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4657446Z     {
2023-01-11T21:41:23.4657564Z         #pragma omp for 
2023-01-11T21:41:23.4657707Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:23.4657824Z         {
2023-01-11T21:41:23.4657966Z             #pragma GCC ivdep
2023-01-11T21:41:23.4658115Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:23.4658228Z             {
2023-01-11T21:41:23.4658341Z                 {
2023-01-11T21:41:23.4658441Z                     {
2023-01-11T21:41:23.4658627Z                         auto tmp0 = in_ptr0[i0 + (20*i1)];
2023-01-11T21:41:23.4658804Z                         auto tmp12 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:23.4658978Z                         auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:23.4659147Z                         auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:23.4659382Z                         auto tmp3 = -tmp2;
2023-01-11T21:41:23.4659545Z                         auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:23.4659800Z                         auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:23.4659959Z                         auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:23.4660125Z                         auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:23.4660352Z                         auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:23.4660523Z                         auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:23.4660702Z                         auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:23.4660864Z                         auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:23.4661121Z                         auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:23.4661268Z                         auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:23.4661423Z                         auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:23.4661585Z                         auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:23.4661747Z                         auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:23.4661909Z                         auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:23.4662077Z                         auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:23.4662249Z                         auto tmp20 = std::log(tmp19);
2023-01-11T21:41:23.4662617Z                         auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:23.4662802Z                         auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:23.4662981Z                         auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:23.4663165Z                         auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:23.4663356Z                         auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:23.4663521Z                         auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:23.4663685Z                         auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:23.4663944Z                         auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:23.4664093Z                         auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:23.4664256Z                         out_ptr0[i0 + (20*i1)] = tmp29;
2023-01-11T21:41:23.4664368Z                     }
2023-01-11T21:41:23.4664482Z                 }
2023-01-11T21:41:23.4664597Z             }
2023-01-11T21:41:23.4664708Z         }
2023-01-11T21:41:23.4664818Z     }
2023-01-11T21:41:23.4664912Z }
2023-01-11T21:41:23.4665049Z ''')
2023-01-11T21:41:23.4665058Z 
2023-01-11T21:41:23.4665065Z 
2023-01-11T21:41:23.4665227Z async_compile.wait(globals())
2023-01-11T21:41:23.4665359Z del async_compile
2023-01-11T21:41:23.4665367Z 
2023-01-11T21:41:23.4665489Z def call(args):
2023-01-11T21:41:23.4665609Z     x1_1, x2_1 = args
2023-01-11T21:41:23.4665730Z     args.clear()
2023-01-11T21:41:23.4666077Z     buf0 = empty_strided((20, 10), (1, 20), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4666354Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4666471Z     del x1_1
2023-01-11T21:41:23.4666581Z     del x2_1
2023-01-11T21:41:23.4666707Z     return (buf0, )
2023-01-11T21:41:23.4666715Z 
2023-01-11T21:41:23.4666723Z 
2023-01-11T21:41:23.4666858Z if __name__ == "__main__":
2023-01-11T21:41:23.4667379Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4667600Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4667949Z     x1_1 = rand_strided((20, 10), (1, 20), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4668302Z     x2_1 = rand_strided((20, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4668495Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:23.4668503Z 
2023-01-11T21:41:23.4668510Z 
2023-01-11T21:41:23.4668680Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4668800Z import torch
2023-01-11T21:41:23.4668924Z import random
2023-01-11T21:41:23.4669132Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4669351Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4669359Z 
2023-01-11T21:41:23.4669480Z aten = torch.ops.aten
2023-01-11T21:41:23.4669720Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4669884Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4669891Z 
2023-01-11T21:41:23.4669899Z 
2023-01-11T21:41:23.4670137Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4670605Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4670816Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4671000Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.4671176Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4671274Z {
2023-01-11T21:41:23.4671448Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4671554Z     {
2023-01-11T21:41:23.4671693Z         #pragma omp for 
2023-01-11T21:41:23.4671835Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.4671945Z         {
2023-01-11T21:41:23.4672185Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4672404Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.4672549Z             auto tmp1 = tmp0.abs();
2023-01-11T21:41:23.4672696Z             auto tmp2 = tmp1.sin();
2023-01-11T21:41:23.4672892Z             auto tmp3 = tmp2.neg();
2023-01-11T21:41:23.4673043Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:23.4673283Z             auto tmp5 = decltype(tmp4)(1)/(decltype(tmp4)(1) + tmp4.neg().exp());
2023-01-11T21:41:23.4673503Z             auto tmp6 = at::vec::clamp_min(tmp5, decltype(tmp5)(0));
2023-01-11T21:41:23.4673635Z             auto tmp7 = tmp6.cos();
2023-01-11T21:41:23.4673835Z             auto tmp8 = tmp7.exp();
2023-01-11T21:41:23.4673987Z             auto tmp9 = tmp8.sqrt();
2023-01-11T21:41:23.4674132Z             auto tmp10 = tmp9 + tmp0;
2023-01-11T21:41:23.4674367Z             auto tmp12 = tmp10 - tmp11;
2023-01-11T21:41:23.4674516Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:23.4674661Z             auto tmp14 = tmp13 / tmp0;
2023-01-11T21:41:23.4674794Z             auto tmp15 = tmp14 * tmp14;
2023-01-11T21:41:23.4674939Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:23.4675087Z             auto tmp17 = tmp16 * tmp14;
2023-01-11T21:41:23.4675243Z             auto tmp18 = tmp17 * tmp17;
2023-01-11T21:41:23.4675393Z             auto tmp19 = tmp18.log();
2023-01-11T21:41:23.4675546Z             auto tmp20 = tmp19.floor();
2023-01-11T21:41:23.4675694Z             auto tmp21 = tmp20.ceil();
2023-01-11T21:41:23.4675822Z             auto tmp22 = tmp21.trunc();
2023-01-11T21:41:23.4675980Z             auto tmp23 = tmp22.lgamma();
2023-01-11T21:41:23.4676144Z             auto tmp24 = tmp23.fmod(tmp11);
2023-01-11T21:41:23.4676453Z             auto tmp25 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), decltype(tmp24)(0) < tmp24);
2023-01-11T21:41:23.4676760Z             auto tmp26 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), tmp24 < decltype(tmp24)(0));
2023-01-11T21:41:23.4676994Z             auto tmp27 = tmp25 - tmp26;
2023-01-11T21:41:23.4677141Z             auto tmp28 = tmp27 + tmp11;
2023-01-11T21:41:23.4677297Z             tmp28.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4677399Z         }
2023-01-11T21:41:23.4677574Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4677717Z         for(long i0=64; i0<70; i0+=1)
2023-01-11T21:41:23.4677828Z         {
2023-01-11T21:41:23.4677974Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4678123Z             auto tmp12 = in_ptr1[i0];
2023-01-11T21:41:23.4678278Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:23.4678417Z             auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:23.4678620Z             auto tmp3 = -tmp2;
2023-01-11T21:41:23.4678767Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:23.4679006Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:23.4679149Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:23.4679297Z             auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:23.4679447Z             auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:23.4679587Z             auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:23.4679744Z             auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:23.4679891Z             auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:23.4680166Z             auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:23.4680312Z             auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:23.4680456Z             auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:23.4680600Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:23.4680730Z             auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:23.4680873Z             auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:23.4681014Z             auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:23.4681171Z             auto tmp20 = std::log(tmp19);
2023-01-11T21:41:23.4681336Z             auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:23.4681495Z             auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:23.4681657Z             auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:23.4681826Z             auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:23.4681986Z             auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:23.4682137Z             auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:23.4682337Z             auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:23.4682566Z             auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:23.4682714Z             auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:23.4682852Z             out_ptr0[i0] = tmp29;
2023-01-11T21:41:23.4682959Z         }
2023-01-11T21:41:23.4683051Z     }
2023-01-11T21:41:23.4683152Z }
2023-01-11T21:41:23.4683289Z ''')
2023-01-11T21:41:23.4683298Z 
2023-01-11T21:41:23.4683305Z 
2023-01-11T21:41:23.4683464Z async_compile.wait(globals())
2023-01-11T21:41:23.4683590Z del async_compile
2023-01-11T21:41:23.4683598Z 
2023-01-11T21:41:23.4683722Z def call(args):
2023-01-11T21:41:23.4683841Z     x1_1, x2_1 = args
2023-01-11T21:41:23.4683950Z     args.clear()
2023-01-11T21:41:23.4684304Z     buf0 = empty_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4684581Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4684693Z     del x1_1
2023-01-11T21:41:23.4684807Z     del x2_1
2023-01-11T21:41:23.4684930Z     return (buf0, )
2023-01-11T21:41:23.4684938Z 
2023-01-11T21:41:23.4684945Z 
2023-01-11T21:41:23.4685076Z if __name__ == "__main__":
2023-01-11T21:41:23.4685273Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4685479Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4685829Z     x1_1 = rand_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4686176Z     x2_1 = rand_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4686369Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:23.4686377Z 
2023-01-11T21:41:23.4686490Z ok (6.559s)
2023-01-11T21:41:23.4687357Z   test_abs_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.4687585Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.4688065Z [2023-01-11 21:24:05,590] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 7
2023-01-11T21:41:23.4688554Z [2023-01-11 21:24:07,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 7
2023-01-11T21:41:23.4688563Z 
2023-01-11T21:41:23.4688713Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4688829Z import torch
2023-01-11T21:41:23.4688956Z import random
2023-01-11T21:41:23.4689161Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4689377Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4689385Z 
2023-01-11T21:41:23.4689521Z aten = torch.ops.aten
2023-01-11T21:41:23.4689761Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4689956Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4689978Z 
2023-01-11T21:41:23.4689986Z 
2023-01-11T21:41:23.4690214Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4690588Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4690799Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4690971Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.4691079Z {
2023-01-11T21:41:23.4691254Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4691359Z     {
2023-01-11T21:41:23.4691477Z         #pragma omp for 
2023-01-11T21:41:23.4691618Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.4691725Z         {
2023-01-11T21:41:23.4691956Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4692105Z             auto tmp1 = tmp0.abs();
2023-01-11T21:41:23.4692380Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.4692530Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.4692661Z             auto tmp4 = tmp0 / tmp3;
2023-01-11T21:41:23.4692824Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4692932Z         }
2023-01-11T21:41:23.4693102Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4693248Z         for(long i0=16; i0<17; i0+=1)
2023-01-11T21:41:23.4693356Z         {
2023-01-11T21:41:23.4693501Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4693641Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:23.4693815Z             auto tmp2 = static_cast<float>(1);
2023-01-11T21:41:23.4693960Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.4694105Z             auto tmp4 = tmp0 / tmp3;
2023-01-11T21:41:23.4694247Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.4694358Z         }
2023-01-11T21:41:23.4694467Z     }
2023-01-11T21:41:23.4694556Z }
2023-01-11T21:41:23.4694692Z ''')
2023-01-11T21:41:23.4694705Z 
2023-01-11T21:41:23.4694716Z 
2023-01-11T21:41:23.4694872Z async_compile.wait(globals())
2023-01-11T21:41:23.4695001Z del async_compile
2023-01-11T21:41:23.4695009Z 
2023-01-11T21:41:23.4695131Z def call(args):
2023-01-11T21:41:23.4695250Z     arg0_1, = args
2023-01-11T21:41:23.4695372Z     args.clear()
2023-01-11T21:41:23.4695699Z     buf0 = empty_strided((17, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4695932Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.4696054Z     del arg0_1
2023-01-11T21:41:23.4696179Z     return (buf0, )
2023-01-11T21:41:23.4696187Z 
2023-01-11T21:41:23.4696194Z 
2023-01-11T21:41:23.4696330Z if __name__ == "__main__":
2023-01-11T21:41:23.4696531Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4696754Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4697117Z     arg0_1 = rand_strided((17, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4697298Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.4697325Z 
2023-01-11T21:41:23.4697424Z ok (1.619s)
2023-01-11T21:41:23.4698327Z   test_adaptive_avg_pool2d1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.4698551Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.4699025Z [2023-01-11 21:24:07,219] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 8
2023-01-11T21:41:23.4699467Z [2023-01-11 21:24:07,228] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._adaptive_avg_pool2d
2023-01-11T21:41:23.4699475Z 
2023-01-11T21:41:23.4699642Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4699820Z import torch
2023-01-11T21:41:23.4699945Z import random
2023-01-11T21:41:23.4700137Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4700355Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4700363Z 
2023-01-11T21:41:23.4700501Z aten = torch.ops.aten
2023-01-11T21:41:23.4700743Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4700908Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4700916Z 
2023-01-11T21:41:23.4700923Z 
2023-01-11T21:41:23.4701166Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4701535Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4701746Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4701920Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.4702078Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.4702187Z {
2023-01-11T21:41:23.4702554Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4702665Z     {
2023-01-11T21:41:23.4702801Z         #pragma omp for 
2023-01-11T21:41:23.4702945Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.4703038Z         {
2023-01-11T21:41:23.4703177Z             #pragma GCC ivdep
2023-01-11T21:41:23.4703326Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:23.4703438Z             {
2023-01-11T21:41:23.4703582Z                 #pragma GCC ivdep
2023-01-11T21:41:23.4703736Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:41:23.4703845Z                 {
2023-01-11T21:41:23.4703947Z                     {
2023-01-11T21:41:23.4704066Z                         {
2023-01-11T21:41:23.4704268Z                             auto tmp0 = static_cast<long>(((8*i1) / 3));
2023-01-11T21:41:23.4704480Z                             auto tmp1 = static_cast<long>(((21 + (16*i1)) / 6));
2023-01-11T21:41:23.4704647Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:23.4704860Z                             auto tmp3 = static_cast<long>(((8*i2) / 3));
2023-01-11T21:41:23.4705067Z                             auto tmp4 = static_cast<long>(((21 + (16*i2)) / 6));
2023-01-11T21:41:23.4705232Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:23.4705381Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:23.4705539Z                             float tmp7 = 0.0;
2023-01-11T21:41:23.4705671Z                             if(tmp6)
2023-01-11T21:41:23.4705801Z                             {
2023-01-11T21:41:23.4706014Z                                 auto tmp8 = in_ptr0[(16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4706164Z                                 tmp7 = tmp8;
2023-01-11T21:41:23.4706287Z                             }
2023-01-11T21:41:23.4706478Z                             auto tmp9 = static_cast<long>(1 + (((8*i2) / 3)));
2023-01-11T21:41:23.4706645Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:23.4706823Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:23.4706977Z                             float tmp12 = 0.0;
2023-01-11T21:41:23.4707120Z                             if(tmp11)
2023-01-11T21:41:23.4707245Z                             {
2023-01-11T21:41:23.4707461Z                                 auto tmp13 = in_ptr0[1 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4707613Z                                 tmp12 = tmp13;
2023-01-11T21:41:23.4707718Z                             }
2023-01-11T21:41:23.4707886Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:23.4708093Z                             auto tmp15 = static_cast<long>(2 + (((8*i2) / 3)));
2023-01-11T21:41:23.4708262Z                             auto tmp16 = tmp15 < tmp4;
2023-01-11T21:41:23.4708428Z                             auto tmp17 = tmp2 & tmp16;
2023-01-11T21:41:23.4708584Z                             float tmp18 = 0.0;
2023-01-11T21:41:23.4708721Z                             if(tmp17)
2023-01-11T21:41:23.4708894Z                             {
2023-01-11T21:41:23.4709113Z                                 auto tmp19 = in_ptr0[2 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4709264Z                                 tmp18 = tmp19;
2023-01-11T21:41:23.4709385Z                             }
2023-01-11T21:41:23.4822785Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:23.4823113Z                             auto tmp21 = static_cast<long>(3 + (((8*i2) / 3)));
2023-01-11T21:41:23.4823291Z                             auto tmp22 = tmp21 < tmp4;
2023-01-11T21:41:23.4823460Z                             auto tmp23 = tmp2 & tmp22;
2023-01-11T21:41:23.4823615Z                             float tmp24 = 0.0;
2023-01-11T21:41:23.4823733Z                             if(tmp23)
2023-01-11T21:41:23.4823852Z                             {
2023-01-11T21:41:23.4824071Z                                 auto tmp25 = in_ptr0[3 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4824401Z                                 tmp24 = tmp25;
2023-01-11T21:41:23.4824524Z                             }
2023-01-11T21:41:23.4824696Z                             auto tmp26 = tmp24 + tmp20;
2023-01-11T21:41:23.4824902Z                             auto tmp27 = static_cast<long>(1 + (((8*i1) / 3)));
2023-01-11T21:41:23.4825056Z                             auto tmp28 = tmp27 < tmp1;
2023-01-11T21:41:23.4825220Z                             auto tmp29 = tmp28 & tmp5;
2023-01-11T21:41:23.4825373Z                             float tmp30 = 0.0;
2023-01-11T21:41:23.4825508Z                             if(tmp29)
2023-01-11T21:41:23.4825630Z                             {
2023-01-11T21:41:23.4825847Z                                 auto tmp31 = in_ptr0[16 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4825997Z                                 tmp30 = tmp31;
2023-01-11T21:41:23.4826118Z                             }
2023-01-11T21:41:23.4826280Z                             auto tmp32 = tmp30 + tmp26;
2023-01-11T21:41:23.4826448Z                             auto tmp33 = tmp28 & tmp10;
2023-01-11T21:41:23.4826602Z                             float tmp34 = 0.0;
2023-01-11T21:41:23.4826735Z                             if(tmp33)
2023-01-11T21:41:23.4826854Z                             {
2023-01-11T21:41:23.4827068Z                                 auto tmp35 = in_ptr0[17 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4827216Z                                 tmp34 = tmp35;
2023-01-11T21:41:23.4827325Z                             }
2023-01-11T21:41:23.4827491Z                             auto tmp36 = tmp34 + tmp32;
2023-01-11T21:41:23.4827655Z                             auto tmp37 = tmp28 & tmp16;
2023-01-11T21:41:23.4827806Z                             float tmp38 = 0.0;
2023-01-11T21:41:23.4827938Z                             if(tmp37)
2023-01-11T21:41:23.4828059Z                             {
2023-01-11T21:41:23.4828279Z                                 auto tmp39 = in_ptr0[18 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4828417Z                                 tmp38 = tmp39;
2023-01-11T21:41:23.4828535Z                             }
2023-01-11T21:41:23.4828702Z                             auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:23.4828869Z                             auto tmp41 = tmp28 & tmp22;
2023-01-11T21:41:23.4829024Z                             float tmp42 = 0.0;
2023-01-11T21:41:23.4829158Z                             if(tmp41)
2023-01-11T21:41:23.4829278Z                             {
2023-01-11T21:41:23.4829490Z                                 auto tmp43 = in_ptr0[19 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4829625Z                                 tmp42 = tmp43;
2023-01-11T21:41:23.4829744Z                             }
2023-01-11T21:41:23.4829912Z                             auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:23.4830118Z                             auto tmp45 = static_cast<long>(2 + (((8*i1) / 3)));
2023-01-11T21:41:23.4830361Z                             auto tmp46 = tmp45 < tmp1;
2023-01-11T21:41:23.4830526Z                             auto tmp47 = tmp46 & tmp5;
2023-01-11T21:41:23.4830677Z                             float tmp48 = 0.0;
2023-01-11T21:41:23.4830797Z                             if(tmp47)
2023-01-11T21:41:23.4830915Z                             {
2023-01-11T21:41:23.4831132Z                                 auto tmp49 = in_ptr0[32 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4831393Z                                 tmp48 = tmp49;
2023-01-11T21:41:23.4831514Z                             }
2023-01-11T21:41:23.4831683Z                             auto tmp50 = tmp48 + tmp44;
2023-01-11T21:41:23.4831850Z                             auto tmp51 = tmp46 & tmp10;
2023-01-11T21:41:23.4831987Z                             float tmp52 = 0.0;
2023-01-11T21:41:23.4832121Z                             if(tmp51)
2023-01-11T21:41:23.4832295Z                             {
2023-01-11T21:41:23.4832509Z                                 auto tmp53 = in_ptr0[33 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4832658Z                                 tmp52 = tmp53;
2023-01-11T21:41:23.4832783Z                             }
2023-01-11T21:41:23.4832948Z                             auto tmp54 = tmp52 + tmp50;
2023-01-11T21:41:23.4833116Z                             auto tmp55 = tmp46 & tmp16;
2023-01-11T21:41:23.4833252Z                             float tmp56 = 0.0;
2023-01-11T21:41:23.4833384Z                             if(tmp55)
2023-01-11T21:41:23.4833500Z                             {
2023-01-11T21:41:23.4833705Z                                 auto tmp57 = in_ptr0[34 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4833915Z                                 tmp56 = tmp57;
2023-01-11T21:41:23.4834029Z                             }
2023-01-11T21:41:23.4834191Z                             auto tmp58 = tmp56 + tmp54;
2023-01-11T21:41:23.4834352Z                             auto tmp59 = tmp46 & tmp22;
2023-01-11T21:41:23.4834505Z                             float tmp60 = 0.0;
2023-01-11T21:41:23.4834637Z                             if(tmp59)
2023-01-11T21:41:23.4834757Z                             {
2023-01-11T21:41:23.4834971Z                                 auto tmp61 = in_ptr0[35 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4835118Z                                 tmp60 = tmp61;
2023-01-11T21:41:23.4835241Z                             }
2023-01-11T21:41:23.4835391Z                             auto tmp62 = tmp60 + tmp58;
2023-01-11T21:41:23.4835597Z                             auto tmp63 = static_cast<long>(3 + (((8*i1) / 3)));
2023-01-11T21:41:23.4835763Z                             auto tmp64 = tmp63 < tmp1;
2023-01-11T21:41:23.4835929Z                             auto tmp65 = tmp64 & tmp5;
2023-01-11T21:41:23.4836081Z                             float tmp66 = 0.0;
2023-01-11T21:41:23.4836221Z                             if(tmp65)
2023-01-11T21:41:23.4836341Z                             {
2023-01-11T21:41:23.4836551Z                                 auto tmp67 = in_ptr0[48 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4836683Z                                 tmp66 = tmp67;
2023-01-11T21:41:23.4836803Z                             }
2023-01-11T21:41:23.4836970Z                             auto tmp68 = tmp66 + tmp62;
2023-01-11T21:41:23.4837132Z                             auto tmp69 = tmp64 & tmp10;
2023-01-11T21:41:23.4837278Z                             float tmp70 = 0.0;
2023-01-11T21:41:23.4837403Z                             if(tmp69)
2023-01-11T21:41:23.4837516Z                             {
2023-01-11T21:41:23.4837716Z                                 auto tmp71 = in_ptr0[49 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4837860Z                                 tmp70 = tmp71;
2023-01-11T21:41:23.4837979Z                             }
2023-01-11T21:41:23.4838206Z                             auto tmp72 = tmp70 + tmp68;
2023-01-11T21:41:23.4838373Z                             auto tmp73 = tmp64 & tmp16;
2023-01-11T21:41:23.4838527Z                             float tmp74 = 0.0;
2023-01-11T21:41:23.4838657Z                             if(tmp73)
2023-01-11T21:41:23.4838768Z                             {
2023-01-11T21:41:23.4838979Z                                 auto tmp75 = in_ptr0[50 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4839126Z                                 tmp74 = tmp75;
2023-01-11T21:41:23.4839243Z                             }
2023-01-11T21:41:23.4839409Z                             auto tmp76 = tmp74 + tmp72;
2023-01-11T21:41:23.4839573Z                             auto tmp77 = tmp64 & tmp22;
2023-01-11T21:41:23.4839723Z                             float tmp78 = 0.0;
2023-01-11T21:41:23.4839856Z                             if(tmp77)
2023-01-11T21:41:23.4839965Z                             {
2023-01-11T21:41:23.4840229Z                                 auto tmp79 = in_ptr0[51 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:23.4840379Z                                 tmp78 = tmp79;
2023-01-11T21:41:23.4840498Z                             }
2023-01-11T21:41:23.4840664Z                             auto tmp80 = tmp78 + tmp76;
2023-01-11T21:41:23.4840816Z                             float tmp81 = 0.0;
2023-01-11T21:41:23.4840945Z                             if(tmp6)
2023-01-11T21:41:23.4841051Z                             {
2023-01-11T21:41:23.4841242Z                                 auto tmp82 = static_cast<float>(1);
2023-01-11T21:41:23.4841392Z                                 tmp81 = tmp82;
2023-01-11T21:41:23.4841510Z                             }
2023-01-11T21:41:23.4841660Z                             float tmp83 = 0.0;
2023-01-11T21:41:23.4841794Z                             if(tmp11)
2023-01-11T21:41:23.4841913Z                             {
2023-01-11T21:41:23.4842102Z                                 auto tmp84 = static_cast<float>(1);
2023-01-11T21:41:23.4842251Z                                 tmp83 = tmp84;
2023-01-11T21:41:23.4842372Z                             }
2023-01-11T21:41:23.4842539Z                             auto tmp85 = tmp83 + tmp81;
2023-01-11T21:41:23.4842690Z                             float tmp86 = 0.0;
2023-01-11T21:41:23.4842822Z                             if(tmp17)
2023-01-11T21:41:23.4842945Z                             {
2023-01-11T21:41:23.4843122Z                                 auto tmp87 = static_cast<float>(1);
2023-01-11T21:41:23.4843269Z                                 tmp86 = tmp87;
2023-01-11T21:41:23.4843392Z                             }
2023-01-11T21:41:23.4843560Z                             auto tmp88 = tmp86 + tmp85;
2023-01-11T21:41:23.4843711Z                             float tmp89 = 0.0;
2023-01-11T21:41:23.4843842Z                             if(tmp23)
2023-01-11T21:41:23.4843965Z                             {
2023-01-11T21:41:23.4844148Z                                 auto tmp90 = static_cast<float>(1);
2023-01-11T21:41:23.4844295Z                                 tmp89 = tmp90;
2023-01-11T21:41:23.4844414Z                             }
2023-01-11T21:41:23.4844579Z                             auto tmp91 = tmp89 + tmp88;
2023-01-11T21:41:23.4844732Z                             float tmp92 = 0.0;
2023-01-11T21:41:23.4844865Z                             if(tmp29)
2023-01-11T21:41:23.4844982Z                             {
2023-01-11T21:41:23.4845160Z                                 auto tmp93 = static_cast<float>(1);
2023-01-11T21:41:23.4845309Z                                 tmp92 = tmp93;
2023-01-11T21:41:23.4845426Z                             }
2023-01-11T21:41:23.4845593Z                             auto tmp94 = tmp92 + tmp91;
2023-01-11T21:41:23.4845744Z                             float tmp95 = 0.0;
2023-01-11T21:41:23.4845876Z                             if(tmp33)
2023-01-11T21:41:23.4845994Z                             {
2023-01-11T21:41:23.4846237Z                                 auto tmp96 = static_cast<float>(1);
2023-01-11T21:41:23.4846371Z                                 tmp95 = tmp96;
2023-01-11T21:41:23.4846488Z                             }
2023-01-11T21:41:23.4846656Z                             auto tmp97 = tmp95 + tmp94;
2023-01-11T21:41:23.4846811Z                             float tmp98 = 0.0;
2023-01-11T21:41:23.4846946Z                             if(tmp37)
2023-01-11T21:41:23.4847065Z                             {
2023-01-11T21:41:23.4847253Z                                 auto tmp99 = static_cast<float>(1);
2023-01-11T21:41:23.4847383Z                                 tmp98 = tmp99;
2023-01-11T21:41:23.4847502Z                             }
2023-01-11T21:41:23.4847672Z                             auto tmp100 = tmp98 + tmp97;
2023-01-11T21:41:23.4847828Z                             float tmp101 = 0.0;
2023-01-11T21:41:23.4847959Z                             if(tmp41)
2023-01-11T21:41:23.4848080Z                             {
2023-01-11T21:41:23.4848319Z                                 auto tmp102 = static_cast<float>(1);
2023-01-11T21:41:23.4848454Z                                 tmp101 = tmp102;
2023-01-11T21:41:23.4848573Z                             }
2023-01-11T21:41:23.4848747Z                             auto tmp103 = tmp101 + tmp100;
2023-01-11T21:41:23.4848904Z                             float tmp104 = 0.0;
2023-01-11T21:41:23.4849035Z                             if(tmp47)
2023-01-11T21:41:23.4849158Z                             {
2023-01-11T21:41:23.4849350Z                                 auto tmp105 = static_cast<float>(1);
2023-01-11T21:41:23.4849484Z                                 tmp104 = tmp105;
2023-01-11T21:41:23.4849604Z                             }
2023-01-11T21:41:23.4849775Z                             auto tmp106 = tmp104 + tmp103;
2023-01-11T21:41:23.4849927Z                             float tmp107 = 0.0;
2023-01-11T21:41:23.4850056Z                             if(tmp51)
2023-01-11T21:41:23.4850182Z                             {
2023-01-11T21:41:23.4850375Z                                 auto tmp108 = static_cast<float>(1);
2023-01-11T21:41:23.4850510Z                                 tmp107 = tmp108;
2023-01-11T21:41:23.4850629Z                             }
2023-01-11T21:41:23.4850795Z                             auto tmp109 = tmp107 + tmp106;
2023-01-11T21:41:23.4850946Z                             float tmp110 = 0.0;
2023-01-11T21:41:23.4851072Z                             if(tmp55)
2023-01-11T21:41:23.4851187Z                             {
2023-01-11T21:41:23.4851375Z                                 auto tmp111 = static_cast<float>(1);
2023-01-11T21:41:23.4851511Z                                 tmp110 = tmp111;
2023-01-11T21:41:23.4851625Z                             }
2023-01-11T21:41:23.4851792Z                             auto tmp112 = tmp110 + tmp109;
2023-01-11T21:41:23.4851944Z                             float tmp113 = 0.0;
2023-01-11T21:41:23.4852073Z                             if(tmp59)
2023-01-11T21:41:23.4852199Z                             {
2023-01-11T21:41:23.4852386Z                                 auto tmp114 = static_cast<float>(1);
2023-01-11T21:41:23.4852526Z                                 tmp113 = tmp114;
2023-01-11T21:41:23.4852634Z                             }
2023-01-11T21:41:23.4852800Z                             auto tmp115 = tmp113 + tmp112;
2023-01-11T21:41:23.4852949Z                             float tmp116 = 0.0;
2023-01-11T21:41:23.4853075Z                             if(tmp65)
2023-01-11T21:41:23.4853192Z                             {
2023-01-11T21:41:23.4853380Z                                 auto tmp117 = static_cast<float>(1);
2023-01-11T21:41:23.4853522Z                                 tmp116 = tmp117;
2023-01-11T21:41:23.4853628Z                             }
2023-01-11T21:41:23.4853794Z                             auto tmp118 = tmp116 + tmp115;
2023-01-11T21:41:23.4853939Z                             float tmp119 = 0.0;
2023-01-11T21:41:23.4854114Z                             if(tmp69)
2023-01-11T21:41:23.4854229Z                             {
2023-01-11T21:41:23.4854417Z                                 auto tmp120 = static_cast<float>(1);
2023-01-11T21:41:23.4854564Z                                 tmp119 = tmp120;
2023-01-11T21:41:23.4854673Z                             }
2023-01-11T21:41:23.4854839Z                             auto tmp121 = tmp119 + tmp118;
2023-01-11T21:41:23.4854992Z                             float tmp122 = 0.0;
2023-01-11T21:41:23.4855114Z                             if(tmp73)
2023-01-11T21:41:23.4855224Z                             {
2023-01-11T21:41:23.4855410Z                                 auto tmp123 = static_cast<float>(1);
2023-01-11T21:41:23.4855557Z                                 tmp122 = tmp123;
2023-01-11T21:41:23.4855667Z                             }
2023-01-11T21:41:23.4855833Z                             auto tmp124 = tmp122 + tmp121;
2023-01-11T21:41:23.4855978Z                             float tmp125 = 0.0;
2023-01-11T21:41:23.4856171Z                             if(tmp77)
2023-01-11T21:41:23.4856286Z                             {
2023-01-11T21:41:23.4856476Z                                 auto tmp126 = static_cast<float>(1);
2023-01-11T21:41:23.4856616Z                                 tmp125 = tmp126;
2023-01-11T21:41:23.4856720Z                             }
2023-01-11T21:41:23.4856883Z                             auto tmp127 = tmp125 + tmp124;
2023-01-11T21:41:23.4857048Z                             auto tmp128 = tmp80 / tmp127;
2023-01-11T21:41:23.4857223Z                             out_ptr0[i2 + (6*i1) + (36*i0)] = tmp128;
2023-01-11T21:41:23.4857333Z                         }
2023-01-11T21:41:23.4857438Z                     }
2023-01-11T21:41:23.4857547Z                 }
2023-01-11T21:41:23.4857644Z             }
2023-01-11T21:41:23.4857747Z         }
2023-01-11T21:41:23.4857879Z         #pragma omp for 
2023-01-11T21:41:23.4858018Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.4858118Z         {
2023-01-11T21:41:23.4858374Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4858601Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.4858734Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4858888Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.4858992Z         }
2023-01-11T21:41:23.4859158Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4859305Z         for(long i0=2048; i0<2048; i0+=1)
2023-01-11T21:41:23.4859414Z         {
2023-01-11T21:41:23.4859558Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4859715Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.4859860Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.4859999Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.4860107Z         }
2023-01-11T21:41:23.4860214Z     }
2023-01-11T21:41:23.4860316Z }
2023-01-11T21:41:23.4860517Z ''')
2023-01-11T21:41:23.4860528Z 
2023-01-11T21:41:23.4860540Z 
2023-01-11T21:41:23.4860688Z async_compile.wait(globals())
2023-01-11T21:41:23.4860814Z del async_compile
2023-01-11T21:41:23.4860822Z 
2023-01-11T21:41:23.4860948Z def call(args):
2023-01-11T21:41:23.4861070Z     arg0_1, = args
2023-01-11T21:41:23.4861192Z     args.clear()
2023-01-11T21:41:23.4861593Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4861995Z     buf1 = empty_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4862286Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.4862509Z     del arg0_1
2023-01-11T21:41:23.4862701Z     buf2 = aten._adaptive_avg_pool2d(buf1, [2, 5])
2023-01-11T21:41:23.4862806Z     del buf1
2023-01-11T21:41:23.4862927Z     buf3 = buf2
2023-01-11T21:41:23.4863107Z     assert_size_stride(buf3, (2, 4, 2, 5), (40, 10, 5, 1))
2023-01-11T21:41:23.4863212Z     del buf2
2023-01-11T21:41:23.4863437Z     return (buf0, buf3, )
2023-01-11T21:41:23.4863446Z 
2023-01-11T21:41:23.4863454Z 
2023-01-11T21:41:23.4863587Z if __name__ == "__main__":
2023-01-11T21:41:23.4863793Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4864013Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4864410Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4864600Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.4865098Z [2023-01-11 21:24:09,417] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 8
2023-01-11T21:41:23.4865901Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.4866191Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.4866667Z [2023-01-11 21:24:09,448] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 9
2023-01-11T21:41:23.4866676Z 
2023-01-11T21:41:23.4866683Z 
2023-01-11T21:41:23.4866850Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4866973Z import torch
2023-01-11T21:41:23.4867095Z import random
2023-01-11T21:41:23.4867286Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4867499Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4867506Z 
2023-01-11T21:41:23.4867644Z aten = torch.ops.aten
2023-01-11T21:41:23.4867883Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4868042Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4868050Z 
2023-01-11T21:41:23.4868057Z 
2023-01-11T21:41:23.4868300Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4868678Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4868890Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4869049Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.4869217Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.4869318Z {
2023-01-11T21:41:23.4869490Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4869597Z     {
2023-01-11T21:41:23.4869729Z         #pragma omp for 
2023-01-11T21:41:23.4869868Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.4869959Z         {
2023-01-11T21:41:23.4870094Z             #pragma GCC ivdep
2023-01-11T21:41:23.4870236Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:23.4870349Z             {
2023-01-11T21:41:23.4870487Z                 #pragma GCC ivdep
2023-01-11T21:41:23.4870631Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:41:23.4870738Z                 {
2023-01-11T21:41:23.4870838Z                     {
2023-01-11T21:41:23.4870959Z                         {
2023-01-11T21:41:23.4871150Z                             auto tmp0 = static_cast<long>((i1 / 2));
2023-01-11T21:41:23.4871353Z                             auto tmp1 = static_cast<long>(((8 + (3*i1)) / 6));
2023-01-11T21:41:23.4871513Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:23.4871698Z                             auto tmp3 = static_cast<long>((i2 / 2));
2023-01-11T21:41:23.4871903Z                             auto tmp4 = static_cast<long>(((8 + (3*i2)) / 6));
2023-01-11T21:41:23.4872055Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:23.4872208Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:23.4872478Z                             float tmp7 = 0.0;
2023-01-11T21:41:23.4872606Z                             if(tmp6)
2023-01-11T21:41:23.4872728Z                             {
2023-01-11T21:41:23.4900041Z                                 auto tmp8 = in_ptr0[(3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:23.4901597Z                                 tmp7 = tmp8;
2023-01-11T21:41:23.4901911Z                             }
2023-01-11T21:41:23.4902956Z                             auto tmp9 = static_cast<long>(1 + (i2 / 2));
2023-01-11T21:41:23.4904000Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:23.4904230Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:23.4904357Z                             float tmp12 = 0.0;
2023-01-11T21:41:23.4904467Z                             if(tmp11)
2023-01-11T21:41:23.4904569Z                             {
2023-01-11T21:41:23.4904965Z                                 auto tmp13 = in_ptr0[1 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:23.4905181Z                                 tmp12 = tmp13;
2023-01-11T21:41:23.4905382Z                             }
2023-01-11T21:41:23.4905529Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:23.4906031Z                             auto tmp15 = static_cast<long>(1 + (i1 / 2));
2023-01-11T21:41:23.4906300Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:41:23.4906447Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:41:23.4906575Z                             float tmp18 = 0.0;
2023-01-11T21:41:23.4907029Z                             if(tmp17)
2023-01-11T21:41:23.4907433Z                             {
2023-01-11T21:41:23.4908005Z                                 auto tmp19 = in_ptr0[3 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:23.4908572Z                                 tmp18 = tmp19;
2023-01-11T21:41:23.4909144Z                             }
2023-01-11T21:41:23.4909670Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:23.4910255Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:41:23.4910372Z                             float tmp22 = 0.0;
2023-01-11T21:41:23.4910474Z                             if(tmp21)
2023-01-11T21:41:23.4910575Z                             {
2023-01-11T21:41:23.4910839Z                                 auto tmp23 = in_ptr0[4 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:23.4910965Z                                 tmp22 = tmp23;
2023-01-11T21:41:23.4911063Z                             }
2023-01-11T21:41:23.4911200Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:41:23.4911325Z                             float tmp25 = 0.0;
2023-01-11T21:41:23.4911416Z                             if(tmp6)
2023-01-11T21:41:23.4911513Z                             {
2023-01-11T21:41:23.4911670Z                                 auto tmp26 = static_cast<float>(1);
2023-01-11T21:41:23.4911790Z                                 tmp25 = tmp26;
2023-01-11T21:41:23.4911886Z                             }
2023-01-11T21:41:23.4912007Z                             float tmp27 = 0.0;
2023-01-11T21:41:23.4912115Z                             if(tmp11)
2023-01-11T21:41:23.4912197Z                             {
2023-01-11T21:41:23.4912358Z                                 auto tmp28 = static_cast<float>(1);
2023-01-11T21:41:23.4912480Z                                 tmp27 = tmp28;
2023-01-11T21:41:23.4912577Z                             }
2023-01-11T21:41:23.4912714Z                             auto tmp29 = tmp27 + tmp25;
2023-01-11T21:41:23.4912842Z                             float tmp30 = 0.0;
2023-01-11T21:41:23.4912949Z                             if(tmp17)
2023-01-11T21:41:23.4913028Z                             {
2023-01-11T21:41:23.4913183Z                                 auto tmp31 = static_cast<float>(1);
2023-01-11T21:41:23.4913299Z                                 tmp30 = tmp31;
2023-01-11T21:41:23.4913394Z                             }
2023-01-11T21:41:23.4913530Z                             auto tmp32 = tmp30 + tmp29;
2023-01-11T21:41:23.4913653Z                             float tmp33 = 0.0;
2023-01-11T21:41:23.4913809Z                             if(tmp21)
2023-01-11T21:41:23.4913889Z                             {
2023-01-11T21:41:23.4914127Z                                 auto tmp34 = static_cast<float>(1);
2023-01-11T21:41:23.4914247Z                                 tmp33 = tmp34;
2023-01-11T21:41:23.4914345Z                             }
2023-01-11T21:41:23.4914481Z                             auto tmp35 = tmp33 + tmp32;
2023-01-11T21:41:23.4914615Z                             auto tmp36 = tmp24 / tmp35;
2023-01-11T21:41:23.4914762Z                             out_ptr0[i2 + (6*i1) + (36*i0)] = tmp36;
2023-01-11T21:41:23.4914843Z                         }
2023-01-11T21:41:23.4914941Z                     }
2023-01-11T21:41:23.4915033Z                 }
2023-01-11T21:41:23.4915128Z             }
2023-01-11T21:41:23.4915214Z         }
2023-01-11T21:41:23.4915324Z         #pragma omp for 
2023-01-11T21:41:23.4915443Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.4915517Z         {
2023-01-11T21:41:23.4915631Z             #pragma GCC ivdep
2023-01-11T21:41:23.4915750Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.4915847Z             {
2023-01-11T21:41:23.4916003Z                 #pragma GCC ivdep
2023-01-11T21:41:23.4916129Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:41:23.4916218Z                 {
2023-01-11T21:41:23.4916296Z                     {
2023-01-11T21:41:23.4916391Z                         {
2023-01-11T21:41:23.4916554Z                             auto tmp0 = static_cast<long>(((3*i1) / 2));
2023-01-11T21:41:23.4916722Z                             auto tmp1 = static_cast<long>(2 + (((3*i1) / 2)));
2023-01-11T21:41:23.4916859Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:23.4917032Z                             auto tmp3 = static_cast<long>(((3*i2) / 5));
2023-01-11T21:41:23.4917198Z                             auto tmp4 = static_cast<long>(((7 + (3*i2)) / 5));
2023-01-11T21:41:23.4917312Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:23.4917440Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:23.4917571Z                             float tmp7 = 0.0;
2023-01-11T21:41:23.4917682Z                             if(tmp6)
2023-01-11T21:41:23.4917780Z                             {
2023-01-11T21:41:23.4917949Z                                 auto tmp8 = in_ptr0[(3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:23.4918108Z                                 auto tmp9 = static_cast<float>(1);
2023-01-11T21:41:23.4918246Z                                 auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:23.4918354Z                                 tmp7 = tmp10;
2023-01-11T21:41:23.4918456Z                             }
2023-01-11T21:41:23.4918624Z                             auto tmp11 = static_cast<long>(1 + (((3*i2) / 5)));
2023-01-11T21:41:23.4918762Z                             auto tmp12 = tmp11 < tmp4;
2023-01-11T21:41:23.4918896Z                             auto tmp13 = tmp2 & tmp12;
2023-01-11T21:41:23.4919022Z                             float tmp14 = 0.0;
2023-01-11T21:41:23.4919131Z                             if(tmp13)
2023-01-11T21:41:23.4919222Z                             {
2023-01-11T21:41:23.4919397Z                                 auto tmp15 = in_ptr0[1 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:23.4919554Z                                 auto tmp16 = static_cast<float>(1);
2023-01-11T21:41:23.4919701Z                                 auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:23.4919824Z                                 tmp14 = tmp17;
2023-01-11T21:41:23.4919922Z                             }
2023-01-11T21:41:23.4920059Z                             auto tmp18 = tmp14 + tmp7;
2023-01-11T21:41:23.4920207Z                             auto tmp19 = static_cast<long>(1 + (((3*i1) / 2)));
2023-01-11T21:41:23.4920342Z                             auto tmp20 = tmp19 < tmp1;
2023-01-11T21:41:23.4920480Z                             auto tmp21 = tmp20 & tmp5;
2023-01-11T21:41:23.4920609Z                             float tmp22 = 0.0;
2023-01-11T21:41:23.4920725Z                             if(tmp21)
2023-01-11T21:41:23.4920894Z                             {
2023-01-11T21:41:23.4921074Z                                 auto tmp23 = in_ptr0[3 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:23.4921237Z                                 auto tmp24 = static_cast<float>(1);
2023-01-11T21:41:23.4921364Z                                 auto tmp25 = tmp23 + tmp24;
2023-01-11T21:41:23.4921491Z                                 tmp22 = tmp25;
2023-01-11T21:41:23.4921598Z                             }
2023-01-11T21:41:23.4921733Z                             auto tmp26 = tmp22 + tmp18;
2023-01-11T21:41:23.4921869Z                             auto tmp27 = tmp20 & tmp12;
2023-01-11T21:41:23.4921993Z                             float tmp28 = 0.0;
2023-01-11T21:41:23.4922103Z                             if(tmp27)
2023-01-11T21:41:23.4922186Z                             {
2023-01-11T21:41:23.4922362Z                                 auto tmp29 = in_ptr0[4 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:23.4922560Z                                 auto tmp30 = static_cast<float>(1);
2023-01-11T21:41:23.4922704Z                                 auto tmp31 = tmp29 + tmp30;
2023-01-11T21:41:23.4922826Z                                 tmp28 = tmp31;
2023-01-11T21:41:23.4922921Z                             }
2023-01-11T21:41:23.4923055Z                             auto tmp32 = tmp28 + tmp26;
2023-01-11T21:41:23.4923162Z                             float tmp33 = 0.0;
2023-01-11T21:41:23.4923272Z                             if(tmp6)
2023-01-11T21:41:23.4923367Z                             {
2023-01-11T21:41:23.4923523Z                                 auto tmp34 = static_cast<float>(1);
2023-01-11T21:41:23.4923644Z                                 tmp33 = tmp34;
2023-01-11T21:41:23.4923744Z                             }
2023-01-11T21:41:23.4923863Z                             float tmp35 = 0.0;
2023-01-11T21:41:23.4923971Z                             if(tmp13)
2023-01-11T21:41:23.4924057Z                             {
2023-01-11T21:41:23.4924220Z                                 auto tmp36 = static_cast<float>(1);
2023-01-11T21:41:23.4924340Z                                 tmp35 = tmp36;
2023-01-11T21:41:23.4924439Z                             }
2023-01-11T21:41:23.4924576Z                             auto tmp37 = tmp35 + tmp33;
2023-01-11T21:41:23.4924701Z                             float tmp38 = 0.0;
2023-01-11T21:41:23.4924805Z                             if(tmp21)
2023-01-11T21:41:23.4924890Z                             {
2023-01-11T21:41:23.4925048Z                                 auto tmp39 = static_cast<float>(1);
2023-01-11T21:41:23.4925168Z                                 tmp38 = tmp39;
2023-01-11T21:41:23.4925264Z                             }
2023-01-11T21:41:23.4925397Z                             auto tmp40 = tmp38 + tmp37;
2023-01-11T21:41:23.4925520Z                             float tmp41 = 0.0;
2023-01-11T21:41:23.4925623Z                             if(tmp27)
2023-01-11T21:41:23.4925709Z                             {
2023-01-11T21:41:23.4925868Z                                 auto tmp42 = static_cast<float>(1);
2023-01-11T21:41:23.4925988Z                                 tmp41 = tmp42;
2023-01-11T21:41:23.4926082Z                             }
2023-01-11T21:41:23.4926217Z                             auto tmp43 = tmp41 + tmp40;
2023-01-11T21:41:23.4926354Z                             auto tmp44 = tmp32 / tmp43;
2023-01-11T21:41:23.4926506Z                             out_ptr1[i2 + (5*i1) + (10*i0)] = tmp44;
2023-01-11T21:41:23.4926590Z                         }
2023-01-11T21:41:23.4926689Z                     }
2023-01-11T21:41:23.4926786Z                 }
2023-01-11T21:41:23.4926875Z             }
2023-01-11T21:41:23.4926967Z         }
2023-01-11T21:41:23.4927054Z     }
2023-01-11T21:41:23.4927136Z }
2023-01-11T21:41:23.4927284Z ''')
2023-01-11T21:41:23.4927294Z 
2023-01-11T21:41:23.4927299Z 
2023-01-11T21:41:23.4927425Z async_compile.wait(globals())
2023-01-11T21:41:23.4927528Z del async_compile
2023-01-11T21:41:23.4927577Z 
2023-01-11T21:41:23.4927682Z def call(args):
2023-01-11T21:41:23.4927780Z     arg0_1, = args
2023-01-11T21:41:23.4927881Z     args.clear()
2023-01-11T21:41:23.4928201Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4928494Z     buf1 = empty_strided((2, 4, 2, 5), (40, 10, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4928732Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.4928827Z     del arg0_1
2023-01-11T21:41:23.4928932Z     return (buf0, buf1, )
2023-01-11T21:41:23.4928938Z 
2023-01-11T21:41:23.4928945Z 
2023-01-11T21:41:23.4929049Z if __name__ == "__main__":
2023-01-11T21:41:23.4929211Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.4929391Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.4929938Z     arg0_1 = rand_strided((2, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.4930133Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.4930548Z [2023-01-11 21:24:11,283] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 9
2023-01-11T21:41:23.4931185Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.4931373Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.4931763Z [2023-01-11 21:24:11,444] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 10
2023-01-11T21:41:23.4931771Z 
2023-01-11T21:41:23.4931778Z 
2023-01-11T21:41:23.4931911Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.4932036Z import torch
2023-01-11T21:41:23.4932145Z import random
2023-01-11T21:41:23.4932313Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.4932471Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.4932496Z 
2023-01-11T21:41:23.4932592Z aten = torch.ops.aten
2023-01-11T21:41:23.4932788Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.4932918Z async_compile = AsyncCompile()
2023-01-11T21:41:23.4932925Z 
2023-01-11T21:41:23.4932930Z 
2023-01-11T21:41:23.4933122Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.4933419Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.4933589Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.4933726Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.4933859Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.4933939Z {
2023-01-11T21:41:23.4934079Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.4934170Z     {
2023-01-11T21:41:23.4934279Z         #pragma omp for 
2023-01-11T21:41:23.4934397Z         for(long i0=0; i0<36; i0+=1)
2023-01-11T21:41:23.4934485Z         {
2023-01-11T21:41:23.4934680Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.4934810Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.4934898Z         }
2023-01-11T21:41:23.4935034Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.4935151Z         for(long i0=288; i0<288; i0+=1)
2023-01-11T21:41:23.4935239Z         {
2023-01-11T21:41:23.4935359Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.4935454Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.4935541Z         }
2023-01-11T21:41:23.4935653Z         #pragma omp for 
2023-01-11T21:41:23.4935768Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.4935855Z         {
2023-01-11T21:41:23.4935968Z             #pragma GCC ivdep
2023-01-11T21:41:23.4936081Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.4936201Z             {
2023-01-11T21:41:23.4936315Z                 #pragma GCC ivdep
2023-01-11T21:41:23.4936436Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:41:23.4936525Z                 {
2023-01-11T21:41:23.4936619Z                     {
2023-01-11T21:41:23.4936713Z                         {
2023-01-11T21:41:23.4936872Z                             auto tmp0 = static_cast<long>(3*i1);
2023-01-11T21:41:23.4937017Z                             auto tmp1 = static_cast<long>(3 + (3*i1));
2023-01-11T21:41:23.4937150Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:23.4937313Z                             auto tmp3 = static_cast<long>(((6*i2) / 5));
2023-01-11T21:41:23.4937484Z                             auto tmp4 = static_cast<long>(2 + (((6*i2) / 5)));
2023-01-11T21:41:23.4937618Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:23.4937757Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:23.4937934Z                             float tmp7 = 0.0;
2023-01-11T21:41:23.4938024Z                             if(tmp6)
2023-01-11T21:41:23.4938123Z                             {
2023-01-11T21:41:23.4938287Z                                 auto tmp8 = in_ptr0[(18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4938445Z                                 auto tmp9 = static_cast<float>(1);
2023-01-11T21:41:23.4938589Z                                 auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:23.4938719Z                                 tmp7 = tmp10;
2023-01-11T21:41:23.4938817Z                             }
2023-01-11T21:41:23.4938981Z                             auto tmp11 = static_cast<long>(1 + (((6*i2) / 5)));
2023-01-11T21:41:23.4939101Z                             auto tmp12 = tmp11 < tmp4;
2023-01-11T21:41:23.4939234Z                             auto tmp13 = tmp2 & tmp12;
2023-01-11T21:41:23.4939367Z                             float tmp14 = 0.0;
2023-01-11T21:41:23.4939474Z                             if(tmp13)
2023-01-11T21:41:23.4939579Z                             {
2023-01-11T21:41:23.4939746Z                                 auto tmp15 = in_ptr0[1 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4939904Z                                 auto tmp16 = static_cast<float>(1);
2023-01-11T21:41:23.4940029Z                                 auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:23.4940149Z                                 tmp14 = tmp17;
2023-01-11T21:41:23.4940245Z                             }
2023-01-11T21:41:23.4940378Z                             auto tmp18 = tmp14 + tmp7;
2023-01-11T21:41:23.4940543Z                             auto tmp19 = static_cast<long>(1 + (3*i1));
2023-01-11T21:41:23.4940676Z                             auto tmp20 = tmp19 < tmp1;
2023-01-11T21:41:23.4940809Z                             auto tmp21 = tmp20 & tmp5;
2023-01-11T21:41:23.4940916Z                             float tmp22 = 0.0;
2023-01-11T21:41:23.4941020Z                             if(tmp21)
2023-01-11T21:41:23.4941125Z                             {
2023-01-11T21:41:23.4941292Z                                 auto tmp23 = in_ptr0[6 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4941451Z                                 auto tmp24 = static_cast<float>(1);
2023-01-11T21:41:23.4941593Z                                 auto tmp25 = tmp23 + tmp24;
2023-01-11T21:41:23.4941714Z                                 tmp22 = tmp25;
2023-01-11T21:41:23.4941810Z                             }
2023-01-11T21:41:23.4941931Z                             auto tmp26 = tmp22 + tmp18;
2023-01-11T21:41:23.4942061Z                             auto tmp27 = tmp20 & tmp12;
2023-01-11T21:41:23.4942186Z                             float tmp28 = 0.0;
2023-01-11T21:41:23.4942290Z                             if(tmp27)
2023-01-11T21:41:23.4942506Z                             {
2023-01-11T21:41:23.4942678Z                                 auto tmp29 = in_ptr0[7 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4942849Z                                 auto tmp30 = static_cast<float>(1);
2023-01-11T21:41:23.4943048Z                                 auto tmp31 = tmp29 + tmp30;
2023-01-11T21:41:23.4943169Z                                 tmp28 = tmp31;
2023-01-11T21:41:23.4943264Z                             }
2023-01-11T21:41:23.4943404Z                             auto tmp32 = tmp28 + tmp26;
2023-01-11T21:41:23.4943565Z                             auto tmp33 = static_cast<long>(2 + (3*i1));
2023-01-11T21:41:23.4943704Z                             auto tmp34 = tmp33 < tmp1;
2023-01-11T21:41:23.4943850Z                             auto tmp35 = tmp34 & tmp5;
2023-01-11T21:41:23.4943972Z                             float tmp36 = 0.0;
2023-01-11T21:41:23.4944068Z                             if(tmp35)
2023-01-11T21:41:23.4944168Z                             {
2023-01-11T21:41:23.4944344Z                                 auto tmp37 = in_ptr0[12 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4944498Z                                 auto tmp38 = static_cast<float>(1);
2023-01-11T21:41:23.4944686Z                                 auto tmp39 = tmp37 + tmp38;
2023-01-11T21:41:23.4944803Z                                 tmp36 = tmp39;
2023-01-11T21:41:23.4944903Z                             }
2023-01-11T21:41:23.4945020Z                             auto tmp40 = tmp36 + tmp32;
2023-01-11T21:41:23.4945154Z                             auto tmp41 = tmp34 & tmp12;
2023-01-11T21:41:23.4945280Z                             float tmp42 = 0.0;
2023-01-11T21:41:23.4945392Z                             if(tmp41)
2023-01-11T21:41:23.4945489Z                             {
2023-01-11T21:41:23.4945658Z                                 auto tmp43 = in_ptr0[13 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4945814Z                                 auto tmp44 = static_cast<float>(1);
2023-01-11T21:41:23.4945940Z                                 auto tmp45 = tmp43 + tmp44;
2023-01-11T21:41:23.4946061Z                                 tmp42 = tmp45;
2023-01-11T21:41:23.4946172Z                             }
2023-01-11T21:41:23.4946307Z                             auto tmp46 = tmp42 + tmp40;
2023-01-11T21:41:23.4946443Z                             auto tmp47 = tmp1 < tmp1;
2023-01-11T21:41:23.4946576Z                             auto tmp48 = tmp47 & tmp5;
2023-01-11T21:41:23.4946696Z                             float tmp49 = 0.0;
2023-01-11T21:41:23.4946805Z                             if(tmp48)
2023-01-11T21:41:23.4946886Z                             {
2023-01-11T21:41:23.4947055Z                                 auto tmp50 = in_ptr0[18 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4947211Z                                 auto tmp51 = static_cast<float>(1);
2023-01-11T21:41:23.4947353Z                                 auto tmp52 = tmp50 + tmp51;
2023-01-11T21:41:23.4947475Z                                 tmp49 = tmp52;
2023-01-11T21:41:23.4947572Z                             }
2023-01-11T21:41:23.4947705Z                             auto tmp53 = tmp49 + tmp46;
2023-01-11T21:41:23.4947833Z                             auto tmp54 = tmp47 & tmp12;
2023-01-11T21:41:23.4947956Z                             float tmp55 = 0.0;
2023-01-11T21:41:23.4948061Z                             if(tmp54)
2023-01-11T21:41:23.4948160Z                             {
2023-01-11T21:41:23.4948323Z                                 auto tmp56 = in_ptr0[19 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:23.4948476Z                                 auto tmp57 = static_cast<float>(1);
2023-01-11T21:41:23.4948617Z                                 auto tmp58 = tmp56 + tmp57;
2023-01-11T21:41:23.4948724Z                                 tmp55 = tmp58;
2023-01-11T21:41:23.4948822Z                             }
2023-01-11T21:41:23.4948966Z                             auto tmp59 = tmp55 + tmp53;
2023-01-11T21:41:23.4949093Z                             float tmp60 = 0.0;
2023-01-11T21:41:23.4949199Z                             if(tmp6)
2023-01-11T21:41:23.4949298Z                             {
2023-01-11T21:41:23.4949490Z                                 auto tmp61 = static_cast<float>(1);
2023-01-11T21:41:23.4949609Z                                 tmp60 = tmp61;
2023-01-11T21:41:23.4949687Z                             }
2023-01-11T21:41:23.4949808Z                             float tmp62 = 0.0;
2023-01-11T21:41:23.4949918Z                             if(tmp13)
2023-01-11T21:41:23.4950020Z                             {
2023-01-11T21:41:23.4950184Z                                 auto tmp63 = static_cast<float>(1);
2023-01-11T21:41:23.4950301Z                                 tmp62 = tmp63;
2023-01-11T21:41:23.4950400Z                             }
2023-01-11T21:41:23.4950517Z                             auto tmp64 = tmp62 + tmp60;
2023-01-11T21:41:23.4950646Z                             float tmp65 = 0.0;
2023-01-11T21:41:23.4950751Z                             if(tmp21)
2023-01-11T21:41:23.4950849Z                             {
2023-01-11T21:41:23.4950998Z                                 auto tmp66 = static_cast<float>(1);
2023-01-11T21:41:23.4951158Z                                 tmp65 = tmp66;
2023-01-11T21:41:23.4951256Z                             }
2023-01-11T21:41:23.4951373Z                             auto tmp67 = tmp65 + tmp64;
2023-01-11T21:41:23.4951497Z                             float tmp68 = 0.0;
2023-01-11T21:41:23.4951604Z                             if(tmp27)
2023-01-11T21:41:23.4951703Z                             {
2023-01-11T21:41:23.4951858Z                                 auto tmp69 = static_cast<float>(1);
2023-01-11T21:41:23.4951974Z                                 tmp68 = tmp69;
2023-01-11T21:41:23.4952075Z                             }
2023-01-11T21:41:23.4952193Z                             auto tmp70 = tmp68 + tmp67;
2023-01-11T21:41:23.4952317Z                             float tmp71 = 0.0;
2023-01-11T21:41:23.4952421Z                             if(tmp35)
2023-01-11T21:41:23.4952519Z                             {
2023-01-11T21:41:23.4952677Z                                 auto tmp72 = static_cast<float>(1);
2023-01-11T21:41:23.4952800Z                                 tmp71 = tmp72;
2023-01-11T21:41:23.4952897Z                             }
2023-01-11T21:41:23.4953018Z                             auto tmp73 = tmp71 + tmp70;
2023-01-11T21:41:23.4953139Z                             float tmp74 = 0.0;
2023-01-11T21:41:23.4953247Z                             if(tmp41)
2023-01-11T21:41:23.4953345Z                             {
2023-01-11T21:41:23.4953502Z                                 auto tmp75 = static_cast<float>(1);
2023-01-11T21:41:23.4953618Z                                 tmp74 = tmp75;
2023-01-11T21:41:23.4953714Z                             }
2023-01-11T21:41:23.4953892Z                             auto tmp76 = tmp74 + tmp73;
2023-01-11T21:41:23.4954015Z                             float tmp77 = 0.0;
2023-01-11T21:41:23.4954126Z                             if(tmp48)
2023-01-11T21:41:23.4954220Z                             {
2023-01-11T21:41:23.4954378Z                                 auto tmp78 = static_cast<float>(1);
2023-01-11T21:41:23.4954502Z                                 tmp77 = tmp78;
2023-01-11T21:41:23.4954601Z                             }
2023-01-11T21:41:23.4954724Z                             auto tmp79 = tmp77 + tmp76;
2023-01-11T21:41:23.4954849Z                             float tmp80 = 0.0;
2023-01-11T21:41:23.4954959Z                             if(tmp54)
2023-01-11T21:41:23.4955057Z                             {
2023-01-11T21:41:23.4955211Z                                 auto tmp81 = static_cast<float>(1);
2023-01-11T21:41:23.4955330Z                                 tmp80 = tmp81;
2023-01-11T21:41:23.4955427Z                             }
2023-01-11T21:41:23.4955564Z                             auto tmp82 = tmp80 + tmp79;
2023-01-11T21:41:23.4955690Z                             auto tmp83 = tmp59 / tmp82;
2023-01-11T21:41:23.4955842Z                             out_ptr1[i2 + (5*i1) + (10*i0)] = tmp83;
2023-01-11T21:41:23.5037960Z                         }
2023-01-11T21:41:23.5038253Z                     }
2023-01-11T21:41:23.5038344Z                 }
2023-01-11T21:41:23.5038430Z             }
2023-01-11T21:41:23.5038518Z         }
2023-01-11T21:41:23.5038584Z     }
2023-01-11T21:41:23.5038664Z }
2023-01-11T21:41:23.5038826Z ''')
2023-01-11T21:41:23.5038834Z 
2023-01-11T21:41:23.5038840Z 
2023-01-11T21:41:23.5038993Z async_compile.wait(globals())
2023-01-11T21:41:23.5039092Z del async_compile
2023-01-11T21:41:23.5039098Z 
2023-01-11T21:41:23.5039195Z def call(args):
2023-01-11T21:41:23.5039293Z     arg0_1, = args
2023-01-11T21:41:23.5039368Z     args.clear()
2023-01-11T21:41:23.5039684Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5039982Z     buf1 = empty_strided((2, 4, 2, 5), (40, 10, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5040214Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.5040312Z     del arg0_1
2023-01-11T21:41:23.5040544Z     return (buf0, buf1, )
2023-01-11T21:41:23.5040554Z 
2023-01-11T21:41:23.5040561Z 
2023-01-11T21:41:23.5040693Z if __name__ == "__main__":
2023-01-11T21:41:23.5040896Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5041103Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5041500Z     arg0_1 = rand_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5041696Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5042204Z [2023-01-11 21:24:13,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 10
2023-01-11T21:41:23.5042213Z 
2023-01-11T21:41:23.5042328Z ok (6.032s)
2023-01-11T21:41:23.5043249Z   test_adaptive_avg_pool2d2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5043487Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5043979Z [2023-01-11 21:24:13,241] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 11
2023-01-11T21:41:23.5044437Z [2023-01-11 21:24:13,247] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._adaptive_avg_pool2d
2023-01-11T21:41:23.5044923Z [2023-01-11 21:24:13,250] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 11
2023-01-11T21:41:23.5044950Z 
2023-01-11T21:41:23.5045102Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5045222Z import torch
2023-01-11T21:41:23.5045343Z import random
2023-01-11T21:41:23.5045557Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5045777Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5045794Z 
2023-01-11T21:41:23.5045932Z aten = torch.ops.aten
2023-01-11T21:41:23.5046175Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5046325Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5046351Z 
2023-01-11T21:41:23.5046359Z 
2023-01-11T21:41:23.5046499Z async_compile.wait(globals())
2023-01-11T21:41:23.5046624Z del async_compile
2023-01-11T21:41:23.5046633Z 
2023-01-11T21:41:23.5046757Z def call(args):
2023-01-11T21:41:23.5046875Z     arg0_1, = args
2023-01-11T21:41:23.5046998Z     args.clear()
2023-01-11T21:41:23.5047192Z     buf0 = aten._adaptive_avg_pool2d(arg0_1, [4, 4])
2023-01-11T21:41:23.5047310Z     del arg0_1
2023-01-11T21:41:23.5047408Z     buf1 = buf0
2023-01-11T21:41:23.5047590Z     assert_size_stride(buf1, (2, 4, 4, 4), (64, 16, 4, 1))
2023-01-11T21:41:23.5047704Z     del buf0
2023-01-11T21:41:23.5047829Z     return (buf1, )
2023-01-11T21:41:23.5047837Z 
2023-01-11T21:41:23.5047844Z 
2023-01-11T21:41:23.5047977Z if __name__ == "__main__":
2023-01-11T21:41:23.5048238Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5048460Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5048860Z     arg0_1 = rand_strided((2, 4, 21, 21), (1764, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5049052Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5049061Z 
2023-01-11T21:41:23.5049174Z ok (0.030s)
2023-01-11T21:41:23.5050086Z   test_add_const_float_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5050313Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5050847Z [2023-01-11 21:24:13,264] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 12
2023-01-11T21:41:23.5051354Z [2023-01-11 21:24:14,899] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 12
2023-01-11T21:41:23.5051363Z 
2023-01-11T21:41:23.5051532Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5051654Z import torch
2023-01-11T21:41:23.5051777Z import random
2023-01-11T21:41:23.5051969Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5052187Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5052195Z 
2023-01-11T21:41:23.5052334Z aten = torch.ops.aten
2023-01-11T21:41:23.5052578Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5052743Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5052752Z 
2023-01-11T21:41:23.5052759Z 
2023-01-11T21:41:23.5053013Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5053398Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5053615Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5053775Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5053878Z {
2023-01-11T21:41:23.5054054Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5054162Z     {
2023-01-11T21:41:23.5054298Z         #pragma omp for 
2023-01-11T21:41:23.5054442Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.5054553Z         {
2023-01-11T21:41:23.5054791Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5055036Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1.5));
2023-01-11T21:41:23.5055182Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5055341Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5055450Z         }
2023-01-11T21:41:23.5055619Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5055766Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:23.5055866Z         {
2023-01-11T21:41:23.5056009Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5056191Z             auto tmp1 = static_cast<float>(1.5);
2023-01-11T21:41:23.5056338Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5056479Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5056586Z         }
2023-01-11T21:41:23.5056690Z     }
2023-01-11T21:41:23.5056778Z }
2023-01-11T21:41:23.5056919Z ''')
2023-01-11T21:41:23.5056927Z 
2023-01-11T21:41:23.5056935Z 
2023-01-11T21:41:23.5057094Z async_compile.wait(globals())
2023-01-11T21:41:23.5057223Z del async_compile
2023-01-11T21:41:23.5057233Z 
2023-01-11T21:41:23.5057357Z def call(args):
2023-01-11T21:41:23.5057477Z     arg0_1, = args
2023-01-11T21:41:23.5057597Z     args.clear()
2023-01-11T21:41:23.5057939Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5058178Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5058354Z     del arg0_1
2023-01-11T21:41:23.5058482Z     return (buf0, )
2023-01-11T21:41:23.5058490Z 
2023-01-11T21:41:23.5058496Z 
2023-01-11T21:41:23.5058630Z if __name__ == "__main__":
2023-01-11T21:41:23.5058839Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5059061Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5059423Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5059600Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5059609Z 
2023-01-11T21:41:23.5059721Z ok (1.649s)
2023-01-11T21:41:23.5060621Z   test_add_const_int_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5060891Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5061388Z [2023-01-11 21:24:14,915] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 13
2023-01-11T21:41:23.5061881Z [2023-01-11 21:24:16,544] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 13
2023-01-11T21:41:23.5061889Z 
2023-01-11T21:41:23.5062058Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5062180Z import torch
2023-01-11T21:41:23.5062299Z import random
2023-01-11T21:41:23.5062705Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5062930Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5062940Z 
2023-01-11T21:41:23.5063078Z aten = torch.ops.aten
2023-01-11T21:41:23.5063320Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5063490Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5063498Z 
2023-01-11T21:41:23.5063513Z 
2023-01-11T21:41:23.5063773Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5064159Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5064372Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5064531Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5064634Z {
2023-01-11T21:41:23.5064810Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5064914Z     {
2023-01-11T21:41:23.5065050Z         #pragma omp for 
2023-01-11T21:41:23.5065194Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.5065304Z         {
2023-01-11T21:41:23.5065529Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5065850Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.5066002Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5066161Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5066275Z         }
2023-01-11T21:41:23.5066455Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5066600Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:23.5066690Z         {
2023-01-11T21:41:23.5069226Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5069417Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.5069567Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5069710Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5069817Z         }
2023-01-11T21:41:23.5069921Z     }
2023-01-11T21:41:23.5070009Z }
2023-01-11T21:41:23.5070159Z ''')
2023-01-11T21:41:23.5070169Z 
2023-01-11T21:41:23.5070176Z 
2023-01-11T21:41:23.5070338Z async_compile.wait(globals())
2023-01-11T21:41:23.5070464Z del async_compile
2023-01-11T21:41:23.5070472Z 
2023-01-11T21:41:23.5070600Z def call(args):
2023-01-11T21:41:23.5070718Z     arg0_1, = args
2023-01-11T21:41:23.5070840Z     args.clear()
2023-01-11T21:41:23.5071191Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5071551Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5071669Z     del arg0_1
2023-01-11T21:41:23.5071792Z     return (buf0, )
2023-01-11T21:41:23.5071801Z 
2023-01-11T21:41:23.5071809Z 
2023-01-11T21:41:23.5071940Z if __name__ == "__main__":
2023-01-11T21:41:23.5072148Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5072372Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5072736Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5072913Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5072936Z 
2023-01-11T21:41:23.5073034Z ok (1.644s)
2023-01-11T21:41:23.5074090Z   test_add_inplace_permuted_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5074332Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5074831Z [2023-01-11 21:24:16,560] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 14
2023-01-11T21:41:23.5075337Z [2023-01-11 21:24:18,280] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 14
2023-01-11T21:41:23.5075347Z 
2023-01-11T21:41:23.5075517Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5075639Z import torch
2023-01-11T21:41:23.5075763Z import random
2023-01-11T21:41:23.5075957Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5076176Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5076185Z 
2023-01-11T21:41:23.5076320Z aten = torch.ops.aten
2023-01-11T21:41:23.5076568Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5076730Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5076739Z 
2023-01-11T21:41:23.5076746Z 
2023-01-11T21:41:23.5076997Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5077381Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5077601Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5077782Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5077939Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5078044Z {
2023-01-11T21:41:23.5078222Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5078329Z     {
2023-01-11T21:41:23.5078490Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.5078630Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.5078736Z         {
2023-01-11T21:41:23.5078869Z             for(long i1=0; i1<12; i1+=1)
2023-01-11T21:41:23.5078978Z             {
2023-01-11T21:41:23.5079142Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:23.5079253Z                 {
2023-01-11T21:41:23.5079529Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (221*i1) + (2652*i0));
2023-01-11T21:41:23.5079787Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i2) + (221*i0));
2023-01-11T21:41:23.5079946Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5080129Z                     tmp2.store(out_ptr0 + (8*i2) + (221*i1) + (2652*i0));
2023-01-11T21:41:23.5080239Z                 }
2023-01-11T21:41:23.5080409Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.5080563Z                 for(long i2=216; i2<221; i2+=1)
2023-01-11T21:41:23.5080673Z                 {
2023-01-11T21:41:23.5080861Z                     auto tmp0 = out_ptr0[i2 + (221*i1) + (2652*i0)];
2023-01-11T21:41:23.5081034Z                     auto tmp1 = in_ptr1[i2 + (221*i0)];
2023-01-11T21:41:23.5081179Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5081411Z                     out_ptr0[i2 + (221*i1) + (2652*i0)] = tmp2;
2023-01-11T21:41:23.5081521Z                 }
2023-01-11T21:41:23.5081635Z             }
2023-01-11T21:41:23.5081745Z         }
2023-01-11T21:41:23.5081851Z     }
2023-01-11T21:41:23.5081953Z }
2023-01-11T21:41:23.5082080Z ''')
2023-01-11T21:41:23.5082089Z 
2023-01-11T21:41:23.5082096Z 
2023-01-11T21:41:23.5082258Z async_compile.wait(globals())
2023-01-11T21:41:23.5082384Z del async_compile
2023-01-11T21:41:23.5082394Z 
2023-01-11T21:41:23.5082515Z def call(args):
2023-01-11T21:41:23.5082646Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5082769Z     args.clear()
2023-01-11T21:41:23.5083060Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:23.5083178Z     del arg1_1
2023-01-11T21:41:23.5083290Z     return (arg0_1, )
2023-01-11T21:41:23.5083298Z 
2023-01-11T21:41:23.5083306Z 
2023-01-11T21:41:23.5083433Z if __name__ == "__main__":
2023-01-11T21:41:23.5083687Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5083912Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5084325Z     arg0_1 = rand_strided((2, 13, 12, 17), (2652, 17, 221, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5084729Z     arg1_1 = rand_strided((2, 13, 1, 17), (221, 17, 17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5084935Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5084943Z 
2023-01-11T21:41:23.5085057Z ok (1.736s)
2023-01-11T21:41:23.5085930Z   test_addmm_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5086165Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5086655Z [2023-01-11 21:24:18,313] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 15
2023-01-11T21:41:23.5087155Z [2023-01-11 21:24:20,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 15
2023-01-11T21:41:23.5087164Z 
2023-01-11T21:41:23.5087331Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5087455Z import torch
2023-01-11T21:41:23.5087578Z import random
2023-01-11T21:41:23.5087786Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5088003Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5088011Z 
2023-01-11T21:41:23.5088132Z aten = torch.ops.aten
2023-01-11T21:41:23.5088380Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5088544Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5088552Z 
2023-01-11T21:41:23.5088559Z 
2023-01-11T21:41:23.5088814Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5089190Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5089403Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5089588Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5089768Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.5089929Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5090099Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.5090269Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.5090373Z {
2023-01-11T21:41:23.5090550Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5090658Z     {
2023-01-11T21:41:23.5090791Z         #pragma omp for 
2023-01-11T21:41:23.5090918Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5091025Z         {
2023-01-11T21:41:23.5091272Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5091568Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.5091723Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5091885Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5091990Z         }
2023-01-11T21:41:23.5092146Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5092292Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.5092398Z         {
2023-01-11T21:41:23.5092548Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5092723Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.5092870Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5093008Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5093101Z         }
2023-01-11T21:41:23.5093236Z         #pragma omp for 
2023-01-11T21:41:23.5093378Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5093489Z         {
2023-01-11T21:41:23.5093767Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.5094010Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.5094159Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5094316Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.5094412Z         }
2023-01-11T21:41:23.5094579Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5094723Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.5094833Z         {
2023-01-11T21:41:23.5094983Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.5095160Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.5095290Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5095428Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.5095534Z         }
2023-01-11T21:41:23.5095670Z         #pragma omp for 
2023-01-11T21:41:23.5095812Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5095917Z         {
2023-01-11T21:41:23.5096161Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:23.5096397Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.5096528Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5096687Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.5096793Z         }
2023-01-11T21:41:23.5096963Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5097107Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.5097212Z         {
2023-01-11T21:41:23.5097342Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:41:23.5097516Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.5097665Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5097805Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:23.5097914Z         }
2023-01-11T21:41:23.5098016Z     }
2023-01-11T21:41:23.5098119Z }
2023-01-11T21:41:23.5098257Z ''')
2023-01-11T21:41:23.5098266Z 
2023-01-11T21:41:23.5098292Z 
2023-01-11T21:41:23.5098525Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.5098919Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5099129Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.5099235Z {
2023-01-11T21:41:23.5099411Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5099519Z     {
2023-01-11T21:41:23.5099655Z         #pragma omp for 
2023-01-11T21:41:23.5099781Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5099890Z         {
2023-01-11T21:41:23.5100135Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.5100379Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:23.5100529Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5100696Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.5100803Z         }
2023-01-11T21:41:23.5100959Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5101106Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.5101267Z         {
2023-01-11T21:41:23.5101420Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.5101596Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:23.5101747Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5101892Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5101981Z         }
2023-01-11T21:41:23.5102085Z     }
2023-01-11T21:41:23.5102192Z }
2023-01-11T21:41:23.5102473Z ''')
2023-01-11T21:41:23.5102483Z 
2023-01-11T21:41:23.5102490Z 
2023-01-11T21:41:23.5102652Z async_compile.wait(globals())
2023-01-11T21:41:23.5102779Z del async_compile
2023-01-11T21:41:23.5102787Z 
2023-01-11T21:41:23.5102909Z def call(args):
2023-01-11T21:41:23.5103038Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.5103159Z     args.clear()
2023-01-11T21:41:23.5103529Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5103890Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5104332Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5104767Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.5104892Z     del arg0_1
2023-01-11T21:41:23.5105007Z     del arg1_1
2023-01-11T21:41:23.5105105Z     del arg2_1
2023-01-11T21:41:23.5105470Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5105691Z     aten.addmm.out(buf0, buf1, buf2, beta=1, alpha=1, out=buf3)
2023-01-11T21:41:23.5105804Z     del buf0
2023-01-11T21:41:23.5105918Z     del buf1
2023-01-11T21:41:23.5106027Z     del buf2
2023-01-11T21:41:23.5106179Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:23.5106345Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.5106465Z     return (buf4, )
2023-01-11T21:41:23.5106475Z 
2023-01-11T21:41:23.5106487Z 
2023-01-11T21:41:23.5106621Z if __name__ == "__main__":
2023-01-11T21:41:23.5106825Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5107045Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5107405Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5107763Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5108101Z     arg2_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5108319Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.5108326Z 
2023-01-11T21:41:23.5108441Z ok (1.721s)
2023-01-11T21:41:23.5109353Z   test_alexnet_prefix_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5109584Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5110078Z [2023-01-11 21:24:20,148] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 16
2023-01-11T21:41:23.5110476Z [2023-01-11 21:24:20,236] torch._inductor.scheduler: [DEBUG] removed dead node: buf2
2023-01-11T21:41:23.5110970Z [2023-01-11 21:24:21,835] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 16
2023-01-11T21:41:23.5110980Z 
2023-01-11T21:41:23.5111150Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5111273Z import torch
2023-01-11T21:41:23.5111379Z import random
2023-01-11T21:41:23.5111589Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5111807Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5111816Z 
2023-01-11T21:41:23.5112028Z aten = torch.ops.aten
2023-01-11T21:41:23.5112272Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5112435Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5112444Z 
2023-01-11T21:41:23.5112452Z 
2023-01-11T21:41:23.5112707Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5113082Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5113283Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5113456Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5113561Z {
2023-01-11T21:41:23.5113989Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5114119Z     {
2023-01-11T21:41:23.5114262Z         #pragma omp for 
2023-01-11T21:41:23.5114403Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.5114495Z         {
2023-01-11T21:41:23.5114634Z             #pragma GCC ivdep
2023-01-11T21:41:23.5114777Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:41:23.5114972Z             {
2023-01-11T21:41:23.5115117Z                 #pragma GCC ivdep
2023-01-11T21:41:23.5115270Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:23.5115381Z                 {
2023-01-11T21:41:23.5115479Z                     {
2023-01-11T21:41:23.5115601Z                         {
2023-01-11T21:41:23.5115802Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5116002Z                             auto tmp2 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5116198Z                             auto tmp5 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5116396Z                             auto tmp8 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5116601Z                             auto tmp11 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5116784Z                             auto tmp14 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5116993Z                             auto tmp17 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5117192Z                             auto tmp20 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5117392Z                             auto tmp23 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5117565Z                             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.5117737Z                             auto tmp3 = tmp2 * (tmp2>0);
2023-01-11T21:41:23.5117972Z                             auto tmp4 = (tmp1 != tmp1) ? tmp1 : std::max(tmp3, tmp1);
2023-01-11T21:41:23.5118143Z                             auto tmp6 = tmp5 * (tmp5>0);
2023-01-11T21:41:23.5118369Z                             auto tmp7 = (tmp4 != tmp4) ? tmp4 : std::max(tmp6, tmp4);
2023-01-11T21:41:23.5118524Z                             auto tmp9 = tmp8 * (tmp8>0);
2023-01-11T21:41:23.5118752Z                             auto tmp10 = (tmp7 != tmp7) ? tmp7 : std::max(tmp9, tmp7);
2023-01-11T21:41:23.5118935Z                             auto tmp12 = tmp11 * (tmp11>0);
2023-01-11T21:41:23.5119172Z                             auto tmp13 = (tmp10 != tmp10) ? tmp10 : std::max(tmp12, tmp10);
2023-01-11T21:41:23.5119350Z                             auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:41:23.5119578Z                             auto tmp16 = (tmp13 != tmp13) ? tmp13 : std::max(tmp15, tmp13);
2023-01-11T21:41:23.5119756Z                             auto tmp18 = tmp17 * (tmp17>0);
2023-01-11T21:41:23.5119979Z                             auto tmp19 = (tmp16 != tmp16) ? tmp16 : std::max(tmp18, tmp16);
2023-01-11T21:41:23.5120138Z                             auto tmp21 = tmp20 * (tmp20>0);
2023-01-11T21:41:23.5120367Z                             auto tmp22 = (tmp19 != tmp19) ? tmp19 : std::max(tmp21, tmp19);
2023-01-11T21:41:23.5120542Z                             auto tmp24 = tmp23 * (tmp23>0);
2023-01-11T21:41:23.5120768Z                             auto tmp25 = (tmp22 != tmp22) ? tmp22 : std::max(tmp24, tmp22);
2023-01-11T21:41:23.5121000Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp25;
2023-01-11T21:41:23.5121115Z                         }
2023-01-11T21:41:23.5121227Z                     }
2023-01-11T21:41:23.5121336Z                 }
2023-01-11T21:41:23.5121428Z             }
2023-01-11T21:41:23.5121534Z         }
2023-01-11T21:41:23.5121641Z     }
2023-01-11T21:41:23.5121745Z }
2023-01-11T21:41:23.5121910Z ''')
2023-01-11T21:41:23.5121919Z 
2023-01-11T21:41:23.5121927Z 
2023-01-11T21:41:23.5122091Z async_compile.wait(globals())
2023-01-11T21:41:23.5122217Z del async_compile
2023-01-11T21:41:23.5122226Z 
2023-01-11T21:41:23.5122332Z def call(args):
2023-01-11T21:41:23.5122477Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.5122602Z     args.clear()
2023-01-11T21:41:23.5122842Z     buf0 = aten.convolution(arg2_1, arg1_1, arg0_1, (4, 4), (2, 2), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:23.5123040Z     assert_size_stride(buf0, (16, 64, 55, 55), (193600, 3025, 55, 1))
2023-01-11T21:41:23.5123210Z     del arg0_1
2023-01-11T21:41:23.5123329Z     del arg1_1
2023-01-11T21:41:23.5123428Z     del arg2_1
2023-01-11T21:41:23.5123852Z     buf1 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5124090Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.5124217Z     return (buf1, )
2023-01-11T21:41:23.5124226Z 
2023-01-11T21:41:23.5124233Z 
2023-01-11T21:41:23.5124368Z if __name__ == "__main__":
2023-01-11T21:41:23.5124574Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5124799Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5125161Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5125550Z     arg1_1 = rand_strided((64, 3, 11, 11), (363, 121, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5125978Z     arg2_1 = rand_strided((16, 3, 224, 224), (150528, 50176, 224, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5126206Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.5126214Z 
2023-01-11T21:41:23.5126329Z ok (1.921s)
2023-01-11T21:41:23.5127213Z   test_any_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5127439Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5127927Z [2023-01-11 21:24:21,960] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 17
2023-01-11T21:41:23.5128424Z [2023-01-11 21:24:23,540] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 17
2023-01-11T21:41:23.5129253Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5129479Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5129959Z [2023-01-11 21:24:23,579] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 18
2023-01-11T21:41:23.5130435Z [2023-01-11 21:24:25,354] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 18
2023-01-11T21:41:23.5130458Z 
2023-01-11T21:41:23.5130611Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5130737Z import torch
2023-01-11T21:41:23.5130859Z import random
2023-01-11T21:41:23.5131068Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5131346Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5131355Z 
2023-01-11T21:41:23.5131490Z aten = torch.ops.aten
2023-01-11T21:41:23.5131729Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5131876Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5131883Z 
2023-01-11T21:41:23.5131907Z 
2023-01-11T21:41:23.5132139Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5132518Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5132727Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.5132901Z                        bool* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.5133088Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5133261Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:23.5133430Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:23.5133517Z {
2023-01-11T21:41:23.5133713Z     auto out_ptr2 = in_out_ptr0;
2023-01-11T21:41:23.5133867Z     auto out_ptr3 = in_out_ptr1;
2023-01-11T21:41:23.5133970Z     {
2023-01-11T21:41:23.5134075Z         {
2023-01-11T21:41:23.5134208Z             bool tmp2 = 0;
2023-01-11T21:41:23.5134339Z             bool tmp4 = 0;
2023-01-11T21:41:23.5134452Z             bool tmp8 = 0;
2023-01-11T21:41:23.5134587Z             bool tmp10 = 0;
2023-01-11T21:41:23.5134774Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5134888Z             {
2023-01-11T21:41:23.5135188Z                 #pragma omp for reduction(||:tmp2) reduction(||:tmp4) reduction(||:tmp8) reduction(||:tmp10) 
2023-01-11T21:41:23.5135345Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.5135455Z                 {
2023-01-11T21:41:23.5135556Z                     {
2023-01-11T21:41:23.5135722Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5135917Z                         auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:23.5136109Z                         auto tmp3 = std::isinf(tmp0);
2023-01-11T21:41:23.5136270Z                         auto tmp5 = tmp3 == 0;
2023-01-11T21:41:23.5136464Z                         auto tmp6 = static_cast<long>(tmp5);
2023-01-11T21:41:23.5136649Z                         auto tmp7 = static_cast<bool>(tmp6);
2023-01-11T21:41:23.5136790Z                         auto tmp9 = tmp5 == 0;
2023-01-11T21:41:23.5136942Z                         tmp2 = tmp2 || tmp1;
2023-01-11T21:41:23.5137088Z                         tmp4 = tmp4 || tmp3;
2023-01-11T21:41:23.5137239Z                         tmp8 = tmp8 || tmp7;
2023-01-11T21:41:23.5137392Z                         tmp10 = tmp10 || tmp9;
2023-01-11T21:41:23.5137506Z                     }
2023-01-11T21:41:23.5137614Z                 }
2023-01-11T21:41:23.5137705Z             }
2023-01-11T21:41:23.5137841Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:23.5137975Z             out_ptr1[0] = tmp4;
2023-01-11T21:41:23.5138107Z             out_ptr2[0] = tmp8;
2023-01-11T21:41:23.5138250Z             out_ptr3[0] = tmp10;
2023-01-11T21:41:23.5138359Z         }
2023-01-11T21:41:23.5138465Z     }
2023-01-11T21:41:23.5138553Z     {
2023-01-11T21:41:23.5138657Z         {
2023-01-11T21:41:23.5138806Z             auto tmp0 = out_ptr2[0];
2023-01-11T21:41:23.5138951Z             auto tmp1 = tmp0 == 0;
2023-01-11T21:41:23.5139089Z             in_out_ptr0[0] = tmp1;
2023-01-11T21:41:23.5139195Z         }
2023-01-11T21:41:23.5139281Z     }
2023-01-11T21:41:23.5139383Z     {
2023-01-11T21:41:23.5139489Z         {
2023-01-11T21:41:23.5139637Z             auto tmp0 = out_ptr3[0];
2023-01-11T21:41:23.5139782Z             auto tmp1 = tmp0 == 0;
2023-01-11T21:41:23.5139922Z             in_out_ptr1[0] = tmp1;
2023-01-11T21:41:23.5140028Z         }
2023-01-11T21:41:23.5140117Z     }
2023-01-11T21:41:23.5140219Z }
2023-01-11T21:41:23.5140363Z ''')
2023-01-11T21:41:23.5140372Z 
2023-01-11T21:41:23.5140379Z 
2023-01-11T21:41:23.5140540Z async_compile.wait(globals())
2023-01-11T21:41:23.5140666Z del async_compile
2023-01-11T21:41:23.5140734Z 
2023-01-11T21:41:23.5140862Z def call(args):
2023-01-11T21:41:23.5140983Z     arg0_1, = args
2023-01-11T21:41:23.5141088Z     args.clear()
2023-01-11T21:41:23.5141425Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5141754Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5142081Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5142585Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5142711Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:41:23.5142821Z     buf5 = buf3; del buf3  # reuse
2023-01-11T21:41:23.5143117Z     kernel_cpp_0(c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.5143228Z     del arg0_1
2023-01-11T21:41:23.5143374Z     return (buf0, buf1, buf4, buf5, )
2023-01-11T21:41:23.5143381Z 
2023-01-11T21:41:23.5143391Z 
2023-01-11T21:41:23.5143579Z if __name__ == "__main__":
2023-01-11T21:41:23.5143742Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5143913Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5144219Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5144375Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5144382Z 
2023-01-11T21:41:23.5144387Z 
2023-01-11T21:41:23.5144513Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5144593Z import torch
2023-01-11T21:41:23.5144683Z import random
2023-01-11T21:41:23.5144844Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5145017Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5145024Z 
2023-01-11T21:41:23.5145175Z aten = torch.ops.aten
2023-01-11T21:41:23.5145473Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5145670Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5145690Z 
2023-01-11T21:41:23.5145699Z 
2023-01-11T21:41:23.5146021Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5146497Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5146769Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.5146990Z                        bool* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.5147220Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5147432Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:23.5147644Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:23.5147780Z {
2023-01-11T21:41:23.5147962Z     auto out_ptr3 = in_out_ptr0;
2023-01-11T21:41:23.5148159Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:23.5148387Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5148523Z     {
2023-01-11T21:41:23.5148699Z         #pragma omp for 
2023-01-11T21:41:23.5148886Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.5149040Z         {
2023-01-11T21:41:23.5149172Z             {
2023-01-11T21:41:23.5149311Z                 {
2023-01-11T21:41:23.5149489Z                     bool tmp2 = 0;
2023-01-11T21:41:23.5149683Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.5149835Z                     {
2023-01-11T21:41:23.5149988Z                         {
2023-01-11T21:41:23.5150210Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.5150440Z                             auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:23.5150640Z                             tmp2 = tmp2 || tmp1;
2023-01-11T21:41:23.5150792Z                         }
2023-01-11T21:41:23.5150941Z                     }
2023-01-11T21:41:23.5151126Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5151263Z                 }
2023-01-11T21:41:23.5151395Z             }
2023-01-11T21:41:23.5151529Z         }
2023-01-11T21:41:23.5151674Z     }
2023-01-11T21:41:23.5151943Z     {
2023-01-11T21:41:23.5152086Z         {
2023-01-11T21:41:23.5152251Z             bool tmp2 = 0;
2023-01-11T21:41:23.5152417Z             bool tmp5 = 0;
2023-01-11T21:41:23.5152652Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5152792Z             {
2023-01-11T21:41:23.5153084Z                 #pragma omp for reduction(||:tmp2) reduction(||:tmp5) 
2023-01-11T21:41:23.5153286Z                 for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.5153432Z                 {
2023-01-11T21:41:23.5153575Z                     {
2023-01-11T21:41:23.5153854Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5154069Z                         auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:23.5154262Z                         auto tmp3 = tmp1 == 0;
2023-01-11T21:41:23.5154466Z                         auto tmp4 = tmp3 == 0;
2023-01-11T21:41:23.5154657Z                         tmp2 = tmp2 || tmp1;
2023-01-11T21:41:23.5154844Z                         tmp5 = tmp5 || tmp4;
2023-01-11T21:41:23.5155066Z                     }
2023-01-11T21:41:23.5155210Z                 }
2023-01-11T21:41:23.5155340Z             }
2023-01-11T21:41:23.5155512Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:23.5155685Z             out_ptr2[0] = tmp5;
2023-01-11T21:41:23.5155822Z         }
2023-01-11T21:41:23.5155954Z     }
2023-01-11T21:41:23.5156182Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5156322Z     {
2023-01-11T21:41:23.5156486Z         #pragma omp for 
2023-01-11T21:41:23.5156672Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5156808Z         {
2023-01-11T21:41:23.5156958Z             {
2023-01-11T21:41:23.5157098Z                 {
2023-01-11T21:41:23.5157276Z                     bool tmp5 = 0;
2023-01-11T21:41:23.5157455Z                     for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:23.5157600Z                     {
2023-01-11T21:41:23.5157746Z                         {
2023-01-11T21:41:23.5157968Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:23.5158214Z                             auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:23.5158422Z                             auto tmp2 = tmp1 == 0;
2023-01-11T21:41:23.5158661Z                             auto tmp3 = static_cast<long>(tmp2);
2023-01-11T21:41:23.5158901Z                             auto tmp4 = static_cast<bool>(tmp3);
2023-01-11T21:41:23.5159080Z                             tmp5 = tmp5 || tmp4;
2023-01-11T21:41:23.5159220Z                         }
2023-01-11T21:41:23.5159368Z                     }
2023-01-11T21:41:23.5159545Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:23.5159695Z                 }
2023-01-11T21:41:23.5159829Z             }
2023-01-11T21:41:23.5159943Z         }
2023-01-11T21:41:23.5160120Z         #pragma omp for 
2023-01-11T21:41:23.5160303Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5160446Z         {
2023-01-11T21:41:23.5160584Z             {
2023-01-11T21:41:23.5160731Z                 {
2023-01-11T21:41:23.5160946Z                     auto tmp0 = out_ptr3[i0];
2023-01-11T21:41:23.5161128Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:23.5161329Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.5161479Z                 }
2023-01-11T21:41:23.5161626Z             }
2023-01-11T21:41:23.5161771Z         }
2023-01-11T21:41:23.5161949Z         #pragma omp single
2023-01-11T21:41:23.5162092Z         {
2023-01-11T21:41:23.5162221Z             {
2023-01-11T21:41:23.5162363Z                 {
2023-01-11T21:41:23.5162561Z                     auto tmp0 = out_ptr2[0];
2023-01-11T21:41:23.5162753Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:23.5162934Z                     in_out_ptr1[0] = tmp1;
2023-01-11T21:41:23.5163076Z                 }
2023-01-11T21:41:23.5163210Z             }
2023-01-11T21:41:23.5163328Z         }
2023-01-11T21:41:23.5163463Z     }
2023-01-11T21:41:23.5163603Z }
2023-01-11T21:41:23.5163806Z ''')
2023-01-11T21:41:23.5163817Z 
2023-01-11T21:41:23.5163826Z 
2023-01-11T21:41:23.5164120Z async_compile.wait(globals())
2023-01-11T21:41:23.5164285Z del async_compile
2023-01-11T21:41:23.5164295Z 
2023-01-11T21:41:23.5164461Z def call(args):
2023-01-11T21:41:23.5164597Z     arg0_1, = args
2023-01-11T21:41:23.5164763Z     args.clear()
2023-01-11T21:41:23.5165203Z     buf0 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5165613Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5166020Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5166450Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5166642Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:23.5166810Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:41:23.5167293Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.5167451Z     del arg0_1
2023-01-11T21:41:23.5167719Z     return (buf0, buf1, buf3, buf5, )
2023-01-11T21:41:23.5167734Z 
2023-01-11T21:41:23.5167743Z 
2023-01-11T21:41:23.5167918Z if __name__ == "__main__":
2023-01-11T21:41:23.5168189Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5168483Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5168929Z     arg0_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5169172Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5169182Z 
2023-01-11T21:41:23.5169338Z ok (3.432s)
2023-01-11T21:41:23.5170538Z   test_arange1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5170838Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5171408Z [2023-01-11 21:24:25,416] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 19
2023-01-11T21:41:23.5172035Z [2023-01-11 21:24:27,020] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 19
2023-01-11T21:41:23.5172046Z 
2023-01-11T21:41:23.5172265Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5172430Z import torch
2023-01-11T21:41:23.5172592Z import random
2023-01-11T21:41:23.5172842Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5173129Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5173139Z 
2023-01-11T21:41:23.5173320Z aten = torch.ops.aten
2023-01-11T21:41:23.5173649Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5173867Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5173877Z 
2023-01-11T21:41:23.5173887Z 
2023-01-11T21:41:23.5174209Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5174716Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5175000Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5175219Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5175421Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.5175557Z {
2023-01-11T21:41:23.5175791Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5175924Z     {
2023-01-11T21:41:23.5176099Z         #pragma omp for 
2023-01-11T21:41:23.5176284Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.5176408Z         {
2023-01-11T21:41:23.5176548Z             {
2023-01-11T21:41:23.5176688Z                 {
2023-01-11T21:41:23.5176895Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5177129Z                     auto tmp1 = static_cast<float>(i0);
2023-01-11T21:41:23.5177328Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5177610Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5177744Z                 }
2023-01-11T21:41:23.5177885Z             }
2023-01-11T21:41:23.5178026Z         }
2023-01-11T21:41:23.5178198Z         #pragma omp for 
2023-01-11T21:41:23.5178381Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5178518Z         {
2023-01-11T21:41:23.5178693Z             #pragma GCC ivdep
2023-01-11T21:41:23.5178859Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.5179000Z             {
2023-01-11T21:41:23.5179141Z                 {
2023-01-11T21:41:23.5179292Z                     {
2023-01-11T21:41:23.5179513Z                         auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.5179764Z                         auto tmp1 = static_cast<long>(10 + i1);
2023-01-11T21:41:23.5180013Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:23.5180206Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.5180472Z                         out_ptr1[i1 + (8*i0)] = tmp3;
2023-01-11T21:41:23.5180626Z                     }
2023-01-11T21:41:23.5180775Z                 }
2023-01-11T21:41:23.5180913Z             }
2023-01-11T21:41:23.5181052Z         }
2023-01-11T21:41:23.5181182Z     }
2023-01-11T21:41:23.5181309Z }
2023-01-11T21:41:23.5181493Z ''')
2023-01-11T21:41:23.5181503Z 
2023-01-11T21:41:23.5181511Z 
2023-01-11T21:41:23.5181716Z async_compile.wait(globals())
2023-01-11T21:41:23.5181878Z del async_compile
2023-01-11T21:41:23.5181886Z 
2023-01-11T21:41:23.5182055Z def call(args):
2023-01-11T21:41:23.5182205Z     arg0_1, = args
2023-01-11T21:41:23.5182490Z     args.clear()
2023-01-11T21:41:23.5182924Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5183369Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5183745Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.5183894Z     del arg0_1
2023-01-11T21:41:23.5184073Z     return (buf0, buf1, )
2023-01-11T21:41:23.5184083Z 
2023-01-11T21:41:23.5184092Z 
2023-01-11T21:41:23.5184261Z if __name__ == "__main__":
2023-01-11T21:41:23.5184531Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5184824Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5185264Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5185510Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5185520Z 
2023-01-11T21:41:23.5185675Z ok (1.667s)
2023-01-11T21:41:23.5186922Z   test_arange2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5187221Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5187840Z [2023-01-11 21:24:27,059] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 20
2023-01-11T21:41:23.5188462Z [2023-01-11 21:24:28,741] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 20
2023-01-11T21:41:23.5188473Z 
2023-01-11T21:41:23.5188692Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5188848Z import torch
2023-01-11T21:41:23.5188996Z import random
2023-01-11T21:41:23.5189262Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5189557Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5189567Z 
2023-01-11T21:41:23.5189747Z aten = torch.ops.aten
2023-01-11T21:41:23.5190057Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5190275Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5190284Z 
2023-01-11T21:41:23.5190292Z 
2023-01-11T21:41:23.5190726Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5191218Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5191464Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.5191670Z                        long* __restrict__ out_ptr0)
2023-01-11T21:41:23.5191800Z {
2023-01-11T21:41:23.5192030Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5192168Z     {
2023-01-11T21:41:23.5192341Z         #pragma omp for 
2023-01-11T21:41:23.5192534Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5192666Z         {
2023-01-11T21:41:23.5192845Z             #pragma GCC ivdep
2023-01-11T21:41:23.5193032Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.5193168Z             {
2023-01-11T21:41:23.5193310Z                 {
2023-01-11T21:41:23.5193451Z                     {
2023-01-11T21:41:23.5193663Z                         auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.5194064Z                         auto tmp1 = static_cast<long>(i1);
2023-01-11T21:41:23.5194282Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5194487Z                         out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:23.5194639Z                     }
2023-01-11T21:41:23.5194782Z                 }
2023-01-11T21:41:23.5194924Z             }
2023-01-11T21:41:23.5195069Z         }
2023-01-11T21:41:23.5195188Z     }
2023-01-11T21:41:23.5195323Z }
2023-01-11T21:41:23.5195511Z ''')
2023-01-11T21:41:23.5195520Z 
2023-01-11T21:41:23.5195529Z 
2023-01-11T21:41:23.5195739Z async_compile.wait(globals())
2023-01-11T21:41:23.5195910Z del async_compile
2023-01-11T21:41:23.5195919Z 
2023-01-11T21:41:23.5196086Z def call(args):
2023-01-11T21:41:23.5196240Z     arg0_1, = args
2023-01-11T21:41:23.5196382Z     args.clear()
2023-01-11T21:41:23.5196845Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5197162Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5197324Z     del arg0_1
2023-01-11T21:41:23.5197491Z     return (buf0, )
2023-01-11T21:41:23.5197500Z 
2023-01-11T21:41:23.5197509Z 
2023-01-11T21:41:23.5197670Z if __name__ == "__main__":
2023-01-11T21:41:23.5197932Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5198236Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5198673Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5198917Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5198926Z 
2023-01-11T21:41:23.5199072Z ok (1.719s)
2023-01-11T21:41:23.5200298Z   test_arange3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5200589Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5201204Z [2023-01-11 21:24:28,782] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 21
2023-01-11T21:41:23.5201842Z [2023-01-11 21:24:30,427] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 21
2023-01-11T21:41:23.5201852Z 
2023-01-11T21:41:23.5202075Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5202234Z import torch
2023-01-11T21:41:23.5202377Z import random
2023-01-11T21:41:23.5202659Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5202943Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5202954Z 
2023-01-11T21:41:23.5203137Z aten = torch.ops.aten
2023-01-11T21:41:23.5203457Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5203664Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5203753Z 
2023-01-11T21:41:23.5203768Z 
2023-01-11T21:41:23.5204079Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5204592Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5204848Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5205077Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5205221Z {
2023-01-11T21:41:23.5205447Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5205587Z     {
2023-01-11T21:41:23.5205760Z         #pragma omp for 
2023-01-11T21:41:23.5205941Z         for(long i0=0; i0<14; i0+=1)
2023-01-11T21:41:23.5206063Z         {
2023-01-11T21:41:23.5206206Z             {
2023-01-11T21:41:23.5206352Z                 {
2023-01-11T21:41:23.5206551Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5206792Z                     auto tmp1 = static_cast<long>(4*i0);
2023-01-11T21:41:23.5207037Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:23.5207345Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.5207513Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.5207656Z                 }
2023-01-11T21:41:23.5207800Z             }
2023-01-11T21:41:23.5207946Z         }
2023-01-11T21:41:23.5208084Z     }
2023-01-11T21:41:23.5208218Z }
2023-01-11T21:41:23.5208378Z ''')
2023-01-11T21:41:23.5208406Z 
2023-01-11T21:41:23.5208414Z 
2023-01-11T21:41:23.5208600Z async_compile.wait(globals())
2023-01-11T21:41:23.5208767Z del async_compile
2023-01-11T21:41:23.5208776Z 
2023-01-11T21:41:23.5208944Z def call(args):
2023-01-11T21:41:23.5209102Z     arg0_1, = args
2023-01-11T21:41:23.5209262Z     args.clear()
2023-01-11T21:41:23.5209705Z     buf0 = empty_strided((14, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5210010Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5210152Z     del arg0_1
2023-01-11T21:41:23.5210320Z     return (buf0, )
2023-01-11T21:41:23.5210334Z 
2023-01-11T21:41:23.5210347Z 
2023-01-11T21:41:23.5210519Z if __name__ == "__main__":
2023-01-11T21:41:23.5210777Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5211065Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5211522Z     arg0_1 = rand_strided((14, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5211768Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5211778Z 
2023-01-11T21:41:23.5211930Z ok (1.687s)
2023-01-11T21:41:23.5213132Z   test_arange4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5213403Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5214022Z [2023-01-11 21:24:30,464] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 22
2023-01-11T21:41:23.5214662Z [2023-01-11 21:24:32,074] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 22
2023-01-11T21:41:23.5214673Z 
2023-01-11T21:41:23.5214899Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5215059Z import torch
2023-01-11T21:41:23.5215219Z import random
2023-01-11T21:41:23.5215492Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5215787Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5215797Z 
2023-01-11T21:41:23.5215952Z aten = torch.ops.aten
2023-01-11T21:41:23.5216258Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5216466Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5216477Z 
2023-01-11T21:41:23.5216486Z 
2023-01-11T21:41:23.5216799Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5217372Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5217641Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5217856Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5217989Z {
2023-01-11T21:41:23.5218201Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5218341Z     {
2023-01-11T21:41:23.5218509Z         #pragma omp for 
2023-01-11T21:41:23.5218689Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.5218823Z         {
2023-01-11T21:41:23.5218965Z             {
2023-01-11T21:41:23.5219104Z                 {
2023-01-11T21:41:23.5219291Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5219671Z                     auto tmp1 = static_cast<long>(512 + ((-1)*i0));
2023-01-11T21:41:23.5219906Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:23.5220208Z                     auto tmp3 = tmp0 - tmp2;
2023-01-11T21:41:23.5220473Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.5220624Z                 }
2023-01-11T21:41:23.5220761Z             }
2023-01-11T21:41:23.5220885Z         }
2023-01-11T21:41:23.5221023Z     }
2023-01-11T21:41:23.5221156Z }
2023-01-11T21:41:23.5221325Z ''')
2023-01-11T21:41:23.5221334Z 
2023-01-11T21:41:23.5221342Z 
2023-01-11T21:41:23.5221544Z async_compile.wait(globals())
2023-01-11T21:41:23.5221703Z del async_compile
2023-01-11T21:41:23.5221712Z 
2023-01-11T21:41:23.5221870Z def call(args):
2023-01-11T21:41:23.5222004Z     arg0_1, = args
2023-01-11T21:41:23.5222160Z     args.clear()
2023-01-11T21:41:23.5222799Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5223105Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5223246Z     del arg0_1
2023-01-11T21:41:23.5223392Z     return (buf0, )
2023-01-11T21:41:23.5223402Z 
2023-01-11T21:41:23.5223410Z 
2023-01-11T21:41:23.5223561Z if __name__ == "__main__":
2023-01-11T21:41:23.5223819Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5224074Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5224505Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5224735Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5224744Z 
2023-01-11T21:41:23.5224877Z ok (1.646s)
2023-01-11T21:41:23.5225997Z   test_argmax_argmin1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5226276Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5226932Z [2023-01-11 21:24:32,115] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 23
2023-01-11T21:41:23.5227576Z [2023-01-11 21:24:33,709] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 23
2023-01-11T21:41:23.5227586Z 
2023-01-11T21:41:23.5227806Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5227944Z import torch
2023-01-11T21:41:23.5228113Z import random
2023-01-11T21:41:23.5228385Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5228672Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5228683Z 
2023-01-11T21:41:23.5228863Z aten = torch.ops.aten
2023-01-11T21:41:23.5229186Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5229406Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5229415Z 
2023-01-11T21:41:23.5229424Z 
2023-01-11T21:41:23.5229733Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5230208Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5230615Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5230836Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:23.5231045Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.5231180Z {
2023-01-11T21:41:23.5231320Z     {
2023-01-11T21:41:23.5231469Z         {
2023-01-11T21:41:23.5231726Z             struct IndexValue_1 {size_t index; float value;};
2023-01-11T21:41:23.5232276Z             IndexValue_1 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.5232601Z             #pragma omp declare reduction(argmax : struct IndexValue_1 :\
2023-01-11T21:41:23.5232945Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.5233283Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.5233898Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.5234256Z             struct IndexValue_2 {size_t index; float value;};
2023-01-11T21:41:23.5234449Z             IndexValue_2 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.5234608Z             #pragma omp declare reduction(argmin : struct IndexValue_2 :\
2023-01-11T21:41:23.5234796Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.5234987Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.5235195Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.5235358Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5235457Z             {
2023-01-11T21:41:23.5235643Z                 #pragma omp for reduction(argmax:tmp1) reduction(argmin:tmp2) 
2023-01-11T21:41:23.5235768Z                 for(long i0=0; i0<524288; i0+=1)
2023-01-11T21:41:23.5235847Z                 {
2023-01-11T21:41:23.5235953Z                     {
2023-01-11T21:41:23.5236091Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5236221Z                         if (tmp1.value < tmp0) {
2023-01-11T21:41:23.5236373Z                             tmp1.index = i0; tmp1.value = tmp0;
2023-01-11T21:41:23.5236472Z                         }
2023-01-11T21:41:23.5236609Z                         if (tmp2.value > tmp0) {
2023-01-11T21:41:23.5236758Z                             tmp2.index = i0; tmp2.value = tmp0;
2023-01-11T21:41:23.5236852Z                         }
2023-01-11T21:41:23.5236957Z                     }
2023-01-11T21:41:23.5237054Z                 }
2023-01-11T21:41:23.5237146Z             }
2023-01-11T21:41:23.5237272Z             out_ptr0[0] = tmp1.index;
2023-01-11T21:41:23.5237388Z             out_ptr1[0] = tmp2.index;
2023-01-11T21:41:23.5237457Z         }
2023-01-11T21:41:23.5237550Z     }
2023-01-11T21:41:23.5237637Z }
2023-01-11T21:41:23.5237781Z ''')
2023-01-11T21:41:23.5237790Z 
2023-01-11T21:41:23.5237801Z 
2023-01-11T21:41:23.5237948Z async_compile.wait(globals())
2023-01-11T21:41:23.5238052Z del async_compile
2023-01-11T21:41:23.5238060Z 
2023-01-11T21:41:23.5238163Z def call(args):
2023-01-11T21:41:23.5238247Z     arg0_1, = args
2023-01-11T21:41:23.5238349Z     args.clear()
2023-01-11T21:41:23.5238612Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5238879Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5239118Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.5239223Z     del arg0_1
2023-01-11T21:41:23.5239336Z     return (buf0, buf1, )
2023-01-11T21:41:23.5239344Z 
2023-01-11T21:41:23.5239350Z 
2023-01-11T21:41:23.5239466Z if __name__ == "__main__":
2023-01-11T21:41:23.5239612Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5239787Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5240117Z     arg0_1 = rand_strided((8, 256, 256), (65536, 256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5240365Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5240373Z 
2023-01-11T21:41:23.5240472Z ok (1.639s)
2023-01-11T21:41:23.5241151Z   test_argmax_argmin2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5241340Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5241742Z [2023-01-11 21:24:33,760] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 24
2023-01-11T21:41:23.5242196Z [2023-01-11 21:24:35,321] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 24
2023-01-11T21:41:23.5242212Z 
2023-01-11T21:41:23.5242351Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5242430Z import torch
2023-01-11T21:41:23.5242544Z import random
2023-01-11T21:41:23.5242706Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5242879Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5242887Z 
2023-01-11T21:41:23.5243002Z aten = torch.ops.aten
2023-01-11T21:41:23.5243187Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5243320Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5243328Z 
2023-01-11T21:41:23.5243334Z 
2023-01-11T21:41:23.5243536Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5243817Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5243993Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5244139Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:23.5244269Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.5244402Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.5244533Z                        long* __restrict__ out_ptr3)
2023-01-11T21:41:23.5244622Z {
2023-01-11T21:41:23.5244742Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5244828Z     {
2023-01-11T21:41:23.5244931Z         #pragma omp for 
2023-01-11T21:41:23.5245040Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:23.5245127Z         {
2023-01-11T21:41:23.5245219Z             {
2023-01-11T21:41:23.5245292Z                 {
2023-01-11T21:41:23.5245461Z                     struct IndexValue_3 {size_t index; float value;};
2023-01-11T21:41:23.5245827Z                     IndexValue_3 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.5246020Z                     #pragma omp declare reduction(argmax : struct IndexValue_3 :\
2023-01-11T21:41:23.5246248Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.5246481Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.5246857Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.5247035Z                     struct IndexValue_4 {size_t index; float value;};
2023-01-11T21:41:23.5247234Z                     IndexValue_4 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.5247422Z                     #pragma omp declare reduction(argmin : struct IndexValue_4 :\
2023-01-11T21:41:23.5247635Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.5247837Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.5248043Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.5248257Z                     for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:23.5248354Z                     {
2023-01-11T21:41:23.5248448Z                         {
2023-01-11T21:41:23.5248599Z                             auto tmp0 = in_ptr0[i0 + (144*i1)];
2023-01-11T21:41:23.5248718Z                             if (tmp1.value < tmp0) {
2023-01-11T21:41:23.5248872Z                                 tmp1.index = i1; tmp1.value = tmp0;
2023-01-11T21:41:23.5248975Z                             }
2023-01-11T21:41:23.5249109Z                             if (tmp2.value > tmp0) {
2023-01-11T21:41:23.5249261Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:41:23.5249358Z                             }
2023-01-11T21:41:23.5249451Z                         }
2023-01-11T21:41:23.5249534Z                     }
2023-01-11T21:41:23.5249673Z                     out_ptr0[i0] = tmp1.index;
2023-01-11T21:41:23.5249803Z                     out_ptr1[i0] = tmp2.index;
2023-01-11T21:41:23.5249897Z                 }
2023-01-11T21:41:23.5250052Z             }
2023-01-11T21:41:23.5250149Z         }
2023-01-11T21:41:23.5250268Z         #pragma omp for 
2023-01-11T21:41:23.5250367Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:23.5250455Z         {
2023-01-11T21:41:23.5250542Z             {
2023-01-11T21:41:23.5250631Z                 {
2023-01-11T21:41:23.5250806Z                     struct IndexValue_5 {size_t index; float value;};
2023-01-11T21:41:23.5251177Z                     IndexValue_5 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.5251375Z                     #pragma omp declare reduction(argmax : struct IndexValue_5 :\
2023-01-11T21:41:23.5274538Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.5274769Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.5275192Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.5275374Z                     struct IndexValue_6 {size_t index; float value;};
2023-01-11T21:41:23.5275578Z                     IndexValue_6 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.5275774Z                     #pragma omp declare reduction(argmin : struct IndexValue_6 :\
2023-01-11T21:41:23.5275983Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.5276211Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.5276421Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.5276567Z                     for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:23.5276661Z                     {
2023-01-11T21:41:23.5276761Z                         {
2023-01-11T21:41:23.5276895Z                             auto tmp0 = in_ptr0[i1 + (144*i0)];
2023-01-11T21:41:23.5277055Z                             if (tmp1.value < tmp0) {
2023-01-11T21:41:23.5277228Z                                 tmp1.index = i1; tmp1.value = tmp0;
2023-01-11T21:41:23.5277339Z                             }
2023-01-11T21:41:23.5277469Z                             if (tmp2.value > tmp0) {
2023-01-11T21:41:23.5277622Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:41:23.5277719Z                             }
2023-01-11T21:41:23.5277810Z                         }
2023-01-11T21:41:23.5277902Z                     }
2023-01-11T21:41:23.5278039Z                     out_ptr2[i0] = tmp1.index;
2023-01-11T21:41:23.5278168Z                     out_ptr3[i0] = tmp2.index;
2023-01-11T21:41:23.5278244Z                 }
2023-01-11T21:41:23.5278330Z             }
2023-01-11T21:41:23.5278412Z         }
2023-01-11T21:41:23.5278498Z     }
2023-01-11T21:41:23.5278574Z }
2023-01-11T21:41:23.5278706Z ''')
2023-01-11T21:41:23.5278717Z 
2023-01-11T21:41:23.5278723Z 
2023-01-11T21:41:23.5279003Z async_compile.wait(globals())
2023-01-11T21:41:23.5279081Z del async_compile
2023-01-11T21:41:23.5279087Z 
2023-01-11T21:41:23.5279200Z def call(args):
2023-01-11T21:41:23.5279295Z     arg0_1, = args
2023-01-11T21:41:23.5279393Z     args.clear()
2023-01-11T21:41:23.5279679Z     buf0 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5279953Z     buf1 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5280231Z     buf2 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5280484Z     buf3 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5280773Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:23.5280874Z     del arg0_1
2023-01-11T21:41:23.5280991Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:23.5280997Z 
2023-01-11T21:41:23.5281008Z 
2023-01-11T21:41:23.5281200Z if __name__ == "__main__":
2023-01-11T21:41:23.5281366Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5281536Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5281837Z     arg0_1 = rand_strided((144, 144), (144, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5281974Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5281995Z 
2023-01-11T21:41:23.5282068Z ok (1.609s)
2023-01-11T21:41:23.5282231Z   test_argmax_argmin3_cpu (__main__.CpuTests) ... skip: 
2023-01-11T21:41:23.5282444Z         FIXME: In the case of having equally max/min elements, our implementation returns
2023-01-11T21:41:23.5282590Z         the last index instead of the first one
2023-01-11T21:41:23.5282684Z          (0.001s)
2023-01-11T21:41:23.5283393Z   test_as_strided_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5283576Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5283952Z [2023-01-11 21:24:35,356] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 25
2023-01-11T21:41:23.5284325Z [2023-01-11 21:24:36,913] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 25
2023-01-11T21:41:23.5284352Z 
2023-01-11T21:41:23.5284470Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5284572Z import torch
2023-01-11T21:41:23.5284674Z import random
2023-01-11T21:41:23.5284826Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5284998Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5285005Z 
2023-01-11T21:41:23.5285112Z aten = torch.ops.aten
2023-01-11T21:41:23.5285314Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5285429Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5285436Z 
2023-01-11T21:41:23.5285459Z 
2023-01-11T21:41:23.5285641Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5285925Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5286090Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.5286232Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:23.5286323Z {
2023-01-11T21:41:23.5286470Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5286558Z     {
2023-01-11T21:41:23.5286651Z         #pragma omp for 
2023-01-11T21:41:23.5286773Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:23.5286859Z         {
2023-01-11T21:41:23.5287062Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5287272Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.5287486Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5287676Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.5287780Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.5287919Z             tmp4.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.5288006Z         }
2023-01-11T21:41:23.5288144Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5288268Z         for(long i0=4096; i0<4096; i0+=1)
2023-01-11T21:41:23.5288361Z         {
2023-01-11T21:41:23.5288477Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5288602Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.5288724Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5288858Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.5288976Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.5289090Z             in_out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.5289179Z         }
2023-01-11T21:41:23.5289333Z     }
2023-01-11T21:41:23.5289409Z }
2023-01-11T21:41:23.5289532Z ''')
2023-01-11T21:41:23.5289540Z 
2023-01-11T21:41:23.5289545Z 
2023-01-11T21:41:23.5289680Z async_compile.wait(globals())
2023-01-11T21:41:23.5289791Z del async_compile
2023-01-11T21:41:23.5289798Z 
2023-01-11T21:41:23.5289897Z def call(args):
2023-01-11T21:41:23.5290000Z     arg0_1, = args
2023-01-11T21:41:23.5290118Z     args.clear()
2023-01-11T21:41:23.5290441Z     buf0 = empty_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5290611Z     buf1 = as_strided(buf0, (8, 8, 64), (512, 64, 1)); del buf0  # reuse
2023-01-11T21:41:23.5290832Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:23.5291013Z     return (as_strided(arg0_1, (8, 8, 64), (512, 64, 1)), buf1, )
2023-01-11T21:41:23.5291021Z 
2023-01-11T21:41:23.5291028Z 
2023-01-11T21:41:23.5291153Z if __name__ == "__main__":
2023-01-11T21:41:23.5291342Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5291534Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5291849Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5291972Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5291994Z 
2023-01-11T21:41:23.5292065Z ok (1.591s)
2023-01-11T21:41:23.5292727Z   test_as_strided_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5292914Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5293329Z [2023-01-11 21:24:36,946] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 26
2023-01-11T21:41:23.5293757Z [2023-01-11 21:24:38,534] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 26
2023-01-11T21:41:23.5293769Z 
2023-01-11T21:41:23.5293917Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5294032Z import torch
2023-01-11T21:41:23.5294137Z import random
2023-01-11T21:41:23.5294322Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5294496Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5294505Z 
2023-01-11T21:41:23.5294622Z aten = torch.ops.aten
2023-01-11T21:41:23.5294812Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5294950Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5294957Z 
2023-01-11T21:41:23.5294964Z 
2023-01-11T21:41:23.5295179Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5295482Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5295665Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5295902Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5296028Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5296118Z {
2023-01-11T21:41:23.5296260Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5296347Z     {
2023-01-11T21:41:23.5296457Z         #pragma omp for 
2023-01-11T21:41:23.5296578Z         for(long i0=0; i0<1280; i0+=1)
2023-01-11T21:41:23.5296668Z         {
2023-01-11T21:41:23.5296850Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5297042Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:23.5297161Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5297351Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:23.5297473Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.5297604Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5297758Z         }
2023-01-11T21:41:23.5297886Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5298010Z         for(long i0=10240; i0<10240; i0+=1)
2023-01-11T21:41:23.5298099Z         {
2023-01-11T21:41:23.5298221Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5298364Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:23.5298486Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5298633Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:41:23.5298736Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.5298843Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.5298938Z         }
2023-01-11T21:41:23.5299042Z         #pragma omp for 
2023-01-11T21:41:23.5299159Z         for(long i0=0; i0<5120; i0+=1)
2023-01-11T21:41:23.5299250Z         {
2023-01-11T21:41:23.5299346Z             {
2023-01-11T21:41:23.5299421Z                 {
2023-01-11T21:41:23.5299553Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.5299710Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.5299843Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5299984Z                     auto tmp3 = static_cast<float>(4);
2023-01-11T21:41:23.5300204Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:23.5300332Z                     out_ptr0[2*i0] = tmp4;
2023-01-11T21:41:23.5300411Z                 }
2023-01-11T21:41:23.5300510Z             }
2023-01-11T21:41:23.5300608Z         }
2023-01-11T21:41:23.5300700Z     }
2023-01-11T21:41:23.5300782Z }
2023-01-11T21:41:23.5300902Z ''')
2023-01-11T21:41:23.5300912Z 
2023-01-11T21:41:23.5300919Z 
2023-01-11T21:41:23.5301046Z async_compile.wait(globals())
2023-01-11T21:41:23.5301133Z del async_compile
2023-01-11T21:41:23.5301140Z 
2023-01-11T21:41:23.5301235Z def call(args):
2023-01-11T21:41:23.5301350Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5301453Z     args.clear()
2023-01-11T21:41:23.5301765Z     buf0 = empty_strided((10, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5302021Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5302123Z     del arg0_1
2023-01-11T21:41:23.5302202Z     del arg1_1
2023-01-11T21:41:23.5302305Z     return (buf0, )
2023-01-11T21:41:23.5302313Z 
2023-01-11T21:41:23.5302318Z 
2023-01-11T21:41:23.5302574Z if __name__ == "__main__":
2023-01-11T21:41:23.5302726Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5302886Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5303196Z     arg0_1 = rand_strided((10, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5303494Z     arg1_1 = rand_strided((10, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5303661Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5303669Z 
2023-01-11T21:41:23.5303741Z ok (1.622s)
2023-01-11T21:41:23.5304451Z   test_avg_pool2d1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5304755Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5305156Z [2023-01-11 21:24:38,555] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 27
2023-01-11T21:41:23.5305547Z [2023-01-11 21:24:40,145] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 27
2023-01-11T21:41:23.5305558Z 
2023-01-11T21:41:23.5305701Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5305801Z import torch
2023-01-11T21:41:23.5305908Z import random
2023-01-11T21:41:23.5306078Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5306346Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5306356Z 
2023-01-11T21:41:23.5306443Z aten = torch.ops.aten
2023-01-11T21:41:23.5306641Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5306775Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5306783Z 
2023-01-11T21:41:23.5306789Z 
2023-01-11T21:41:23.5307003Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5307296Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5307468Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5307612Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5307698Z {
2023-01-11T21:41:23.5307821Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5307907Z     {
2023-01-11T21:41:23.5308021Z         #pragma omp for 
2023-01-11T21:41:23.5308140Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5308232Z         {
2023-01-11T21:41:23.5308364Z             #pragma GCC ivdep
2023-01-11T21:41:23.5308483Z             for(long i1=0; i1<7; i1+=1)
2023-01-11T21:41:23.5308565Z             {
2023-01-11T21:41:23.5308684Z                 #pragma GCC ivdep
2023-01-11T21:41:23.5308802Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:41:23.5308896Z                 {
2023-01-11T21:41:23.5308995Z                     {
2023-01-11T21:41:23.5309090Z                         {
2023-01-11T21:41:23.5309221Z                             auto tmp0 = in_ptr0[(2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5309388Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5309546Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5309701Z                             auto tmp5 = in_ptr0[16 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5309857Z                             auto tmp7 = in_ptr0[17 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5310012Z                             auto tmp9 = in_ptr0[18 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5310172Z                             auto tmp11 = in_ptr0[32 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5310340Z                             auto tmp13 = in_ptr0[33 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5310501Z                             auto tmp15 = in_ptr0[34 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.5310618Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:23.5310753Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:23.5310887Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:23.5311016Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:41:23.5311156Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:41:23.5311292Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:41:23.5311433Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:23.5311637Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:41:23.5311816Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:23.5311955Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:41:23.5312107Z                             out_ptr0[i2 + (7*i1) + (49*i0)] = tmp18;
2023-01-11T21:41:23.5312204Z                         }
2023-01-11T21:41:23.5312304Z                     }
2023-01-11T21:41:23.5312392Z                 }
2023-01-11T21:41:23.5312461Z             }
2023-01-11T21:41:23.5312546Z         }
2023-01-11T21:41:23.5312638Z     }
2023-01-11T21:41:23.5312723Z }
2023-01-11T21:41:23.5312858Z ''')
2023-01-11T21:41:23.5312866Z 
2023-01-11T21:41:23.5312872Z 
2023-01-11T21:41:23.5312996Z async_compile.wait(globals())
2023-01-11T21:41:23.5313097Z del async_compile
2023-01-11T21:41:23.5313103Z 
2023-01-11T21:41:23.5313185Z def call(args):
2023-01-11T21:41:23.5313281Z     arg0_1, = args
2023-01-11T21:41:23.5313382Z     args.clear()
2023-01-11T21:41:23.5313847Z     buf0 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5314036Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5314125Z     del arg0_1
2023-01-11T21:41:23.5314215Z     return (buf0, )
2023-01-11T21:41:23.5314222Z 
2023-01-11T21:41:23.5314228Z 
2023-01-11T21:41:23.5314328Z if __name__ == "__main__":
2023-01-11T21:41:23.5314479Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5314662Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5315008Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5315161Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5315167Z 
2023-01-11T21:41:23.5315264Z ok (1.610s)
2023-01-11T21:41:23.5315964Z   test_avg_pool2d2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5316159Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5316557Z [2023-01-11 21:24:40,216] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 28
2023-01-11T21:41:23.5316957Z [2023-01-11 21:24:41,816] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 28
2023-01-11T21:41:23.5316964Z 
2023-01-11T21:41:23.5317097Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5317184Z import torch
2023-01-11T21:41:23.5317284Z import random
2023-01-11T21:41:23.5317443Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5317622Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5317634Z 
2023-01-11T21:41:23.5317747Z aten = torch.ops.aten
2023-01-11T21:41:23.5317938Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5318073Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5318080Z 
2023-01-11T21:41:23.5318085Z 
2023-01-11T21:41:23.5318289Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5318579Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5318748Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5318890Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5318982Z {
2023-01-11T21:41:23.5319122Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5319217Z     {
2023-01-11T21:41:23.5319324Z         #pragma omp for 
2023-01-11T21:41:23.5319428Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.5319518Z         {
2023-01-11T21:41:23.5319633Z             #pragma GCC ivdep
2023-01-11T21:41:23.5319772Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:41:23.5319986Z             {
2023-01-11T21:41:23.5320095Z                 #pragma GCC ivdep
2023-01-11T21:41:23.5320208Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:23.5320299Z                 {
2023-01-11T21:41:23.5320402Z                     {
2023-01-11T21:41:23.5320496Z                         {
2023-01-11T21:41:23.5320656Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5320820Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5320983Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5321149Z                             auto tmp5 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5321291Z                             auto tmp7 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5321455Z                             auto tmp9 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5321676Z                             auto tmp11 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5321842Z                             auto tmp13 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5322001Z                             auto tmp15 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.5322138Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:23.5322268Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:23.5322399Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:23.5322515Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:41:23.5322657Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:41:23.5322795Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:41:23.5322936Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:23.5323080Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:41:23.5323260Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:23.5323402Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:41:23.5323542Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp18;
2023-01-11T21:41:23.5323645Z                         }
2023-01-11T21:41:23.5323742Z                     }
2023-01-11T21:41:23.5323841Z                 }
2023-01-11T21:41:23.5323937Z             }
2023-01-11T21:41:23.5324022Z         }
2023-01-11T21:41:23.5324108Z     }
2023-01-11T21:41:23.5324180Z }
2023-01-11T21:41:23.5324311Z ''')
2023-01-11T21:41:23.5324320Z 
2023-01-11T21:41:23.5324327Z 
2023-01-11T21:41:23.5324454Z async_compile.wait(globals())
2023-01-11T21:41:23.5324564Z del async_compile
2023-01-11T21:41:23.5324571Z 
2023-01-11T21:41:23.5324674Z def call(args):
2023-01-11T21:41:23.5324770Z     arg0_1, = args
2023-01-11T21:41:23.5324868Z     args.clear()
2023-01-11T21:41:23.5325203Z     buf0 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5325402Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5325504Z     del arg0_1
2023-01-11T21:41:23.5325608Z     return (buf0, )
2023-01-11T21:41:23.5325617Z 
2023-01-11T21:41:23.5325623Z 
2023-01-11T21:41:23.5325736Z if __name__ == "__main__":
2023-01-11T21:41:23.5325905Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5326085Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5326428Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5326571Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5326594Z 
2023-01-11T21:41:23.5326679Z ok (1.746s)
2023-01-11T21:41:23.5327386Z   test_avg_pool2d3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5327647Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5328042Z [2023-01-11 21:24:41,913] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 29
2023-01-11T21:41:23.5328435Z [2023-01-11 21:24:43,522] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 29
2023-01-11T21:41:23.5328442Z 
2023-01-11T21:41:23.5328571Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5328678Z import torch
2023-01-11T21:41:23.5328780Z import random
2023-01-11T21:41:23.5328933Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5329108Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5329116Z 
2023-01-11T21:41:23.5329236Z aten = torch.ops.aten
2023-01-11T21:41:23.5329527Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5329667Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5329674Z 
2023-01-11T21:41:23.5329679Z 
2023-01-11T21:41:23.5329889Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5330200Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5330377Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5330520Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5330599Z {
2023-01-11T21:41:23.5330741Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5330832Z     {
2023-01-11T21:41:23.5330940Z         #pragma omp for 
2023-01-11T21:41:23.5331052Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.5331138Z         {
2023-01-11T21:41:23.5331248Z             #pragma GCC ivdep
2023-01-11T21:41:23.5331367Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.5331464Z             {
2023-01-11T21:41:23.5331563Z                 {
2023-01-11T21:41:23.5331657Z                     {
2023-01-11T21:41:23.5332551Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:23.5332726Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.5332849Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.5332999Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:23.5333126Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.5333251Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:23.5333526Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:23.5333662Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:23.5333786Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:23.5333924Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:23.5334042Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:23.5334176Z                         float tmp11 = 0.0;
2023-01-11T21:41:23.5334280Z                         if(tmp10)
2023-01-11T21:41:23.5334430Z                         {
2023-01-11T21:41:23.5334732Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5334886Z                             tmp11 = tmp12;
2023-01-11T21:41:23.5335025Z                         }
2023-01-11T21:41:23.5335160Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:23.5335331Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:23.5335580Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:23.5335746Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:23.5335860Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:23.5336032Z                         float tmp18 = 0.0;
2023-01-11T21:41:23.5336173Z                         if(tmp17)
2023-01-11T21:41:23.5336398Z                         {
2023-01-11T21:41:23.5336696Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5336863Z                             tmp18 = tmp19;
2023-01-11T21:41:23.5337007Z                         }
2023-01-11T21:41:23.5337126Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:41:23.5337434Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:23.5337602Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:23.5337762Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:23.5337926Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:23.5338093Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:23.5338251Z                         float tmp26 = 0.0;
2023-01-11T21:41:23.5416435Z                         if(tmp25)
2023-01-11T21:41:23.5416657Z                         {
2023-01-11T21:41:23.5416998Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5417298Z                             tmp26 = tmp27;
2023-01-11T21:41:23.5417402Z                         }
2023-01-11T21:41:23.5417544Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:41:23.5417704Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:23.5417839Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:23.5417943Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:23.5418062Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:23.5418197Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:23.5418315Z                         float tmp34 = 0.0;
2023-01-11T21:41:23.5418421Z                         if(tmp33)
2023-01-11T21:41:23.5418521Z                         {
2023-01-11T21:41:23.5418782Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5418872Z                             tmp34 = tmp35;
2023-01-11T21:41:23.5418976Z                         }
2023-01-11T21:41:23.5419096Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:41:23.5419226Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:23.5419352Z                         float tmp38 = 0.0;
2023-01-11T21:41:23.5419453Z                         if(tmp37)
2023-01-11T21:41:23.5419548Z                         {
2023-01-11T21:41:23.5419682Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:23.5419797Z                             tmp38 = tmp39;
2023-01-11T21:41:23.5419887Z                         }
2023-01-11T21:41:23.5420022Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:23.5420144Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:23.5420258Z                         float tmp42 = 0.0;
2023-01-11T21:41:23.5420360Z                         if(tmp41)
2023-01-11T21:41:23.5420449Z                         {
2023-01-11T21:41:23.5420606Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5420726Z                             tmp42 = tmp43;
2023-01-11T21:41:23.5420824Z                         }
2023-01-11T21:41:23.5420955Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:23.5421111Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:23.5421251Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:23.5421385Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:23.5421507Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:23.5421639Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:23.5421755Z                         float tmp50 = 0.0;
2023-01-11T21:41:23.5421846Z                         if(tmp49)
2023-01-11T21:41:23.5421942Z                         {
2023-01-11T21:41:23.5422093Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5422212Z                             tmp50 = tmp51;
2023-01-11T21:41:23.5422550Z                         }
2023-01-11T21:41:23.5422677Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:41:23.5422799Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:23.5422912Z                         float tmp54 = 0.0;
2023-01-11T21:41:23.5423003Z                         if(tmp53)
2023-01-11T21:41:23.5423093Z                         {
2023-01-11T21:41:23.5423237Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5423337Z                             tmp54 = tmp55;
2023-01-11T21:41:23.5423417Z                         }
2023-01-11T21:41:23.5423547Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:41:23.5423678Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:23.5423794Z                         float tmp58 = 0.0;
2023-01-11T21:41:23.5423900Z                         if(tmp57)
2023-01-11T21:41:23.5423994Z                         {
2023-01-11T21:41:23.5424250Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5424373Z                             tmp58 = tmp59;
2023-01-11T21:41:23.5424461Z                         }
2023-01-11T21:41:23.5424589Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:41:23.5425685Z                         auto tmp61 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:23.5425819Z                         auto tmp62 = tmp60 * tmp61;
2023-01-11T21:41:23.5425961Z                         out_ptr0[i1 + (4*i0)] = tmp62;
2023-01-11T21:41:23.5426041Z                     }
2023-01-11T21:41:23.5426126Z                 }
2023-01-11T21:41:23.5426209Z             }
2023-01-11T21:41:23.5426293Z         }
2023-01-11T21:41:23.5426366Z     }
2023-01-11T21:41:23.5426441Z }
2023-01-11T21:41:23.5426588Z ''')
2023-01-11T21:41:23.5426598Z 
2023-01-11T21:41:23.5426603Z 
2023-01-11T21:41:23.5426725Z async_compile.wait(globals())
2023-01-11T21:41:23.5426821Z del async_compile
2023-01-11T21:41:23.5426833Z 
2023-01-11T21:41:23.5426924Z def call(args):
2023-01-11T21:41:23.5427029Z     arg0_1, = args
2023-01-11T21:41:23.5427128Z     args.clear()
2023-01-11T21:41:23.5427428Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5427612Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5427699Z     del arg0_1
2023-01-11T21:41:23.5427785Z     return (buf0, )
2023-01-11T21:41:23.5427791Z 
2023-01-11T21:41:23.5427797Z 
2023-01-11T21:41:23.5427889Z if __name__ == "__main__":
2023-01-11T21:41:23.5428050Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5428223Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5428523Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5428672Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5428679Z 
2023-01-11T21:41:23.5428767Z ok (1.630s)
2023-01-11T21:41:23.5429466Z   test_avg_pool2d4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5429646Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5430006Z [2023-01-11 21:24:43,547] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 30
2023-01-11T21:41:23.5430353Z [2023-01-11 21:24:45,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 30
2023-01-11T21:41:23.5430360Z 
2023-01-11T21:41:23.5430474Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5430554Z import torch
2023-01-11T21:41:23.5430634Z import random
2023-01-11T21:41:23.5430775Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5431072Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5431081Z 
2023-01-11T21:41:23.5431201Z aten = torch.ops.aten
2023-01-11T21:41:23.5431392Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5432388Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5432396Z 
2023-01-11T21:41:23.5432401Z 
2023-01-11T21:41:23.5432849Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5433171Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5433345Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5433489Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5433582Z {
2023-01-11T21:41:23.5433786Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5433858Z     {
2023-01-11T21:41:23.5433959Z         #pragma omp for 
2023-01-11T21:41:23.5434081Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.5434254Z         {
2023-01-11T21:41:23.5434369Z             #pragma GCC ivdep
2023-01-11T21:41:23.5434488Z             for(long i1=0; i1<55; i1+=1)
2023-01-11T21:41:23.5434563Z             {
2023-01-11T21:41:23.5434677Z                 #pragma GCC ivdep
2023-01-11T21:41:23.5434804Z                 for(long i2=0; i2<55; i2+=1)
2023-01-11T21:41:23.5434893Z                 {
2023-01-11T21:41:23.5434989Z                     {
2023-01-11T21:41:23.5435085Z                         {
2023-01-11T21:41:23.5435244Z                             auto tmp0 = in_ptr0[(2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5435384Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5435537Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5435694Z                             auto tmp5 = in_ptr0[111 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5436750Z                             auto tmp7 = in_ptr0[112 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5436913Z                             auto tmp9 = in_ptr0[113 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5437066Z                             auto tmp11 = in_ptr0[222 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5437223Z                             auto tmp13 = in_ptr0[223 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5437381Z                             auto tmp15 = in_ptr0[224 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.5437513Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:23.5437629Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:23.5437757Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:23.5437890Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:41:23.5438022Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:41:23.5438154Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:41:23.5438288Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:23.5438417Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:41:23.5438564Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:23.5438695Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:41:23.5438843Z                             out_ptr0[i2 + (55*i1) + (3025*i0)] = tmp18;
2023-01-11T21:41:23.5438940Z                         }
2023-01-11T21:41:23.5439041Z                     }
2023-01-11T21:41:23.5439135Z                 }
2023-01-11T21:41:23.5439220Z             }
2023-01-11T21:41:23.5439290Z         }
2023-01-11T21:41:23.5439377Z     }
2023-01-11T21:41:23.5439459Z }
2023-01-11T21:41:23.5439593Z ''')
2023-01-11T21:41:23.5439602Z 
2023-01-11T21:41:23.5439607Z 
2023-01-11T21:41:23.5439733Z async_compile.wait(globals())
2023-01-11T21:41:23.5439836Z del async_compile
2023-01-11T21:41:23.5439843Z 
2023-01-11T21:41:23.5439942Z def call(args):
2023-01-11T21:41:23.5440113Z     arg0_1, = args
2023-01-11T21:41:23.5440209Z     args.clear()
2023-01-11T21:41:23.5440544Z     buf0 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5440733Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5440826Z     del arg0_1
2023-01-11T21:41:23.5440928Z     return (buf0, )
2023-01-11T21:41:23.5440935Z 
2023-01-11T21:41:23.5440940Z 
2023-01-11T21:41:23.5441044Z if __name__ == "__main__":
2023-01-11T21:41:23.5441209Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5441366Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5441697Z     arg0_1 = rand_strided((2, 8, 111, 111), (98568, 12321, 111, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5441845Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5441852Z 
2023-01-11T21:41:23.5441930Z ok (1.658s)
2023-01-11T21:41:23.5442689Z   test_avg_pool2d5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5442865Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5443266Z [2023-01-11 21:24:45,202] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 31
2023-01-11T21:41:23.5443273Z 
2023-01-11T21:41:23.5443402Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5443497Z import torch
2023-01-11T21:41:23.5443576Z import random
2023-01-11T21:41:23.5443732Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5443890Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5443896Z 
2023-01-11T21:41:23.5444005Z aten = torch.ops.aten
2023-01-11T21:41:23.5444185Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5444306Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5444313Z 
2023-01-11T21:41:23.5444319Z 
2023-01-11T21:41:23.5444518Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5444828Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5444982Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5445124Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5445211Z {
2023-01-11T21:41:23.5445352Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5445444Z     {
2023-01-11T21:41:23.5445550Z         #pragma omp for 
2023-01-11T21:41:23.5445662Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.5445733Z         {
2023-01-11T21:41:23.5445841Z             #pragma GCC ivdep
2023-01-11T21:41:23.5445952Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.5446051Z             {
2023-01-11T21:41:23.5446140Z                 {
2023-01-11T21:41:23.5446233Z                     {
2023-01-11T21:41:23.5446486Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:23.5446613Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.5446740Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.5446881Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:23.5447011Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.5447141Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:23.5447386Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:23.5447512Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:23.5447620Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:23.5447747Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:23.5447875Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:23.5448064Z                         float tmp11 = 0.0;
2023-01-11T21:41:23.5448170Z                         if(tmp10)
2023-01-11T21:41:23.5448256Z                         {
2023-01-11T21:41:23.5448498Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5448599Z                             tmp11 = tmp12;
2023-01-11T21:41:23.5448691Z                         }
2023-01-11T21:41:23.5448845Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:23.5448974Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:23.5449099Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:23.5449228Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:23.5449354Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:23.5449472Z                         float tmp18 = 0.0;
2023-01-11T21:41:23.5449558Z                         if(tmp17)
2023-01-11T21:41:23.5449712Z                         {
2023-01-11T21:41:23.5449965Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5450076Z                             tmp18 = tmp19;
2023-01-11T21:41:23.5450168Z                         }
2023-01-11T21:41:23.5450293Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:41:23.5450441Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:23.5450548Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:23.5450667Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:23.5450789Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:23.5450918Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:23.5451032Z                         float tmp26 = 0.0;
2023-01-11T21:41:23.5451130Z                         if(tmp25)
2023-01-11T21:41:23.5451222Z                         {
2023-01-11T21:41:23.5451456Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5451575Z                             tmp26 = tmp27;
2023-01-11T21:41:23.5451663Z                         }
2023-01-11T21:41:23.5451792Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:41:23.5451945Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:23.5452074Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:23.5452195Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:23.5452309Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:23.5452440Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:23.5452554Z                         float tmp34 = 0.0;
2023-01-11T21:41:23.5452652Z                         if(tmp33)
2023-01-11T21:41:23.5452741Z                         {
2023-01-11T21:41:23.5452986Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5453097Z                             tmp34 = tmp35;
2023-01-11T21:41:23.5453181Z                         }
2023-01-11T21:41:23.5453305Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:41:23.5453433Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:23.5453545Z                         float tmp38 = 0.0;
2023-01-11T21:41:23.5453650Z                         if(tmp37)
2023-01-11T21:41:23.5453743Z                         {
2023-01-11T21:41:23.5453893Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:23.5453989Z                             tmp38 = tmp39;
2023-01-11T21:41:23.5454084Z                         }
2023-01-11T21:41:23.5454207Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:23.5454338Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:23.5454453Z                         float tmp42 = 0.0;
2023-01-11T21:41:23.5454557Z                         if(tmp41)
2023-01-11T21:41:23.5454651Z                         {
2023-01-11T21:41:23.5454785Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5454976Z                             tmp42 = tmp43;
2023-01-11T21:41:23.5455076Z                         }
2023-01-11T21:41:23.5455206Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:23.5455360Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:23.5455492Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:23.5455610Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:23.5455732Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:23.5455844Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:23.5455960Z                         float tmp50 = 0.0;
2023-01-11T21:41:23.5456066Z                         if(tmp49)
2023-01-11T21:41:23.5456159Z                         {
2023-01-11T21:41:23.5456308Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5456426Z                             tmp50 = tmp51;
2023-01-11T21:41:23.5456581Z                         }
2023-01-11T21:41:23.5456700Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:41:23.5456829Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:23.5456942Z                         float tmp54 = 0.0;
2023-01-11T21:41:23.5457043Z                         if(tmp53)
2023-01-11T21:41:23.5457139Z                         {
2023-01-11T21:41:23.5457288Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5457403Z                             tmp54 = tmp55;
2023-01-11T21:41:23.5457479Z                         }
2023-01-11T21:41:23.5457601Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:41:23.5457723Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:23.5457832Z                         float tmp58 = 0.0;
2023-01-11T21:41:23.5457925Z                         if(tmp57)
2023-01-11T21:41:23.5458015Z                         {
2023-01-11T21:41:23.5458164Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5458258Z                             tmp58 = tmp59;
2023-01-11T21:41:23.5458351Z                         }
2023-01-11T21:41:23.5458472Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:41:23.5458587Z                         float tmp61 = 0.0;
2023-01-11T21:41:23.5458687Z                         if(tmp10)
2023-01-11T21:41:23.5458780Z                         {
2023-01-11T21:41:23.5458932Z                             auto tmp62 = static_cast<float>(1);
2023-01-11T21:41:23.5459029Z                             tmp61 = tmp62;
2023-01-11T21:41:23.5459119Z                         }
2023-01-11T21:41:23.5459233Z                         float tmp63 = 0.0;
2023-01-11T21:41:23.5459334Z                         if(tmp17)
2023-01-11T21:41:23.5459428Z                         {
2023-01-11T21:41:23.5459577Z                             auto tmp64 = static_cast<float>(1);
2023-01-11T21:41:23.5459704Z                             tmp63 = tmp64;
2023-01-11T21:41:23.5459795Z                         }
2023-01-11T21:41:23.5459951Z                         auto tmp65 = tmp63 + tmp61;
2023-01-11T21:41:23.5460074Z                         float tmp66 = 0.0;
2023-01-11T21:41:23.5460178Z                         if(tmp25)
2023-01-11T21:41:23.5460287Z                         {
2023-01-11T21:41:23.5460440Z                             auto tmp67 = static_cast<float>(1);
2023-01-11T21:41:23.5460551Z                             tmp66 = tmp67;
2023-01-11T21:41:23.5460639Z                         }
2023-01-11T21:41:23.5460775Z                         auto tmp68 = tmp66 + tmp65;
2023-01-11T21:41:23.5460896Z                         float tmp69 = 0.0;
2023-01-11T21:41:23.5461002Z                         if(tmp33)
2023-01-11T21:41:23.5461098Z                         {
2023-01-11T21:41:23.5461246Z                             auto tmp70 = static_cast<float>(1);
2023-01-11T21:41:23.5461356Z                             tmp69 = tmp70;
2023-01-11T21:41:23.5461515Z                         }
2023-01-11T21:41:23.5461656Z                         auto tmp71 = tmp69 + tmp68;
2023-01-11T21:41:23.5461777Z                         float tmp72 = 0.0;
2023-01-11T21:41:23.5461887Z                         if(tmp37)
2023-01-11T21:41:23.5461985Z                         {
2023-01-11T21:41:23.5462145Z                             auto tmp73 = static_cast<float>(1);
2023-01-11T21:41:23.5462255Z                             tmp72 = tmp73;
2023-01-11T21:41:23.5462475Z                         }
2023-01-11T21:41:23.5462624Z                         auto tmp74 = tmp72 + tmp71;
2023-01-11T21:41:23.5462747Z                         float tmp75 = 0.0;
2023-01-11T21:41:23.5462854Z                         if(tmp41)
2023-01-11T21:41:23.5462950Z                         {
2023-01-11T21:41:23.5463093Z                             auto tmp76 = static_cast<float>(1);
2023-01-11T21:41:23.5463213Z                             tmp75 = tmp76;
2023-01-11T21:41:23.5463293Z                         }
2023-01-11T21:41:23.5463515Z                         auto tmp77 = tmp75 + tmp74;
2023-01-11T21:41:23.5463636Z                         float tmp78 = 0.0;
2023-01-11T21:41:23.5463736Z                         if(tmp49)
2023-01-11T21:41:23.5463820Z                         {
2023-01-11T21:41:23.5463960Z                             auto tmp79 = static_cast<float>(1);
2023-01-11T21:41:23.5464061Z                             tmp78 = tmp79;
2023-01-11T21:41:23.5464135Z                         }
2023-01-11T21:41:23.5464261Z                         auto tmp80 = tmp78 + tmp77;
2023-01-11T21:41:23.5464363Z                         float tmp81 = 0.0;
2023-01-11T21:41:23.5464460Z                         if(tmp53)
2023-01-11T21:41:23.5464554Z                         {
2023-01-11T21:41:23.5464705Z                             auto tmp82 = static_cast<float>(1);
2023-01-11T21:41:23.5464810Z                             tmp81 = tmp82;
2023-01-11T21:41:23.5464904Z                         }
2023-01-11T21:41:23.5465027Z                         auto tmp83 = tmp81 + tmp80;
2023-01-11T21:41:23.5465148Z                         float tmp84 = 0.0;
2023-01-11T21:41:23.5465253Z                         if(tmp57)
2023-01-11T21:41:23.5465346Z                         {
2023-01-11T21:41:23.5465493Z                             auto tmp85 = static_cast<float>(1);
2023-01-11T21:41:23.5465600Z                             tmp84 = tmp85;
2023-01-11T21:41:23.5465698Z                         }
2023-01-11T21:41:23.5465817Z                         auto tmp86 = tmp84 + tmp83;
2023-01-11T21:41:23.5465938Z                         auto tmp87 = tmp60 / tmp86;
2023-01-11T21:41:23.5466066Z                         out_ptr0[i1 + (4*i0)] = tmp87;
2023-01-11T21:41:23.5466156Z                     }
2023-01-11T21:41:23.5466246Z                 }
2023-01-11T21:41:23.5466334Z             }
2023-01-11T21:41:23.5466404Z         }
2023-01-11T21:41:23.5466490Z     }
2023-01-11T21:41:23.5466571Z }
2023-01-11T21:41:23.5466711Z ''')
2023-01-11T21:41:23.5466721Z 
2023-01-11T21:41:23.5466732Z 
2023-01-11T21:41:23.5466863Z async_compile.wait(globals())
2023-01-11T21:41:23.5466966Z del async_compile
2023-01-11T21:41:23.5466974Z 
2023-01-11T21:41:23.5467071Z def call(args):
2023-01-11T21:41:23.5467171Z     arg0_1, = args
2023-01-11T21:41:23.5467252Z     args.clear()
2023-01-11T21:41:23.5467572Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5467762Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5467857Z     del arg0_1
2023-01-11T21:41:23.5467956Z     return (buf0, )
2023-01-11T21:41:23.5467963Z 
2023-01-11T21:41:23.5467968Z 
2023-01-11T21:41:23.5468074Z if __name__ == "__main__":
2023-01-11T21:41:23.5468223Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5468382Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5468706Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5468962Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5469363Z [2023-01-11 21:24:46,856] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 31
2023-01-11T21:41:23.5469372Z 
2023-01-11T21:41:23.5469465Z ok (1.676s)
2023-01-11T21:41:23.5470143Z   test_avg_pool2d6_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5470307Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5470668Z [2023-01-11 21:24:46,877] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 32
2023-01-11T21:41:23.5471120Z [2023-01-11 21:24:48,513] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 32
2023-01-11T21:41:23.5471135Z 
2023-01-11T21:41:23.5471270Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5471351Z import torch
2023-01-11T21:41:23.5471455Z import random
2023-01-11T21:41:23.5471628Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5471798Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5471805Z 
2023-01-11T21:41:23.5471923Z aten = torch.ops.aten
2023-01-11T21:41:23.5472121Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5472250Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5472257Z 
2023-01-11T21:41:23.5472262Z 
2023-01-11T21:41:23.5472465Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5472748Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5472928Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5473069Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5473163Z {
2023-01-11T21:41:23.5473305Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5473394Z     {
2023-01-11T21:41:23.5473503Z         #pragma omp for 
2023-01-11T21:41:23.5473606Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.5473700Z         {
2023-01-11T21:41:23.5473887Z             #pragma GCC ivdep
2023-01-11T21:41:23.5474014Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.5474108Z             {
2023-01-11T21:41:23.5474202Z                 {
2023-01-11T21:41:23.5474293Z                     {
2023-01-11T21:41:23.5474549Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:23.5474701Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.5474833Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.5474983Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:23.5475114Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.5475251Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:23.5475512Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:23.5475631Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:23.5475761Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:23.5475887Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:23.5476011Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:23.5476133Z                         float tmp11 = 0.0;
2023-01-11T21:41:23.5476241Z                         if(tmp10)
2023-01-11T21:41:23.5476333Z                         {
2023-01-11T21:41:23.5476580Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5476694Z                             tmp11 = tmp12;
2023-01-11T21:41:23.5476782Z                         }
2023-01-11T21:41:23.5476937Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:23.5477144Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:23.5477276Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:23.5477399Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:23.5477506Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:23.5477616Z                         float tmp18 = 0.0;
2023-01-11T21:41:23.5477717Z                         if(tmp17)
2023-01-11T21:41:23.5477804Z                         {
2023-01-11T21:41:23.5478072Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5478188Z                             tmp18 = tmp19;
2023-01-11T21:41:23.5478279Z                         }
2023-01-11T21:41:23.5478405Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:41:23.5478546Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:23.5478678Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:23.5478885Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:23.5479016Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:23.5479147Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:23.5479265Z                         float tmp26 = 0.0;
2023-01-11T21:41:23.5479366Z                         if(tmp25)
2023-01-11T21:41:23.5479444Z                         {
2023-01-11T21:41:23.5479839Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5479950Z                             tmp26 = tmp27;
2023-01-11T21:41:23.5480046Z                         }
2023-01-11T21:41:23.5480171Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:41:23.5480312Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:23.5480447Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:23.5480559Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:23.5480690Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:23.5480826Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:23.5480939Z                         float tmp34 = 0.0;
2023-01-11T21:41:23.5481033Z                         if(tmp33)
2023-01-11T21:41:23.5481120Z                         {
2023-01-11T21:41:23.5481383Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5481487Z                             tmp34 = tmp35;
2023-01-11T21:41:23.5481602Z                         }
2023-01-11T21:41:23.5481743Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:41:23.5481898Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:23.5482032Z                         float tmp38 = 0.0;
2023-01-11T21:41:23.5482129Z                         if(tmp37)
2023-01-11T21:41:23.5482220Z                         {
2023-01-11T21:41:23.5482347Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:23.5482461Z                             tmp38 = tmp39;
2023-01-11T21:41:23.5482567Z                         }
2023-01-11T21:41:23.5482707Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:23.5482850Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:23.5482976Z                         float tmp42 = 0.0;
2023-01-11T21:41:23.5483090Z                         if(tmp41)
2023-01-11T21:41:23.5483170Z                         {
2023-01-11T21:41:23.5483320Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5483436Z                             tmp42 = tmp43;
2023-01-11T21:41:23.5483527Z                         }
2023-01-11T21:41:23.5483651Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:23.5483807Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:23.5483928Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:23.5484050Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:23.5484167Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:23.5484368Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:23.5484475Z                         float tmp50 = 0.0;
2023-01-11T21:41:23.5484570Z                         if(tmp49)
2023-01-11T21:41:23.5484658Z                         {
2023-01-11T21:41:23.5485759Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5485869Z                             tmp50 = tmp51;
2023-01-11T21:41:23.5485945Z                         }
2023-01-11T21:41:23.5486064Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:41:23.5486183Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:23.5486286Z                         float tmp54 = 0.0;
2023-01-11T21:41:23.5486389Z                         if(tmp53)
2023-01-11T21:41:23.5486474Z                         {
2023-01-11T21:41:23.5486616Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5486779Z                             tmp54 = tmp55;
2023-01-11T21:41:23.5486864Z                         }
2023-01-11T21:41:23.5486979Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:41:23.5487101Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:23.5487210Z                         float tmp58 = 0.0;
2023-01-11T21:41:23.5487301Z                         if(tmp57)
2023-01-11T21:41:23.5487383Z                         {
2023-01-11T21:41:23.5487512Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.5487618Z                             tmp58 = tmp59;
2023-01-11T21:41:23.5487708Z                         }
2023-01-11T21:41:23.5487827Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:41:23.5487982Z                         auto tmp61 = static_cast<float>(0.3333333333333333);
2023-01-11T21:41:23.5488103Z                         auto tmp62 = tmp60 * tmp61;
2023-01-11T21:41:23.5488231Z                         out_ptr0[i1 + (4*i0)] = tmp62;
2023-01-11T21:41:23.5488315Z                     }
2023-01-11T21:41:23.5488471Z                 }
2023-01-11T21:41:23.5488557Z             }
2023-01-11T21:41:23.5488634Z         }
2023-01-11T21:41:23.5488714Z     }
2023-01-11T21:41:23.5488788Z }
2023-01-11T21:41:23.5488909Z ''')
2023-01-11T21:41:23.5488918Z 
2023-01-11T21:41:23.5488924Z 
2023-01-11T21:41:23.5489035Z async_compile.wait(globals())
2023-01-11T21:41:23.5489123Z del async_compile
2023-01-11T21:41:23.5489131Z 
2023-01-11T21:41:23.5489222Z def call(args):
2023-01-11T21:41:23.5489307Z     arg0_1, = args
2023-01-11T21:41:23.5490873Z     args.clear()
2023-01-11T21:41:23.5492004Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5492175Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5492264Z     del arg0_1
2023-01-11T21:41:23.5492341Z     return (buf0, )
2023-01-11T21:41:23.5492349Z 
2023-01-11T21:41:23.5492355Z 
2023-01-11T21:41:23.5492461Z if __name__ == "__main__":
2023-01-11T21:41:23.5492621Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5492786Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5493992Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5494143Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5494149Z 
2023-01-11T21:41:23.5494238Z ok (1.656s)
2023-01-11T21:41:23.5494898Z   test_avg_pool2d7_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5495075Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5495454Z [2023-01-11 21:24:48,535] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 33
2023-01-11T21:41:23.5495914Z [2023-01-11 21:24:48,540] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.avg_pool2d
2023-01-11T21:41:23.5496290Z [2023-01-11 21:24:48,544] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 33
2023-01-11T21:41:23.5496298Z 
2023-01-11T21:41:23.5496422Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5496520Z import torch
2023-01-11T21:41:23.5496613Z import random
2023-01-11T21:41:23.5496767Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5496936Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5496942Z 
2023-01-11T21:41:23.5497038Z aten = torch.ops.aten
2023-01-11T21:41:23.5497222Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5497346Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5497353Z 
2023-01-11T21:41:23.5497359Z 
2023-01-11T21:41:23.5497536Z async_compile.wait(globals())
2023-01-11T21:41:23.5497633Z del async_compile
2023-01-11T21:41:23.5497639Z 
2023-01-11T21:41:23.5497730Z def call(args):
2023-01-11T21:41:23.5497819Z     arg0_1, = args
2023-01-11T21:41:23.5497912Z     args.clear()
2023-01-11T21:41:23.5498073Z     buf0 = aten.avg_pool2d(arg0_1, [13, 13], [1, 1], [0, 0], False, True, None)
2023-01-11T21:41:23.5498160Z     del arg0_1
2023-01-11T21:41:23.5498248Z     buf1 = buf0
2023-01-11T21:41:23.5498392Z     assert_size_stride(buf1, (1, 1, 12, 12), (144, 144, 12, 1))
2023-01-11T21:41:23.5498472Z     del buf0
2023-01-11T21:41:23.5498557Z     return (buf1, )
2023-01-11T21:41:23.5498564Z 
2023-01-11T21:41:23.5498569Z 
2023-01-11T21:41:23.5498667Z if __name__ == "__main__":
2023-01-11T21:41:23.5498815Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5498985Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5499342Z     arg0_1 = rand_strided((1, 1, 24, 24), (576, 576, 24, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5499526Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5499534Z 
2023-01-11T21:41:23.5499637Z ok (0.031s)
2023-01-11T21:41:23.5500427Z   test_avg_pool2d_backward2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5500645Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5501065Z [2023-01-11 21:24:48,565] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 34
2023-01-11T21:41:23.5501073Z 
2023-01-11T21:41:23.5501199Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5501290Z import torch
2023-01-11T21:41:23.5501372Z import random
2023-01-11T21:41:23.5501542Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5501694Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5501702Z 
2023-01-11T21:41:23.5501796Z aten = torch.ops.aten
2023-01-11T21:41:23.5501970Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5502092Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5502098Z 
2023-01-11T21:41:23.5502105Z 
2023-01-11T21:41:23.5502300Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5502820Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5502988Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5503125Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5503211Z {
2023-01-11T21:41:23.5503349Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5503434Z     {
2023-01-11T21:41:23.5503544Z         #pragma omp for 
2023-01-11T21:41:23.5503779Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:23.5503871Z         {
2023-01-11T21:41:23.5503992Z             #pragma GCC ivdep
2023-01-11T21:41:23.5504123Z             for(long i1=0; i1<15; i1+=1)
2023-01-11T21:41:23.5504219Z             {
2023-01-11T21:41:23.5504317Z                 {
2023-01-11T21:41:23.5504418Z                     {
2023-01-11T21:41:23.5504696Z                         auto tmp0 = static_cast<int>((-1) + i0);
2023-01-11T21:41:23.5504965Z                         auto tmp1 = static_cast<int>((-1) + i1);
2023-01-11T21:41:23.5505136Z                         auto tmp2 = static_cast<int>(2 + i0);
2023-01-11T21:41:23.5505281Z                         auto tmp3 = static_cast<int>(2 + i1);
2023-01-11T21:41:23.5505414Z                         auto tmp4 = static_cast<int>(0);
2023-01-11T21:41:23.5505607Z                         auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:41:23.5505786Z                         auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:41:23.5506024Z                         auto tmp7 = static_cast<int>(20);
2023-01-11T21:41:23.5506188Z                         auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:41:23.5506332Z                         auto tmp9 = static_cast<int>(15);
2023-01-11T21:41:23.5506507Z                         auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp3, tmp9);
2023-01-11T21:41:23.5506632Z                         auto tmp11 = tmp5 + tmp4;
2023-01-11T21:41:23.5506752Z                         auto tmp12 = tmp6 + tmp4;
2023-01-11T21:41:23.5506898Z                         auto tmp13 = static_cast<int>(1);
2023-01-11T21:41:23.5507035Z                         auto tmp14 = static_cast<int>(3);
2023-01-11T21:41:23.5507156Z                         auto tmp15 = tmp11 * tmp13;
2023-01-11T21:41:23.5507361Z                         auto tmp16 = tmp15 - tmp13;
2023-01-11T21:41:23.5507484Z                         auto tmp17 = tmp12 * tmp13;
2023-01-11T21:41:23.5507683Z                         auto tmp18 = tmp17 - tmp13;
2023-01-11T21:41:23.5507810Z                         auto tmp19 = tmp16 + tmp14;
2023-01-11T21:41:23.5507933Z                         auto tmp20 = tmp7 + tmp13;
2023-01-11T21:41:23.5508113Z                         auto tmp21 = (tmp20 != tmp20) ? tmp20 : std::min(tmp19, tmp20);
2023-01-11T21:41:23.5508236Z                         auto tmp22 = tmp18 + tmp14;
2023-01-11T21:41:23.5508357Z                         auto tmp23 = tmp9 + tmp13;
2023-01-11T21:41:23.5508514Z                         auto tmp24 = (tmp23 != tmp23) ? tmp23 : std::min(tmp22, tmp23);
2023-01-11T21:41:23.5508691Z                         auto tmp25 = (tmp4 != tmp4) ? tmp4 : std::max(tmp16, tmp4);
2023-01-11T21:41:23.5508866Z                         auto tmp26 = (tmp4 != tmp4) ? tmp4 : std::max(tmp18, tmp4);
2023-01-11T21:41:23.5509037Z                         auto tmp27 = (tmp7 != tmp7) ? tmp7 : std::min(tmp21, tmp7);
2023-01-11T21:41:23.5509205Z                         auto tmp28 = (tmp9 != tmp9) ? tmp9 : std::min(tmp24, tmp9);
2023-01-11T21:41:23.5509411Z                         auto tmp29 = tmp27 - tmp25;
2023-01-11T21:41:23.5509604Z                         auto tmp30 = tmp28 - tmp26;
2023-01-11T21:41:23.5509732Z                         auto tmp31 = tmp29 * tmp30;
2023-01-11T21:41:23.5509916Z                         auto tmp32 = tmp8 - tmp13;
2023-01-11T21:41:23.5510089Z                         auto tmp33 = (tmp32 != tmp32) ? tmp32 : std::min(tmp11, tmp32);
2023-01-11T21:41:23.5510288Z                         auto tmp34 = tmp10 - tmp13;
2023-01-11T21:41:23.5510456Z                         auto tmp35 = (tmp34 != tmp34) ? tmp34 : std::min(tmp12, tmp34);
2023-01-11T21:41:23.5510606Z                         auto tmp36 = in_ptr0[tmp35 + (15*tmp33)];
2023-01-11T21:41:23.5510727Z                         auto tmp37 = tmp36 / tmp31;
2023-01-11T21:41:23.5510856Z                         auto tmp38 = tmp11 < tmp8;
2023-01-11T21:41:23.5510973Z                         auto tmp39 = tmp12 < tmp10;
2023-01-11T21:41:23.5511173Z                         auto tmp40 = tmp38 & tmp39;
2023-01-11T21:41:23.5511321Z                         auto tmp41 = static_cast<float>(0.0);
2023-01-11T21:41:23.5511457Z                         auto tmp42 = tmp40 ? tmp37 : tmp41;
2023-01-11T21:41:23.5511587Z                         auto tmp43 = tmp6 + tmp13;
2023-01-11T21:41:23.5511711Z                         auto tmp44 = tmp43 * tmp13;
2023-01-11T21:41:23.5511910Z                         auto tmp45 = tmp44 - tmp13;
2023-01-11T21:41:23.5512032Z                         auto tmp46 = tmp45 + tmp14;
2023-01-11T21:41:23.5512214Z                         auto tmp47 = (tmp23 != tmp23) ? tmp23 : std::min(tmp46, tmp23);
2023-01-11T21:41:23.5512398Z                         auto tmp48 = (tmp4 != tmp4) ? tmp4 : std::max(tmp45, tmp4);
2023-01-11T21:41:23.5512570Z                         auto tmp49 = (tmp9 != tmp9) ? tmp9 : std::min(tmp47, tmp9);
2023-01-11T21:41:23.5512772Z                         auto tmp50 = tmp49 - tmp48;
2023-01-11T21:41:23.5512955Z                         auto tmp51 = tmp29 * tmp50;
2023-01-11T21:41:23.5513133Z                         auto tmp52 = (tmp34 != tmp34) ? tmp34 : std::min(tmp43, tmp34);
2023-01-11T21:41:23.5513270Z                         auto tmp53 = in_ptr0[tmp52 + (15*tmp33)];
2023-01-11T21:41:23.5513405Z                         auto tmp54 = tmp53 / tmp51;
2023-01-11T21:41:23.5513530Z                         auto tmp55 = tmp43 < tmp10;
2023-01-11T21:41:23.5513636Z                         auto tmp56 = tmp38 & tmp55;
2023-01-11T21:41:23.5513821Z                         auto tmp57 = tmp42 + tmp54;
2023-01-11T21:41:23.5513966Z                         auto tmp58 = tmp56 ? tmp57 : tmp42;
2023-01-11T21:41:23.5514105Z                         auto tmp59 = static_cast<int>(2);
2023-01-11T21:41:23.5514231Z                         auto tmp60 = tmp6 + tmp59;
2023-01-11T21:41:23.5514357Z                         auto tmp61 = tmp60 * tmp13;
2023-01-11T21:41:23.5514568Z                         auto tmp62 = tmp61 - tmp13;
2023-01-11T21:41:23.5514681Z                         auto tmp63 = tmp62 + tmp14;
2023-01-11T21:41:23.5514850Z                         auto tmp64 = (tmp23 != tmp23) ? tmp23 : std::min(tmp63, tmp23);
2023-01-11T21:41:23.5515011Z                         auto tmp65 = (tmp4 != tmp4) ? tmp4 : std::max(tmp62, tmp4);
2023-01-11T21:41:23.5515169Z                         auto tmp66 = (tmp9 != tmp9) ? tmp9 : std::min(tmp64, tmp9);
2023-01-11T21:41:23.5515366Z                         auto tmp67 = tmp66 - tmp65;
2023-01-11T21:41:23.5515489Z                         auto tmp68 = tmp29 * tmp67;
2023-01-11T21:41:23.5515659Z                         auto tmp69 = (tmp34 != tmp34) ? tmp34 : std::min(tmp60, tmp34);
2023-01-11T21:41:23.5515806Z                         auto tmp70 = in_ptr0[tmp69 + (15*tmp33)];
2023-01-11T21:41:23.5515928Z                         auto tmp71 = tmp70 / tmp68;
2023-01-11T21:41:23.5516036Z                         auto tmp72 = tmp60 < tmp10;
2023-01-11T21:41:23.5516151Z                         auto tmp73 = tmp38 & tmp72;
2023-01-11T21:41:23.5516285Z                         auto tmp74 = tmp58 + tmp71;
2023-01-11T21:41:23.5516418Z                         auto tmp75 = tmp73 ? tmp74 : tmp58;
2023-01-11T21:41:23.5516541Z                         auto tmp76 = tmp5 + tmp13;
2023-01-11T21:41:23.5516661Z                         auto tmp77 = tmp76 * tmp13;
2023-01-11T21:41:23.5516864Z                         auto tmp78 = tmp77 - tmp13;
2023-01-11T21:41:23.5516980Z                         auto tmp79 = tmp78 + tmp14;
2023-01-11T21:41:23.5517154Z                         auto tmp80 = (tmp20 != tmp20) ? tmp20 : std::min(tmp79, tmp20);
2023-01-11T21:41:23.5517334Z                         auto tmp81 = (tmp4 != tmp4) ? tmp4 : std::max(tmp78, tmp4);
2023-01-11T21:41:23.5517503Z                         auto tmp82 = (tmp7 != tmp7) ? tmp7 : std::min(tmp80, tmp7);
2023-01-11T21:41:23.5517705Z                         auto tmp83 = tmp82 - tmp81;
2023-01-11T21:41:23.5517816Z                         auto tmp84 = tmp83 * tmp30;
2023-01-11T21:41:23.5517978Z                         auto tmp85 = (tmp32 != tmp32) ? tmp32 : std::min(tmp76, tmp32);
2023-01-11T21:41:23.5518206Z                         auto tmp86 = in_ptr0[tmp35 + (15*tmp85)];
2023-01-11T21:41:23.5518322Z                         auto tmp87 = tmp86 / tmp84;
2023-01-11T21:41:23.5518440Z                         auto tmp88 = tmp76 < tmp8;
2023-01-11T21:41:23.5518558Z                         auto tmp89 = tmp88 & tmp39;
2023-01-11T21:41:23.5518685Z                         auto tmp90 = tmp75 + tmp87;
2023-01-11T21:41:23.5518827Z                         auto tmp91 = tmp89 ? tmp90 : tmp75;
2023-01-11T21:41:23.5518947Z                         auto tmp92 = tmp83 * tmp50;
2023-01-11T21:41:23.5519083Z                         auto tmp93 = in_ptr0[tmp52 + (15*tmp85)];
2023-01-11T21:41:23.5519204Z                         auto tmp94 = tmp93 / tmp92;
2023-01-11T21:41:23.5519305Z                         auto tmp95 = tmp88 & tmp55;
2023-01-11T21:41:23.5519415Z                         auto tmp96 = tmp91 + tmp94;
2023-01-11T21:41:23.5519608Z                         auto tmp97 = tmp95 ? tmp96 : tmp91;
2023-01-11T21:41:23.5519735Z                         auto tmp98 = tmp83 * tmp67;
2023-01-11T21:41:23.5519869Z                         auto tmp99 = in_ptr0[tmp69 + (15*tmp85)];
2023-01-11T21:41:23.5519995Z                         auto tmp100 = tmp99 / tmp98;
2023-01-11T21:41:23.5520120Z                         auto tmp101 = tmp88 & tmp72;
2023-01-11T21:41:23.5520237Z                         auto tmp102 = tmp97 + tmp100;
2023-01-11T21:41:23.5520374Z                         auto tmp103 = tmp101 ? tmp102 : tmp97;
2023-01-11T21:41:23.5520496Z                         auto tmp104 = tmp5 + tmp59;
2023-01-11T21:41:23.5520622Z                         auto tmp105 = tmp104 * tmp13;
2023-01-11T21:41:23.5520835Z                         auto tmp106 = tmp105 - tmp13;
2023-01-11T21:41:23.5520958Z                         auto tmp107 = tmp106 + tmp14;
2023-01-11T21:41:23.5521135Z                         auto tmp108 = (tmp20 != tmp20) ? tmp20 : std::min(tmp107, tmp20);
2023-01-11T21:41:23.5521321Z                         auto tmp109 = (tmp4 != tmp4) ? tmp4 : std::max(tmp106, tmp4);
2023-01-11T21:41:23.5521480Z                         auto tmp110 = (tmp7 != tmp7) ? tmp7 : std::min(tmp108, tmp7);
2023-01-11T21:41:23.5521696Z                         auto tmp111 = tmp110 - tmp109;
2023-01-11T21:41:23.5521818Z                         auto tmp112 = tmp111 * tmp30;
2023-01-11T21:41:23.5522000Z                         auto tmp113 = (tmp32 != tmp32) ? tmp32 : std::min(tmp104, tmp32);
2023-01-11T21:41:23.5522152Z                         auto tmp114 = in_ptr0[tmp35 + (15*tmp113)];
2023-01-11T21:41:23.5522281Z                         auto tmp115 = tmp114 / tmp112;
2023-01-11T21:41:23.5522411Z                         auto tmp116 = tmp104 < tmp8;
2023-01-11T21:41:23.5522542Z                         auto tmp117 = tmp116 & tmp39;
2023-01-11T21:41:23.5522654Z                         auto tmp118 = tmp103 + tmp115;
2023-01-11T21:41:23.5522794Z                         auto tmp119 = tmp117 ? tmp118 : tmp103;
2023-01-11T21:41:23.5522933Z                         auto tmp120 = tmp111 * tmp50;
2023-01-11T21:41:23.5523082Z                         auto tmp121 = in_ptr0[tmp52 + (15*tmp113)];
2023-01-11T21:41:23.5523210Z                         auto tmp122 = tmp121 / tmp120;
2023-01-11T21:41:23.5523343Z                         auto tmp123 = tmp116 & tmp55;
2023-01-11T21:41:23.5523470Z                         auto tmp124 = tmp119 + tmp122;
2023-01-11T21:41:23.5523599Z                         auto tmp125 = tmp123 ? tmp124 : tmp119;
2023-01-11T21:41:23.5523725Z                         auto tmp126 = tmp111 * tmp67;
2023-01-11T21:41:23.5523872Z                         auto tmp127 = in_ptr0[tmp69 + (15*tmp113)];
2023-01-11T21:41:23.5524005Z                         auto tmp128 = tmp127 / tmp126;
2023-01-11T21:41:23.5524135Z                         auto tmp129 = tmp116 & tmp72;
2023-01-11T21:41:23.5524256Z                         auto tmp130 = tmp125 + tmp128;
2023-01-11T21:41:23.5524406Z                         auto tmp131 = tmp129 ? tmp130 : tmp125;
2023-01-11T21:41:23.5524609Z                         out_ptr0[i1 + (15*i0)] = tmp131;
2023-01-11T21:41:23.5524685Z                     }
2023-01-11T21:41:23.5524775Z                 }
2023-01-11T21:41:23.5524869Z             }
2023-01-11T21:41:23.5524957Z         }
2023-01-11T21:41:23.5525032Z     }
2023-01-11T21:41:23.5525113Z }
2023-01-11T21:41:23.5525215Z ''')
2023-01-11T21:41:23.5525239Z 
2023-01-11T21:41:23.5525245Z 
2023-01-11T21:41:23.5525358Z async_compile.wait(globals())
2023-01-11T21:41:23.5525447Z del async_compile
2023-01-11T21:41:23.5525454Z 
2023-01-11T21:41:23.5525555Z def call(args):
2023-01-11T21:41:23.5525658Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5525762Z     args.clear()
2023-01-11T21:41:23.5526095Z     buf0 = empty_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5526293Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5526372Z     del arg0_1
2023-01-11T21:41:23.5526528Z     return (buf0, )
2023-01-11T21:41:23.5526537Z 
2023-01-11T21:41:23.5526544Z 
2023-01-11T21:41:23.5526654Z if __name__ == "__main__":
2023-01-11T21:41:23.5526818Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5526995Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5527310Z     arg0_1 = rand_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5527626Z     arg1_1 = rand_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5527785Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5528161Z [2023-01-11 21:24:50,245] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 34
2023-01-11T21:41:23.5528187Z 
2023-01-11T21:41:23.5528265Z ok (1.701s)
2023-01-11T21:41:23.5528951Z   test_avg_pool2d_backward3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5529136Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5529518Z [2023-01-11 21:24:50,286] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 35
2023-01-11T21:41:23.5529921Z [2023-01-11 21:24:52,072] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 35
2023-01-11T21:41:23.5529929Z 
2023-01-11T21:41:23.5530053Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5530147Z import torch
2023-01-11T21:41:23.5530241Z import random
2023-01-11T21:41:23.5530393Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5530546Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5530559Z 
2023-01-11T21:41:23.5530670Z aten = torch.ops.aten
2023-01-11T21:41:23.5530858Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5530993Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5531000Z 
2023-01-11T21:41:23.5531006Z 
2023-01-11T21:41:23.5531206Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5531505Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5531690Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5531832Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5531909Z {
2023-01-11T21:41:23.5532052Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5532130Z     {
2023-01-11T21:41:23.5532244Z         #pragma omp for 
2023-01-11T21:41:23.5532362Z         for(long i0=0; i0<2016; i0+=1)
2023-01-11T21:41:23.5532447Z         {
2023-01-11T21:41:23.5532542Z             #pragma GCC ivdep
2023-01-11T21:41:23.5532749Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:41:23.5532833Z             {
2023-01-11T21:41:23.5532945Z                 #pragma GCC ivdep
2023-01-11T21:41:23.5533074Z                 for(long i2=0; i2<21; i2+=1)
2023-01-11T21:41:23.5533164Z                 {
2023-01-11T21:41:23.5533261Z                     {
2023-01-11T21:41:23.5533342Z                         {
2023-01-11T21:41:23.5533502Z                             auto tmp0 = static_cast<int>(((1 + i1) / 2));
2023-01-11T21:41:23.5533673Z                             auto tmp1 = static_cast<int>(((1 + i2) / 2));
2023-01-11T21:41:23.5533829Z                             auto tmp2 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:23.5533977Z                             auto tmp3 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:23.5534128Z                             auto tmp4 = static_cast<int>(0);
2023-01-11T21:41:23.5534314Z                             auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:41:23.5534575Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:41:23.5534708Z                             auto tmp7 = static_cast<int>(11);
2023-01-11T21:41:23.5534889Z                             auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:41:23.5535061Z                             auto tmp9 = (tmp7 != tmp7) ? tmp7 : std::min(tmp3, tmp7);
2023-01-11T21:41:23.5535201Z                             auto tmp10 = tmp5 + tmp4;
2023-01-11T21:41:23.5535324Z                             auto tmp11 = tmp6 + tmp4;
2023-01-11T21:41:23.5535473Z                             auto tmp12 = static_cast<int>(1);
2023-01-11T21:41:23.5535703Z                             auto tmp13 = tmp8 - tmp12;
2023-01-11T21:41:23.5535883Z                             auto tmp14 = (tmp13 != tmp13) ? tmp13 : std::min(tmp10, tmp13);
2023-01-11T21:41:23.5536073Z                             auto tmp15 = tmp9 - tmp12;
2023-01-11T21:41:23.5536261Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp11, tmp15);
2023-01-11T21:41:23.5536423Z                             auto tmp17 = in_ptr0[tmp16 + (11*tmp14) + (121*i0)];
2023-01-11T21:41:23.5536553Z                             auto tmp18 = tmp17 / 1;
2023-01-11T21:41:23.5536684Z                             auto tmp19 = tmp10 < tmp8;
2023-01-11T21:41:23.5536815Z                             auto tmp20 = tmp11 < tmp9;
2023-01-11T21:41:23.5536948Z                             auto tmp21 = tmp19 & tmp20;
2023-01-11T21:41:23.5537102Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:23.5537233Z                             auto tmp23 = tmp21 ? tmp18 : tmp22;
2023-01-11T21:41:23.5537376Z                             out_ptr0[i2 + (21*i1) + (441*i0)] = tmp23;
2023-01-11T21:41:23.5537470Z                         }
2023-01-11T21:41:23.5537560Z                     }
2023-01-11T21:41:23.5537647Z                 }
2023-01-11T21:41:23.5537734Z             }
2023-01-11T21:41:23.5537816Z         }
2023-01-11T21:41:23.5537897Z     }
2023-01-11T21:41:23.5537977Z }
2023-01-11T21:41:23.5538085Z ''')
2023-01-11T21:41:23.5538094Z 
2023-01-11T21:41:23.5538100Z 
2023-01-11T21:41:23.5538223Z async_compile.wait(globals())
2023-01-11T21:41:23.5538324Z del async_compile
2023-01-11T21:41:23.5538330Z 
2023-01-11T21:41:23.5538426Z def call(args):
2023-01-11T21:41:23.5538530Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5538612Z     args.clear()
2023-01-11T21:41:23.5538935Z     buf0 = empty_strided((1, 2016, 21, 21), (889056, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5539119Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5539213Z     del arg0_1
2023-01-11T21:41:23.5539312Z     return (buf0, )
2023-01-11T21:41:23.5539319Z 
2023-01-11T21:41:23.5539325Z 
2023-01-11T21:41:23.5539430Z if __name__ == "__main__":
2023-01-11T21:41:23.5539589Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5539765Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5540167Z     arg0_1 = rand_strided((1, 2016, 11, 11), (243936, 121, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5540492Z     arg1_1 = rand_strided((1, 2016, 21, 21), (889056, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5540656Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5540664Z 
2023-01-11T21:41:23.5540758Z ok (1.917s)
2023-01-11T21:41:23.5541462Z   test_avg_pool2d_backward4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5541645Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5542089Z [2023-01-11 21:24:52,195] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 36
2023-01-11T21:41:23.5542662Z [2023-01-11 21:24:52,210] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.avg_pool2d_backward
2023-01-11T21:41:23.5543079Z [2023-01-11 21:24:52,214] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 36
2023-01-11T21:41:23.5543088Z 
2023-01-11T21:41:23.5543220Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5543299Z import torch
2023-01-11T21:41:23.5543403Z import random
2023-01-11T21:41:23.5543563Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5543740Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5543747Z 
2023-01-11T21:41:23.5543856Z aten = torch.ops.aten
2023-01-11T21:41:23.5544038Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5544165Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5544172Z 
2023-01-11T21:41:23.5544178Z 
2023-01-11T21:41:23.5544310Z async_compile.wait(globals())
2023-01-11T21:41:23.5544395Z del async_compile
2023-01-11T21:41:23.5544402Z 
2023-01-11T21:41:23.5544497Z def call(args):
2023-01-11T21:41:23.5544601Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5544698Z     args.clear()
2023-01-11T21:41:23.5544906Z     buf0 = aten.avg_pool2d_backward(arg0_1, arg1_1, [13, 13], [1, 1], [0, 0], True, False, None)
2023-01-11T21:41:23.5544997Z     del arg0_1
2023-01-11T21:41:23.5545086Z     del arg1_1
2023-01-11T21:41:23.5545161Z     buf1 = buf0
2023-01-11T21:41:23.5545306Z     assert_size_stride(buf1, (1, 16, 24, 24), (9216, 576, 24, 1))
2023-01-11T21:41:23.5545389Z     del buf0
2023-01-11T21:41:23.5545485Z     return (buf1, )
2023-01-11T21:41:23.5545491Z 
2023-01-11T21:41:23.5545496Z 
2023-01-11T21:41:23.5545601Z if __name__ == "__main__":
2023-01-11T21:41:23.5545759Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5545933Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5546266Z     arg0_1 = rand_strided((1, 16, 12, 12), (2304, 144, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5546567Z     arg1_1 = rand_strided((1, 16, 24, 24), (9216, 576, 24, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5546730Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5546738Z 
2023-01-11T21:41:23.5546828Z ok (0.054s)
2023-01-11T21:41:23.5547524Z   test_avg_pool2d_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5547704Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5548104Z [2023-01-11 21:24:52,248] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 37
2023-01-11T21:41:23.5548639Z [2023-01-11 21:24:53,858] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 37
2023-01-11T21:41:23.5548647Z 
2023-01-11T21:41:23.5548778Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5548877Z import torch
2023-01-11T21:41:23.5548959Z import random
2023-01-11T21:41:23.5549127Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5549297Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5549304Z 
2023-01-11T21:41:23.5549414Z aten = torch.ops.aten
2023-01-11T21:41:23.5549600Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5549727Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5549734Z 
2023-01-11T21:41:23.5549740Z 
2023-01-11T21:41:23.5549941Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5550221Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5550467Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5550600Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5550688Z {
2023-01-11T21:41:23.5550824Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5550903Z     {
2023-01-11T21:41:23.5551012Z         #pragma omp for 
2023-01-11T21:41:23.5551126Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5551202Z         {
2023-01-11T21:41:23.5551314Z             #pragma GCC ivdep
2023-01-11T21:41:23.5551432Z             for(long i1=0; i1<14; i1+=1)
2023-01-11T21:41:23.5551523Z             {
2023-01-11T21:41:23.5551633Z                 #pragma GCC ivdep
2023-01-11T21:41:23.5551753Z                 for(long i2=0; i2<14; i2+=1)
2023-01-11T21:41:23.5551840Z                 {
2023-01-11T21:41:23.5551909Z                     {
2023-01-11T21:41:23.5551998Z                         {
2023-01-11T21:41:23.5552148Z                             auto tmp0 = static_cast<int>((i1 / 2));
2023-01-11T21:41:23.5552295Z                             auto tmp1 = static_cast<int>((i2 / 2));
2023-01-11T21:41:23.5553365Z                             auto tmp2 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:23.5553528Z                             auto tmp3 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:23.5553677Z                             auto tmp4 = static_cast<int>(0);
2023-01-11T21:41:23.5553929Z                             auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:41:23.5554108Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:41:23.5554254Z                             auto tmp7 = static_cast<int>(7);
2023-01-11T21:41:23.5554436Z                             auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:41:23.5554617Z                             auto tmp9 = (tmp7 != tmp7) ? tmp7 : std::min(tmp3, tmp7);
2023-01-11T21:41:23.5554750Z                             auto tmp10 = tmp5 + tmp4;
2023-01-11T21:41:23.5554881Z                             auto tmp11 = tmp6 + tmp4;
2023-01-11T21:41:23.5555029Z                             auto tmp12 = static_cast<int>(1);
2023-01-11T21:41:23.5555269Z                             auto tmp13 = tmp8 - tmp12;
2023-01-11T21:41:23.5555435Z                             auto tmp14 = (tmp13 != tmp13) ? tmp13 : std::min(tmp10, tmp13);
2023-01-11T21:41:23.5555654Z                             auto tmp15 = tmp9 - tmp12;
2023-01-11T21:41:23.5555836Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp11, tmp15);
2023-01-11T21:41:23.5556004Z                             auto tmp17 = in_ptr0[tmp16 + (7*tmp14) + (49*i0)];
2023-01-11T21:41:23.5556135Z                             auto tmp18 = tmp17 / 4;
2023-01-11T21:41:23.5556265Z                             auto tmp19 = tmp10 < tmp8;
2023-01-11T21:41:23.5556391Z                             auto tmp20 = tmp11 < tmp9;
2023-01-11T21:41:23.5556526Z                             auto tmp21 = tmp19 & tmp20;
2023-01-11T21:41:23.5556666Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:23.5556896Z                             auto tmp23 = tmp21 ? tmp18 : tmp22;
2023-01-11T21:41:23.5557035Z                             out_ptr0[i2 + (14*i1) + (196*i0)] = tmp23;
2023-01-11T21:41:23.5557125Z                         }
2023-01-11T21:41:23.5557214Z                     }
2023-01-11T21:41:23.5557300Z                 }
2023-01-11T21:41:23.5557385Z             }
2023-01-11T21:41:23.5557457Z         }
2023-01-11T21:41:23.5557542Z     }
2023-01-11T21:41:23.5557628Z }
2023-01-11T21:41:23.5557747Z ''')
2023-01-11T21:41:23.5557754Z 
2023-01-11T21:41:23.5557760Z 
2023-01-11T21:41:23.5557889Z async_compile.wait(globals())
2023-01-11T21:41:23.5557989Z del async_compile
2023-01-11T21:41:23.5557996Z 
2023-01-11T21:41:23.5558097Z def call(args):
2023-01-11T21:41:23.5558185Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5558285Z     args.clear()
2023-01-11T21:41:23.5558658Z     buf0 = empty_strided((2, 4, 14, 14), (784, 196, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5558853Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5558944Z     del arg0_1
2023-01-11T21:41:23.5559039Z     return (buf0, )
2023-01-11T21:41:23.5559046Z 
2023-01-11T21:41:23.5559052Z 
2023-01-11T21:41:23.5559155Z if __name__ == "__main__":
2023-01-11T21:41:23.5559333Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5559755Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5560085Z     arg0_1 = rand_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5560387Z     arg1_1 = rand_strided((2, 4, 14, 14), (784, 196, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5560545Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5560553Z 
2023-01-11T21:41:23.5560640Z ok (1.643s)
2023-01-11T21:41:23.5561314Z   test_baddbmm_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5561492Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5561861Z [2023-01-11 21:24:53,883] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 38
2023-01-11T21:41:23.5562254Z [2023-01-11 21:24:55,612] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 38
2023-01-11T21:41:23.5562262Z 
2023-01-11T21:41:23.5562394Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5562476Z import torch
2023-01-11T21:41:23.5562570Z import random
2023-01-11T21:41:23.5562735Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5562899Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5562913Z 
2023-01-11T21:41:23.5563020Z aten = torch.ops.aten
2023-01-11T21:41:23.5563212Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5563345Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5563353Z 
2023-01-11T21:41:23.5563359Z 
2023-01-11T21:41:23.5563554Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5563854Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5564025Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.5564172Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:23.5564251Z {
2023-01-11T21:41:23.5564386Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5564465Z     {
2023-01-11T21:41:23.5564574Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.5564679Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:23.5564758Z         {
2023-01-11T21:41:23.5564874Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:23.5565048Z             {
2023-01-11T21:41:23.5565175Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:23.5565265Z                 {
2023-01-11T21:41:23.5565454Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (100*i0));
2023-01-11T21:41:23.5565675Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + (8*i2) + (100*i1) + (12800*i0));
2023-01-11T21:41:23.5565795Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5565954Z                     tmp2.store(in_out_ptr0 + (8*i2) + (100*i1) + (12800*i0));
2023-01-11T21:41:23.5566043Z                 }
2023-01-11T21:41:23.5566172Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.5566293Z                 for(long i2=96; i2<100; i2+=1)
2023-01-11T21:41:23.5566377Z                 {
2023-01-11T21:41:23.5566497Z                     auto tmp0 = in_ptr0[i2 + (100*i0)];
2023-01-11T21:41:23.5566698Z                     auto tmp1 = in_out_ptr0[i2 + (100*i1) + (12800*i0)];
2023-01-11T21:41:23.5566831Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5566980Z                     in_out_ptr0[i2 + (100*i1) + (12800*i0)] = tmp2;
2023-01-11T21:41:23.5567073Z                 }
2023-01-11T21:41:23.5567159Z             }
2023-01-11T21:41:23.5567245Z         }
2023-01-11T21:41:23.5567313Z     }
2023-01-11T21:41:23.5567397Z }
2023-01-11T21:41:23.5567521Z ''')
2023-01-11T21:41:23.5567529Z 
2023-01-11T21:41:23.5567534Z 
2023-01-11T21:41:23.5567649Z async_compile.wait(globals())
2023-01-11T21:41:23.5567751Z del async_compile
2023-01-11T21:41:23.5567757Z 
2023-01-11T21:41:23.5567857Z def call(args):
2023-01-11T21:41:23.5567985Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.5568068Z     args.clear()
2023-01-11T21:41:23.5568386Z     buf0 = empty_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5568522Z     aten.bmm.out(arg1_1, arg2_1, out=buf0)
2023-01-11T21:41:23.5568620Z     del arg1_1
2023-01-11T21:41:23.5568714Z     del arg2_1
2023-01-11T21:41:23.5568829Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:23.5569013Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:23.5569091Z     del arg0_1
2023-01-11T21:41:23.5569183Z     return (buf1, )
2023-01-11T21:41:23.5569190Z 
2023-01-11T21:41:23.5569196Z 
2023-01-11T21:41:23.5569299Z if __name__ == "__main__":
2023-01-11T21:41:23.5569450Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5569621Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5569926Z     arg0_1 = rand_strided((6, 1, 100), (100, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5570229Z     arg1_1 = rand_strided((6, 128, 64), (8192, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5570537Z     arg2_1 = rand_strided((6, 64, 100), (6400, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5570699Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.5570714Z 
2023-01-11T21:41:23.5570788Z ok (1.757s)
2023-01-11T21:41:23.5571469Z   test_batch_norm_2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5571645Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5572022Z [2023-01-11 21:24:55,896] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 39
2023-01-11T21:41:23.5572402Z [2023-01-11 21:24:57,733] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 39
2023-01-11T21:41:23.5573023Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5573281Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5573653Z [2023-01-11 21:24:58,036] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 40
2023-01-11T21:41:23.5573662Z 
2023-01-11T21:41:23.5573790Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5573885Z import torch
2023-01-11T21:41:23.5573965Z import random
2023-01-11T21:41:23.5574127Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5574289Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5574297Z 
2023-01-11T21:41:23.5574548Z aten = torch.ops.aten
2023-01-11T21:41:23.5574736Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5574927Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5574936Z 
2023-01-11T21:41:23.5574942Z 
2023-01-11T21:41:23.5575142Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5575431Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5575584Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5575723Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5575857Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.5575992Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:23.5576125Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:23.5576253Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5576378Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.5576504Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.5576615Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:41:23.5576697Z {
2023-01-11T21:41:23.5576832Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5576915Z     {
2023-01-11T21:41:23.5577019Z         #pragma omp for 
2023-01-11T21:41:23.5577129Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.5577196Z         {
2023-01-11T21:41:23.5577380Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5577498Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5577583Z         }
2023-01-11T21:41:23.5577715Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5577828Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.5577912Z         {
2023-01-11T21:41:23.5578025Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5578118Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.5578197Z         }
2023-01-11T21:41:23.5578299Z         #pragma omp for 
2023-01-11T21:41:23.5578409Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.5578495Z         {
2023-01-11T21:41:23.5578678Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.5578783Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.5578868Z         }
2023-01-11T21:41:23.5578999Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5579110Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.5579199Z         {
2023-01-11T21:41:23.5579313Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.5579416Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:23.5579484Z         }
2023-01-11T21:41:23.5579606Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.5579712Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.5579793Z         {
2023-01-11T21:41:23.5579911Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:23.5579991Z             {
2023-01-11T21:41:23.5580110Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:23.5580185Z                 {
2023-01-11T21:41:23.5580399Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + (8*i2) + (64*i1) + (640*i0));
2023-01-11T21:41:23.5580660Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i1]);
2023-01-11T21:41:23.5580831Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr1[i1]);
2023-01-11T21:41:23.5581006Z                     auto tmp11 = at::vec::Vectorized<float>(in_ptr3[i1]);
2023-01-11T21:41:23.5581188Z                     auto tmp13 = at::vec::Vectorized<float>(in_ptr4[i1]);
2023-01-11T21:41:23.5581403Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.5581695Z                     auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:23.5581813Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.5581916Z                     auto tmp6 = tmp5.sqrt();
2023-01-11T21:41:23.5582049Z                     auto tmp7 = tmp6.reciprocal();
2023-01-11T21:41:23.5582228Z                     auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.5582597Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.5582733Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:23.5582861Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:23.5582981Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:23.5583151Z                     auto tmp15 = at::vec::clamp_min(tmp14, decltype(tmp14)(0));
2023-01-11T21:41:23.5583306Z                     tmp15.store(out_ptr2 + (8*i2) + (64*i1) + (640*i0));
2023-01-11T21:41:23.5583393Z                 }
2023-01-11T21:41:23.5583529Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.5583650Z                 for(long i2=64; i2<64; i2+=1)
2023-01-11T21:41:23.5583740Z                 {
2023-01-11T21:41:23.5583885Z                     auto tmp0 = in_ptr2[i2 + (64*i1) + (640*i0)];
2023-01-11T21:41:23.5583995Z                     auto tmp1 = out_ptr0[i1];
2023-01-11T21:41:23.5584120Z                     auto tmp3 = out_ptr1[i1];
2023-01-11T21:41:23.5584249Z                     auto tmp11 = in_ptr3[i1];
2023-01-11T21:41:23.5584373Z                     auto tmp13 = in_ptr4[i1];
2023-01-11T21:41:23.5584589Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.5584819Z                     auto tmp4 = static_cast<float>(1e-05);
2023-01-11T21:41:23.5584944Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.5585078Z                     auto tmp6 = std::sqrt(tmp5);
2023-01-11T21:41:23.5585183Z                     auto tmp7 = 1 / tmp6;
2023-01-11T21:41:23.5585325Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:41:23.5585442Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.5585566Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:23.5585688Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:23.5585812Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:23.5585940Z                     auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:41:23.5586065Z                     out_ptr2[i2 + (64*i1) + (640*i0)] = tmp15;
2023-01-11T21:41:23.5586155Z                 }
2023-01-11T21:41:23.5586246Z             }
2023-01-11T21:41:23.5586332Z         }
2023-01-11T21:41:23.5586439Z         #pragma omp for 
2023-01-11T21:41:23.5586549Z         for(long i0=0; i0<1280; i0+=1)
2023-01-11T21:41:23.5586634Z         {
2023-01-11T21:41:23.5586709Z             {
2023-01-11T21:41:23.5586799Z                 {
2023-01-11T21:41:23.5586927Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:23.5587065Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:23.5587191Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:41:23.5587305Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:23.5587396Z                 }
2023-01-11T21:41:23.5587467Z             }
2023-01-11T21:41:23.5587551Z         }
2023-01-11T21:41:23.5587634Z     }
2023-01-11T21:41:23.5587719Z }
2023-01-11T21:41:23.5587836Z ''')
2023-01-11T21:41:23.5587845Z 
2023-01-11T21:41:23.5587938Z 
2023-01-11T21:41:23.5588076Z async_compile.wait(globals())
2023-01-11T21:41:23.5588176Z del async_compile
2023-01-11T21:41:23.5588182Z 
2023-01-11T21:41:23.5588261Z def call(args):
2023-01-11T21:41:23.5588450Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
2023-01-11T21:41:23.5588548Z     args.clear()
2023-01-11T21:41:23.5588838Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5589109Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5589409Z     buf2 = empty_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5589705Z     buf3 = empty_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5590254Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(primals_6.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:23.5590342Z     del primals_1
2023-01-11T21:41:23.5590438Z     del primals_2
2023-01-11T21:41:23.5590538Z     del primals_3
2023-01-11T21:41:23.5590633Z     del primals_4
2023-01-11T21:41:23.5590787Z     return (buf0, buf1, buf2, primals_6, buf0, buf1, buf3, )
2023-01-11T21:41:23.5590795Z 
2023-01-11T21:41:23.5590801Z 
2023-01-11T21:41:23.5590900Z if __name__ == "__main__":
2023-01-11T21:41:23.5591051Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5591223Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5591633Z     primals_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5591914Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5592199Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5592481Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5592749Z     primals_5 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5593056Z     primals_6 = rand_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5593290Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]))
2023-01-11T21:41:23.5593299Z 
2023-01-11T21:41:23.5593691Z [2023-01-11 21:24:59,779] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 40
2023-01-11T21:41:23.5593699Z 
2023-01-11T21:41:23.5593901Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5593982Z import torch
2023-01-11T21:41:23.5594081Z import random
2023-01-11T21:41:23.5594239Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5594401Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5594407Z 
2023-01-11T21:41:23.5594513Z aten = torch.ops.aten
2023-01-11T21:41:23.5594704Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5594953Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5594960Z 
2023-01-11T21:41:23.5594967Z 
2023-01-11T21:41:23.5595148Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5595430Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5595590Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5595729Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5595867Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.5596007Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:23.5596144Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:23.5596282Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5596391Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.5596514Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.5596727Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:41:23.5596813Z {
2023-01-11T21:41:23.5596950Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5597031Z     {
2023-01-11T21:41:23.5597134Z         #pragma omp for 
2023-01-11T21:41:23.5597231Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.5597318Z         {
2023-01-11T21:41:23.5597510Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5597639Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5597727Z         }
2023-01-11T21:41:23.5597852Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5597967Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.5598039Z         {
2023-01-11T21:41:23.5598154Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5598263Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.5598349Z         }
2023-01-11T21:41:23.5598454Z         #pragma omp for 
2023-01-11T21:41:23.5598623Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.5598697Z         {
2023-01-11T21:41:23.5598883Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.5599002Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.5599083Z         }
2023-01-11T21:41:23.5599209Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5599320Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.5599407Z         {
2023-01-11T21:41:23.5599509Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.5599622Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:23.5599704Z         }
2023-01-11T21:41:23.5599830Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.5599947Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.5600030Z         {
2023-01-11T21:41:23.5600146Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:23.5600218Z             {
2023-01-11T21:41:23.5600338Z                 for(long i2=0; i2<32; i2+=1)
2023-01-11T21:41:23.5600429Z                 {
2023-01-11T21:41:23.5600640Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + (8*i2) + (256*i1) + (2560*i0));
2023-01-11T21:41:23.5600813Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i1]);
2023-01-11T21:41:23.5600984Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr1[i1]);
2023-01-11T21:41:23.5601155Z                     auto tmp11 = at::vec::Vectorized<float>(in_ptr3[i1]);
2023-01-11T21:41:23.5601339Z                     auto tmp13 = at::vec::Vectorized<float>(in_ptr4[i1]);
2023-01-11T21:41:23.5601533Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.5601832Z                     auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:23.5601957Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.5602079Z                     auto tmp6 = tmp5.sqrt();
2023-01-11T21:41:23.5602221Z                     auto tmp7 = tmp6.reciprocal();
2023-01-11T21:41:23.5602409Z                     auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.5602536Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.5602660Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:23.5602771Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:23.5602894Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:23.5603073Z                     auto tmp15 = at::vec::clamp_min(tmp14, decltype(tmp14)(0));
2023-01-11T21:41:23.5603223Z                     tmp15.store(out_ptr2 + (8*i2) + (256*i1) + (2560*i0));
2023-01-11T21:41:23.5603314Z                 }
2023-01-11T21:41:23.5603448Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.5603575Z                 for(long i2=256; i2<256; i2+=1)
2023-01-11T21:41:23.5603645Z                 {
2023-01-11T21:41:23.5603790Z                     auto tmp0 = in_ptr2[i2 + (256*i1) + (2560*i0)];
2023-01-11T21:41:23.5603919Z                     auto tmp1 = out_ptr0[i1];
2023-01-11T21:41:23.5604245Z                     auto tmp3 = out_ptr1[i1];
2023-01-11T21:41:23.5604365Z                     auto tmp11 = in_ptr3[i1];
2023-01-11T21:41:23.5604486Z                     auto tmp13 = in_ptr4[i1];
2023-01-11T21:41:23.5604685Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.5604910Z                     auto tmp4 = static_cast<float>(1e-05);
2023-01-11T21:41:23.5605015Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.5605157Z                     auto tmp6 = std::sqrt(tmp5);
2023-01-11T21:41:23.5605274Z                     auto tmp7 = 1 / tmp6;
2023-01-11T21:41:23.5605411Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:41:23.5605528Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.5605650Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:23.5605778Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:23.5605888Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:23.5606081Z                     auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:41:23.5606230Z                     out_ptr2[i2 + (256*i1) + (2560*i0)] = tmp15;
2023-01-11T21:41:23.5606326Z                 }
2023-01-11T21:41:23.5606412Z             }
2023-01-11T21:41:23.5606498Z         }
2023-01-11T21:41:23.5606607Z         #pragma omp for 
2023-01-11T21:41:23.5606707Z         for(long i0=0; i0<7680; i0+=1)
2023-01-11T21:41:23.5606787Z         {
2023-01-11T21:41:23.5606871Z             {
2023-01-11T21:41:23.5606958Z                 {
2023-01-11T21:41:23.5607083Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:23.5607223Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:23.5607343Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:41:23.5607443Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:23.5607526Z                 }
2023-01-11T21:41:23.5607614Z             }
2023-01-11T21:41:23.5607701Z         }
2023-01-11T21:41:23.5607786Z     }
2023-01-11T21:41:23.5607873Z }
2023-01-11T21:41:23.5607987Z ''')
2023-01-11T21:41:23.5608000Z 
2023-01-11T21:41:23.5608021Z 
2023-01-11T21:41:23.5608135Z async_compile.wait(globals())
2023-01-11T21:41:23.5608239Z del async_compile
2023-01-11T21:41:23.5608246Z 
2023-01-11T21:41:23.5608346Z def call(args):
2023-01-11T21:41:23.5608545Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
2023-01-11T21:41:23.5608645Z     args.clear()
2023-01-11T21:41:23.5611299Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5611577Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5611874Z     buf2 = empty_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5612182Z     buf3 = empty_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5612665Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(primals_6.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:23.5612773Z     del primals_1
2023-01-11T21:41:23.5612880Z     del primals_2
2023-01-11T21:41:23.5612982Z     del primals_3
2023-01-11T21:41:23.5613079Z     del primals_4
2023-01-11T21:41:23.5613245Z     return (buf0, buf1, buf2, primals_6, buf0, buf1, buf3, )
2023-01-11T21:41:23.5613360Z 
2023-01-11T21:41:23.5613367Z 
2023-01-11T21:41:23.5613470Z if __name__ == "__main__":
2023-01-11T21:41:23.5613618Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5613788Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5614092Z     primals_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5614388Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5614674Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5615030Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5615295Z     primals_5 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5615629Z     primals_6 = rand_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5615869Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]))
2023-01-11T21:41:23.5615876Z 
2023-01-11T21:41:23.5615967Z ok (4.165s)
2023-01-11T21:41:23.5616653Z   test_bernoulli1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5616934Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5617330Z [2023-01-11 21:24:59,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 41
2023-01-11T21:41:23.5617721Z [2023-01-11 21:25:01,468] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 41
2023-01-11T21:41:23.5617729Z 
2023-01-11T21:41:23.5617862Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5617970Z import torch
2023-01-11T21:41:23.5618069Z import random
2023-01-11T21:41:23.5618215Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5618381Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5618388Z 
2023-01-11T21:41:23.5618498Z aten = torch.ops.aten
2023-01-11T21:41:23.5618673Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5618802Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5618810Z 
2023-01-11T21:41:23.5618816Z 
2023-01-11T21:41:23.5619023Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5619318Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5619477Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5619546Z {
2023-01-11T21:41:23.5619692Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5619783Z     {
2023-01-11T21:41:23.5619890Z         #pragma omp for 
2023-01-11T21:41:23.5620003Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.5620093Z         {
2023-01-11T21:41:23.5620296Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:23.5620406Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5620497Z         }
2023-01-11T21:41:23.5620641Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5620754Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:23.5620840Z         {
2023-01-11T21:41:23.5620983Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:23.5621103Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.5621171Z         }
2023-01-11T21:41:23.5621254Z     }
2023-01-11T21:41:23.5621341Z }
2023-01-11T21:41:23.5621461Z ''')
2023-01-11T21:41:23.5621468Z 
2023-01-11T21:41:23.5621474Z 
2023-01-11T21:41:23.5621603Z async_compile.wait(globals())
2023-01-11T21:41:23.5621701Z del async_compile
2023-01-11T21:41:23.5621708Z 
2023-01-11T21:41:23.5621812Z def call(args):
2023-01-11T21:41:23.5621893Z     arg0_1, = args
2023-01-11T21:41:23.5621990Z     args.clear()
2023-01-11T21:41:23.5622289Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5622646Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5622771Z     aten.bernoulli_(buf0, )
2023-01-11T21:41:23.5622878Z     return (buf0, buf0, )
2023-01-11T21:41:23.5622885Z 
2023-01-11T21:41:23.5622891Z 
2023-01-11T21:41:23.5622998Z if __name__ == "__main__":
2023-01-11T21:41:23.5623171Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5623441Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5623750Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5623903Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5623910Z 
2023-01-11T21:41:23.5624008Z ok (1.688s)
2023-01-11T21:41:23.5624717Z   test_bernoulli2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5624894Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5625291Z [2023-01-11 21:25:01,495] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 42
2023-01-11T21:41:23.5625762Z [2023-01-11 21:25:01,495] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.5626157Z [2023-01-11 21:25:03,039] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 42
2023-01-11T21:41:23.5626165Z 
2023-01-11T21:41:23.5626306Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5626383Z import torch
2023-01-11T21:41:23.5626479Z import random
2023-01-11T21:41:23.5626634Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5626799Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5626806Z 
2023-01-11T21:41:23.5626913Z aten = torch.ops.aten
2023-01-11T21:41:23.5627101Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5627226Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5627575Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.5627585Z 
2023-01-11T21:41:23.5627606Z 
2023-01-11T21:41:23.5627808Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5628107Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5628287Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:23.5628434Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5628578Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:23.5628672Z {
2023-01-11T21:41:23.5628815Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5628883Z     {
2023-01-11T21:41:23.5628993Z         #pragma omp for 
2023-01-11T21:41:23.5629109Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5629203Z         {
2023-01-11T21:41:23.5629291Z             {
2023-01-11T21:41:23.5629377Z                 {
2023-01-11T21:41:23.5629487Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.5629628Z                     auto tmp5 = in_ptr1[i0];
2023-01-11T21:41:23.5629774Z                     auto tmp1 = static_cast<int>(65535);
2023-01-11T21:41:23.5629910Z                     auto tmp2 = tmp0 ^ tmp1;
2023-01-11T21:41:23.5630048Z                     auto tmp3 = static_cast<int>(i0);
2023-01-11T21:41:23.5630243Z                     auto tmp4 = static_cast<float>(normalized_rand_cpu(tmp2, tmp3));;
2023-01-11T21:41:23.5630362Z                     auto tmp6 = tmp4 < tmp5;
2023-01-11T21:41:23.5630477Z                     out_ptr0[i0] = tmp6;
2023-01-11T21:41:23.5630554Z                 }
2023-01-11T21:41:23.5630642Z             }
2023-01-11T21:41:23.5630731Z         }
2023-01-11T21:41:23.5630813Z     }
2023-01-11T21:41:23.5630898Z }
2023-01-11T21:41:23.5631019Z ''')
2023-01-11T21:41:23.5631025Z 
2023-01-11T21:41:23.5631031Z 
2023-01-11T21:41:23.5631152Z async_compile.wait(globals())
2023-01-11T21:41:23.5631238Z del async_compile
2023-01-11T21:41:23.5631245Z 
2023-01-11T21:41:23.5631341Z def call(args):
2023-01-11T21:41:23.5631437Z     arg0_1, = args
2023-01-11T21:41:23.5631541Z     args.clear()
2023-01-11T21:41:23.5631729Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.5632068Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5632303Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.5632378Z     del arg0_1
2023-01-11T21:41:23.5632477Z     return (buf0, )
2023-01-11T21:41:23.5632484Z 
2023-01-11T21:41:23.5632490Z 
2023-01-11T21:41:23.5632594Z if __name__ == "__main__":
2023-01-11T21:41:23.5632756Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5632933Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5633201Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.5633479Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5633627Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5633634Z 
2023-01-11T21:41:23.5633708Z ok (1.571s)
2023-01-11T21:41:23.5634566Z   test_bitwise2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5634756Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5635181Z [2023-01-11 21:25:03,063] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 43
2023-01-11T21:41:23.5635592Z [2023-01-11 21:25:04,592] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 43
2023-01-11T21:41:23.5635600Z 
2023-01-11T21:41:23.5635741Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5635845Z import torch
2023-01-11T21:41:23.5635946Z import random
2023-01-11T21:41:23.5636120Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5636287Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5636294Z 
2023-01-11T21:41:23.5636408Z aten = torch.ops.aten
2023-01-11T21:41:23.5636615Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5636754Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5636762Z 
2023-01-11T21:41:23.5636769Z 
2023-01-11T21:41:23.5636980Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5637290Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5637463Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.5637608Z                        const bool* __restrict__ in_ptr1,
2023-01-11T21:41:23.5637728Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:23.5637869Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:41:23.5638003Z                        bool* __restrict__ out_ptr2,
2023-01-11T21:41:23.5638146Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:41:23.5638231Z {
2023-01-11T21:41:23.5638375Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5638461Z     {
2023-01-11T21:41:23.5638555Z         #pragma omp for 
2023-01-11T21:41:23.5638676Z         for(long i0=0; i0<40; i0+=1)
2023-01-11T21:41:23.5638765Z         {
2023-01-11T21:41:23.5638852Z             {
2023-01-11T21:41:23.5638945Z                 {
2023-01-11T21:41:23.5639074Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5639204Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.5639319Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:23.5639446Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:41:23.5639573Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:41:23.5639699Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:41:23.5639818Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.5639939Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.5640147Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.5640251Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:23.5640346Z                 }
2023-01-11T21:41:23.5640435Z             }
2023-01-11T21:41:23.5640526Z         }
2023-01-11T21:41:23.5640613Z     }
2023-01-11T21:41:23.5640695Z }
2023-01-11T21:41:23.5640807Z ''')
2023-01-11T21:41:23.5640833Z 
2023-01-11T21:41:23.5640839Z 
2023-01-11T21:41:23.5640953Z async_compile.wait(globals())
2023-01-11T21:41:23.5641056Z del async_compile
2023-01-11T21:41:23.5641064Z 
2023-01-11T21:41:23.5641166Z def call(args):
2023-01-11T21:41:23.5641274Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5641377Z     args.clear()
2023-01-11T21:41:23.5641692Z     buf0 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5641987Z     buf1 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5642265Z     buf2 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5642635Z     buf3 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5642977Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:23.5643074Z     del arg0_1
2023-01-11T21:41:23.5643172Z     del arg1_1
2023-01-11T21:41:23.5643297Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:23.5643305Z 
2023-01-11T21:41:23.5643310Z 
2023-01-11T21:41:23.5643419Z if __name__ == "__main__":
2023-01-11T21:41:23.5643585Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5643750Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5644051Z     arg0_1 = rand_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5644329Z     arg1_1 = rand_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5644506Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5644518Z 
2023-01-11T21:41:23.5644615Z ok (1.554s)
2023-01-11T21:41:23.5645330Z   test_bitwise_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5645517Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5645913Z [2023-01-11 21:25:04,615] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 44
2023-01-11T21:41:23.5646315Z [2023-01-11 21:25:06,126] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 44
2023-01-11T21:41:23.5646322Z 
2023-01-11T21:41:23.5646458Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5646549Z import torch
2023-01-11T21:41:23.5646653Z import random
2023-01-11T21:41:23.5646819Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5646997Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5647005Z 
2023-01-11T21:41:23.5647114Z aten = torch.ops.aten
2023-01-11T21:41:23.5647313Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5647444Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5647452Z 
2023-01-11T21:41:23.5647458Z 
2023-01-11T21:41:23.5647665Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5647959Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5648129Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.5648280Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:23.5648418Z                        int* __restrict__ out_ptr0,
2023-01-11T21:41:23.5648560Z                        int* __restrict__ out_ptr1,
2023-01-11T21:41:23.5648772Z                        int* __restrict__ out_ptr2,
2023-01-11T21:41:23.5648898Z                        int* __restrict__ out_ptr3)
2023-01-11T21:41:23.5648969Z {
2023-01-11T21:41:23.5649105Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5649192Z     {
2023-01-11T21:41:23.5649300Z         #pragma omp for 
2023-01-11T21:41:23.5649425Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.5649512Z         {
2023-01-11T21:41:23.5649602Z             {
2023-01-11T21:41:23.5649682Z                 {
2023-01-11T21:41:23.5649814Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5649939Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.5650054Z                     auto tmp1 = ~tmp0;
2023-01-11T21:41:23.5650175Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:41:23.5650296Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:41:23.5650418Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:41:23.5650601Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.5650718Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.5650833Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.5650941Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:23.5651029Z                 }
2023-01-11T21:41:23.5651115Z             }
2023-01-11T21:41:23.5651198Z         }
2023-01-11T21:41:23.5651265Z     }
2023-01-11T21:41:23.5651351Z }
2023-01-11T21:41:23.5651476Z ''')
2023-01-11T21:41:23.5651484Z 
2023-01-11T21:41:23.5651489Z 
2023-01-11T21:41:23.5651615Z async_compile.wait(globals())
2023-01-11T21:41:23.5651715Z del async_compile
2023-01-11T21:41:23.5651721Z 
2023-01-11T21:41:23.5651823Z def call(args):
2023-01-11T21:41:23.5651921Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5652005Z     args.clear()
2023-01-11T21:41:23.5652286Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.5652551Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.5652826Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.5653079Z     buf3 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.5653411Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:23.5653503Z     del arg0_1
2023-01-11T21:41:23.5653595Z     del arg1_1
2023-01-11T21:41:23.5653703Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:23.5653710Z 
2023-01-11T21:41:23.5653716Z 
2023-01-11T21:41:23.5653827Z if __name__ == "__main__":
2023-01-11T21:41:23.5653991Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5654166Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5654433Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.5654701Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.5654862Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5654869Z 
2023-01-11T21:41:23.5654961Z ok (1.534s)
2023-01-11T21:41:23.5655575Z   test_bmm1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5655743Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5656134Z [2023-01-11 21:25:06,154] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 45
2023-01-11T21:41:23.5656518Z [2023-01-11 21:25:07,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 45
2023-01-11T21:41:23.5657215Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5657392Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5657777Z [2023-01-11 21:25:07,774] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 46
2023-01-11T21:41:23.5658168Z [2023-01-11 21:25:09,387] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 46
2023-01-11T21:41:23.5658177Z 
2023-01-11T21:41:23.5658309Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5658405Z import torch
2023-01-11T21:41:23.5658487Z import random
2023-01-11T21:41:23.5658654Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5658894Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5658903Z 
2023-01-11T21:41:23.5659016Z aten = torch.ops.aten
2023-01-11T21:41:23.5659197Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5659324Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5659331Z 
2023-01-11T21:41:23.5659338Z 
2023-01-11T21:41:23.5659538Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5659829Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5659980Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5660395Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5660553Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5660686Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.5660768Z {
2023-01-11T21:41:23.5660912Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5661000Z     {
2023-01-11T21:41:23.5661097Z         #pragma omp for 
2023-01-11T21:41:23.5661211Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.5661296Z         {
2023-01-11T21:41:23.5661494Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5661690Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.5661807Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5661932Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5662021Z         }
2023-01-11T21:41:23.5662144Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5662262Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.5662466Z         {
2023-01-11T21:41:23.5662608Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5662749Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.5662867Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5662985Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5663067Z         }
2023-01-11T21:41:23.5663181Z         #pragma omp for 
2023-01-11T21:41:23.5663298Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.5663384Z         {
2023-01-11T21:41:23.5663577Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.5663762Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.5663881Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5663991Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.5664076Z         }
2023-01-11T21:41:23.5664206Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5664326Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.5664415Z         {
2023-01-11T21:41:23.5664532Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.5664670Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.5664776Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5664881Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.5665080Z         }
2023-01-11T21:41:23.5665166Z     }
2023-01-11T21:41:23.5665252Z }
2023-01-11T21:41:23.5665389Z ''')
2023-01-11T21:41:23.5665398Z 
2023-01-11T21:41:23.5665404Z 
2023-01-11T21:41:23.5665592Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.5665858Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5666022Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.5666105Z {
2023-01-11T21:41:23.5666241Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5666328Z     {
2023-01-11T21:41:23.5666434Z         #pragma omp for 
2023-01-11T21:41:23.5666550Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.5666628Z         {
2023-01-11T21:41:23.5666818Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.5667011Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.5667126Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5667339Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.5667430Z         }
2023-01-11T21:41:23.5667560Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5667658Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.5667743Z         {
2023-01-11T21:41:23.5667870Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.5668008Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.5668132Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5668247Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5668337Z         }
2023-01-11T21:41:23.5668411Z     }
2023-01-11T21:41:23.5668496Z }
2023-01-11T21:41:23.5668615Z ''')
2023-01-11T21:41:23.5668624Z 
2023-01-11T21:41:23.5668630Z 
2023-01-11T21:41:23.5668756Z async_compile.wait(globals())
2023-01-11T21:41:23.5668854Z del async_compile
2023-01-11T21:41:23.5668860Z 
2023-01-11T21:41:23.5668959Z def call(args):
2023-01-11T21:41:23.5669061Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5669149Z     args.clear()
2023-01-11T21:41:23.5669447Z     buf0 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5669587Z     aten.bmm.out(arg0_1, arg1_1, out=buf0)
2023-01-11T21:41:23.5669959Z     buf1 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5670366Z     buf2 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5670724Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.5670861Z     del arg0_1
2023-01-11T21:41:23.5679488Z     del arg1_1
2023-01-11T21:41:23.5679921Z     buf3 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5680110Z     aten.bmm.out(buf1, buf2, out=buf3)
2023-01-11T21:41:23.5680244Z     del buf1
2023-01-11T21:41:23.5680372Z     del buf2
2023-01-11T21:41:23.5680539Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:23.5680750Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.5680899Z     return (buf0, buf4, )
2023-01-11T21:41:23.5680908Z 
2023-01-11T21:41:23.5680916Z 
2023-01-11T21:41:23.5681051Z if __name__ == "__main__":
2023-01-11T21:41:23.5681277Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5681527Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5681924Z     arg0_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5682301Z     arg1_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5682519Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5682526Z 
2023-01-11T21:41:23.5682534Z 
2023-01-11T21:41:23.5682715Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5682846Z import torch
2023-01-11T21:41:23.5682956Z import random
2023-01-11T21:41:23.5683183Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5683524Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5683533Z 
2023-01-11T21:41:23.5683691Z aten = torch.ops.aten
2023-01-11T21:41:23.5683959Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5684146Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5684154Z 
2023-01-11T21:41:23.5684162Z 
2023-01-11T21:41:23.5684432Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5684837Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5685055Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5685248Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5685431Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5685618Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.5685743Z {
2023-01-11T21:41:23.5685927Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5686048Z     {
2023-01-11T21:41:23.5686257Z         #pragma omp for 
2023-01-11T21:41:23.5686414Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.5686539Z         {
2023-01-11T21:41:23.5686793Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.5687044Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.5687210Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5687381Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.5687491Z         }
2023-01-11T21:41:23.5687682Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5687844Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.5687970Z         {
2023-01-11T21:41:23.5688136Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5688321Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.5688489Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5688629Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5688758Z         }
2023-01-11T21:41:23.5688917Z         #pragma omp for 
2023-01-11T21:41:23.5689078Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:23.5689201Z         {
2023-01-11T21:41:23.5689459Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.5689717Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.5689882Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5690040Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.5690166Z         }
2023-01-11T21:41:23.5690354Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5690516Z         for(long i0=80; i0<80; i0+=1)
2023-01-11T21:41:23.5690643Z         {
2023-01-11T21:41:23.5690812Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.5690991Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.5691158Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5691313Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.5691433Z         }
2023-01-11T21:41:23.5691564Z     }
2023-01-11T21:41:23.5691678Z }
2023-01-11T21:41:23.5691844Z ''')
2023-01-11T21:41:23.5691853Z 
2023-01-11T21:41:23.5691860Z 
2023-01-11T21:41:23.5692107Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.5692512Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5692744Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.5692865Z {
2023-01-11T21:41:23.5693059Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5693184Z     {
2023-01-11T21:41:23.5693338Z         #pragma omp for 
2023-01-11T21:41:23.5693482Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:23.5693604Z         {
2023-01-11T21:41:23.5693862Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.5694117Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.5694279Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5694550Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.5713371Z         }
2023-01-11T21:41:23.5713623Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.5713832Z         for(long i0=160; i0<160; i0+=1)
2023-01-11T21:41:23.5713964Z         {
2023-01-11T21:41:23.5714131Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.5714324Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.5714475Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5714631Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5714747Z         }
2023-01-11T21:41:23.5714854Z     }
2023-01-11T21:41:23.5714976Z }
2023-01-11T21:41:23.5715150Z ''')
2023-01-11T21:41:23.5715159Z 
2023-01-11T21:41:23.5715166Z 
2023-01-11T21:41:23.5715336Z async_compile.wait(globals())
2023-01-11T21:41:23.5715478Z del async_compile
2023-01-11T21:41:23.5715486Z 
2023-01-11T21:41:23.5715628Z def call(args):
2023-01-11T21:41:23.5715764Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5715889Z     args.clear()
2023-01-11T21:41:23.5716421Z     buf0 = empty_strided((1, 16, 10), (160, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5716731Z     aten.mm.out(as_strided(arg0_1, (16, 8), (8, 1)), as_strided(arg1_1, (8, 10), (10, 1)), out=as_strided(buf0, (16, 10), (10, 1)))
2023-01-11T21:41:23.5717113Z     buf1 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5717495Z     buf2 = empty_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5717855Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.5717962Z     del arg0_1
2023-01-11T21:41:23.5718075Z     del arg1_1
2023-01-11T21:41:23.5718444Z     buf3 = empty_strided((1, 16, 10), (160, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5718740Z     aten.mm.out(as_strided(buf1, (16, 8), (8, 1)), as_strided(buf2, (8, 10), (10, 1)), out=as_strided(buf3, (16, 10), (10, 1)))
2023-01-11T21:41:23.5718876Z     del buf1
2023-01-11T21:41:23.5719005Z     del buf2
2023-01-11T21:41:23.5719174Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:23.5719363Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.5719513Z     return (buf0, buf4, )
2023-01-11T21:41:23.5719521Z 
2023-01-11T21:41:23.5719529Z 
2023-01-11T21:41:23.5719679Z if __name__ == "__main__":
2023-01-11T21:41:23.5719887Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5720133Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5720525Z     arg0_1 = rand_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5720905Z     arg1_1 = rand_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5721132Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5721140Z 
2023-01-11T21:41:23.5721274Z ok (3.262s)
2023-01-11T21:41:23.5722218Z   test_bmm2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5722462Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5722960Z [2023-01-11 21:25:09,412] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 47
2023-01-11T21:41:23.5723442Z [2023-01-11 21:25:09,417] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 47
2023-01-11T21:41:23.5723450Z 
2023-01-11T21:41:23.5723634Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5723778Z import torch
2023-01-11T21:41:23.5723922Z import random
2023-01-11T21:41:23.5724156Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5724404Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5724513Z 
2023-01-11T21:41:23.5724676Z aten = torch.ops.aten
2023-01-11T21:41:23.5724940Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5725110Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5725118Z 
2023-01-11T21:41:23.5725125Z 
2023-01-11T21:41:23.5725296Z async_compile.wait(globals())
2023-01-11T21:41:23.5725444Z del async_compile
2023-01-11T21:41:23.5725452Z 
2023-01-11T21:41:23.5725598Z def call(args):
2023-01-11T21:41:23.5725746Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5725890Z     args.clear()
2023-01-11T21:41:23.5726276Z     buf0 = empty_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5726560Z     aten.mm.out(as_strided(arg0_1, (8, 8), (1, 8)), as_strided(arg1_1, (8, 8), (8, 1)), out=as_strided(buf0, (8, 8), (8, 1)))
2023-01-11T21:41:23.5726698Z     del arg0_1
2023-01-11T21:41:23.5726826Z     del arg1_1
2023-01-11T21:41:23.5726968Z     return (buf0, )
2023-01-11T21:41:23.5727045Z 
2023-01-11T21:41:23.5727054Z 
2023-01-11T21:41:23.5727199Z if __name__ == "__main__":
2023-01-11T21:41:23.5727418Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5727657Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5728051Z     arg0_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5728411Z     arg1_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5728630Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5728638Z 
2023-01-11T21:41:23.5728774Z ok (0.029s)
2023-01-11T21:41:23.5729692Z   test_bool_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5729939Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5730440Z [2023-01-11 21:25:09,450] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 48
2023-01-11T21:41:23.5730948Z [2023-01-11 21:25:10,999] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 48
2023-01-11T21:41:23.5730956Z 
2023-01-11T21:41:23.5731140Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5731281Z import torch
2023-01-11T21:41:23.5731425Z import random
2023-01-11T21:41:23.5731642Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5731883Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5731891Z 
2023-01-11T21:41:23.5732048Z aten = torch.ops.aten
2023-01-11T21:41:23.5732307Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5732490Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5732503Z 
2023-01-11T21:41:23.5732513Z 
2023-01-11T21:41:23.5732777Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5733179Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5733409Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.5733584Z                        const bool* __restrict__ in_ptr1,
2023-01-11T21:41:23.5733770Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:23.5733943Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:41:23.5734116Z                        bool* __restrict__ out_ptr2,
2023-01-11T21:41:23.5734299Z                        bool* __restrict__ out_ptr3,
2023-01-11T21:41:23.5734482Z                        bool* __restrict__ out_ptr4,
2023-01-11T21:41:23.5734657Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:41:23.5734813Z                        bool* __restrict__ out_ptr6,
2023-01-11T21:41:23.5734995Z                        bool* __restrict__ out_ptr7,
2023-01-11T21:41:23.5735249Z                        bool* __restrict__ out_ptr8)
2023-01-11T21:41:23.5735377Z {
2023-01-11T21:41:23.5735573Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5735697Z     {
2023-01-11T21:41:23.5735851Z         #pragma omp for 
2023-01-11T21:41:23.5735991Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.5736119Z         {
2023-01-11T21:41:23.5736240Z             {
2023-01-11T21:41:23.5736365Z                 {
2023-01-11T21:41:23.5736535Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5736705Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.5736867Z                     auto tmp2 = tmp0 || tmp1;
2023-01-11T21:41:23.5737028Z                     auto tmp3 = tmp0 && tmp1;
2023-01-11T21:41:23.5737184Z                     auto tmp4 = tmp0 & tmp1;
2023-01-11T21:41:23.5737350Z                     auto tmp5 = tmp0 | tmp1;
2023-01-11T21:41:23.5737506Z                     auto tmp6 = tmp0 ^ tmp1;
2023-01-11T21:41:23.5737726Z                     auto tmp7 = tmp0 == 0;
2023-01-11T21:41:23.5737897Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.5738047Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.5738187Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.5738345Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:23.5738504Z                     out_ptr4[i0] = tmp6;
2023-01-11T21:41:23.5738663Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:41:23.5738823Z                     out_ptr6[i0] = tmp2;
2023-01-11T21:41:23.5738979Z                     out_ptr7[i0] = tmp7;
2023-01-11T21:41:23.5739139Z                     out_ptr8[i0] = tmp1;
2023-01-11T21:41:23.5739249Z                 }
2023-01-11T21:41:23.5739370Z             }
2023-01-11T21:41:23.5739495Z         }
2023-01-11T21:41:23.5739619Z     }
2023-01-11T21:41:23.5739736Z }
2023-01-11T21:41:23.5739908Z ''')
2023-01-11T21:41:23.5739917Z 
2023-01-11T21:41:23.5739924Z 
2023-01-11T21:41:23.5740111Z async_compile.wait(globals())
2023-01-11T21:41:23.5740242Z del async_compile
2023-01-11T21:41:23.5740249Z 
2023-01-11T21:41:23.5740392Z def call(args):
2023-01-11T21:41:23.5740537Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.5740673Z     args.clear()
2023-01-11T21:41:23.5741033Z     buf0 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5741381Z     buf1 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5741733Z     buf2 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5742067Z     buf3 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5742579Z     buf4 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5742931Z     buf5 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5743265Z     buf6 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5743610Z     buf7 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5743963Z     buf8 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5744638Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:23.5744778Z     del arg0_1
2023-01-11T21:41:23.5744920Z     del arg1_1
2023-01-11T21:41:23.5745132Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, )
2023-01-11T21:41:23.5745140Z 
2023-01-11T21:41:23.5745147Z 
2023-01-11T21:41:23.5745297Z if __name__ == "__main__":
2023-01-11T21:41:23.5745533Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5745783Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5746159Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5746654Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.5746893Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.5746902Z 
2023-01-11T21:41:23.5747037Z ok (1.582s)
2023-01-11T21:41:23.5800143Z   test_both_scalars_cpu (__main__.CpuTests) ... [2023-01-11 21:25:11,061] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 49
2023-01-11T21:41:23.5800718Z [2023-01-11 21:25:12,581] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 49
2023-01-11T21:41:23.5800729Z 
2023-01-11T21:41:23.5800908Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5801046Z import torch
2023-01-11T21:41:23.5801192Z import random
2023-01-11T21:41:23.5801398Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5801618Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5801626Z 
2023-01-11T21:41:23.5801744Z aten = torch.ops.aten
2023-01-11T21:41:23.5802108Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5802248Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5802255Z 
2023-01-11T21:41:23.5802260Z 
2023-01-11T21:41:23.5802479Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5802791Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5802986Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5803136Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.5803294Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.5803444Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.5803588Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:23.5803744Z                        float* __restrict__ out_ptr5)
2023-01-11T21:41:23.5803845Z {
2023-01-11T21:41:23.5803968Z     {
2023-01-11T21:41:23.5804070Z         {
2023-01-11T21:41:23.5804266Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:41:23.5804435Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:41:23.5804559Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5804703Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:23.5804824Z         }
2023-01-11T21:41:23.5804942Z     }
2023-01-11T21:41:23.5805050Z     {
2023-01-11T21:41:23.5805174Z         {
2023-01-11T21:41:23.5805342Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:41:23.5805525Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:23.5805678Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5805826Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:23.5805936Z         }
2023-01-11T21:41:23.5806046Z     }
2023-01-11T21:41:23.5806153Z     {
2023-01-11T21:41:23.5806246Z         {
2023-01-11T21:41:23.5806421Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:41:23.5806601Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:41:23.5806852Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.5807000Z             out_ptr2[0] = tmp2;
2023-01-11T21:41:23.5807124Z         }
2023-01-11T21:41:23.5807231Z     }
2023-01-11T21:41:23.5807324Z     {
2023-01-11T21:41:23.5807447Z         {
2023-01-11T21:41:23.5807625Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:41:23.5807790Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:23.5808005Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.5808149Z             out_ptr3[0] = tmp2;
2023-01-11T21:41:23.5808245Z         }
2023-01-11T21:41:23.5808350Z     }
2023-01-11T21:41:23.5808451Z     {
2023-01-11T21:41:23.5808556Z         {
2023-01-11T21:41:23.5808727Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:41:23.5808900Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:41:23.5809055Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5809177Z             out_ptr4[0] = tmp2;
2023-01-11T21:41:23.5809291Z         }
2023-01-11T21:41:23.5809397Z     }
2023-01-11T21:41:23.5809627Z     {
2023-01-11T21:41:23.5809733Z         {
2023-01-11T21:41:23.5809910Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:41:23.5810087Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:23.5810232Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5810375Z             out_ptr5[0] = tmp2;
2023-01-11T21:41:23.5810493Z         }
2023-01-11T21:41:23.5810603Z     }
2023-01-11T21:41:23.5810723Z }
2023-01-11T21:41:23.5810869Z ''')
2023-01-11T21:41:23.5810879Z 
2023-01-11T21:41:23.5810887Z 
2023-01-11T21:41:23.5811044Z async_compile.wait(globals())
2023-01-11T21:41:23.5811161Z del async_compile
2023-01-11T21:41:23.5811169Z 
2023-01-11T21:41:23.5811299Z def call(args):
2023-01-11T21:41:23.5811618Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5811914Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5812205Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5812594Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5812917Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5813225Z     buf5 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5813590Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:23.5813777Z     return (buf0, buf1, buf2, buf3, buf4, buf5, )
2023-01-11T21:41:23.5813786Z 
2023-01-11T21:41:23.5813793Z 
2023-01-11T21:41:23.5813935Z if __name__ == "__main__":
2023-01-11T21:41:23.5814154Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5814373Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5814550Z     print_performance(lambda: call([]))
2023-01-11T21:41:23.5814558Z 
2023-01-11T21:41:23.5814679Z ok (1.582s)
2023-01-11T21:41:23.5815596Z   test_cat_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5815823Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5816287Z [2023-01-11 21:25:12,633] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 50
2023-01-11T21:41:23.5816768Z [2023-01-11 21:25:14,241] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 50
2023-01-11T21:41:23.5817579Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5817815Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5818270Z [2023-01-11 21:25:14,291] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 51
2023-01-11T21:41:23.5818749Z [2023-01-11 21:25:15,975] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 51
2023-01-11T21:41:23.5818758Z 
2023-01-11T21:41:23.5818926Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5819058Z import torch
2023-01-11T21:41:23.5819187Z import random
2023-01-11T21:41:23.5819389Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5819612Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5819621Z 
2023-01-11T21:41:23.5819751Z aten = torch.ops.aten
2023-01-11T21:41:23.5819979Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5820229Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5820236Z 
2023-01-11T21:41:23.5820243Z 
2023-01-11T21:41:23.5820501Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5820876Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5821099Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5821274Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5821433Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.5821600Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.5821769Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.5821937Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:23.5822105Z                        double* __restrict__ out_ptr5,
2023-01-11T21:41:23.5822296Z                        double* __restrict__ out_ptr6)
2023-01-11T21:41:23.5822553Z {
2023-01-11T21:41:23.5822808Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5822913Z     {
2023-01-11T21:41:23.5823055Z         #pragma omp for 
2023-01-11T21:41:23.5823186Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5823295Z         {
2023-01-11T21:41:23.5823438Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.5823548Z             {
2023-01-11T21:41:23.5823784Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:23.5824005Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.5824155Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5824332Z                 tmp0.store(out_ptr0 + (8*i1) + (36*i0));
2023-01-11T21:41:23.5824495Z                 tmp2.store(out_ptr1 + (8*i1) + (36*i0));
2023-01-11T21:41:23.5824614Z             }
2023-01-11T21:41:23.5824773Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.5824910Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:23.5825033Z             {
2023-01-11T21:41:23.5825209Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:23.5825384Z                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.5825538Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5825699Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:41:23.5825858Z                 out_ptr1[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:23.5825954Z             }
2023-01-11T21:41:23.5826061Z         }
2023-01-11T21:41:23.5826202Z         #pragma omp for 
2023-01-11T21:41:23.5826335Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.5826449Z         {
2023-01-11T21:41:23.5826590Z             #pragma GCC ivdep
2023-01-11T21:41:23.5826745Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.5826856Z             {
2023-01-11T21:41:23.5826975Z                 {
2023-01-11T21:41:23.5827103Z                     {
2023-01-11T21:41:23.5827289Z                         auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:23.5827489Z                         auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.5827649Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5827809Z                         out_ptr2[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:23.5827915Z                     }
2023-01-11T21:41:23.5828031Z                 }
2023-01-11T21:41:23.5828144Z             }
2023-01-11T21:41:23.5828259Z         }
2023-01-11T21:41:23.5828393Z         #pragma omp for 
2023-01-11T21:41:23.5828531Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.5828627Z         {
2023-01-11T21:41:23.5828739Z             {
2023-01-11T21:41:23.5828863Z                 {
2023-01-11T21:41:23.5829025Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.5829201Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.5829349Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5829535Z                     auto tmp3 = static_cast<double>(tmp2);
2023-01-11T21:41:23.5829780Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:23.5829926Z                     out_ptr4[i0] = tmp2;
2023-01-11T21:41:23.5830072Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:41:23.5830215Z                     out_ptr6[i0] = tmp3;
2023-01-11T21:41:23.5830336Z                 }
2023-01-11T21:41:23.5830451Z             }
2023-01-11T21:41:23.5830565Z         }
2023-01-11T21:41:23.5830674Z     }
2023-01-11T21:41:23.5830784Z }
2023-01-11T21:41:23.5830944Z ''')
2023-01-11T21:41:23.5830953Z 
2023-01-11T21:41:23.5830960Z 
2023-01-11T21:41:23.5831132Z async_compile.wait(globals())
2023-01-11T21:41:23.5831252Z del async_compile
2023-01-11T21:41:23.5831260Z 
2023-01-11T21:41:23.5831391Z def call(args):
2023-01-11T21:41:23.5831522Z     arg0_1, = args
2023-01-11T21:41:23.5831641Z     args.clear()
2023-01-11T21:41:23.5832006Z     buf3 = empty_strided((8, 36), (36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5832180Z     buf0 = as_strided(buf3, (8, 16), (36, 1))  # alias
2023-01-11T21:41:23.5832413Z     buf2 = as_strided(buf3, (8, 16), (36, 1), 20)  # alias
2023-01-11T21:41:23.5832598Z     buf1 = as_strided(buf3, (8, 4), (36, 1), 16)  # alias
2023-01-11T21:41:23.5832959Z     buf6 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5833135Z     buf4 = as_strided(buf6, (8, 16), (16, 1))  # alias
2023-01-11T21:41:23.5833311Z     buf5 = as_strided(buf6, (8, 16), (16, 1), 128)  # alias
2023-01-11T21:41:23.5833638Z     buf9 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.5833877Z     buf7 = as_strided(buf9, (8, 16), (16, 1))  # alias
2023-01-11T21:41:23.5834059Z     buf8 = as_strided(buf9, (8, 16), (16, 1), 128)  # alias
2023-01-11T21:41:23.5834512Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:23.5834634Z     del arg0_1
2023-01-11T21:41:23.5834781Z     return (buf3, buf6, buf9, )
2023-01-11T21:41:23.5834789Z 
2023-01-11T21:41:23.5834795Z 
2023-01-11T21:41:23.5834922Z if __name__ == "__main__":
2023-01-11T21:41:23.5835121Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5835324Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5835680Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5835870Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5835878Z 
2023-01-11T21:41:23.5835885Z 
2023-01-11T21:41:23.5836047Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5836185Z import torch
2023-01-11T21:41:23.5836303Z import random
2023-01-11T21:41:23.5836508Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5836735Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5836742Z 
2023-01-11T21:41:23.5836849Z aten = torch.ops.aten
2023-01-11T21:41:23.5837081Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5837243Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5837249Z 
2023-01-11T21:41:23.5837255Z 
2023-01-11T21:41:23.5837484Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5837837Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5876773Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5877053Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.5877255Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.5877447Z                        const double* __restrict__ in_ptr3,
2023-01-11T21:41:23.5877639Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5877828Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.5878011Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.5878196Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.5878519Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:23.5878706Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:23.5878900Z                        double* __restrict__ out_ptr6,
2023-01-11T21:41:23.5879075Z                        double* __restrict__ out_ptr7,
2023-01-11T21:41:23.5879260Z                        float* __restrict__ out_ptr8,
2023-01-11T21:41:23.5879446Z                        double* __restrict__ out_ptr9)
2023-01-11T21:41:23.5879572Z {
2023-01-11T21:41:23.5879769Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5879898Z     {
2023-01-11T21:41:23.5880064Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.5880225Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.5880356Z         {
2023-01-11T21:41:23.5880521Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.5880651Z             {
2023-01-11T21:41:23.5880813Z                 #pragma GCC ivdep
2023-01-11T21:41:23.5881059Z                 for(long i2=0; i2<16; i2+=1)
2023-01-11T21:41:23.5881183Z                 {
2023-01-11T21:41:23.5881322Z                     {
2023-01-11T21:41:23.5881459Z                         {
2023-01-11T21:41:23.5881664Z                             auto tmp0 = in_ptr0[i0 + (3*i2) + (48*i1)];
2023-01-11T21:41:23.5881875Z                             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.5882058Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.5882263Z                             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.5882432Z                             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.5882627Z                             out_ptr0[i2 + (48*i1) + (144*i0)] = tmp0;
2023-01-11T21:41:23.5882817Z                             out_ptr1[i2 + (48*i1) + (144*i0)] = tmp2;
2023-01-11T21:41:23.5883005Z                             out_ptr2[i2 + (48*i1) + (144*i0)] = tmp4;
2023-01-11T21:41:23.5883142Z                         }
2023-01-11T21:41:23.5883278Z                     }
2023-01-11T21:41:23.5883413Z                 }
2023-01-11T21:41:23.5883544Z             }
2023-01-11T21:41:23.5883661Z         }
2023-01-11T21:41:23.5883844Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.5884007Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.5884137Z         {
2023-01-11T21:41:23.5884304Z             for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:23.5884436Z             {
2023-01-11T21:41:23.5884553Z                 {
2023-01-11T21:41:23.5884688Z                     {
2023-01-11T21:41:23.5884883Z                         auto tmp0 = in_ptr1[i1 + (144*i0)];
2023-01-11T21:41:23.5885059Z                         out_ptr3[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.5885192Z                     }
2023-01-11T21:41:23.5885324Z                 }
2023-01-11T21:41:23.5885452Z             }
2023-01-11T21:41:23.5885568Z         }
2023-01-11T21:41:23.5885829Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.5886029Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.5886179Z         {
2023-01-11T21:41:23.5886556Z             for(long i1=0; i1<48; i1+=1)
2023-01-11T21:41:23.5886725Z             {
2023-01-11T21:41:23.5886890Z                 {
2023-01-11T21:41:23.5887057Z                     {
2023-01-11T21:41:23.5887290Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.5887528Z                         auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.5887692Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.5887908Z                         out_ptr4[i1 + (48*i0)] = tmp2;
2023-01-11T21:41:23.5888091Z                     }
2023-01-11T21:41:23.5888296Z                 }
2023-01-11T21:41:23.5888460Z             }
2023-01-11T21:41:23.5888626Z         }
2023-01-11T21:41:23.5888816Z         #pragma omp for 
2023-01-11T21:41:23.5888966Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:23.5889126Z         {
2023-01-11T21:41:23.5889328Z             {
2023-01-11T21:41:23.5889509Z                 {
2023-01-11T21:41:23.5889791Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:23.5890072Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:23.5890223Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:41:23.5890421Z                     out_ptr6[i0] = tmp1;
2023-01-11T21:41:23.5890617Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:41:23.5890783Z                 }
2023-01-11T21:41:23.5890948Z             }
2023-01-11T21:41:23.5891124Z         }
2023-01-11T21:41:23.5891348Z         #pragma omp for  collapse(3)
2023-01-11T21:41:23.5891497Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.5891660Z         {
2023-01-11T21:41:23.5891894Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.5892059Z             {
2023-01-11T21:41:23.5892268Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:23.5892437Z                 {
2023-01-11T21:41:23.5892648Z                     {
2023-01-11T21:41:23.5892772Z                         {
2023-01-11T21:41:23.5893078Z                             auto tmp0 = in_ptr2[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:23.5893310Z                             out_ptr8[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:23.5893480Z                         }
2023-01-11T21:41:23.5893680Z                     }
2023-01-11T21:41:23.5893851Z                 }
2023-01-11T21:41:23.5894029Z             }
2023-01-11T21:41:23.5894145Z         }
2023-01-11T21:41:23.5894362Z         #pragma omp for  collapse(3)
2023-01-11T21:41:23.5894562Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.5894729Z         {
2023-01-11T21:41:23.5894922Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.5895093Z             {
2023-01-11T21:41:23.5895248Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:23.5895445Z                 {
2023-01-11T21:41:23.5895622Z                     {
2023-01-11T21:41:23.5895793Z                         {
2023-01-11T21:41:23.5896067Z                             auto tmp0 = in_ptr3[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:23.5896303Z                             out_ptr9[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:23.5896478Z                         }
2023-01-11T21:41:23.5896597Z                     }
2023-01-11T21:41:23.5896763Z                 }
2023-01-11T21:41:23.5896926Z             }
2023-01-11T21:41:23.5897133Z         }
2023-01-11T21:41:23.5897297Z     }
2023-01-11T21:41:23.5897457Z }
2023-01-11T21:41:23.5897683Z ''')
2023-01-11T21:41:23.5897693Z 
2023-01-11T21:41:23.5897700Z 
2023-01-11T21:41:23.5897870Z async_compile.wait(globals())
2023-01-11T21:41:23.5898057Z del async_compile
2023-01-11T21:41:23.5898065Z 
2023-01-11T21:41:23.5898247Z def call(args):
2023-01-11T21:41:23.5898427Z     arg0_1, = args
2023-01-11T21:41:23.5898618Z     args.clear()
2023-01-11T21:41:23.5899099Z     buf3 = empty_strided((1, 3, 3, 48), (432, 144, 48, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5899344Z     buf0 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1))  # alias
2023-01-11T21:41:23.5899590Z     buf1 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1), 16)  # alias
2023-01-11T21:41:23.5899785Z     buf2 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1), 32)  # alias
2023-01-11T21:41:23.5900262Z     buf4 = empty_strided((1, 3, 3, 48), (432, 1, 144, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5900689Z     buf7 = empty_strided((2, 3, 3, 16), (144, 48, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5900927Z     buf5 = as_strided(buf7, (1, 3, 3, 16), (144, 48, 16, 1))  # alias
2023-01-11T21:41:23.5901181Z     buf6 = as_strided(buf7, (1, 3, 3, 16), (144, 48, 16, 1), 144)  # alias
2023-01-11T21:41:23.5901618Z     buf11 = empty_strided((2, 3, 3, 16), (144, 48, 16, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.5901905Z     buf9 = as_strided(buf11, (1, 3, 3, 16), (144, 48, 16, 1))  # alias
2023-01-11T21:41:23.5902150Z     buf10 = as_strided(buf11, (1, 3, 3, 16), (144, 48, 16, 1), 144)  # alias
2023-01-11T21:41:23.5902673Z     buf8 = empty_strided((2, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5903215Z     buf12 = empty_strided((2, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.5904068Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf12.data_ptr()))
2023-01-11T21:41:23.5904247Z     del arg0_1
2023-01-11T21:41:23.5904437Z     del buf0
2023-01-11T21:41:23.5904609Z     del buf1
2023-01-11T21:41:23.5904782Z     del buf10
2023-01-11T21:41:23.5905023Z     del buf11
2023-01-11T21:41:23.5905144Z     del buf2
2023-01-11T21:41:23.5905320Z     del buf3
2023-01-11T21:41:23.5905526Z     del buf5
2023-01-11T21:41:23.5905697Z     del buf6
2023-01-11T21:41:23.5905880Z     del buf7
2023-01-11T21:41:23.5906125Z     del buf9
2023-01-11T21:41:23.5906286Z     return (buf4, buf8, buf12, )
2023-01-11T21:41:23.5906351Z 
2023-01-11T21:41:23.5906358Z 
2023-01-11T21:41:23.5906500Z if __name__ == "__main__":
2023-01-11T21:41:23.5906760Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5907081Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5907521Z     arg0_1 = rand_strided((1, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5907782Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.5907791Z 
2023-01-11T21:41:23.5907963Z ok (3.394s)
2023-01-11T21:41:23.5908954Z   test_cat_extern_kernel_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5909241Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5909773Z [2023-01-11 21:25:16,035] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 52
2023-01-11T21:41:23.5910263Z [2023-01-11 21:25:17,554] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 52
2023-01-11T21:41:23.5910324Z 
2023-01-11T21:41:23.5910502Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5910709Z import torch
2023-01-11T21:41:23.5910938Z import random
2023-01-11T21:41:23.5911203Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5911478Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5911487Z 
2023-01-11T21:41:23.5911685Z aten = torch.ops.aten
2023-01-11T21:41:23.5911977Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5912149Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5912164Z 
2023-01-11T21:41:23.5912171Z 
2023-01-11T21:41:23.5912470Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5912889Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5913198Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5913426Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.5913585Z {
2023-01-11T21:41:23.5913890Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5914005Z     {
2023-01-11T21:41:23.5914204Z         #pragma omp for 
2023-01-11T21:41:23.5914406Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.5914571Z         {
2023-01-11T21:41:23.5914784Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:23.5915027Z             {
2023-01-11T21:41:23.5915351Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (256*i0));
2023-01-11T21:41:23.5915584Z                 tmp0.store(out_ptr0 + (8*i1) + (512*i0));
2023-01-11T21:41:23.5915778Z             }
2023-01-11T21:41:23.5915997Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.5916202Z             for(long i1=256; i1<256; i1+=1)
2023-01-11T21:41:23.5916369Z             {
2023-01-11T21:41:23.5916602Z                 auto tmp0 = in_ptr0[i1 + (256*i0)];
2023-01-11T21:41:23.5916818Z                 out_ptr0[i1 + (512*i0)] = tmp0;
2023-01-11T21:41:23.5916934Z             }
2023-01-11T21:41:23.5917131Z         }
2023-01-11T21:41:23.5917292Z     }
2023-01-11T21:41:23.5917455Z }
2023-01-11T21:41:23.5917668Z ''')
2023-01-11T21:41:23.5917677Z 
2023-01-11T21:41:23.5917684Z 
2023-01-11T21:41:23.5917906Z async_compile.wait(globals())
2023-01-11T21:41:23.5918104Z del async_compile
2023-01-11T21:41:23.5918112Z 
2023-01-11T21:41:23.5918292Z def call(args):
2023-01-11T21:41:23.5918454Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.5918675Z     args.clear()
2023-01-11T21:41:23.5919182Z     buf0 = empty_strided((256, 1600), (1600, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5919407Z     aten.mm.out(arg1_1, arg2_1, out=buf0)
2023-01-11T21:41:23.5919582Z     del arg1_1
2023-01-11T21:41:23.5919770Z     del arg2_1
2023-01-11T21:41:23.5920186Z     buf3 = empty_strided((256, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5920372Z     buf1 = as_strided(buf3, (256, 256), (512, 1))  # alias
2023-01-11T21:41:23.5920634Z     aten.mm.out(as_strided(buf0, (256, 100), (1600, 1)), arg3_1, out=buf1)
2023-01-11T21:41:23.5920806Z     del arg3_1
2023-01-11T21:41:23.5920976Z     del buf0
2023-01-11T21:41:23.5921246Z     buf2 = as_strided(buf3, (256, 256), (512, 1), 256)  # alias
2023-01-11T21:41:23.5921535Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.5921722Z     del arg0_1
2023-01-11T21:41:23.5921855Z     return (buf3, )
2023-01-11T21:41:23.5921863Z 
2023-01-11T21:41:23.5921870Z 
2023-01-11T21:41:23.5922063Z if __name__ == "__main__":
2023-01-11T21:41:23.5922325Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.5922603Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.5923061Z     arg0_1 = rand_strided((256, 256), (256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5923478Z     arg1_1 = rand_strided((256, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5923926Z     arg2_1 = rand_strided((1024, 1600), (1600, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5924350Z     arg3_1 = rand_strided((100, 256), (256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.5924584Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.5924645Z 
2023-01-11T21:41:23.5924769Z ok (1.622s)
2023-01-11T21:41:23.5925751Z   test_cat_upcasting_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.5926034Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.5926564Z [2023-01-11 21:25:17,620] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 53
2023-01-11T21:41:23.5927103Z [2023-01-11 21:25:19,154] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 53
2023-01-11T21:41:23.5927111Z 
2023-01-11T21:41:23.5927335Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.5927569Z import torch
2023-01-11T21:41:23.5927753Z import random
2023-01-11T21:41:23.5927970Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.5928246Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.5928254Z 
2023-01-11T21:41:23.5928450Z aten = torch.ops.aten
2023-01-11T21:41:23.5928741Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.5950597Z async_compile = AsyncCompile()
2023-01-11T21:41:23.5950618Z 
2023-01-11T21:41:23.5950626Z 
2023-01-11T21:41:23.5951033Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.5951485Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.5951768Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.5952012Z                        const half* __restrict__ in_ptr1,
2023-01-11T21:41:23.5952216Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.5952445Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.5952612Z {
2023-01-11T21:41:23.5952924Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.5953088Z     {
2023-01-11T21:41:23.5953272Z         #pragma omp for 
2023-01-11T21:41:23.6013999Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.6014187Z         {
2023-01-11T21:41:23.6014386Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.6014705Z             {
2023-01-11T21:41:23.6015052Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:23.6015279Z                 tmp0.store(out_ptr0 + (8*i1) + (36*i0));
2023-01-11T21:41:23.6015425Z             }
2023-01-11T21:41:23.6015642Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.6015828Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:23.6015963Z             {
2023-01-11T21:41:23.6016172Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:23.6016344Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:41:23.6016483Z             }
2023-01-11T21:41:23.6016621Z         }
2023-01-11T21:41:23.6016789Z         #pragma omp for 
2023-01-11T21:41:23.6016969Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.6017106Z         {
2023-01-11T21:41:23.6017283Z             #pragma GCC ivdep
2023-01-11T21:41:23.6017453Z             for(long i1=0; i1<20; i1+=1)
2023-01-11T21:41:23.6017607Z             {
2023-01-11T21:41:23.6017759Z                 {
2023-01-11T21:41:23.6017907Z                     {
2023-01-11T21:41:23.6018182Z                         auto tmp0 = static_cast<float>(in_ptr1[i1 + (20*i0)]);
2023-01-11T21:41:23.6018434Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.6018637Z                         out_ptr1[i1 + (36*i0)] = tmp1;
2023-01-11T21:41:23.6018769Z                     }
2023-01-11T21:41:23.6018914Z                 }
2023-01-11T21:41:23.6019054Z             }
2023-01-11T21:41:23.6019189Z         }
2023-01-11T21:41:23.6019332Z     }
2023-01-11T21:41:23.6019468Z }
2023-01-11T21:41:23.6019682Z ''')
2023-01-11T21:41:23.6019694Z 
2023-01-11T21:41:23.6019703Z 
2023-01-11T21:41:23.6019894Z async_compile.wait(globals())
2023-01-11T21:41:23.6020058Z del async_compile
2023-01-11T21:41:23.6020067Z 
2023-01-11T21:41:23.6020233Z def call(args):
2023-01-11T21:41:23.6020401Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.6020562Z     args.clear()
2023-01-11T21:41:23.6021032Z     buf2 = empty_strided((8, 36), (36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6021260Z     buf0 = as_strided(buf2, (8, 16), (36, 1))  # alias
2023-01-11T21:41:23.6021493Z     buf1 = as_strided(buf2, (8, 20), (36, 1), 16)  # alias
2023-01-11T21:41:23.6021922Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.6022077Z     del arg0_1
2023-01-11T21:41:23.6022225Z     del arg1_1
2023-01-11T21:41:23.6022535Z     return (buf2, )
2023-01-11T21:41:23.6022547Z 
2023-01-11T21:41:23.6022556Z 
2023-01-11T21:41:23.6022735Z if __name__ == "__main__":
2023-01-11T21:41:23.6022997Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6023268Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6023721Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6024151Z     arg1_1 = rand_strided((8, 20), (20, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.6024526Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.6024535Z 
2023-01-11T21:41:23.6024677Z ok (1.557s)
2023-01-11T21:41:23.6025897Z   test_cauchy_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6026184Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6026814Z [2023-01-11 21:25:19,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 54
2023-01-11T21:41:23.6027454Z [2023-01-11 21:25:20,707] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 54
2023-01-11T21:41:23.6027551Z 
2023-01-11T21:41:23.6027772Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6027936Z import torch
2023-01-11T21:41:23.6028076Z import random
2023-01-11T21:41:23.6028359Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6028642Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6028651Z 
2023-01-11T21:41:23.6028832Z aten = torch.ops.aten
2023-01-11T21:41:23.6029146Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6029358Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6029368Z 
2023-01-11T21:41:23.6029377Z 
2023-01-11T21:41:23.6029700Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6030207Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6030466Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6030677Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.6030907Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6031043Z {
2023-01-11T21:41:23.6031177Z     {
2023-01-11T21:41:23.6031320Z         {
2023-01-11T21:41:23.6031498Z             float tmp6 = 0;
2023-01-11T21:41:23.6031746Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6031876Z             {
2023-01-11T21:41:23.6032108Z                 #pragma omp for reduction(+:tmp6) 
2023-01-11T21:41:23.6032301Z                 for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6032449Z                 {
2023-01-11T21:41:23.6032642Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:23.6032786Z                     {
2023-01-11T21:41:23.6032936Z                         {
2023-01-11T21:41:23.6033125Z                             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.6033341Z                             auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:23.6033677Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.6033936Z                             auto tmp3 = 1 / tmp2;
2023-01-11T21:41:23.6034194Z                             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:23.6034396Z                             auto tmp5 = tmp3 * tmp4;
2023-01-11T21:41:23.6034579Z                             tmp6 += tmp5;
2023-01-11T21:41:23.6034716Z                         }
2023-01-11T21:41:23.6034862Z                     }
2023-01-11T21:41:23.6035011Z                 }
2023-01-11T21:41:23.6035152Z             }
2023-01-11T21:41:23.6035320Z             out_ptr0[0] = tmp6;
2023-01-11T21:41:23.6035459Z         }
2023-01-11T21:41:23.6035592Z     }
2023-01-11T21:41:23.6035709Z }
2023-01-11T21:41:23.6035883Z ''')
2023-01-11T21:41:23.6035894Z 
2023-01-11T21:41:23.6035903Z 
2023-01-11T21:41:23.6036118Z async_compile.wait(globals())
2023-01-11T21:41:23.6036290Z del async_compile
2023-01-11T21:41:23.6036300Z 
2023-01-11T21:41:23.6036464Z def call(args):
2023-01-11T21:41:23.6036634Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.6036800Z     args.clear()
2023-01-11T21:41:23.6037315Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6037702Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6037855Z     del arg0_1
2023-01-11T21:41:23.6038007Z     del arg1_1
2023-01-11T21:41:23.6038173Z     return (buf0, )
2023-01-11T21:41:23.6038182Z 
2023-01-11T21:41:23.6038191Z 
2023-01-11T21:41:23.6038361Z if __name__ == "__main__":
2023-01-11T21:41:23.6038627Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6038926Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6039363Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6039794Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6040067Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.6040077Z 
2023-01-11T21:41:23.6040224Z ok (1.552s)
2023-01-11T21:41:23.6041481Z   test_clamp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6041773Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6042405Z [2023-01-11 21:25:20,738] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 55
2023-01-11T21:41:23.6043044Z [2023-01-11 21:25:22,268] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 55
2023-01-11T21:41:23.6043055Z 
2023-01-11T21:41:23.6043268Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6043429Z import torch
2023-01-11T21:41:23.6043570Z import random
2023-01-11T21:41:23.6043842Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6044138Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6044148Z 
2023-01-11T21:41:23.6044321Z aten = torch.ops.aten
2023-01-11T21:41:23.6044630Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6044833Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6044843Z 
2023-01-11T21:41:23.6044850Z 
2023-01-11T21:41:23.6045152Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6045651Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6045904Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6046138Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.6046354Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.6046563Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.6046779Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.6046919Z {
2023-01-11T21:41:23.6047143Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6047272Z     {
2023-01-11T21:41:23.6047439Z         #pragma omp for 
2023-01-11T21:41:23.6047611Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.6047746Z         {
2023-01-11T21:41:23.6048051Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.6048342Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.6048855Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-0.10000000149011612));
2023-01-11T21:41:23.6049093Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:23.6049406Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.10000000149011612));
2023-01-11T21:41:23.6049648Z             auto tmp4 = at::vec::minimum(tmp2, tmp3);
2023-01-11T21:41:23.6049958Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:41:23.6050285Z             auto tmp7 = at::vec::maximum(tmp5, tmp6);
2023-01-11T21:41:23.6050470Z             auto tmp8 = tmp0 + tmp5;
2023-01-11T21:41:23.6050702Z             auto tmp9 = at::vec::minimum(tmp8, tmp6);
2023-01-11T21:41:23.6050903Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.6051092Z             tmp7.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.6051269Z             tmp9.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.6051405Z         }
2023-01-11T21:41:23.6051618Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.6051799Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.6051937Z         {
2023-01-11T21:41:23.6052120Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.6052306Z             auto tmp5 = in_ptr1[i0];
2023-01-11T21:41:23.6052689Z             auto tmp1 = static_cast<float>(-0.10000000149011612);
2023-01-11T21:41:23.6052951Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:23.6053193Z             auto tmp3 = static_cast<float>(0.10000000149011612);
2023-01-11T21:41:23.6053524Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:41:23.6053751Z             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:23.6054015Z             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:41:23.6054193Z             auto tmp8 = tmp0 + tmp5;
2023-01-11T21:41:23.6054439Z             auto tmp9 = (tmp6 != tmp6) ? tmp6 : std::min(tmp8, tmp6);
2023-01-11T21:41:23.6054601Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.6054775Z             out_ptr1[i0] = tmp7;
2023-01-11T21:41:23.6054952Z             out_ptr2[i0] = tmp9;
2023-01-11T21:41:23.6055093Z         }
2023-01-11T21:41:23.6055227Z     }
2023-01-11T21:41:23.6055355Z }
2023-01-11T21:41:23.6055509Z ''')
2023-01-11T21:41:23.6055534Z 
2023-01-11T21:41:23.6055543Z 
2023-01-11T21:41:23.6055723Z async_compile.wait(globals())
2023-01-11T21:41:23.6055881Z del async_compile
2023-01-11T21:41:23.6055891Z 
2023-01-11T21:41:23.6056048Z def call(args):
2023-01-11T21:41:23.6056218Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.6056367Z     args.clear()
2023-01-11T21:41:23.6056806Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6057219Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6057625Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6058095Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6058243Z     del arg0_1
2023-01-11T21:41:23.6058389Z     del arg1_1
2023-01-11T21:41:23.6058570Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.6058581Z 
2023-01-11T21:41:23.6058590Z 
2023-01-11T21:41:23.6058751Z if __name__ == "__main__":
2023-01-11T21:41:23.6059003Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6059277Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6059696Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6060119Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6060362Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.6060372Z 
2023-01-11T21:41:23.6060507Z ok (1.562s)
2023-01-11T21:41:23.6061631Z   test_clone_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6061906Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6062730Z [2023-01-11 21:25:22,300] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 56
2023-01-11T21:41:23.6063479Z [2023-01-11 21:25:23,815] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 56
2023-01-11T21:41:23.6063491Z 
2023-01-11T21:41:23.6063707Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6063862Z import torch
2023-01-11T21:41:23.6064011Z import random
2023-01-11T21:41:23.6064273Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6064553Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6064563Z 
2023-01-11T21:41:23.6064742Z aten = torch.ops.aten
2023-01-11T21:41:23.6065050Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6065254Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6065263Z 
2023-01-11T21:41:23.6065271Z 
2023-01-11T21:41:23.6065583Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6066091Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6066453Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6066679Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.6066903Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.6067035Z {
2023-01-11T21:41:23.6067262Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6067397Z     {
2023-01-11T21:41:23.6067561Z         #pragma omp for 
2023-01-11T21:41:23.6067721Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6067858Z         {
2023-01-11T21:41:23.6068155Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.6068460Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.6068647Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.6068953Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.6069150Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.6069336Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.6069547Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.6069685Z         }
2023-01-11T21:41:23.6069907Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.6070085Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.6070222Z         {
2023-01-11T21:41:23.6070417Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.6070618Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.6070800Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.6071023Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:23.6071197Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.6071360Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.6071469Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.6071549Z         }
2023-01-11T21:41:23.6071611Z     }
2023-01-11T21:41:23.6071689Z }
2023-01-11T21:41:23.6071812Z ''')
2023-01-11T21:41:23.6071821Z 
2023-01-11T21:41:23.6071826Z 
2023-01-11T21:41:23.6071941Z async_compile.wait(globals())
2023-01-11T21:41:23.6072049Z del async_compile
2023-01-11T21:41:23.6072055Z 
2023-01-11T21:41:23.6072154Z def call(args):
2023-01-11T21:41:23.6072262Z     arg0_1, = args
2023-01-11T21:41:23.6072353Z     args.clear()
2023-01-11T21:41:23.6072653Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6072939Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6073181Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.6073282Z     del arg0_1
2023-01-11T21:41:23.6073387Z     return (buf0, buf1, )
2023-01-11T21:41:23.6073396Z 
2023-01-11T21:41:23.6073402Z 
2023-01-11T21:41:23.6073514Z if __name__ == "__main__":
2023-01-11T21:41:23.6073680Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6073917Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6074220Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6074490Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.6074498Z 
2023-01-11T21:41:23.6074589Z ok (1.547s)
2023-01-11T21:41:23.6075279Z   test_constant_pad_1d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6075453Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6075860Z [2023-01-11 21:25:23,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 57
2023-01-11T21:41:23.6076232Z [2023-01-11 21:25:25,358] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 57
2023-01-11T21:41:23.6076239Z 
2023-01-11T21:41:23.6076443Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6076534Z import torch
2023-01-11T21:41:23.6076626Z import random
2023-01-11T21:41:23.6076793Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6076957Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6076964Z 
2023-01-11T21:41:23.6077069Z aten = torch.ops.aten
2023-01-11T21:41:23.6077267Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6077397Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6077404Z 
2023-01-11T21:41:23.6077411Z 
2023-01-11T21:41:23.6077628Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6077900Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6078066Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6078217Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.6078348Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.6078435Z {
2023-01-11T21:41:23.6078577Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6078662Z     {
2023-01-11T21:41:23.6078754Z         #pragma omp for 
2023-01-11T21:41:23.6078880Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6078966Z         {
2023-01-11T21:41:23.6079069Z             #pragma GCC ivdep
2023-01-11T21:41:23.6079192Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:23.6079286Z             {
2023-01-11T21:41:23.6079376Z                 {
2023-01-11T21:41:23.6079450Z                     {
2023-01-11T21:41:23.6079602Z                         auto tmp0 = static_cast<long>(i1);
2023-01-11T21:41:23.6079755Z                         auto tmp1 = static_cast<long>(31);
2023-01-11T21:41:23.6079891Z                         auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:23.6080010Z                         float tmp3 = 6.0;
2023-01-11T21:41:23.6080118Z                         if(tmp2)
2023-01-11T21:41:23.6080212Z                         {
2023-01-11T21:41:23.6080353Z                             auto tmp4 = in_ptr0[i1 + (31*i0)];
2023-01-11T21:41:23.6080462Z                             tmp3 = tmp4;
2023-01-11T21:41:23.6080546Z                         }
2023-01-11T21:41:23.6080686Z                         out_ptr0[i1 + (32*i0)] = tmp3;
2023-01-11T21:41:23.6080777Z                     }
2023-01-11T21:41:23.6080865Z                 }
2023-01-11T21:41:23.6080948Z             }
2023-01-11T21:41:23.6081022Z         }
2023-01-11T21:41:23.6081130Z         #pragma omp for 
2023-01-11T21:41:23.6081239Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6081323Z         {
2023-01-11T21:41:23.6081432Z             #pragma GCC ivdep
2023-01-11T21:41:23.6081545Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:23.6081632Z             {
2023-01-11T21:41:23.6081710Z                 {
2023-01-11T21:41:23.6081803Z                     {
2023-01-11T21:41:23.6082053Z                         auto tmp0 = static_cast<long>((-2) + i1);
2023-01-11T21:41:23.6082289Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.6082423Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.6082582Z                         auto tmp3 = static_cast<long>(31);
2023-01-11T21:41:23.6082716Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.6082830Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:23.6082951Z                         float tmp6 = 99.0;
2023-01-11T21:41:23.6083060Z                         if(tmp5)
2023-01-11T21:41:23.6083160Z                         {
2023-01-11T21:41:23.6083417Z                             auto tmp7 = in_ptr0[(-2) + i1 + (31*i0)];
2023-01-11T21:41:23.6083531Z                             tmp6 = tmp7;
2023-01-11T21:41:23.6083624Z                         }
2023-01-11T21:41:23.6083740Z                         out_ptr1[i1 + (36*i0)] = tmp6;
2023-01-11T21:41:23.6083829Z                     }
2023-01-11T21:41:23.6083918Z                 }
2023-01-11T21:41:23.6083999Z             }
2023-01-11T21:41:23.6084157Z         }
2023-01-11T21:41:23.6084246Z     }
2023-01-11T21:41:23.6084314Z }
2023-01-11T21:41:23.6084427Z ''')
2023-01-11T21:41:23.6084435Z 
2023-01-11T21:41:23.6084441Z 
2023-01-11T21:41:23.6084569Z async_compile.wait(globals())
2023-01-11T21:41:23.6084672Z del async_compile
2023-01-11T21:41:23.6084679Z 
2023-01-11T21:41:23.6084785Z def call(args):
2023-01-11T21:41:23.6084879Z     arg0_1, = args
2023-01-11T21:41:23.6084969Z     args.clear()
2023-01-11T21:41:23.6085268Z     buf0 = empty_strided((2, 16, 32), (512, 32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6085546Z     buf1 = empty_strided((2, 16, 36), (576, 36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6085781Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.6085879Z     del arg0_1
2023-01-11T21:41:23.6085978Z     return (buf0, buf1, )
2023-01-11T21:41:23.6085985Z 
2023-01-11T21:41:23.6085991Z 
2023-01-11T21:41:23.6086100Z if __name__ == "__main__":
2023-01-11T21:41:23.6086265Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6086446Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6086747Z     arg0_1 = rand_strided((2, 16, 31), (496, 31, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6086886Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.6086893Z 
2023-01-11T21:41:23.6086976Z ok (1.543s)
2023-01-11T21:41:23.6087644Z   test_constant_pad_2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6087821Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6088224Z [2023-01-11 21:25:25,388] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 58
2023-01-11T21:41:23.6088610Z [2023-01-11 21:25:26,949] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 58
2023-01-11T21:41:23.6088618Z 
2023-01-11T21:41:23.6088745Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6088841Z import torch
2023-01-11T21:41:23.6088936Z import random
2023-01-11T21:41:23.6089088Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6089254Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6089261Z 
2023-01-11T21:41:23.6089379Z aten = torch.ops.aten
2023-01-11T21:41:23.6089577Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6089709Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6089716Z 
2023-01-11T21:41:23.6089721Z 
2023-01-11T21:41:23.6089921Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6090218Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6090530Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6090663Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.6090787Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.6090872Z {
2023-01-11T21:41:23.6091009Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6091093Z     {
2023-01-11T21:41:23.6091201Z         #pragma omp for 
2023-01-11T21:41:23.6091310Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:23.6091386Z         {
2023-01-11T21:41:23.6091504Z             #pragma GCC ivdep
2023-01-11T21:41:23.6091616Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:23.6091705Z             {
2023-01-11T21:41:23.6091797Z                 {
2023-01-11T21:41:23.6091885Z                     {
2023-01-11T21:41:23.6092140Z                         auto tmp0 = static_cast<long>((-1) + i0);
2023-01-11T21:41:23.6092276Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.6092478Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.6092612Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:23.6092732Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.6092957Z                         auto tmp5 = static_cast<long>((-1) + i1);
2023-01-11T21:41:23.6093080Z                         auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:41:23.6093211Z                         auto tmp7 = tmp5 < tmp3;
2023-01-11T21:41:23.6093318Z                         auto tmp8 = tmp2 & tmp4;
2023-01-11T21:41:23.6093436Z                         auto tmp9 = tmp8 & tmp6;
2023-01-11T21:41:23.6093557Z                         auto tmp10 = tmp9 & tmp7;
2023-01-11T21:41:23.6093669Z                         float tmp11 = 6.0;
2023-01-11T21:41:23.6093766Z                         if(tmp10)
2023-01-11T21:41:23.6093855Z                         {
2023-01-11T21:41:23.6094112Z                             auto tmp12 = in_ptr0[(-9) + i1 + (8*i0)];
2023-01-11T21:41:23.6094221Z                             tmp11 = tmp12;
2023-01-11T21:41:23.6094319Z                         }
2023-01-11T21:41:23.6094461Z                         out_ptr0[i1 + (10*i0)] = tmp11;
2023-01-11T21:41:23.6094553Z                     }
2023-01-11T21:41:23.6094643Z                 }
2023-01-11T21:41:23.6094732Z             }
2023-01-11T21:41:23.6094819Z         }
2023-01-11T21:41:23.6094925Z         #pragma omp for 
2023-01-11T21:41:23.6095042Z         for(long i0=0; i0<15; i0+=1)
2023-01-11T21:41:23.6095134Z         {
2023-01-11T21:41:23.6095240Z             #pragma GCC ivdep
2023-01-11T21:41:23.6095356Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:41:23.6095449Z             {
2023-01-11T21:41:23.6095541Z                 {
2023-01-11T21:41:23.6095626Z                     {
2023-01-11T21:41:23.6095883Z                         auto tmp0 = static_cast<long>((-3) + i0);
2023-01-11T21:41:23.6096032Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.6096171Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.6096314Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:23.6096442Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.6096683Z                         auto tmp5 = static_cast<long>((-1) + i1);
2023-01-11T21:41:23.6096798Z                         auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:41:23.6096928Z                         auto tmp7 = tmp5 < tmp3;
2023-01-11T21:41:23.6097048Z                         auto tmp8 = tmp2 & tmp4;
2023-01-11T21:41:23.6097176Z                         auto tmp9 = tmp8 & tmp6;
2023-01-11T21:41:23.6097299Z                         auto tmp10 = tmp9 & tmp7;
2023-01-11T21:41:23.6097413Z                         float tmp11 = 99.0;
2023-01-11T21:41:23.6097517Z                         if(tmp10)
2023-01-11T21:41:23.6097597Z                         {
2023-01-11T21:41:23.6097849Z                             auto tmp12 = in_ptr0[(-25) + i1 + (8*i0)];
2023-01-11T21:41:23.6098052Z                             tmp11 = tmp12;
2023-01-11T21:41:23.6098149Z                         }
2023-01-11T21:41:23.6098274Z                         out_ptr1[i1 + (11*i0)] = tmp11;
2023-01-11T21:41:23.6098369Z                     }
2023-01-11T21:41:23.6098459Z                 }
2023-01-11T21:41:23.6098526Z             }
2023-01-11T21:41:23.6098605Z         }
2023-01-11T21:41:23.6098690Z     }
2023-01-11T21:41:23.6098779Z }
2023-01-11T21:41:23.6098895Z ''')
2023-01-11T21:41:23.6098903Z 
2023-01-11T21:41:23.6098908Z 
2023-01-11T21:41:23.6099030Z async_compile.wait(globals())
2023-01-11T21:41:23.6099138Z del async_compile
2023-01-11T21:41:23.6099145Z 
2023-01-11T21:41:23.6099237Z def call(args):
2023-01-11T21:41:23.6099330Z     arg0_1, = args
2023-01-11T21:41:23.6099431Z     args.clear()
2023-01-11T21:41:23.6099756Z     buf0 = empty_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6100142Z     buf1 = empty_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6100369Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.6100464Z     del arg0_1
2023-01-11T21:41:23.6100569Z     return (buf0, buf1, )
2023-01-11T21:41:23.6100577Z 
2023-01-11T21:41:23.6100584Z 
2023-01-11T21:41:23.6100684Z if __name__ == "__main__":
2023-01-11T21:41:23.6100847Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6101027Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6101346Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6101493Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.6101500Z 
2023-01-11T21:41:23.6101582Z ok (1.590s)
2023-01-11T21:41:23.6102217Z   test_constant_pad_3d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6102538Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6102916Z [2023-01-11 21:25:26,979] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 59
2023-01-11T21:41:23.6103303Z [2023-01-11 21:25:28,549] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 59
2023-01-11T21:41:23.6103321Z 
2023-01-11T21:41:23.6103434Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6103533Z import torch
2023-01-11T21:41:23.6103634Z import random
2023-01-11T21:41:23.6103809Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6103985Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6103993Z 
2023-01-11T21:41:23.6104105Z aten = torch.ops.aten
2023-01-11T21:41:23.6104315Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6104436Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6104443Z 
2023-01-11T21:41:23.6104464Z 
2023-01-11T21:41:23.6104653Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6104951Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6105133Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6105276Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.6105417Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.6105502Z {
2023-01-11T21:41:23.6105646Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6105727Z     {
2023-01-11T21:41:23.6105855Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6105970Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.6106060Z         {
2023-01-11T21:41:23.6106174Z             for(long i1=0; i1<15; i1+=1)
2023-01-11T21:41:23.6106395Z             {
2023-01-11T21:41:23.6106496Z                 #pragma GCC ivdep
2023-01-11T21:41:23.6106618Z                 for(long i2=0; i2<11; i2+=1)
2023-01-11T21:41:23.6106707Z                 {
2023-01-11T21:41:23.6106828Z                     #pragma GCC ivdep
2023-01-11T21:41:23.6106958Z                     for(long i3=0; i3<7; i3+=1)
2023-01-11T21:41:23.6107050Z                     {
2023-01-11T21:41:23.6107147Z                         {
2023-01-11T21:41:23.6107232Z                             {
2023-01-11T21:41:23.6107495Z                                 auto tmp0 = static_cast<long>((-5) + i1);
2023-01-11T21:41:23.6107650Z                                 auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.6107781Z                                 auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.6107929Z                                 auto tmp3 = static_cast<long>(4);
2023-01-11T21:41:23.6108070Z                                 auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.6108427Z                                 auto tmp5 = static_cast<long>((-3) + i2);
2023-01-11T21:41:23.6108568Z                                 auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:41:23.6108693Z                                 auto tmp7 = tmp5 < tmp3;
2023-01-11T21:41:23.6108947Z                                 auto tmp8 = static_cast<long>((-1) + i3);
2023-01-11T21:41:23.6109089Z                                 auto tmp9 = tmp8 >= tmp1;
2023-01-11T21:41:23.6109212Z                                 auto tmp10 = tmp8 < tmp3;
2023-01-11T21:41:23.6109349Z                                 auto tmp11 = tmp2 & tmp4;
2023-01-11T21:41:23.6109494Z                                 auto tmp12 = tmp11 & tmp6;
2023-01-11T21:41:23.6109621Z                                 auto tmp13 = tmp12 & tmp7;
2023-01-11T21:41:23.6109739Z                                 auto tmp14 = tmp13 & tmp9;
2023-01-11T21:41:23.6109877Z                                 auto tmp15 = tmp14 & tmp10;
2023-01-11T21:41:23.6110009Z                                 float tmp16 = 6.0;
2023-01-11T21:41:23.6110123Z                                 if(tmp15)
2023-01-11T21:41:23.6110227Z                                 {
2023-01-11T21:41:23.6110512Z                                     auto tmp17 = in_ptr0[(-93) + i3 + (4*i2) + (16*i1) + (64*i0)];
2023-01-11T21:41:23.6110636Z                                     tmp16 = tmp17;
2023-01-11T21:41:23.6110740Z                                 }
2023-01-11T21:41:23.6110900Z                                 out_ptr0[i3 + (7*i2) + (77*i1) + (1155*i0)] = tmp16;
2023-01-11T21:41:23.6111001Z                             }
2023-01-11T21:41:23.6111108Z                         }
2023-01-11T21:41:23.6111213Z                     }
2023-01-11T21:41:23.6111306Z                 }
2023-01-11T21:41:23.6111396Z             }
2023-01-11T21:41:23.6111478Z         }
2023-01-11T21:41:23.6111584Z         #pragma omp for 
2023-01-11T21:41:23.6111713Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.6111814Z         {
2023-01-11T21:41:23.6111947Z             #pragma GCC ivdep
2023-01-11T21:41:23.6112072Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:41:23.6112155Z             {
2023-01-11T21:41:23.6112257Z                 #pragma GCC ivdep
2023-01-11T21:41:23.6112376Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:23.6112466Z                 {
2023-01-11T21:41:23.6112559Z                     {
2023-01-11T21:41:23.6112661Z                         {
2023-01-11T21:41:23.6112925Z                             auto tmp0 = static_cast<long>((-3) + i1);
2023-01-11T21:41:23.6113074Z                             auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.6113189Z                             auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.6113337Z                             auto tmp3 = static_cast<long>(4);
2023-01-11T21:41:23.6113468Z                             auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.6113590Z                             auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:23.6113790Z                             float tmp6 = 6.0;
2023-01-11T21:41:23.6113992Z                             if(tmp5)
2023-01-11T21:41:23.6114096Z                             {
2023-01-11T21:41:23.6114372Z                                 auto tmp7 = in_ptr0[(-12) + i2 + (4*i1) + (16*i0)];
2023-01-11T21:41:23.6114478Z                                 tmp6 = tmp7;
2023-01-11T21:41:23.6114570Z                             }
2023-01-11T21:41:23.6114717Z                             out_ptr1[i2 + (4*i1) + (44*i0)] = tmp6;
2023-01-11T21:41:23.6114806Z                         }
2023-01-11T21:41:23.6114895Z                     }
2023-01-11T21:41:23.6114985Z                 }
2023-01-11T21:41:23.6115074Z             }
2023-01-11T21:41:23.6115140Z         }
2023-01-11T21:41:23.6115219Z     }
2023-01-11T21:41:23.6115298Z }
2023-01-11T21:41:23.6115408Z ''')
2023-01-11T21:41:23.6115417Z 
2023-01-11T21:41:23.6115424Z 
2023-01-11T21:41:23.6115550Z async_compile.wait(globals())
2023-01-11T21:41:23.6115645Z del async_compile
2023-01-11T21:41:23.6115656Z 
2023-01-11T21:41:23.6115820Z def call(args):
2023-01-11T21:41:23.6115901Z     arg0_1, = args
2023-01-11T21:41:23.6116000Z     args.clear()
2023-01-11T21:41:23.6116319Z     buf0 = empty_strided((2, 15, 11, 7), (1155, 77, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6116629Z     buf1 = empty_strided((2, 4, 11, 4), (176, 44, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6116855Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.6116947Z     del arg0_1
2023-01-11T21:41:23.6117046Z     return (buf0, buf1, )
2023-01-11T21:41:23.6117054Z 
2023-01-11T21:41:23.6117061Z 
2023-01-11T21:41:23.6117161Z if __name__ == "__main__":
2023-01-11T21:41:23.6117321Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6117510Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6117817Z     arg0_1 = rand_strided((2, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6117974Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.6117981Z 
2023-01-11T21:41:23.6118070Z ok (1.601s)
2023-01-11T21:41:23.6118752Z   test_conv2d_backward_channels_last_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6118930Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6119304Z [2023-01-11 21:25:28,601] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 60
2023-01-11T21:41:23.6119686Z [2023-01-11 21:25:28,620] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 60
2023-01-11T21:41:23.6119693Z 
2023-01-11T21:41:23.6119821Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6119916Z import torch
2023-01-11T21:41:23.6120015Z import random
2023-01-11T21:41:23.6120180Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6120344Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6120351Z 
2023-01-11T21:41:23.6120460Z aten = torch.ops.aten
2023-01-11T21:41:23.6120640Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6120774Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6120781Z 
2023-01-11T21:41:23.6120786Z 
2023-01-11T21:41:23.6120899Z async_compile.wait(globals())
2023-01-11T21:41:23.6121004Z del async_compile
2023-01-11T21:41:23.6121011Z 
2023-01-11T21:41:23.6121112Z def call(args):
2023-01-11T21:41:23.6121228Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.6121319Z     args.clear()
2023-01-11T21:41:23.6121561Z     buf0 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [320], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, True, True])
2023-01-11T21:41:23.6121751Z     del arg0_1
2023-01-11T21:41:23.6121833Z     del arg1_1
2023-01-11T21:41:23.6121917Z     del arg2_1
2023-01-11T21:41:23.6122014Z     buf1 = buf0[0]
2023-01-11T21:41:23.6122177Z     assert_size_stride(buf1, (2, 2048, 8, 8), (131072, 1, 16384, 2048))
2023-01-11T21:41:23.6122276Z     buf2 = buf0[1]
2023-01-11T21:41:23.6122431Z     assert_size_stride(buf2, (320, 2048, 1, 1), (2048, 1, 2048, 2048))
2023-01-11T21:41:23.6122530Z     buf3 = buf0[2]
2023-01-11T21:41:23.6122655Z     assert_size_stride(buf3, (320, ), (1, ))
2023-01-11T21:41:23.6122741Z     del buf0
2023-01-11T21:41:23.6122856Z     return (buf1, buf2, buf3, )
2023-01-11T21:41:23.6122864Z 
2023-01-11T21:41:23.6122869Z 
2023-01-11T21:41:23.6122973Z if __name__ == "__main__":
2023-01-11T21:41:23.6123130Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6123302Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6123721Z     arg0_1 = rand_strided((2, 320, 8, 8), (20480, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6124058Z     arg1_1 = rand_strided((2, 2048, 8, 8), (131072, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6124407Z     arg2_1 = rand_strided((320, 2048, 1, 1), (2048, 1, 2048, 2048), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6124607Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.6124616Z 
2023-01-11T21:41:23.6124710Z ok (0.103s)
2023-01-11T21:41:23.6125034Z   test_conv2d_binary_cpu (__main__.CpuTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:41:23.6125832Z   test_conv2d_channels_last_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6126014Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6126386Z [2023-01-11 21:25:28,728] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 61
2023-01-11T21:41:23.6126785Z [2023-01-11 21:25:30,312] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 61
2023-01-11T21:41:23.6127435Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6127621Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6128038Z [2023-01-11 21:25:30,385] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 62
2023-01-11T21:41:23.6128454Z [2023-01-11 21:25:30,408] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 62
2023-01-11T21:41:23.6129096Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6129277Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6129674Z [2023-01-11 21:25:30,480] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 63
2023-01-11T21:41:23.6130075Z [2023-01-11 21:25:30,502] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 63
2023-01-11T21:41:23.6130084Z 
2023-01-11T21:41:23.6130220Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6130416Z import torch
2023-01-11T21:41:23.6130510Z import random
2023-01-11T21:41:23.6130684Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6130849Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6130871Z 
2023-01-11T21:41:23.6130962Z aten = torch.ops.aten
2023-01-11T21:41:23.6131157Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6131286Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6131293Z 
2023-01-11T21:41:23.6131300Z 
2023-01-11T21:41:23.6131507Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6131819Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6131985Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6132134Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6132206Z {
2023-01-11T21:41:23.6132348Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6132434Z     {
2023-01-11T21:41:23.6132641Z         #pragma omp for  collapse(3)
2023-01-11T21:41:23.6132755Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.6132853Z         {
2023-01-11T21:41:23.6132962Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.6133038Z             {
2023-01-11T21:41:23.6133167Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:41:23.6133262Z                 {
2023-01-11T21:41:23.6133354Z                     {
2023-01-11T21:41:23.6133444Z                         {
2023-01-11T21:41:23.6133600Z                             auto tmp0 = in_ptr0[i2 + (256*i1) + (768*i0)];
2023-01-11T21:41:23.6133754Z                             out_ptr0[i1 + (3*i2) + (768*i0)] = tmp0;
2023-01-11T21:41:23.6133831Z                         }
2023-01-11T21:41:23.6133919Z                     }
2023-01-11T21:41:23.6134007Z                 }
2023-01-11T21:41:23.6134102Z             }
2023-01-11T21:41:23.6134190Z         }
2023-01-11T21:41:23.6134278Z     }
2023-01-11T21:41:23.6134367Z }
2023-01-11T21:41:23.6134488Z ''')
2023-01-11T21:41:23.6134497Z 
2023-01-11T21:41:23.6134503Z 
2023-01-11T21:41:23.6134628Z async_compile.wait(globals())
2023-01-11T21:41:23.6134732Z del async_compile
2023-01-11T21:41:23.6134739Z 
2023-01-11T21:41:23.6134840Z def call(args):
2023-01-11T21:41:23.6134982Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.6135071Z     args.clear()
2023-01-11T21:41:23.6135405Z     buf0 = empty_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6135591Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6135814Z     buf1 = aten.convolution(buf0, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:23.6135971Z     assert_size_stride(buf1, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:41:23.6136062Z     del buf0
2023-01-11T21:41:23.6136166Z     del primals_2
2023-01-11T21:41:23.6136287Z     return (buf1, primals_1, primals_3, )
2023-01-11T21:41:23.6136297Z 
2023-01-11T21:41:23.6136306Z 
2023-01-11T21:41:23.6136402Z if __name__ == "__main__":
2023-01-11T21:41:23.6136543Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6136684Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6137009Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6137305Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6137632Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6137830Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.6137838Z 
2023-01-11T21:41:23.6137844Z 
2023-01-11T21:41:23.6137982Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6138083Z import torch
2023-01-11T21:41:23.6138181Z import random
2023-01-11T21:41:23.6138334Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6138605Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6138613Z 
2023-01-11T21:41:23.6138718Z aten = torch.ops.aten
2023-01-11T21:41:23.6138896Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6139023Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6139031Z 
2023-01-11T21:41:23.6139037Z 
2023-01-11T21:41:23.6139169Z async_compile.wait(globals())
2023-01-11T21:41:23.6139276Z del async_compile
2023-01-11T21:41:23.6139283Z 
2023-01-11T21:41:23.6139383Z def call(args):
2023-01-11T21:41:23.6139522Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.6139625Z     args.clear()
2023-01-11T21:41:23.6139842Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:23.6139972Z     assert_size_stride(buf0, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:41:23.6140065Z     del primals_2
2023-01-11T21:41:23.6140203Z     return (buf0, primals_1, primals_3, )
2023-01-11T21:41:23.6140215Z 
2023-01-11T21:41:23.6140285Z 
2023-01-11T21:41:23.6140390Z if __name__ == "__main__":
2023-01-11T21:41:23.6140551Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6140714Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6141038Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6141329Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6141658Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6141857Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.6141866Z 
2023-01-11T21:41:23.6141872Z 
2023-01-11T21:41:23.6142010Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6142114Z import torch
2023-01-11T21:41:23.6142214Z import random
2023-01-11T21:41:23.6142492Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6142679Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6142686Z 
2023-01-11T21:41:23.6142803Z aten = torch.ops.aten
2023-01-11T21:41:23.6142985Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6143120Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6143127Z 
2023-01-11T21:41:23.6143133Z 
2023-01-11T21:41:23.6143258Z async_compile.wait(globals())
2023-01-11T21:41:23.6143354Z del async_compile
2023-01-11T21:41:23.6143360Z 
2023-01-11T21:41:23.6143455Z def call(args):
2023-01-11T21:41:23.6143587Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.6143685Z     args.clear()
2023-01-11T21:41:23.6143893Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:23.6144048Z     assert_size_stride(buf0, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:41:23.6144157Z     del primals_2
2023-01-11T21:41:23.6144291Z     return (buf0, primals_1, primals_3, )
2023-01-11T21:41:23.6144298Z 
2023-01-11T21:41:23.6144308Z 
2023-01-11T21:41:23.6144411Z if __name__ == "__main__":
2023-01-11T21:41:23.6144578Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6144747Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6145093Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6145402Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6145736Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6145939Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.6145949Z 
2023-01-11T21:41:23.6146044Z ok (1.848s)
2023-01-11T21:41:23.6146718Z   test_conv2d_packed_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6147024Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6147409Z [2023-01-11 21:25:30,554] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 64
2023-01-11T21:41:23.6147773Z [2023-01-11 21:25:32,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 64
2023-01-11T21:41:23.6147796Z 
2023-01-11T21:41:23.6147923Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6148018Z import torch
2023-01-11T21:41:23.6148125Z import random
2023-01-11T21:41:23.6148295Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6148469Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6148477Z 
2023-01-11T21:41:23.6148588Z aten = torch.ops.aten
2023-01-11T21:41:23.6148856Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6148978Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6148985Z 
2023-01-11T21:41:23.6149006Z 
2023-01-11T21:41:23.6149203Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6149501Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6149670Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6149809Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6149891Z {
2023-01-11T21:41:23.6150034Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6150116Z     {
2023-01-11T21:41:23.6150235Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6150345Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6150429Z         {
2023-01-11T21:41:23.6150555Z             for(long i1=0; i1<3136; i1+=1)
2023-01-11T21:41:23.6150643Z             {
2023-01-11T21:41:23.6150725Z                 {
2023-01-11T21:41:23.6150814Z                     {
2023-01-11T21:41:23.6150961Z                         auto tmp0 = in_ptr0[i1 + (3136*i0)];
2023-01-11T21:41:23.6151096Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6151188Z                     }
2023-01-11T21:41:23.6151279Z                 }
2023-01-11T21:41:23.6151373Z             }
2023-01-11T21:41:23.6151457Z         }
2023-01-11T21:41:23.6151531Z     }
2023-01-11T21:41:23.6151613Z }
2023-01-11T21:41:23.6151741Z ''')
2023-01-11T21:41:23.6151750Z 
2023-01-11T21:41:23.6151757Z 
2023-01-11T21:41:23.6151959Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6152246Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6152414Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6152561Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6152646Z {
2023-01-11T21:41:23.6152772Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6152859Z     {
2023-01-11T21:41:23.6152978Z         #pragma omp for 
2023-01-11T21:41:23.6153093Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.6153172Z         {
2023-01-11T21:41:23.6153274Z             #pragma GCC ivdep
2023-01-11T21:41:23.6153384Z             for(long i1=0; i1<324; i1+=1)
2023-01-11T21:41:23.6153476Z             {
2023-01-11T21:41:23.6153568Z                 {
2023-01-11T21:41:23.6153657Z                     {
2023-01-11T21:41:23.6153871Z                         auto tmp0 = in_ptr0[i0 + (64*i1)];
2023-01-11T21:41:23.6154014Z                         out_ptr0[i1 + (324*i0)] = tmp0;
2023-01-11T21:41:23.6154102Z                     }
2023-01-11T21:41:23.6154177Z                 }
2023-01-11T21:41:23.6154268Z             }
2023-01-11T21:41:23.6154355Z         }
2023-01-11T21:41:23.6154438Z     }
2023-01-11T21:41:23.6154520Z }
2023-01-11T21:41:23.6154645Z ''')
2023-01-11T21:41:23.6154653Z 
2023-01-11T21:41:23.6154659Z 
2023-01-11T21:41:23.6154786Z async_compile.wait(globals())
2023-01-11T21:41:23.6154956Z del async_compile
2023-01-11T21:41:23.6154963Z 
2023-01-11T21:41:23.6155064Z def call(args):
2023-01-11T21:41:23.6155185Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.6155280Z     args.clear()
2023-01-11T21:41:23.6155609Z     buf0 = empty_strided((1, 3, 56, 56), (9408, 1, 168, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6155808Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6155900Z     del arg2_1
2023-01-11T21:41:23.6156269Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (3, 3), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6156427Z     assert_size_stride(buf1, (1, 64, 18, 18), (20736, 1, 1152, 64))
2023-01-11T21:41:23.6156533Z     del arg0_1
2023-01-11T21:41:23.6156641Z     del arg1_1
2023-01-11T21:41:23.6156732Z     del buf0
2023-01-11T21:41:23.6157066Z     buf2 = empty_strided((1, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6157321Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6157433Z     return (buf2, )
2023-01-11T21:41:23.6157440Z 
2023-01-11T21:41:23.6157445Z 
2023-01-11T21:41:23.6157537Z if __name__ == "__main__":
2023-01-11T21:41:23.6157706Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6157886Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6158204Z     arg0_1 = rand_strided((64, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6158494Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6158816Z     arg2_1 = rand_strided((1, 3, 56, 56), (9408, 3136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6158991Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.6158997Z 
2023-01-11T21:41:23.6159103Z ok (1.643s)
2023-01-11T21:41:23.6159365Z   test_conv2d_unary_cpu (__main__.CpuTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:41:23.6160096Z   test_conv3d_channels_last_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6160279Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6160667Z [2023-01-11 21:25:32,219] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 65
2023-01-11T21:41:23.6161059Z [2023-01-11 21:25:33,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 65
2023-01-11T21:41:23.6161713Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6161908Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6162302Z [2023-01-11 21:25:33,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 66
2023-01-11T21:41:23.6162707Z [2023-01-11 21:25:35,443] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 66
2023-01-11T21:41:23.6163363Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6163546Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6164013Z [2023-01-11 21:25:35,516] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 67
2023-01-11T21:41:23.6164416Z [2023-01-11 21:25:35,548] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 67
2023-01-11T21:41:23.6164426Z 
2023-01-11T21:41:23.6164550Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6164653Z import torch
2023-01-11T21:41:23.6164758Z import random
2023-01-11T21:41:23.6164915Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6165084Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6165092Z 
2023-01-11T21:41:23.6165206Z aten = torch.ops.aten
2023-01-11T21:41:23.6165398Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6165507Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6165514Z 
2023-01-11T21:41:23.6165538Z 
2023-01-11T21:41:23.6165728Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6166105Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6166293Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6166434Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6166522Z {
2023-01-11T21:41:23.6166659Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6166744Z     {
2023-01-11T21:41:23.6166836Z         #pragma omp for 
2023-01-11T21:41:23.6166951Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.6167036Z         {
2023-01-11T21:41:23.6167231Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.6167361Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.6167445Z         }
2023-01-11T21:41:23.6167576Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.6167671Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:23.6167756Z         {
2023-01-11T21:41:23.6167880Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.6167993Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.6168081Z         }
2023-01-11T21:41:23.6168169Z     }
2023-01-11T21:41:23.6168243Z }
2023-01-11T21:41:23.6168359Z ''')
2023-01-11T21:41:23.6168370Z 
2023-01-11T21:41:23.6168377Z 
2023-01-11T21:41:23.6168504Z async_compile.wait(globals())
2023-01-11T21:41:23.6168612Z del async_compile
2023-01-11T21:41:23.6168619Z 
2023-01-11T21:41:23.6168712Z def call(args):
2023-01-11T21:41:23.6168851Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.6168946Z     args.clear()
2023-01-11T21:41:23.6169273Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6169455Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6169675Z     buf1 = aten.convolution(primals_3, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6169835Z     assert_size_stride(buf1, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:41:23.6169932Z     del buf0
2023-01-11T21:41:23.6170034Z     del primals_2
2023-01-11T21:41:23.6170168Z     return (buf1, primals_1, primals_3, )
2023-01-11T21:41:23.6170175Z 
2023-01-11T21:41:23.6170182Z 
2023-01-11T21:41:23.6170289Z if __name__ == "__main__":
2023-01-11T21:41:23.6170461Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6170632Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6170985Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6171283Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6171615Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6171807Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.6171815Z 
2023-01-11T21:41:23.6171820Z 
2023-01-11T21:41:23.6172035Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6172133Z import torch
2023-01-11T21:41:23.6172226Z import random
2023-01-11T21:41:23.6172372Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6172546Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6172553Z 
2023-01-11T21:41:23.6172668Z aten = torch.ops.aten
2023-01-11T21:41:23.6172847Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6173126Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6173133Z 
2023-01-11T21:41:23.6173139Z 
2023-01-11T21:41:23.6173353Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6173655Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6173821Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6173942Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.6174077Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.6174289Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.6174378Z {
2023-01-11T21:41:23.6174519Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6174609Z     {
2023-01-11T21:41:23.6174725Z         #pragma omp for 
2023-01-11T21:41:23.6174828Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.6174917Z         {
2023-01-11T21:41:23.6175111Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.6175242Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.6175319Z         }
2023-01-11T21:41:23.6175453Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.6175564Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:23.6175639Z         {
2023-01-11T21:41:23.6175756Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.6175865Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.6175948Z         }
2023-01-11T21:41:23.6176079Z         #pragma omp for  collapse(3)
2023-01-11T21:41:23.6176198Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.6176287Z         {
2023-01-11T21:41:23.6176381Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.6176464Z             {
2023-01-11T21:41:23.6176584Z                 for(long i2=0; i2<4096; i2+=1)
2023-01-11T21:41:23.6176669Z                 {
2023-01-11T21:41:23.6176764Z                     {
2023-01-11T21:41:23.6176865Z                         {
2023-01-11T21:41:23.6177016Z                             auto tmp0 = in_ptr1[i1 + (3*i2) + (12288*i0)];
2023-01-11T21:41:23.6177144Z                             out_ptr1[i2 + (4096*i1) + (12288*i0)] = tmp0;
2023-01-11T21:41:23.6177239Z                         }
2023-01-11T21:41:23.6177327Z                     }
2023-01-11T21:41:23.6177419Z                 }
2023-01-11T21:41:23.6177505Z             }
2023-01-11T21:41:23.6177582Z         }
2023-01-11T21:41:23.6177646Z     }
2023-01-11T21:41:23.6177728Z }
2023-01-11T21:41:23.6177873Z ''')
2023-01-11T21:41:23.6177884Z 
2023-01-11T21:41:23.6177894Z 
2023-01-11T21:41:23.6178040Z async_compile.wait(globals())
2023-01-11T21:41:23.6178150Z del async_compile
2023-01-11T21:41:23.6178158Z 
2023-01-11T21:41:23.6178258Z def call(args):
2023-01-11T21:41:23.6178412Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.6178506Z     args.clear()
2023-01-11T21:41:23.6178814Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6179142Z     buf1 = empty_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6179435Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.6179643Z     buf2 = aten.convolution(buf1, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6179794Z     assert_size_stride(buf2, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:41:23.6179887Z     del buf0
2023-01-11T21:41:23.6180067Z     del buf1
2023-01-11T21:41:23.6180163Z     del primals_2
2023-01-11T21:41:23.6180290Z     return (buf2, primals_1, primals_3, )
2023-01-11T21:41:23.6180298Z 
2023-01-11T21:41:23.6180304Z 
2023-01-11T21:41:23.6180409Z if __name__ == "__main__":
2023-01-11T21:41:23.6180579Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6180755Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6181081Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6181366Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6181706Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 1, 768, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6181907Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.6181914Z 
2023-01-11T21:41:23.6181921Z 
2023-01-11T21:41:23.6182042Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6182213Z import torch
2023-01-11T21:41:23.6182309Z import random
2023-01-11T21:41:23.6182645Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6182818Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6182826Z 
2023-01-11T21:41:23.6182939Z aten = torch.ops.aten
2023-01-11T21:41:23.6183128Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6183257Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6183264Z 
2023-01-11T21:41:23.6183269Z 
2023-01-11T21:41:23.6183463Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6183762Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6183929Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6184084Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.6184212Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.6184355Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.6184433Z {
2023-01-11T21:41:23.6184546Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6184627Z     {
2023-01-11T21:41:23.6184730Z         #pragma omp for 
2023-01-11T21:41:23.6184831Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.6184915Z         {
2023-01-11T21:41:23.6185102Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.6185234Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.6185301Z         }
2023-01-11T21:41:23.6185422Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.6185526Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:23.6185611Z         {
2023-01-11T21:41:23.6185731Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.6185839Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.6185922Z         }
2023-01-11T21:41:23.6186022Z         #pragma omp for  collapse(3)
2023-01-11T21:41:23.6186124Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.6186219Z         {
2023-01-11T21:41:23.6186333Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.6186420Z             {
2023-01-11T21:41:23.6186537Z                 for(long i2=0; i2<4096; i2+=1)
2023-01-11T21:41:23.6186623Z                 {
2023-01-11T21:41:23.6186706Z                     {
2023-01-11T21:41:23.6186797Z                         {
2023-01-11T21:41:23.6186956Z                             auto tmp0 = in_ptr1[i1 + (3*i2) + (12288*i0)];
2023-01-11T21:41:23.6187096Z                             out_ptr1[i2 + (4096*i1) + (12288*i0)] = tmp0;
2023-01-11T21:41:23.6187189Z                         }
2023-01-11T21:41:23.6187280Z                     }
2023-01-11T21:41:23.6187374Z                 }
2023-01-11T21:41:23.6187442Z             }
2023-01-11T21:41:23.6187517Z         }
2023-01-11T21:41:23.6187601Z     }
2023-01-11T21:41:23.6187688Z }
2023-01-11T21:41:23.6187820Z ''')
2023-01-11T21:41:23.6187828Z 
2023-01-11T21:41:23.6187834Z 
2023-01-11T21:41:23.6187954Z async_compile.wait(globals())
2023-01-11T21:41:23.6188184Z del async_compile
2023-01-11T21:41:23.6188191Z 
2023-01-11T21:41:23.6188277Z def call(args):
2023-01-11T21:41:23.6188429Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.6188524Z     args.clear()
2023-01-11T21:41:23.6188826Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6189133Z     buf1 = empty_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6189423Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.6189621Z     buf2 = aten.convolution(buf1, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6189780Z     assert_size_stride(buf2, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:41:23.6189857Z     del buf0
2023-01-11T21:41:23.6189955Z     del buf1
2023-01-11T21:41:23.6190135Z     del primals_2
2023-01-11T21:41:23.6190286Z     return (buf2, primals_1, primals_3, )
2023-01-11T21:41:23.6190294Z 
2023-01-11T21:41:23.6190299Z 
2023-01-11T21:41:23.6190407Z if __name__ == "__main__":
2023-01-11T21:41:23.6190567Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6190744Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6191067Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6191360Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6191704Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 1, 768, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6191891Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.6191899Z 
2023-01-11T21:41:23.6191988Z ok (3.403s)
2023-01-11T21:41:23.6192194Z   test_conv_autotune_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:23.6192708Z   test_conv_backward_cpu (__main__.CpuTests) ... [2023-01-11 21:25:35,630] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 68
2023-01-11T21:41:23.6193094Z [2023-01-11 21:25:35,700] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 68
2023-01-11T21:41:23.6193102Z 
2023-01-11T21:41:23.6193229Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6193305Z import torch
2023-01-11T21:41:23.6193405Z import random
2023-01-11T21:41:23.6193571Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6193819Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6193827Z 
2023-01-11T21:41:23.6193931Z aten = torch.ops.aten
2023-01-11T21:41:23.6194118Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6194253Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6194260Z 
2023-01-11T21:41:23.6194266Z 
2023-01-11T21:41:23.6194386Z async_compile.wait(globals())
2023-01-11T21:41:23.6194477Z del async_compile
2023-01-11T21:41:23.6194484Z 
2023-01-11T21:41:23.6194589Z def call(args):
2023-01-11T21:41:23.6194769Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1 = args
2023-01-11T21:41:23.6194867Z     args.clear()
2023-01-11T21:41:23.6195093Z     buf0 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [4], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, True, True])
2023-01-11T21:41:23.6195194Z     buf1 = buf0[0]
2023-01-11T21:41:23.6195338Z     assert_size_stride(buf1, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:41:23.6195428Z     buf2 = buf0[1]
2023-01-11T21:41:23.6195559Z     assert_size_stride(buf2, (4, 4, 3, 3), (36, 9, 3, 1))
2023-01-11T21:41:23.6195651Z     buf3 = buf0[2]
2023-01-11T21:41:23.6195768Z     assert_size_stride(buf3, (4, ), (1, ))
2023-01-11T21:41:23.6195851Z     del buf0
2023-01-11T21:41:23.6196078Z     buf4 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [4], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, False, False])
2023-01-11T21:41:23.6196259Z     del arg0_1
2023-01-11T21:41:23.6196351Z     del arg1_1
2023-01-11T21:41:23.6196437Z     del arg2_1
2023-01-11T21:41:23.6196530Z     buf5 = buf4[0]
2023-01-11T21:41:23.6196664Z     assert_size_stride(buf5, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:41:23.6196759Z     del buf4
2023-01-11T21:41:23.6196976Z     buf6 = aten.convolution_backward(arg3_1, arg4_1, arg5_1, [4], [1], [0], [1], False, [0], 1, [True, True, True])
2023-01-11T21:41:23.6197077Z     del arg3_1
2023-01-11T21:41:23.6197158Z     del arg4_1
2023-01-11T21:41:23.6197232Z     del arg5_1
2023-01-11T21:41:23.6197326Z     buf7 = buf6[0]
2023-01-11T21:41:23.6197476Z     assert_size_stride(buf7, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:41:23.6197564Z     buf8 = buf6[1]
2023-01-11T21:41:23.6197695Z     assert_size_stride(buf8, (4, 4, 3, 3), (36, 9, 3, 1))
2023-01-11T21:41:23.6197788Z     buf9 = buf6[2]
2023-01-11T21:41:23.6197908Z     assert_size_stride(buf9, (4, ), (1, ))
2023-01-11T21:41:23.6197997Z     del buf6
2023-01-11T21:41:23.6198298Z     buf10 = aten.convolution_backward(arg6_1, arg7_1, arg8_1, [4], [1, 1, 1], [0, 0, 0], [1, 1, 1], False, [0, 0, 0], 1, [True, True, True])
2023-01-11T21:41:23.6198400Z     del arg6_1
2023-01-11T21:41:23.6198491Z     del arg7_1
2023-01-11T21:41:23.6198572Z     del arg8_1
2023-01-11T21:41:23.6198662Z     buf11 = buf10[0]
2023-01-11T21:41:23.6198803Z     assert_size_stride(buf11, (3, 4, 5, 5, 5), (500, 125, 25, 5, 1))
2023-01-11T21:41:23.6198905Z     buf12 = buf10[1]
2023-01-11T21:41:23.6199048Z     assert_size_stride(buf12, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
2023-01-11T21:41:23.6199146Z     buf13 = buf10[2]
2023-01-11T21:41:23.6199280Z     assert_size_stride(buf13, (4, ), (1, ))
2023-01-11T21:41:23.6199384Z     del buf10
2023-01-11T21:41:23.6199547Z     return (buf1, buf2, buf3, buf5, buf7, buf8, buf9, buf11, buf12, buf13, )
2023-01-11T21:41:23.6199557Z 
2023-01-11T21:41:23.6199563Z 
2023-01-11T21:41:23.6199665Z if __name__ == "__main__":
2023-01-11T21:41:23.6199827Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6200000Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6200310Z     arg0_1 = rand_strided((3, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6200631Z     arg1_1 = rand_strided((3, 4, 5, 5), (100, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6200942Z     arg2_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6201241Z     arg3_1 = rand_strided((3, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6201550Z     arg4_1 = rand_strided((3, 4, 5, 5), (100, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6201842Z     arg5_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6202139Z     arg6_1 = rand_strided((3, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6202464Z     arg7_1 = rand_strided((3, 4, 5, 5, 5), (500, 125, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6202778Z     arg8_1 = rand_strided((4, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6203017Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1]))
2023-01-11T21:41:23.6203025Z 
2023-01-11T21:41:23.6203120Z ok (0.153s)
2023-01-11T21:41:23.6203795Z   test_conv_bn_fuse_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6203971Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6204365Z [2023-01-11 21:25:35,816] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 69
2023-01-11T21:41:23.6204859Z [2023-01-11 21:25:35,838] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 69
2023-01-11T21:41:23.6205478Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6205648Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6206000Z [2023-01-11 21:25:35,986] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 70
2023-01-11T21:41:23.6206394Z [2023-01-11 21:25:36,009] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 70
2023-01-11T21:41:23.6207086Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6207278Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6207655Z [2023-01-11 21:25:36,120] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 71
2023-01-11T21:41:23.6208040Z [2023-01-11 21:25:36,142] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 71
2023-01-11T21:41:23.6208653Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6208853Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6209229Z [2023-01-11 21:25:36,256] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 72
2023-01-11T21:41:23.6209622Z [2023-01-11 21:25:36,279] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 72
2023-01-11T21:41:23.6210220Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6210409Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6210780Z [2023-01-11 21:25:36,516] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 73
2023-01-11T21:41:23.6211171Z [2023-01-11 21:25:36,538] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 73
2023-01-11T21:41:23.6211801Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6212005Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6212398Z [2023-01-11 21:25:36,649] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 74
2023-01-11T21:41:23.6212407Z 
2023-01-11T21:41:23.6212544Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6212641Z import torch
2023-01-11T21:41:23.6212752Z import random
2023-01-11T21:41:23.6213006Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6213167Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6213191Z 
2023-01-11T21:41:23.6213299Z aten = torch.ops.aten
2023-01-11T21:41:23.6213482Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6213613Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6213620Z 
2023-01-11T21:41:23.6213626Z 
2023-01-11T21:41:23.6213752Z async_compile.wait(globals())
2023-01-11T21:41:23.6213860Z del async_compile
2023-01-11T21:41:23.6213866Z 
2023-01-11T21:41:23.6213955Z def call(args):
2023-01-11T21:41:23.6214110Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6214200Z     args.clear()
2023-01-11T21:41:23.6214397Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:23.6214545Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:23.6214647Z     del arg0_1
2023-01-11T21:41:23.6214851Z     del arg1_1
2023-01-11T21:41:23.6214945Z     del arg7_1
2023-01-11T21:41:23.6215042Z     return (buf0, )
2023-01-11T21:41:23.6215049Z 
2023-01-11T21:41:23.6215054Z 
2023-01-11T21:41:23.6215162Z if __name__ == "__main__":
2023-01-11T21:41:23.6215316Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6215494Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6215821Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6216119Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6216406Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6216680Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6216955Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6217248Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6217509Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6217822Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6218048Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6218056Z 
2023-01-11T21:41:23.6218063Z 
2023-01-11T21:41:23.6218205Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6218308Z import torch
2023-01-11T21:41:23.6218414Z import random
2023-01-11T21:41:23.6218582Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6218768Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6218776Z 
2023-01-11T21:41:23.6218871Z aten = torch.ops.aten
2023-01-11T21:41:23.6219080Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6219209Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6219216Z 
2023-01-11T21:41:23.6219228Z 
2023-01-11T21:41:23.6219370Z async_compile.wait(globals())
2023-01-11T21:41:23.6219474Z del async_compile
2023-01-11T21:41:23.6219481Z 
2023-01-11T21:41:23.6219581Z def call(args):
2023-01-11T21:41:23.6219751Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6219846Z     args.clear()
2023-01-11T21:41:23.6220034Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:23.6220191Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:23.6220294Z     del arg0_1
2023-01-11T21:41:23.6220387Z     del arg1_1
2023-01-11T21:41:23.6220477Z     del arg7_1
2023-01-11T21:41:23.6220585Z     return (buf0, )
2023-01-11T21:41:23.6220591Z 
2023-01-11T21:41:23.6220597Z 
2023-01-11T21:41:23.6220708Z if __name__ == "__main__":
2023-01-11T21:41:23.6220862Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6221054Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6221470Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6221765Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6222052Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6222475Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6222772Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6223050Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6223291Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6223599Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6223833Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6223843Z 
2023-01-11T21:41:23.6223855Z 
2023-01-11T21:41:23.6224092Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6224203Z import torch
2023-01-11T21:41:23.6224304Z import random
2023-01-11T21:41:23.6224476Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6224655Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6224662Z 
2023-01-11T21:41:23.6224764Z aten = torch.ops.aten
2023-01-11T21:41:23.6224967Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6225103Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6225110Z 
2023-01-11T21:41:23.6225116Z 
2023-01-11T21:41:23.6225251Z async_compile.wait(globals())
2023-01-11T21:41:23.6225356Z del async_compile
2023-01-11T21:41:23.6225362Z 
2023-01-11T21:41:23.6225466Z def call(args):
2023-01-11T21:41:23.6225639Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6225726Z     args.clear()
2023-01-11T21:41:23.6225882Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:23.6226037Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:23.6226130Z     del arg0_1
2023-01-11T21:41:23.6226223Z     del arg1_1
2023-01-11T21:41:23.6226317Z     del arg7_1
2023-01-11T21:41:23.6226421Z     return (buf0, )
2023-01-11T21:41:23.6226427Z 
2023-01-11T21:41:23.6226432Z 
2023-01-11T21:41:23.6226540Z if __name__ == "__main__":
2023-01-11T21:41:23.6226690Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6226871Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6227196Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6227492Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6227771Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6228039Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6228295Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6228567Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6228806Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6229099Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6229317Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6229326Z 
2023-01-11T21:41:23.6229333Z 
2023-01-11T21:41:23.6229461Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6229557Z import torch
2023-01-11T21:41:23.6229657Z import random
2023-01-11T21:41:23.6229810Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6229976Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6229982Z 
2023-01-11T21:41:23.6230085Z aten = torch.ops.aten
2023-01-11T21:41:23.6230374Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6230499Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6230507Z 
2023-01-11T21:41:23.6230512Z 
2023-01-11T21:41:23.6230638Z async_compile.wait(globals())
2023-01-11T21:41:23.6230741Z del async_compile
2023-01-11T21:41:23.6230747Z 
2023-01-11T21:41:23.6230845Z def call(args):
2023-01-11T21:41:23.6231002Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6231102Z     args.clear()
2023-01-11T21:41:23.6231269Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:23.6231418Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:23.6231510Z     del arg0_1
2023-01-11T21:41:23.6231603Z     del arg1_1
2023-01-11T21:41:23.6231699Z     del arg7_1
2023-01-11T21:41:23.6231800Z     return (buf0, )
2023-01-11T21:41:23.6231807Z 
2023-01-11T21:41:23.6231813Z 
2023-01-11T21:41:23.6231919Z if __name__ == "__main__":
2023-01-11T21:41:23.6232150Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6232311Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6232626Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6232903Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6233180Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6233446Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6233811Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6234094Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6234346Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6234634Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6234863Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6234871Z 
2023-01-11T21:41:23.6234877Z 
2023-01-11T21:41:23.6235010Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6235103Z import torch
2023-01-11T21:41:23.6235199Z import random
2023-01-11T21:41:23.6235364Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6235535Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6235542Z 
2023-01-11T21:41:23.6235652Z aten = torch.ops.aten
2023-01-11T21:41:23.6235831Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6235953Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6235961Z 
2023-01-11T21:41:23.6235966Z 
2023-01-11T21:41:23.6236091Z async_compile.wait(globals())
2023-01-11T21:41:23.6236191Z del async_compile
2023-01-11T21:41:23.6236197Z 
2023-01-11T21:41:23.6236293Z def call(args):
2023-01-11T21:41:23.6236459Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6236560Z     args.clear()
2023-01-11T21:41:23.6236742Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:23.6236870Z     assert_size_stride(buf0, (1, 32, 110), (3520, 110, 1))
2023-01-11T21:41:23.6236963Z     del arg0_1
2023-01-11T21:41:23.6237055Z     del arg1_1
2023-01-11T21:41:23.6237149Z     del arg7_1
2023-01-11T21:41:23.6237247Z     return (buf0, )
2023-01-11T21:41:23.6237254Z 
2023-01-11T21:41:23.6237260Z 
2023-01-11T21:41:23.6237366Z if __name__ == "__main__":
2023-01-11T21:41:23.6237521Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6237676Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6237986Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6238276Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6238545Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6238912Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6239181Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6239451Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6239704Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6239983Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6240205Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6240213Z 
2023-01-11T21:41:23.6240612Z [2023-01-11 21:25:36,672] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 74
2023-01-11T21:41:23.6241309Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6241505Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6241896Z [2023-01-11 21:25:36,783] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 75
2023-01-11T21:41:23.6242288Z [2023-01-11 21:25:36,806] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 75
2023-01-11T21:41:23.6242929Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6243125Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6243511Z [2023-01-11 21:25:36,916] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 76
2023-01-11T21:41:23.6243912Z [2023-01-11 21:25:36,938] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 76
2023-01-11T21:41:23.6244528Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6244692Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6245062Z [2023-01-11 21:25:37,050] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 77
2023-01-11T21:41:23.6245459Z [2023-01-11 21:25:37,072] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 77
2023-01-11T21:41:23.6246098Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6246268Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6246647Z [2023-01-11 21:25:37,182] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 78
2023-01-11T21:41:23.6246992Z [2023-01-11 21:25:37,204] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 78
2023-01-11T21:41:23.6247612Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6247874Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6248240Z [2023-01-11 21:25:37,315] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 79
2023-01-11T21:41:23.6248248Z 
2023-01-11T21:41:23.6248387Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6248471Z import torch
2023-01-11T21:41:23.6248573Z import random
2023-01-11T21:41:23.6248737Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6248910Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6248918Z 
2023-01-11T21:41:23.6249026Z aten = torch.ops.aten
2023-01-11T21:41:23.6249225Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6249416Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6249424Z 
2023-01-11T21:41:23.6249430Z 
2023-01-11T21:41:23.6249557Z async_compile.wait(globals())
2023-01-11T21:41:23.6249646Z del async_compile
2023-01-11T21:41:23.6249654Z 
2023-01-11T21:41:23.6249743Z def call(args):
2023-01-11T21:41:23.6249916Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6250021Z     args.clear()
2023-01-11T21:41:23.6250212Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:23.6250354Z     assert_size_stride(buf0, (1, 128, 110), (14080, 110, 1))
2023-01-11T21:41:23.6250450Z     del arg0_1
2023-01-11T21:41:23.6250526Z     del arg1_1
2023-01-11T21:41:23.6250623Z     del arg7_1
2023-01-11T21:41:23.6250717Z     return (buf0, )
2023-01-11T21:41:23.6250724Z 
2023-01-11T21:41:23.6250729Z 
2023-01-11T21:41:23.6250837Z if __name__ == "__main__":
2023-01-11T21:41:23.6250997Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6251171Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6251495Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6251789Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6252065Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6252350Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6252639Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6252923Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6253181Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6253487Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6253713Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6253726Z 
2023-01-11T21:41:23.6253734Z 
2023-01-11T21:41:23.6253872Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6253968Z import torch
2023-01-11T21:41:23.6254047Z import random
2023-01-11T21:41:23.6254218Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6254383Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6254389Z 
2023-01-11T21:41:23.6254500Z aten = torch.ops.aten
2023-01-11T21:41:23.6254691Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6254820Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6254827Z 
2023-01-11T21:41:23.6254832Z 
2023-01-11T21:41:23.6254949Z async_compile.wait(globals())
2023-01-11T21:41:23.6255036Z del async_compile
2023-01-11T21:41:23.6255057Z 
2023-01-11T21:41:23.6255147Z def call(args):
2023-01-11T21:41:23.6255315Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6255497Z     args.clear()
2023-01-11T21:41:23.6255684Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:23.6255835Z     assert_size_stride(buf0, (1, 32, 108), (3456, 108, 1))
2023-01-11T21:41:23.6255927Z     del arg0_1
2023-01-11T21:41:23.6256010Z     del arg1_1
2023-01-11T21:41:23.6256087Z     del arg7_1
2023-01-11T21:41:23.6256196Z     return (buf0, )
2023-01-11T21:41:23.6256204Z 
2023-01-11T21:41:23.6256210Z 
2023-01-11T21:41:23.6256325Z if __name__ == "__main__":
2023-01-11T21:41:23.6256489Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6256658Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6256970Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6257249Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6257530Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6257852Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6258102Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6258346Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6258581Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6258856Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6259057Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6259064Z 
2023-01-11T21:41:23.6259071Z 
2023-01-11T21:41:23.6259189Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6259275Z import torch
2023-01-11T21:41:23.6259351Z import random
2023-01-11T21:41:23.6259494Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6259646Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6259657Z 
2023-01-11T21:41:23.6259768Z aten = torch.ops.aten
2023-01-11T21:41:23.6259980Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6260119Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6260125Z 
2023-01-11T21:41:23.6260132Z 
2023-01-11T21:41:23.6260248Z async_compile.wait(globals())
2023-01-11T21:41:23.6260342Z del async_compile
2023-01-11T21:41:23.6260348Z 
2023-01-11T21:41:23.6260426Z def call(args):
2023-01-11T21:41:23.6260588Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6260689Z     args.clear()
2023-01-11T21:41:23.6260881Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:23.6261030Z     assert_size_stride(buf0, (1, 128, 108), (13824, 108, 1))
2023-01-11T21:41:23.6261123Z     del arg0_1
2023-01-11T21:41:23.6261217Z     del arg1_1
2023-01-11T21:41:23.6261300Z     del arg7_1
2023-01-11T21:41:23.6261402Z     return (buf0, )
2023-01-11T21:41:23.6261414Z 
2023-01-11T21:41:23.6261424Z 
2023-01-11T21:41:23.6261529Z if __name__ == "__main__":
2023-01-11T21:41:23.6261686Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6261857Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6262192Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6262673Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6262988Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6263289Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6263613Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6263956Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6264277Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6264780Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6265071Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6265080Z 
2023-01-11T21:41:23.6265087Z 
2023-01-11T21:41:23.6265251Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6265356Z import torch
2023-01-11T21:41:23.6265440Z import random
2023-01-11T21:41:23.6265596Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6265782Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6265789Z 
2023-01-11T21:41:23.6265922Z aten = torch.ops.aten
2023-01-11T21:41:23.6266171Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6266318Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6266326Z 
2023-01-11T21:41:23.6266331Z 
2023-01-11T21:41:23.6266492Z async_compile.wait(globals())
2023-01-11T21:41:23.6266619Z del async_compile
2023-01-11T21:41:23.6266630Z 
2023-01-11T21:41:23.6266807Z def call(args):
2023-01-11T21:41:23.6266996Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6267117Z     args.clear()
2023-01-11T21:41:23.6267353Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:23.6267533Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:23.6267650Z     del arg0_1
2023-01-11T21:41:23.6267761Z     del arg1_1
2023-01-11T21:41:23.6267861Z     del arg7_1
2023-01-11T21:41:23.6267979Z     return (buf0, )
2023-01-11T21:41:23.6267988Z 
2023-01-11T21:41:23.6267999Z 
2023-01-11T21:41:23.6268129Z if __name__ == "__main__":
2023-01-11T21:41:23.6268308Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6268532Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6268907Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6269267Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6269612Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6269939Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6270287Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6270627Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6270937Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6271325Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6271595Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6271604Z 
2023-01-11T21:41:23.6271609Z 
2023-01-11T21:41:23.6271785Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6271908Z import torch
2023-01-11T21:41:23.6272018Z import random
2023-01-11T21:41:23.6272245Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6272471Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6272479Z 
2023-01-11T21:41:23.6272616Z aten = torch.ops.aten
2023-01-11T21:41:23.6272864Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6273030Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6273039Z 
2023-01-11T21:41:23.6273046Z 
2023-01-11T21:41:23.6273212Z async_compile.wait(globals())
2023-01-11T21:41:23.6273339Z del async_compile
2023-01-11T21:41:23.6273347Z 
2023-01-11T21:41:23.6273468Z def call(args):
2023-01-11T21:41:23.6273668Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6273856Z     args.clear()
2023-01-11T21:41:23.6274104Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:23.6274290Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:23.6274489Z     del arg0_1
2023-01-11T21:41:23.6274613Z     del arg1_1
2023-01-11T21:41:23.6274732Z     del arg7_1
2023-01-11T21:41:23.6274842Z     return (buf0, )
2023-01-11T21:41:23.6274850Z 
2023-01-11T21:41:23.6274857Z 
2023-01-11T21:41:23.6274993Z if __name__ == "__main__":
2023-01-11T21:41:23.6275185Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6275403Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6275782Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6276136Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6276483Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6276824Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6277172Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6277591Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6277932Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6278330Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6278629Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6278638Z 
2023-01-11T21:41:23.6279157Z [2023-01-11 21:25:37,337] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 79
2023-01-11T21:41:23.6280079Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6280301Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6280798Z [2023-01-11 21:25:37,446] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 80
2023-01-11T21:41:23.6281285Z [2023-01-11 21:25:37,468] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 80
2023-01-11T21:41:23.6281975Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6282170Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6282615Z [2023-01-11 21:25:37,579] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 81
2023-01-11T21:41:23.6283038Z [2023-01-11 21:25:37,601] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 81
2023-01-11T21:41:23.6283858Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6284089Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6284572Z [2023-01-11 21:25:37,711] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 82
2023-01-11T21:41:23.6285056Z [2023-01-11 21:25:37,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 82
2023-01-11T21:41:23.6285958Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6286234Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6286638Z [2023-01-11 21:25:37,843] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 83
2023-01-11T21:41:23.6287124Z [2023-01-11 21:25:37,864] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 83
2023-01-11T21:41:23.6287719Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6287944Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6288315Z [2023-01-11 21:25:37,976] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 84
2023-01-11T21:41:23.6288323Z 
2023-01-11T21:41:23.6288455Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6288548Z import torch
2023-01-11T21:41:23.6288642Z import random
2023-01-11T21:41:23.6288797Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6288964Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6288971Z 
2023-01-11T21:41:23.6289075Z aten = torch.ops.aten
2023-01-11T21:41:23.6289248Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6289380Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6289387Z 
2023-01-11T21:41:23.6289392Z 
2023-01-11T21:41:23.6289516Z async_compile.wait(globals())
2023-01-11T21:41:23.6289620Z del async_compile
2023-01-11T21:41:23.6289627Z 
2023-01-11T21:41:23.6289734Z def call(args):
2023-01-11T21:41:23.6289939Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6290039Z     args.clear()
2023-01-11T21:41:23.6290211Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:23.6290357Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:23.6290451Z     del arg0_1
2023-01-11T21:41:23.6290541Z     del arg1_1
2023-01-11T21:41:23.6290629Z     del arg7_1
2023-01-11T21:41:23.6290726Z     return (buf0, )
2023-01-11T21:41:23.6290734Z 
2023-01-11T21:41:23.6290740Z 
2023-01-11T21:41:23.6290852Z if __name__ == "__main__":
2023-01-11T21:41:23.6291051Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6291215Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6291603Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6291944Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6292272Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6292605Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6292909Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6293234Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6293546Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6293901Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6294185Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6294194Z 
2023-01-11T21:41:23.6294203Z 
2023-01-11T21:41:23.6294355Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6294461Z import torch
2023-01-11T21:41:23.6294576Z import random
2023-01-11T21:41:23.6294752Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6295036Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6295044Z 
2023-01-11T21:41:23.6295167Z aten = torch.ops.aten
2023-01-11T21:41:23.6295372Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6295513Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6295520Z 
2023-01-11T21:41:23.6295527Z 
2023-01-11T21:41:23.6295668Z async_compile.wait(globals())
2023-01-11T21:41:23.6295781Z del async_compile
2023-01-11T21:41:23.6295788Z 
2023-01-11T21:41:23.6295905Z def call(args):
2023-01-11T21:41:23.6296091Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6296201Z     args.clear()
2023-01-11T21:41:23.6296404Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:23.6296562Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:23.6296652Z     del arg0_1
2023-01-11T21:41:23.6296754Z     del arg1_1
2023-01-11T21:41:23.6296862Z     del arg7_1
2023-01-11T21:41:23.6297039Z     return (buf0, )
2023-01-11T21:41:23.6297050Z 
2023-01-11T21:41:23.6297057Z 
2023-01-11T21:41:23.6297186Z if __name__ == "__main__":
2023-01-11T21:41:23.6297345Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6297500Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6297820Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6298153Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6298489Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6298818Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6299149Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6299460Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6299773Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6300147Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6300435Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6300442Z 
2023-01-11T21:41:23.6300447Z 
2023-01-11T21:41:23.6300603Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6300716Z import torch
2023-01-11T21:41:23.6300838Z import random
2023-01-11T21:41:23.6301036Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6301260Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6301268Z 
2023-01-11T21:41:23.6301401Z aten = torch.ops.aten
2023-01-11T21:41:23.6301636Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6301797Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6301805Z 
2023-01-11T21:41:23.6301812Z 
2023-01-11T21:41:23.6301971Z async_compile.wait(globals())
2023-01-11T21:41:23.6302106Z del async_compile
2023-01-11T21:41:23.6302114Z 
2023-01-11T21:41:23.6302237Z def call(args):
2023-01-11T21:41:23.6302567Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6302692Z     args.clear()
2023-01-11T21:41:23.6302927Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:23.6303091Z     assert_size_stride(buf0, (1, 32, 110), (3520, 110, 1))
2023-01-11T21:41:23.6303212Z     del arg0_1
2023-01-11T21:41:23.6303313Z     del arg1_1
2023-01-11T21:41:23.6303421Z     del arg7_1
2023-01-11T21:41:23.6303537Z     return (buf0, )
2023-01-11T21:41:23.6303543Z 
2023-01-11T21:41:23.6303548Z 
2023-01-11T21:41:23.6303657Z if __name__ == "__main__":
2023-01-11T21:41:23.6303852Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6304077Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6304477Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6304952Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6305309Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6305634Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6305942Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6306285Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6306614Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6306985Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6307268Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6307277Z 
2023-01-11T21:41:23.6307284Z 
2023-01-11T21:41:23.6307448Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6307643Z import torch
2023-01-11T21:41:23.6307772Z import random
2023-01-11T21:41:23.6307996Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6308236Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6308245Z 
2023-01-11T21:41:23.6308383Z aten = torch.ops.aten
2023-01-11T21:41:23.6308628Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6308794Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6308802Z 
2023-01-11T21:41:23.6308810Z 
2023-01-11T21:41:23.6308971Z async_compile.wait(globals())
2023-01-11T21:41:23.6309100Z del async_compile
2023-01-11T21:41:23.6309107Z 
2023-01-11T21:41:23.6309230Z def call(args):
2023-01-11T21:41:23.6309439Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6309567Z     args.clear()
2023-01-11T21:41:23.6309810Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:23.6309990Z     assert_size_stride(buf0, (1, 128, 110), (14080, 110, 1))
2023-01-11T21:41:23.6310104Z     del arg0_1
2023-01-11T21:41:23.6310219Z     del arg1_1
2023-01-11T21:41:23.6310330Z     del arg7_1
2023-01-11T21:41:23.6310451Z     return (buf0, )
2023-01-11T21:41:23.6310457Z 
2023-01-11T21:41:23.6310463Z 
2023-01-11T21:41:23.6310582Z if __name__ == "__main__":
2023-01-11T21:41:23.6310784Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6310993Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6311353Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6311720Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6312078Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6312439Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6312768Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6313121Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6313446Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6313907Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6314198Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6314207Z 
2023-01-11T21:41:23.6314229Z 
2023-01-11T21:41:23.6314381Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6314507Z import torch
2023-01-11T21:41:23.6314633Z import random
2023-01-11T21:41:23.6314836Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6315067Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6315073Z 
2023-01-11T21:41:23.6315188Z aten = torch.ops.aten
2023-01-11T21:41:23.6315399Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6315613Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6315622Z 
2023-01-11T21:41:23.6315631Z 
2023-01-11T21:41:23.6315791Z async_compile.wait(globals())
2023-01-11T21:41:23.6315906Z del async_compile
2023-01-11T21:41:23.6315915Z 
2023-01-11T21:41:23.6316043Z def call(args):
2023-01-11T21:41:23.6316215Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6316326Z     args.clear()
2023-01-11T21:41:23.6316553Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:23.6316740Z     assert_size_stride(buf0, (1, 32, 108), (3456, 108, 1))
2023-01-11T21:41:23.6316847Z     del arg0_1
2023-01-11T21:41:23.6316955Z     del arg1_1
2023-01-11T21:41:23.6317071Z     del arg7_1
2023-01-11T21:41:23.6317187Z     return (buf0, )
2023-01-11T21:41:23.6317194Z 
2023-01-11T21:41:23.6317200Z 
2023-01-11T21:41:23.6317333Z if __name__ == "__main__":
2023-01-11T21:41:23.6317545Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6317816Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6318176Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6318536Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6318898Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6319223Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6319566Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6319904Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6320239Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6320622Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6320902Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6320930Z 
2023-01-11T21:41:23.6321414Z [2023-01-11 21:25:37,997] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 84
2023-01-11T21:41:23.6322312Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6322537Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6323027Z [2023-01-11 21:25:38,103] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 85
2023-01-11T21:41:23.6323538Z [2023-01-11 21:25:39,706] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 85
2023-01-11T21:41:23.6324440Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6324664Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6325156Z [2023-01-11 21:25:39,857] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 86
2023-01-11T21:41:23.6325644Z [2023-01-11 21:25:39,879] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 86
2023-01-11T21:41:23.6326553Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6326842Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6327305Z [2023-01-11 21:25:39,996] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 87
2023-01-11T21:41:23.6327316Z 
2023-01-11T21:41:23.6327477Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6327601Z import torch
2023-01-11T21:41:23.6327727Z import random
2023-01-11T21:41:23.6327947Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6328182Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6328191Z 
2023-01-11T21:41:23.6328323Z aten = torch.ops.aten
2023-01-11T21:41:23.6328582Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6328726Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6328747Z 
2023-01-11T21:41:23.6328754Z 
2023-01-11T21:41:23.6328892Z async_compile.wait(globals())
2023-01-11T21:41:23.6329065Z del async_compile
2023-01-11T21:41:23.6329075Z 
2023-01-11T21:41:23.6329199Z def call(args):
2023-01-11T21:41:23.6329413Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6329531Z     args.clear()
2023-01-11T21:41:23.6329777Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:23.6329967Z     assert_size_stride(buf0, (1, 128, 108), (13824, 108, 1))
2023-01-11T21:41:23.6330071Z     del arg0_1
2023-01-11T21:41:23.6330187Z     del arg1_1
2023-01-11T21:41:23.6330303Z     del arg7_1
2023-01-11T21:41:23.6330421Z     return (buf0, )
2023-01-11T21:41:23.6330429Z 
2023-01-11T21:41:23.6330437Z 
2023-01-11T21:41:23.6330563Z if __name__ == "__main__":
2023-01-11T21:41:23.6330763Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6330999Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6331385Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6331744Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6332093Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6332447Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6332792Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6333313Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6333648Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6334013Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6334302Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6334310Z 
2023-01-11T21:41:23.6334315Z 
2023-01-11T21:41:23.6334471Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6334588Z import torch
2023-01-11T21:41:23.6334711Z import random
2023-01-11T21:41:23.6334906Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6335127Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6335136Z 
2023-01-11T21:41:23.6335278Z aten = torch.ops.aten
2023-01-11T21:41:23.6335511Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6335660Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6335681Z 
2023-01-11T21:41:23.6335689Z 
2023-01-11T21:41:23.6335928Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6336340Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6336559Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6336722Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6336824Z {
2023-01-11T21:41:23.6336992Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6337196Z     {
2023-01-11T21:41:23.6337335Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6337480Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6337586Z         {
2023-01-11T21:41:23.6337745Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6337853Z             {
2023-01-11T21:41:23.6337953Z                 {
2023-01-11T21:41:23.6338067Z                     {
2023-01-11T21:41:23.6338233Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6338392Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6338498Z                     }
2023-01-11T21:41:23.6338604Z                 }
2023-01-11T21:41:23.6338713Z             }
2023-01-11T21:41:23.6338812Z         }
2023-01-11T21:41:23.6338916Z     }
2023-01-11T21:41:23.6339007Z }
2023-01-11T21:41:23.6339138Z ''')
2023-01-11T21:41:23.6339146Z 
2023-01-11T21:41:23.6339152Z 
2023-01-11T21:41:23.6339387Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6339861Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6340074Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6340233Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6340321Z {
2023-01-11T21:41:23.6340454Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6340542Z     {
2023-01-11T21:41:23.6340653Z         #pragma omp for 
2023-01-11T21:41:23.6340774Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6340862Z         {
2023-01-11T21:41:23.6340976Z             #pragma GCC ivdep
2023-01-11T21:41:23.6341097Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6341174Z             {
2023-01-11T21:41:23.6341270Z                 {
2023-01-11T21:41:23.6341364Z                     {
2023-01-11T21:41:23.6341507Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6341648Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6341748Z                     }
2023-01-11T21:41:23.6341835Z                 }
2023-01-11T21:41:23.6341903Z             }
2023-01-11T21:41:23.6341987Z         }
2023-01-11T21:41:23.6342077Z     }
2023-01-11T21:41:23.6342158Z }
2023-01-11T21:41:23.6342267Z ''')
2023-01-11T21:41:23.6342275Z 
2023-01-11T21:41:23.6342280Z 
2023-01-11T21:41:23.6342538Z async_compile.wait(globals())
2023-01-11T21:41:23.6342633Z del async_compile
2023-01-11T21:41:23.6342654Z 
2023-01-11T21:41:23.6342735Z def call(args):
2023-01-11T21:41:23.6342905Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6343006Z     args.clear()
2023-01-11T21:41:23.6343344Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6343576Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6343676Z     del arg7_1
2023-01-11T21:41:23.6344084Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6344239Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6344342Z     del arg0_1
2023-01-11T21:41:23.6344449Z     del arg1_1
2023-01-11T21:41:23.6344545Z     del buf0
2023-01-11T21:41:23.6344912Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6345126Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6345250Z     return (buf2, )
2023-01-11T21:41:23.6345257Z 
2023-01-11T21:41:23.6345264Z 
2023-01-11T21:41:23.6345394Z if __name__ == "__main__":
2023-01-11T21:41:23.6345555Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6345741Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6346057Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6346358Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6346764Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6347078Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6347385Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6347673Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6347935Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6348306Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6348554Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6348562Z 
2023-01-11T21:41:23.6348571Z 
2023-01-11T21:41:23.6348719Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6348826Z import torch
2023-01-11T21:41:23.6348939Z import random
2023-01-11T21:41:23.6349204Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6349399Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6349406Z 
2023-01-11T21:41:23.6349511Z aten = torch.ops.aten
2023-01-11T21:41:23.6349721Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6349856Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6349863Z 
2023-01-11T21:41:23.6349870Z 
2023-01-11T21:41:23.6350007Z async_compile.wait(globals())
2023-01-11T21:41:23.6350108Z del async_compile
2023-01-11T21:41:23.6350115Z 
2023-01-11T21:41:23.6350224Z def call(args):
2023-01-11T21:41:23.6350415Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6350538Z     args.clear()
2023-01-11T21:41:23.6350987Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6351158Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6351271Z     del arg0_1
2023-01-11T21:41:23.6351371Z     del arg1_1
2023-01-11T21:41:23.6351472Z     del arg7_1
2023-01-11T21:41:23.6351575Z     return (buf0, )
2023-01-11T21:41:23.6351583Z 
2023-01-11T21:41:23.6351590Z 
2023-01-11T21:41:23.6351704Z if __name__ == "__main__":
2023-01-11T21:41:23.6351862Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6352053Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6352385Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6352734Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6353087Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6353428Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6353830Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6354178Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6354478Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6354871Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6355150Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6355159Z 
2023-01-11T21:41:23.6355654Z [2023-01-11 21:25:41,625] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 87
2023-01-11T21:41:23.6356510Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6356836Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6357315Z [2023-01-11 21:25:41,774] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 88
2023-01-11T21:41:23.6357742Z [2023-01-11 21:25:41,797] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 88
2023-01-11T21:41:23.6358356Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6358536Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6358905Z [2023-01-11 21:25:41,937] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 89
2023-01-11T21:41:23.6358918Z 
2023-01-11T21:41:23.6359109Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6359191Z import torch
2023-01-11T21:41:23.6359284Z import random
2023-01-11T21:41:23.6359441Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6359607Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6359613Z 
2023-01-11T21:41:23.6359718Z aten = torch.ops.aten
2023-01-11T21:41:23.6359945Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6360080Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6360087Z 
2023-01-11T21:41:23.6360093Z 
2023-01-11T21:41:23.6360279Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6360651Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6360867Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6361049Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6361152Z {
2023-01-11T21:41:23.6361348Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6361450Z     {
2023-01-11T21:41:23.6361573Z         #pragma omp for 
2023-01-11T21:41:23.6361698Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6361801Z         {
2023-01-11T21:41:23.6361944Z             #pragma GCC ivdep
2023-01-11T21:41:23.6362091Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6362203Z             {
2023-01-11T21:41:23.6362307Z                 {
2023-01-11T21:41:23.6362398Z                     {
2023-01-11T21:41:23.6362581Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6362742Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6362858Z                     }
2023-01-11T21:41:23.6362973Z                 }
2023-01-11T21:41:23.6363082Z             }
2023-01-11T21:41:23.6363192Z         }
2023-01-11T21:41:23.6363285Z     }
2023-01-11T21:41:23.6363395Z }
2023-01-11T21:41:23.6363532Z ''')
2023-01-11T21:41:23.6363540Z 
2023-01-11T21:41:23.6363553Z 
2023-01-11T21:41:23.6363808Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6364222Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6364446Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6364622Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6364712Z {
2023-01-11T21:41:23.6364874Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6364972Z     {
2023-01-11T21:41:23.6365106Z         #pragma omp for 
2023-01-11T21:41:23.6365252Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6365360Z         {
2023-01-11T21:41:23.6365506Z             #pragma GCC ivdep
2023-01-11T21:41:23.6365641Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6365747Z             {
2023-01-11T21:41:23.6365852Z                 {
2023-01-11T21:41:23.6365951Z                     {
2023-01-11T21:41:23.6366116Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6366401Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6366517Z                     }
2023-01-11T21:41:23.6366614Z                 }
2023-01-11T21:41:23.6366721Z             }
2023-01-11T21:41:23.6366813Z         }
2023-01-11T21:41:23.6366894Z     }
2023-01-11T21:41:23.6366978Z }
2023-01-11T21:41:23.6367090Z ''')
2023-01-11T21:41:23.6367097Z 
2023-01-11T21:41:23.6367103Z 
2023-01-11T21:41:23.6367217Z async_compile.wait(globals())
2023-01-11T21:41:23.6367299Z del async_compile
2023-01-11T21:41:23.6367307Z 
2023-01-11T21:41:23.6367402Z def call(args):
2023-01-11T21:41:23.6367566Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6367663Z     args.clear()
2023-01-11T21:41:23.6367989Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6368178Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6368276Z     del arg7_1
2023-01-11T21:41:23.6368702Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6368870Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6368957Z     del arg0_1
2023-01-11T21:41:23.6369047Z     del arg1_1
2023-01-11T21:41:23.6369128Z     del buf0
2023-01-11T21:41:23.6369460Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6369651Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6369749Z     return (buf2, )
2023-01-11T21:41:23.6369756Z 
2023-01-11T21:41:23.6369762Z 
2023-01-11T21:41:23.6369860Z if __name__ == "__main__":
2023-01-11T21:41:23.6370029Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6370191Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6370507Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6370795Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6371078Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6371343Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6371616Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6371881Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6372143Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6372463Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6372683Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6372690Z 
2023-01-11T21:41:23.6372695Z 
2023-01-11T21:41:23.6372833Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6372928Z import torch
2023-01-11T21:41:23.6373028Z import random
2023-01-11T21:41:23.6373188Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6373339Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6373359Z 
2023-01-11T21:41:23.6373454Z aten = torch.ops.aten
2023-01-11T21:41:23.6373635Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6373763Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6373769Z 
2023-01-11T21:41:23.6373775Z 
2023-01-11T21:41:23.6373890Z async_compile.wait(globals())
2023-01-11T21:41:23.6373981Z del async_compile
2023-01-11T21:41:23.6373987Z 
2023-01-11T21:41:23.6374075Z def call(args):
2023-01-11T21:41:23.6374229Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6374307Z     args.clear()
2023-01-11T21:41:23.6374677Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6374920Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6375015Z     del arg0_1
2023-01-11T21:41:23.6375105Z     del arg1_1
2023-01-11T21:41:23.6375196Z     del arg7_1
2023-01-11T21:41:23.6375294Z     return (buf0, )
2023-01-11T21:41:23.6375301Z 
2023-01-11T21:41:23.6375306Z 
2023-01-11T21:41:23.6375407Z if __name__ == "__main__":
2023-01-11T21:41:23.6375557Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6375730Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6376041Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6376332Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6376616Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6376898Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6377256Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6377533Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6377762Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6378070Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6378277Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6378284Z 
2023-01-11T21:41:23.6378658Z [2023-01-11 21:25:41,970] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 89
2023-01-11T21:41:23.6379298Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6379475Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6379864Z [2023-01-11 21:25:42,081] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 90
2023-01-11T21:41:23.6380260Z [2023-01-11 21:25:42,103] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 90
2023-01-11T21:41:23.6380897Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6381072Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6381445Z [2023-01-11 21:25:42,227] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 91
2023-01-11T21:41:23.6381809Z [2023-01-11 21:25:42,260] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 91
2023-01-11T21:41:23.6382538Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6382716Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6383109Z [2023-01-11 21:25:42,411] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 92
2023-01-11T21:41:23.6383117Z 
2023-01-11T21:41:23.6383257Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6383358Z import torch
2023-01-11T21:41:23.6383563Z import random
2023-01-11T21:41:23.6383728Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6383893Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6383899Z 
2023-01-11T21:41:23.6383991Z aten = torch.ops.aten
2023-01-11T21:41:23.6384173Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6384297Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6384303Z 
2023-01-11T21:41:23.6384308Z 
2023-01-11T21:41:23.6384506Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6384791Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6384962Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6385103Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6385192Z {
2023-01-11T21:41:23.6385328Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6385417Z     {
2023-01-11T21:41:23.6385550Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6385758Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6385844Z         {
2023-01-11T21:41:23.6385966Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6386054Z             {
2023-01-11T21:41:23.6386124Z                 {
2023-01-11T21:41:23.6386214Z                     {
2023-01-11T21:41:23.6386364Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6386493Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6386582Z                     }
2023-01-11T21:41:23.6386672Z                 }
2023-01-11T21:41:23.6386745Z             }
2023-01-11T21:41:23.6386835Z         }
2023-01-11T21:41:23.6386925Z     }
2023-01-11T21:41:23.6387013Z }
2023-01-11T21:41:23.6387133Z ''')
2023-01-11T21:41:23.6387141Z 
2023-01-11T21:41:23.6387147Z 
2023-01-11T21:41:23.6387341Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6387645Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6387824Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6387953Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6388037Z {
2023-01-11T21:41:23.6388180Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6388267Z     {
2023-01-11T21:41:23.6388372Z         #pragma omp for 
2023-01-11T21:41:23.6388484Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6388555Z         {
2023-01-11T21:41:23.6388665Z             #pragma GCC ivdep
2023-01-11T21:41:23.6388789Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6388872Z             {
2023-01-11T21:41:23.6388960Z                 {
2023-01-11T21:41:23.6389050Z                     {
2023-01-11T21:41:23.6389187Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6389304Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6389399Z                     }
2023-01-11T21:41:23.6389488Z                 }
2023-01-11T21:41:23.6389574Z             }
2023-01-11T21:41:23.6389656Z         }
2023-01-11T21:41:23.6389737Z     }
2023-01-11T21:41:23.6389817Z }
2023-01-11T21:41:23.6389925Z ''')
2023-01-11T21:41:23.6389933Z 
2023-01-11T21:41:23.6389939Z 
2023-01-11T21:41:23.6390067Z async_compile.wait(globals())
2023-01-11T21:41:23.6390170Z del async_compile
2023-01-11T21:41:23.6390177Z 
2023-01-11T21:41:23.6390269Z def call(args):
2023-01-11T21:41:23.6390434Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6390532Z     args.clear()
2023-01-11T21:41:23.6390851Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6391040Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6391123Z     del arg7_1
2023-01-11T21:41:23.6391497Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6391661Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6391832Z     del arg0_1
2023-01-11T21:41:23.6391920Z     del arg1_1
2023-01-11T21:41:23.6392010Z     del buf0
2023-01-11T21:41:23.6392335Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6392509Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6392614Z     return (buf2, )
2023-01-11T21:41:23.6392622Z 
2023-01-11T21:41:23.6392628Z 
2023-01-11T21:41:23.6392740Z if __name__ == "__main__":
2023-01-11T21:41:23.6392905Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6393081Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6393371Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6393637Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6394054Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6394325Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6394603Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6394882Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6395144Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6395475Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6395706Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6395715Z 
2023-01-11T21:41:23.6395721Z 
2023-01-11T21:41:23.6395856Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6395958Z import torch
2023-01-11T21:41:23.6396050Z import random
2023-01-11T21:41:23.6396227Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6396420Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6396427Z 
2023-01-11T21:41:23.6396542Z aten = torch.ops.aten
2023-01-11T21:41:23.6396746Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6396886Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6396894Z 
2023-01-11T21:41:23.6396900Z 
2023-01-11T21:41:23.6397036Z async_compile.wait(globals())
2023-01-11T21:41:23.6397136Z del async_compile
2023-01-11T21:41:23.6397143Z 
2023-01-11T21:41:23.6397237Z def call(args):
2023-01-11T21:41:23.6397411Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6397515Z     args.clear()
2023-01-11T21:41:23.6397904Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6398066Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6398162Z     del arg0_1
2023-01-11T21:41:23.6398264Z     del arg1_1
2023-01-11T21:41:23.6398355Z     del arg7_1
2023-01-11T21:41:23.6398440Z     return (buf0, )
2023-01-11T21:41:23.6398447Z 
2023-01-11T21:41:23.6398454Z 
2023-01-11T21:41:23.6398559Z if __name__ == "__main__":
2023-01-11T21:41:23.6398727Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6398909Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6399223Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6399509Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6399773Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6400045Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6400314Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6400580Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6400948Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6401255Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6401475Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6401483Z 
2023-01-11T21:41:23.6401489Z 
2023-01-11T21:41:23.6401626Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6401724Z import torch
2023-01-11T21:41:23.6401821Z import random
2023-01-11T21:41:23.6401969Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6402143Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6402150Z 
2023-01-11T21:41:23.6402268Z aten = torch.ops.aten
2023-01-11T21:41:23.6402451Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6402575Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6402583Z 
2023-01-11T21:41:23.6402593Z 
2023-01-11T21:41:23.6402857Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6403143Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6403299Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6403413Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6403492Z {
2023-01-11T21:41:23.6403622Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6403704Z     {
2023-01-11T21:41:23.6403807Z         #pragma omp for 
2023-01-11T21:41:23.6403916Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6403996Z         {
2023-01-11T21:41:23.6404087Z             #pragma GCC ivdep
2023-01-11T21:41:23.6404203Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6404290Z             {
2023-01-11T21:41:23.6404376Z                 {
2023-01-11T21:41:23.6404461Z                     {
2023-01-11T21:41:23.6404600Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6404723Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6404817Z                     }
2023-01-11T21:41:23.6404906Z                 }
2023-01-11T21:41:23.6404996Z             }
2023-01-11T21:41:23.6405087Z         }
2023-01-11T21:41:23.6405173Z     }
2023-01-11T21:41:23.6405259Z }
2023-01-11T21:41:23.6405365Z ''')
2023-01-11T21:41:23.6405372Z 
2023-01-11T21:41:23.6405393Z 
2023-01-11T21:41:23.6405581Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6405878Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6406047Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6406183Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6406270Z {
2023-01-11T21:41:23.6406407Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6406492Z     {
2023-01-11T21:41:23.6406584Z         #pragma omp for 
2023-01-11T21:41:23.6406695Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6406793Z         {
2023-01-11T21:41:23.6406908Z             #pragma GCC ivdep
2023-01-11T21:41:23.6407038Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6407128Z             {
2023-01-11T21:41:23.6407206Z                 {
2023-01-11T21:41:23.6407297Z                     {
2023-01-11T21:41:23.6407445Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6407584Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6407679Z                     }
2023-01-11T21:41:23.6407773Z                 }
2023-01-11T21:41:23.6407865Z             }
2023-01-11T21:41:23.6407941Z         }
2023-01-11T21:41:23.6408024Z     }
2023-01-11T21:41:23.6408110Z }
2023-01-11T21:41:23.6408237Z ''')
2023-01-11T21:41:23.6408244Z 
2023-01-11T21:41:23.6408250Z 
2023-01-11T21:41:23.6408378Z async_compile.wait(globals())
2023-01-11T21:41:23.6408484Z del async_compile
2023-01-11T21:41:23.6408492Z 
2023-01-11T21:41:23.6408589Z def call(args):
2023-01-11T21:41:23.6408841Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6408943Z     args.clear()
2023-01-11T21:41:23.6409296Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6409476Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6409564Z     del arg7_1
2023-01-11T21:41:23.6409944Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6410098Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6410194Z     del arg0_1
2023-01-11T21:41:23.6410267Z     del arg1_1
2023-01-11T21:41:23.6410355Z     del buf0
2023-01-11T21:41:23.6410689Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6410879Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6411058Z     return (buf2, )
2023-01-11T21:41:23.6411068Z 
2023-01-11T21:41:23.6411074Z 
2023-01-11T21:41:23.6411186Z if __name__ == "__main__":
2023-01-11T21:41:23.6411349Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6411529Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6411826Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6412107Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6412379Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6412642Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6412897Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6413152Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6413404Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6413717Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6413915Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6413923Z 
2023-01-11T21:41:23.6414296Z [2023-01-11 21:25:42,435] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 92
2023-01-11T21:41:23.6414947Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6415132Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6415506Z [2023-01-11 21:25:42,591] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 93
2023-01-11T21:41:23.6415890Z [2023-01-11 21:25:44,158] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 93
2023-01-11T21:41:23.6416459Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6416625Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6416986Z [2023-01-11 21:25:44,269] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 94
2023-01-11T21:41:23.6417345Z [2023-01-11 21:25:44,291] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 94
2023-01-11T21:41:23.6418014Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6418182Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6418518Z [2023-01-11 21:25:44,408] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 95
2023-01-11T21:41:23.6418540Z 
2023-01-11T21:41:23.6418650Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6418747Z import torch
2023-01-11T21:41:23.6418845Z import random
2023-01-11T21:41:23.6418993Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6419151Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6419159Z 
2023-01-11T21:41:23.6419257Z aten = torch.ops.aten
2023-01-11T21:41:23.6419505Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6419613Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6419619Z 
2023-01-11T21:41:23.6419624Z 
2023-01-11T21:41:23.6419737Z async_compile.wait(globals())
2023-01-11T21:41:23.6419830Z del async_compile
2023-01-11T21:41:23.6419837Z 
2023-01-11T21:41:23.6419924Z def call(args):
2023-01-11T21:41:23.6420078Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6420177Z     args.clear()
2023-01-11T21:41:23.6420573Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6420731Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6420812Z     del arg0_1
2023-01-11T21:41:23.6420907Z     del arg1_1
2023-01-11T21:41:23.6421005Z     del arg7_1
2023-01-11T21:41:23.6421108Z     return (buf0, )
2023-01-11T21:41:23.6421115Z 
2023-01-11T21:41:23.6421125Z 
2023-01-11T21:41:23.6421233Z if __name__ == "__main__":
2023-01-11T21:41:23.6421391Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6421564Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6421883Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6422168Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6422612Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6422905Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6423180Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6423458Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6423709Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6424031Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6424245Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6424267Z 
2023-01-11T21:41:23.6424274Z 
2023-01-11T21:41:23.6424396Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6424490Z import torch
2023-01-11T21:41:23.6424597Z import random
2023-01-11T21:41:23.6424760Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6424933Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6424940Z 
2023-01-11T21:41:23.6425051Z aten = torch.ops.aten
2023-01-11T21:41:23.6425245Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6425359Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6425379Z 
2023-01-11T21:41:23.6425385Z 
2023-01-11T21:41:23.6425577Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6425858Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6426138Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6426272Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6426356Z {
2023-01-11T21:41:23.6426491Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6426576Z     {
2023-01-11T21:41:23.6426683Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6426793Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6426875Z         {
2023-01-11T21:41:23.6426995Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6427083Z             {
2023-01-11T21:41:23.6427171Z                 {
2023-01-11T21:41:23.6427261Z                     {
2023-01-11T21:41:23.6427391Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6427518Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6427607Z                     }
2023-01-11T21:41:23.6427694Z                 }
2023-01-11T21:41:23.6427858Z             }
2023-01-11T21:41:23.6427945Z         }
2023-01-11T21:41:23.6428010Z     }
2023-01-11T21:41:23.6428088Z }
2023-01-11T21:41:23.6428205Z ''')
2023-01-11T21:41:23.6428213Z 
2023-01-11T21:41:23.6428218Z 
2023-01-11T21:41:23.6428420Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6428734Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6428903Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6429043Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6429124Z {
2023-01-11T21:41:23.6429241Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6429322Z     {
2023-01-11T21:41:23.6429426Z         #pragma omp for 
2023-01-11T21:41:23.6429537Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6429620Z         {
2023-01-11T21:41:23.6429730Z             #pragma GCC ivdep
2023-01-11T21:41:23.6429849Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:23.6429935Z             {
2023-01-11T21:41:23.6430021Z                 {
2023-01-11T21:41:23.6430110Z                     {
2023-01-11T21:41:23.6430252Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6430383Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:23.6430474Z                     }
2023-01-11T21:41:23.6430553Z                 }
2023-01-11T21:41:23.6430636Z             }
2023-01-11T21:41:23.6430728Z         }
2023-01-11T21:41:23.6430811Z     }
2023-01-11T21:41:23.6430892Z }
2023-01-11T21:41:23.6431015Z ''')
2023-01-11T21:41:23.6431024Z 
2023-01-11T21:41:23.6431030Z 
2023-01-11T21:41:23.6431164Z async_compile.wait(globals())
2023-01-11T21:41:23.6431252Z del async_compile
2023-01-11T21:41:23.6431273Z 
2023-01-11T21:41:23.6431356Z def call(args):
2023-01-11T21:41:23.6431522Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6431622Z     args.clear()
2023-01-11T21:41:23.6431946Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6432133Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6432225Z     del arg7_1
2023-01-11T21:41:23.6432581Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6432719Z     assert_size_stride(buf1, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:23.6432809Z     del arg0_1
2023-01-11T21:41:23.6432897Z     del arg1_1
2023-01-11T21:41:23.6432988Z     del buf0
2023-01-11T21:41:23.6433314Z     buf2 = empty_strided((1, 32, 110, 110), (387200, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6433493Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6433594Z     return (buf2, )
2023-01-11T21:41:23.6433601Z 
2023-01-11T21:41:23.6433606Z 
2023-01-11T21:41:23.6433703Z if __name__ == "__main__":
2023-01-11T21:41:23.6434003Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6434167Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6434477Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6434747Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6435009Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6435266Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6435523Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6435787Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6436056Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6436384Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6436681Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6436691Z 
2023-01-11T21:41:23.6436698Z 
2023-01-11T21:41:23.6436836Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6436937Z import torch
2023-01-11T21:41:23.6437039Z import random
2023-01-11T21:41:23.6437197Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6437362Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6437368Z 
2023-01-11T21:41:23.6437456Z aten = torch.ops.aten
2023-01-11T21:41:23.6437639Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6437760Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6437767Z 
2023-01-11T21:41:23.6437772Z 
2023-01-11T21:41:23.6437888Z async_compile.wait(globals())
2023-01-11T21:41:23.6437983Z del async_compile
2023-01-11T21:41:23.6437990Z 
2023-01-11T21:41:23.6438082Z def call(args):
2023-01-11T21:41:23.6438251Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6438351Z     args.clear()
2023-01-11T21:41:23.6438724Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6438874Z     assert_size_stride(buf0, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:23.6438970Z     del arg0_1
2023-01-11T21:41:23.6439060Z     del arg1_1
2023-01-11T21:41:23.6439146Z     del arg7_1
2023-01-11T21:41:23.6439239Z     return (buf0, )
2023-01-11T21:41:23.6439247Z 
2023-01-11T21:41:23.6439253Z 
2023-01-11T21:41:23.6439352Z if __name__ == "__main__":
2023-01-11T21:41:23.6439498Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6439678Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6439987Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6440262Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6440543Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6440817Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6441092Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6441375Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6441617Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6441922Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6442136Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6442144Z 
2023-01-11T21:41:23.6442533Z [2023-01-11 21:25:46,088] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 95
2023-01-11T21:41:23.6443203Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6443478Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6443877Z [2023-01-11 21:25:46,239] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 96
2023-01-11T21:41:23.6444273Z [2023-01-11 21:25:46,262] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 96
2023-01-11T21:41:23.6445010Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6445261Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6445670Z [2023-01-11 21:25:46,402] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 97
2023-01-11T21:41:23.6445679Z 
2023-01-11T21:41:23.6445850Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6445939Z import torch
2023-01-11T21:41:23.6446079Z import random
2023-01-11T21:41:23.6446296Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6446712Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6446720Z 
2023-01-11T21:41:23.6446869Z aten = torch.ops.aten
2023-01-11T21:41:23.6447048Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6447219Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6447227Z 
2023-01-11T21:41:23.6447233Z 
2023-01-11T21:41:23.6447475Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6447809Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6448023Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6448203Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6448335Z {
2023-01-11T21:41:23.6448586Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6448660Z     {
2023-01-11T21:41:23.6448820Z         #pragma omp for 
2023-01-11T21:41:23.6448973Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6449098Z         {
2023-01-11T21:41:23.6449248Z             #pragma GCC ivdep
2023-01-11T21:41:23.6449411Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6449482Z             {
2023-01-11T21:41:23.6449610Z                 {
2023-01-11T21:41:23.6449784Z                     {
2023-01-11T21:41:23.6450018Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6450192Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6450332Z                     }
2023-01-11T21:41:23.6450456Z                 }
2023-01-11T21:41:23.6450528Z             }
2023-01-11T21:41:23.6450649Z         }
2023-01-11T21:41:23.6450769Z     }
2023-01-11T21:41:23.6450887Z }
2023-01-11T21:41:23.6451059Z ''')
2023-01-11T21:41:23.6451067Z 
2023-01-11T21:41:23.6451073Z 
2023-01-11T21:41:23.6451353Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6451699Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6451904Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6452024Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6452153Z {
2023-01-11T21:41:23.6452336Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6452458Z     {
2023-01-11T21:41:23.6452613Z         #pragma omp for 
2023-01-11T21:41:23.6452807Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6452883Z         {
2023-01-11T21:41:23.6453080Z             #pragma GCC ivdep
2023-01-11T21:41:23.6453318Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:23.6453450Z             {
2023-01-11T21:41:23.6453572Z                 {
2023-01-11T21:41:23.6453704Z                     {
2023-01-11T21:41:23.6453897Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6454024Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:23.6454154Z                     }
2023-01-11T21:41:23.6454284Z                 }
2023-01-11T21:41:23.6454452Z             }
2023-01-11T21:41:23.6454577Z         }
2023-01-11T21:41:23.6454699Z     }
2023-01-11T21:41:23.6454819Z }
2023-01-11T21:41:23.6454936Z ''')
2023-01-11T21:41:23.6454945Z 
2023-01-11T21:41:23.6454951Z 
2023-01-11T21:41:23.6455131Z async_compile.wait(globals())
2023-01-11T21:41:23.6455272Z del async_compile
2023-01-11T21:41:23.6455279Z 
2023-01-11T21:41:23.6455454Z def call(args):
2023-01-11T21:41:23.6455659Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6455901Z     args.clear()
2023-01-11T21:41:23.6456290Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6456470Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6456613Z     del arg7_1
2023-01-11T21:41:23.6457029Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6457230Z     assert_size_stride(buf1, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:23.6457364Z     del arg0_1
2023-01-11T21:41:23.6457503Z     del arg1_1
2023-01-11T21:41:23.6457633Z     del buf0
2023-01-11T21:41:23.6458063Z     buf2 = empty_strided((1, 128, 110, 110), (1548800, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6458254Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6458405Z     return (buf2, )
2023-01-11T21:41:23.6458420Z 
2023-01-11T21:41:23.6458431Z 
2023-01-11T21:41:23.6458584Z if __name__ == "__main__":
2023-01-11T21:41:23.6458802Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6459034Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6459451Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6459787Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6460097Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6460361Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6460726Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6461035Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6461326Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6461718Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6461983Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6461992Z 
2023-01-11T21:41:23.6461998Z 
2023-01-11T21:41:23.6462164Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6462301Z import torch
2023-01-11T21:41:23.6462534Z import random
2023-01-11T21:41:23.6462757Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6463023Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6463031Z 
2023-01-11T21:41:23.6463177Z aten = torch.ops.aten
2023-01-11T21:41:23.6463407Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6463571Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6463578Z 
2023-01-11T21:41:23.6463584Z 
2023-01-11T21:41:23.6463780Z async_compile.wait(globals())
2023-01-11T21:41:23.6463917Z del async_compile
2023-01-11T21:41:23.6464029Z 
2023-01-11T21:41:23.6464117Z def call(args):
2023-01-11T21:41:23.6464335Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6464470Z     args.clear()
2023-01-11T21:41:23.6464940Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6465129Z     assert_size_stride(buf0, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:23.6465253Z     del arg0_1
2023-01-11T21:41:23.6465381Z     del arg1_1
2023-01-11T21:41:23.6465459Z     del arg7_1
2023-01-11T21:41:23.6465594Z     return (buf0, )
2023-01-11T21:41:23.6465601Z 
2023-01-11T21:41:23.6465608Z 
2023-01-11T21:41:23.6465760Z if __name__ == "__main__":
2023-01-11T21:41:23.6465959Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6466164Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6466576Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6467004Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6467323Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6467582Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6467947Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6468249Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6468541Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6468886Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6469134Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6469142Z 
2023-01-11T21:41:23.6469623Z [2023-01-11 21:25:48,026] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 97
2023-01-11T21:41:23.6470271Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6470490Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6470909Z [2023-01-11 21:25:48,140] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 98
2023-01-11T21:41:23.6471268Z [2023-01-11 21:25:48,163] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 98
2023-01-11T21:41:23.6471925Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6472142Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6472564Z [2023-01-11 21:25:48,283] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 99
2023-01-11T21:41:23.6472992Z [2023-01-11 21:25:49,900] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 99
2023-01-11T21:41:23.6473714Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6474024Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6474524Z [2023-01-11 21:25:50,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 100
2023-01-11T21:41:23.6474534Z 
2023-01-11T21:41:23.6474701Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6474833Z import torch
2023-01-11T21:41:23.6474960Z import random
2023-01-11T21:41:23.6475114Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6475325Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6475334Z 
2023-01-11T21:41:23.6475478Z aten = torch.ops.aten
2023-01-11T21:41:23.6475767Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6475936Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6475943Z 
2023-01-11T21:41:23.6475949Z 
2023-01-11T21:41:23.6476190Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6476507Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6476786Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6476910Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6477031Z {
2023-01-11T21:41:23.6477210Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6477333Z     {
2023-01-11T21:41:23.6477532Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6477716Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6477840Z         {
2023-01-11T21:41:23.6477942Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6478066Z             {
2023-01-11T21:41:23.6478187Z                 {
2023-01-11T21:41:23.6478325Z                     {
2023-01-11T21:41:23.6478501Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6478663Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6478823Z                     }
2023-01-11T21:41:23.6478899Z                 }
2023-01-11T21:41:23.6479024Z             }
2023-01-11T21:41:23.6479147Z         }
2023-01-11T21:41:23.6479276Z     }
2023-01-11T21:41:23.6479412Z }
2023-01-11T21:41:23.6479568Z ''')
2023-01-11T21:41:23.6479576Z 
2023-01-11T21:41:23.6479582Z 
2023-01-11T21:41:23.6479807Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6480078Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6480273Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6480522Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6480638Z {
2023-01-11T21:41:23.6480809Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6480938Z     {
2023-01-11T21:41:23.6481076Z         #pragma omp for 
2023-01-11T21:41:23.6481174Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6481298Z         {
2023-01-11T21:41:23.6481441Z             #pragma GCC ivdep
2023-01-11T21:41:23.6481593Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:23.6481743Z             {
2023-01-11T21:41:23.6481860Z                 {
2023-01-11T21:41:23.6481944Z                     {
2023-01-11T21:41:23.6482126Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6482291Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:23.6482418Z                     }
2023-01-11T21:41:23.6482539Z                 }
2023-01-11T21:41:23.6482658Z             }
2023-01-11T21:41:23.6482778Z         }
2023-01-11T21:41:23.6482845Z     }
2023-01-11T21:41:23.6483031Z }
2023-01-11T21:41:23.6483196Z ''')
2023-01-11T21:41:23.6483203Z 
2023-01-11T21:41:23.6483209Z 
2023-01-11T21:41:23.6483364Z async_compile.wait(globals())
2023-01-11T21:41:23.6483498Z del async_compile
2023-01-11T21:41:23.6483505Z 
2023-01-11T21:41:23.6483635Z def call(args):
2023-01-11T21:41:23.6483835Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6483920Z     args.clear()
2023-01-11T21:41:23.6484267Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6484555Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6484730Z     del arg7_1
2023-01-11T21:41:23.6485125Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6485318Z     assert_size_stride(buf1, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:23.6485448Z     del arg0_1
2023-01-11T21:41:23.6485574Z     del arg1_1
2023-01-11T21:41:23.6485645Z     del buf0
2023-01-11T21:41:23.6486001Z     buf2 = empty_strided((1, 32, 108, 108), (373248, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6486214Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6486391Z     return (buf2, )
2023-01-11T21:41:23.6486399Z 
2023-01-11T21:41:23.6486405Z 
2023-01-11T21:41:23.6486575Z if __name__ == "__main__":
2023-01-11T21:41:23.6486765Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6487040Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6487374Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6487635Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6487940Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6488250Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6488559Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6488853Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6489190Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6489526Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6489815Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6489829Z 
2023-01-11T21:41:23.6489835Z 
2023-01-11T21:41:23.6490007Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6490093Z import torch
2023-01-11T21:41:23.6490239Z import random
2023-01-11T21:41:23.6490445Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6490701Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6490709Z 
2023-01-11T21:41:23.6490868Z aten = torch.ops.aten
2023-01-11T21:41:23.6491153Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6491317Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6491325Z 
2023-01-11T21:41:23.6491331Z 
2023-01-11T21:41:23.6491438Z async_compile.wait(globals())
2023-01-11T21:41:23.6491570Z del async_compile
2023-01-11T21:41:23.6491577Z 
2023-01-11T21:41:23.6491713Z def call(args):
2023-01-11T21:41:23.6491913Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6591092Z     args.clear()
2023-01-11T21:41:23.6591718Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6591879Z     assert_size_stride(buf0, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:23.6591972Z     del arg0_1
2023-01-11T21:41:23.6592058Z     del arg1_1
2023-01-11T21:41:23.6592134Z     del arg7_1
2023-01-11T21:41:23.6592227Z     return (buf0, )
2023-01-11T21:41:23.6592235Z 
2023-01-11T21:41:23.6592241Z 
2023-01-11T21:41:23.6592337Z if __name__ == "__main__":
2023-01-11T21:41:23.6592492Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6592657Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6592948Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6593249Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6593527Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6594131Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6594395Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6594667Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6594924Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6595242Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6595464Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6595474Z 
2023-01-11T21:41:23.6595481Z 
2023-01-11T21:41:23.6595617Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6595716Z import torch
2023-01-11T21:41:23.6595808Z import random
2023-01-11T21:41:23.6595951Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6596216Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6596228Z 
2023-01-11T21:41:23.6596368Z aten = torch.ops.aten
2023-01-11T21:41:23.6596586Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6596699Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6596706Z 
2023-01-11T21:41:23.6596713Z 
2023-01-11T21:41:23.6596937Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6597232Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6597400Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6597553Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6597635Z {
2023-01-11T21:41:23.6597769Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6597855Z     {
2023-01-11T21:41:23.6597964Z         #pragma omp for 
2023-01-11T21:41:23.6598071Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6598144Z         {
2023-01-11T21:41:23.6598254Z             #pragma GCC ivdep
2023-01-11T21:41:23.6598378Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6598466Z             {
2023-01-11T21:41:23.6598577Z                 {
2023-01-11T21:41:23.6598661Z                     {
2023-01-11T21:41:23.6598793Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6598924Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6599011Z                     }
2023-01-11T21:41:23.6599095Z                 }
2023-01-11T21:41:23.6599175Z             }
2023-01-11T21:41:23.6599251Z         }
2023-01-11T21:41:23.6599323Z     }
2023-01-11T21:41:23.6599397Z }
2023-01-11T21:41:23.6599507Z ''')
2023-01-11T21:41:23.6599514Z 
2023-01-11T21:41:23.6599519Z 
2023-01-11T21:41:23.6599696Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6599983Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6600155Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6600299Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6600374Z {
2023-01-11T21:41:23.6600512Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6600588Z     {
2023-01-11T21:41:23.6600688Z         #pragma omp for 
2023-01-11T21:41:23.6600819Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6600896Z         {
2023-01-11T21:41:23.6600993Z             #pragma GCC ivdep
2023-01-11T21:41:23.6601100Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:23.6601177Z             {
2023-01-11T21:41:23.6601248Z                 {
2023-01-11T21:41:23.6601329Z                     {
2023-01-11T21:41:23.6601458Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6601580Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:23.6601661Z                     }
2023-01-11T21:41:23.6601743Z                 }
2023-01-11T21:41:23.6601822Z             }
2023-01-11T21:41:23.6601890Z         }
2023-01-11T21:41:23.6602052Z     }
2023-01-11T21:41:23.6602129Z }
2023-01-11T21:41:23.6602240Z ''')
2023-01-11T21:41:23.6602247Z 
2023-01-11T21:41:23.6602252Z 
2023-01-11T21:41:23.6602367Z async_compile.wait(globals())
2023-01-11T21:41:23.6602454Z del async_compile
2023-01-11T21:41:23.6602460Z 
2023-01-11T21:41:23.6602556Z def call(args):
2023-01-11T21:41:23.6602719Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6602819Z     args.clear()
2023-01-11T21:41:23.6603143Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6603319Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6603404Z     del arg7_1
2023-01-11T21:41:23.6603739Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6603890Z     assert_size_stride(buf1, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:23.6603977Z     del arg0_1
2023-01-11T21:41:23.6604108Z     del arg1_1
2023-01-11T21:41:23.6604189Z     del buf0
2023-01-11T21:41:23.6604503Z     buf2 = empty_strided((1, 128, 108, 108), (1492992, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6604671Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6604760Z     return (buf2, )
2023-01-11T21:41:23.6604767Z 
2023-01-11T21:41:23.6604773Z 
2023-01-11T21:41:23.6604867Z if __name__ == "__main__":
2023-01-11T21:41:23.6605019Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6605173Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6605486Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6605783Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6606040Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6606306Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6606562Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6606820Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6607062Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6607352Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6607557Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6607564Z 
2023-01-11T21:41:23.6607925Z [2023-01-11 21:25:50,056] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 100
2023-01-11T21:41:23.6608525Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6608713Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6609069Z [2023-01-11 21:25:50,182] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 101
2023-01-11T21:41:23.6609429Z [2023-01-11 21:25:50,214] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 101
2023-01-11T21:41:23.6610004Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6610177Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6610602Z [2023-01-11 21:25:50,325] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 102
2023-01-11T21:41:23.6610958Z [2023-01-11 21:25:50,348] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 102
2023-01-11T21:41:23.6611531Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6611691Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6612027Z [2023-01-11 21:25:50,466] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 103
2023-01-11T21:41:23.6612034Z 
2023-01-11T21:41:23.6612172Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6612346Z import torch
2023-01-11T21:41:23.6612444Z import random
2023-01-11T21:41:23.6612590Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6612750Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6612757Z 
2023-01-11T21:41:23.6612856Z aten = torch.ops.aten
2023-01-11T21:41:23.6613029Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6613133Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6613139Z 
2023-01-11T21:41:23.6613144Z 
2023-01-11T21:41:23.6613253Z async_compile.wait(globals())
2023-01-11T21:41:23.6613348Z del async_compile
2023-01-11T21:41:23.6613354Z 
2023-01-11T21:41:23.6613440Z def call(args):
2023-01-11T21:41:23.6613598Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6613690Z     args.clear()
2023-01-11T21:41:23.6614036Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6614191Z     assert_size_stride(buf0, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:23.6614266Z     del arg0_1
2023-01-11T21:41:23.6614347Z     del arg1_1
2023-01-11T21:41:23.6614433Z     del arg7_1
2023-01-11T21:41:23.6614520Z     return (buf0, )
2023-01-11T21:41:23.6614526Z 
2023-01-11T21:41:23.6614531Z 
2023-01-11T21:41:23.6614626Z if __name__ == "__main__":
2023-01-11T21:41:23.6614771Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6614929Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6615200Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6615461Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6615725Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6615977Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6616250Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6616498Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6616733Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6617027Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6617217Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6617231Z 
2023-01-11T21:41:23.6617237Z 
2023-01-11T21:41:23.6617345Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6617435Z import torch
2023-01-11T21:41:23.6617523Z import random
2023-01-11T21:41:23.6617672Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6617825Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6617831Z 
2023-01-11T21:41:23.6617926Z aten = torch.ops.aten
2023-01-11T21:41:23.6618177Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6618286Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6618293Z 
2023-01-11T21:41:23.6618309Z 
2023-01-11T21:41:23.6618478Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6618747Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6618900Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6619028Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6619105Z {
2023-01-11T21:41:23.6639978Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6640127Z     {
2023-01-11T21:41:23.6640252Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6640348Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6640422Z         {
2023-01-11T21:41:23.6640527Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6640598Z             {
2023-01-11T21:41:23.6640673Z                 {
2023-01-11T21:41:23.6640863Z                     {
2023-01-11T21:41:23.6640995Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6641107Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6641182Z                     }
2023-01-11T21:41:23.6641254Z                 }
2023-01-11T21:41:23.6641323Z             }
2023-01-11T21:41:23.6641394Z         }
2023-01-11T21:41:23.6641457Z     }
2023-01-11T21:41:23.6641522Z }
2023-01-11T21:41:23.6641650Z ''')
2023-01-11T21:41:23.6641659Z 
2023-01-11T21:41:23.6641664Z 
2023-01-11T21:41:23.6641849Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6642121Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6642267Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6642386Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6642453Z {
2023-01-11T21:41:23.6642565Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6642639Z     {
2023-01-11T21:41:23.6642728Z         #pragma omp for 
2023-01-11T21:41:23.6642824Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6642893Z         {
2023-01-11T21:41:23.6642984Z             #pragma GCC ivdep
2023-01-11T21:41:23.6643082Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6643155Z             {
2023-01-11T21:41:23.6643230Z                 {
2023-01-11T21:41:23.6643304Z                     {
2023-01-11T21:41:23.6643425Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6643539Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6643612Z                     }
2023-01-11T21:41:23.6643680Z                 }
2023-01-11T21:41:23.6643751Z             }
2023-01-11T21:41:23.6643822Z         }
2023-01-11T21:41:23.6643890Z     }
2023-01-11T21:41:23.6643956Z }
2023-01-11T21:41:23.6644052Z ''')
2023-01-11T21:41:23.6644060Z 
2023-01-11T21:41:23.6644065Z 
2023-01-11T21:41:23.6644176Z async_compile.wait(globals())
2023-01-11T21:41:23.6644251Z del async_compile
2023-01-11T21:41:23.6644257Z 
2023-01-11T21:41:23.6644336Z def call(args):
2023-01-11T21:41:23.6644480Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6644560Z     args.clear()
2023-01-11T21:41:23.6644854Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6645016Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6645092Z     del arg7_1
2023-01-11T21:41:23.6645425Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6645552Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6645627Z     del arg0_1
2023-01-11T21:41:23.6645700Z     del arg1_1
2023-01-11T21:41:23.6645773Z     del buf0
2023-01-11T21:41:23.6646064Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6646322Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6646405Z     return (buf2, )
2023-01-11T21:41:23.6646412Z 
2023-01-11T21:41:23.6646417Z 
2023-01-11T21:41:23.6646503Z if __name__ == "__main__":
2023-01-11T21:41:23.6646636Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6646783Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6647056Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6647301Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6647542Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6647775Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6648009Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6648328Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6648563Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6648850Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6649042Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6649048Z 
2023-01-11T21:41:23.6649054Z 
2023-01-11T21:41:23.6649164Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6649244Z import torch
2023-01-11T21:41:23.6649323Z import random
2023-01-11T21:41:23.6649461Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6649602Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6649614Z 
2023-01-11T21:41:23.6649695Z aten = torch.ops.aten
2023-01-11T21:41:23.6649858Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6649974Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6649980Z 
2023-01-11T21:41:23.6649986Z 
2023-01-11T21:41:23.6650090Z async_compile.wait(globals())
2023-01-11T21:41:23.6650173Z del async_compile
2023-01-11T21:41:23.6650180Z 
2023-01-11T21:41:23.6650258Z def call(args):
2023-01-11T21:41:23.6650397Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6650470Z     args.clear()
2023-01-11T21:41:23.6650797Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6650928Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6651009Z     del arg0_1
2023-01-11T21:41:23.6651085Z     del arg1_1
2023-01-11T21:41:23.6651158Z     del arg7_1
2023-01-11T21:41:23.6651238Z     return (buf0, )
2023-01-11T21:41:23.6651244Z 
2023-01-11T21:41:23.6651249Z 
2023-01-11T21:41:23.6651332Z if __name__ == "__main__":
2023-01-11T21:41:23.6651469Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6651619Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6651887Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6652133Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6652372Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6652601Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6652842Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6653078Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6653302Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6653586Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6653785Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6653862Z 
2023-01-11T21:41:23.6654221Z [2023-01-11 21:25:50,498] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 103
2023-01-11T21:41:23.6654787Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6654940Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6655276Z [2023-01-11 21:25:50,634] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 104
2023-01-11T21:41:23.6655623Z [2023-01-11 21:25:50,657] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 104
2023-01-11T21:41:23.6656258Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6656417Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6656754Z [2023-01-11 21:25:50,784] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 105
2023-01-11T21:41:23.6656762Z 
2023-01-11T21:41:23.6656866Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6656945Z import torch
2023-01-11T21:41:23.6657023Z import random
2023-01-11T21:41:23.6657160Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6657302Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6657308Z 
2023-01-11T21:41:23.6657405Z aten = torch.ops.aten
2023-01-11T21:41:23.6657564Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6657670Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6657677Z 
2023-01-11T21:41:23.6657682Z 
2023-01-11T21:41:23.6657846Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6658108Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6658255Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6658371Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6658441Z {
2023-01-11T21:41:23.6658556Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6658626Z     {
2023-01-11T21:41:23.6658708Z         #pragma omp for 
2023-01-11T21:41:23.6658802Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6658874Z         {
2023-01-11T21:41:23.6658965Z             #pragma GCC ivdep
2023-01-11T21:41:23.6659068Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6659145Z             {
2023-01-11T21:41:23.6659217Z                 {
2023-01-11T21:41:23.6659285Z                     {
2023-01-11T21:41:23.6659406Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6659516Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6659590Z                     }
2023-01-11T21:41:23.6659668Z                 }
2023-01-11T21:41:23.6659742Z             }
2023-01-11T21:41:23.6659810Z         }
2023-01-11T21:41:23.6659873Z     }
2023-01-11T21:41:23.6659943Z }
2023-01-11T21:41:23.6660043Z ''')
2023-01-11T21:41:23.6660050Z 
2023-01-11T21:41:23.6660056Z 
2023-01-11T21:41:23.6660219Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6660478Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6660621Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6660740Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6660865Z {
2023-01-11T21:41:23.6660980Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6661051Z     {
2023-01-11T21:41:23.6661138Z         #pragma omp for 
2023-01-11T21:41:23.6661230Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6661298Z         {
2023-01-11T21:41:23.6661388Z             #pragma GCC ivdep
2023-01-11T21:41:23.6661487Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6661563Z             {
2023-01-11T21:41:23.6661648Z                 {
2023-01-11T21:41:23.6661731Z                     {
2023-01-11T21:41:23.6661846Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6661970Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6662055Z                     }
2023-01-11T21:41:23.6662135Z                 }
2023-01-11T21:41:23.6662212Z             }
2023-01-11T21:41:23.6662285Z         }
2023-01-11T21:41:23.6662593Z     }
2023-01-11T21:41:23.6662659Z }
2023-01-11T21:41:23.6662778Z ''')
2023-01-11T21:41:23.6662866Z 
2023-01-11T21:41:23.6662873Z 
2023-01-11T21:41:23.6662990Z async_compile.wait(globals())
2023-01-11T21:41:23.6663083Z del async_compile
2023-01-11T21:41:23.6663089Z 
2023-01-11T21:41:23.6663175Z def call(args):
2023-01-11T21:41:23.6663325Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6663412Z     args.clear()
2023-01-11T21:41:23.6663701Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6663871Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6663956Z     del arg7_1
2023-01-11T21:41:23.6664288Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6664427Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6664515Z     del arg0_1
2023-01-11T21:41:23.6664597Z     del arg1_1
2023-01-11T21:41:23.6664683Z     del buf0
2023-01-11T21:41:23.6664975Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6665144Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6665234Z     return (buf2, )
2023-01-11T21:41:23.6665240Z 
2023-01-11T21:41:23.6665245Z 
2023-01-11T21:41:23.6665339Z if __name__ == "__main__":
2023-01-11T21:41:23.6665481Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6665641Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6665918Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6666171Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6666417Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6666665Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6666919Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6667166Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6667402Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6667700Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6667898Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6667906Z 
2023-01-11T21:41:23.6667910Z 
2023-01-11T21:41:23.6668028Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6668099Z import torch
2023-01-11T21:41:23.6668189Z import random
2023-01-11T21:41:23.6668331Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6668481Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6668487Z 
2023-01-11T21:41:23.6668581Z aten = torch.ops.aten
2023-01-11T21:41:23.6668842Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6668953Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6668959Z 
2023-01-11T21:41:23.6668964Z 
2023-01-11T21:41:23.6669075Z async_compile.wait(globals())
2023-01-11T21:41:23.6669152Z del async_compile
2023-01-11T21:41:23.6669158Z 
2023-01-11T21:41:23.6669242Z def call(args):
2023-01-11T21:41:23.6669391Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6669476Z     args.clear()
2023-01-11T21:41:23.6669812Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6669956Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6670039Z     del arg0_1
2023-01-11T21:41:23.6670118Z     del arg1_1
2023-01-11T21:41:23.6670188Z     del arg7_1
2023-01-11T21:41:23.6670276Z     return (buf0, )
2023-01-11T21:41:23.6670282Z 
2023-01-11T21:41:23.6670290Z 
2023-01-11T21:41:23.6670473Z if __name__ == "__main__":
2023-01-11T21:41:23.6670620Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6670775Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6671054Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6671307Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6671545Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6671794Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6672160Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6672513Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6672851Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6673233Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6673533Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6673542Z 
2023-01-11T21:41:23.6674133Z [2023-01-11 21:25:50,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 105
2023-01-11T21:41:23.6675018Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6675251Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6675747Z [2023-01-11 21:25:50,927] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 106
2023-01-11T21:41:23.6676263Z [2023-01-11 21:25:50,956] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 106
2023-01-11T21:41:23.6677143Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6677382Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6677878Z [2023-01-11 21:25:51,097] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 107
2023-01-11T21:41:23.6678416Z [2023-01-11 21:25:51,129] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 107
2023-01-11T21:41:23.6679294Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6679617Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6680123Z [2023-01-11 21:25:51,252] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 108
2023-01-11T21:41:23.6680134Z 
2023-01-11T21:41:23.6680319Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6680462Z import torch
2023-01-11T21:41:23.6680589Z import random
2023-01-11T21:41:23.6680812Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6681042Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6681051Z 
2023-01-11T21:41:23.6681191Z aten = torch.ops.aten
2023-01-11T21:41:23.6681443Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6681684Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6681693Z 
2023-01-11T21:41:23.6681700Z 
2023-01-11T21:41:23.6681962Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6682360Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6682580Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6682762Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6682881Z {
2023-01-11T21:41:23.6683055Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6683163Z     {
2023-01-11T21:41:23.6683310Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6683465Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6683558Z         {
2023-01-11T21:41:23.6683722Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6683830Z             {
2023-01-11T21:41:23.6683952Z                 {
2023-01-11T21:41:23.6684072Z                     {
2023-01-11T21:41:23.6684260Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6684417Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6684529Z                     }
2023-01-11T21:41:23.6684657Z                 }
2023-01-11T21:41:23.6684776Z             }
2023-01-11T21:41:23.6684896Z         }
2023-01-11T21:41:23.6685012Z     }
2023-01-11T21:41:23.6685124Z }
2023-01-11T21:41:23.6685261Z ''')
2023-01-11T21:41:23.6685270Z 
2023-01-11T21:41:23.6685290Z 
2023-01-11T21:41:23.6685530Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6685922Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6686156Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6686345Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6686464Z {
2023-01-11T21:41:23.6686655Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6686774Z     {
2023-01-11T21:41:23.6686909Z         #pragma omp for 
2023-01-11T21:41:23.6687058Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6687174Z         {
2023-01-11T21:41:23.6687329Z             #pragma GCC ivdep
2023-01-11T21:41:23.6687492Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6687619Z             {
2023-01-11T21:41:23.6687727Z                 {
2023-01-11T21:41:23.6687853Z                     {
2023-01-11T21:41:23.6688042Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6688218Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6688347Z                     }
2023-01-11T21:41:23.6688473Z                 }
2023-01-11T21:41:23.6688590Z             }
2023-01-11T21:41:23.6688665Z         }
2023-01-11T21:41:23.6688743Z     }
2023-01-11T21:41:23.6688824Z }
2023-01-11T21:41:23.6688936Z ''')
2023-01-11T21:41:23.6688945Z 
2023-01-11T21:41:23.6688951Z 
2023-01-11T21:41:23.6689074Z async_compile.wait(globals())
2023-01-11T21:41:23.6689177Z del async_compile
2023-01-11T21:41:23.6689261Z 
2023-01-11T21:41:23.6689365Z def call(args):
2023-01-11T21:41:23.6689500Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6689627Z     args.clear()
2023-01-11T21:41:23.6690053Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6690239Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6690372Z     del arg7_1
2023-01-11T21:41:23.6690849Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6691056Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6691191Z     del arg0_1
2023-01-11T21:41:23.6691313Z     del arg1_1
2023-01-11T21:41:23.6691441Z     del buf0
2023-01-11T21:41:23.6691867Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6692188Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6692336Z     return (buf2, )
2023-01-11T21:41:23.6692344Z 
2023-01-11T21:41:23.6692351Z 
2023-01-11T21:41:23.6692488Z if __name__ == "__main__":
2023-01-11T21:41:23.6692707Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6692944Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6693327Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6693694Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6694056Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6694398Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6694767Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6695100Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6695442Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6695858Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6696141Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6696150Z 
2023-01-11T21:41:23.6696170Z 
2023-01-11T21:41:23.6696346Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6696482Z import torch
2023-01-11T21:41:23.6696625Z import random
2023-01-11T21:41:23.6696852Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6697093Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6697101Z 
2023-01-11T21:41:23.6697256Z aten = torch.ops.aten
2023-01-11T21:41:23.6697520Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6697692Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6697700Z 
2023-01-11T21:41:23.6697713Z 
2023-01-11T21:41:23.6697896Z async_compile.wait(globals())
2023-01-11T21:41:23.6698042Z del async_compile
2023-01-11T21:41:23.6698050Z 
2023-01-11T21:41:23.6698191Z def call(args):
2023-01-11T21:41:23.6698411Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6698552Z     args.clear()
2023-01-11T21:41:23.6699037Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6699233Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:23.6699339Z     del arg0_1
2023-01-11T21:41:23.6699450Z     del arg1_1
2023-01-11T21:41:23.6699555Z     del arg7_1
2023-01-11T21:41:23.6699673Z     return (buf0, )
2023-01-11T21:41:23.6699680Z 
2023-01-11T21:41:23.6699686Z 
2023-01-11T21:41:23.6699833Z if __name__ == "__main__":
2023-01-11T21:41:23.6700056Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6700301Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6700758Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6701125Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6701497Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6701853Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6702185Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6702592Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6702839Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6703225Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6703516Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6703633Z 
2023-01-11T21:41:23.6703642Z 
2023-01-11T21:41:23.6703816Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6703956Z import torch
2023-01-11T21:41:23.6704104Z import random
2023-01-11T21:41:23.6704331Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6704571Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6704580Z 
2023-01-11T21:41:23.6704735Z aten = torch.ops.aten
2023-01-11T21:41:23.6704998Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6705168Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6705190Z 
2023-01-11T21:41:23.6705197Z 
2023-01-11T21:41:23.6705454Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6705849Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6706086Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6706274Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6706396Z {
2023-01-11T21:41:23.6706584Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6706707Z     {
2023-01-11T21:41:23.6706855Z         #pragma omp for 
2023-01-11T21:41:23.6707014Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6707144Z         {
2023-01-11T21:41:23.6707299Z             #pragma GCC ivdep
2023-01-11T21:41:23.6707445Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6707560Z             {
2023-01-11T21:41:23.6707668Z                 {
2023-01-11T21:41:23.6707775Z                     {
2023-01-11T21:41:23.6707977Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6708156Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6708283Z                     }
2023-01-11T21:41:23.6708400Z                 }
2023-01-11T21:41:23.6708520Z             }
2023-01-11T21:41:23.6708624Z         }
2023-01-11T21:41:23.6708736Z     }
2023-01-11T21:41:23.6708860Z }
2023-01-11T21:41:23.6709027Z ''')
2023-01-11T21:41:23.6709040Z 
2023-01-11T21:41:23.6709047Z 
2023-01-11T21:41:23.6709314Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6709715Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6709951Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6710140Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6710251Z {
2023-01-11T21:41:23.6710449Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6710572Z     {
2023-01-11T21:41:23.6710721Z         #pragma omp for 
2023-01-11T21:41:23.6710877Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6711007Z         {
2023-01-11T21:41:23.6711155Z             #pragma GCC ivdep
2023-01-11T21:41:23.6711327Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6711456Z             {
2023-01-11T21:41:23.6711586Z                 {
2023-01-11T21:41:23.6711717Z                     {
2023-01-11T21:41:23.6712017Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6712197Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:23.6712315Z                     }
2023-01-11T21:41:23.6712445Z                 }
2023-01-11T21:41:23.6712572Z             }
2023-01-11T21:41:23.6712700Z         }
2023-01-11T21:41:23.6712824Z     }
2023-01-11T21:41:23.6712943Z }
2023-01-11T21:41:23.6713110Z ''')
2023-01-11T21:41:23.6713119Z 
2023-01-11T21:41:23.6713126Z 
2023-01-11T21:41:23.6713288Z async_compile.wait(globals())
2023-01-11T21:41:23.6713416Z del async_compile
2023-01-11T21:41:23.6713423Z 
2023-01-11T21:41:23.6713546Z def call(args):
2023-01-11T21:41:23.6713846Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6713986Z     args.clear()
2023-01-11T21:41:23.6714402Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6714737Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6714880Z     del arg7_1
2023-01-11T21:41:23.6715346Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6715537Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6715677Z     del arg0_1
2023-01-11T21:41:23.6715812Z     del arg1_1
2023-01-11T21:41:23.6715943Z     del buf0
2023-01-11T21:41:23.6716365Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6716582Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6716674Z     return (buf2, )
2023-01-11T21:41:23.6716681Z 
2023-01-11T21:41:23.6716701Z 
2023-01-11T21:41:23.6716804Z if __name__ == "__main__":
2023-01-11T21:41:23.6716999Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6717207Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6717588Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6717931Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6718275Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6718591Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6718875Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6719187Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6719482Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6719907Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6720200Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6720214Z 
2023-01-11T21:41:23.6720704Z [2023-01-11 21:25:51,274] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 108
2023-01-11T21:41:23.6721543Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6721787Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6722292Z [2023-01-11 21:25:51,394] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 109
2023-01-11T21:41:23.6722809Z [2023-01-11 21:25:51,427] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 109
2023-01-11T21:41:23.6723710Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6724020Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6724529Z [2023-01-11 21:25:51,535] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 110
2023-01-11T21:41:23.6724996Z [2023-01-11 21:25:51,557] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 110
2023-01-11T21:41:23.6725938Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6726190Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6726703Z [2023-01-11 21:25:51,669] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 111
2023-01-11T21:41:23.6726714Z 
2023-01-11T21:41:23.6726904Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6727047Z import torch
2023-01-11T21:41:23.6727198Z import random
2023-01-11T21:41:23.6727428Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6727656Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6727664Z 
2023-01-11T21:41:23.6727823Z aten = torch.ops.aten
2023-01-11T21:41:23.6728090Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6728276Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6728285Z 
2023-01-11T21:41:23.6728292Z 
2023-01-11T21:41:23.6728471Z async_compile.wait(globals())
2023-01-11T21:41:23.6728619Z del async_compile
2023-01-11T21:41:23.6728631Z 
2023-01-11T21:41:23.6728776Z def call(args):
2023-01-11T21:41:23.6729000Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6729130Z     args.clear()
2023-01-11T21:41:23.6729621Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6729833Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:23.6729971Z     del arg0_1
2023-01-11T21:41:23.6730107Z     del arg1_1
2023-01-11T21:41:23.6730236Z     del arg7_1
2023-01-11T21:41:23.6730381Z     return (buf0, )
2023-01-11T21:41:23.6730390Z 
2023-01-11T21:41:23.6730396Z 
2023-01-11T21:41:23.6730533Z if __name__ == "__main__":
2023-01-11T21:41:23.6730759Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6731003Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6731409Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6731774Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6732087Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6732410Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6732770Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6733120Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6733467Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6733883Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6734155Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6734163Z 
2023-01-11T21:41:23.6734170Z 
2023-01-11T21:41:23.6734335Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6734526Z import torch
2023-01-11T21:41:23.6734650Z import random
2023-01-11T21:41:23.6734846Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6735047Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6735055Z 
2023-01-11T21:41:23.6735185Z aten = torch.ops.aten
2023-01-11T21:41:23.6735402Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6735567Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6735576Z 
2023-01-11T21:41:23.6735583Z 
2023-01-11T21:41:23.6735827Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6736186Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6736425Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6736600Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6736692Z {
2023-01-11T21:41:23.6736875Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6736998Z     {
2023-01-11T21:41:23.6737243Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6737400Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6737516Z         {
2023-01-11T21:41:23.6737680Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6737788Z             {
2023-01-11T21:41:23.6737913Z                 {
2023-01-11T21:41:23.6738045Z                     {
2023-01-11T21:41:23.6738234Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6738407Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6738538Z                     }
2023-01-11T21:41:23.6738667Z                 }
2023-01-11T21:41:23.6738779Z             }
2023-01-11T21:41:23.6738904Z         }
2023-01-11T21:41:23.6739030Z     }
2023-01-11T21:41:23.6739155Z }
2023-01-11T21:41:23.6739296Z ''')
2023-01-11T21:41:23.6739306Z 
2023-01-11T21:41:23.6739313Z 
2023-01-11T21:41:23.6739560Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6739960Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6740172Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6740338Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6740454Z {
2023-01-11T21:41:23.6740654Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6740779Z     {
2023-01-11T21:41:23.6740928Z         #pragma omp for 
2023-01-11T21:41:23.6741081Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6741187Z         {
2023-01-11T21:41:23.6741326Z             #pragma GCC ivdep
2023-01-11T21:41:23.6741484Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:23.6741592Z             {
2023-01-11T21:41:23.6741693Z                 {
2023-01-11T21:41:23.6741804Z                     {
2023-01-11T21:41:23.6741948Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6742122Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:23.6742263Z                     }
2023-01-11T21:41:23.6742551Z                 }
2023-01-11T21:41:23.6742684Z             }
2023-01-11T21:41:23.6742798Z         }
2023-01-11T21:41:23.6742924Z     }
2023-01-11T21:41:23.6743035Z }
2023-01-11T21:41:23.6743191Z ''')
2023-01-11T21:41:23.6743200Z 
2023-01-11T21:41:23.6743208Z 
2023-01-11T21:41:23.6743391Z async_compile.wait(globals())
2023-01-11T21:41:23.6743541Z del async_compile
2023-01-11T21:41:23.6743549Z 
2023-01-11T21:41:23.6743691Z def call(args):
2023-01-11T21:41:23.6743920Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6744066Z     args.clear()
2023-01-11T21:41:23.6744481Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6744725Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6744857Z     del arg7_1
2023-01-11T21:41:23.6745347Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6745658Z     assert_size_stride(buf1, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:23.6745795Z     del arg0_1
2023-01-11T21:41:23.6745929Z     del arg1_1
2023-01-11T21:41:23.6746054Z     del buf0
2023-01-11T21:41:23.6746473Z     buf2 = empty_strided((1, 32, 110, 110), (387200, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6746735Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6746879Z     return (buf2, )
2023-01-11T21:41:23.6746887Z 
2023-01-11T21:41:23.6746895Z 
2023-01-11T21:41:23.6747049Z if __name__ == "__main__":
2023-01-11T21:41:23.6747281Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6747527Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6747917Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6748363Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6748721Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6749079Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6749446Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6749800Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6750136Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6750560Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6750859Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6750868Z 
2023-01-11T21:41:23.6750875Z 
2023-01-11T21:41:23.6751064Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6751208Z import torch
2023-01-11T21:41:23.6751338Z import random
2023-01-11T21:41:23.6751578Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6751821Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6751830Z 
2023-01-11T21:41:23.6751987Z aten = torch.ops.aten
2023-01-11T21:41:23.6752228Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6752389Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6752397Z 
2023-01-11T21:41:23.6752404Z 
2023-01-11T21:41:23.6752568Z async_compile.wait(globals())
2023-01-11T21:41:23.6752698Z del async_compile
2023-01-11T21:41:23.6752707Z 
2023-01-11T21:41:23.6752823Z def call(args):
2023-01-11T21:41:23.6753040Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6753183Z     args.clear()
2023-01-11T21:41:23.6753628Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.6753880Z     assert_size_stride(buf0, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:23.6754012Z     del arg0_1
2023-01-11T21:41:23.6754149Z     del arg1_1
2023-01-11T21:41:23.6754264Z     del arg7_1
2023-01-11T21:41:23.6754410Z     return (buf0, )
2023-01-11T21:41:23.6754419Z 
2023-01-11T21:41:23.6754426Z 
2023-01-11T21:41:23.6754574Z if __name__ == "__main__":
2023-01-11T21:41:23.6754783Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6755012Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6755398Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6755760Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6756107Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6756457Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6756798Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6757243Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6757548Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6757939Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6758234Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6758242Z 
2023-01-11T21:41:23.6758776Z [2023-01-11 21:25:51,703] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 111
2023-01-11T21:41:23.6759654Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6759970Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6760490Z [2023-01-11 21:25:51,826] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 112
2023-01-11T21:41:23.6760994Z [2023-01-11 21:25:51,848] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 112
2023-01-11T21:41:23.6761867Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6762068Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6762581Z [2023-01-11 21:25:51,968] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 113
2023-01-11T21:41:23.6762590Z 
2023-01-11T21:41:23.6762776Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6762920Z import torch
2023-01-11T21:41:23.6763067Z import random
2023-01-11T21:41:23.6763301Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6763551Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6763561Z 
2023-01-11T21:41:23.6763706Z aten = torch.ops.aten
2023-01-11T21:41:23.6763973Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6764161Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6764168Z 
2023-01-11T21:41:23.6764176Z 
2023-01-11T21:41:23.6764442Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6764846Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6765083Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6765276Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6765405Z {
2023-01-11T21:41:23.6765591Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6765721Z     {
2023-01-11T21:41:23.6765869Z         #pragma omp for 
2023-01-11T21:41:23.6766021Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6766141Z         {
2023-01-11T21:41:23.6766281Z             #pragma GCC ivdep
2023-01-11T21:41:23.6766432Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6766558Z             {
2023-01-11T21:41:23.6766674Z                 {
2023-01-11T21:41:23.6766800Z                     {
2023-01-11T21:41:23.6766992Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6767168Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6767299Z                     }
2023-01-11T21:41:23.6767413Z                 }
2023-01-11T21:41:23.6767524Z             }
2023-01-11T21:41:23.6767648Z         }
2023-01-11T21:41:23.6767746Z     }
2023-01-11T21:41:23.6767851Z }
2023-01-11T21:41:23.6768013Z ''')
2023-01-11T21:41:23.6768021Z 
2023-01-11T21:41:23.6768028Z 
2023-01-11T21:41:23.6768379Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6768753Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6768986Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6769177Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6769301Z {
2023-01-11T21:41:23.6769499Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6769629Z     {
2023-01-11T21:41:23.6769787Z         #pragma omp for 
2023-01-11T21:41:23.6769935Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6770060Z         {
2023-01-11T21:41:23.6770212Z             #pragma GCC ivdep
2023-01-11T21:41:23.6770378Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:23.6770510Z             {
2023-01-11T21:41:23.6770643Z                 {
2023-01-11T21:41:23.6770780Z                     {
2023-01-11T21:41:23.6770959Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6771204Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:23.6771340Z                     }
2023-01-11T21:41:23.6771466Z                 }
2023-01-11T21:41:23.6771589Z             }
2023-01-11T21:41:23.6771715Z         }
2023-01-11T21:41:23.6771839Z     }
2023-01-11T21:41:23.6771949Z }
2023-01-11T21:41:23.6772101Z ''')
2023-01-11T21:41:23.6772109Z 
2023-01-11T21:41:23.6772116Z 
2023-01-11T21:41:23.6772297Z async_compile.wait(globals())
2023-01-11T21:41:23.6772444Z del async_compile
2023-01-11T21:41:23.6772452Z 
2023-01-11T21:41:23.6772596Z def call(args):
2023-01-11T21:41:23.6772818Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6772957Z     args.clear()
2023-01-11T21:41:23.6773367Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6773623Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6773762Z     del arg7_1
2023-01-11T21:41:23.6774244Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6774441Z     assert_size_stride(buf1, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:23.6774579Z     del arg0_1
2023-01-11T21:41:23.6774695Z     del arg1_1
2023-01-11T21:41:23.6774809Z     del buf0
2023-01-11T21:41:23.6775247Z     buf2 = empty_strided((1, 128, 110, 110), (1548800, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6775507Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6775638Z     return (buf2, )
2023-01-11T21:41:23.6775646Z 
2023-01-11T21:41:23.6775654Z 
2023-01-11T21:41:23.6775796Z if __name__ == "__main__":
2023-01-11T21:41:23.6776016Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6776262Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6776641Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6776996Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6777336Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6777672Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6777995Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6778320Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6778626Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6779029Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6779284Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6779292Z 
2023-01-11T21:41:23.6779299Z 
2023-01-11T21:41:23.6779454Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6779637Z import torch
2023-01-11T21:41:23.6779751Z import random
2023-01-11T21:41:23.6779941Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6780132Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6780139Z 
2023-01-11T21:41:23.6780261Z aten = torch.ops.aten
2023-01-11T21:41:23.6780474Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6780613Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6780620Z 
2023-01-11T21:41:23.6780626Z 
2023-01-11T21:41:23.6780774Z async_compile.wait(globals())
2023-01-11T21:41:23.6780864Z del async_compile
2023-01-11T21:41:23.6780871Z 
2023-01-11T21:41:23.6780983Z def call(args):
2023-01-11T21:41:23.6781164Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6781277Z     args.clear()
2023-01-11T21:41:23.6781678Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:23.6781913Z     assert_size_stride(buf0, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:23.6782019Z     del arg0_1
2023-01-11T21:41:23.6782112Z     del arg1_1
2023-01-11T21:41:23.6782218Z     del arg7_1
2023-01-11T21:41:23.6782457Z     return (buf0, )
2023-01-11T21:41:23.6782469Z 
2023-01-11T21:41:23.6782474Z 
2023-01-11T21:41:23.6782601Z if __name__ == "__main__":
2023-01-11T21:41:23.6782775Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6782952Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6783286Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6783586Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6783866Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6784173Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6784497Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6784811Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6785120Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6785517Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6785792Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6785800Z 
2023-01-11T21:41:23.6786294Z [2023-01-11 21:25:52,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 113
2023-01-11T21:41:23.6787121Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6787337Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6787784Z [2023-01-11 21:25:52,109] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 114
2023-01-11T21:41:23.6788290Z [2023-01-11 21:25:52,131] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 114
2023-01-11T21:41:23.6789136Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6789353Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6789832Z [2023-01-11 21:25:52,283] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 115
2023-01-11T21:41:23.6790381Z [2023-01-11 21:25:52,315] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 115
2023-01-11T21:41:23.6791065Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6791302Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6791796Z [2023-01-11 21:25:52,441] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 116
2023-01-11T21:41:23.6791805Z 
2023-01-11T21:41:23.6791985Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6792124Z import torch
2023-01-11T21:41:23.6792253Z import random
2023-01-11T21:41:23.6792583Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6792826Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6792834Z 
2023-01-11T21:41:23.6792978Z aten = torch.ops.aten
2023-01-11T21:41:23.6793222Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6793397Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6793404Z 
2023-01-11T21:41:23.6793411Z 
2023-01-11T21:41:23.6793657Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6794119Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6794313Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6794498Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6794615Z {
2023-01-11T21:41:23.6794807Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6794929Z     {
2023-01-11T21:41:23.6795098Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6795266Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6795373Z         {
2023-01-11T21:41:23.6795531Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6795653Z             {
2023-01-11T21:41:23.6795775Z                 {
2023-01-11T21:41:23.6795897Z                     {
2023-01-11T21:41:23.6796081Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6796237Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.6796344Z                     }
2023-01-11T21:41:23.6796461Z                 }
2023-01-11T21:41:23.6796572Z             }
2023-01-11T21:41:23.6796683Z         }
2023-01-11T21:41:23.6796798Z     }
2023-01-11T21:41:23.6796914Z }
2023-01-11T21:41:23.6797045Z ''')
2023-01-11T21:41:23.6797054Z 
2023-01-11T21:41:23.6797061Z 
2023-01-11T21:41:23.6797300Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6797663Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6797887Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6798061Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6798170Z {
2023-01-11T21:41:23.6798356Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6798462Z     {
2023-01-11T21:41:23.6798599Z         #pragma omp for 
2023-01-11T21:41:23.6798740Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.6798854Z         {
2023-01-11T21:41:23.6798999Z             #pragma GCC ivdep
2023-01-11T21:41:23.6799153Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:23.6799264Z             {
2023-01-11T21:41:23.6799356Z                 {
2023-01-11T21:41:23.6799463Z                     {
2023-01-11T21:41:23.6799645Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.6799814Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:23.6799937Z                     }
2023-01-11T21:41:23.6800049Z                 }
2023-01-11T21:41:23.6800256Z             }
2023-01-11T21:41:23.6800359Z         }
2023-01-11T21:41:23.6800477Z     }
2023-01-11T21:41:23.6800595Z }
2023-01-11T21:41:23.6800755Z ''')
2023-01-11T21:41:23.6800764Z 
2023-01-11T21:41:23.6800771Z 
2023-01-11T21:41:23.6800952Z async_compile.wait(globals())
2023-01-11T21:41:23.6801095Z del async_compile
2023-01-11T21:41:23.6801103Z 
2023-01-11T21:41:23.6801240Z def call(args):
2023-01-11T21:41:23.6801434Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6801566Z     args.clear()
2023-01-11T21:41:23.6801971Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6802206Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6802308Z     del arg7_1
2023-01-11T21:41:23.6802716Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6802977Z     assert_size_stride(buf1, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:23.6803119Z     del arg0_1
2023-01-11T21:41:23.6803221Z     del arg1_1
2023-01-11T21:41:23.6803333Z     del buf0
2023-01-11T21:41:23.6803762Z     buf2 = empty_strided((1, 32, 108, 108), (373248, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6803995Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6804129Z     return (buf2, )
2023-01-11T21:41:23.6804137Z 
2023-01-11T21:41:23.6804144Z 
2023-01-11T21:41:23.6804261Z if __name__ == "__main__":
2023-01-11T21:41:23.6804488Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6804721Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6805126Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6805462Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6805829Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6806155Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6806495Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6806791Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6807041Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6807407Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6807710Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6807719Z 
2023-01-11T21:41:23.6807727Z 
2023-01-11T21:41:23.6807903Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6808046Z import torch
2023-01-11T21:41:23.6808191Z import random
2023-01-11T21:41:23.6808411Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6808620Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6808628Z 
2023-01-11T21:41:23.6808753Z aten = torch.ops.aten
2023-01-11T21:41:23.6809013Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6809162Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6809168Z 
2023-01-11T21:41:23.6809174Z 
2023-01-11T21:41:23.6809291Z async_compile.wait(globals())
2023-01-11T21:41:23.6809386Z del async_compile
2023-01-11T21:41:23.6809392Z 
2023-01-11T21:41:23.6809484Z def call(args):
2023-01-11T21:41:23.6809638Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6809733Z     args.clear()
2023-01-11T21:41:23.6810086Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:23.6810243Z     assert_size_stride(buf0, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:23.6810364Z     del arg0_1
2023-01-11T21:41:23.6810553Z     del arg1_1
2023-01-11T21:41:23.6810659Z     del arg7_1
2023-01-11T21:41:23.6810780Z     return (buf0, )
2023-01-11T21:41:23.6810788Z 
2023-01-11T21:41:23.6810795Z 
2023-01-11T21:41:23.6810937Z if __name__ == "__main__":
2023-01-11T21:41:23.6811115Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6811354Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6811723Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6812084Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6812459Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6812821Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6813132Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6813446Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6813854Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6814266Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6814509Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6814533Z 
2023-01-11T21:41:23.6814540Z 
2023-01-11T21:41:23.6814658Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6814758Z import torch
2023-01-11T21:41:23.6814860Z import random
2023-01-11T21:41:23.6815025Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6815213Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6815221Z 
2023-01-11T21:41:23.6815367Z aten = torch.ops.aten
2023-01-11T21:41:23.6815597Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6815746Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6815753Z 
2023-01-11T21:41:23.6815779Z 
2023-01-11T21:41:23.6816019Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6816373Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6816582Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6816745Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6816859Z {
2023-01-11T21:41:23.6817036Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6817146Z     {
2023-01-11T21:41:23.6817286Z         #pragma omp for 
2023-01-11T21:41:23.6817455Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6817563Z         {
2023-01-11T21:41:23.6817687Z             #pragma GCC ivdep
2023-01-11T21:41:23.6817840Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.6817949Z             {
2023-01-11T21:41:23.6818040Z                 {
2023-01-11T21:41:23.6818171Z                     {
2023-01-11T21:41:23.6818353Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:23.6818494Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:23.6818595Z                     }
2023-01-11T21:41:23.6818689Z                 }
2023-01-11T21:41:23.6818784Z             }
2023-01-11T21:41:23.6818885Z         }
2023-01-11T21:41:23.6818992Z     }
2023-01-11T21:41:23.6819115Z }
2023-01-11T21:41:23.6819290Z ''')
2023-01-11T21:41:23.6819300Z 
2023-01-11T21:41:23.6819307Z 
2023-01-11T21:41:23.6819577Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.6819975Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6820209Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6820391Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6820492Z {
2023-01-11T21:41:23.6820690Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6820814Z     {
2023-01-11T21:41:23.6820968Z         #pragma omp for 
2023-01-11T21:41:23.6821130Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.6821340Z         {
2023-01-11T21:41:23.6821483Z             #pragma GCC ivdep
2023-01-11T21:41:23.6821646Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:23.6821774Z             {
2023-01-11T21:41:23.6821906Z                 {
2023-01-11T21:41:23.6822039Z                     {
2023-01-11T21:41:23.6822228Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:23.6822510Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:23.6822609Z                     }
2023-01-11T21:41:23.6822712Z                 }
2023-01-11T21:41:23.6822830Z             }
2023-01-11T21:41:23.6822958Z         }
2023-01-11T21:41:23.6823080Z     }
2023-01-11T21:41:23.6823206Z }
2023-01-11T21:41:23.6823376Z ''')
2023-01-11T21:41:23.6823385Z 
2023-01-11T21:41:23.6823392Z 
2023-01-11T21:41:23.6823559Z async_compile.wait(globals())
2023-01-11T21:41:23.6823708Z del async_compile
2023-01-11T21:41:23.6823716Z 
2023-01-11T21:41:23.6823853Z def call(args):
2023-01-11T21:41:23.6824181Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6824310Z     args.clear()
2023-01-11T21:41:23.6824733Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6824969Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6825082Z     del arg7_1
2023-01-11T21:41:23.6825461Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6825635Z     assert_size_stride(buf1, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:23.6825747Z     del arg0_1
2023-01-11T21:41:23.6825858Z     del arg1_1
2023-01-11T21:41:23.6825959Z     del buf0
2023-01-11T21:41:23.6826311Z     buf2 = empty_strided((1, 128, 108, 108), (1492992, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6826532Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.6826645Z     return (buf2, )
2023-01-11T21:41:23.6826653Z 
2023-01-11T21:41:23.6826675Z 
2023-01-11T21:41:23.6826785Z if __name__ == "__main__":
2023-01-11T21:41:23.6826973Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6827167Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6827514Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6827815Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6828122Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6828426Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6828711Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6829012Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6829271Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6829671Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6829928Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6829937Z 
2023-01-11T21:41:23.6830376Z [2023-01-11 21:25:52,463] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 116
2023-01-11T21:41:23.6831124Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6831340Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6831788Z [2023-01-11 21:25:52,608] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 117
2023-01-11T21:41:23.6832422Z [2023-01-11 21:25:52,631] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 117
2023-01-11T21:41:23.6833311Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6833536Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6834133Z [2023-01-11 21:25:52,884] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 118
2023-01-11T21:41:23.6834624Z [2023-01-11 21:25:54,425] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 118
2023-01-11T21:41:23.6835574Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6835813Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6836310Z [2023-01-11 21:25:54,775] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 119
2023-01-11T21:41:23.6836827Z [2023-01-11 21:25:54,799] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 119
2023-01-11T21:41:23.6837704Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6837937Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6838421Z [2023-01-11 21:25:55,537] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 120
2023-01-11T21:41:23.6838431Z 
2023-01-11T21:41:23.6838617Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6838743Z import torch
2023-01-11T21:41:23.6838885Z import random
2023-01-11T21:41:23.6839112Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6839348Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6839358Z 
2023-01-11T21:41:23.6839515Z aten = torch.ops.aten
2023-01-11T21:41:23.6839776Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6839951Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6839959Z 
2023-01-11T21:41:23.6839966Z 
2023-01-11T21:41:23.6840150Z async_compile.wait(globals())
2023-01-11T21:41:23.6840282Z del async_compile
2023-01-11T21:41:23.6840289Z 
2023-01-11T21:41:23.6840427Z def call(args):
2023-01-11T21:41:23.6840658Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6840799Z     args.clear()
2023-01-11T21:41:23.6841251Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:23.6841469Z     assert_size_stride(buf0, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:23.6841605Z     del arg0_1
2023-01-11T21:41:23.6841724Z     del arg1_1
2023-01-11T21:41:23.6841859Z     del arg7_1
2023-01-11T21:41:23.6841997Z     return (buf0, )
2023-01-11T21:41:23.6842005Z 
2023-01-11T21:41:23.6842012Z 
2023-01-11T21:41:23.6842163Z if __name__ == "__main__":
2023-01-11T21:41:23.6842383Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6842632Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6843137Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6843449Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6843781Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6844087Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6844374Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6844652Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6844924Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6845316Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6845584Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6845598Z 
2023-01-11T21:41:23.6845680Z 
2023-01-11T21:41:23.6845867Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6845997Z import torch
2023-01-11T21:41:23.6846101Z import random
2023-01-11T21:41:23.6846304Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6846501Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6846509Z 
2023-01-11T21:41:23.6846636Z aten = torch.ops.aten
2023-01-11T21:41:23.6846887Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6847048Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6847054Z 
2023-01-11T21:41:23.6847060Z 
2023-01-11T21:41:23.6847203Z async_compile.wait(globals())
2023-01-11T21:41:23.6847314Z del async_compile
2023-01-11T21:41:23.6847335Z 
2023-01-11T21:41:23.6847433Z def call(args):
2023-01-11T21:41:23.6847633Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6847759Z     args.clear()
2023-01-11T21:41:23.6847972Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6848152Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6848266Z     del arg0_1
2023-01-11T21:41:23.6848385Z     del arg1_1
2023-01-11T21:41:23.6848482Z     del arg7_1
2023-01-11T21:41:23.6848596Z     return (buf0, )
2023-01-11T21:41:23.6848604Z 
2023-01-11T21:41:23.6848611Z 
2023-01-11T21:41:23.6848734Z if __name__ == "__main__":
2023-01-11T21:41:23.6848921Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6849122Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6849486Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6849819Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6850143Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6850473Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6850790Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6851149Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6851465Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6851872Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6852125Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6852134Z 
2023-01-11T21:41:23.6852141Z 
2023-01-11T21:41:23.6852295Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6852411Z import torch
2023-01-11T21:41:23.6852505Z import random
2023-01-11T21:41:23.6852710Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6852940Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6853033Z 
2023-01-11T21:41:23.6853191Z aten = torch.ops.aten
2023-01-11T21:41:23.6853456Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6853629Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6853637Z 
2023-01-11T21:41:23.6853645Z 
2023-01-11T21:41:23.6853908Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6854301Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6854504Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6854665Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6854765Z {
2023-01-11T21:41:23.6854946Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6855079Z     {
2023-01-11T21:41:23.6855247Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6855410Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6855522Z         {
2023-01-11T21:41:23.6855694Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.6855865Z             {
2023-01-11T21:41:23.6855957Z                 {
2023-01-11T21:41:23.6856061Z                     {
2023-01-11T21:41:23.6856232Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.6856413Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.6856533Z                     }
2023-01-11T21:41:23.6856663Z                 }
2023-01-11T21:41:23.6856780Z             }
2023-01-11T21:41:23.6856901Z         }
2023-01-11T21:41:23.6857022Z     }
2023-01-11T21:41:23.6857151Z }
2023-01-11T21:41:23.6857304Z ''')
2023-01-11T21:41:23.6857328Z 
2023-01-11T21:41:23.6857335Z 
2023-01-11T21:41:23.6857503Z async_compile.wait(globals())
2023-01-11T21:41:23.6857653Z del async_compile
2023-01-11T21:41:23.6857661Z 
2023-01-11T21:41:23.6857805Z def call(args):
2023-01-11T21:41:23.6858025Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6858149Z     args.clear()
2023-01-11T21:41:23.6858596Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6858848Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6858944Z     del arg7_1
2023-01-11T21:41:23.6859205Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6859420Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6859555Z     del arg0_1
2023-01-11T21:41:23.6859683Z     del arg1_1
2023-01-11T21:41:23.6859826Z     return (buf1, )
2023-01-11T21:41:23.6859833Z 
2023-01-11T21:41:23.6859840Z 
2023-01-11T21:41:23.6859976Z if __name__ == "__main__":
2023-01-11T21:41:23.6860164Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6860370Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6860791Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6861169Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6861523Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6861888Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6862211Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6862656Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6862945Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6863321Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6863589Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6863597Z 
2023-01-11T21:41:23.6863604Z 
2023-01-11T21:41:23.6863761Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6863987Z import torch
2023-01-11T21:41:23.6864104Z import random
2023-01-11T21:41:23.6864284Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6864494Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6864502Z 
2023-01-11T21:41:23.6864630Z aten = torch.ops.aten
2023-01-11T21:41:23.6864884Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6865051Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6865058Z 
2023-01-11T21:41:23.6865065Z 
2023-01-11T21:41:23.6865233Z async_compile.wait(globals())
2023-01-11T21:41:23.6865342Z del async_compile
2023-01-11T21:41:23.6865350Z 
2023-01-11T21:41:23.6865471Z def call(args):
2023-01-11T21:41:23.6865655Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6865769Z     args.clear()
2023-01-11T21:41:23.6866013Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.6866298Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6866421Z     del arg0_1
2023-01-11T21:41:23.6866532Z     del arg1_1
2023-01-11T21:41:23.6866652Z     del arg7_1
2023-01-11T21:41:23.6866784Z     return (buf0, )
2023-01-11T21:41:23.6866792Z 
2023-01-11T21:41:23.6866800Z 
2023-01-11T21:41:23.6866950Z if __name__ == "__main__":
2023-01-11T21:41:23.6867159Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6867364Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6867745Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6868057Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6868392Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6868743Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6869109Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6869452Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6869789Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6870211Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6870489Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6870499Z 
2023-01-11T21:41:23.6870947Z [2023-01-11 21:25:57,117] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 120
2023-01-11T21:41:23.6871823Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6872038Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6872504Z [2023-01-11 21:25:57,737] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 121
2023-01-11T21:41:23.6873018Z [2023-01-11 21:25:57,760] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 121
2023-01-11T21:41:23.6873861Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6874110Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6874632Z [2023-01-11 21:25:57,998] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 122
2023-01-11T21:41:23.6875237Z [2023-01-11 21:25:58,027] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 122
2023-01-11T21:41:23.6876148Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6876356Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6876749Z [2023-01-11 21:25:58,344] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 123
2023-01-11T21:41:23.6877157Z [2023-01-11 21:25:58,367] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 123
2023-01-11T21:41:23.6877976Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6878143Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6878576Z [2023-01-11 21:25:59,106] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 124
2023-01-11T21:41:23.6878585Z 
2023-01-11T21:41:23.6878753Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6878878Z import torch
2023-01-11T21:41:23.6878999Z import random
2023-01-11T21:41:23.6879225Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6879426Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6879452Z 
2023-01-11T21:41:23.6879571Z aten = torch.ops.aten
2023-01-11T21:41:23.6879822Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6879981Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6879989Z 
2023-01-11T21:41:23.6879996Z 
2023-01-11T21:41:23.6880260Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6880666Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6880905Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6881098Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6881209Z {
2023-01-11T21:41:23.6881391Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6881520Z     {
2023-01-11T21:41:23.6881677Z         #pragma omp for 
2023-01-11T21:41:23.6881841Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6881971Z         {
2023-01-11T21:41:23.6882134Z             #pragma GCC ivdep
2023-01-11T21:41:23.6882292Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.6882430Z             {
2023-01-11T21:41:23.6882558Z                 {
2023-01-11T21:41:23.6882683Z                     {
2023-01-11T21:41:23.6882871Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.6883029Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.6883117Z                     }
2023-01-11T21:41:23.6883188Z                 }
2023-01-11T21:41:23.6883283Z             }
2023-01-11T21:41:23.6883410Z         }
2023-01-11T21:41:23.6883520Z     }
2023-01-11T21:41:23.6883641Z }
2023-01-11T21:41:23.6883803Z ''')
2023-01-11T21:41:23.6883812Z 
2023-01-11T21:41:23.6883820Z 
2023-01-11T21:41:23.6883999Z async_compile.wait(globals())
2023-01-11T21:41:23.6884132Z del async_compile
2023-01-11T21:41:23.6884140Z 
2023-01-11T21:41:23.6884283Z def call(args):
2023-01-11T21:41:23.6884478Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6884599Z     args.clear()
2023-01-11T21:41:23.6885030Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6885369Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6885504Z     del arg7_1
2023-01-11T21:41:23.6885717Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.6885889Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6885978Z     del arg0_1
2023-01-11T21:41:23.6886065Z     del arg1_1
2023-01-11T21:41:23.6886167Z     return (buf1, )
2023-01-11T21:41:23.6886175Z 
2023-01-11T21:41:23.6886184Z 
2023-01-11T21:41:23.6886332Z if __name__ == "__main__":
2023-01-11T21:41:23.6886540Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6886763Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6887170Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6887572Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6887834Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6888161Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6888463Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6888730Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6888962Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6889315Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6889525Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6889550Z 
2023-01-11T21:41:23.6889555Z 
2023-01-11T21:41:23.6889657Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6889761Z import torch
2023-01-11T21:41:23.6889852Z import random
2023-01-11T21:41:23.6890002Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6890157Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6890163Z 
2023-01-11T21:41:23.6890261Z aten = torch.ops.aten
2023-01-11T21:41:23.6890515Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6890620Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6890644Z 
2023-01-11T21:41:23.6890649Z 
2023-01-11T21:41:23.6890745Z async_compile.wait(globals())
2023-01-11T21:41:23.6890837Z del async_compile
2023-01-11T21:41:23.6890844Z 
2023-01-11T21:41:23.6890940Z def call(args):
2023-01-11T21:41:23.6891087Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6891176Z     args.clear()
2023-01-11T21:41:23.6891355Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6891511Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6891582Z     del arg0_1
2023-01-11T21:41:23.6891709Z     del arg1_1
2023-01-11T21:41:23.6891808Z     del arg7_1
2023-01-11T21:41:23.6891899Z     return (buf0, )
2023-01-11T21:41:23.6891906Z 
2023-01-11T21:41:23.6891911Z 
2023-01-11T21:41:23.6892006Z if __name__ == "__main__":
2023-01-11T21:41:23.6892152Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6892314Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6892617Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6892859Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6893109Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6893377Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6893631Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6893966Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6894202Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6894511Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6894717Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6894725Z 
2023-01-11T21:41:23.6894729Z 
2023-01-11T21:41:23.6894874Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6894947Z import torch
2023-01-11T21:41:23.6895039Z import random
2023-01-11T21:41:23.6895187Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6895345Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6895351Z 
2023-01-11T21:41:23.6895455Z aten = torch.ops.aten
2023-01-11T21:41:23.6895696Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6895818Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6895824Z 
2023-01-11T21:41:23.6895830Z 
2023-01-11T21:41:23.6895998Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6896256Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6896417Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6896547Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6896624Z {
2023-01-11T21:41:23.6896771Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6896851Z     {
2023-01-11T21:41:23.6896950Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6897053Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6897132Z         {
2023-01-11T21:41:23.6897244Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.6897326Z             {
2023-01-11T21:41:23.6897406Z                 {
2023-01-11T21:41:23.6897497Z                     {
2023-01-11T21:41:23.6897608Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.6897733Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.6897817Z                     }
2023-01-11T21:41:23.6897898Z                 }
2023-01-11T21:41:23.6897977Z             }
2023-01-11T21:41:23.6898055Z         }
2023-01-11T21:41:23.6898130Z     }
2023-01-11T21:41:23.6898188Z }
2023-01-11T21:41:23.6898301Z ''')
2023-01-11T21:41:23.6898308Z 
2023-01-11T21:41:23.6898313Z 
2023-01-11T21:41:23.6898428Z async_compile.wait(globals())
2023-01-11T21:41:23.6898537Z del async_compile
2023-01-11T21:41:23.6898543Z 
2023-01-11T21:41:23.6898632Z def call(args):
2023-01-11T21:41:23.6898783Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6898872Z     args.clear()
2023-01-11T21:41:23.6899174Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6899353Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6899442Z     del arg7_1
2023-01-11T21:41:23.6899622Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6899769Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6899851Z     del arg0_1
2023-01-11T21:41:23.6899936Z     del arg1_1
2023-01-11T21:41:23.6900026Z     return (buf1, )
2023-01-11T21:41:23.6900033Z 
2023-01-11T21:41:23.6900039Z 
2023-01-11T21:41:23.6900136Z if __name__ == "__main__":
2023-01-11T21:41:23.6900281Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6900435Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6900727Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6900980Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6901316Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6901561Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6901826Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6902048Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6902285Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6902736Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6902939Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6902947Z 
2023-01-11T21:41:23.6902952Z 
2023-01-11T21:41:23.6903070Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6903159Z import torch
2023-01-11T21:41:23.6903248Z import random
2023-01-11T21:41:23.6903506Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6903648Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6903654Z 
2023-01-11T21:41:23.6903757Z aten = torch.ops.aten
2023-01-11T21:41:23.6903926Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6904043Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6904049Z 
2023-01-11T21:41:23.6904054Z 
2023-01-11T21:41:23.6904162Z async_compile.wait(globals())
2023-01-11T21:41:23.6904254Z del async_compile
2023-01-11T21:41:23.6904260Z 
2023-01-11T21:41:23.6904347Z def call(args):
2023-01-11T21:41:23.6904494Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6904569Z     args.clear()
2023-01-11T21:41:23.6904744Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.6904892Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6904996Z     del arg0_1
2023-01-11T21:41:23.6905080Z     del arg1_1
2023-01-11T21:41:23.6905164Z     del arg7_1
2023-01-11T21:41:23.6905255Z     return (buf0, )
2023-01-11T21:41:23.6905261Z 
2023-01-11T21:41:23.6905267Z 
2023-01-11T21:41:23.6905359Z if __name__ == "__main__":
2023-01-11T21:41:23.6905486Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6905640Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6905944Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6906200Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6906470Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6906718Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6906957Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6907210Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6907427Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6907736Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6907953Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6907962Z 
2023-01-11T21:41:23.6908319Z [2023-01-11 21:25:59,135] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 124
2023-01-11T21:41:23.6908867Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6909146Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6909487Z [2023-01-11 21:25:59,852] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 125
2023-01-11T21:41:23.6909832Z [2023-01-11 21:25:59,875] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 125
2023-01-11T21:41:23.6910385Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6910547Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6910879Z [2023-01-11 21:26:00,155] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 126
2023-01-11T21:41:23.6911278Z [2023-01-11 21:26:00,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 126
2023-01-11T21:41:23.6911848Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6912006Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6912347Z [2023-01-11 21:26:00,581] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 127
2023-01-11T21:41:23.6912700Z [2023-01-11 21:26:00,604] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 127
2023-01-11T21:41:23.6913239Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6913400Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6913819Z [2023-01-11 21:26:01,411] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 128
2023-01-11T21:41:23.6913831Z 
2023-01-11T21:41:23.6913950Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6914039Z import torch
2023-01-11T21:41:23.6914109Z import random
2023-01-11T21:41:23.6914254Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6914413Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6914420Z 
2023-01-11T21:41:23.6914518Z aten = torch.ops.aten
2023-01-11T21:41:23.6914685Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6914807Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6914814Z 
2023-01-11T21:41:23.6914819Z 
2023-01-11T21:41:23.6914995Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6915255Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6915403Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6915513Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6915589Z {
2023-01-11T21:41:23.6915709Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6915787Z     {
2023-01-11T21:41:23.6915892Z         #pragma omp for 
2023-01-11T21:41:23.6915993Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6916054Z         {
2023-01-11T21:41:23.6916154Z             #pragma GCC ivdep
2023-01-11T21:41:23.6916265Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.6916344Z             {
2023-01-11T21:41:23.6916424Z                 {
2023-01-11T21:41:23.6916512Z                     {
2023-01-11T21:41:23.6916731Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.6916836Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.6916917Z                     }
2023-01-11T21:41:23.6917019Z                 }
2023-01-11T21:41:23.6917095Z             }
2023-01-11T21:41:23.6917171Z         }
2023-01-11T21:41:23.6917246Z     }
2023-01-11T21:41:23.6917320Z }
2023-01-11T21:41:23.6917410Z ''')
2023-01-11T21:41:23.6917417Z 
2023-01-11T21:41:23.6917422Z 
2023-01-11T21:41:23.6917538Z async_compile.wait(globals())
2023-01-11T21:41:23.6917628Z del async_compile
2023-01-11T21:41:23.6917634Z 
2023-01-11T21:41:23.6917719Z def call(args):
2023-01-11T21:41:23.6917876Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6917964Z     args.clear()
2023-01-11T21:41:23.6918285Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6918494Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6918581Z     del arg7_1
2023-01-11T21:41:23.6918762Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.6918912Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.6918993Z     del arg0_1
2023-01-11T21:41:23.6919076Z     del arg1_1
2023-01-11T21:41:23.6919173Z     return (buf1, )
2023-01-11T21:41:23.6919179Z 
2023-01-11T21:41:23.6919184Z 
2023-01-11T21:41:23.6919275Z if __name__ == "__main__":
2023-01-11T21:41:23.6919402Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6919564Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6919860Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6920118Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6920426Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6920781Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6921132Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6921485Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6921804Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6922234Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6922526Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6922535Z 
2023-01-11T21:41:23.6922542Z 
2023-01-11T21:41:23.6922730Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6922874Z import torch
2023-01-11T21:41:23.6923017Z import random
2023-01-11T21:41:23.6923247Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6923484Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6923492Z 
2023-01-11T21:41:23.6923634Z aten = torch.ops.aten
2023-01-11T21:41:23.6923890Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6924071Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6924079Z 
2023-01-11T21:41:23.6924086Z 
2023-01-11T21:41:23.6924268Z async_compile.wait(globals())
2023-01-11T21:41:23.6924415Z del async_compile
2023-01-11T21:41:23.6924422Z 
2023-01-11T21:41:23.6924565Z def call(args):
2023-01-11T21:41:23.6924784Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6924926Z     args.clear()
2023-01-11T21:41:23.6925172Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6925384Z     assert_size_stride(buf0, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:23.6925611Z     del arg0_1
2023-01-11T21:41:23.6925753Z     del arg1_1
2023-01-11T21:41:23.6925891Z     del arg7_1
2023-01-11T21:41:23.6926036Z     return (buf0, )
2023-01-11T21:41:23.6926043Z 
2023-01-11T21:41:23.6926050Z 
2023-01-11T21:41:23.6926200Z if __name__ == "__main__":
2023-01-11T21:41:23.6926422Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6926648Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6927058Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6927418Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6927774Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6928123Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6928473Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6928911Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6929260Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6929678Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6929967Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6929976Z 
2023-01-11T21:41:23.6929983Z 
2023-01-11T21:41:23.6930171Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6930313Z import torch
2023-01-11T21:41:23.6930458Z import random
2023-01-11T21:41:23.6930686Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6930923Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6930932Z 
2023-01-11T21:41:23.6931084Z aten = torch.ops.aten
2023-01-11T21:41:23.6931330Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6931512Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6931527Z 
2023-01-11T21:41:23.6931534Z 
2023-01-11T21:41:23.6931792Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6932169Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6932403Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6932593Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6932720Z {
2023-01-11T21:41:23.6932916Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6933029Z     {
2023-01-11T21:41:23.6933208Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6933370Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6933498Z         {
2023-01-11T21:41:23.6933666Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.6933796Z             {
2023-01-11T21:41:23.6933914Z                 {
2023-01-11T21:41:23.6934049Z                     {
2023-01-11T21:41:23.6934241Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.6934432Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.6934564Z                     }
2023-01-11T21:41:23.6934696Z                 }
2023-01-11T21:41:23.6934823Z             }
2023-01-11T21:41:23.6934938Z         }
2023-01-11T21:41:23.6935064Z     }
2023-01-11T21:41:23.6935189Z }
2023-01-11T21:41:23.6935350Z ''')
2023-01-11T21:41:23.6935358Z 
2023-01-11T21:41:23.6935364Z 
2023-01-11T21:41:23.6935547Z async_compile.wait(globals())
2023-01-11T21:41:23.6935699Z del async_compile
2023-01-11T21:41:23.6935706Z 
2023-01-11T21:41:23.6935849Z def call(args):
2023-01-11T21:41:23.6936071Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6936200Z     args.clear()
2023-01-11T21:41:23.6936628Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6936886Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6937102Z     del arg7_1
2023-01-11T21:41:23.6937361Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6937574Z     assert_size_stride(buf1, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:23.6937709Z     del arg0_1
2023-01-11T21:41:23.6937835Z     del arg1_1
2023-01-11T21:41:23.6937980Z     return (buf1, )
2023-01-11T21:41:23.6937988Z 
2023-01-11T21:41:23.6937994Z 
2023-01-11T21:41:23.6938144Z if __name__ == "__main__":
2023-01-11T21:41:23.6938365Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6938605Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6939010Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6939371Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6939724Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6940119Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6940477Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6940830Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6941169Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6941589Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6941880Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6941888Z 
2023-01-11T21:41:23.6941895Z 
2023-01-11T21:41:23.6942082Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6942226Z import torch
2023-01-11T21:41:23.6942491Z import random
2023-01-11T21:41:23.6942721Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6942964Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6942977Z 
2023-01-11T21:41:23.6943135Z aten = torch.ops.aten
2023-01-11T21:41:23.6943393Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6943574Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6943582Z 
2023-01-11T21:41:23.6943588Z 
2023-01-11T21:41:23.6943768Z async_compile.wait(globals())
2023-01-11T21:41:23.6943919Z del async_compile
2023-01-11T21:41:23.6943927Z 
2023-01-11T21:41:23.6944058Z def call(args):
2023-01-11T21:41:23.6944276Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6944419Z     args.clear()
2023-01-11T21:41:23.6944677Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.6944895Z     assert_size_stride(buf0, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:23.6945034Z     del arg0_1
2023-01-11T21:41:23.6945170Z     del arg1_1
2023-01-11T21:41:23.6945310Z     del arg7_1
2023-01-11T21:41:23.6945444Z     return (buf0, )
2023-01-11T21:41:23.6945452Z 
2023-01-11T21:41:23.6945459Z 
2023-01-11T21:41:23.6945609Z if __name__ == "__main__":
2023-01-11T21:41:23.6945836Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6946077Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6946495Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6946857Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6947215Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6947572Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6947914Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6948269Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6948712Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6949149Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6949441Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6949450Z 
2023-01-11T21:41:23.6949956Z [2023-01-11 21:26:01,440] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 128
2023-01-11T21:41:23.6950817Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6951053Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6951620Z [2023-01-11 21:26:02,157] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 129
2023-01-11T21:41:23.6952125Z [2023-01-11 21:26:02,184] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 129
2023-01-11T21:41:23.6952972Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6953212Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6953699Z [2023-01-11 21:26:02,500] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 130
2023-01-11T21:41:23.6954272Z [2023-01-11 21:26:02,529] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 130
2023-01-11T21:41:23.6955131Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6955371Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6955857Z [2023-01-11 21:26:02,919] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 131
2023-01-11T21:41:23.6956358Z [2023-01-11 21:26:02,943] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 131
2023-01-11T21:41:23.6957209Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6957455Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6957939Z [2023-01-11 21:26:03,671] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 132
2023-01-11T21:41:23.6957950Z 
2023-01-11T21:41:23.6958134Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6958263Z import torch
2023-01-11T21:41:23.6958411Z import random
2023-01-11T21:41:23.6958639Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6958875Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6958883Z 
2023-01-11T21:41:23.6959040Z aten = torch.ops.aten
2023-01-11T21:41:23.6959301Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6959483Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6959491Z 
2023-01-11T21:41:23.6959498Z 
2023-01-11T21:41:23.6959834Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6960205Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6960439Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6960633Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6960758Z {
2023-01-11T21:41:23.6960952Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6961079Z     {
2023-01-11T21:41:23.6961234Z         #pragma omp for 
2023-01-11T21:41:23.6961382Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.6961509Z         {
2023-01-11T21:41:23.6961670Z             #pragma GCC ivdep
2023-01-11T21:41:23.6961841Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.6961971Z             {
2023-01-11T21:41:23.6962104Z                 {
2023-01-11T21:41:23.6962225Z                     {
2023-01-11T21:41:23.6962417Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.6962655Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.6962793Z                     }
2023-01-11T21:41:23.6962925Z                 }
2023-01-11T21:41:23.6963056Z             }
2023-01-11T21:41:23.6963184Z         }
2023-01-11T21:41:23.6963297Z     }
2023-01-11T21:41:23.6963422Z }
2023-01-11T21:41:23.6963584Z ''')
2023-01-11T21:41:23.6963592Z 
2023-01-11T21:41:23.6963599Z 
2023-01-11T21:41:23.6963780Z async_compile.wait(globals())
2023-01-11T21:41:23.6963929Z del async_compile
2023-01-11T21:41:23.6963937Z 
2023-01-11T21:41:23.6964082Z def call(args):
2023-01-11T21:41:23.6964306Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6964435Z     args.clear()
2023-01-11T21:41:23.6964876Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6965134Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6965273Z     del arg7_1
2023-01-11T21:41:23.6965540Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.6965759Z     assert_size_stride(buf1, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:23.6965896Z     del arg0_1
2023-01-11T21:41:23.6966031Z     del arg1_1
2023-01-11T21:41:23.6966162Z     return (buf1, )
2023-01-11T21:41:23.6966170Z 
2023-01-11T21:41:23.6966190Z 
2023-01-11T21:41:23.6966326Z if __name__ == "__main__":
2023-01-11T21:41:23.6966550Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6966793Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6967202Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6967567Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6967928Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6968290Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6968632Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6968985Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6969324Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6969758Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6970051Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6970059Z 
2023-01-11T21:41:23.6970066Z 
2023-01-11T21:41:23.6970256Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6970400Z import torch
2023-01-11T21:41:23.6970546Z import random
2023-01-11T21:41:23.6970760Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6970999Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6971079Z 
2023-01-11T21:41:23.6971240Z aten = torch.ops.aten
2023-01-11T21:41:23.6971500Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6971683Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6971691Z 
2023-01-11T21:41:23.6971698Z 
2023-01-11T21:41:23.6971879Z async_compile.wait(globals())
2023-01-11T21:41:23.6972028Z del async_compile
2023-01-11T21:41:23.6972035Z 
2023-01-11T21:41:23.6972180Z def call(args):
2023-01-11T21:41:23.6972389Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6972530Z     args.clear()
2023-01-11T21:41:23.6972789Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6973001Z     assert_size_stride(buf0, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:23.6973141Z     del arg0_1
2023-01-11T21:41:23.6973277Z     del arg1_1
2023-01-11T21:41:23.6973414Z     del arg7_1
2023-01-11T21:41:23.6973602Z     return (buf0, )
2023-01-11T21:41:23.6973625Z 
2023-01-11T21:41:23.6973631Z 
2023-01-11T21:41:23.6973768Z if __name__ == "__main__":
2023-01-11T21:41:23.6973994Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6974239Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6974651Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6975008Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6975367Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6975720Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6976076Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6976414Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6976754Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6977188Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6977481Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6977489Z 
2023-01-11T21:41:23.6977496Z 
2023-01-11T21:41:23.6977684Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6977826Z import torch
2023-01-11T21:41:23.6977970Z import random
2023-01-11T21:41:23.6978197Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6978419Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6978427Z 
2023-01-11T21:41:23.6978586Z aten = torch.ops.aten
2023-01-11T21:41:23.6978846Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6979029Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6979037Z 
2023-01-11T21:41:23.6979044Z 
2023-01-11T21:41:23.6979304Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.6979689Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.6979921Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.6980110Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.6980223Z {
2023-01-11T21:41:23.6980416Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.6980542Z     {
2023-01-11T21:41:23.6980720Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.6980882Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.6981009Z         {
2023-01-11T21:41:23.6981179Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.6981292Z             {
2023-01-11T21:41:23.6981420Z                 {
2023-01-11T21:41:23.6981552Z                     {
2023-01-11T21:41:23.6981743Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.6981924Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.6982130Z                     }
2023-01-11T21:41:23.6982262Z                 }
2023-01-11T21:41:23.6982504Z             }
2023-01-11T21:41:23.6982639Z         }
2023-01-11T21:41:23.6982765Z     }
2023-01-11T21:41:23.6982891Z }
2023-01-11T21:41:23.6983054Z ''')
2023-01-11T21:41:23.6983061Z 
2023-01-11T21:41:23.6983068Z 
2023-01-11T21:41:23.6983252Z async_compile.wait(globals())
2023-01-11T21:41:23.6983400Z del async_compile
2023-01-11T21:41:23.6983409Z 
2023-01-11T21:41:23.6983538Z def call(args):
2023-01-11T21:41:23.6983765Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6983908Z     args.clear()
2023-01-11T21:41:23.6984342Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6984596Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.6984735Z     del arg7_1
2023-01-11T21:41:23.6985082Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.6985290Z     assert_size_stride(buf1, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:23.6985427Z     del arg0_1
2023-01-11T21:41:23.6985564Z     del arg1_1
2023-01-11T21:41:23.6985709Z     return (buf1, )
2023-01-11T21:41:23.6985717Z 
2023-01-11T21:41:23.6985724Z 
2023-01-11T21:41:23.6985879Z if __name__ == "__main__":
2023-01-11T21:41:23.6986100Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6986338Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6986745Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6987090Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6987450Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6987808Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6988171Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6988522Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6988860Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6989285Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6989579Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6989589Z 
2023-01-11T21:41:23.6989596Z 
2023-01-11T21:41:23.6989783Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.6989912Z import torch
2023-01-11T21:41:23.6990057Z import random
2023-01-11T21:41:23.6990287Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.6990522Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.6990529Z 
2023-01-11T21:41:23.6990685Z aten = torch.ops.aten
2023-01-11T21:41:23.6990950Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.6991132Z async_compile = AsyncCompile()
2023-01-11T21:41:23.6991140Z 
2023-01-11T21:41:23.6991147Z 
2023-01-11T21:41:23.6991325Z async_compile.wait(globals())
2023-01-11T21:41:23.6991459Z del async_compile
2023-01-11T21:41:23.6991466Z 
2023-01-11T21:41:23.6991613Z def call(args):
2023-01-11T21:41:23.6991833Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.6991975Z     args.clear()
2023-01-11T21:41:23.6992234Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.6992450Z     assert_size_stride(buf0, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:23.6992588Z     del arg0_1
2023-01-11T21:41:23.6992712Z     del arg1_1
2023-01-11T21:41:23.6992847Z     del arg7_1
2023-01-11T21:41:23.6992995Z     return (buf0, )
2023-01-11T21:41:23.6993003Z 
2023-01-11T21:41:23.6993103Z 
2023-01-11T21:41:23.6993276Z if __name__ == "__main__":
2023-01-11T21:41:23.6993502Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.6993805Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.6994227Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6994589Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6994933Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6995294Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6995652Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6996007Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6996344Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.6996894Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.6997192Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.6997200Z 
2023-01-11T21:41:23.6997708Z [2023-01-11 21:26:03,700] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 132
2023-01-11T21:41:23.6998566Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.6998790Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.6999278Z [2023-01-11 21:26:04,347] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 133
2023-01-11T21:41:23.6999793Z [2023-01-11 21:26:04,370] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 133
2023-01-11T21:41:23.7000651Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7000890Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7001377Z [2023-01-11 21:26:04,630] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 134
2023-01-11T21:41:23.7001875Z [2023-01-11 21:26:04,658] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 134
2023-01-11T21:41:23.7002733Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7002976Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7003461Z [2023-01-11 21:26:05,003] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 135
2023-01-11T21:41:23.7003960Z [2023-01-11 21:26:05,025] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 135
2023-01-11T21:41:23.7004817Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7005126Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7005613Z [2023-01-11 21:26:05,771] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 136
2023-01-11T21:41:23.7005623Z 
2023-01-11T21:41:23.7005811Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7005958Z import torch
2023-01-11T21:41:23.7006104Z import random
2023-01-11T21:41:23.7006332Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7006569Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7006577Z 
2023-01-11T21:41:23.7006737Z aten = torch.ops.aten
2023-01-11T21:41:23.7006980Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7007165Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7007173Z 
2023-01-11T21:41:23.7007180Z 
2023-01-11T21:41:23.7007445Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7007908Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7008146Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7008335Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7008463Z {
2023-01-11T21:41:23.7008657Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7008770Z     {
2023-01-11T21:41:23.7008925Z         #pragma omp for 
2023-01-11T21:41:23.7009087Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.7009217Z         {
2023-01-11T21:41:23.7009374Z             #pragma GCC ivdep
2023-01-11T21:41:23.7009542Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7009673Z             {
2023-01-11T21:41:23.7009787Z                 {
2023-01-11T21:41:23.7009919Z                     {
2023-01-11T21:41:23.7010111Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.7010295Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7010430Z                     }
2023-01-11T21:41:23.7010563Z                 }
2023-01-11T21:41:23.7010677Z             }
2023-01-11T21:41:23.7010806Z         }
2023-01-11T21:41:23.7010932Z     }
2023-01-11T21:41:23.7011055Z }
2023-01-11T21:41:23.7011214Z ''')
2023-01-11T21:41:23.7011223Z 
2023-01-11T21:41:23.7011230Z 
2023-01-11T21:41:23.7011410Z async_compile.wait(globals())
2023-01-11T21:41:23.7011560Z del async_compile
2023-01-11T21:41:23.7011567Z 
2023-01-11T21:41:23.7011711Z def call(args):
2023-01-11T21:41:23.7011920Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7012063Z     args.clear()
2023-01-11T21:41:23.7012504Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7012759Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7012897Z     del arg7_1
2023-01-11T21:41:23.7013159Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7013382Z     assert_size_stride(buf1, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:23.7013506Z     del arg0_1
2023-01-11T21:41:23.7013641Z     del arg1_1
2023-01-11T21:41:23.7013788Z     return (buf1, )
2023-01-11T21:41:23.7013796Z 
2023-01-11T21:41:23.7013803Z 
2023-01-11T21:41:23.7013952Z if __name__ == "__main__":
2023-01-11T21:41:23.7014174Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7014413Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7014823Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7015184Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7015529Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7015885Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7016322Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7016680Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7017013Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7017443Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7017734Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7017743Z 
2023-01-11T21:41:23.7017750Z 
2023-01-11T21:41:23.7017937Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7018082Z import torch
2023-01-11T21:41:23.7018211Z import random
2023-01-11T21:41:23.7018437Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7018674Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7018682Z 
2023-01-11T21:41:23.7018839Z aten = torch.ops.aten
2023-01-11T21:41:23.7019157Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7019345Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7019354Z 
2023-01-11T21:41:23.7019360Z 
2023-01-11T21:41:23.7019539Z async_compile.wait(globals())
2023-01-11T21:41:23.7019674Z del async_compile
2023-01-11T21:41:23.7019695Z 
2023-01-11T21:41:23.7019829Z def call(args):
2023-01-11T21:41:23.7020050Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7020195Z     args.clear()
2023-01-11T21:41:23.7020450Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7020663Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7020803Z     del arg0_1
2023-01-11T21:41:23.7020941Z     del arg1_1
2023-01-11T21:41:23.7021066Z     del arg7_1
2023-01-11T21:41:23.7021213Z     return (buf0, )
2023-01-11T21:41:23.7021221Z 
2023-01-11T21:41:23.7021232Z 
2023-01-11T21:41:23.7021389Z if __name__ == "__main__":
2023-01-11T21:41:23.7021610Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7021848Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7022253Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7022744Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7023104Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7023447Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7023802Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7024155Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7024494Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7024933Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7025227Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7025235Z 
2023-01-11T21:41:23.7025242Z 
2023-01-11T21:41:23.7025430Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7025573Z import torch
2023-01-11T21:41:23.7025702Z import random
2023-01-11T21:41:23.7025926Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7026164Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7026172Z 
2023-01-11T21:41:23.7026329Z aten = torch.ops.aten
2023-01-11T21:41:23.7026586Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7026766Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7026775Z 
2023-01-11T21:41:23.7026782Z 
2023-01-11T21:41:23.7027042Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7027430Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7027751Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7027942Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7028068Z {
2023-01-11T21:41:23.7028264Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7028391Z     {
2023-01-11T21:41:23.7028571Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7028729Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7028845Z         {
2023-01-11T21:41:23.7029019Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7029154Z             {
2023-01-11T21:41:23.7029284Z                 {
2023-01-11T21:41:23.7029418Z                     {
2023-01-11T21:41:23.7029609Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.7029789Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7029908Z                     }
2023-01-11T21:41:23.7030039Z                 }
2023-01-11T21:41:23.7030233Z             }
2023-01-11T21:41:23.7030365Z         }
2023-01-11T21:41:23.7030490Z     }
2023-01-11T21:41:23.7030614Z }
2023-01-11T21:41:23.7030759Z ''')
2023-01-11T21:41:23.7030781Z 
2023-01-11T21:41:23.7030788Z 
2023-01-11T21:41:23.7030953Z async_compile.wait(globals())
2023-01-11T21:41:23.7031100Z del async_compile
2023-01-11T21:41:23.7031108Z 
2023-01-11T21:41:23.7031253Z def call(args):
2023-01-11T21:41:23.7031473Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7031614Z     args.clear()
2023-01-11T21:41:23.7032050Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7032306Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7032431Z     del arg7_1
2023-01-11T21:41:23.7032690Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7032910Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7033052Z     del arg0_1
2023-01-11T21:41:23.7033191Z     del arg1_1
2023-01-11T21:41:23.7033336Z     return (buf1, )
2023-01-11T21:41:23.7033344Z 
2023-01-11T21:41:23.7033350Z 
2023-01-11T21:41:23.7033502Z if __name__ == "__main__":
2023-01-11T21:41:23.7033789Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7034018Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7034424Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7034788Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7035148Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7035507Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7035859Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7036219Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7036560Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7036970Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7037264Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7037273Z 
2023-01-11T21:41:23.7037280Z 
2023-01-11T21:41:23.7037471Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7037617Z import torch
2023-01-11T21:41:23.7037762Z import random
2023-01-11T21:41:23.7037989Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7038226Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7038234Z 
2023-01-11T21:41:23.7038391Z aten = torch.ops.aten
2023-01-11T21:41:23.7038633Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7038904Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7038912Z 
2023-01-11T21:41:23.7038919Z 
2023-01-11T21:41:23.7039098Z async_compile.wait(globals())
2023-01-11T21:41:23.7039246Z del async_compile
2023-01-11T21:41:23.7039253Z 
2023-01-11T21:41:23.7039396Z def call(args):
2023-01-11T21:41:23.7039620Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7039763Z     args.clear()
2023-01-11T21:41:23.7040022Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7040224Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7040363Z     del arg0_1
2023-01-11T21:41:23.7040502Z     del arg1_1
2023-01-11T21:41:23.7040637Z     del arg7_1
2023-01-11T21:41:23.7040784Z     return (buf0, )
2023-01-11T21:41:23.7040793Z 
2023-01-11T21:41:23.7040799Z 
2023-01-11T21:41:23.7040950Z if __name__ == "__main__":
2023-01-11T21:41:23.7041223Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7041468Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7041860Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7042224Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7042587Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7042947Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7043305Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7043661Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7043999Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7044439Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7044727Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7044735Z 
2023-01-11T21:41:23.7045247Z [2023-01-11 21:26:05,799] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 136
2023-01-11T21:41:23.7046105Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7046345Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7046836Z [2023-01-11 21:26:06,493] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 137
2023-01-11T21:41:23.7047347Z [2023-01-11 21:26:06,515] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 137
2023-01-11T21:41:23.7048203Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7048442Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7048928Z [2023-01-11 21:26:06,752] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 138
2023-01-11T21:41:23.7049431Z [2023-01-11 21:26:06,781] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 138
2023-01-11T21:41:23.7050291Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7050596Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7051070Z [2023-01-11 21:26:07,099] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 139
2023-01-11T21:41:23.7051574Z [2023-01-11 21:26:07,121] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 139
2023-01-11T21:41:23.7052425Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7052665Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7053206Z [2023-01-11 21:26:07,854] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 140
2023-01-11T21:41:23.7053216Z 
2023-01-11T21:41:23.7053409Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7053551Z import torch
2023-01-11T21:41:23.7053697Z import random
2023-01-11T21:41:23.7053921Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7054144Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7054170Z 
2023-01-11T21:41:23.7054315Z aten = torch.ops.aten
2023-01-11T21:41:23.7054577Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7054762Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7054770Z 
2023-01-11T21:41:23.7054777Z 
2023-01-11T21:41:23.7055034Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7055416Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7055658Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7055847Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7055958Z {
2023-01-11T21:41:23.7056154Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7056281Z     {
2023-01-11T21:41:23.7056439Z         #pragma omp for 
2023-01-11T21:41:23.7056602Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.7056731Z         {
2023-01-11T21:41:23.7056888Z             #pragma GCC ivdep
2023-01-11T21:41:23.7057046Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7057175Z             {
2023-01-11T21:41:23.7057307Z                 {
2023-01-11T21:41:23.7057441Z                     {
2023-01-11T21:41:23.7057636Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.7057821Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7057954Z                     }
2023-01-11T21:41:23.7058071Z                 }
2023-01-11T21:41:23.7058199Z             }
2023-01-11T21:41:23.7058336Z         }
2023-01-11T21:41:23.7058463Z     }
2023-01-11T21:41:23.7058587Z }
2023-01-11T21:41:23.7058743Z ''')
2023-01-11T21:41:23.7058751Z 
2023-01-11T21:41:23.7058757Z 
2023-01-11T21:41:23.7058940Z async_compile.wait(globals())
2023-01-11T21:41:23.7059076Z del async_compile
2023-01-11T21:41:23.7059084Z 
2023-01-11T21:41:23.7059229Z def call(args):
2023-01-11T21:41:23.7059453Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7059599Z     args.clear()
2023-01-11T21:41:23.7060040Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7060296Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7060435Z     del arg7_1
2023-01-11T21:41:23.7060682Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7060902Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7061100Z     del arg0_1
2023-01-11T21:41:23.7061238Z     del arg1_1
2023-01-11T21:41:23.7061381Z     return (buf1, )
2023-01-11T21:41:23.7061389Z 
2023-01-11T21:41:23.7061396Z 
2023-01-11T21:41:23.7061546Z if __name__ == "__main__":
2023-01-11T21:41:23.7061768Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7062008Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7062526Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7062898Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7063261Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7063619Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7063976Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7064409Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7064747Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7065177Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7065455Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7065479Z 
2023-01-11T21:41:23.7065486Z 
2023-01-11T21:41:23.7065663Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7065805Z import torch
2023-01-11T21:41:23.7065951Z import random
2023-01-11T21:41:23.7066176Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7066416Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7066424Z 
2023-01-11T21:41:23.7066583Z aten = torch.ops.aten
2023-01-11T21:41:23.7066841Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7067017Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7067024Z 
2023-01-11T21:41:23.7067048Z 
2023-01-11T21:41:23.7067212Z async_compile.wait(globals())
2023-01-11T21:41:23.7067360Z del async_compile
2023-01-11T21:41:23.7067369Z 
2023-01-11T21:41:23.7067502Z def call(args):
2023-01-11T21:41:23.7067709Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7067812Z     args.clear()
2023-01-11T21:41:23.7068016Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7068184Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7068266Z     del arg0_1
2023-01-11T21:41:23.7068353Z     del arg1_1
2023-01-11T21:41:23.7068444Z     del arg7_1
2023-01-11T21:41:23.7068538Z     return (buf0, )
2023-01-11T21:41:23.7068545Z 
2023-01-11T21:41:23.7068550Z 
2023-01-11T21:41:23.7068651Z if __name__ == "__main__":
2023-01-11T21:41:23.7068820Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7068998Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7069322Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7069598Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7069890Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7070169Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7070447Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7070728Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7071008Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7071357Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7071685Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7071694Z 
2023-01-11T21:41:23.7071700Z 
2023-01-11T21:41:23.7071832Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7071917Z import torch
2023-01-11T21:41:23.7072015Z import random
2023-01-11T21:41:23.7072178Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7072349Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7072356Z 
2023-01-11T21:41:23.7072471Z aten = torch.ops.aten
2023-01-11T21:41:23.7072650Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7072780Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7072787Z 
2023-01-11T21:41:23.7072792Z 
2023-01-11T21:41:23.7072968Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7073256Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7073427Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7073626Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7073712Z {
2023-01-11T21:41:23.7073923Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7074017Z     {
2023-01-11T21:41:23.7074135Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7074250Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7074342Z         {
2023-01-11T21:41:23.7074469Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7074556Z             {
2023-01-11T21:41:23.7074641Z                 {
2023-01-11T21:41:23.7074732Z                     {
2023-01-11T21:41:23.7074864Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.7075000Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7075093Z                     }
2023-01-11T21:41:23.7075184Z                 }
2023-01-11T21:41:23.7075276Z             }
2023-01-11T21:41:23.7075362Z         }
2023-01-11T21:41:23.7075449Z     }
2023-01-11T21:41:23.7075522Z }
2023-01-11T21:41:23.7075645Z ''')
2023-01-11T21:41:23.7075653Z 
2023-01-11T21:41:23.7075659Z 
2023-01-11T21:41:23.7075785Z async_compile.wait(globals())
2023-01-11T21:41:23.7075890Z del async_compile
2023-01-11T21:41:23.7075898Z 
2023-01-11T21:41:23.7075986Z def call(args):
2023-01-11T21:41:23.7076156Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7076258Z     args.clear()
2023-01-11T21:41:23.7076597Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7076795Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7076891Z     del arg7_1
2023-01-11T21:41:23.7077091Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7077261Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7077359Z     del arg0_1
2023-01-11T21:41:23.7077459Z     del arg1_1
2023-01-11T21:41:23.7077553Z     return (buf1, )
2023-01-11T21:41:23.7077561Z 
2023-01-11T21:41:23.7077568Z 
2023-01-11T21:41:23.7077665Z if __name__ == "__main__":
2023-01-11T21:41:23.7077830Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7078013Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7078343Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7078639Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7078915Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7079203Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7079482Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7079745Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7080099Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7080452Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7080676Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7080685Z 
2023-01-11T21:41:23.7080690Z 
2023-01-11T21:41:23.7080822Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7080924Z import torch
2023-01-11T21:41:23.7081025Z import random
2023-01-11T21:41:23.7081181Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7081341Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7081348Z 
2023-01-11T21:41:23.7081455Z aten = torch.ops.aten
2023-01-11T21:41:23.7081648Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7081784Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7081790Z 
2023-01-11T21:41:23.7081801Z 
2023-01-11T21:41:23.7082009Z async_compile.wait(globals())
2023-01-11T21:41:23.7082116Z del async_compile
2023-01-11T21:41:23.7082122Z 
2023-01-11T21:41:23.7082223Z def call(args):
2023-01-11T21:41:23.7082393Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7082486Z     args.clear()
2023-01-11T21:41:23.7082679Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7082850Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7082939Z     del arg0_1
2023-01-11T21:41:23.7083026Z     del arg1_1
2023-01-11T21:41:23.7083118Z     del arg7_1
2023-01-11T21:41:23.7083217Z     return (buf0, )
2023-01-11T21:41:23.7083224Z 
2023-01-11T21:41:23.7083231Z 
2023-01-11T21:41:23.7083338Z if __name__ == "__main__":
2023-01-11T21:41:23.7083495Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7083667Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7084045Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7084367Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7084674Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7084980Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7085293Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7085602Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7085876Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7086258Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7086512Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7086529Z 
2023-01-11T21:41:23.7086984Z [2023-01-11 21:26:07,882] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 140
2023-01-11T21:41:23.7087718Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7087918Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7088353Z [2023-01-11 21:26:08,571] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 141
2023-01-11T21:41:23.7088794Z [2023-01-11 21:26:08,593] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 141
2023-01-11T21:41:23.7089517Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7089805Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7090234Z [2023-01-11 21:26:08,865] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 142
2023-01-11T21:41:23.7090654Z [2023-01-11 21:26:08,893] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 142
2023-01-11T21:41:23.7091416Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7091626Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7092054Z [2023-01-11 21:26:09,277] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 143
2023-01-11T21:41:23.7092497Z [2023-01-11 21:26:09,299] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 143
2023-01-11T21:41:23.7093229Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7093428Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7093859Z [2023-01-11 21:26:10,089] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 144
2023-01-11T21:41:23.7093874Z 
2023-01-11T21:41:23.7094023Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7094127Z import torch
2023-01-11T21:41:23.7094228Z import random
2023-01-11T21:41:23.7094416Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7094609Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7094618Z 
2023-01-11T21:41:23.7094744Z aten = torch.ops.aten
2023-01-11T21:41:23.7094958Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7095104Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7095111Z 
2023-01-11T21:41:23.7095118Z 
2023-01-11T21:41:23.7095346Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7095687Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7095874Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7096016Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7096123Z {
2023-01-11T21:41:23.7096286Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7096387Z     {
2023-01-11T21:41:23.7096514Z         #pragma omp for 
2023-01-11T21:41:23.7096643Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.7096728Z         {
2023-01-11T21:41:23.7096862Z             #pragma GCC ivdep
2023-01-11T21:41:23.7097007Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7097112Z             {
2023-01-11T21:41:23.7097220Z                 {
2023-01-11T21:41:23.7097326Z                     {
2023-01-11T21:41:23.7097490Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.7097633Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7097744Z                     }
2023-01-11T21:41:23.7097850Z                 }
2023-01-11T21:41:23.7097953Z             }
2023-01-11T21:41:23.7098055Z         }
2023-01-11T21:41:23.7098153Z     }
2023-01-11T21:41:23.7098253Z }
2023-01-11T21:41:23.7098454Z ''')
2023-01-11T21:41:23.7098466Z 
2023-01-11T21:41:23.7098474Z 
2023-01-11T21:41:23.7098627Z async_compile.wait(globals())
2023-01-11T21:41:23.7098743Z del async_compile
2023-01-11T21:41:23.7098751Z 
2023-01-11T21:41:23.7098863Z def call(args):
2023-01-11T21:41:23.7099049Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7099162Z     args.clear()
2023-01-11T21:41:23.7099563Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7099758Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7099865Z     del arg7_1
2023-01-11T21:41:23.7100086Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7100267Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:23.7100380Z     del arg0_1
2023-01-11T21:41:23.7100487Z     del arg1_1
2023-01-11T21:41:23.7100662Z     return (buf1, )
2023-01-11T21:41:23.7100673Z 
2023-01-11T21:41:23.7100679Z 
2023-01-11T21:41:23.7100805Z if __name__ == "__main__":
2023-01-11T21:41:23.7100974Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7101172Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7101533Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7101847Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7102146Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7102572Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7102885Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7103190Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7103478Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7103866Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7104111Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7104122Z 
2023-01-11T21:41:23.7104129Z 
2023-01-11T21:41:23.7104277Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7104386Z import torch
2023-01-11T21:41:23.7104496Z import random
2023-01-11T21:41:23.7104680Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7104879Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7104886Z 
2023-01-11T21:41:23.7104988Z aten = torch.ops.aten
2023-01-11T21:41:23.7105202Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7105341Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7105349Z 
2023-01-11T21:41:23.7105356Z 
2023-01-11T21:41:23.7105504Z async_compile.wait(globals())
2023-01-11T21:41:23.7105613Z del async_compile
2023-01-11T21:41:23.7105620Z 
2023-01-11T21:41:23.7105726Z def call(args):
2023-01-11T21:41:23.7105907Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7106019Z     args.clear()
2023-01-11T21:41:23.7106233Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7106420Z     assert_size_stride(buf0, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:23.7106526Z     del arg0_1
2023-01-11T21:41:23.7106630Z     del arg1_1
2023-01-11T21:41:23.7106733Z     del arg7_1
2023-01-11T21:41:23.7106845Z     return (buf0, )
2023-01-11T21:41:23.7106852Z 
2023-01-11T21:41:23.7106859Z 
2023-01-11T21:41:23.7106978Z if __name__ == "__main__":
2023-01-11T21:41:23.7107165Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7107352Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7107842Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7108161Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7108477Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7108782Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7109086Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7109388Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7109677Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7110044Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7110291Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7110305Z 
2023-01-11T21:41:23.7110392Z 
2023-01-11T21:41:23.7110546Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7110663Z import torch
2023-01-11T21:41:23.7110775Z import random
2023-01-11T21:41:23.7110958Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7111150Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7111157Z 
2023-01-11T21:41:23.7111278Z aten = torch.ops.aten
2023-01-11T21:41:23.7111486Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7111635Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7111643Z 
2023-01-11T21:41:23.7111649Z 
2023-01-11T21:41:23.7111875Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7112205Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7112401Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7112557Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7112654Z {
2023-01-11T21:41:23.7112815Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7112892Z     {
2023-01-11T21:41:23.7113033Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7113166Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7113264Z         {
2023-01-11T21:41:23.7113405Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7113503Z             {
2023-01-11T21:41:23.7113588Z                 {
2023-01-11T21:41:23.7113689Z                     {
2023-01-11T21:41:23.7113923Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.7114083Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7114186Z                     }
2023-01-11T21:41:23.7114287Z                 }
2023-01-11T21:41:23.7114382Z             }
2023-01-11T21:41:23.7114466Z         }
2023-01-11T21:41:23.7114563Z     }
2023-01-11T21:41:23.7114655Z }
2023-01-11T21:41:23.7114792Z ''')
2023-01-11T21:41:23.7114801Z 
2023-01-11T21:41:23.7114813Z 
2023-01-11T21:41:23.7114967Z async_compile.wait(globals())
2023-01-11T21:41:23.7115090Z del async_compile
2023-01-11T21:41:23.7115097Z 
2023-01-11T21:41:23.7115209Z def call(args):
2023-01-11T21:41:23.7115388Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7115503Z     args.clear()
2023-01-11T21:41:23.7115896Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7116115Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7116225Z     del arg7_1
2023-01-11T21:41:23.7116455Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7116638Z     assert_size_stride(buf1, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:23.7116748Z     del arg0_1
2023-01-11T21:41:23.7116843Z     del arg1_1
2023-01-11T21:41:23.7116957Z     return (buf1, )
2023-01-11T21:41:23.7117041Z 
2023-01-11T21:41:23.7117052Z 
2023-01-11T21:41:23.7117176Z if __name__ == "__main__":
2023-01-11T21:41:23.7117357Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7117531Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7117860Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7118149Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7118430Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7118717Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7119025Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7119333Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7119623Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7120063Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7120316Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7120324Z 
2023-01-11T21:41:23.7120331Z 
2023-01-11T21:41:23.7120479Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7120592Z import torch
2023-01-11T21:41:23.7120680Z import random
2023-01-11T21:41:23.7120844Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7121029Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7121038Z 
2023-01-11T21:41:23.7121161Z aten = torch.ops.aten
2023-01-11T21:41:23.7121355Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7121498Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7121506Z 
2023-01-11T21:41:23.7121513Z 
2023-01-11T21:41:23.7121656Z async_compile.wait(globals())
2023-01-11T21:41:23.7121772Z del async_compile
2023-01-11T21:41:23.7121789Z 
2023-01-11T21:41:23.7121885Z def call(args):
2023-01-11T21:41:23.7122054Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7122153Z     args.clear()
2023-01-11T21:41:23.7122381Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7122549Z     assert_size_stride(buf0, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:23.7122644Z     del arg0_1
2023-01-11T21:41:23.7122745Z     del arg1_1
2023-01-11T21:41:23.7122845Z     del arg7_1
2023-01-11T21:41:23.7122936Z     return (buf0, )
2023-01-11T21:41:23.7122944Z 
2023-01-11T21:41:23.7122949Z 
2023-01-11T21:41:23.7123053Z if __name__ == "__main__":
2023-01-11T21:41:23.7123233Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7123425Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7123766Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7124082Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7124373Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7124672Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7124964Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7125244Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7125503Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7125859Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7126113Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7126122Z 
2023-01-11T21:41:23.7126529Z [2023-01-11 21:26:10,117] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 144
2023-01-11T21:41:23.7127214Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7127387Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7127755Z [2023-01-11 21:26:10,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 145
2023-01-11T21:41:23.7128126Z [2023-01-11 21:26:10,844] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 145
2023-01-11T21:41:23.7128779Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7128960Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7129335Z [2023-01-11 21:26:11,159] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 146
2023-01-11T21:41:23.7129729Z [2023-01-11 21:26:11,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 146
2023-01-11T21:41:23.7130328Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7130496Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7130863Z [2023-01-11 21:26:11,575] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 147
2023-01-11T21:41:23.7131236Z [2023-01-11 21:26:11,597] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 147
2023-01-11T21:41:23.7131838Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7132011Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7132379Z [2023-01-11 21:26:12,268] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 148
2023-01-11T21:41:23.7132387Z 
2023-01-11T21:41:23.7132520Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7132599Z import torch
2023-01-11T21:41:23.7132693Z import random
2023-01-11T21:41:23.7132851Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7133014Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7133021Z 
2023-01-11T21:41:23.7133126Z aten = torch.ops.aten
2023-01-11T21:41:23.7133306Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7133428Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7133435Z 
2023-01-11T21:41:23.7133442Z 
2023-01-11T21:41:23.7133625Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7133895Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7134056Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7134191Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7134273Z {
2023-01-11T21:41:23.7134407Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7134553Z     {
2023-01-11T21:41:23.7134657Z         #pragma omp for 
2023-01-11T21:41:23.7134751Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.7134839Z         {
2023-01-11T21:41:23.7134949Z             #pragma GCC ivdep
2023-01-11T21:41:23.7135069Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7135155Z             {
2023-01-11T21:41:23.7135241Z                 {
2023-01-11T21:41:23.7135315Z                     {
2023-01-11T21:41:23.7135452Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.7135583Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7135676Z                     }
2023-01-11T21:41:23.7135765Z                 }
2023-01-11T21:41:23.7135852Z             }
2023-01-11T21:41:23.7135934Z         }
2023-01-11T21:41:23.7136003Z     }
2023-01-11T21:41:23.7136079Z }
2023-01-11T21:41:23.7136192Z ''')
2023-01-11T21:41:23.7136199Z 
2023-01-11T21:41:23.7136204Z 
2023-01-11T21:41:23.7136380Z async_compile.wait(globals())
2023-01-11T21:41:23.7136483Z del async_compile
2023-01-11T21:41:23.7136489Z 
2023-01-11T21:41:23.7136583Z def call(args):
2023-01-11T21:41:23.7136753Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7136834Z     args.clear()
2023-01-11T21:41:23.7137171Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7137354Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7137445Z     del arg7_1
2023-01-11T21:41:23.7137648Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7137813Z     assert_size_stride(buf1, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:23.7137906Z     del arg0_1
2023-01-11T21:41:23.7138001Z     del arg1_1
2023-01-11T21:41:23.7138092Z     return (buf1, )
2023-01-11T21:41:23.7138099Z 
2023-01-11T21:41:23.7138110Z 
2023-01-11T21:41:23.7138207Z if __name__ == "__main__":
2023-01-11T21:41:23.7138358Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7138518Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7138836Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7139112Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7139383Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7139670Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7139958Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7140237Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7140503Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7140838Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7141061Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7141069Z 
2023-01-11T21:41:23.7141076Z 
2023-01-11T21:41:23.7141206Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7141302Z import torch
2023-01-11T21:41:23.7141404Z import random
2023-01-11T21:41:23.7141560Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7141740Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7141747Z 
2023-01-11T21:41:23.7141855Z aten = torch.ops.aten
2023-01-11T21:41:23.7142054Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7142185Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7142193Z 
2023-01-11T21:41:23.7142199Z 
2023-01-11T21:41:23.7142488Z async_compile.wait(globals())
2023-01-11T21:41:23.7142601Z del async_compile
2023-01-11T21:41:23.7142720Z 
2023-01-11T21:41:23.7142822Z def call(args):
2023-01-11T21:41:23.7142975Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7143075Z     args.clear()
2023-01-11T21:41:23.7143272Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7143445Z     assert_size_stride(buf0, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:23.7143545Z     del arg0_1
2023-01-11T21:41:23.7143639Z     del arg1_1
2023-01-11T21:41:23.7143727Z     del arg7_1
2023-01-11T21:41:23.7143817Z     return (buf0, )
2023-01-11T21:41:23.7143839Z 
2023-01-11T21:41:23.7143846Z 
2023-01-11T21:41:23.7143941Z if __name__ == "__main__":
2023-01-11T21:41:23.7144114Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7144299Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7144654Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7145042Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7145333Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7145613Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7145884Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7146122Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7146353Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7146675Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7146884Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7146892Z 
2023-01-11T21:41:23.7146897Z 
2023-01-11T21:41:23.7147019Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7147114Z import torch
2023-01-11T21:41:23.7147207Z import random
2023-01-11T21:41:23.7147364Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7147514Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7147521Z 
2023-01-11T21:41:23.7147624Z aten = torch.ops.aten
2023-01-11T21:41:23.7147805Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7147925Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7147932Z 
2023-01-11T21:41:23.7147938Z 
2023-01-11T21:41:23.7148128Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7148410Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7148571Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7148705Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7148772Z {
2023-01-11T21:41:23.7148907Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7148993Z     {
2023-01-11T21:41:23.7149125Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7149240Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7149325Z         {
2023-01-11T21:41:23.7149449Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7149524Z             {
2023-01-11T21:41:23.7149615Z                 {
2023-01-11T21:41:23.7149703Z                     {
2023-01-11T21:41:23.7149841Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.7149980Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7150075Z                     }
2023-01-11T21:41:23.7150166Z                 }
2023-01-11T21:41:23.7150244Z             }
2023-01-11T21:41:23.7150332Z         }
2023-01-11T21:41:23.7150419Z     }
2023-01-11T21:41:23.7150507Z }
2023-01-11T21:41:23.7150619Z ''')
2023-01-11T21:41:23.7150628Z 
2023-01-11T21:41:23.7150634Z 
2023-01-11T21:41:23.7150762Z async_compile.wait(globals())
2023-01-11T21:41:23.7150858Z del async_compile
2023-01-11T21:41:23.7150969Z 
2023-01-11T21:41:23.7151050Z def call(args):
2023-01-11T21:41:23.7151212Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7151302Z     args.clear()
2023-01-11T21:41:23.7151691Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7151918Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7152027Z     del arg7_1
2023-01-11T21:41:23.7152265Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:23.7152444Z     assert_size_stride(buf1, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:23.7152556Z     del arg0_1
2023-01-11T21:41:23.7152666Z     del arg1_1
2023-01-11T21:41:23.7152780Z     return (buf1, )
2023-01-11T21:41:23.7152789Z 
2023-01-11T21:41:23.7152796Z 
2023-01-11T21:41:23.7152919Z if __name__ == "__main__":
2023-01-11T21:41:23.7153171Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7153379Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7153834Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7154155Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7154487Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7154819Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7155151Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7155481Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7155797Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7156201Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7156477Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7156487Z 
2023-01-11T21:41:23.7156495Z 
2023-01-11T21:41:23.7156657Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7156761Z import torch
2023-01-11T21:41:23.7156879Z import random
2023-01-11T21:41:23.7157082Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7157288Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7157296Z 
2023-01-11T21:41:23.7157430Z aten = torch.ops.aten
2023-01-11T21:41:23.7157668Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7157824Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7157831Z 
2023-01-11T21:41:23.7157838Z 
2023-01-11T21:41:23.7157976Z async_compile.wait(globals())
2023-01-11T21:41:23.7158101Z del async_compile
2023-01-11T21:41:23.7158108Z 
2023-01-11T21:41:23.7158228Z def call(args):
2023-01-11T21:41:23.7158437Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7158551Z     args.clear()
2023-01-11T21:41:23.7158794Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7158994Z     assert_size_stride(buf0, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:23.7159106Z     del arg0_1
2023-01-11T21:41:23.7159207Z     del arg1_1
2023-01-11T21:41:23.7159318Z     del arg7_1
2023-01-11T21:41:23.7159438Z     return (buf0, )
2023-01-11T21:41:23.7159445Z 
2023-01-11T21:41:23.7159453Z 
2023-01-11T21:41:23.7159582Z if __name__ == "__main__":
2023-01-11T21:41:23.7159779Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7159988Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7160382Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7160730Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7161117Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7161467Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7161801Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7162144Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7162467Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7162889Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7163156Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7163164Z 
2023-01-11T21:41:23.7163654Z [2023-01-11 21:26:12,295] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 148
2023-01-11T21:41:23.7163664Z 
2023-01-11T21:41:23.7163871Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7163973Z import torch
2023-01-11T21:41:23.7164091Z import random
2023-01-11T21:41:23.7164291Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7164500Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7164508Z 
2023-01-11T21:41:23.7164641Z aten = torch.ops.aten
2023-01-11T21:41:23.7164875Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7165027Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7165036Z 
2023-01-11T21:41:23.7165043Z 
2023-01-11T21:41:23.7165262Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7165627Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7165830Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7165994Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7166099Z {
2023-01-11T21:41:23.7166272Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7166377Z     {
2023-01-11T21:41:23.7166492Z         #pragma omp for 
2023-01-11T21:41:23.7166632Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.7166739Z         {
2023-01-11T21:41:23.7166873Z             #pragma GCC ivdep
2023-01-11T21:41:23.7167024Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:23.7167133Z             {
2023-01-11T21:41:23.7167241Z                 {
2023-01-11T21:41:23.7167338Z                     {
2023-01-11T21:41:23.7167509Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:23.7167672Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:23.7167782Z                     }
2023-01-11T21:41:23.7167889Z                 }
2023-01-11T21:41:23.7167993Z             }
2023-01-11T21:41:23.7168097Z         }
2023-01-11T21:41:23.7168183Z     }
2023-01-11T21:41:23.7168281Z }
2023-01-11T21:41:23.7168416Z ''')
2023-01-11T21:41:23.7168423Z 
2023-01-11T21:41:23.7168435Z 
2023-01-11T21:41:23.7168588Z async_compile.wait(globals())
2023-01-11T21:41:23.7178381Z del async_compile
2023-01-11T21:41:23.7178399Z 
2023-01-11T21:41:23.7178555Z def call(args):
2023-01-11T21:41:23.7178739Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:23.7178838Z     args.clear()
2023-01-11T21:41:23.7179234Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7179412Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7179488Z     del arg7_1
2023-01-11T21:41:23.7179684Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:23.7179849Z     assert_size_stride(buf1, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:23.7179938Z     del arg0_1
2023-01-11T21:41:23.7180028Z     del arg1_1
2023-01-11T21:41:23.7180125Z     return (buf1, )
2023-01-11T21:41:23.7180239Z 
2023-01-11T21:41:23.7180250Z 
2023-01-11T21:41:23.7180348Z if __name__ == "__main__":
2023-01-11T21:41:23.7180509Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7180658Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7180986Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7181259Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7181525Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7181793Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7182079Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7182566Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7182842Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7183292Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7183508Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:23.7183515Z 
2023-01-11T21:41:23.7183611Z ok (37.036s)
2023-01-11T21:41:23.7184344Z   test_conv_functional_bn_fuse_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7184535Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7184934Z [2023-01-11 21:26:12,853] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 149
2023-01-11T21:41:23.7185328Z [2023-01-11 21:26:14,406] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 149
2023-01-11T21:41:23.7185336Z 
2023-01-11T21:41:23.7185463Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7185557Z import torch
2023-01-11T21:41:23.7185639Z import random
2023-01-11T21:41:23.7185805Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7185977Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7185985Z 
2023-01-11T21:41:23.7186090Z aten = torch.ops.aten
2023-01-11T21:41:23.7186283Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7186403Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7186410Z 
2023-01-11T21:41:23.7186416Z 
2023-01-11T21:41:23.7186610Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7186914Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7187097Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7187226Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7187315Z {
2023-01-11T21:41:23.7187457Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7187539Z     {
2023-01-11T21:41:23.7187669Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7187787Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7187865Z         {
2023-01-11T21:41:23.7187992Z             for(long i1=0; i1<31136; i1+=1)
2023-01-11T21:41:23.7188085Z             {
2023-01-11T21:41:23.7188173Z                 {
2023-01-11T21:41:23.7188265Z                     {
2023-01-11T21:41:23.7188412Z                         auto tmp0 = in_ptr0[i1 + (31136*i0)];
2023-01-11T21:41:23.7188540Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.7188618Z                     }
2023-01-11T21:41:23.7188707Z                 }
2023-01-11T21:41:23.7188795Z             }
2023-01-11T21:41:23.7188883Z         }
2023-01-11T21:41:23.7189062Z     }
2023-01-11T21:41:23.7189149Z }
2023-01-11T21:41:23.7189270Z ''')
2023-01-11T21:41:23.7189279Z 
2023-01-11T21:41:23.7189284Z 
2023-01-11T21:41:23.7189466Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.7189767Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7189935Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7190078Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7190162Z {
2023-01-11T21:41:23.7190303Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7190393Z     {
2023-01-11T21:41:23.7190491Z         #pragma omp for 
2023-01-11T21:41:23.7190607Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7190690Z         {
2023-01-11T21:41:23.7190802Z             #pragma GCC ivdep
2023-01-11T21:41:23.7190931Z             for(long i1=0; i1<29916; i1+=1)
2023-01-11T21:41:23.7191016Z             {
2023-01-11T21:41:23.7191105Z                 {
2023-01-11T21:41:23.7191236Z                     {
2023-01-11T21:41:23.7191379Z                         auto tmp0 = in_ptr0[i0 + (64*i1)];
2023-01-11T21:41:23.7191518Z                         out_ptr0[i1 + (29916*i0)] = tmp0;
2023-01-11T21:41:23.7191610Z                     }
2023-01-11T21:41:23.7191701Z                 }
2023-01-11T21:41:23.7191791Z             }
2023-01-11T21:41:23.7191879Z         }
2023-01-11T21:41:23.7191949Z     }
2023-01-11T21:41:23.7192030Z }
2023-01-11T21:41:23.7192143Z ''')
2023-01-11T21:41:23.7192151Z 
2023-01-11T21:41:23.7192157Z 
2023-01-11T21:41:23.7192284Z async_compile.wait(globals())
2023-01-11T21:41:23.7192381Z del async_compile
2023-01-11T21:41:23.7192387Z 
2023-01-11T21:41:23.7192484Z def call(args):
2023-01-11T21:41:23.7192642Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1 = args
2023-01-11T21:41:23.7192727Z     args.clear()
2023-01-11T21:41:23.7193047Z     buf0 = empty_strided((1, 3, 556, 56), (93408, 1, 168, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7193253Z     kernel_cpp_0(c_void_p(arg6_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7193348Z     del arg6_1
2023-01-11T21:41:23.7193816Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg2_1, arg3_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:23.7193975Z     assert_size_stride(buf1, (1, 64, 554, 54), (1914624, 1, 3456, 64))
2023-01-11T21:41:23.7194070Z     del arg2_1
2023-01-11T21:41:23.7194149Z     del arg3_1
2023-01-11T21:41:23.7194247Z     del buf0
2023-01-11T21:41:23.7194582Z     buf2 = empty_strided((1, 64, 554, 54), (1914624, 29916, 54, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7194776Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.7194876Z     return (buf2, )
2023-01-11T21:41:23.7194883Z 
2023-01-11T21:41:23.7194889Z 
2023-01-11T21:41:23.7194997Z if __name__ == "__main__":
2023-01-11T21:41:23.7195164Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7195348Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7195623Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7195901Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7196215Z     arg2_1 = rand_strided((64, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7196501Z     arg3_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7196779Z     arg4_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7197059Z     arg5_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7197396Z     arg6_1 = rand_strided((1, 3, 556, 56), (93408, 31136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7197619Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1]))
2023-01-11T21:41:23.7197626Z 
2023-01-11T21:41:23.7197717Z ok (1.709s)
2023-01-11T21:41:23.7198502Z   test_convolution1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7198685Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7199100Z [2023-01-11 21:26:14,534] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 150
2023-01-11T21:41:23.7199513Z [2023-01-11 21:26:16,103] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 150
2023-01-11T21:41:23.7199522Z 
2023-01-11T21:41:23.7199665Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7199766Z import torch
2023-01-11T21:41:23.7199861Z import random
2023-01-11T21:41:23.7200088Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7200277Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7200284Z 
2023-01-11T21:41:23.7200382Z aten = torch.ops.aten
2023-01-11T21:41:23.7200581Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7200716Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7200724Z 
2023-01-11T21:41:23.7200729Z 
2023-01-11T21:41:23.7200932Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7201236Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7201412Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.7201549Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:23.7201637Z {
2023-01-11T21:41:23.7201774Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7201860Z     {
2023-01-11T21:41:23.7201972Z         #pragma omp for 
2023-01-11T21:41:23.7202095Z         for(long i0=0; i0<2352; i0+=1)
2023-01-11T21:41:23.7202195Z         {
2023-01-11T21:41:23.7202282Z             {
2023-01-11T21:41:23.7202357Z                 {
2023-01-11T21:41:23.7202495Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7202631Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.7202786Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:41:23.7202923Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:41:23.7203054Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.7203175Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.7203254Z                 }
2023-01-11T21:41:23.7203347Z             }
2023-01-11T21:41:23.7203439Z         }
2023-01-11T21:41:23.7203524Z     }
2023-01-11T21:41:23.7203612Z }
2023-01-11T21:41:23.7203734Z ''')
2023-01-11T21:41:23.7203743Z 
2023-01-11T21:41:23.7203749Z 
2023-01-11T21:41:23.7203882Z async_compile.wait(globals())
2023-01-11T21:41:23.7203973Z del async_compile
2023-01-11T21:41:23.7203996Z 
2023-01-11T21:41:23.7204087Z def call(args):
2023-01-11T21:41:23.7204238Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.7204348Z     args.clear()
2023-01-11T21:41:23.7204575Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:23.7204733Z     assert_size_stride(buf0, (2, 6, 14, 14), (1176, 196, 14, 1))
2023-01-11T21:41:23.7204838Z     del primals_2
2023-01-11T21:41:23.7205010Z     buf1 = as_strided(buf0, (2, 6, 14, 14), (1176, 196, 14, 1)); del buf0  # reuse
2023-01-11T21:41:23.7205331Z     buf2 = empty_strided((2, 6, 14, 14), (1176, 196, 14, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.7205526Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.7205679Z     return (buf1, primals_1, primals_3, buf2, )
2023-01-11T21:41:23.7205686Z 
2023-01-11T21:41:23.7205693Z 
2023-01-11T21:41:23.7205801Z if __name__ == "__main__":
2023-01-11T21:41:23.7205969Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7206222Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7206559Z     primals_1 = rand_strided((6, 5, 3, 3), (45, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7206862Z     primals_2 = rand_strided((6, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7207187Z     primals_3 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7207390Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.7207399Z 
2023-01-11T21:41:23.7207496Z ok (1.655s)
2023-01-11T21:41:23.7208202Z   test_convolution2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7208442Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7208841Z [2023-01-11 21:26:16,146] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 151
2023-01-11T21:41:23.7209241Z [2023-01-11 21:26:16,168] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 151
2023-01-11T21:41:23.7209250Z 
2023-01-11T21:41:23.7209388Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7209492Z import torch
2023-01-11T21:41:23.7209590Z import random
2023-01-11T21:41:23.7209742Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7209911Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7209918Z 
2023-01-11T21:41:23.7210020Z aten = torch.ops.aten
2023-01-11T21:41:23.7210206Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7210323Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7210330Z 
2023-01-11T21:41:23.7210340Z 
2023-01-11T21:41:23.7210455Z async_compile.wait(globals())
2023-01-11T21:41:23.7210547Z del async_compile
2023-01-11T21:41:23.7210553Z 
2023-01-11T21:41:23.7210642Z def call(args):
2023-01-11T21:41:23.7210740Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.7210845Z     args.clear()
2023-01-11T21:41:23.7211064Z     buf0 = aten.convolution(arg0_1, arg1_1, arg2_1, (4,), (0,), (1,), True, (0,), 1)
2023-01-11T21:41:23.7211221Z     assert_size_stride(buf0, (2, 16, 364), (5824, 364, 1))
2023-01-11T21:41:23.7211321Z     del arg0_1
2023-01-11T21:41:23.7211428Z     del arg1_1
2023-01-11T21:41:23.7211517Z     del arg2_1
2023-01-11T21:41:23.7211623Z     return (buf0, )
2023-01-11T21:41:23.7211629Z 
2023-01-11T21:41:23.7211636Z 
2023-01-11T21:41:23.7211744Z if __name__ == "__main__":
2023-01-11T21:41:23.7211897Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7212080Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7212412Z     arg0_1 = rand_strided((2, 32, 90), (2880, 90, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7212719Z     arg1_1 = rand_strided((32, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7212993Z     arg2_1 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7213144Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.7213166Z 
2023-01-11T21:41:23.7213240Z ok (0.065s)
2023-01-11T21:41:23.7213876Z   test_cos_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7214042Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7214422Z [2023-01-11 21:26:16,201] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 152
2023-01-11T21:41:23.7214896Z [2023-01-11 21:26:17,708] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 152
2023-01-11T21:41:23.7214904Z 
2023-01-11T21:41:23.7215040Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7215137Z import torch
2023-01-11T21:41:23.7215231Z import random
2023-01-11T21:41:23.7215400Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7215564Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7215571Z 
2023-01-11T21:41:23.7215680Z aten = torch.ops.aten
2023-01-11T21:41:23.7215876Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7216007Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7216015Z 
2023-01-11T21:41:23.7216022Z 
2023-01-11T21:41:23.7216224Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7216606Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7216779Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7216926Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7217049Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.7217136Z {
2023-01-11T21:41:23.7217286Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7217371Z     {
2023-01-11T21:41:23.7217474Z         #pragma omp for 
2023-01-11T21:41:23.7217595Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.7217677Z         {
2023-01-11T21:41:23.7217873Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7217994Z             auto tmp1 = tmp0.cos();
2023-01-11T21:41:23.7218188Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.7218311Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.7218497Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.7218623Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:23.7218743Z             auto tmp6 = tmp5.cos();
2023-01-11T21:41:23.7218857Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7218980Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7219063Z         }
2023-01-11T21:41:23.7219197Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7219319Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.7219405Z         {
2023-01-11T21:41:23.7219520Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7219627Z             auto tmp1 = std::cos(tmp0);
2023-01-11T21:41:23.7219763Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:23.7219881Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.7220017Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:23.7220131Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:23.7220253Z             auto tmp6 = std::cos(tmp5);
2023-01-11T21:41:23.7220370Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.7220475Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:23.7220560Z         }
2023-01-11T21:41:23.7220639Z     }
2023-01-11T21:41:23.7220719Z }
2023-01-11T21:41:23.7220842Z ''')
2023-01-11T21:41:23.7220850Z 
2023-01-11T21:41:23.7220856Z 
2023-01-11T21:41:23.7220986Z async_compile.wait(globals())
2023-01-11T21:41:23.7221088Z del async_compile
2023-01-11T21:41:23.7221095Z 
2023-01-11T21:41:23.7221180Z def call(args):
2023-01-11T21:41:23.7221276Z     arg0_1, = args
2023-01-11T21:41:23.7221381Z     args.clear()
2023-01-11T21:41:23.7221689Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7221983Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7222212Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.7222306Z     del arg0_1
2023-01-11T21:41:23.7222586Z     return (buf0, buf1, )
2023-01-11T21:41:23.7222702Z 
2023-01-11T21:41:23.7222712Z 
2023-01-11T21:41:23.7222814Z if __name__ == "__main__":
2023-01-11T21:41:23.7222988Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7223170Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7223482Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7223639Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7223646Z 
2023-01-11T21:41:23.7223734Z ok (1.540s)
2023-01-11T21:41:23.7224433Z   test_cpp_wrapper_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7224608Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7225075Z [2023-01-11 21:26:17,741] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 153
2023-01-11T21:41:23.7225084Z 
2023-01-11T21:41:23.7225205Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7225299Z import torch
2023-01-11T21:41:23.7225401Z import random
2023-01-11T21:41:23.7225571Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7225750Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7225758Z 
2023-01-11T21:41:23.7225861Z aten = torch.ops.aten
2023-01-11T21:41:23.7226052Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7226185Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7226192Z 
2023-01-11T21:41:23.7226199Z 
2023-01-11T21:41:23.7226383Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7226678Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7226855Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.7227008Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:23.7227096Z {
2023-01-11T21:41:23.7227232Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7227318Z     {
2023-01-11T21:41:23.7227414Z         #pragma omp for 
2023-01-11T21:41:23.7227531Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:23.7227618Z         {
2023-01-11T21:41:23.7227808Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7228002Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.7228117Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7228302Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.7228415Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.7228539Z             tmp4.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7228627Z         }
2023-01-11T21:41:23.7228771Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7228901Z         for(long i0=4096; i0<4096; i0+=1)
2023-01-11T21:41:23.7228988Z         {
2023-01-11T21:41:23.7229108Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7229246Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.7229357Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7229497Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.7229620Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.7229734Z             in_out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.7229826Z         }
2023-01-11T21:41:23.7229916Z     }
2023-01-11T21:41:23.7229988Z }
2023-01-11T21:41:23.7230110Z ''')
2023-01-11T21:41:23.7230118Z 
2023-01-11T21:41:23.7230248Z async_compile.wait(globals())
2023-01-11T21:41:23.7230349Z del async_compile
2023-01-11T21:41:23.7230517Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7230614Z wrapper = (
2023-01-11T21:41:23.7230723Z '''
2023-01-11T21:41:23.7230815Z #include <dlfcn.h>
2023-01-11T21:41:23.7230997Z #include <assert.h>
2023-01-11T21:41:23.7231182Z     std::vector<at::Tensor> call_0(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7231300Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7231405Z     arg0_1 = args[0];
2023-01-11T21:41:23.7231596Z     auto buf0 = at::empty_strided({64, 64}, {64, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7231779Z     auto buf1 = at::as_strided(buf0, {8, 8, 64}, {512, 64, 1}); buf0.reset();  // reuse
2023-01-11T21:41:23.7232150Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/7t/c7teerg2txdzjxddrmv3s7fxnncw2uo4ovbnvjzi3xo2blybtvpk.so", RTLD_NOW);
2023-01-11T21:41:23.7232278Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7232430Z     void (*kernel_cpp_0)(float*,const float*);
2023-01-11T21:41:23.7232609Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7232796Z     kernel_cpp_0((float*)(buf1.data_ptr()), (float*)(arg0_1.data_ptr()));
2023-01-11T21:41:23.7233205Z     return std::vector<at::Tensor>({at::as_strided(arg0_1, {8, 8, 64}, {512, 64, 1}), buf1}); }''' )
2023-01-11T21:41:23.7233217Z 
2023-01-11T21:41:23.7233323Z module = load_inline(
2023-01-11T21:41:23.7233703Z     name='inline_extension_cvr6w7wpa3pkttzmjiie5bttvbnlvilrhuls2ejiiqdffyagh3el',
2023-01-11T21:41:23.7233893Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7234037Z     functions=['call_0'],
2023-01-11T21:41:23.7234596Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7234806Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7235693Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7235706Z 
2023-01-11T21:41:23.7235815Z def _wrap_func(f):
2023-01-11T21:41:23.7235909Z     def g(args):
2023-01-11T21:41:23.7236006Z         return f(args)
2023-01-11T21:41:23.7236091Z     return g
2023-01-11T21:41:23.7236197Z call = _wrap_func(module.call_0)
2023-01-11T21:41:23.7236217Z 
2023-01-11T21:41:23.7236224Z 
2023-01-11T21:41:23.7236315Z if __name__ == "__main__":
2023-01-11T21:41:23.7236465Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7236660Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7236951Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7237107Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7237114Z 
2023-01-11T21:41:23.7237530Z [2023-01-11 21:26:36,863] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 153
2023-01-11T21:41:23.7238208Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7238396Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7238789Z [2023-01-11 21:26:36,886] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 154
2023-01-11T21:41:23.7238797Z 
2023-01-11T21:41:23.7238934Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7239018Z import torch
2023-01-11T21:41:23.7239113Z import random
2023-01-11T21:41:23.7239279Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7239448Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7239456Z 
2023-01-11T21:41:23.7239568Z aten = torch.ops.aten
2023-01-11T21:41:23.7239756Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7239971Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7239980Z 
2023-01-11T21:41:23.7239987Z 
2023-01-11T21:41:23.7240171Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7240470Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7240644Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.7240787Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:23.7240927Z                        int* __restrict__ out_ptr0,
2023-01-11T21:41:23.7241056Z                        int* __restrict__ out_ptr1,
2023-01-11T21:41:23.7241186Z                        int* __restrict__ out_ptr2,
2023-01-11T21:41:23.7241316Z                        int* __restrict__ out_ptr3)
2023-01-11T21:41:23.7241390Z {
2023-01-11T21:41:23.7241534Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7241619Z     {
2023-01-11T21:41:23.7241727Z         #pragma omp for 
2023-01-11T21:41:23.7241843Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7241990Z         {
2023-01-11T21:41:23.7242068Z             {
2023-01-11T21:41:23.7242160Z                 {
2023-01-11T21:41:23.7242292Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7242419Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.7242532Z                     auto tmp1 = ~tmp0;
2023-01-11T21:41:23.7242660Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:41:23.7242797Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:41:23.7242910Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:41:23.7243034Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.7243152Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.7243276Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.7243394Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:23.7243482Z                 }
2023-01-11T21:41:23.7243566Z             }
2023-01-11T21:41:23.7243635Z         }
2023-01-11T21:41:23.7243717Z     }
2023-01-11T21:41:23.7243805Z }
2023-01-11T21:41:23.7243931Z ''')
2023-01-11T21:41:23.7243939Z 
2023-01-11T21:41:23.7244078Z async_compile.wait(globals())
2023-01-11T21:41:23.7244179Z del async_compile
2023-01-11T21:41:23.7244354Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7244440Z wrapper = (
2023-01-11T21:41:23.7244550Z '''
2023-01-11T21:41:23.7244658Z #include <dlfcn.h>
2023-01-11T21:41:23.7244764Z #include <assert.h>
2023-01-11T21:41:23.7244959Z     std::vector<at::Tensor> call_1(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7245073Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:23.7245180Z     arg0_1 = args[0];
2023-01-11T21:41:23.7245266Z     arg1_1 = args[1];
2023-01-11T21:41:23.7245458Z     auto buf0 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:23.7245655Z     auto buf1 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:23.7245845Z     auto buf2 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:23.7246038Z     auto buf3 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:23.7246466Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/rv/crvxoskxizzxrfommbf7bg5tkfmti76e7o2dzmn36stpkt3uvwnx.so", RTLD_NOW);
2023-01-11T21:41:23.7246627Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7246808Z     void (*kernel_cpp_0)(const int*,const int*,int*,int*,int*,int*);
2023-01-11T21:41:23.7246971Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7247281Z     kernel_cpp_0((int*)(arg0_1.data_ptr()), (int*)(arg1_1.data_ptr()), (int*)(buf0.data_ptr()), (int*)(buf1.data_ptr()), (int*)(buf2.data_ptr()), (int*)(buf3.data_ptr()));
2023-01-11T21:41:23.7247376Z     arg0_1.reset();
2023-01-11T21:41:23.7247471Z     arg1_1.reset();
2023-01-11T21:41:23.7247755Z     return std::vector<at::Tensor>({buf0, buf1, buf2, buf3}); }''' )
2023-01-11T21:41:23.7247765Z 
2023-01-11T21:41:23.7247872Z module = load_inline(
2023-01-11T21:41:23.7248330Z     name='inline_extension_cuyauoq6gdklrmicb22pr47u4ph7mongsidlmteo5v4kpvsi5a4d',
2023-01-11T21:41:23.7248447Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7248594Z     functions=['call_1'],
2023-01-11T21:41:23.7249159Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7249380Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7250302Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7250311Z 
2023-01-11T21:41:23.7250417Z def _wrap_func(f):
2023-01-11T21:41:23.7250516Z     def g(args):
2023-01-11T21:41:23.7250620Z         return f(args)
2023-01-11T21:41:23.7250711Z     return g
2023-01-11T21:41:23.7250888Z call = _wrap_func(module.call_1)
2023-01-11T21:41:23.7250913Z 
2023-01-11T21:41:23.7250919Z 
2023-01-11T21:41:23.7251013Z if __name__ == "__main__":
2023-01-11T21:41:23.7251188Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7251364Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7251653Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.7251959Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.7252139Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7252147Z 
2023-01-11T21:41:23.7252543Z [2023-01-11 21:26:56,481] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 154
2023-01-11T21:41:23.7253209Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7253400Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7253790Z [2023-01-11 21:26:56,504] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 155
2023-01-11T21:41:23.7253812Z 
2023-01-11T21:41:23.7253940Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7254030Z import torch
2023-01-11T21:41:23.7254134Z import random
2023-01-11T21:41:23.7254296Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7254477Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7254484Z 
2023-01-11T21:41:23.7254596Z aten = torch.ops.aten
2023-01-11T21:41:23.7254790Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7254906Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7254913Z 
2023-01-11T21:41:23.7254925Z 
2023-01-11T21:41:23.7255121Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7255423Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7255596Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7255742Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7255886Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7256026Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.7256109Z {
2023-01-11T21:41:23.7256236Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7256322Z     {
2023-01-11T21:41:23.7256435Z         #pragma omp for 
2023-01-11T21:41:23.7256553Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.7256637Z         {
2023-01-11T21:41:23.7256832Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7257017Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.7257206Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7257344Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7257432Z         }
2023-01-11T21:41:23.7257565Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7257681Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.7257768Z         {
2023-01-11T21:41:23.7257882Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7258010Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.7258127Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7258242Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7258336Z         }
2023-01-11T21:41:23.7258446Z         #pragma omp for 
2023-01-11T21:41:23.7258560Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.7258635Z         {
2023-01-11T21:41:23.7258824Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7259018Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.7259205Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7259343Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7259429Z         }
2023-01-11T21:41:23.7259581Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7259708Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.7259791Z         {
2023-01-11T21:41:23.7259912Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.7260054Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.7260171Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7260285Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.7260371Z         }
2023-01-11T21:41:23.7260445Z     }
2023-01-11T21:41:23.7260532Z }
2023-01-11T21:41:23.7260670Z ''')
2023-01-11T21:41:23.7260679Z 
2023-01-11T21:41:23.7260686Z 
2023-01-11T21:41:23.7260888Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.7261207Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7261392Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.7261479Z {
2023-01-11T21:41:23.7261620Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7261692Z     {
2023-01-11T21:41:23.7261799Z         #pragma omp for 
2023-01-11T21:41:23.7261914Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.7262005Z         {
2023-01-11T21:41:23.7262213Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7262552Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.7262679Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7262807Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7262896Z         }
2023-01-11T21:41:23.7263033Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7263154Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.7263243Z         {
2023-01-11T21:41:23.7263374Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7263527Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.7263630Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7263745Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7263832Z         }
2023-01-11T21:41:23.7263917Z     }
2023-01-11T21:41:23.7263999Z }
2023-01-11T21:41:23.7264125Z ''')
2023-01-11T21:41:23.7264134Z 
2023-01-11T21:41:23.7264264Z async_compile.wait(globals())
2023-01-11T21:41:23.7264357Z del async_compile
2023-01-11T21:41:23.7264528Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7264629Z wrapper = (
2023-01-11T21:41:23.7264744Z '''
2023-01-11T21:41:23.7264847Z #include <dlfcn.h>
2023-01-11T21:41:23.7264950Z #include <assert.h>
2023-01-11T21:41:23.7265117Z     std::vector<at::Tensor> call_2(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7265232Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:23.7265334Z     arg0_1 = args[0];
2023-01-11T21:41:23.7265434Z     arg1_1 = args[1];
2023-01-11T21:41:23.7265748Z     auto buf0 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7265885Z     at::bmm_out(buf0, arg0_1, arg1_1);
2023-01-11T21:41:23.7266080Z     auto buf1 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7266273Z     auto buf2 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7266701Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/pc/cpc65tdxpyhvrndvlki6qhxomio7koznfdtyy2it7cwxowywkzk6.so", RTLD_NOW);
2023-01-11T21:41:23.7266837Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7267040Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:23.7267225Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7267499Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()));
2023-01-11T21:41:23.7267608Z     arg0_1.reset();
2023-01-11T21:41:23.7267811Z     arg1_1.reset();
2023-01-11T21:41:23.7268025Z     auto buf3 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7268145Z     at::bmm_out(buf3, buf1, buf2);
2023-01-11T21:41:23.7268253Z     buf1.reset();
2023-01-11T21:41:23.7268359Z     buf2.reset();
2023-01-11T21:41:23.7268505Z     auto buf4 = buf3; buf3.reset();  // reuse
2023-01-11T21:41:23.7268878Z     auto kernel_cpp_1_lib = dlopen("/tmp/torchinductor_jenkins/2i/c2iaojccekodbchj66prruw6huckuqrbwyh2qznbbrfrnmn36tp4.so", RTLD_NOW);
2023-01-11T21:41:23.7269012Z     assert(kernel_cpp_1_lib != nullptr);
2023-01-11T21:41:23.7269139Z     void (*kernel_cpp_1)(float*);
2023-01-11T21:41:23.7269305Z     *(void **) (&kernel_cpp_1) = dlsym(kernel_cpp_1_lib, "kernel");
2023-01-11T21:41:23.7269444Z     kernel_cpp_1((float*)(buf4.data_ptr()));
2023-01-11T21:41:23.7269716Z     return std::vector<at::Tensor>({buf0, buf4}); }''' )
2023-01-11T21:41:23.7269726Z 
2023-01-11T21:41:23.7269842Z module = load_inline(
2023-01-11T21:41:23.7270297Z     name='inline_extension_chfnadliirwsn3ujofkzbsnpr6hburp722b4pdqce6kt2trz36ns',
2023-01-11T21:41:23.7270433Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7270609Z     functions=['call_2'],
2023-01-11T21:41:23.7271157Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7271389Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7272326Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7272351Z 
2023-01-11T21:41:23.7272444Z def _wrap_func(f):
2023-01-11T21:41:23.7272545Z     def g(args):
2023-01-11T21:41:23.7272652Z         return f(args)
2023-01-11T21:41:23.7272759Z     return g
2023-01-11T21:41:23.7272892Z call = _wrap_func(module.call_2)
2023-01-11T21:41:23.7272900Z 
2023-01-11T21:41:23.7272906Z 
2023-01-11T21:41:23.7273004Z if __name__ == "__main__":
2023-01-11T21:41:23.7273162Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7273330Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7273653Z     arg0_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7274061Z     arg1_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7274223Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7274231Z 
2023-01-11T21:41:23.7274621Z [2023-01-11 21:27:16,459] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 155
2023-01-11T21:41:23.7275240Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7275498Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7275888Z [2023-01-11 21:27:16,483] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 156
2023-01-11T21:41:23.7275896Z 
2023-01-11T21:41:23.7276023Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7276119Z import torch
2023-01-11T21:41:23.7276208Z import random
2023-01-11T21:41:23.7276378Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7276552Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7276560Z 
2023-01-11T21:41:23.7276673Z aten = torch.ops.aten
2023-01-11T21:41:23.7276858Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7276991Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7277002Z 
2023-01-11T21:41:23.7277086Z 
2023-01-11T21:41:23.7277293Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7277592Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7277754Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7277901Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7278035Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7278163Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.7278245Z {
2023-01-11T21:41:23.7278383Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7278464Z     {
2023-01-11T21:41:23.7278553Z         #pragma omp for 
2023-01-11T21:41:23.7278664Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.7278747Z         {
2023-01-11T21:41:23.7278936Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7279121Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.7279247Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7279376Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7279449Z         }
2023-01-11T21:41:23.7279583Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7279701Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.7279792Z         {
2023-01-11T21:41:23.7279912Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7280058Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.7280181Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7280292Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7280373Z         }
2023-01-11T21:41:23.7280482Z         #pragma omp for 
2023-01-11T21:41:23.7280592Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:23.7280676Z         {
2023-01-11T21:41:23.7280859Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7281056Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.7281166Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7281300Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7281382Z         }
2023-01-11T21:41:23.7281520Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7281633Z         for(long i0=80; i0<80; i0+=1)
2023-01-11T21:41:23.7281718Z         {
2023-01-11T21:41:23.7281832Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.7281962Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.7282083Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7282195Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.7282283Z         }
2023-01-11T21:41:23.7282369Z     }
2023-01-11T21:41:23.7282452Z }
2023-01-11T21:41:23.7282567Z ''')
2023-01-11T21:41:23.7282589Z 
2023-01-11T21:41:23.7282595Z 
2023-01-11T21:41:23.7282785Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.7283098Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7283354Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.7283442Z {
2023-01-11T21:41:23.7283584Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7283670Z     {
2023-01-11T21:41:23.7283777Z         #pragma omp for 
2023-01-11T21:41:23.7283876Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:23.7283961Z         {
2023-01-11T21:41:23.7284160Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7284354Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.7284477Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7284611Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7284699Z         }
2023-01-11T21:41:23.7284826Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7284945Z         for(long i0=160; i0<160; i0+=1)
2023-01-11T21:41:23.7285032Z         {
2023-01-11T21:41:23.7285218Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7285354Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.7285477Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7285595Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7285666Z         }
2023-01-11T21:41:23.7285748Z     }
2023-01-11T21:41:23.7285830Z }
2023-01-11T21:41:23.7285951Z ''')
2023-01-11T21:41:23.7285959Z 
2023-01-11T21:41:23.7286090Z async_compile.wait(globals())
2023-01-11T21:41:23.7286191Z del async_compile
2023-01-11T21:41:23.7286365Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7286455Z wrapper = (
2023-01-11T21:41:23.7286574Z '''
2023-01-11T21:41:23.7286682Z #include <dlfcn.h>
2023-01-11T21:41:23.7286786Z #include <assert.h>
2023-01-11T21:41:23.7286970Z     std::vector<at::Tensor> call_3(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7287083Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:23.7287179Z     arg0_1 = args[0];
2023-01-11T21:41:23.7287267Z     arg1_1 = args[1];
2023-01-11T21:41:23.7287470Z     auto buf0 = at::empty_strided({1, 16, 10}, {160, 10, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7287663Z     auto buf0_as_strided = at::as_strided(buf0, {16, 10}, {10, 1});
2023-01-11T21:41:23.7287905Z     at::mm_out(buf0_as_strided, at::as_strided(arg0_1, {16, 8}, {8, 1}), at::as_strided(arg1_1, {8, 10}, {10, 1}));
2023-01-11T21:41:23.7288111Z     auto buf1 = at::empty_strided({1, 16, 8}, {128, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7288311Z     auto buf2 = at::empty_strided({1, 8, 10}, {80, 10, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7288706Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/ra/cralzjg7p7jfrh7lq7f5aawuy5ztcxsmezjq7ltkvv7n75g5gmjr.so", RTLD_NOW);
2023-01-11T21:41:23.7288838Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7289001Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:23.7289150Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7289405Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()));
2023-01-11T21:41:23.7289505Z     arg0_1.reset();
2023-01-11T21:41:23.7289607Z     arg1_1.reset();
2023-01-11T21:41:23.7289813Z     auto buf3 = at::empty_strided({1, 16, 10}, {160, 10, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7289987Z     auto buf3_as_strided = at::as_strided(buf3, {16, 10}, {10, 1});
2023-01-11T21:41:23.7290226Z     at::mm_out(buf3_as_strided, at::as_strided(buf1, {16, 8}, {8, 1}), at::as_strided(buf2, {8, 10}, {10, 1}));
2023-01-11T21:41:23.7290330Z     buf1.reset();
2023-01-11T21:41:23.7290417Z     buf2.reset();
2023-01-11T21:41:23.7290556Z     auto buf4 = buf3; buf3.reset();  // reuse
2023-01-11T21:41:23.7290917Z     auto kernel_cpp_1_lib = dlopen("/tmp/torchinductor_jenkins/yc/cycbydl6b3o6njowlq7op2e3gon6ehl777xoi27mhfawtegw4ri4.so", RTLD_NOW);
2023-01-11T21:41:23.7291062Z     assert(kernel_cpp_1_lib != nullptr);
2023-01-11T21:41:23.7291266Z     void (*kernel_cpp_1)(float*);
2023-01-11T21:41:23.7291441Z     *(void **) (&kernel_cpp_1) = dlsym(kernel_cpp_1_lib, "kernel");
2023-01-11T21:41:23.7291588Z     kernel_cpp_1((float*)(buf4.data_ptr()));
2023-01-11T21:41:23.7291837Z     return std::vector<at::Tensor>({buf0, buf4}); }''' )
2023-01-11T21:41:23.7291859Z 
2023-01-11T21:41:23.7291961Z module = load_inline(
2023-01-11T21:41:23.7292357Z     name='inline_extension_cfk34ucpswp45ihunrnj5h3vvmenylbjrdehpsrzmjuafwmv7mzt',
2023-01-11T21:41:23.7292476Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7292640Z     functions=['call_3'],
2023-01-11T21:41:23.7293199Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7293424Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7294494Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7294510Z 
2023-01-11T21:41:23.7294612Z def _wrap_func(f):
2023-01-11T21:41:23.7294697Z     def g(args):
2023-01-11T21:41:23.7294798Z         return f(args)
2023-01-11T21:41:23.7294892Z     return g
2023-01-11T21:41:23.7295025Z call = _wrap_func(module.call_3)
2023-01-11T21:41:23.7295033Z 
2023-01-11T21:41:23.7295040Z 
2023-01-11T21:41:23.7295148Z if __name__ == "__main__":
2023-01-11T21:41:23.7295327Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7295522Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7295833Z     arg0_1 = rand_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7296117Z     arg1_1 = rand_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7296269Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7296279Z 
2023-01-11T21:41:23.7296647Z [2023-01-11 21:27:36,682] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 156
2023-01-11T21:41:23.7297215Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7297381Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7297735Z [2023-01-11 21:27:36,702] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 157
2023-01-11T21:41:23.7297742Z 
2023-01-11T21:41:23.7297865Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7297953Z import torch
2023-01-11T21:41:23.7298042Z import random
2023-01-11T21:41:23.7298183Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7298337Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7298343Z 
2023-01-11T21:41:23.7298442Z aten = torch.ops.aten
2023-01-11T21:41:23.7298616Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7298737Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7298744Z 
2023-01-11T21:41:23.7298855Z async_compile.wait(globals())
2023-01-11T21:41:23.7298946Z del async_compile
2023-01-11T21:41:23.7299097Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7299171Z wrapper = (
2023-01-11T21:41:23.7299277Z '''
2023-01-11T21:41:23.7299374Z #include <dlfcn.h>
2023-01-11T21:41:23.7299471Z #include <assert.h>
2023-01-11T21:41:23.7299629Z     at::Tensor call_4(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7299736Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:23.7299837Z     arg0_1 = args[0];
2023-01-11T21:41:23.7299926Z     arg1_1 = args[1];
2023-01-11T21:41:23.7300202Z     auto buf0 = at::empty_strided({1, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7300379Z     auto buf0_as_strided = at::as_strided(buf0, {8, 8}, {8, 1});
2023-01-11T21:41:23.7300614Z     at::mm_out(buf0_as_strided, at::as_strided(arg0_1, {8, 8}, {1, 8}), at::as_strided(arg1_1, {8, 8}, {8, 1}));
2023-01-11T21:41:23.7300715Z     arg0_1.reset();
2023-01-11T21:41:23.7300813Z     arg1_1.reset();
2023-01-11T21:41:23.7300978Z     return buf0; }''' )
2023-01-11T21:41:23.7300985Z 
2023-01-11T21:41:23.7301096Z module = load_inline(
2023-01-11T21:41:23.7301457Z     name='inline_extension_chbemg2ahqboherkh3dfy5k24qgsw5gtkd5ehfcdmncijlslqgpn',
2023-01-11T21:41:23.7301574Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7301733Z     functions=['call_4'],
2023-01-11T21:41:23.7302269Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7302745Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7303649Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7303659Z 
2023-01-11T21:41:23.7303759Z def _wrap_func(f):
2023-01-11T21:41:23.7303861Z     def g(args):
2023-01-11T21:41:23.7303950Z         return f(args)
2023-01-11T21:41:23.7304039Z     return g
2023-01-11T21:41:23.7304166Z call = _wrap_func(module.call_4)
2023-01-11T21:41:23.7304173Z 
2023-01-11T21:41:23.7304181Z 
2023-01-11T21:41:23.7304288Z if __name__ == "__main__":
2023-01-11T21:41:23.7304453Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7304632Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7304938Z     arg0_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7305233Z     arg1_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7305386Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7305393Z 
2023-01-11T21:41:23.7305788Z [2023-01-11 21:27:56,449] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 157
2023-01-11T21:41:23.7306421Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7306589Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7306982Z [2023-01-11 21:27:56,532] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 158
2023-01-11T21:41:23.7306998Z 
2023-01-11T21:41:23.7307144Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7307244Z import torch
2023-01-11T21:41:23.7307353Z import random
2023-01-11T21:41:23.7307515Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7307678Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7307685Z 
2023-01-11T21:41:23.7307796Z aten = torch.ops.aten
2023-01-11T21:41:23.7307982Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7308113Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7308121Z 
2023-01-11T21:41:23.7308126Z 
2023-01-11T21:41:23.7308315Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7308602Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7308769Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7308902Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7309125Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.7309256Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.7309391Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7309521Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:23.7309704Z                        double* __restrict__ out_ptr5,
2023-01-11T21:41:23.7309904Z                        double* __restrict__ out_ptr6)
2023-01-11T21:41:23.7310030Z {
2023-01-11T21:41:23.7310232Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7310362Z     {
2023-01-11T21:41:23.7310536Z         #pragma omp for 
2023-01-11T21:41:23.7310713Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.7310852Z         {
2023-01-11T21:41:23.7311035Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.7311173Z             {
2023-01-11T21:41:23.7311494Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:23.7311876Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.7312063Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7312285Z                 tmp0.store(out_ptr0 + (8*i1) + (36*i0));
2023-01-11T21:41:23.7312506Z                 tmp2.store(out_ptr1 + (8*i1) + (36*i0));
2023-01-11T21:41:23.7312648Z             }
2023-01-11T21:41:23.7312858Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.7313036Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:23.7313162Z             {
2023-01-11T21:41:23.7313363Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:23.7313576Z                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.7313831Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7314025Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:41:23.7314219Z                 out_ptr1[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:23.7314352Z             }
2023-01-11T21:41:23.7314476Z         }
2023-01-11T21:41:23.7314662Z         #pragma omp for 
2023-01-11T21:41:23.7314833Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.7314960Z         {
2023-01-11T21:41:23.7315122Z             #pragma GCC ivdep
2023-01-11T21:41:23.7315290Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.7315409Z             {
2023-01-11T21:41:23.7315541Z                 {
2023-01-11T21:41:23.7315686Z                     {
2023-01-11T21:41:23.7315909Z                         auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:23.7316145Z                         auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.7316345Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7316548Z                         out_ptr2[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:23.7316678Z                     }
2023-01-11T21:41:23.7316817Z                 }
2023-01-11T21:41:23.7316955Z             }
2023-01-11T21:41:23.7317095Z         }
2023-01-11T21:41:23.7317264Z         #pragma omp for 
2023-01-11T21:41:23.7317448Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.7317595Z         {
2023-01-11T21:41:23.7317724Z             {
2023-01-11T21:41:23.7317870Z                 {
2023-01-11T21:41:23.7318078Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7318316Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.7318511Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7318760Z                     auto tmp3 = static_cast<double>(tmp2);
2023-01-11T21:41:23.7318946Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:23.7319113Z                     out_ptr4[i0] = tmp2;
2023-01-11T21:41:23.7319300Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:41:23.7319481Z                     out_ptr6[i0] = tmp3;
2023-01-11T21:41:23.7319621Z                 }
2023-01-11T21:41:23.7319758Z             }
2023-01-11T21:41:23.7319900Z         }
2023-01-11T21:41:23.7320034Z     }
2023-01-11T21:41:23.7320152Z }
2023-01-11T21:41:23.7320349Z ''')
2023-01-11T21:41:23.7320361Z 
2023-01-11T21:41:23.7320664Z async_compile.wait(globals())
2023-01-11T21:41:23.7320825Z del async_compile
2023-01-11T21:41:23.7321098Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7321253Z wrapper = (
2023-01-11T21:41:23.7321403Z '''
2023-01-11T21:41:23.7321564Z #include <dlfcn.h>
2023-01-11T21:41:23.7321731Z #include <assert.h>
2023-01-11T21:41:23.7322017Z     std::vector<at::Tensor> call_5(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7322181Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7322330Z     arg0_1 = args[0];
2023-01-11T21:41:23.7322622Z     auto buf3 = at::empty_strided({8, 36}, {36, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7322863Z     auto buf0 = at::as_strided(buf3, {8, 16}, {36, 1});  // alias
2023-01-11T21:41:23.7323106Z     auto buf2 = at::as_strided(buf3, {8, 16}, {36, 1}, 20);  // alias
2023-01-11T21:41:23.7323349Z     auto buf1 = at::as_strided(buf3, {8, 4}, {36, 1}, 16);  // alias
2023-01-11T21:41:23.7323639Z     auto buf6 = at::empty_strided({16, 16}, {16, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7323941Z     auto buf4 = at::as_strided(buf6, {8, 16}, {16, 1});  // alias
2023-01-11T21:41:23.7324186Z     auto buf5 = at::as_strided(buf6, {8, 16}, {16, 1}, 128);  // alias
2023-01-11T21:41:23.7324479Z     auto buf9 = at::empty_strided({16, 16}, {16, 1}, at::ScalarType::Double); 
2023-01-11T21:41:23.7324721Z     auto buf7 = at::as_strided(buf9, {8, 16}, {16, 1});  // alias
2023-01-11T21:41:23.7324970Z     auto buf8 = at::as_strided(buf9, {8, 16}, {16, 1}, 128);  // alias
2023-01-11T21:41:23.7325633Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/xx/cxxvcduhvysrcz4iolqfqobmycduemx7yqmtsfsczg53dn526b2j.so", RTLD_NOW);
2023-01-11T21:41:23.7325837Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7326189Z     void (*kernel_cpp_0)(const float*,float*,float*,float*,float*,float*,double*,double*);
2023-01-11T21:41:23.7326474Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7327164Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf2.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf4.data_ptr()), (float*)(buf5.data_ptr()), (double*)(buf7.data_ptr()), (double*)(buf8.data_ptr()));
2023-01-11T21:41:23.7327333Z     arg0_1.reset();
2023-01-11T21:41:23.7327746Z     return std::vector<at::Tensor>({buf3, buf6, buf9}); }''' )
2023-01-11T21:41:23.7327757Z 
2023-01-11T21:41:23.7327929Z module = load_inline(
2023-01-11T21:41:23.7328546Z     name='inline_extension_cji7g5r4jyhbjebn7otwyol5fhfvue57jqht5etdlccgyremizfh',
2023-01-11T21:41:23.7328741Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7328974Z     functions=['call_5'],
2023-01-11T21:41:23.7329842Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7330177Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7331729Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7331746Z 
2023-01-11T21:41:23.7331917Z def _wrap_func(f):
2023-01-11T21:41:23.7332071Z     def g(args):
2023-01-11T21:41:23.7332230Z         return f(args)
2023-01-11T21:41:23.7332373Z     return g
2023-01-11T21:41:23.7332560Z call = _wrap_func(module.call_5)
2023-01-11T21:41:23.7332570Z 
2023-01-11T21:41:23.7332594Z 
2023-01-11T21:41:23.7332747Z if __name__ == "__main__":
2023-01-11T21:41:23.7333008Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7333309Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7333760Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7334009Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7334020Z 
2023-01-11T21:41:23.7334741Z [2023-01-11 21:28:15,662] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 158
2023-01-11T21:41:23.7335905Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7336182Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7336789Z [2023-01-11 21:28:15,713] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 159
2023-01-11T21:41:23.7336800Z 
2023-01-11T21:41:23.7336997Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7337155Z import torch
2023-01-11T21:41:23.7337307Z import random
2023-01-11T21:41:23.7337561Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7337904Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7337916Z 
2023-01-11T21:41:23.7338086Z aten = torch.ops.aten
2023-01-11T21:41:23.7338393Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7338582Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7338592Z 
2023-01-11T21:41:23.7338617Z 
2023-01-11T21:41:23.7338916Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7339427Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7339697Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7339932Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7340166Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.7340402Z                        const double* __restrict__ in_ptr3,
2023-01-11T21:41:23.7340626Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7340831Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.7341044Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.7341265Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7341480Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:23.7341686Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:23.7341913Z                        double* __restrict__ out_ptr6,
2023-01-11T21:41:23.7342144Z                        double* __restrict__ out_ptr7,
2023-01-11T21:41:23.7342502Z                        float* __restrict__ out_ptr8,
2023-01-11T21:41:23.7342707Z                        double* __restrict__ out_ptr9)
2023-01-11T21:41:23.7342838Z {
2023-01-11T21:41:23.7343061Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7343190Z     {
2023-01-11T21:41:23.7343398Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7343578Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7343701Z         {
2023-01-11T21:41:23.7343888Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.7344027Z             {
2023-01-11T21:41:23.7344212Z                 #pragma GCC ivdep
2023-01-11T21:41:23.7344409Z                 for(long i2=0; i2<16; i2+=1)
2023-01-11T21:41:23.7344550Z                 {
2023-01-11T21:41:23.7344693Z                     {
2023-01-11T21:41:23.7344827Z                         {
2023-01-11T21:41:23.7345070Z                             auto tmp0 = in_ptr0[i0 + (3*i2) + (48*i1)];
2023-01-11T21:41:23.7345309Z                             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.7345517Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7345757Z                             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.7345957Z                             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.7346176Z                             out_ptr0[i2 + (48*i1) + (144*i0)] = tmp0;
2023-01-11T21:41:23.7346376Z                             out_ptr1[i2 + (48*i1) + (144*i0)] = tmp2;
2023-01-11T21:41:23.7346710Z                             out_ptr2[i2 + (48*i1) + (144*i0)] = tmp4;
2023-01-11T21:41:23.7346864Z                         }
2023-01-11T21:41:23.7347011Z                     }
2023-01-11T21:41:23.7347157Z                 }
2023-01-11T21:41:23.7347295Z             }
2023-01-11T21:41:23.7347429Z         }
2023-01-11T21:41:23.7347617Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7347801Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7347943Z         {
2023-01-11T21:41:23.7348128Z             for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:23.7348275Z             {
2023-01-11T21:41:23.7348417Z                 {
2023-01-11T21:41:23.7348564Z                     {
2023-01-11T21:41:23.7348767Z                         auto tmp0 = in_ptr1[i1 + (144*i0)];
2023-01-11T21:41:23.7348971Z                         out_ptr3[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:23.7349116Z                     }
2023-01-11T21:41:23.7349258Z                 }
2023-01-11T21:41:23.7349402Z             }
2023-01-11T21:41:23.7349656Z         }
2023-01-11T21:41:23.7349863Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.7350024Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7350170Z         {
2023-01-11T21:41:23.7350352Z             for(long i1=0; i1<48; i1+=1)
2023-01-11T21:41:23.7350492Z             {
2023-01-11T21:41:23.7350631Z                 {
2023-01-11T21:41:23.7350776Z                     {
2023-01-11T21:41:23.7350981Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:23.7351221Z                         auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.7351428Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7351628Z                         out_ptr4[i1 + (48*i0)] = tmp2;
2023-01-11T21:41:23.7351774Z                     }
2023-01-11T21:41:23.7351922Z                 }
2023-01-11T21:41:23.7352060Z             }
2023-01-11T21:41:23.7352182Z         }
2023-01-11T21:41:23.7352355Z         #pragma omp for 
2023-01-11T21:41:23.7352545Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:23.7352684Z         {
2023-01-11T21:41:23.7352827Z             {
2023-01-11T21:41:23.7352963Z                 {
2023-01-11T21:41:23.7353169Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:23.7353407Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:23.7353588Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:41:23.7353843Z                     out_ptr6[i0] = tmp1;
2023-01-11T21:41:23.7354027Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:41:23.7354174Z                 }
2023-01-11T21:41:23.7354311Z             }
2023-01-11T21:41:23.7354453Z         }
2023-01-11T21:41:23.7354649Z         #pragma omp for  collapse(3)
2023-01-11T21:41:23.7354831Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.7354970Z         {
2023-01-11T21:41:23.7355158Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.7355297Z             {
2023-01-11T21:41:23.7355486Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:23.7355638Z                 {
2023-01-11T21:41:23.7355772Z                     {
2023-01-11T21:41:23.7355917Z                         {
2023-01-11T21:41:23.7356147Z                             auto tmp0 = in_ptr2[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:23.7356366Z                             out_ptr8[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:23.7356510Z                         }
2023-01-11T21:41:23.7356653Z                     }
2023-01-11T21:41:23.7356797Z                 }
2023-01-11T21:41:23.7356920Z             }
2023-01-11T21:41:23.7357065Z         }
2023-01-11T21:41:23.7357272Z         #pragma omp for  collapse(3)
2023-01-11T21:41:23.7357450Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.7357584Z         {
2023-01-11T21:41:23.7357768Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.7357894Z             {
2023-01-11T21:41:23.7358095Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:23.7358236Z                 {
2023-01-11T21:41:23.7358459Z                     {
2023-01-11T21:41:23.7358606Z                         {
2023-01-11T21:41:23.7358843Z                             auto tmp0 = in_ptr3[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:23.7359066Z                             out_ptr9[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:23.7359200Z                         }
2023-01-11T21:41:23.7359342Z                     }
2023-01-11T21:41:23.7359483Z                 }
2023-01-11T21:41:23.7359623Z             }
2023-01-11T21:41:23.7359760Z         }
2023-01-11T21:41:23.7359896Z     }
2023-01-11T21:41:23.7360034Z }
2023-01-11T21:41:23.7360207Z ''')
2023-01-11T21:41:23.7360219Z 
2023-01-11T21:41:23.7360424Z async_compile.wait(globals())
2023-01-11T21:41:23.7360595Z del async_compile
2023-01-11T21:41:23.7360869Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7361025Z wrapper = (
2023-01-11T21:41:23.7361193Z '''
2023-01-11T21:41:23.7361353Z #include <dlfcn.h>
2023-01-11T21:41:23.7361504Z #include <assert.h>
2023-01-11T21:41:23.7361860Z     std::vector<at::Tensor> call_6(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7362034Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7362187Z     arg0_1 = args[0];
2023-01-11T21:41:23.7362493Z     auto buf3 = at::empty_strided({1, 3, 3, 48}, {432, 144, 48, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7362762Z     auto buf0 = at::as_strided(buf3, {1, 3, 3, 16}, {432, 144, 48, 1});  // alias
2023-01-11T21:41:23.7363025Z     auto buf1 = at::as_strided(buf3, {1, 3, 3, 16}, {432, 144, 48, 1}, 16);  // alias
2023-01-11T21:41:23.7363292Z     auto buf2 = at::as_strided(buf3, {1, 3, 3, 16}, {432, 144, 48, 1}, 32);  // alias
2023-01-11T21:41:23.7363591Z     auto buf4 = at::empty_strided({1, 3, 3, 48}, {432, 1, 144, 3}, at::ScalarType::Float); 
2023-01-11T21:41:23.7363893Z     auto buf7 = at::empty_strided({2, 3, 3, 16}, {144, 48, 16, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7364160Z     auto buf5 = at::as_strided(buf7, {1, 3, 3, 16}, {144, 48, 16, 1});  // alias
2023-01-11T21:41:23.7364437Z     auto buf6 = at::as_strided(buf7, {1, 3, 3, 16}, {144, 48, 16, 1}, 144);  // alias
2023-01-11T21:41:23.7364755Z     auto buf11 = at::empty_strided({2, 3, 3, 16}, {144, 48, 16, 1}, at::ScalarType::Double); 
2023-01-11T21:41:23.7365014Z     auto buf9 = at::as_strided(buf11, {1, 3, 3, 16}, {144, 48, 16, 1});  // alias
2023-01-11T21:41:23.7365286Z     auto buf10 = at::as_strided(buf11, {1, 3, 3, 16}, {144, 48, 16, 1}, 144);  // alias
2023-01-11T21:41:23.7365594Z     auto buf8 = at::empty_strided({2, 3, 3, 16}, {144, 1, 48, 3}, at::ScalarType::Float); 
2023-01-11T21:41:23.7365908Z     auto buf12 = at::empty_strided({2, 3, 3, 16}, {144, 1, 48, 3}, at::ScalarType::Double); 
2023-01-11T21:41:23.7366533Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/tf/ctf2pptj3ayxmvzwwc77hmhshbex7tnnbmizfndel6malv4tyfpu.so", RTLD_NOW);
2023-01-11T21:41:23.7366738Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7367260Z     void (*kernel_cpp_0)(const float*,const float*,const float*,const double*,float*,float*,float*,float*,float*,float*,double*,double*,float*,double*);
2023-01-11T21:41:23.7367542Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7368590Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf3.data_ptr()), (float*)(buf7.data_ptr()), (double*)(buf11.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()), (float*)(buf4.data_ptr()), (float*)(buf5.data_ptr()), (float*)(buf6.data_ptr()), (double*)(buf9.data_ptr()), (double*)(buf10.data_ptr()), (float*)(buf8.data_ptr()), (double*)(buf12.data_ptr()));
2023-01-11T21:41:23.7368746Z     arg0_1.reset();
2023-01-11T21:41:23.7368903Z     buf0.reset();
2023-01-11T21:41:23.7369060Z     buf1.reset();
2023-01-11T21:41:23.7369214Z     buf10.reset();
2023-01-11T21:41:23.7369374Z     buf11.reset();
2023-01-11T21:41:23.7369518Z     buf2.reset();
2023-01-11T21:41:23.7369669Z     buf3.reset();
2023-01-11T21:41:23.7369820Z     buf5.reset();
2023-01-11T21:41:23.7370056Z     buf6.reset();
2023-01-11T21:41:23.7370216Z     buf7.reset();
2023-01-11T21:41:23.7370374Z     buf9.reset();
2023-01-11T21:41:23.7370788Z     return std::vector<at::Tensor>({buf4, buf8, buf12}); }''' )
2023-01-11T21:41:23.7370815Z 
2023-01-11T21:41:23.7370973Z module = load_inline(
2023-01-11T21:41:23.7371619Z     name='inline_extension_cwwd52gevofbwzpjosptdssanute3m5y5v4upouhvxxh6jtkg7ny',
2023-01-11T21:41:23.7371815Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7372066Z     functions=['call_6'],
2023-01-11T21:41:23.7372926Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7373258Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7374905Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7374928Z 
2023-01-11T21:41:23.7375094Z def _wrap_func(f):
2023-01-11T21:41:23.7375230Z     def g(args):
2023-01-11T21:41:23.7375398Z         return f(args)
2023-01-11T21:41:23.7375549Z     return g
2023-01-11T21:41:23.7375748Z call = _wrap_func(module.call_6)
2023-01-11T21:41:23.7375758Z 
2023-01-11T21:41:23.7375767Z 
2023-01-11T21:41:23.7375934Z if __name__ == "__main__":
2023-01-11T21:41:23.7376198Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7376475Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7376975Z     arg0_1 = rand_strided((1, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7377212Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7377222Z 
2023-01-11T21:41:23.7377855Z [2023-01-11 21:28:35,060] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 159
2023-01-11T21:41:23.7378974Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7379264Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7379882Z [2023-01-11 21:28:35,113] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 160
2023-01-11T21:41:23.7379893Z 
2023-01-11T21:41:23.7380109Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7380267Z import torch
2023-01-11T21:41:23.7380427Z import random
2023-01-11T21:41:23.7380709Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7380972Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7380983Z 
2023-01-11T21:41:23.7381156Z aten = torch.ops.aten
2023-01-11T21:41:23.7381472Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7381681Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7381691Z 
2023-01-11T21:41:23.7381699Z 
2023-01-11T21:41:23.7382003Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7382639Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7382907Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.7383040Z {
2023-01-11T21:41:23.7383255Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7383384Z     {
2023-01-11T21:41:23.7383562Z         #pragma omp for 
2023-01-11T21:41:23.7383743Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.7383887Z         {
2023-01-11T21:41:23.7384197Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7384498Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:23.7384813Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7384953Z         }
2023-01-11T21:41:23.7385169Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7385347Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:23.7385483Z         {
2023-01-11T21:41:23.7385687Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7386008Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:23.7386170Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:23.7386351Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7386488Z         }
2023-01-11T21:41:23.7386619Z     }
2023-01-11T21:41:23.7386749Z }
2023-01-11T21:41:23.7386919Z ''')
2023-01-11T21:41:23.7386927Z 
2023-01-11T21:41:23.7387129Z async_compile.wait(globals())
2023-01-11T21:41:23.7387275Z del async_compile
2023-01-11T21:41:23.7387547Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7387702Z wrapper = (
2023-01-11T21:41:23.7387869Z '''
2023-01-11T21:41:23.7388035Z #include <dlfcn.h>
2023-01-11T21:41:23.7388293Z #include <assert.h>
2023-01-11T21:41:23.7388595Z     std::vector<at::Tensor> call_7(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7388827Z     at::Tensor primals_1, primals_2, primals_3;
2023-01-11T21:41:23.7389005Z     primals_1 = args[0];
2023-01-11T21:41:23.7389174Z     primals_2 = args[1];
2023-01-11T21:41:23.7389344Z     primals_3 = args[2];
2023-01-11T21:41:23.7389644Z     auto buf0 = at::empty_strided({2, 16}, {16, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7389976Z     at::addmm_out(buf0, primals_2, primals_3, at::as_strided(primals_1, {8, 16}, {1, 8}), 1, 1);
2023-01-11T21:41:23.7390149Z     primals_1.reset();
2023-01-11T21:41:23.7390306Z     primals_2.reset();
2023-01-11T21:41:23.7390522Z     auto buf1 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:23.7391146Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/iw/ciwiq5etlmci4iuytny63x3zhsv2pxo7evslwt4lbr6dhqxlpznj.so", RTLD_NOW);
2023-01-11T21:41:23.7391361Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7391565Z     void (*kernel_cpp_0)(float*);
2023-01-11T21:41:23.7391845Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7392077Z     kernel_cpp_0((float*)(buf1.data_ptr()));
2023-01-11T21:41:23.7392511Z     return std::vector<at::Tensor>({buf1, primals_3, buf1}); }''' )
2023-01-11T21:41:23.7392523Z 
2023-01-11T21:41:23.7392686Z module = load_inline(
2023-01-11T21:41:23.7393296Z     name='inline_extension_cfo4kg3ofnmy3tbyyrnmb4ko7l4ctd5apfqvog3nnpfe7zmqeqge',
2023-01-11T21:41:23.7393484Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7393798Z     functions=['call_7'],
2023-01-11T21:41:23.7394677Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7395015Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7396572Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7396588Z 
2023-01-11T21:41:23.7396750Z def _wrap_func(f):
2023-01-11T21:41:23.7396906Z     def g(args):
2023-01-11T21:41:23.7397044Z         return f(args)
2023-01-11T21:41:23.7397187Z     return g
2023-01-11T21:41:23.7397394Z call = _wrap_func(module.call_7)
2023-01-11T21:41:23.7397404Z 
2023-01-11T21:41:23.7397413Z 
2023-01-11T21:41:23.7397580Z if __name__ == "__main__":
2023-01-11T21:41:23.7397841Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7398122Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7398613Z     primals_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7399060Z     primals_2 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7399631Z     primals_3 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7399957Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.7399968Z 
2023-01-11T21:41:23.7400607Z [2023-01-11 21:28:56,424] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 160
2023-01-11T21:41:23.7401766Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7402046Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7402653Z [2023-01-11 21:28:56,622] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 161
2023-01-11T21:41:23.7402668Z 
2023-01-11T21:41:23.7402944Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7403104Z import torch
2023-01-11T21:41:23.7403264Z import random
2023-01-11T21:41:23.7403531Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7403815Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7403826Z 
2023-01-11T21:41:23.7404005Z aten = torch.ops.aten
2023-01-11T21:41:23.7404307Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7404515Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7404525Z 
2023-01-11T21:41:23.7404534Z 
2023-01-11T21:41:23.7404836Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7405339Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7405608Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.7405733Z {
2023-01-11T21:41:23.7405955Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7406094Z     {
2023-01-11T21:41:23.7406279Z         #pragma omp for 
2023-01-11T21:41:23.7406458Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.7406599Z         {
2023-01-11T21:41:23.7406904Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7407189Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.7407399Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7407495Z         }
2023-01-11T21:41:23.7407621Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7407728Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.7407805Z         {
2023-01-11T21:41:23.7407915Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7408009Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.7408114Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.7408203Z         }
2023-01-11T21:41:23.7408293Z     }
2023-01-11T21:41:23.7408385Z }
2023-01-11T21:41:23.7408509Z ''')
2023-01-11T21:41:23.7408517Z 
2023-01-11T21:41:23.7408527Z 
2023-01-11T21:41:23.7408720Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.7408996Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7409159Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.7409243Z {
2023-01-11T21:41:23.7409393Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7409480Z     {
2023-01-11T21:41:23.7409581Z         #pragma omp for 
2023-01-11T21:41:23.7409700Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.7409776Z         {
2023-01-11T21:41:23.7409985Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7410174Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.7410310Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7410403Z         }
2023-01-11T21:41:23.7410548Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7410671Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.7410836Z         {
2023-01-11T21:41:23.7410968Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7411099Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.7411223Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.7411313Z         }
2023-01-11T21:41:23.7411405Z     }
2023-01-11T21:41:23.7411495Z }
2023-01-11T21:41:23.7411612Z ''')
2023-01-11T21:41:23.7411620Z 
2023-01-11T21:41:23.7411626Z 
2023-01-11T21:41:23.7411844Z kernel_cpp_2 = async_compile.cpp('''
2023-01-11T21:41:23.7412167Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7412342Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.7412428Z {
2023-01-11T21:41:23.7412578Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7412675Z     {
2023-01-11T21:41:23.7412765Z         #pragma omp for 
2023-01-11T21:41:23.7412882Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.7412980Z         {
2023-01-11T21:41:23.7413250Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7413426Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.7413556Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.7413642Z         }
2023-01-11T21:41:23.7413756Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7413863Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.7413955Z         {
2023-01-11T21:41:23.7414081Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7414193Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.7414307Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.7414394Z         }
2023-01-11T21:41:23.7414456Z     }
2023-01-11T21:41:23.7414531Z }
2023-01-11T21:41:23.7414659Z ''')
2023-01-11T21:41:23.7414666Z 
2023-01-11T21:41:23.7414671Z 
2023-01-11T21:41:23.7414854Z kernel_cpp_3 = async_compile.cpp('''
2023-01-11T21:41:23.7415156Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7415328Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.7415467Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:23.7415544Z {
2023-01-11T21:41:23.7415668Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7415749Z     {
2023-01-11T21:41:23.7415857Z         #pragma omp for 
2023-01-11T21:41:23.7415981Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.7416084Z         {
2023-01-11T21:41:23.7416192Z             {
2023-01-11T21:41:23.7416288Z                 {
2023-01-11T21:41:23.7416449Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.7416605Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.7416780Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:41:23.7416933Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:41:23.7417080Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.7417215Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.7417295Z                 }
2023-01-11T21:41:23.7417393Z             }
2023-01-11T21:41:23.7417486Z         }
2023-01-11T21:41:23.7417573Z     }
2023-01-11T21:41:23.7417663Z }
2023-01-11T21:41:23.7417802Z ''')
2023-01-11T21:41:23.7417811Z 
2023-01-11T21:41:23.7417952Z async_compile.wait(globals())
2023-01-11T21:41:23.7418051Z del async_compile
2023-01-11T21:41:23.7418232Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7418343Z wrapper = (
2023-01-11T21:41:23.7418470Z '''
2023-01-11T21:41:23.7418583Z #include <dlfcn.h>
2023-01-11T21:41:23.7418697Z #include <assert.h>
2023-01-11T21:41:23.7418888Z     std::vector<at::Tensor> call_8(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7419129Z     at::Tensor primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9;
2023-01-11T21:41:23.7419232Z     primals_1 = args[0];
2023-01-11T21:41:23.7419339Z     primals_2 = args[1];
2023-01-11T21:41:23.7419541Z     primals_3 = args[2];
2023-01-11T21:41:23.7419647Z     primals_4 = args[3];
2023-01-11T21:41:23.7419753Z     primals_5 = args[4];
2023-01-11T21:41:23.7419857Z     primals_6 = args[5];
2023-01-11T21:41:23.7419952Z     primals_7 = args[6];
2023-01-11T21:41:23.7420071Z     primals_8 = args[7];
2023-01-11T21:41:23.7420187Z     primals_9 = args[8];
2023-01-11T21:41:23.7420385Z     auto buf0 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7420600Z     at::addmm_out(buf0, primals_2, primals_9, at::as_strided(primals_1, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:23.7420715Z     primals_1.reset();
2023-01-11T21:41:23.7420817Z     primals_2.reset();
2023-01-11T21:41:23.7420947Z     auto buf1 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:23.7421328Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/qf/cqfoag4edkqhhwar6dcfekr7xrl4rhfowlfpqtdov7pzkh5vony6.so", RTLD_NOW);
2023-01-11T21:41:23.7421471Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7421663Z     void (*kernel_cpp_0)(float*);
2023-01-11T21:41:23.7421853Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7422001Z     kernel_cpp_0((float*)(buf1.data_ptr()));
2023-01-11T21:41:23.7422188Z     auto buf2 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7422531Z     at::addmm_out(buf2, primals_4, buf1, at::as_strided(primals_3, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:23.7422629Z     primals_4.reset();
2023-01-11T21:41:23.7422755Z     auto buf3 = buf2; buf2.reset();  // reuse
2023-01-11T21:41:23.7423105Z     auto kernel_cpp_1_lib = dlopen("/tmp/torchinductor_jenkins/qf/cqfoag4edkqhhwar6dcfekr7xrl4rhfowlfpqtdov7pzkh5vony6.so", RTLD_NOW);
2023-01-11T21:41:23.7423246Z     assert(kernel_cpp_1_lib != nullptr);
2023-01-11T21:41:23.7423366Z     void (*kernel_cpp_1)(float*);
2023-01-11T21:41:23.7423540Z     *(void **) (&kernel_cpp_1) = dlsym(kernel_cpp_1_lib, "kernel");
2023-01-11T21:41:23.7423675Z     kernel_cpp_1((float*)(buf3.data_ptr()));
2023-01-11T21:41:23.7423872Z     auto buf4 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7424061Z     at::addmm_out(buf4, primals_6, buf3, at::as_strided(primals_5, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:23.7424165Z     primals_6.reset();
2023-01-11T21:41:23.7424300Z     auto buf5 = buf4; buf4.reset();  // reuse
2023-01-11T21:41:23.7424673Z     auto kernel_cpp_2_lib = dlopen("/tmp/torchinductor_jenkins/qf/cqfoag4edkqhhwar6dcfekr7xrl4rhfowlfpqtdov7pzkh5vony6.so", RTLD_NOW);
2023-01-11T21:41:23.7424814Z     assert(kernel_cpp_2_lib != nullptr);
2023-01-11T21:41:23.7424935Z     void (*kernel_cpp_2)(float*);
2023-01-11T21:41:23.7425104Z     *(void **) (&kernel_cpp_2) = dlsym(kernel_cpp_2_lib, "kernel");
2023-01-11T21:41:23.7425247Z     kernel_cpp_2((float*)(buf5.data_ptr()));
2023-01-11T21:41:23.7425416Z     auto buf6 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7425618Z     at::addmm_out(buf6, primals_8, buf5, at::as_strided(primals_7, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:23.7425723Z     primals_8.reset();
2023-01-11T21:41:23.7425860Z     auto buf7 = buf6; buf6.reset();  // reuse
2023-01-11T21:41:23.7426042Z     auto buf8 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Bool); 
2023-01-11T21:41:23.7426383Z     auto kernel_cpp_3_lib = dlopen("/tmp/torchinductor_jenkins/by/cby2ob4hd4n2ixdn4t25zfpvb6vyfd66nfdv33d5vsx36mxcwhy4.so", RTLD_NOW);
2023-01-11T21:41:23.7426520Z     assert(kernel_cpp_3_lib != nullptr);
2023-01-11T21:41:23.7426656Z     void (*kernel_cpp_3)(float*,bool*);
2023-01-11T21:41:23.7426821Z     *(void **) (&kernel_cpp_3) = dlsym(kernel_cpp_3_lib, "kernel");
2023-01-11T21:41:23.7427007Z     kernel_cpp_3((float*)(buf7.data_ptr()), (bool*)(buf8.data_ptr()));
2023-01-11T21:41:23.7427610Z     return std::vector<at::Tensor>({buf7, primals_9, buf1, buf3, buf5, buf8, at::as_strided(primals_7, {8, 8}, {8, 1}), at::as_strided(primals_5, {8, 8}, {8, 1}), at::as_strided(primals_3, {8, 8}, {8, 1})}); }''' )
2023-01-11T21:41:23.7427734Z 
2023-01-11T21:41:23.7427847Z module = load_inline(
2023-01-11T21:41:23.7428227Z     name='inline_extension_csdpljql3gdtwyefwna55hislgjyk4wkihbuoidk4i5vw3bqzfra',
2023-01-11T21:41:23.7428349Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7428516Z     functions=['call_8'],
2023-01-11T21:41:23.7429052Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7429268Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7430118Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7430142Z 
2023-01-11T21:41:23.7430250Z def _wrap_func(f):
2023-01-11T21:41:23.7430352Z     def g(args):
2023-01-11T21:41:23.7430572Z         return f(args)
2023-01-11T21:41:23.7430671Z     return g
2023-01-11T21:41:23.7430810Z call = _wrap_func(module.call_8)
2023-01-11T21:41:23.7430818Z 
2023-01-11T21:41:23.7430824Z 
2023-01-11T21:41:23.7430925Z if __name__ == "__main__":
2023-01-11T21:41:23.7431083Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7431234Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7431540Z     primals_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7431842Z     primals_2 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7432131Z     primals_3 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7432423Z     primals_4 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7432698Z     primals_5 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7432981Z     primals_6 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7433290Z     primals_7 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7433590Z     primals_8 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7433970Z     primals_9 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7434251Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]))
2023-01-11T21:41:23.7434260Z 
2023-01-11T21:41:23.7434684Z [2023-01-11 21:29:18,007] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 161
2023-01-11T21:41:23.7435064Z [2023-01-11 21:29:18,029] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 162
2023-01-11T21:41:23.7435072Z 
2023-01-11T21:41:23.7435201Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7435306Z import torch
2023-01-11T21:41:23.7435408Z import random
2023-01-11T21:41:23.7435580Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7435734Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7435741Z 
2023-01-11T21:41:23.7435841Z aten = torch.ops.aten
2023-01-11T21:41:23.7436025Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7436148Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7436155Z 
2023-01-11T21:41:23.7436161Z 
2023-01-11T21:41:23.7436351Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7436647Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7436828Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7436977Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7437108Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7437198Z {
2023-01-11T21:41:23.7437348Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7437532Z     {
2023-01-11T21:41:23.7437634Z         #pragma omp for 
2023-01-11T21:41:23.7437743Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:23.7437826Z         {
2023-01-11T21:41:23.7438007Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7438182Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7438302Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7438429Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7438512Z         }
2023-01-11T21:41:23.7438634Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7438750Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:23.7438814Z         {
2023-01-11T21:41:23.7438921Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7439031Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.7439139Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7439244Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7439386Z         }
2023-01-11T21:41:23.7439486Z     }
2023-01-11T21:41:23.7439568Z }
2023-01-11T21:41:23.7439693Z ''')
2023-01-11T21:41:23.7439702Z 
2023-01-11T21:41:23.7439834Z async_compile.wait(globals())
2023-01-11T21:41:23.7439942Z del async_compile
2023-01-11T21:41:23.7440102Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7440198Z wrapper = (
2023-01-11T21:41:23.7440311Z '''
2023-01-11T21:41:23.7440398Z #include <dlfcn.h>
2023-01-11T21:41:23.7440503Z #include <assert.h>
2023-01-11T21:41:23.7440703Z     std::vector<at::Tensor> call_9(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7440846Z     at::Tensor primals_1, primals_2;
2023-01-11T21:41:23.7440954Z     primals_1 = args[0];
2023-01-11T21:41:23.7441060Z     primals_2 = args[1];
2023-01-11T21:41:23.7441244Z     auto buf0 = at::empty_strided({100000, }, {1, }, at::ScalarType::Float); 
2023-01-11T21:41:23.7441568Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/76/c76av2fn5v7sv2ienoi5x7je4ut64tlvvozup7k5kastmftp5d3z.so", RTLD_NOW);
2023-01-11T21:41:23.7441722Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7441897Z     void (*kernel_cpp_0)(const float*,const float*,float*);
2023-01-11T21:41:23.7442081Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7442325Z     kernel_cpp_0((float*)(primals_1.data_ptr()), (float*)(primals_2.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:23.7442438Z     primals_2.reset();
2023-01-11T21:41:23.7442717Z     return std::vector<at::Tensor>({buf0, primals_1}); }''' )
2023-01-11T21:41:23.7442726Z 
2023-01-11T21:41:23.7442834Z module = load_inline(
2023-01-11T21:41:23.7443197Z     name='inline_extension_c5mwnoxgxnxcege5ed3o2rm3o6dlemfn2y763tsgjbijuxitr5bs',
2023-01-11T21:41:23.7443307Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7443463Z     functions=['call_9'],
2023-01-11T21:41:23.7443991Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7444209Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7445125Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7445136Z 
2023-01-11T21:41:23.7445250Z def _wrap_func(f):
2023-01-11T21:41:23.7445358Z     def g(args):
2023-01-11T21:41:23.7445450Z         return f(args)
2023-01-11T21:41:23.7445550Z     return g
2023-01-11T21:41:23.7445676Z call = _wrap_func(module.call_9)
2023-01-11T21:41:23.7445683Z 
2023-01-11T21:41:23.7445688Z 
2023-01-11T21:41:23.7445785Z if __name__ == "__main__":
2023-01-11T21:41:23.7445935Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7446099Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7446514Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7446828Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7447001Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:23.7447023Z 
2023-01-11T21:41:23.7447437Z [2023-01-11 21:29:39,052] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 162
2023-01-11T21:41:23.7447850Z [2023-01-11 21:29:39,056] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 162
2023-01-11T21:41:23.7447858Z 
2023-01-11T21:41:23.7448016Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7448124Z import torch
2023-01-11T21:41:23.7448239Z import random
2023-01-11T21:41:23.7448427Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7448609Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7448616Z 
2023-01-11T21:41:23.7448813Z aten = torch.ops.aten
2023-01-11T21:41:23.7449013Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7449155Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7449163Z 
2023-01-11T21:41:23.7449169Z 
2023-01-11T21:41:23.7449384Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7449677Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7449854Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7450011Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7450152Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7450228Z {
2023-01-11T21:41:23.7450371Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7450464Z     {
2023-01-11T21:41:23.7450573Z         #pragma omp for 
2023-01-11T21:41:23.7450691Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:23.7450788Z         {
2023-01-11T21:41:23.7450991Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7451175Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7451272Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7451397Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7451490Z         }
2023-01-11T21:41:23.7451617Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7451744Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:23.7451829Z         {
2023-01-11T21:41:23.7451933Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7452061Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.7452172Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7452277Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7452367Z         }
2023-01-11T21:41:23.7452456Z     }
2023-01-11T21:41:23.7452542Z }
2023-01-11T21:41:23.7452649Z ''')
2023-01-11T21:41:23.7452657Z 
2023-01-11T21:41:23.7452777Z async_compile.wait(globals())
2023-01-11T21:41:23.7452884Z del async_compile
2023-01-11T21:41:23.7453040Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7453137Z wrapper = (
2023-01-11T21:41:23.7453248Z '''
2023-01-11T21:41:23.7453344Z #include <dlfcn.h>
2023-01-11T21:41:23.7453427Z #include <assert.h>
2023-01-11T21:41:23.7453601Z     std::vector<at::Tensor> call_10(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7453731Z     at::Tensor primals_1, tangents_1;
2023-01-11T21:41:23.7453837Z     primals_1 = args[0];
2023-01-11T21:41:23.7453941Z     tangents_1 = args[1];
2023-01-11T21:41:23.7454133Z     auto buf0 = at::empty_strided({100000, }, {1, }, at::ScalarType::Float); 
2023-01-11T21:41:23.7454484Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/76/c76av2fn5v7sv2ienoi5x7je4ut64tlvvozup7k5kastmftp5d3z.so", RTLD_NOW);
2023-01-11T21:41:23.7454641Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7454801Z     void (*kernel_cpp_0)(const float*,const float*,float*);
2023-01-11T21:41:23.7455087Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7455353Z     kernel_cpp_0((float*)(tangents_1.data_ptr()), (float*)(primals_1.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:23.7455468Z     primals_1.reset();
2023-01-11T21:41:23.7455583Z     tangents_1.reset();
2023-01-11T21:41:23.7456056Z     return std::vector<at::Tensor>({at::Tensor(), buf0}); }''' )
2023-01-11T21:41:23.7456065Z 
2023-01-11T21:41:23.7456161Z module = load_inline(
2023-01-11T21:41:23.7456516Z     name='inline_extension_cw4gwewvco6iido3xvjhqkdjwb76c4nhgeylgbiva5xor4layxbv',
2023-01-11T21:41:23.7456645Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7456821Z     functions=['call_10'],
2023-01-11T21:41:23.7457395Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7457611Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7458537Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7458555Z 
2023-01-11T21:41:23.7458655Z def _wrap_func(f):
2023-01-11T21:41:23.7458745Z     def g(args):
2023-01-11T21:41:23.7458839Z         return f(args)
2023-01-11T21:41:23.7458912Z     return g
2023-01-11T21:41:23.7459032Z call = _wrap_func(module.call_10)
2023-01-11T21:41:23.7459038Z 
2023-01-11T21:41:23.7459044Z 
2023-01-11T21:41:23.7459143Z if __name__ == "__main__":
2023-01-11T21:41:23.7459289Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7459448Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7459738Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7460027Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7460194Z     print_performance(lambda: call([primals_1, tangents_1]))
2023-01-11T21:41:23.7460201Z 
2023-01-11T21:41:23.7460597Z [2023-01-11 21:29:58,620] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 162
2023-01-11T21:41:23.7460978Z [2023-01-11 21:29:58,741] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 163
2023-01-11T21:41:23.7461349Z [2023-01-11 21:29:58,743] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.7461702Z [2023-01-11 21:29:58,745] torch._inductor.graph: [DEBUG] Set _can_use_cpp_wrapper to False due to Constants
2023-01-11T21:41:23.7462070Z [2023-01-11 21:30:00,349] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 163
2023-01-11T21:41:23.7462621Z [2023-01-11 21:30:00,352] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 163
2023-01-11T21:41:23.7462640Z 
2023-01-11T21:41:23.7462769Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7462864Z import torch
2023-01-11T21:41:23.7462964Z import random
2023-01-11T21:41:23.7463120Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7463311Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7463318Z 
2023-01-11T21:41:23.7463433Z aten = torch.ops.aten
2023-01-11T21:41:23.7463642Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7463775Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7464029Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.7464038Z 
2023-01-11T21:41:23.7464043Z 
2023-01-11T21:41:23.7464250Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7464554Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7464714Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:23.7465049Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7465211Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.7465353Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7465443Z {
2023-01-11T21:41:23.7465581Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7465663Z     {
2023-01-11T21:41:23.7465751Z         #pragma omp for 
2023-01-11T21:41:23.7465872Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:23.7465960Z         {
2023-01-11T21:41:23.7466048Z             {
2023-01-11T21:41:23.7466137Z                 {
2023-01-11T21:41:23.7466255Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.7466380Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.7466487Z                     auto tmp7 = in_ptr2[i0];
2023-01-11T21:41:23.7466623Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.7466898Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.7467049Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:23.7467176Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.7467317Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.7467438Z                     auto tmp8 = tmp6 * tmp7;
2023-01-11T21:41:23.7467546Z                     auto tmp9 = tmp5 * tmp8;
2023-01-11T21:41:23.7467708Z                     auto tmp10 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:23.7467841Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:23.7467963Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:23.7468058Z                 }
2023-01-11T21:41:23.7468145Z             }
2023-01-11T21:41:23.7468233Z         }
2023-01-11T21:41:23.7468302Z     }
2023-01-11T21:41:23.7468379Z }
2023-01-11T21:41:23.7468513Z ''')
2023-01-11T21:41:23.7468523Z 
2023-01-11T21:41:23.7468528Z 
2023-01-11T21:41:23.7468655Z async_compile.wait(globals())
2023-01-11T21:41:23.7468768Z del async_compile
2023-01-11T21:41:23.7468775Z 
2023-01-11T21:41:23.7468872Z def call(args):
2023-01-11T21:41:23.7468991Z     primals_1, primals_2 = args
2023-01-11T21:41:23.7469078Z     args.clear()
2023-01-11T21:41:23.7469269Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.7469574Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7469866Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7469964Z     del primals_2
2023-01-11T21:41:23.7470124Z     return (buf0, primals_1, seed_cpu_None.clone(), )
2023-01-11T21:41:23.7470132Z 
2023-01-11T21:41:23.7470137Z 
2023-01-11T21:41:23.7470241Z if __name__ == "__main__":
2023-01-11T21:41:23.7470392Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7470546Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7470834Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7471118Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7471396Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7471567Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:23.7471574Z 
2023-01-11T21:41:23.7471580Z 
2023-01-11T21:41:23.7471707Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7471793Z import torch
2023-01-11T21:41:23.7471886Z import random
2023-01-11T21:41:23.7472033Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7472194Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7472201Z 
2023-01-11T21:41:23.7472303Z aten = torch.ops.aten
2023-01-11T21:41:23.7472480Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7472598Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7472690Z 
2023-01-11T21:41:23.7472696Z 
2023-01-11T21:41:23.7472883Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7473149Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7473303Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7473459Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7473596Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.7473807Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7473899Z {
2023-01-11T21:41:23.7474042Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7474130Z     {
2023-01-11T21:41:23.7474253Z         #pragma omp for 
2023-01-11T21:41:23.7474360Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:23.7474452Z         {
2023-01-11T21:41:23.7474545Z             {
2023-01-11T21:41:23.7474636Z                 {
2023-01-11T21:41:23.7474838Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:23.7474968Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.7475093Z                     auto tmp10 = in_ptr2[i0];
2023-01-11T21:41:23.7475218Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.7475409Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.7475555Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:23.7475684Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.7475835Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.7475961Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.7476109Z                     auto tmp8 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:23.7476234Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.7476338Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:23.7476446Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:23.7476540Z                 }
2023-01-11T21:41:23.7476626Z             }
2023-01-11T21:41:23.7476707Z         }
2023-01-11T21:41:23.7476784Z     }
2023-01-11T21:41:23.7476856Z }
2023-01-11T21:41:23.7476984Z ''')
2023-01-11T21:41:23.7476992Z 
2023-01-11T21:41:23.7477118Z async_compile.wait(globals())
2023-01-11T21:41:23.7477221Z del async_compile
2023-01-11T21:41:23.7477381Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7477482Z wrapper = (
2023-01-11T21:41:23.7477598Z '''
2023-01-11T21:41:23.7477697Z #include <dlfcn.h>
2023-01-11T21:41:23.7477805Z #include <assert.h>
2023-01-11T21:41:23.7478004Z     std::vector<at::Tensor> call_11(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7478164Z     at::Tensor primals_1, philox_seed_like, tangents_1;
2023-01-11T21:41:23.7478266Z     primals_1 = args[0];
2023-01-11T21:41:23.7478381Z     philox_seed_like = args[1];
2023-01-11T21:41:23.7478484Z     tangents_1 = args[2];
2023-01-11T21:41:23.7478653Z     auto buf0 = at::empty_strided({100000, }, {1, }, at::ScalarType::Float); 
2023-01-11T21:41:23.7479025Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/lq/clqibsqxpldu4echxzc3z6phb5szhugslpae44irpgfrltvzbhob.so", RTLD_NOW);
2023-01-11T21:41:23.7479176Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7479388Z     void (*kernel_cpp_0)(const long*,const float*,const float*,float*);
2023-01-11T21:41:23.7479576Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7479895Z     kernel_cpp_0((long*)(philox_seed_like.data_ptr()), (float*)(tangents_1.data_ptr()), (float*)(primals_1.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:23.7480019Z     philox_seed_like.reset();
2023-01-11T21:41:23.7480124Z     primals_1.reset();
2023-01-11T21:41:23.7480220Z     tangents_1.reset();
2023-01-11T21:41:23.7480508Z     return std::vector<at::Tensor>({at::Tensor(), buf0}); }''' )
2023-01-11T21:41:23.7480519Z 
2023-01-11T21:41:23.7480633Z module = load_inline(
2023-01-11T21:41:23.7481130Z     name='inline_extension_c7pb2bael7vzlyb6qu32uoilu6pwiy3hiolshidh4zeg5hqyeqvp',
2023-01-11T21:41:23.7481260Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7481432Z     functions=['call_11'],
2023-01-11T21:41:23.7482007Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7482242Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7483205Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7483215Z 
2023-01-11T21:41:23.7483317Z def _wrap_func(f):
2023-01-11T21:41:23.7483422Z     def g(args):
2023-01-11T21:41:23.7483533Z         return f(args)
2023-01-11T21:41:23.7483621Z     return g
2023-01-11T21:41:23.7483843Z call = _wrap_func(module.call_11)
2023-01-11T21:41:23.7483854Z 
2023-01-11T21:41:23.7483861Z 
2023-01-11T21:41:23.7483977Z if __name__ == "__main__":
2023-01-11T21:41:23.7484158Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7484342Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7484655Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7484968Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7485287Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7485518Z     print_performance(lambda: call([primals_1, philox_seed_like, tangents_1]))
2023-01-11T21:41:23.7485526Z 
2023-01-11T21:41:23.7485962Z [2023-01-11 21:30:21,467] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 163
2023-01-11T21:41:23.7486651Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7486838Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7487244Z [2023-01-11 21:30:21,493] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 164
2023-01-11T21:41:23.7487252Z 
2023-01-11T21:41:23.7487392Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7487500Z import torch
2023-01-11T21:41:23.7487590Z import random
2023-01-11T21:41:23.7487770Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7487961Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7487969Z 
2023-01-11T21:41:23.7488090Z aten = torch.ops.aten
2023-01-11T21:41:23.7488304Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7488448Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7488454Z 
2023-01-11T21:41:23.7488593Z async_compile.wait(globals())
2023-01-11T21:41:23.7488690Z del async_compile
2023-01-11T21:41:23.7488875Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7488981Z wrapper = (
2023-01-11T21:41:23.7489110Z '''
2023-01-11T21:41:23.7489215Z #include <dlfcn.h>
2023-01-11T21:41:23.7489326Z #include <assert.h>
2023-01-11T21:41:23.7489505Z     at::Tensor call_12(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7489627Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:23.7489737Z     arg0_1 = args[0];
2023-01-11T21:41:23.7489848Z     arg1_1 = args[1];
2023-01-11T21:41:23.7490051Z     auto buf0 = at::empty_strided({32, 32}, {32, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7490233Z     at::mm_out(buf0, arg0_1, at::as_strided(arg1_1, {32, 32}, {32, 1}));
2023-01-11T21:41:23.7490343Z     arg0_1.reset();
2023-01-11T21:41:23.7490528Z     arg1_1.reset();
2023-01-11T21:41:23.7490675Z     return buf0; }''' )
2023-01-11T21:41:23.7490685Z 
2023-01-11T21:41:23.7490791Z module = load_inline(
2023-01-11T21:41:23.7491186Z     name='inline_extension_cbk5vdefanataz2xcfa5gu6egrblcazbommb7bzzso26owifwayb',
2023-01-11T21:41:23.7491316Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7491480Z     functions=['call_12'],
2023-01-11T21:41:23.7492040Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7492265Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7493203Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7493219Z 
2023-01-11T21:41:23.7493429Z def _wrap_func(f):
2023-01-11T21:41:23.7493524Z     def g(args):
2023-01-11T21:41:23.7493643Z         return f(args)
2023-01-11T21:41:23.7493737Z     return g
2023-01-11T21:41:23.7493858Z call = _wrap_func(module.call_12)
2023-01-11T21:41:23.7493865Z 
2023-01-11T21:41:23.7493871Z 
2023-01-11T21:41:23.7493975Z if __name__ == "__main__":
2023-01-11T21:41:23.7494132Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7494312Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7494600Z     arg0_1 = rand_strided((32, 32), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7494910Z     arg1_1 = rand_strided((32, 1, 32), (32, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7495067Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7495075Z 
2023-01-11T21:41:23.7495479Z [2023-01-11 21:30:22,840] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 164
2023-01-11T21:41:23.7495841Z STAGE:2023-01-11 21:30:22 1454:1454 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:41:23.7496209Z [2023-01-11 21:30:22,853] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 165
2023-01-11T21:41:23.7496592Z [2023-01-11 21:30:22,854] torch._inductor.graph: [DEBUG] Set _can_use_cpp_wrapper to False due to profiler not supported
2023-01-11T21:41:23.7496966Z [2023-01-11 21:30:24,436] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 165
2023-01-11T21:41:23.7497318Z STAGE:2023-01-11 21:30:24 1454:1454 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:41:23.7497678Z STAGE:2023-01-11 21:30:24 1454:1454 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:41:23.7498275Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7498454Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7498839Z [2023-01-11 21:30:24,458] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 166
2023-01-11T21:41:23.7498847Z 
2023-01-11T21:41:23.7498989Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7499103Z import torch
2023-01-11T21:41:23.7499210Z import random
2023-01-11T21:41:23.7499388Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7499563Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7499571Z 
2023-01-11T21:41:23.7499683Z aten = torch.ops.aten
2023-01-11T21:41:23.7499859Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7499991Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7500085Z 
2023-01-11T21:41:23.7500095Z 
2023-01-11T21:41:23.7500298Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7500582Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7500739Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7500887Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7501032Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7501103Z {
2023-01-11T21:41:23.7501242Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7501321Z     {
2023-01-11T21:41:23.7501423Z         #pragma omp for 
2023-01-11T21:41:23.7501530Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.7501619Z         {
2023-01-11T21:41:23.7501803Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7501988Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7502089Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7502276Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7502504Z         }
2023-01-11T21:41:23.7502642Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7502756Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:23.7502837Z         {
2023-01-11T21:41:23.7502940Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7503047Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.7503161Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7503272Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7503356Z         }
2023-01-11T21:41:23.7503434Z     }
2023-01-11T21:41:23.7503510Z }
2023-01-11T21:41:23.7503621Z ''')
2023-01-11T21:41:23.7503629Z 
2023-01-11T21:41:23.7503650Z 
2023-01-11T21:41:23.7503756Z async_compile.wait(globals())
2023-01-11T21:41:23.7503858Z del async_compile
2023-01-11T21:41:23.7503865Z 
2023-01-11T21:41:23.7503963Z def call(args):
2023-01-11T21:41:23.7504057Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7504155Z     args.clear()
2023-01-11T21:41:23.7504306Z     from torch.profiler import record_function
2023-01-11T21:41:23.7504533Z     with record_function('inductor_wrapper_call'):
2023-01-11T21:41:23.7504809Z         buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7505041Z         kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7505138Z         del arg0_1
2023-01-11T21:41:23.7505233Z         del arg1_1
2023-01-11T21:41:23.7505335Z         return (buf0, )
2023-01-11T21:41:23.7505342Z 
2023-01-11T21:41:23.7505349Z 
2023-01-11T21:41:23.7505451Z if __name__ == "__main__":
2023-01-11T21:41:23.7505607Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7505764Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7506044Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7506325Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7506493Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7506500Z 
2023-01-11T21:41:23.7506506Z 
2023-01-11T21:41:23.7506636Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7506732Z import torch
2023-01-11T21:41:23.7506829Z import random
2023-01-11T21:41:23.7506991Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7507143Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7507166Z 
2023-01-11T21:41:23.7507257Z aten = torch.ops.aten
2023-01-11T21:41:23.7507441Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7507570Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7507576Z 
2023-01-11T21:41:23.7507581Z 
2023-01-11T21:41:23.7507761Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7508039Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7508211Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7508464Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7508577Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.7508699Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.7508833Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:23.7508963Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7509047Z {
2023-01-11T21:41:23.7509131Z     {
2023-01-11T21:41:23.7509217Z         {
2023-01-11T21:41:23.7509317Z             float tmp1 = 0;
2023-01-11T21:41:23.7509649Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.7509816Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.7509977Z             struct IndexValue_7 {size_t index; float value;};
2023-01-11T21:41:23.7510302Z             IndexValue_7 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.7510576Z             #pragma omp declare reduction(argmax : struct IndexValue_7 :\
2023-01-11T21:41:23.7510790Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.7510993Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.7511339Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.7511475Z             struct IndexValue_8 {size_t index; float value;};
2023-01-11T21:41:23.7511644Z             IndexValue_8 tmp5{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:23.7511857Z             #pragma omp declare reduction(argmin : struct IndexValue_8 :\
2023-01-11T21:41:23.7512085Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:23.7512308Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:23.7512527Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:23.7512661Z             for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.7512762Z             {
2023-01-11T21:41:23.7512845Z                 {
2023-01-11T21:41:23.7512984Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7513096Z                     tmp1 += tmp0;
2023-01-11T21:41:23.7513240Z                     tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:23.7513375Z                     tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:23.7513504Z                     if (tmp4.value < tmp0) {
2023-01-11T21:41:23.7513653Z                         tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:23.7513806Z                     }
2023-01-11T21:41:23.7513938Z                     if (tmp5.value > tmp0) {
2023-01-11T21:41:23.7514082Z                         tmp5.index = i0; tmp5.value = tmp0;
2023-01-11T21:41:23.7514175Z                     }
2023-01-11T21:41:23.7514265Z                 }
2023-01-11T21:41:23.7514351Z             }
2023-01-11T21:41:23.7514463Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:23.7514554Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:23.7514656Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:23.7514769Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:23.7514876Z             out_ptr4[0] = tmp5.index;
2023-01-11T21:41:23.7514962Z         }
2023-01-11T21:41:23.7515045Z     }
2023-01-11T21:41:23.7515110Z }
2023-01-11T21:41:23.7515240Z ''')
2023-01-11T21:41:23.7515247Z 
2023-01-11T21:41:23.7515367Z async_compile.wait(globals())
2023-01-11T21:41:23.7515466Z del async_compile
2023-01-11T21:41:23.7515626Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7515722Z wrapper = (
2023-01-11T21:41:23.7515832Z '''
2023-01-11T21:41:23.7515923Z #include <dlfcn.h>
2023-01-11T21:41:23.7516021Z #include <assert.h>
2023-01-11T21:41:23.7516204Z     std::vector<at::Tensor> call_13(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7516324Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7516516Z     arg0_1 = args[0];
2023-01-11T21:41:23.7516712Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Float); 
2023-01-11T21:41:23.7516907Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Float); 
2023-01-11T21:41:23.7517096Z     auto buf2 = at::empty_strided({}, {}, at::ScalarType::Float); 
2023-01-11T21:41:23.7517266Z     auto buf3 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7517452Z     auto buf4 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7517846Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/5l/c5l5a67pncykxc4k6lcjmgu7elavbipctmegtckslekz4senmd6h.so", RTLD_NOW);
2023-01-11T21:41:23.7517983Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7518175Z     void (*kernel_cpp_0)(const float*,float*,float*,float*,long*,long*);
2023-01-11T21:41:23.7518357Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7518756Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()), (long*)(buf3.data_ptr()), (long*)(buf4.data_ptr()));
2023-01-11T21:41:23.7518864Z     arg0_1.reset();
2023-01-11T21:41:23.7519143Z     return std::vector<at::Tensor>({buf0, buf1, buf2, buf3, buf4}); }''' )
2023-01-11T21:41:23.7519165Z 
2023-01-11T21:41:23.7519259Z module = load_inline(
2023-01-11T21:41:23.7519666Z     name='inline_extension_cnves6oopp5lj6abdi7pghxpzsce42tujpsuwbgsp347d4fyecyk',
2023-01-11T21:41:23.7519795Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7519961Z     functions=['call_13'],
2023-01-11T21:41:23.7520479Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7520698Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7521689Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7521706Z 
2023-01-11T21:41:23.7521829Z def _wrap_func(f):
2023-01-11T21:41:23.7521918Z     def g(args):
2023-01-11T21:41:23.7522029Z         return f(args)
2023-01-11T21:41:23.7522137Z     return g
2023-01-11T21:41:23.7522279Z call = _wrap_func(module.call_13)
2023-01-11T21:41:23.7522288Z 
2023-01-11T21:41:23.7522294Z 
2023-01-11T21:41:23.7522397Z if __name__ == "__main__":
2023-01-11T21:41:23.7522552Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7522725Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7523027Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7523158Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7523164Z 
2023-01-11T21:41:23.7523562Z [2023-01-11 21:30:46,345] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 166
2023-01-11T21:41:23.7524228Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7524426Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7524813Z [2023-01-11 21:30:46,370] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 167
2023-01-11T21:41:23.7524820Z 
2023-01-11T21:41:23.7524952Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7525053Z import torch
2023-01-11T21:41:23.7525160Z import random
2023-01-11T21:41:23.7525327Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7525490Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7525579Z 
2023-01-11T21:41:23.7525706Z aten = torch.ops.aten
2023-01-11T21:41:23.7525894Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7526024Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7526032Z 
2023-01-11T21:41:23.7526039Z 
2023-01-11T21:41:23.7526239Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7526534Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7526707Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7526854Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7526977Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7527113Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.7527199Z {
2023-01-11T21:41:23.7527350Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7527444Z     {
2023-01-11T21:41:23.7527560Z         #pragma omp for 
2023-01-11T21:41:23.7527739Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.7527816Z         {
2023-01-11T21:41:23.7528010Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7528194Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7528382Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.7528497Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.7528676Z             auto tmp4 = at::vec::clamp_min(tmp3, decltype(tmp3)(0));
2023-01-11T21:41:23.7528862Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:23.7528982Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:23.7529090Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7529205Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7529296Z         }
2023-01-11T21:41:23.7529429Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7529545Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.7529643Z         {
2023-01-11T21:41:23.7529762Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7529864Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.7529994Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.7530115Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.7530247Z             auto tmp4 = tmp3 * (tmp3>0);
2023-01-11T21:41:23.7530397Z             auto tmp5 = static_cast<float>(10);
2023-01-11T21:41:23.7530510Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:23.7530627Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.7530729Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:23.7530816Z         }
2023-01-11T21:41:23.7530899Z     }
2023-01-11T21:41:23.7530984Z }
2023-01-11T21:41:23.7531115Z ''')
2023-01-11T21:41:23.7531124Z 
2023-01-11T21:41:23.7531260Z async_compile.wait(globals())
2023-01-11T21:41:23.7531368Z del async_compile
2023-01-11T21:41:23.7531532Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7531649Z wrapper = (
2023-01-11T21:41:23.7531774Z '''
2023-01-11T21:41:23.7531889Z #include <dlfcn.h>
2023-01-11T21:41:23.7532003Z #include <assert.h>
2023-01-11T21:41:23.7532200Z     std::vector<at::Tensor> call_14(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7532298Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:23.7532406Z     arg0_1 = args[0];
2023-01-11T21:41:23.7532516Z     arg1_1 = args[1];
2023-01-11T21:41:23.7532711Z     auto buf0 = at::empty_strided({8, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7532910Z     auto buf1 = at::empty_strided({8, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7533307Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/7x/c7xp2x2aeezqsrsqkwdk4izewjw4zicynazuew4aormeadx7we4w.so", RTLD_NOW);
2023-01-11T21:41:23.7533451Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7533635Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:23.7533802Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7534180Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()));
2023-01-11T21:41:23.7534289Z     arg0_1.reset();
2023-01-11T21:41:23.7534393Z     arg1_1.reset();
2023-01-11T21:41:23.7534658Z     return std::vector<at::Tensor>({buf0, buf1}); }''' )
2023-01-11T21:41:23.7534668Z 
2023-01-11T21:41:23.7534772Z module = load_inline(
2023-01-11T21:41:23.7535145Z     name='inline_extension_calgsbsip6x4xs4ezhgcztxahbh3ege57zlzguw75tt24wn43c6r',
2023-01-11T21:41:23.7535260Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7535404Z     functions=['call_14'],
2023-01-11T21:41:23.7535926Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7536130Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7537064Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7537081Z 
2023-01-11T21:41:23.7537192Z def _wrap_func(f):
2023-01-11T21:41:23.7537289Z     def g(args):
2023-01-11T21:41:23.7537397Z         return f(args)
2023-01-11T21:41:23.7537490Z     return g
2023-01-11T21:41:23.7537617Z call = _wrap_func(module.call_14)
2023-01-11T21:41:23.7537640Z 
2023-01-11T21:41:23.7537648Z 
2023-01-11T21:41:23.7537741Z if __name__ == "__main__":
2023-01-11T21:41:23.7537909Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7538090Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7538389Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7538677Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7538845Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7538854Z 
2023-01-11T21:41:23.7539239Z [2023-01-11 21:31:07,805] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 167
2023-01-11T21:41:23.7539865Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7540041Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7540415Z [2023-01-11 21:31:07,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 168
2023-01-11T21:41:23.7540440Z 
2023-01-11T21:41:23.7540560Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7540659Z import torch
2023-01-11T21:41:23.7540759Z import random
2023-01-11T21:41:23.7540916Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7541082Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7541090Z 
2023-01-11T21:41:23.7541201Z aten = torch.ops.aten
2023-01-11T21:41:23.7541391Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7541510Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7541518Z 
2023-01-11T21:41:23.7541524Z 
2023-01-11T21:41:23.7541720Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7542002Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7542171Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7542302Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7542529Z {
2023-01-11T21:41:23.7542658Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7542720Z     {
2023-01-11T21:41:23.7542927Z         #pragma omp for 
2023-01-11T21:41:23.7543030Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.7543114Z         {
2023-01-11T21:41:23.7543291Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7543468Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:23.7543576Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7543688Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7543755Z         }
2023-01-11T21:41:23.7543874Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7543985Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.7544065Z         {
2023-01-11T21:41:23.7544181Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7544407Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:23.7544517Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:23.7544610Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:23.7544714Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.7544878Z         }
2023-01-11T21:41:23.7544956Z     }
2023-01-11T21:41:23.7545039Z }
2023-01-11T21:41:23.7545153Z ''')
2023-01-11T21:41:23.7545161Z 
2023-01-11T21:41:23.7545271Z async_compile.wait(globals())
2023-01-11T21:41:23.7545373Z del async_compile
2023-01-11T21:41:23.7545543Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7545640Z wrapper = (
2023-01-11T21:41:23.7545749Z '''
2023-01-11T21:41:23.7545849Z #include <dlfcn.h>
2023-01-11T21:41:23.7545949Z #include <assert.h>
2023-01-11T21:41:23.7546098Z     at::Tensor call_15(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7546209Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7546309Z     arg0_1 = args[0];
2023-01-11T21:41:23.7546481Z     auto buf0 = at::empty_strided({8, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:23.7546827Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/35/c35igtmj5ncrr7wfhg5mi3p27d5wx6jrpmulflkkuspmmqmzjoe5.so", RTLD_NOW);
2023-01-11T21:41:23.7546972Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7547107Z     void (*kernel_cpp_0)(const float*,float*);
2023-01-11T21:41:23.7547266Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7547420Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:23.7547510Z     arg0_1.reset();
2023-01-11T21:41:23.7547658Z     return buf0; }''' )
2023-01-11T21:41:23.7547666Z 
2023-01-11T21:41:23.7547765Z module = load_inline(
2023-01-11T21:41:23.7548109Z     name='inline_extension_cjm5ytgzhvhh3tc653lkgnoh5sagh4gzhwzzhzx6xddloix3de7v',
2023-01-11T21:41:23.7548218Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7548373Z     functions=['call_15'],
2023-01-11T21:41:23.7548925Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7549146Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7550018Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7550032Z 
2023-01-11T21:41:23.7550139Z def _wrap_func(f):
2023-01-11T21:41:23.7550231Z     def g(args):
2023-01-11T21:41:23.7550334Z         return f(args)
2023-01-11T21:41:23.7550425Z     return g
2023-01-11T21:41:23.7550541Z call = _wrap_func(module.call_15)
2023-01-11T21:41:23.7550549Z 
2023-01-11T21:41:23.7550554Z 
2023-01-11T21:41:23.7550659Z if __name__ == "__main__":
2023-01-11T21:41:23.7550797Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7550961Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7551242Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7551391Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7551482Z 
2023-01-11T21:41:23.7551887Z [2023-01-11 21:31:28,637] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 168
2023-01-11T21:41:23.7552491Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7552665Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7553068Z [2023-01-11 21:31:28,669] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 169
2023-01-11T21:41:23.7553076Z 
2023-01-11T21:41:23.7553223Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7553314Z import torch
2023-01-11T21:41:23.7553422Z import random
2023-01-11T21:41:23.7553704Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7553963Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7553972Z 
2023-01-11T21:41:23.7554085Z aten = torch.ops.aten
2023-01-11T21:41:23.7554290Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7554427Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7554437Z 
2023-01-11T21:41:23.7554443Z 
2023-01-11T21:41:23.7554651Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7554934Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7555121Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7555279Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:23.7555423Z                        double* __restrict__ out_ptr1,
2023-01-11T21:41:23.7555570Z                        double* __restrict__ out_ptr2)
2023-01-11T21:41:23.7555667Z {
2023-01-11T21:41:23.7555812Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7555898Z     {
2023-01-11T21:41:23.7556016Z         #pragma omp for 
2023-01-11T21:41:23.7556141Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.7556244Z         {
2023-01-11T21:41:23.7556352Z             {
2023-01-11T21:41:23.7556455Z                 {
2023-01-11T21:41:23.7556580Z                     double tmp2 = 0;
2023-01-11T21:41:23.7556700Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:23.7556796Z                     {
2023-01-11T21:41:23.7556898Z                         {
2023-01-11T21:41:23.7557054Z                             auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:23.7557212Z                             auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:23.7557324Z                             tmp2 += tmp1;
2023-01-11T21:41:23.7557421Z                         }
2023-01-11T21:41:23.7557510Z                     }
2023-01-11T21:41:23.7557631Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7557722Z                 }
2023-01-11T21:41:23.7557812Z             }
2023-01-11T21:41:23.7557903Z         }
2023-01-11T21:41:23.7557992Z     }
2023-01-11T21:41:23.7558076Z     {
2023-01-11T21:41:23.7558144Z         {
2023-01-11T21:41:23.7558258Z             double tmp2 = 0;
2023-01-11T21:41:23.7558414Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7558514Z             {
2023-01-11T21:41:23.7558675Z                 #pragma omp for reduction(+:tmp2) 
2023-01-11T21:41:23.7558806Z                 for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.7558891Z                 {
2023-01-11T21:41:23.7558984Z                     {
2023-01-11T21:41:23.7559130Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7559288Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:23.7559399Z                         tmp2 += tmp1;
2023-01-11T21:41:23.7559489Z                     }
2023-01-11T21:41:23.7559574Z                 }
2023-01-11T21:41:23.7559643Z             }
2023-01-11T21:41:23.7559856Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:23.7559939Z         }
2023-01-11T21:41:23.7560024Z     }
2023-01-11T21:41:23.7560174Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7560258Z     {
2023-01-11T21:41:23.7560368Z         #pragma omp for 
2023-01-11T21:41:23.7560478Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.7560565Z         {
2023-01-11T21:41:23.7560673Z             #pragma GCC ivdep
2023-01-11T21:41:23.7560784Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:23.7560869Z             {
2023-01-11T21:41:23.7560958Z                 {
2023-01-11T21:41:23.7561034Z                     {
2023-01-11T21:41:23.7561171Z                         auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:23.7561310Z                         auto tmp2 = out_ptr0[i1];
2023-01-11T21:41:23.7561445Z                         auto tmp4 = out_ptr1[0];
2023-01-11T21:41:23.7561596Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:23.7561794Z                         auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.7561937Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.7562077Z                         out_ptr2[i1 + (32*i0)] = tmp5;
2023-01-11T21:41:23.7562161Z                     }
2023-01-11T21:41:23.7562264Z                 }
2023-01-11T21:41:23.7562360Z             }
2023-01-11T21:41:23.7562446Z         }
2023-01-11T21:41:23.7562538Z     }
2023-01-11T21:41:23.7562623Z }
2023-01-11T21:41:23.7562749Z ''')
2023-01-11T21:41:23.7562758Z 
2023-01-11T21:41:23.7562889Z async_compile.wait(globals())
2023-01-11T21:41:23.7562987Z del async_compile
2023-01-11T21:41:23.7563161Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7563269Z wrapper = (
2023-01-11T21:41:23.7563385Z '''
2023-01-11T21:41:23.7563497Z #include <dlfcn.h>
2023-01-11T21:41:23.7563587Z #include <assert.h>
2023-01-11T21:41:23.7563756Z     at::Tensor call_16(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7563853Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7563964Z     arg0_1 = args[0];
2023-01-11T21:41:23.7564147Z     auto buf0 = at::empty_strided({32, }, {1, }, at::ScalarType::Double); 
2023-01-11T21:41:23.7564314Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Double); 
2023-01-11T21:41:23.7564485Z     auto buf2 = at::empty_strided({32, 32}, {32, 1}, at::ScalarType::Double); 
2023-01-11T21:41:23.7564822Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/zh/czhbish5ijarri5zlmhjqoss3r374biyjp3mnifm6ga2y5j5hx55.so", RTLD_NOW);
2023-01-11T21:41:23.7564940Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7565122Z     void (*kernel_cpp_0)(const float*,double*,double*,double*);
2023-01-11T21:41:23.7565307Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7565557Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (double*)(buf0.data_ptr()), (double*)(buf1.data_ptr()), (double*)(buf2.data_ptr()));
2023-01-11T21:41:23.7565665Z     arg0_1.reset();
2023-01-11T21:41:23.7565823Z     return buf2; }''' )
2023-01-11T21:41:23.7565834Z 
2023-01-11T21:41:23.7565934Z module = load_inline(
2023-01-11T21:41:23.7566287Z     name='inline_extension_cch76d6suiocuagkhrlcimmdoy5nhjvwl4xulurcswqzhcc7kzny',
2023-01-11T21:41:23.7566381Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7566535Z     functions=['call_16'],
2023-01-11T21:41:23.7567083Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7567305Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7568205Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7568214Z 
2023-01-11T21:41:23.7568319Z def _wrap_func(f):
2023-01-11T21:41:23.7568504Z     def g(args):
2023-01-11T21:41:23.7568610Z         return f(args)
2023-01-11T21:41:23.7568689Z     return g
2023-01-11T21:41:23.7568813Z call = _wrap_func(module.call_16)
2023-01-11T21:41:23.7568820Z 
2023-01-11T21:41:23.7568826Z 
2023-01-11T21:41:23.7568925Z if __name__ == "__main__":
2023-01-11T21:41:23.7569089Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7569264Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7569566Z     arg0_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7569725Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7569732Z 
2023-01-11T21:41:23.7570116Z [2023-01-11 21:31:49,478] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 169
2023-01-11T21:41:23.7570800Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7570983Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7571340Z [2023-01-11 21:31:49,500] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 170
2023-01-11T21:41:23.7571348Z 
2023-01-11T21:41:23.7571471Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7571564Z import torch
2023-01-11T21:41:23.7571656Z import random
2023-01-11T21:41:23.7571819Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7571978Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7571985Z 
2023-01-11T21:41:23.7572098Z aten = torch.ops.aten
2023-01-11T21:41:23.7572275Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7572401Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7572412Z 
2023-01-11T21:41:23.7572421Z 
2023-01-11T21:41:23.7572615Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7572884Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7573058Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.7573209Z                        const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.7573351Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.7573430Z {
2023-01-11T21:41:23.7573541Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:23.7573622Z     {
2023-01-11T21:41:23.7573708Z         {
2023-01-11T21:41:23.7573802Z             long tmp2 = 0;
2023-01-11T21:41:23.7573899Z             long tmp3 = 0;
2023-01-11T21:41:23.7574040Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7574120Z             {
2023-01-11T21:41:23.7574285Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:23.7574405Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7574498Z                 {
2023-01-11T21:41:23.7574590Z                     {
2023-01-11T21:41:23.7574722Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7574874Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:23.7574980Z                         tmp2 += tmp1;
2023-01-11T21:41:23.7575071Z                         tmp3 += tmp1;
2023-01-11T21:41:23.7575160Z                     }
2023-01-11T21:41:23.7575251Z                 }
2023-01-11T21:41:23.7575338Z             }
2023-01-11T21:41:23.7575447Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:23.7575551Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:23.7575658Z         }
2023-01-11T21:41:23.7575738Z     }
2023-01-11T21:41:23.7575827Z     {
2023-01-11T21:41:23.7575925Z         {
2023-01-11T21:41:23.7576050Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:23.7576172Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:23.7576320Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:23.7576516Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7576635Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.7576750Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:23.7576833Z         }
2023-01-11T21:41:23.7576917Z     }
2023-01-11T21:41:23.7577022Z }
2023-01-11T21:41:23.7577156Z ''')
2023-01-11T21:41:23.7577164Z 
2023-01-11T21:41:23.7577277Z async_compile.wait(globals())
2023-01-11T21:41:23.7577382Z del async_compile
2023-01-11T21:41:23.7577551Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7577653Z wrapper = (
2023-01-11T21:41:23.7577765Z '''
2023-01-11T21:41:23.7577868Z #include <dlfcn.h>
2023-01-11T21:41:23.7577969Z #include <assert.h>
2023-01-11T21:41:23.7578131Z     at::Tensor call_17(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7578245Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7578354Z     arg0_1 = args[0];
2023-01-11T21:41:23.7578525Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7578777Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7578931Z     auto buf2 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:23.7579301Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/7i/c7ikfrx6rninixt746snmnigf7nld7jz6nxxc6lhqudu4kpzv6cu.so", RTLD_NOW);
2023-01-11T21:41:23.7579449Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7579577Z     void (*kernel_cpp_0)(long*,const bool*,long*);
2023-01-11T21:41:23.7579747Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7579954Z     kernel_cpp_0((long*)(buf2.data_ptr()), (bool*)(arg0_1.data_ptr()), (long*)(buf1.data_ptr()));
2023-01-11T21:41:23.7580048Z     arg0_1.reset();
2023-01-11T21:41:23.7580204Z     return buf2; }''' )
2023-01-11T21:41:23.7580213Z 
2023-01-11T21:41:23.7580309Z module = load_inline(
2023-01-11T21:41:23.7580665Z     name='inline_extension_cilgouchaeqshp6duams4dexs6tbjv33w7cmpjeqyecqjxdnhm6c',
2023-01-11T21:41:23.7580786Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7580955Z     functions=['call_17'],
2023-01-11T21:41:23.7581477Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7581686Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7582705Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7582715Z 
2023-01-11T21:41:23.7582823Z def _wrap_func(f):
2023-01-11T21:41:23.7582926Z     def g(args):
2023-01-11T21:41:23.7583036Z         return f(args)
2023-01-11T21:41:23.7583117Z     return g
2023-01-11T21:41:23.7583251Z call = _wrap_func(module.call_17)
2023-01-11T21:41:23.7583258Z 
2023-01-11T21:41:23.7583271Z 
2023-01-11T21:41:23.7583386Z if __name__ == "__main__":
2023-01-11T21:41:23.7583554Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7583727Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7584017Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.7584182Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7584190Z 
2023-01-11T21:41:23.7584599Z [2023-01-11 21:32:10,515] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 170
2023-01-11T21:41:23.7585260Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7585451Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7585960Z [2023-01-11 21:32:10,535] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 171
2023-01-11T21:41:23.7585983Z 
2023-01-11T21:41:23.7586103Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7586201Z import torch
2023-01-11T21:41:23.7586291Z import random
2023-01-11T21:41:23.7586440Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7586614Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7586622Z 
2023-01-11T21:41:23.7586724Z aten = torch.ops.aten
2023-01-11T21:41:23.7586906Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7587014Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7587021Z 
2023-01-11T21:41:23.7587026Z 
2023-01-11T21:41:23.7587225Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7587490Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7587730Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.7587889Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:41:23.7588019Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.7588105Z {
2023-01-11T21:41:23.7588213Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:23.7588303Z     {
2023-01-11T21:41:23.7588392Z         {
2023-01-11T21:41:23.7588496Z             long tmp2 = 0;
2023-01-11T21:41:23.7588608Z             long tmp3 = 0;
2023-01-11T21:41:23.7588757Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7588844Z             {
2023-01-11T21:41:23.7589018Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:23.7589140Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7589240Z                 {
2023-01-11T21:41:23.7589337Z                     {
2023-01-11T21:41:23.7589468Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7589635Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:23.7589747Z                         tmp2 += tmp1;
2023-01-11T21:41:23.7589848Z                         tmp3 += tmp1;
2023-01-11T21:41:23.7589945Z                     }
2023-01-11T21:41:23.7590038Z                 }
2023-01-11T21:41:23.7590124Z             }
2023-01-11T21:41:23.7590236Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:23.7590347Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:23.7590438Z         }
2023-01-11T21:41:23.7590511Z     }
2023-01-11T21:41:23.7590588Z     {
2023-01-11T21:41:23.7590672Z         {
2023-01-11T21:41:23.7590794Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:23.7590913Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:23.7591047Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:23.7591154Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7591271Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.7591380Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:23.7591472Z         }
2023-01-11T21:41:23.7591550Z     }
2023-01-11T21:41:23.7591633Z }
2023-01-11T21:41:23.7591764Z ''')
2023-01-11T21:41:23.7591772Z 
2023-01-11T21:41:23.7591887Z async_compile.wait(globals())
2023-01-11T21:41:23.7591990Z del async_compile
2023-01-11T21:41:23.7592154Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7592250Z wrapper = (
2023-01-11T21:41:23.7592362Z '''
2023-01-11T21:41:23.7592468Z #include <dlfcn.h>
2023-01-11T21:41:23.7592570Z #include <assert.h>
2023-01-11T21:41:23.7592724Z     at::Tensor call_18(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7592825Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7592923Z     arg0_1 = args[0];
2023-01-11T21:41:23.7593092Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7593255Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7593387Z     auto buf2 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:23.7593818Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/tb/ctbh34bqrqepgalem35dqwj3753otv7p7fp3h5othhadkxb5qbmw.so", RTLD_NOW);
2023-01-11T21:41:23.7594037Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7594193Z     void (*kernel_cpp_0)(long*,const unsigned char*,long*);
2023-01-11T21:41:23.7594363Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7594595Z     kernel_cpp_0((long*)(buf2.data_ptr()), (unsigned char*)(arg0_1.data_ptr()), (long*)(buf1.data_ptr()));
2023-01-11T21:41:23.7594696Z     arg0_1.reset();
2023-01-11T21:41:23.7594845Z     return buf2; }''' )
2023-01-11T21:41:23.7594853Z 
2023-01-11T21:41:23.7594945Z module = load_inline(
2023-01-11T21:41:23.7595277Z     name='inline_extension_cd35e6efhhtvu2vyxgwximssv66dj3ogpttagtwmu3k3432jc22z',
2023-01-11T21:41:23.7595369Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7595511Z     functions=['call_18'],
2023-01-11T21:41:23.7596160Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7596378Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7597378Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7597386Z 
2023-01-11T21:41:23.7597492Z def _wrap_func(f):
2023-01-11T21:41:23.7597586Z     def g(args):
2023-01-11T21:41:23.7597682Z         return f(args)
2023-01-11T21:41:23.7597776Z     return g
2023-01-11T21:41:23.7597886Z call = _wrap_func(module.call_18)
2023-01-11T21:41:23.7597892Z 
2023-01-11T21:41:23.7597897Z 
2023-01-11T21:41:23.7597996Z if __name__ == "__main__":
2023-01-11T21:41:23.7598159Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7598343Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7598640Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.uint8)
2023-01-11T21:41:23.7598789Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7598797Z 
2023-01-11T21:41:23.7599185Z [2023-01-11 21:32:31,244] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 171
2023-01-11T21:41:23.7599864Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7600078Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7600481Z [2023-01-11 21:32:31,264] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 172
2023-01-11T21:41:23.7600507Z 
2023-01-11T21:41:23.7600629Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7600726Z import torch
2023-01-11T21:41:23.7600834Z import random
2023-01-11T21:41:23.7600997Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7601167Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7601174Z 
2023-01-11T21:41:23.7601274Z aten = torch.ops.aten
2023-01-11T21:41:23.7601478Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7601622Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7601632Z 
2023-01-11T21:41:23.7601658Z 
2023-01-11T21:41:23.7601876Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7602280Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7602488Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.7602658Z                        const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.7602805Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.7602979Z {
2023-01-11T21:41:23.7603102Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:23.7603175Z     {
2023-01-11T21:41:23.7603264Z         {
2023-01-11T21:41:23.7603370Z             long tmp2 = 0;
2023-01-11T21:41:23.7603472Z             long tmp3 = 0;
2023-01-11T21:41:23.7603647Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7603740Z             {
2023-01-11T21:41:23.7603915Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:23.7604034Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7604127Z                 {
2023-01-11T21:41:23.7604221Z                     {
2023-01-11T21:41:23.7604358Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7604531Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:23.7604658Z                         tmp2 += tmp1;
2023-01-11T21:41:23.7604765Z                         tmp3 += tmp1;
2023-01-11T21:41:23.7604871Z                     }
2023-01-11T21:41:23.7605035Z                 }
2023-01-11T21:41:23.7605145Z             }
2023-01-11T21:41:23.7605273Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:23.7605397Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:23.7605498Z         }
2023-01-11T21:41:23.7605580Z     }
2023-01-11T21:41:23.7605677Z     {
2023-01-11T21:41:23.7605775Z         {
2023-01-11T21:41:23.7605893Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:23.7606016Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:23.7606170Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:23.7606302Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7606414Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.7606539Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:23.7606636Z         }
2023-01-11T21:41:23.7606727Z     }
2023-01-11T21:41:23.7606819Z }
2023-01-11T21:41:23.7606961Z ''')
2023-01-11T21:41:23.7606972Z 
2023-01-11T21:41:23.7607105Z async_compile.wait(globals())
2023-01-11T21:41:23.7607228Z del async_compile
2023-01-11T21:41:23.7607421Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7607527Z wrapper = (
2023-01-11T21:41:23.7607657Z '''
2023-01-11T21:41:23.7607773Z #include <dlfcn.h>
2023-01-11T21:41:23.7607888Z #include <assert.h>
2023-01-11T21:41:23.7608050Z     at::Tensor call_19(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7608154Z     at::Tensor arg0_1;
2023-01-11T21:41:23.7608253Z     arg0_1 = args[0];
2023-01-11T21:41:23.7608449Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7608629Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:23.7608757Z     auto buf2 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:23.7609188Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/3h/c3h2ropcb3kmljlkoaniup2ktv5vwaget4hkir3pmqeix6qdyoww.so", RTLD_NOW);
2023-01-11T21:41:23.7609318Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7609462Z     void (*kernel_cpp_0)(long*,const int*,long*);
2023-01-11T21:41:23.7609646Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7609876Z     kernel_cpp_0((long*)(buf2.data_ptr()), (int*)(arg0_1.data_ptr()), (long*)(buf1.data_ptr()));
2023-01-11T21:41:23.7609971Z     arg0_1.reset();
2023-01-11T21:41:23.7610132Z     return buf2; }''' )
2023-01-11T21:41:23.7610144Z 
2023-01-11T21:41:23.7610246Z module = load_inline(
2023-01-11T21:41:23.7610637Z     name='inline_extension_cnlotoitsz6eiybqu2bw3lgfkvhrhrmri7y2brdfjlaghnyjsytr',
2023-01-11T21:41:23.7610746Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7610883Z     functions=['call_19'],
2023-01-11T21:41:23.7611399Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7611610Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7612480Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7612568Z 
2023-01-11T21:41:23.7612667Z def _wrap_func(f):
2023-01-11T21:41:23.7612758Z     def g(args):
2023-01-11T21:41:23.7612853Z         return f(args)
2023-01-11T21:41:23.7612940Z     return g
2023-01-11T21:41:23.7613051Z call = _wrap_func(module.call_19)
2023-01-11T21:41:23.7613058Z 
2023-01-11T21:41:23.7613063Z 
2023-01-11T21:41:23.7613160Z if __name__ == "__main__":
2023-01-11T21:41:23.7613310Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7613484Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7613761Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.7613902Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7613908Z 
2023-01-11T21:41:23.7614390Z [2023-01-11 21:32:52,225] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 172
2023-01-11T21:41:23.7615019Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7615190Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7615546Z [2023-01-11 21:32:52,250] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 173
2023-01-11T21:41:23.7615569Z 
2023-01-11T21:41:23.7615684Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7615777Z import torch
2023-01-11T21:41:23.7615873Z import random
2023-01-11T21:41:23.7616040Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7616216Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7616223Z 
2023-01-11T21:41:23.7616329Z aten = torch.ops.aten
2023-01-11T21:41:23.7616522Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7616636Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7616642Z 
2023-01-11T21:41:23.7616662Z 
2023-01-11T21:41:23.7616842Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7617133Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7617294Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7617442Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7617581Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7617706Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.7617784Z {
2023-01-11T21:41:23.7617902Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7617990Z     {
2023-01-11T21:41:23.7618100Z         #pragma omp for 
2023-01-11T21:41:23.7618214Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.7618292Z         {
2023-01-11T21:41:23.7618401Z             #pragma GCC ivdep
2023-01-11T21:41:23.7618515Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.7618583Z             {
2023-01-11T21:41:23.7618671Z                 {
2023-01-11T21:41:23.7618755Z                     {
2023-01-11T21:41:23.7618892Z                         auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:23.7619028Z                         auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:23.7619177Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.7619342Z                         out_ptr0[i0 + (8*i1)] = tmp2;
2023-01-11T21:41:23.7619426Z                     }
2023-01-11T21:41:23.7619530Z                 }
2023-01-11T21:41:23.7619633Z             }
2023-01-11T21:41:23.7619737Z         }
2023-01-11T21:41:23.7619866Z         #pragma omp for 
2023-01-11T21:41:23.7620077Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.7620170Z         {
2023-01-11T21:41:23.7620366Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7620551Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.7620670Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7620852Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:23.7620968Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.7621083Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7621161Z         }
2023-01-11T21:41:23.7621272Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7621378Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.7621460Z         {
2023-01-11T21:41:23.7621576Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.7621723Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.7621841Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.7622059Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:41:23.7622164Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.7622274Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.7622508Z         }
2023-01-11T21:41:23.7622605Z     }
2023-01-11T21:41:23.7622688Z }
2023-01-11T21:41:23.7622815Z ''')
2023-01-11T21:41:23.7622822Z 
2023-01-11T21:41:23.7622923Z async_compile.wait(globals())
2023-01-11T21:41:23.7623018Z del async_compile
2023-01-11T21:41:23.7623172Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:23.7623263Z wrapper = (
2023-01-11T21:41:23.7623372Z '''
2023-01-11T21:41:23.7623471Z #include <dlfcn.h>
2023-01-11T21:41:23.7623572Z #include <assert.h>
2023-01-11T21:41:23.7623729Z     std::vector<at::Tensor> call_20(std::vector<at::Tensor> args) {
2023-01-11T21:41:23.7623837Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:23.7623927Z     arg0_1 = args[0];
2023-01-11T21:41:23.7624014Z     arg1_1 = args[1];
2023-01-11T21:41:23.7624191Z     auto buf0 = at::empty_strided({8, 8}, {1, 8}, at::ScalarType::Float); 
2023-01-11T21:41:23.7624363Z     auto buf1 = at::empty_strided({8, 8}, {1, 8}, at::ScalarType::Float); 
2023-01-11T21:41:23.7624691Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/ue/cueituey67usa3z6yqp6aokcyslwfb22e2edl237c4xqodiu4ah2.so", RTLD_NOW);
2023-01-11T21:41:23.7624821Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:23.7624972Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:23.7625166Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:23.7625450Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()));
2023-01-11T21:41:23.7625550Z     arg0_1.reset();
2023-01-11T21:41:23.7625663Z     arg1_1.reset();
2023-01-11T21:41:23.7625993Z     return std::vector<at::Tensor>({buf0, buf1}); }''' )
2023-01-11T21:41:23.7626004Z 
2023-01-11T21:41:23.7626123Z module = load_inline(
2023-01-11T21:41:23.7626497Z     name='inline_extension_cjhlg7jjaalr6ljwfilqinh666vxmfig6blf4xoze25v3hhrk5f6',
2023-01-11T21:41:23.7626599Z     cpp_sources=[wrapper],
2023-01-11T21:41:23.7626782Z     functions=['call_20'],
2023-01-11T21:41:23.7627297Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:23.7627496Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:23.7628321Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:23.7628329Z 
2023-01-11T21:41:23.7628420Z def _wrap_func(f):
2023-01-11T21:41:23.7628511Z     def g(args):
2023-01-11T21:41:23.7628607Z         return f(args)
2023-01-11T21:41:23.7628812Z     return g
2023-01-11T21:41:23.7628945Z call = _wrap_func(module.call_20)
2023-01-11T21:41:23.7628953Z 
2023-01-11T21:41:23.7628959Z 
2023-01-11T21:41:23.7629063Z if __name__ == "__main__":
2023-01-11T21:41:23.7629252Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7629450Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7629806Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7630155Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7630366Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7630376Z 
2023-01-11T21:41:23.7630915Z [2023-01-11 21:33:13,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 173
2023-01-11T21:41:23.7631016Z ok (415.558s)
2023-01-11T21:41:23.7631235Z   test_cudnn_rnn_cpu (__main__.CpuTests) ... skip: requires CUDA (0.002s)
2023-01-11T21:41:23.7631602Z   test_dense_mask_index_cpu (__main__.CpuTests) ... skip: https://github.com/pytorch/torchdynamo/issues/1697 (0.001s)
2023-01-11T21:41:23.7632437Z   test_div1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7632599Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7632968Z [2023-01-11 21:33:13,329] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 174
2023-01-11T21:41:23.7633329Z [2023-01-11 21:33:14,937] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 174
2023-01-11T21:41:23.7633337Z 
2023-01-11T21:41:23.7633455Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7633548Z import torch
2023-01-11T21:41:23.7633629Z import random
2023-01-11T21:41:23.7633870Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7634031Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7634037Z 
2023-01-11T21:41:23.7634144Z aten = torch.ops.aten
2023-01-11T21:41:23.7634320Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7634439Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7634447Z 
2023-01-11T21:41:23.7634452Z 
2023-01-11T21:41:23.7634637Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7634899Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7635041Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7635176Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7635306Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7635436Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.7635565Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.7635688Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7635813Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:23.7635880Z {
2023-01-11T21:41:23.7636013Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7636094Z     {
2023-01-11T21:41:23.7636197Z         #pragma omp for 
2023-01-11T21:41:23.7636304Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.7636385Z         {
2023-01-11T21:41:23.7636575Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7636752Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7636851Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7636966Z             auto tmp3 = tmp2.floor();
2023-01-11T21:41:23.7637075Z             auto tmp4 = tmp2.trunc();
2023-01-11T21:41:23.7637191Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7637409Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7637539Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.7637657Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:23.7637764Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:23.7637844Z         }
2023-01-11T21:41:23.7637984Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7638128Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.7638235Z         {
2023-01-11T21:41:23.7638378Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7638514Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.7638663Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7638830Z             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:23.7638974Z             auto tmp4 = std::trunc(tmp2);
2023-01-11T21:41:23.7639112Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7639249Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.7639386Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.7639570Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:23.7639717Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:23.7639825Z         }
2023-01-11T21:41:23.7639932Z     }
2023-01-11T21:41:23.7640036Z }
2023-01-11T21:41:23.7640181Z ''')
2023-01-11T21:41:23.7640193Z 
2023-01-11T21:41:23.7640200Z 
2023-01-11T21:41:23.7640365Z async_compile.wait(globals())
2023-01-11T21:41:23.7640472Z del async_compile
2023-01-11T21:41:23.7640494Z 
2023-01-11T21:41:23.7640591Z def call(args):
2023-01-11T21:41:23.7640698Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7640803Z     args.clear()
2023-01-11T21:41:23.7641167Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7641436Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7641723Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7641980Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7642234Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7642570Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7642661Z     del arg0_1
2023-01-11T21:41:23.7642746Z     del arg1_1
2023-01-11T21:41:23.7642869Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7642876Z 
2023-01-11T21:41:23.7642881Z 
2023-01-11T21:41:23.7642979Z if __name__ == "__main__":
2023-01-11T21:41:23.7643135Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7643295Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7643549Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7643807Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7643964Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7643971Z 
2023-01-11T21:41:23.7644059Z ok (1.669s)
2023-01-11T21:41:23.7644669Z   test_div2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7644835Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7645206Z [2023-01-11 21:33:15,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 175
2023-01-11T21:41:23.7645585Z [2023-01-11 21:33:16,596] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 175
2023-01-11T21:41:23.7645594Z 
2023-01-11T21:41:23.7645731Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7645932Z import torch
2023-01-11T21:41:23.7646045Z import random
2023-01-11T21:41:23.7646268Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7646496Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7646504Z 
2023-01-11T21:41:23.7646647Z aten = torch.ops.aten
2023-01-11T21:41:23.7646906Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7647074Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7647083Z 
2023-01-11T21:41:23.7647091Z 
2023-01-11T21:41:23.7647351Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7647778Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7647991Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7648183Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7648367Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7648614Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.7648792Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.7648942Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7649109Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:23.7649200Z {
2023-01-11T21:41:23.7649391Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7649485Z     {
2023-01-11T21:41:23.7649610Z         #pragma omp for 
2023-01-11T21:41:23.7649755Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7649865Z         {
2023-01-11T21:41:23.7649976Z             {
2023-01-11T21:41:23.7650075Z                 {
2023-01-11T21:41:23.7650239Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7650406Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.7650602Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7650721Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:23.7650864Z                     auto tmp4 = std::floor(tmp3);
2023-01-11T21:41:23.7650994Z                     auto tmp5 = std::trunc(tmp3);
2023-01-11T21:41:23.7651090Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.7651200Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.7651308Z                     out_ptr2[i0] = tmp5;
2023-01-11T21:41:23.7651414Z                     out_ptr3[i0] = tmp3;
2023-01-11T21:41:23.7651518Z                     out_ptr4[i0] = tmp4;
2023-01-11T21:41:23.7651600Z                 }
2023-01-11T21:41:23.7651684Z             }
2023-01-11T21:41:23.7651749Z         }
2023-01-11T21:41:23.7651826Z     }
2023-01-11T21:41:23.7651905Z }
2023-01-11T21:41:23.7652024Z ''')
2023-01-11T21:41:23.7652032Z 
2023-01-11T21:41:23.7652038Z 
2023-01-11T21:41:23.7652173Z async_compile.wait(globals())
2023-01-11T21:41:23.7652278Z del async_compile
2023-01-11T21:41:23.7652284Z 
2023-01-11T21:41:23.7652380Z def call(args):
2023-01-11T21:41:23.7652465Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7652582Z     args.clear()
2023-01-11T21:41:23.7652971Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7653350Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7653720Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7654081Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7654442Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7654974Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7655079Z     del arg0_1
2023-01-11T21:41:23.7655195Z     del arg1_1
2023-01-11T21:41:23.7655372Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7655382Z 
2023-01-11T21:41:23.7655466Z 
2023-01-11T21:41:23.7655609Z if __name__ == "__main__":
2023-01-11T21:41:23.7655822Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7656059Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7656430Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7656801Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7657003Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7657012Z 
2023-01-11T21:41:23.7657133Z ok (1.659s)
2023-01-11T21:41:23.7658156Z   test_div3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7658463Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7658996Z [2023-01-11 21:33:16,646] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 176
2023-01-11T21:41:23.7659469Z [2023-01-11 21:33:18,260] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 176
2023-01-11T21:41:23.7659476Z 
2023-01-11T21:41:23.7659598Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7659691Z import torch
2023-01-11T21:41:23.7659781Z import random
2023-01-11T21:41:23.7659918Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7660074Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7660081Z 
2023-01-11T21:41:23.7660182Z aten = torch.ops.aten
2023-01-11T21:41:23.7660372Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7660505Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7660511Z 
2023-01-11T21:41:23.7660516Z 
2023-01-11T21:41:23.7660775Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7661211Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7661435Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7661604Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.7661783Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7661959Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7662129Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.7662303Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7662615Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7662720Z {
2023-01-11T21:41:23.7662888Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7662993Z     {
2023-01-11T21:41:23.7663128Z         #pragma omp for 
2023-01-11T21:41:23.7681377Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7681556Z         {
2023-01-11T21:41:23.7681666Z             {
2023-01-11T21:41:23.7681771Z                 {
2023-01-11T21:41:23.7681940Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7682097Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.7682294Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7682483Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:23.7682640Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:23.7683147Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:23.7683303Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:23.7683438Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.7683582Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:23.7683723Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:23.7683865Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:23.7684165Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:23.7684270Z                 }
2023-01-11T21:41:23.7684373Z             }
2023-01-11T21:41:23.7684465Z         }
2023-01-11T21:41:23.7684566Z     }
2023-01-11T21:41:23.7684665Z }
2023-01-11T21:41:23.7684797Z ''')
2023-01-11T21:41:23.7684807Z 
2023-01-11T21:41:23.7684814Z 
2023-01-11T21:41:23.7684975Z async_compile.wait(globals())
2023-01-11T21:41:23.7685098Z del async_compile
2023-01-11T21:41:23.7685106Z 
2023-01-11T21:41:23.7685228Z def call(args):
2023-01-11T21:41:23.7685347Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7685464Z     args.clear()
2023-01-11T21:41:23.7685793Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7686147Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7686503Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7686923Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7687280Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7687815Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7687921Z     del arg0_1
2023-01-11T21:41:23.7688035Z     del arg1_1
2023-01-11T21:41:23.7688204Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7688214Z 
2023-01-11T21:41:23.7688222Z 
2023-01-11T21:41:23.7688346Z if __name__ == "__main__":
2023-01-11T21:41:23.7688559Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7688791Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7689151Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7689504Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7689713Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7689729Z 
2023-01-11T21:41:23.7689828Z ok (1.663s)
2023-01-11T21:41:23.7690846Z   test_div4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7691077Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7691599Z [2023-01-11 21:33:18,310] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 177
2023-01-11T21:41:23.7692114Z [2023-01-11 21:33:18,330] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 177
2023-01-11T21:41:23.7692123Z 
2023-01-11T21:41:23.7692289Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7692408Z import torch
2023-01-11T21:41:23.7692519Z import random
2023-01-11T21:41:23.7692730Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7692963Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7692973Z 
2023-01-11T21:41:23.7693105Z aten = torch.ops.aten
2023-01-11T21:41:23.7693365Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7693528Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7693536Z 
2023-01-11T21:41:23.7693544Z 
2023-01-11T21:41:23.7693796Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7694217Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7694436Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7694616Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.7694778Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7695017Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7695178Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.7695348Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7695508Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7695599Z {
2023-01-11T21:41:23.7695778Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7695870Z     {
2023-01-11T21:41:23.7696000Z         #pragma omp for 
2023-01-11T21:41:23.7696125Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7696226Z         {
2023-01-11T21:41:23.7696326Z             {
2023-01-11T21:41:23.7696428Z                 {
2023-01-11T21:41:23.7696580Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7696732Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.7696926Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7697114Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:23.7697348Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:23.7697832Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:23.7697987Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:23.7698127Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.7698264Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:23.7698403Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:23.7698538Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:23.7698673Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:23.7698775Z                 }
2023-01-11T21:41:23.7698876Z             }
2023-01-11T21:41:23.7698966Z         }
2023-01-11T21:41:23.7699066Z     }
2023-01-11T21:41:23.7699161Z }
2023-01-11T21:41:23.7699288Z ''')
2023-01-11T21:41:23.7699297Z 
2023-01-11T21:41:23.7699305Z 
2023-01-11T21:41:23.7699470Z async_compile.wait(globals())
2023-01-11T21:41:23.7699594Z del async_compile
2023-01-11T21:41:23.7699607Z 
2023-01-11T21:41:23.7699728Z def call(args):
2023-01-11T21:41:23.7699863Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7699974Z     args.clear()
2023-01-11T21:41:23.7700357Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7700732Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7701096Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7701465Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7701829Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7702579Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7702697Z     del arg0_1
2023-01-11T21:41:23.7702803Z     del arg1_1
2023-01-11T21:41:23.7702982Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7702992Z 
2023-01-11T21:41:23.7702999Z 
2023-01-11T21:41:23.7703135Z if __name__ == "__main__":
2023-01-11T21:41:23.7703359Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7703603Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7703980Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7704350Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7704570Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7704579Z 
2023-01-11T21:41:23.7704685Z ok (0.070s)
2023-01-11T21:41:23.7705737Z   test_div5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7706090Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7706626Z [2023-01-11 21:33:18,379] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 178
2023-01-11T21:41:23.7707165Z [2023-01-11 21:33:19,945] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 178
2023-01-11T21:41:23.7707177Z 
2023-01-11T21:41:23.7707354Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7707481Z import torch
2023-01-11T21:41:23.7707609Z import random
2023-01-11T21:41:23.7707833Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7708057Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7708066Z 
2023-01-11T21:41:23.7708211Z aten = torch.ops.aten
2023-01-11T21:41:23.7708475Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7708718Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7708727Z 
2023-01-11T21:41:23.7708733Z 
2023-01-11T21:41:23.7708987Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7709424Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7709651Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7709834Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7709993Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7710170Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.7710348Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7710516Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7710621Z {
2023-01-11T21:41:23.7710803Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7710911Z     {
2023-01-11T21:41:23.7711032Z         #pragma omp for 
2023-01-11T21:41:23.7711185Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7711301Z         {
2023-01-11T21:41:23.7711410Z             {
2023-01-11T21:41:23.7711519Z                 {
2023-01-11T21:41:23.7711680Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7711882Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7712060Z                     auto tmp2 = static_cast<float>(16);
2023-01-11T21:41:23.7712222Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:23.7712410Z                     auto tmp4 = static_cast<long>(16);
2023-01-11T21:41:23.7712888Z                     auto tmp5 = ((tmp0 < 0) != (tmp4 < 0) ? (tmp0 % tmp4 != 0 ? tmp0 / tmp4 - 1 : tmp0 / tmp4) : tmp0 / tmp4);
2023-01-11T21:41:23.7713051Z                     auto tmp6 = tmp0 / tmp4;
2023-01-11T21:41:23.7713199Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.7713350Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:23.7713481Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:23.7713644Z                     out_ptr3[i0] = tmp3;
2023-01-11T21:41:23.7713860Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:23.7713976Z                 }
2023-01-11T21:41:23.7714090Z             }
2023-01-11T21:41:23.7714200Z         }
2023-01-11T21:41:23.7714309Z     }
2023-01-11T21:41:23.7714399Z }
2023-01-11T21:41:23.7714539Z ''')
2023-01-11T21:41:23.7714548Z 
2023-01-11T21:41:23.7714555Z 
2023-01-11T21:41:23.7714725Z async_compile.wait(globals())
2023-01-11T21:41:23.7714853Z del async_compile
2023-01-11T21:41:23.7714861Z 
2023-01-11T21:41:23.7714986Z def call(args):
2023-01-11T21:41:23.7715105Z     arg0_1, = args
2023-01-11T21:41:23.7715230Z     args.clear()
2023-01-11T21:41:23.7715594Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7715965Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7716331Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7716782Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7717143Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7717625Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7717744Z     del arg0_1
2023-01-11T21:41:23.7717922Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7717931Z 
2023-01-11T21:41:23.7717939Z 
2023-01-11T21:41:23.7718074Z if __name__ == "__main__":
2023-01-11T21:41:23.7718272Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7718505Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7718866Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7719075Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7719083Z 
2023-01-11T21:41:23.7719255Z ok (1.615s)
2023-01-11T21:41:23.7720305Z   test_div6_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7720545Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7721067Z [2023-01-11 21:33:19,995] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 179
2023-01-11T21:41:23.7721609Z [2023-01-11 21:33:21,604] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 179
2023-01-11T21:41:23.7721619Z 
2023-01-11T21:41:23.7721784Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7721911Z import torch
2023-01-11T21:41:23.7722041Z import random
2023-01-11T21:41:23.7722272Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7722510Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7722519Z 
2023-01-11T21:41:23.7722664Z aten = torch.ops.aten
2023-01-11T21:41:23.7722928Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7723088Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7723111Z 
2023-01-11T21:41:23.7723119Z 
2023-01-11T21:41:23.7723362Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7723794Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7724020Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.7724213Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.7724390Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7724564Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7724735Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.7724902Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7725049Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7725134Z {
2023-01-11T21:41:23.7725268Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7725356Z     {
2023-01-11T21:41:23.7725462Z         #pragma omp for 
2023-01-11T21:41:23.7725573Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.7725642Z         {
2023-01-11T21:41:23.7725736Z             {
2023-01-11T21:41:23.7725825Z                 {
2023-01-11T21:41:23.7725957Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7726090Z                     auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:23.7726235Z                     auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:23.7726387Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:23.7726519Z                     auto tmp4 = static_cast<float>(tmp3);
2023-01-11T21:41:23.7726645Z                     auto tmp5 = tmp2 / tmp4;
2023-01-11T21:41:23.7727082Z                     auto tmp6 = ((tmp1 < 0) != (tmp3 < 0) ? (tmp1 % tmp3 != 0 ? tmp1 / tmp3 - 1 : tmp1 / tmp3) : tmp1 / tmp3);
2023-01-11T21:41:23.7727209Z                     auto tmp7 = tmp1 / tmp3;
2023-01-11T21:41:23.7727364Z                     auto tmp8 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7727495Z                     auto tmp9 = tmp8 / tmp4;
2023-01-11T21:41:23.7727611Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:41:23.7727709Z                     out_ptr1[i0] = tmp6;
2023-01-11T21:41:23.7727820Z                     out_ptr2[i0] = tmp7;
2023-01-11T21:41:23.7727938Z                     out_ptr3[i0] = tmp9;
2023-01-11T21:41:23.7728050Z                     out_ptr4[i0] = tmp6;
2023-01-11T21:41:23.7728137Z                 }
2023-01-11T21:41:23.7728224Z             }
2023-01-11T21:41:23.7728307Z         }
2023-01-11T21:41:23.7728375Z     }
2023-01-11T21:41:23.7728457Z }
2023-01-11T21:41:23.7728572Z ''')
2023-01-11T21:41:23.7728586Z 
2023-01-11T21:41:23.7728648Z 
2023-01-11T21:41:23.7728776Z async_compile.wait(globals())
2023-01-11T21:41:23.7728875Z del async_compile
2023-01-11T21:41:23.7728881Z 
2023-01-11T21:41:23.7728973Z def call(args):
2023-01-11T21:41:23.7729074Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7729153Z     args.clear()
2023-01-11T21:41:23.7729432Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7729714Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7729972Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7730234Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7730496Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7730872Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7730978Z     del arg0_1
2023-01-11T21:41:23.7731069Z     del arg1_1
2023-01-11T21:41:23.7731189Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7731198Z 
2023-01-11T21:41:23.7731204Z 
2023-01-11T21:41:23.7731308Z if __name__ == "__main__":
2023-01-11T21:41:23.7731470Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7731649Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7731929Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.7732208Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7732372Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7732378Z 
2023-01-11T21:41:23.7732471Z ok (1.659s)
2023-01-11T21:41:23.7733123Z   test_div7_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7733304Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7733687Z [2023-01-11 21:33:21,656] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 180
2023-01-11T21:41:23.7734060Z [2023-01-11 21:33:23,234] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 180
2023-01-11T21:41:23.7734068Z 
2023-01-11T21:41:23.7734201Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7734300Z import torch
2023-01-11T21:41:23.7734399Z import random
2023-01-11T21:41:23.7734558Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7734720Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7734727Z 
2023-01-11T21:41:23.7734906Z aten = torch.ops.aten
2023-01-11T21:41:23.7735102Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7735236Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7735243Z 
2023-01-11T21:41:23.7735249Z 
2023-01-11T21:41:23.7735444Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7735716Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7735883Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7736028Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.7736160Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7736283Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7736413Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.7736544Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7736674Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7736821Z {
2023-01-11T21:41:23.7736968Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7737052Z     {
2023-01-11T21:41:23.7737146Z         #pragma omp for 
2023-01-11T21:41:23.7737268Z         for(long i0=0; i0<10000; i0+=1)
2023-01-11T21:41:23.7737355Z         {
2023-01-11T21:41:23.7737443Z             {
2023-01-11T21:41:23.7737542Z                 {
2023-01-11T21:41:23.7737680Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7737795Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.7737940Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7738084Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:23.7738209Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:23.7738571Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:23.7738689Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:23.7738810Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.7738925Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:23.7739026Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:23.7739140Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:23.7739252Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:23.7739334Z                 }
2023-01-11T21:41:23.7739421Z             }
2023-01-11T21:41:23.7739506Z         }
2023-01-11T21:41:23.7739592Z     }
2023-01-11T21:41:23.7739661Z }
2023-01-11T21:41:23.7739781Z ''')
2023-01-11T21:41:23.7739789Z 
2023-01-11T21:41:23.7739795Z 
2023-01-11T21:41:23.7739927Z async_compile.wait(globals())
2023-01-11T21:41:23.7740028Z del async_compile
2023-01-11T21:41:23.7740035Z 
2023-01-11T21:41:23.7740132Z def call(args):
2023-01-11T21:41:23.7740235Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7740334Z     args.clear()
2023-01-11T21:41:23.7740617Z     buf0 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7740910Z     buf1 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7741202Z     buf2 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7741483Z     buf3 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7741780Z     buf4 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7742147Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7742245Z     del arg0_1
2023-01-11T21:41:23.7742486Z     del arg1_1
2023-01-11T21:41:23.7742607Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7742616Z 
2023-01-11T21:41:23.7742640Z 
2023-01-11T21:41:23.7742729Z if __name__ == "__main__":
2023-01-11T21:41:23.7742895Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7743188Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7743487Z     arg0_1 = rand_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7743783Z     arg1_1 = rand_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7743945Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7743953Z 
2023-01-11T21:41:23.7744038Z ok (1.630s)
2023-01-11T21:41:23.7744492Z   test_div8_cpu (__main__.CpuTests) ... [2023-01-11 21:33:23,278] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 181
2023-01-11T21:41:23.7744894Z [2023-01-11 21:33:24,854] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 181
2023-01-11T21:41:23.7744904Z 
2023-01-11T21:41:23.7745033Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7745129Z import torch
2023-01-11T21:41:23.7745228Z import random
2023-01-11T21:41:23.7745469Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7745645Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7745653Z 
2023-01-11T21:41:23.7745764Z aten = torch.ops.aten
2023-01-11T21:41:23.7745938Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7746058Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7746064Z 
2023-01-11T21:41:23.7746070Z 
2023-01-11T21:41:23.7746262Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7746534Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7746696Z extern "C" void kernel(long* __restrict__ out_ptr0,
2023-01-11T21:41:23.7746822Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7746944Z                        long* __restrict__ out_ptr2)
2023-01-11T21:41:23.7747026Z {
2023-01-11T21:41:23.7747095Z     {
2023-01-11T21:41:23.7747185Z         {
2023-01-11T21:41:23.7747326Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:41:23.7747469Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:41:23.7747582Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7747687Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:23.7747771Z         }
2023-01-11T21:41:23.7747834Z     }
2023-01-11T21:41:23.7747913Z     {
2023-01-11T21:41:23.7747994Z         {
2023-01-11T21:41:23.7748127Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:41:23.7748270Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:41:23.7748627Z             auto tmp2 = ((tmp0 < 0) != (tmp1 < 0) ? (tmp0 % tmp1 != 0 ? tmp0 / tmp1 - 1 : tmp0 / tmp1) : tmp0 / tmp1);
2023-01-11T21:41:23.7748738Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:23.7748809Z         }
2023-01-11T21:41:23.7748895Z     }
2023-01-11T21:41:23.7748980Z     {
2023-01-11T21:41:23.7749065Z         {
2023-01-11T21:41:23.7749209Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:41:23.7749343Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:41:23.7749450Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7749568Z             out_ptr2[0] = tmp2;
2023-01-11T21:41:23.7749653Z         }
2023-01-11T21:41:23.7749735Z     }
2023-01-11T21:41:23.7749818Z }
2023-01-11T21:41:23.7749928Z ''')
2023-01-11T21:41:23.7749936Z 
2023-01-11T21:41:23.7749941Z 
2023-01-11T21:41:23.7750071Z async_compile.wait(globals())
2023-01-11T21:41:23.7750153Z del async_compile
2023-01-11T21:41:23.7750175Z 
2023-01-11T21:41:23.7750253Z def call(args):
2023-01-11T21:41:23.7750517Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7750782Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7751042Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7751267Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.7751382Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.7751388Z 
2023-01-11T21:41:23.7751473Z 
2023-01-11T21:41:23.7751582Z if __name__ == "__main__":
2023-01-11T21:41:23.7751736Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7751896Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7752032Z     print_performance(lambda: call([]))
2023-01-11T21:41:23.7752039Z 
2023-01-11T21:41:23.7752124Z ok (1.621s)
2023-01-11T21:41:23.7752813Z   test_div_prim_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7752988Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7753382Z [2023-01-11 21:33:24,919] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 182
2023-01-11T21:41:23.7753925Z [2023-01-11 21:33:26,489] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 182
2023-01-11T21:41:23.7754548Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7754723Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7755102Z [2023-01-11 21:33:26,531] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 183
2023-01-11T21:41:23.7755466Z [2023-01-11 21:33:28,092] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 183
2023-01-11T21:41:23.7755491Z 
2023-01-11T21:41:23.7755610Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7755720Z import torch
2023-01-11T21:41:23.7755824Z import random
2023-01-11T21:41:23.7755987Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7756156Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7756165Z 
2023-01-11T21:41:23.7756273Z aten = torch.ops.aten
2023-01-11T21:41:23.7756461Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7756575Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7756582Z 
2023-01-11T21:41:23.7756603Z 
2023-01-11T21:41:23.7756781Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7757070Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7757245Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7757395Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7757539Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7757631Z {
2023-01-11T21:41:23.7757773Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7757843Z     {
2023-01-11T21:41:23.7757951Z         #pragma omp for 
2023-01-11T21:41:23.7758069Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.7758375Z         {
2023-01-11T21:41:23.7758708Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7759117Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7759462Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7759759Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7760002Z         }
2023-01-11T21:41:23.7760266Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7760557Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:23.7760791Z         {
2023-01-11T21:41:23.7761042Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7761315Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.7761580Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7762008Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7762262Z         }
2023-01-11T21:41:23.7762462Z     }
2023-01-11T21:41:23.7762671Z }
2023-01-11T21:41:23.7762930Z ''')
2023-01-11T21:41:23.7763070Z 
2023-01-11T21:41:23.7763077Z 
2023-01-11T21:41:23.7763193Z async_compile.wait(globals())
2023-01-11T21:41:23.7763468Z del async_compile
2023-01-11T21:41:23.7763628Z 
2023-01-11T21:41:23.7763725Z def call(args):
2023-01-11T21:41:23.7763990Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7764257Z     args.clear()
2023-01-11T21:41:23.7764692Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7765160Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7765494Z     del arg0_1
2023-01-11T21:41:23.7765734Z     del arg1_1
2023-01-11T21:41:23.7766022Z     return (buf0, )
2023-01-11T21:41:23.7766183Z 
2023-01-11T21:41:23.7766190Z 
2023-01-11T21:41:23.7766299Z if __name__ == "__main__":
2023-01-11T21:41:23.7766700Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7767078Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7767610Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7768110Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7768507Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7768722Z 
2023-01-11T21:41:23.7768727Z 
2023-01-11T21:41:23.7768863Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7769117Z import torch
2023-01-11T21:41:23.7769343Z import random
2023-01-11T21:41:23.7769642Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7770006Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7770200Z 
2023-01-11T21:41:23.7770306Z aten = torch.ops.aten
2023-01-11T21:41:23.7770640Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7770997Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7771156Z 
2023-01-11T21:41:23.7771180Z 
2023-01-11T21:41:23.7771375Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7771843Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7772320Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7772648Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.7772969Z                        long* __restrict__ out_ptr0)
2023-01-11T21:41:23.7773222Z {
2023-01-11T21:41:23.7773471Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7773717Z     {
2023-01-11T21:41:23.7773948Z         #pragma omp for 
2023-01-11T21:41:23.7774211Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:23.7774438Z         {
2023-01-11T21:41:23.7774655Z             {
2023-01-11T21:41:23.7774880Z                 {
2023-01-11T21:41:23.7775127Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7775426Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.7775713Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7775984Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7776243Z                 }
2023-01-11T21:41:23.7776461Z             }
2023-01-11T21:41:23.7776658Z         }
2023-01-11T21:41:23.7776869Z     }
2023-01-11T21:41:23.7777067Z }
2023-01-11T21:41:23.7777303Z ''')
2023-01-11T21:41:23.7777436Z 
2023-01-11T21:41:23.7777442Z 
2023-01-11T21:41:23.7777568Z async_compile.wait(globals())
2023-01-11T21:41:23.7777861Z del async_compile
2023-01-11T21:41:23.7778017Z 
2023-01-11T21:41:23.7778103Z def call(args):
2023-01-11T21:41:23.7778347Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7778605Z     args.clear()
2023-01-11T21:41:23.7779038Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7779497Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7779937Z     del arg0_1
2023-01-11T21:41:23.7780185Z     del arg1_1
2023-01-11T21:41:23.7780425Z     return (buf0, )
2023-01-11T21:41:23.7780592Z 
2023-01-11T21:41:23.7780598Z 
2023-01-11T21:41:23.7780709Z if __name__ == "__main__":
2023-01-11T21:41:23.7781036Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7781411Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7781926Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7782580Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7782933Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7783133Z 
2023-01-11T21:41:23.7783227Z ok (3.236s)
2023-01-11T21:41:23.7784148Z   test_div_zero_dim_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7785020Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7785656Z [2023-01-11 21:33:28,148] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 184
2023-01-11T21:41:23.7786293Z [2023-01-11 21:33:29,804] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 184
2023-01-11T21:41:23.7787215Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7788025Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7788655Z [2023-01-11 21:33:29,919] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 185
2023-01-11T21:41:23.7789323Z [2023-01-11 21:33:31,582] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 185
2023-01-11T21:41:23.7790192Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7790966Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7791552Z [2023-01-11 21:33:31,631] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 186
2023-01-11T21:41:23.7791847Z 
2023-01-11T21:41:23.7791983Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7792266Z import torch
2023-01-11T21:41:23.7792503Z import random
2023-01-11T21:41:23.7792805Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7793157Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7793376Z 
2023-01-11T21:41:23.7793491Z aten = torch.ops.aten
2023-01-11T21:41:23.7793924Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7794272Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7794463Z 
2023-01-11T21:41:23.7794469Z 
2023-01-11T21:41:23.7794680Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7795155Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7795641Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7795972Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7796298Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7796617Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.7797032Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.7797337Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7797630Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:23.7797867Z {
2023-01-11T21:41:23.7798135Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7798408Z     {
2023-01-11T21:41:23.7798616Z         #pragma omp for 
2023-01-11T21:41:23.7798879Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.7799112Z         {
2023-01-11T21:41:23.7799435Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.7799849Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:23.7800155Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7800429Z             auto tmp3 = tmp2.floor();
2023-01-11T21:41:23.7800689Z             auto tmp4 = tmp2.trunc();
2023-01-11T21:41:23.7800967Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7801341Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7801628Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.7801928Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:23.7802224Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:23.7802450Z         }
2023-01-11T21:41:23.7802710Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7803007Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.7803246Z         {
2023-01-11T21:41:23.7803491Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7803782Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:23.7804045Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7804351Z             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:23.7804652Z             auto tmp4 = std::trunc(tmp2);
2023-01-11T21:41:23.7804921Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7805185Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.7805446Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.7805723Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:23.7805988Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:23.7806211Z         }
2023-01-11T21:41:23.7806403Z     }
2023-01-11T21:41:23.7806610Z }
2023-01-11T21:41:23.7806869Z ''')
2023-01-11T21:41:23.7806996Z 
2023-01-11T21:41:23.7807002Z 
2023-01-11T21:41:23.7807128Z async_compile.wait(globals())
2023-01-11T21:41:23.7807378Z del async_compile
2023-01-11T21:41:23.7807539Z 
2023-01-11T21:41:23.7807631Z def call(args):
2023-01-11T21:41:23.7807880Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7808104Z     args.clear()
2023-01-11T21:41:23.7808553Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7809075Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7809566Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7810059Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7810534Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7811119Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7811584Z     del arg0_1
2023-01-11T21:41:23.7811809Z     del arg1_1
2023-01-11T21:41:23.7812173Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7812446Z 
2023-01-11T21:41:23.7812454Z 
2023-01-11T21:41:23.7812592Z if __name__ == "__main__":
2023-01-11T21:41:23.7813004Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7813540Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7814218Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7814890Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7815547Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7815838Z 
2023-01-11T21:41:23.7815846Z 
2023-01-11T21:41:23.7816040Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7816425Z import torch
2023-01-11T21:41:23.7816782Z import random
2023-01-11T21:41:23.7817212Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7817736Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7818048Z 
2023-01-11T21:41:23.7818208Z aten = torch.ops.aten
2023-01-11T21:41:23.7818699Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7819195Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7819456Z 
2023-01-11T21:41:23.7819462Z 
2023-01-11T21:41:23.7819744Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7820348Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7820985Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7821530Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7821968Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7822549Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.7822967Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.7823395Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7823821Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:23.7824162Z {
2023-01-11T21:41:23.7824534Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7824924Z     {
2023-01-11T21:41:23.7825230Z         #pragma omp for 
2023-01-11T21:41:23.7825611Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.7825967Z         {
2023-01-11T21:41:23.7826401Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:23.7826925Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.7827403Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7827812Z             auto tmp3 = tmp2.floor();
2023-01-11T21:41:23.7828201Z             auto tmp4 = tmp2.trunc();
2023-01-11T21:41:23.7828617Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.7829037Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.7829436Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.7829853Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:23.7830269Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:23.7830620Z         }
2023-01-11T21:41:23.7831002Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.7831419Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.7831760Z         {
2023-01-11T21:41:23.7832112Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:23.7832512Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.7832877Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7833298Z             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:23.7833805Z             auto tmp4 = std::trunc(tmp2);
2023-01-11T21:41:23.7834223Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.7834586Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.7834964Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:23.7835309Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:23.7835668Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:23.7836008Z         }
2023-01-11T21:41:23.7836308Z     }
2023-01-11T21:41:23.7836584Z }
2023-01-11T21:41:23.7836929Z ''')
2023-01-11T21:41:23.7837115Z 
2023-01-11T21:41:23.7837122Z 
2023-01-11T21:41:23.7837293Z async_compile.wait(globals())
2023-01-11T21:41:23.7837638Z del async_compile
2023-01-11T21:41:23.7837853Z 
2023-01-11T21:41:23.7837989Z def call(args):
2023-01-11T21:41:23.7838319Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7838642Z     args.clear()
2023-01-11T21:41:23.7839202Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7839814Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7840610Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7841257Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7841922Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7842690Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7843341Z     del arg0_1
2023-01-11T21:41:23.7843660Z     del arg1_1
2023-01-11T21:41:23.7844042Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7844281Z 
2023-01-11T21:41:23.7844288Z 
2023-01-11T21:41:23.7844426Z if __name__ == "__main__":
2023-01-11T21:41:23.7844866Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7845390Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7846139Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7846745Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7847196Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7847437Z 
2023-01-11T21:41:23.7847955Z [2023-01-11 21:33:33,212] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 186
2023-01-11T21:41:23.7849221Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.7850383Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.7851194Z [2023-01-11 21:33:33,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 187
2023-01-11T21:41:23.7852117Z [2023-01-11 21:33:34,851] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 187
2023-01-11T21:41:23.7852520Z 
2023-01-11T21:41:23.7852706Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7853068Z import torch
2023-01-11T21:41:23.7853411Z import random
2023-01-11T21:41:23.7853841Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7854360Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7854675Z 
2023-01-11T21:41:23.7854834Z aten = torch.ops.aten
2023-01-11T21:41:23.7855321Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7855779Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7856031Z 
2023-01-11T21:41:23.7856038Z 
2023-01-11T21:41:23.7856315Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7856957Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7857624Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7858082Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.7858519Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7858907Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7859302Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.7859728Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7860136Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7860469Z {
2023-01-11T21:41:23.7860836Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7861189Z     {
2023-01-11T21:41:23.7861443Z         #pragma omp for 
2023-01-11T21:41:23.7861776Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:23.7862105Z         {
2023-01-11T21:41:23.7862523Z             {
2023-01-11T21:41:23.7862837Z                 {
2023-01-11T21:41:23.7863167Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.7863686Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:41:23.7864077Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7864551Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:23.7864953Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:23.7865573Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:23.7865966Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:23.7866309Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.7866673Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:23.7867048Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:23.7867408Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:23.7867745Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:23.7868072Z                 }
2023-01-11T21:41:23.7868374Z             }
2023-01-11T21:41:23.7868664Z         }
2023-01-11T21:41:23.7869079Z     }
2023-01-11T21:41:23.7869387Z }
2023-01-11T21:41:23.7869687Z ''')
2023-01-11T21:41:23.7869865Z 
2023-01-11T21:41:23.7869873Z 
2023-01-11T21:41:23.7870042Z async_compile.wait(globals())
2023-01-11T21:41:23.7870390Z del async_compile
2023-01-11T21:41:23.7870584Z 
2023-01-11T21:41:23.7870717Z def call(args):
2023-01-11T21:41:23.7871029Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7871355Z     args.clear()
2023-01-11T21:41:23.7871889Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7872506Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7873119Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7873818Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7874346Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7875117Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7875779Z     del arg0_1
2023-01-11T21:41:23.7876077Z     del arg1_1
2023-01-11T21:41:23.7876370Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7876621Z 
2023-01-11T21:41:23.7876627Z 
2023-01-11T21:41:23.7876728Z if __name__ == "__main__":
2023-01-11T21:41:23.7877018Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7877348Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7877869Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7878416Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7878787Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7879044Z 
2023-01-11T21:41:23.7879051Z 
2023-01-11T21:41:23.7879222Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7879581Z import torch
2023-01-11T21:41:23.7879881Z import random
2023-01-11T21:41:23.7880254Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7880782Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7881047Z 
2023-01-11T21:41:23.7881153Z aten = torch.ops.aten
2023-01-11T21:41:23.7881568Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7882066Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7882309Z 
2023-01-11T21:41:23.7882317Z 
2023-01-11T21:41:23.7882509Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7883109Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7883741Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.7884139Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.7884523Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7884886Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:23.7885370Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:23.7885767Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.7886158Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:23.7886508Z {
2023-01-11T21:41:23.7886839Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7887159Z     {
2023-01-11T21:41:23.7887466Z         #pragma omp for 
2023-01-11T21:41:23.7887743Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:23.7888049Z         {
2023-01-11T21:41:23.7888330Z             {
2023-01-11T21:41:23.7888622Z                 {
2023-01-11T21:41:23.7888941Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:23.7889300Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.7889731Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.7890155Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:23.7890642Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:23.7891344Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:23.7891786Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:23.7892127Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.7892452Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:23.7892777Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:23.7893080Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:23.7893424Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:23.7893739Z                 }
2023-01-11T21:41:23.7894021Z             }
2023-01-11T21:41:23.7894311Z         }
2023-01-11T21:41:23.7894574Z     }
2023-01-11T21:41:23.7894841Z }
2023-01-11T21:41:23.7895171Z ''')
2023-01-11T21:41:23.7895365Z 
2023-01-11T21:41:23.7895372Z 
2023-01-11T21:41:23.7895555Z async_compile.wait(globals())
2023-01-11T21:41:23.7895920Z del async_compile
2023-01-11T21:41:23.7896141Z 
2023-01-11T21:41:23.7896284Z def call(args):
2023-01-11T21:41:23.7896630Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.7896960Z     args.clear()
2023-01-11T21:41:23.7897513Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7898117Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7898691Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7899238Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7899783Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7900498Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.7901092Z     del arg0_1
2023-01-11T21:41:23.7901420Z     del arg1_1
2023-01-11T21:41:23.7901754Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.7901969Z 
2023-01-11T21:41:23.7901975Z 
2023-01-11T21:41:23.7902123Z if __name__ == "__main__":
2023-01-11T21:41:23.7902805Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7903304Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7903939Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7904486Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7904970Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.7905233Z 
2023-01-11T21:41:23.7905353Z ok (6.760s)
2023-01-11T21:41:23.7906128Z   test_dropout_cpu (__main__.CpuTests) ... [2023-01-11 21:33:34,926] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 188
2023-01-11T21:41:23.7907032Z [2023-01-11 21:33:34,927] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.7908051Z [2023-01-11 21:33:36,483] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 188
2023-01-11T21:41:23.7908921Z [2023-01-11 21:33:36,549] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 189
2023-01-11T21:41:23.7909762Z [2023-01-11 21:33:36,550] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.7910640Z [2023-01-11 21:33:36,558] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 189
2023-01-11T21:41:23.7911029Z 
2023-01-11T21:41:23.7911215Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7911609Z import torch
2023-01-11T21:41:23.7911946Z import random
2023-01-11T21:41:23.7912382Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7912909Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7913214Z 
2023-01-11T21:41:23.7913358Z aten = torch.ops.aten
2023-01-11T21:41:23.7914059Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7914568Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7915100Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.7915453Z 
2023-01-11T21:41:23.7915460Z 
2023-01-11T21:41:23.7915744Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7916383Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7917037Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:23.7917504Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7917946Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7918315Z {
2023-01-11T21:41:23.7918670Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7919051Z     {
2023-01-11T21:41:23.7919388Z         #pragma omp for 
2023-01-11T21:41:23.7919752Z         for(long i0=0; i0<1000; i0+=1)
2023-01-11T21:41:23.7920103Z         {
2023-01-11T21:41:23.7920425Z             {
2023-01-11T21:41:23.7920730Z                 {
2023-01-11T21:41:23.7921091Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.7921492Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.7921906Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.7922429Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.7922951Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:23.7923384Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.7923805Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.7924237Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.7924667Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:23.7925080Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.7925475Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.7925830Z                 }
2023-01-11T21:41:23.7926144Z             }
2023-01-11T21:41:23.7926464Z         }
2023-01-11T21:41:23.7926773Z     }
2023-01-11T21:41:23.7927063Z }
2023-01-11T21:41:23.7927412Z ''')
2023-01-11T21:41:23.7927606Z 
2023-01-11T21:41:23.7927614Z 
2023-01-11T21:41:23.7927799Z async_compile.wait(globals())
2023-01-11T21:41:23.7928171Z del async_compile
2023-01-11T21:41:23.7928390Z 
2023-01-11T21:41:23.7928533Z def call(args):
2023-01-11T21:41:23.7928880Z     arg0_1, = args
2023-01-11T21:41:23.7929207Z     args.clear()
2023-01-11T21:41:23.7929668Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.7930349Z     buf0 = empty_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7930982Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7931473Z     del arg0_1
2023-01-11T21:41:23.7931815Z     return (buf0, )
2023-01-11T21:41:23.7932032Z 
2023-01-11T21:41:23.7932124Z 
2023-01-11T21:41:23.7932284Z if __name__ == "__main__":
2023-01-11T21:41:23.7932702Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7933213Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7933845Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7934476Z     arg0_1 = rand_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7934938Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7935194Z 
2023-01-11T21:41:23.7935200Z 
2023-01-11T21:41:23.7935374Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7935735Z import torch
2023-01-11T21:41:23.7936045Z import random
2023-01-11T21:41:23.7936447Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7936935Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7937217Z 
2023-01-11T21:41:23.7937347Z aten = torch.ops.aten
2023-01-11T21:41:23.7937798Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7938337Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7938838Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.7939169Z 
2023-01-11T21:41:23.7939175Z 
2023-01-11T21:41:23.7939428Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7940030Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7940643Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:23.7941078Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7941492Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7941836Z {
2023-01-11T21:41:23.7942168Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7942666Z     {
2023-01-11T21:41:23.7942980Z         #pragma omp for 
2023-01-11T21:41:23.7943321Z         for(long i0=0; i0<1000; i0+=1)
2023-01-11T21:41:23.7943651Z         {
2023-01-11T21:41:23.7943954Z             {
2023-01-11T21:41:23.7944242Z                 {
2023-01-11T21:41:23.7944578Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.7944956Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.7945343Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.7945825Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.7946305Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:23.7946704Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.7947099Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.7947501Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.7947904Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:23.7948288Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.7948654Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.7948981Z                 }
2023-01-11T21:41:23.7949277Z             }
2023-01-11T21:41:23.7949571Z         }
2023-01-11T21:41:23.7949860Z     }
2023-01-11T21:41:23.7950129Z }
2023-01-11T21:41:23.7950458Z ''')
2023-01-11T21:41:23.7950636Z 
2023-01-11T21:41:23.7950643Z 
2023-01-11T21:41:23.7950813Z async_compile.wait(globals())
2023-01-11T21:41:23.7951163Z del async_compile
2023-01-11T21:41:23.7951366Z 
2023-01-11T21:41:23.7951500Z def call(args):
2023-01-11T21:41:23.7951815Z     arg0_1, = args
2023-01-11T21:41:23.7952131Z     args.clear()
2023-01-11T21:41:23.7952541Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.7953180Z     buf0 = empty_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7953845Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7954306Z     del arg0_1
2023-01-11T21:41:23.7954621Z     return (buf0, )
2023-01-11T21:41:23.7954823Z 
2023-01-11T21:41:23.7954830Z 
2023-01-11T21:41:23.7955096Z if __name__ == "__main__":
2023-01-11T21:41:23.7955491Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7955970Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7956598Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7957234Z     arg0_1 = rand_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7957692Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7957949Z 
2023-01-11T21:41:23.7958077Z ok (1.706s)
2023-01-11T21:41:23.7958877Z   test_dropout_deterministic_cpu (__main__.CpuTests) ... [2023-01-11 21:33:36,627] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 190
2023-01-11T21:41:23.7959759Z [2023-01-11 21:33:36,628] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.7960575Z [2023-01-11 21:33:38,199] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 190
2023-01-11T21:41:23.7961477Z [2023-01-11 21:33:38,265] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 191
2023-01-11T21:41:23.7962282Z [2023-01-11 21:33:38,265] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.7963084Z [2023-01-11 21:33:38,274] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 191
2023-01-11T21:41:23.7963453Z 
2023-01-11T21:41:23.7963629Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7963992Z import torch
2023-01-11T21:41:23.7964302Z import random
2023-01-11T21:41:23.7964707Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7965196Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7965477Z 
2023-01-11T21:41:23.7965624Z aten = torch.ops.aten
2023-01-11T21:41:23.7966055Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7966511Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7967022Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.7967356Z 
2023-01-11T21:41:23.7967363Z 
2023-01-11T21:41:23.7967596Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7968198Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7968807Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:23.7968998Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7969176Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7969279Z {
2023-01-11T21:41:23.7969459Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7969578Z     {
2023-01-11T21:41:23.7969722Z         #pragma omp for 
2023-01-11T21:41:23.7969873Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.7969994Z         {
2023-01-11T21:41:23.7970112Z             {
2023-01-11T21:41:23.7970216Z                 {
2023-01-11T21:41:23.7970380Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.7970545Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.7970725Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.7970972Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.7971162Z                     auto tmp3 = static_cast<float>(0.55);
2023-01-11T21:41:23.7971325Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.7971500Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.7971663Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.7971861Z                     auto tmp8 = static_cast<float>(2.2222222222222223);
2023-01-11T21:41:23.7972021Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.7972173Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.7972295Z                 }
2023-01-11T21:41:23.7972414Z             }
2023-01-11T21:41:23.7972521Z         }
2023-01-11T21:41:23.7972720Z     }
2023-01-11T21:41:23.7972839Z }
2023-01-11T21:41:23.7972992Z ''')
2023-01-11T21:41:23.7973000Z 
2023-01-11T21:41:23.7973006Z 
2023-01-11T21:41:23.7973175Z async_compile.wait(globals())
2023-01-11T21:41:23.7973314Z del async_compile
2023-01-11T21:41:23.7973321Z 
2023-01-11T21:41:23.7973453Z def call(args):
2023-01-11T21:41:23.7973568Z     arg0_1, = args
2023-01-11T21:41:23.7973699Z     args.clear()
2023-01-11T21:41:23.7973931Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.7974272Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7974529Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7974625Z     del arg0_1
2023-01-11T21:41:23.7974723Z     return (buf0, )
2023-01-11T21:41:23.7974729Z 
2023-01-11T21:41:23.7974734Z 
2023-01-11T21:41:23.7974840Z if __name__ == "__main__":
2023-01-11T21:41:23.7975036Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7975213Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7975485Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7975759Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7975905Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7975912Z 
2023-01-11T21:41:23.7975917Z 
2023-01-11T21:41:23.7976044Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7976138Z import torch
2023-01-11T21:41:23.7976242Z import random
2023-01-11T21:41:23.7976385Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7976562Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7976568Z 
2023-01-11T21:41:23.7976673Z aten = torch.ops.aten
2023-01-11T21:41:23.7976862Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7977002Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7977247Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.7977254Z 
2023-01-11T21:41:23.7977259Z 
2023-01-11T21:41:23.7977444Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7977745Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7977888Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:23.7978029Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.7978160Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.7978238Z {
2023-01-11T21:41:23.7978369Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7978454Z     {
2023-01-11T21:41:23.7978561Z         #pragma omp for 
2023-01-11T21:41:23.7978660Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.7978738Z         {
2023-01-11T21:41:23.7978820Z             {
2023-01-11T21:41:23.7978920Z                 {
2023-01-11T21:41:23.7979045Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.7979171Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.7979291Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.7979478Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.7979621Z                     auto tmp3 = static_cast<float>(0.55);
2023-01-11T21:41:23.7979743Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.7979884Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.7980004Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.7980153Z                     auto tmp8 = static_cast<float>(2.2222222222222223);
2023-01-11T21:41:23.7980277Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.7980374Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.7980456Z                 }
2023-01-11T21:41:23.7980539Z             }
2023-01-11T21:41:23.7980637Z         }
2023-01-11T21:41:23.7980789Z     }
2023-01-11T21:41:23.7980870Z }
2023-01-11T21:41:23.7980974Z ''')
2023-01-11T21:41:23.7980998Z 
2023-01-11T21:41:23.7981003Z 
2023-01-11T21:41:23.7981121Z async_compile.wait(globals())
2023-01-11T21:41:23.7981245Z del async_compile
2023-01-11T21:41:23.7981252Z 
2023-01-11T21:41:23.7981367Z def call(args):
2023-01-11T21:41:23.7981470Z     arg0_1, = args
2023-01-11T21:41:23.7981578Z     args.clear()
2023-01-11T21:41:23.7981775Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.7982069Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7982291Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.7982515Z     del arg0_1
2023-01-11T21:41:23.7982615Z     return (buf0, )
2023-01-11T21:41:23.7982622Z 
2023-01-11T21:41:23.7982627Z 
2023-01-11T21:41:23.7982732Z if __name__ == "__main__":
2023-01-11T21:41:23.7982980Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7983152Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7983428Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.7983693Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7983819Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7983842Z 
2023-01-11T21:41:23.7983912Z ok (1.716s)
2023-01-11T21:41:23.7984410Z   test_dtype_mismatch_issue_cpu (__main__.CpuTests) ... [2023-01-11 21:33:38,300] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 192
2023-01-11T21:41:23.7984791Z [2023-01-11 21:33:39,918] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 192
2023-01-11T21:41:23.7984799Z 
2023-01-11T21:41:23.7984930Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.7985030Z import torch
2023-01-11T21:41:23.7985123Z import random
2023-01-11T21:41:23.7985298Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.7985454Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.7985476Z 
2023-01-11T21:41:23.7985570Z aten = torch.ops.aten
2023-01-11T21:41:23.7985765Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.7985903Z async_compile = AsyncCompile()
2023-01-11T21:41:23.7985910Z 
2023-01-11T21:41:23.7985916Z 
2023-01-11T21:41:23.7986104Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.7986396Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.7986557Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.7986696Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.7986811Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.7986943Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.7987029Z {
2023-01-11T21:41:23.7987150Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:23.7987293Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.7987378Z     {
2023-01-11T21:41:23.7987486Z         #pragma omp for 
2023-01-11T21:41:23.7987583Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:23.7987666Z         {
2023-01-11T21:41:23.7987751Z             {
2023-01-11T21:41:23.7987836Z                 {
2023-01-11T21:41:23.7988194Z                     float tmp5 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.7988331Z                     for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:23.7988423Z                     {
2023-01-11T21:41:23.7988504Z                         {
2023-01-11T21:41:23.7988661Z                             auto tmp0 = static_cast<long>(i1);
2023-01-11T21:41:23.7988806Z                             auto tmp1 = static_cast<long>(63);
2023-01-11T21:41:23.7988929Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:23.7989041Z                             float tmp3 = 0.0;
2023-01-11T21:41:23.7989242Z                             if(tmp2)
2023-01-11T21:41:23.7989332Z                             {
2023-01-11T21:41:23.7989448Z                                 auto tmp4 = in_ptr0[i1 + (63*i0)];
2023-01-11T21:41:23.7989565Z                                 tmp3 = tmp4;
2023-01-11T21:41:23.7989656Z                             }
2023-01-11T21:41:23.7989795Z                             tmp5 = std::max(tmp5, tmp3);
2023-01-11T21:41:23.7989888Z                         }
2023-01-11T21:41:23.7989977Z                     }
2023-01-11T21:41:23.7990093Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:41:23.7990163Z                 }
2023-01-11T21:41:23.7990245Z             }
2023-01-11T21:41:23.7990327Z         }
2023-01-11T21:41:23.7990429Z         #pragma omp for 
2023-01-11T21:41:23.7990547Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:23.7990628Z         {
2023-01-11T21:41:23.7990710Z             {
2023-01-11T21:41:23.7990781Z                 {
2023-01-11T21:41:23.7990891Z                     float tmp8 = 0;
2023-01-11T21:41:23.7991065Z                     for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:23.7991153Z                     {
2023-01-11T21:41:23.7991242Z                         {
2023-01-11T21:41:23.7991368Z                             auto tmp5 = out_ptr0[i0];
2023-01-11T21:41:23.7991510Z                             auto tmp0 = static_cast<long>(i1);
2023-01-11T21:41:23.7991641Z                             auto tmp1 = static_cast<long>(63);
2023-01-11T21:41:23.7991770Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:23.7991889Z                             float tmp3 = 0.0;
2023-01-11T21:41:23.7991992Z                             if(tmp2)
2023-01-11T21:41:23.7992087Z                             {
2023-01-11T21:41:23.7992233Z                                 auto tmp4 = in_ptr0[i1 + (63*i0)];
2023-01-11T21:41:23.7992345Z                                 tmp3 = tmp4;
2023-01-11T21:41:23.7992426Z                             }
2023-01-11T21:41:23.7992648Z                             auto tmp6 = tmp3 - tmp5;
2023-01-11T21:41:23.7992795Z                             auto tmp7 = std::exp(tmp6);
2023-01-11T21:41:23.7992930Z                             out_ptr1[i1 + (64*i0)] = tmp7;
2023-01-11T21:41:23.7993045Z                             tmp8 += tmp7;
2023-01-11T21:41:23.7993147Z                         }
2023-01-11T21:41:23.7993243Z                     }
2023-01-11T21:41:23.7993345Z                     out_ptr2[i0] = tmp8;
2023-01-11T21:41:23.7993430Z                 }
2023-01-11T21:41:23.7993514Z             }
2023-01-11T21:41:23.7993597Z         }
2023-01-11T21:41:23.7993699Z         #pragma omp for 
2023-01-11T21:41:23.7993883Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:23.7993975Z         {
2023-01-11T21:41:23.7994070Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.7994164Z             {
2023-01-11T21:41:23.7994368Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (8*i1) + (64*i0));
2023-01-11T21:41:23.7994543Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:23.7994670Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7994816Z                 tmp2.store(in_out_ptr0 + (8*i1) + (64*i0));
2023-01-11T21:41:23.7994903Z             }
2023-01-11T21:41:23.7995016Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.7995138Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:41:23.7995222Z             {
2023-01-11T21:41:23.7995352Z                 auto tmp0 = out_ptr1[i1 + (64*i0)];
2023-01-11T21:41:23.7995470Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:23.7995580Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.7995701Z                 in_out_ptr0[i1 + (64*i0)] = tmp2;
2023-01-11T21:41:23.7995769Z             }
2023-01-11T21:41:23.7995851Z         }
2023-01-11T21:41:23.7995931Z     }
2023-01-11T21:41:23.7996011Z }
2023-01-11T21:41:23.7996133Z ''')
2023-01-11T21:41:23.7996142Z 
2023-01-11T21:41:23.7996147Z 
2023-01-11T21:41:23.7996268Z async_compile.wait(globals())
2023-01-11T21:41:23.7996443Z del async_compile
2023-01-11T21:41:23.7996450Z 
2023-01-11T21:41:23.7996525Z def call(args):
2023-01-11T21:41:23.7996615Z     arg0_1, = args
2023-01-11T21:41:23.7996711Z     args.clear()
2023-01-11T21:41:23.7997030Z     buf0 = empty_strided((128, 32, 1), (32, 1, 4096), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7997316Z     buf1 = empty_strided((128, 32, 64), (2048, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7997605Z     buf2 = empty_strided((128, 32, 1), (32, 1, 4096), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7997720Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:23.7997984Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.7998062Z     del arg0_1
2023-01-11T21:41:23.7998161Z     return (buf3, )
2023-01-11T21:41:23.7998168Z 
2023-01-11T21:41:23.7998174Z 
2023-01-11T21:41:23.7998275Z if __name__ == "__main__":
2023-01-11T21:41:23.7998496Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.7998670Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.7998968Z     arg0_1 = rand_strided((128, 32, 63), (2016, 63, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.7999109Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.7999116Z 
2023-01-11T21:41:23.7999205Z ok (1.647s)
2023-01-11T21:41:23.7999869Z   test_elu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8000024Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8000403Z [2023-01-11 21:33:39,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 193
2023-01-11T21:41:23.8000793Z [2023-01-11 21:33:41,584] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 193
2023-01-11T21:41:23.8000800Z 
2023-01-11T21:41:23.8000935Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8001036Z import torch
2023-01-11T21:41:23.8001132Z import random
2023-01-11T21:41:23.8001299Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8001476Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8001484Z 
2023-01-11T21:41:23.8001584Z aten = torch.ops.aten
2023-01-11T21:41:23.8001773Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8001899Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8001905Z 
2023-01-11T21:41:23.8001911Z 
2023-01-11T21:41:23.8002093Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8002378Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8002541Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8002670Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8002801Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8002871Z {
2023-01-11T21:41:23.8003003Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8003085Z     {
2023-01-11T21:41:23.8003186Z         #pragma omp for 
2023-01-11T21:41:23.8003295Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.8003381Z         {
2023-01-11T21:41:23.8003463Z             {
2023-01-11T21:41:23.8003533Z                 {
2023-01-11T21:41:23.8003657Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8003801Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:23.8003926Z                     auto tmp2 = tmp0 > tmp1;
2023-01-11T21:41:23.8004081Z                     auto tmp3 = static_cast<float>(1.0507009873554805);
2023-01-11T21:41:23.8004204Z                     auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8004423Z                     auto tmp5 = static_cast<float>(1.0);
2023-01-11T21:41:23.8004527Z                     auto tmp6 = tmp0 * tmp5;
2023-01-11T21:41:23.8004669Z                     auto tmp7 = std::expm1(tmp6);
2023-01-11T21:41:23.8004820Z                     auto tmp8 = static_cast<float>(1.7580993408473766);
2023-01-11T21:41:23.8004942Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.8005083Z                     auto tmp10 = tmp2 ? tmp4 : tmp9;
2023-01-11T21:41:23.8005225Z                     auto tmp11 = static_cast<float>(2);
2023-01-11T21:41:23.8005353Z                     auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:23.8005478Z                     auto tmp13 = static_cast<float>(1);
2023-01-11T21:41:23.8005599Z                     auto tmp14 = tmp0 + tmp13;
2023-01-11T21:41:23.8005726Z                     auto tmp15 = tmp14 > tmp1;
2023-01-11T21:41:23.8005864Z                     auto tmp16 = static_cast<float>(3);
2023-01-11T21:41:23.8006084Z                     auto tmp17 = tmp14 * tmp16;
2023-01-11T21:41:23.8006233Z                     auto tmp18 = static_cast<float>(4);
2023-01-11T21:41:23.8006362Z                     auto tmp19 = tmp14 * tmp18;
2023-01-11T21:41:23.8006503Z                     auto tmp20 = std::expm1(tmp19);
2023-01-11T21:41:23.8006626Z                     auto tmp21 = static_cast<float>(6);
2023-01-11T21:41:23.8006751Z                     auto tmp22 = tmp20 * tmp21;
2023-01-11T21:41:23.8006883Z                     auto tmp23 = tmp15 ? tmp17 : tmp22;
2023-01-11T21:41:23.8006994Z                     out_ptr0[i0] = tmp12;
2023-01-11T21:41:23.8007110Z                     out_ptr1[i0] = tmp23;
2023-01-11T21:41:23.8007194Z                 }
2023-01-11T21:41:23.8007295Z             }
2023-01-11T21:41:23.8007381Z         }
2023-01-11T21:41:23.8007479Z     }
2023-01-11T21:41:23.8007580Z }
2023-01-11T21:41:23.8007721Z ''')
2023-01-11T21:41:23.8007730Z 
2023-01-11T21:41:23.8007736Z 
2023-01-11T21:41:23.8007884Z async_compile.wait(globals())
2023-01-11T21:41:23.8008012Z del async_compile
2023-01-11T21:41:23.8008019Z 
2023-01-11T21:41:23.8008132Z def call(args):
2023-01-11T21:41:23.8008227Z     arg0_1, = args
2023-01-11T21:41:23.8008342Z     args.clear()
2023-01-11T21:41:23.8008666Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8008977Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8009239Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8009350Z     del arg0_1
2023-01-11T21:41:23.8009475Z     return (buf0, buf1, )
2023-01-11T21:41:23.8009484Z 
2023-01-11T21:41:23.8009490Z 
2023-01-11T21:41:23.8009608Z if __name__ == "__main__":
2023-01-11T21:41:23.8009774Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8009971Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8010295Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8010470Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8010477Z 
2023-01-11T21:41:23.8010587Z ok (1.664s)
2023-01-11T21:41:23.8011398Z   test_embedding_bag_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8011598Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8012032Z [2023-01-11 21:33:41,610] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 194
2023-01-11T21:41:23.8012400Z [2023-01-11 21:33:41,625] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._embedding_bag
2023-01-11T21:41:23.8012915Z [2023-01-11 21:33:41,628] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 194
2023-01-11T21:41:23.8012923Z 
2023-01-11T21:41:23.8013077Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8013191Z import torch
2023-01-11T21:41:23.8013307Z import random
2023-01-11T21:41:23.8013492Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8013692Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8013699Z 
2023-01-11T21:41:23.8013827Z aten = torch.ops.aten
2023-01-11T21:41:23.8014044Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8014180Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8014187Z 
2023-01-11T21:41:23.8014194Z 
2023-01-11T21:41:23.8014335Z async_compile.wait(globals())
2023-01-11T21:41:23.8014455Z del async_compile
2023-01-11T21:41:23.8014462Z 
2023-01-11T21:41:23.8014577Z def call(args):
2023-01-11T21:41:23.8014704Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8014823Z     args.clear()
2023-01-11T21:41:23.8015114Z     buf0 = aten._embedding_bag(arg0_1, arg1_1, arg2_1)
2023-01-11T21:41:23.8015217Z     del arg0_1
2023-01-11T21:41:23.8015323Z     del arg1_1
2023-01-11T21:41:23.8015429Z     del arg2_1
2023-01-11T21:41:23.8015541Z     buf1 = buf0[0]
2023-01-11T21:41:23.8015697Z     assert_size_stride(buf1, (3, 4), (4, 1))
2023-01-11T21:41:23.8015806Z     buf2 = buf0[1]
2023-01-11T21:41:23.8015953Z     assert_size_stride(buf2, (0, ), (1, ))
2023-01-11T21:41:23.8016045Z     buf3 = buf0[2]
2023-01-11T21:41:23.8016184Z     assert_size_stride(buf3, (3, ), (1, ))
2023-01-11T21:41:23.8016296Z     buf4 = buf0[3]
2023-01-11T21:41:23.8016440Z     assert_size_stride(buf4, (3, ), (1, ))
2023-01-11T21:41:23.8016542Z     del buf0
2023-01-11T21:41:23.8016686Z     return (buf1, buf2, buf3, buf4, )
2023-01-11T21:41:23.8016694Z 
2023-01-11T21:41:23.8016700Z 
2023-01-11T21:41:23.8016822Z if __name__ == "__main__":
2023-01-11T21:41:23.8016989Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8017196Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8017531Z     arg0_1 = rand_strided((10, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8017832Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8018127Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8018322Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8018330Z 
2023-01-11T21:41:23.8018437Z ok (0.044s)
2023-01-11T21:41:23.8019218Z   test_embedding_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8019422Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8019842Z [2023-01-11 21:33:41,734] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 195
2023-01-11T21:41:23.8020272Z [2023-01-11 21:33:43,307] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 195
2023-01-11T21:41:23.8020281Z 
2023-01-11T21:41:23.8020435Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8020539Z import torch
2023-01-11T21:41:23.8020634Z import random
2023-01-11T21:41:23.8020823Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8021016Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8021024Z 
2023-01-11T21:41:23.8021150Z aten = torch.ops.aten
2023-01-11T21:41:23.8021351Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8021496Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8021504Z 
2023-01-11T21:41:23.8021509Z 
2023-01-11T21:41:23.8021730Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8022138Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8022457Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8022626Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8022783Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8022934Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:41:23.8023066Z                        long* __restrict__ out_ptr2)
2023-01-11T21:41:23.8023164Z {
2023-01-11T21:41:23.8023324Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8023423Z     {
2023-01-11T21:41:23.8023545Z         #pragma omp for 
2023-01-11T21:41:23.8023676Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8023776Z         {
2023-01-11T21:41:23.8023906Z             #pragma GCC ivdep
2023-01-11T21:41:23.8024054Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8024166Z             {
2023-01-11T21:41:23.8024382Z                 {
2023-01-11T21:41:23.8024507Z                     {
2023-01-11T21:41:23.8024665Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8024837Z                         auto tmp1 = in_ptr1[i1 + (4*tmp0)];
2023-01-11T21:41:23.8024989Z                         auto tmp2 = tmp1 * (tmp1>0);
2023-01-11T21:41:23.8025151Z                         out_ptr0[i1 + (4*i0)] = tmp2;
2023-01-11T21:41:23.8025282Z                     }
2023-01-11T21:41:23.8025401Z                 }
2023-01-11T21:41:23.8025517Z             }
2023-01-11T21:41:23.8025638Z         }
2023-01-11T21:41:23.8025761Z         #pragma omp for 
2023-01-11T21:41:23.8025913Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.8026031Z         {
2023-01-11T21:41:23.8026145Z             {
2023-01-11T21:41:23.8026262Z                 {
2023-01-11T21:41:23.8026421Z                     auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8026601Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:23.8026750Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:41:23.8026902Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8027019Z                 }
2023-01-11T21:41:23.8027136Z             }
2023-01-11T21:41:23.8027253Z         }
2023-01-11T21:41:23.8027390Z         #pragma omp for 
2023-01-11T21:41:23.8027534Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8027631Z         {
2023-01-11T21:41:23.8027746Z             {
2023-01-11T21:41:23.8027858Z                 {
2023-01-11T21:41:23.8028014Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8028197Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.8028374Z                     auto tmp2 = static_cast<long>(tmp1);
2023-01-11T21:41:23.8028521Z                     out_ptr2[i0] = tmp2;
2023-01-11T21:41:23.8028623Z                 }
2023-01-11T21:41:23.8028738Z             }
2023-01-11T21:41:23.8028851Z         }
2023-01-11T21:41:23.8028964Z     }
2023-01-11T21:41:23.8029077Z }
2023-01-11T21:41:23.8029248Z ''')
2023-01-11T21:41:23.8029258Z 
2023-01-11T21:41:23.8029264Z 
2023-01-11T21:41:23.8029426Z async_compile.wait(globals())
2023-01-11T21:41:23.8029541Z del async_compile
2023-01-11T21:41:23.8029548Z 
2023-01-11T21:41:23.8029673Z def call(args):
2023-01-11T21:41:23.8029830Z     primals_1, primals_2 = args
2023-01-11T21:41:23.8029957Z     args.clear()
2023-01-11T21:41:23.8030300Z     buf0 = empty_strided((2, 8, 4), (32, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8030626Z     buf1 = empty_strided((2, 8, 4), (32, 4, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8030948Z     buf2 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8031299Z     kernel_cpp_0(c_void_p(primals_2.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8031433Z     del primals_1
2023-01-11T21:41:23.8031558Z     del primals_2
2023-01-11T21:41:23.8031818Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8031826Z 
2023-01-11T21:41:23.8031832Z 
2023-01-11T21:41:23.8031964Z if __name__ == "__main__":
2023-01-11T21:41:23.8032162Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8032372Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8032724Z     primals_1 = rand_strided((10, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8033031Z     primals_2 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8033254Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:23.8033262Z 
2023-01-11T21:41:23.8033384Z ok (1.679s)
2023-01-11T21:41:23.8034360Z   test_exp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8034591Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8035040Z [2023-01-11 21:33:43,326] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 196
2023-01-11T21:41:23.8035488Z [2023-01-11 21:33:45,162] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 196
2023-01-11T21:41:23.8035496Z 
2023-01-11T21:41:23.8035660Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8035788Z import torch
2023-01-11T21:41:23.8035912Z import random
2023-01-11T21:41:23.8036094Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8036304Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8036312Z 
2023-01-11T21:41:23.8036451Z aten = torch.ops.aten
2023-01-11T21:41:23.8036679Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8036849Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8036857Z 
2023-01-11T21:41:23.8036863Z 
2023-01-11T21:41:23.8037093Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8037437Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8037644Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8037809Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8037980Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8038141Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8038258Z {
2023-01-11T21:41:23.8038430Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8038537Z     {
2023-01-11T21:41:23.8038674Z         #pragma omp for 
2023-01-11T21:41:23.8038800Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8038914Z         {
2023-01-11T21:41:23.8039147Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8039379Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8039526Z             auto tmp1 = tmp0.exp();
2023-01-11T21:41:23.8039672Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.8039814Z             auto tmp4 = tmp3.exp();
2023-01-11T21:41:23.8039956Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8040122Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8040253Z         }
2023-01-11T21:41:23.8040455Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8040631Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8040761Z         {
2023-01-11T21:41:23.8040942Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8041105Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:23.8041293Z             auto tmp1 = std::exp(tmp0);
2023-01-11T21:41:23.8041477Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.8041672Z             auto tmp4 = std::exp(tmp3);
2023-01-11T21:41:23.8041838Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8042115Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.8042246Z         }
2023-01-11T21:41:23.8042363Z     }
2023-01-11T21:41:23.8042496Z }
2023-01-11T21:41:23.8042679Z ''')
2023-01-11T21:41:23.8042690Z 
2023-01-11T21:41:23.8042699Z 
2023-01-11T21:41:23.8042895Z async_compile.wait(globals())
2023-01-11T21:41:23.8043058Z del async_compile
2023-01-11T21:41:23.8043068Z 
2023-01-11T21:41:23.8043221Z def call(args):
2023-01-11T21:41:23.8043382Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8043516Z     args.clear()
2023-01-11T21:41:23.8043961Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8044380Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8044804Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8044949Z     del arg0_1
2023-01-11T21:41:23.8045096Z     del arg1_1
2023-01-11T21:41:23.8045342Z     return (buf0, buf1, )
2023-01-11T21:41:23.8045354Z 
2023-01-11T21:41:23.8045362Z 
2023-01-11T21:41:23.8045527Z if __name__ == "__main__":
2023-01-11T21:41:23.8045767Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8046039Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8046479Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8046896Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8047143Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8047153Z 
2023-01-11T21:41:23.8047291Z ok (1.855s)
2023-01-11T21:41:23.8048458Z   test_expand_as_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8048749Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8049361Z [2023-01-11 21:33:45,206] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 197
2023-01-11T21:41:23.8049954Z [2023-01-11 21:33:47,044] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 197
2023-01-11T21:41:23.8049983Z 
2023-01-11T21:41:23.8050182Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8050332Z import torch
2023-01-11T21:41:23.8050480Z import random
2023-01-11T21:41:23.8050732Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8051002Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8051012Z 
2023-01-11T21:41:23.8051184Z aten = torch.ops.aten
2023-01-11T21:41:23.8051476Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8051668Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8051678Z 
2023-01-11T21:41:23.8051686Z 
2023-01-11T21:41:23.8051989Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8052456Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8052718Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8052861Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8052949Z {
2023-01-11T21:41:23.8053090Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8053167Z     {
2023-01-11T21:41:23.8053298Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.8053419Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:23.8053505Z         {
2023-01-11T21:41:23.8053634Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:23.8053721Z             {
2023-01-11T21:41:23.8053851Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:23.8053919Z                 {
2023-01-11T21:41:23.8054225Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (100*i0));
2023-01-11T21:41:23.8054428Z                     auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8054555Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8054685Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:23.8054846Z                     tmp3.store(out_ptr0 + (8*i2) + (100*i1) + (12800*i0));
2023-01-11T21:41:23.8054931Z                 }
2023-01-11T21:41:23.8055071Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.8055179Z                 for(long i2=96; i2<100; i2+=1)
2023-01-11T21:41:23.8055259Z                 {
2023-01-11T21:41:23.8055411Z                     auto tmp0 = in_ptr0[i2 + (100*i0)];
2023-01-11T21:41:23.8055543Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8055659Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8055790Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:23.8056004Z                     out_ptr0[i2 + (100*i1) + (12800*i0)] = tmp3;
2023-01-11T21:41:23.8056078Z                 }
2023-01-11T21:41:23.8056170Z             }
2023-01-11T21:41:23.8056261Z         }
2023-01-11T21:41:23.8056347Z     }
2023-01-11T21:41:23.8056433Z }
2023-01-11T21:41:23.8056565Z ''')
2023-01-11T21:41:23.8056574Z 
2023-01-11T21:41:23.8056579Z 
2023-01-11T21:41:23.8056723Z async_compile.wait(globals())
2023-01-11T21:41:23.8056805Z del async_compile
2023-01-11T21:41:23.8056812Z 
2023-01-11T21:41:23.8056900Z def call(args):
2023-01-11T21:41:23.8056998Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8057102Z     args.clear()
2023-01-11T21:41:23.8057447Z     buf0 = empty_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8057665Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8057827Z     return (as_strided(arg0_1, (6, 128, 100), (100, 0, 1)), buf0, )
2023-01-11T21:41:23.8057835Z 
2023-01-11T21:41:23.8057848Z 
2023-01-11T21:41:23.8057955Z if __name__ == "__main__":
2023-01-11T21:41:23.8058108Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8058289Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8058612Z     arg0_1 = rand_strided((6, 1, 100), (100, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8058924Z     arg1_1 = rand_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8059083Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8059091Z 
2023-01-11T21:41:23.8059189Z ok (1.884s)
2023-01-11T21:41:23.8059924Z   test_expand_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8060106Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8060496Z [2023-01-11 21:33:47,078] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 198
2023-01-11T21:41:23.8060856Z [2023-01-11 21:33:48,675] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 198
2023-01-11T21:41:23.8060878Z 
2023-01-11T21:41:23.8060988Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8061082Z import torch
2023-01-11T21:41:23.8061178Z import random
2023-01-11T21:41:23.8061338Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8061523Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8061531Z 
2023-01-11T21:41:23.8061649Z aten = torch.ops.aten
2023-01-11T21:41:23.8061868Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8061994Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8062083Z 
2023-01-11T21:41:23.8062109Z 
2023-01-11T21:41:23.8062308Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8062720Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8062904Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8063060Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8063214Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8063300Z {
2023-01-11T21:41:23.8063450Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8063516Z     {
2023-01-11T21:41:23.8063628Z         #pragma omp for 
2023-01-11T21:41:23.8063744Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.8063837Z         {
2023-01-11T21:41:23.8063961Z             #pragma GCC ivdep
2023-01-11T21:41:23.8064084Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.8064167Z             {
2023-01-11T21:41:23.8064294Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8064548Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:41:23.8064645Z                 {
2023-01-11T21:41:23.8064760Z                     #pragma GCC ivdep
2023-01-11T21:41:23.8064883Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:41:23.8064976Z                     {
2023-01-11T21:41:23.8065053Z                         {
2023-01-11T21:41:23.8065151Z                             {
2023-01-11T21:41:23.8065297Z                                 auto tmp0 = in_ptr0[i3 + (2*i1)];
2023-01-11T21:41:23.8065444Z                                 auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8065580Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8065743Z                                 auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.8065880Z                                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.8066026Z                                 out_ptr0[i3 + (2*i2) + (6*i1) + (12*i0)] = tmp4;
2023-01-11T21:41:23.8066113Z                             }
2023-01-11T21:41:23.8066218Z                         }
2023-01-11T21:41:23.8066316Z                     }
2023-01-11T21:41:23.8066401Z                 }
2023-01-11T21:41:23.8066484Z             }
2023-01-11T21:41:23.8066568Z         }
2023-01-11T21:41:23.8066672Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.8066782Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8066869Z         {
2023-01-11T21:41:23.8066986Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.8067074Z             {
2023-01-11T21:41:23.8067194Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8067321Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:41:23.8067403Z                 {
2023-01-11T21:41:23.8067520Z                     #pragma GCC ivdep
2023-01-11T21:41:23.8067639Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:41:23.8067730Z                     {
2023-01-11T21:41:23.8067823Z                         {
2023-01-11T21:41:23.8067919Z                             {
2023-01-11T21:41:23.8068065Z                                 auto tmp0 = in_ptr0[i3 + (2*i1)];
2023-01-11T21:41:23.8068196Z                                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.8068344Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8068512Z                                 out_ptr1[i3 + (2*i2) + (6*i1) + (12*i0)] = tmp2;
2023-01-11T21:41:23.8068612Z                             }
2023-01-11T21:41:23.8068711Z                         }
2023-01-11T21:41:23.8068808Z                     }
2023-01-11T21:41:23.8068903Z                 }
2023-01-11T21:41:23.8068986Z             }
2023-01-11T21:41:23.8069082Z         }
2023-01-11T21:41:23.8069172Z     }
2023-01-11T21:41:23.8069273Z }
2023-01-11T21:41:23.8069431Z ''')
2023-01-11T21:41:23.8069440Z 
2023-01-11T21:41:23.8069447Z 
2023-01-11T21:41:23.8069595Z async_compile.wait(globals())
2023-01-11T21:41:23.8069715Z del async_compile
2023-01-11T21:41:23.8069722Z 
2023-01-11T21:41:23.8069818Z def call(args):
2023-01-11T21:41:23.8070036Z     arg0_1, = args
2023-01-11T21:41:23.8070148Z     args.clear()
2023-01-11T21:41:23.8070511Z     buf0 = empty_strided((3, 4, 2, 3, 2), (48, 12, 6, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8070855Z     buf1 = empty_strided((2, 1, 2, 3, 2), (12, 12, 6, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8071107Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8071291Z     return (buf0, buf1, as_strided(arg0_1, (2, 2, 5, 2), (0, 2, 0, 1)), )
2023-01-11T21:41:23.8071300Z 
2023-01-11T21:41:23.8071307Z 
2023-01-11T21:41:23.8071423Z if __name__ == "__main__":
2023-01-11T21:41:23.8071576Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8071775Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8072102Z     arg0_1 = rand_strided((2, 1, 2), (2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8072246Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8072358Z 
2023-01-11T21:41:23.8072451Z ok (1.629s)
2023-01-11T21:41:23.8073451Z   test_expanded_reduction_cpu (__main__.CpuTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/87157 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:41:23.8074198Z   test_expm1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8074384Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8074775Z [2023-01-11 21:33:48,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 199
2023-01-11T21:41:23.8075162Z [2023-01-11 21:33:50,312] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 199
2023-01-11T21:41:23.8075835Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8076017Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8076382Z [2023-01-11 21:33:50,330] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 200
2023-01-11T21:41:23.8076753Z [2023-01-11 21:33:51,887] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 200
2023-01-11T21:41:23.8077402Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8077584Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8077964Z [2023-01-11 21:33:51,906] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 201
2023-01-11T21:41:23.8078340Z [2023-01-11 21:33:53,498] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 201
2023-01-11T21:41:23.8078964Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8079252Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8079682Z [2023-01-11 21:33:53,515] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 202
2023-01-11T21:41:23.8080103Z [2023-01-11 21:33:55,094] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 202
2023-01-11T21:41:23.8080770Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8080955Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8081408Z [2023-01-11 21:33:55,120] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 203
2023-01-11T21:41:23.8081437Z 
2023-01-11T21:41:23.8081552Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8081659Z import torch
2023-01-11T21:41:23.8081767Z import random
2023-01-11T21:41:23.8081956Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8082147Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8082156Z 
2023-01-11T21:41:23.8082283Z aten = torch.ops.aten
2023-01-11T21:41:23.8082499Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8082621Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8082629Z 
2023-01-11T21:41:23.8082634Z 
2023-01-11T21:41:23.8082847Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8083170Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8083340Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:23.8083486Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:23.8083633Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:23.8083727Z {
2023-01-11T21:41:23.8083883Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8083961Z     {
2023-01-11T21:41:23.8084081Z         #pragma omp for 
2023-01-11T21:41:23.8084212Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.8084308Z         {
2023-01-11T21:41:23.8084409Z             {
2023-01-11T21:41:23.8084508Z                 {
2023-01-11T21:41:23.8084680Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:23.8084849Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:23.8085007Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:23.8085147Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8085266Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8085384Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.8085479Z                 }
2023-01-11T21:41:23.8085553Z             }
2023-01-11T21:41:23.8085641Z         }
2023-01-11T21:41:23.8085729Z     }
2023-01-11T21:41:23.8085820Z }
2023-01-11T21:41:23.8085950Z ''')
2023-01-11T21:41:23.8085958Z 
2023-01-11T21:41:23.8085965Z 
2023-01-11T21:41:23.8086095Z async_compile.wait(globals())
2023-01-11T21:41:23.8086190Z del async_compile
2023-01-11T21:41:23.8086198Z 
2023-01-11T21:41:23.8086283Z def call(args):
2023-01-11T21:41:23.8086383Z     arg0_1, = args
2023-01-11T21:41:23.8086481Z     args.clear()
2023-01-11T21:41:23.8086765Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.8087058Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.8087303Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8087401Z     del arg0_1
2023-01-11T21:41:23.8087511Z     return (buf0, buf1, )
2023-01-11T21:41:23.8087601Z 
2023-01-11T21:41:23.8087612Z 
2023-01-11T21:41:23.8087708Z if __name__ == "__main__":
2023-01-11T21:41:23.8087875Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8088048Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8088342Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.8088494Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8088503Z 
2023-01-11T21:41:23.8088508Z 
2023-01-11T21:41:23.8088642Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8088739Z import torch
2023-01-11T21:41:23.8088842Z import random
2023-01-11T21:41:23.8088982Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8089144Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8089150Z 
2023-01-11T21:41:23.8089248Z aten = torch.ops.aten
2023-01-11T21:41:23.8089425Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8089540Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8089617Z 
2023-01-11T21:41:23.8089624Z 
2023-01-11T21:41:23.8089812Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8090078Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8090240Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:23.8090352Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:23.8090495Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:23.8090585Z {
2023-01-11T21:41:23.8090726Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8090811Z     {
2023-01-11T21:41:23.8090919Z         #pragma omp for 
2023-01-11T21:41:23.8091031Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.8091102Z         {
2023-01-11T21:41:23.8091183Z             {
2023-01-11T21:41:23.8091267Z                 {
2023-01-11T21:41:23.8091425Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:23.8091575Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:23.8091723Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:23.8091848Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8091950Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8092075Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.8092169Z                 }
2023-01-11T21:41:23.8092257Z             }
2023-01-11T21:41:23.8092339Z         }
2023-01-11T21:41:23.8092426Z     }
2023-01-11T21:41:23.8092491Z }
2023-01-11T21:41:23.8092614Z ''')
2023-01-11T21:41:23.8092624Z 
2023-01-11T21:41:23.8092630Z 
2023-01-11T21:41:23.8092759Z async_compile.wait(globals())
2023-01-11T21:41:23.8092866Z del async_compile
2023-01-11T21:41:23.8092874Z 
2023-01-11T21:41:23.8092981Z def call(args):
2023-01-11T21:41:23.8093084Z     arg0_1, = args
2023-01-11T21:41:23.8093195Z     args.clear()
2023-01-11T21:41:23.8093514Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.8093809Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.8094056Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8094157Z     del arg0_1
2023-01-11T21:41:23.8094267Z     return (buf0, buf1, )
2023-01-11T21:41:23.8094274Z 
2023-01-11T21:41:23.8094281Z 
2023-01-11T21:41:23.8094397Z if __name__ == "__main__":
2023-01-11T21:41:23.8094579Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8094767Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8095087Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.8095249Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8095257Z 
2023-01-11T21:41:23.8095263Z 
2023-01-11T21:41:23.8095410Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8095516Z import torch
2023-01-11T21:41:23.8095629Z import random
2023-01-11T21:41:23.8095898Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8096066Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8096073Z 
2023-01-11T21:41:23.8096178Z aten = torch.ops.aten
2023-01-11T21:41:23.8096377Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8096491Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8096497Z 
2023-01-11T21:41:23.8096503Z 
2023-01-11T21:41:23.8096729Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8097048Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8097226Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8097373Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8097511Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8097590Z {
2023-01-11T21:41:23.8097722Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8097810Z     {
2023-01-11T21:41:23.8098002Z         #pragma omp for 
2023-01-11T21:41:23.8098125Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8098216Z         {
2023-01-11T21:41:23.8098426Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8098554Z             auto tmp1 = tmp0.expm1();
2023-01-11T21:41:23.8098725Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8098837Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8098964Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8099098Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8099187Z         }
2023-01-11T21:41:23.8099331Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8099454Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8099531Z         {
2023-01-11T21:41:23.8099654Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8099796Z             auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:23.8099937Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:23.8100057Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8100171Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8100285Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.8100367Z         }
2023-01-11T21:41:23.8100456Z     }
2023-01-11T21:41:23.8100544Z }
2023-01-11T21:41:23.8100678Z ''')
2023-01-11T21:41:23.8100686Z 
2023-01-11T21:41:23.8100692Z 
2023-01-11T21:41:23.8100819Z async_compile.wait(globals())
2023-01-11T21:41:23.8100926Z del async_compile
2023-01-11T21:41:23.8100933Z 
2023-01-11T21:41:23.8101033Z def call(args):
2023-01-11T21:41:23.8101133Z     arg0_1, = args
2023-01-11T21:41:23.8101219Z     args.clear()
2023-01-11T21:41:23.8101501Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8101767Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8101990Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8102098Z     del arg0_1
2023-01-11T21:41:23.8102218Z     return (buf0, buf1, )
2023-01-11T21:41:23.8102226Z 
2023-01-11T21:41:23.8102232Z 
2023-01-11T21:41:23.8102504Z if __name__ == "__main__":
2023-01-11T21:41:23.8102653Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8102821Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8103107Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8103257Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8103264Z 
2023-01-11T21:41:23.8103269Z 
2023-01-11T21:41:23.8103397Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8103495Z import torch
2023-01-11T21:41:23.8103592Z import random
2023-01-11T21:41:23.8103761Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8103946Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8103953Z 
2023-01-11T21:41:23.8104079Z aten = torch.ops.aten
2023-01-11T21:41:23.8104386Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8104538Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8104546Z 
2023-01-11T21:41:23.8104553Z 
2023-01-11T21:41:23.8104777Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8105090Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8105269Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8105406Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8105541Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8105637Z {
2023-01-11T21:41:23.8105781Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8105875Z     {
2023-01-11T21:41:23.8105982Z         #pragma omp for 
2023-01-11T21:41:23.8106092Z         for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:23.8106176Z         {
2023-01-11T21:41:23.8106346Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8106565Z             auto tmp1 = tmp0.expm1();
2023-01-11T21:41:23.8106759Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8106873Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8106995Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8107122Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8107213Z         }
2023-01-11T21:41:23.8107341Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8107464Z         for(long i0=200; i0<201; i0+=1)
2023-01-11T21:41:23.8107560Z         {
2023-01-11T21:41:23.8107677Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8107815Z             auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:23.8107971Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:23.8108103Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8108210Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8108331Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.8108432Z         }
2023-01-11T21:41:23.8108528Z     }
2023-01-11T21:41:23.8108621Z }
2023-01-11T21:41:23.8108768Z ''')
2023-01-11T21:41:23.8108777Z 
2023-01-11T21:41:23.8108783Z 
2023-01-11T21:41:23.8108914Z async_compile.wait(globals())
2023-01-11T21:41:23.8109008Z del async_compile
2023-01-11T21:41:23.8109032Z 
2023-01-11T21:41:23.8109112Z def call(args):
2023-01-11T21:41:23.8109206Z     arg0_1, = args
2023-01-11T21:41:23.8109300Z     args.clear()
2023-01-11T21:41:23.8109588Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8109862Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8110075Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8110164Z     del arg0_1
2023-01-11T21:41:23.8110250Z     return (buf0, buf1, )
2023-01-11T21:41:23.8110257Z 
2023-01-11T21:41:23.8110263Z 
2023-01-11T21:41:23.8110373Z if __name__ == "__main__":
2023-01-11T21:41:23.8110553Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8110741Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8111057Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8111224Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8111231Z 
2023-01-11T21:41:23.8111661Z [2023-01-11 21:33:56,662] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 203
2023-01-11T21:41:23.8112317Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8112505Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8112995Z [2023-01-11 21:33:56,679] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 204
2023-01-11T21:41:23.8113410Z [2023-01-11 21:33:58,236] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 204
2023-01-11T21:41:23.8114148Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8114339Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8114801Z [2023-01-11 21:33:58,255] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 205
2023-01-11T21:41:23.8115281Z [2023-01-11 21:33:59,825] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 205
2023-01-11T21:41:23.8116238Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8116464Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8116941Z [2023-01-11 21:33:59,844] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 206
2023-01-11T21:41:23.8117429Z [2023-01-11 21:34:01,430] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 206
2023-01-11T21:41:23.8118301Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8118522Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8118973Z [2023-01-11 21:34:01,449] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 207
2023-01-11T21:41:23.8119000Z 
2023-01-11T21:41:23.8119145Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8119260Z import torch
2023-01-11T21:41:23.8119379Z import random
2023-01-11T21:41:23.8119585Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8119801Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8119810Z 
2023-01-11T21:41:23.8119941Z aten = torch.ops.aten
2023-01-11T21:41:23.8120179Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8120322Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8120330Z 
2023-01-11T21:41:23.8120352Z 
2023-01-11T21:41:23.8120582Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8120970Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8121177Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:23.8121341Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:23.8121506Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:23.8121603Z {
2023-01-11T21:41:23.8121767Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8121850Z     {
2023-01-11T21:41:23.8121977Z         #pragma omp for 
2023-01-11T21:41:23.8122110Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.8122209Z         {
2023-01-11T21:41:23.8122310Z             {
2023-01-11T21:41:23.8122414Z                 {
2023-01-11T21:41:23.8122550Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8122723Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:23.8122904Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:23.8123136Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8123274Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8123408Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.8123510Z                 }
2023-01-11T21:41:23.8123595Z             }
2023-01-11T21:41:23.8123695Z         }
2023-01-11T21:41:23.8123791Z     }
2023-01-11T21:41:23.8123887Z }
2023-01-11T21:41:23.8124024Z ''')
2023-01-11T21:41:23.8124034Z 
2023-01-11T21:41:23.8124042Z 
2023-01-11T21:41:23.8124197Z async_compile.wait(globals())
2023-01-11T21:41:23.8124316Z del async_compile
2023-01-11T21:41:23.8124323Z 
2023-01-11T21:41:23.8124438Z def call(args):
2023-01-11T21:41:23.8124533Z     arg0_1, = args
2023-01-11T21:41:23.8124650Z     args.clear()
2023-01-11T21:41:23.8124991Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8125322Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8125680Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8125797Z     del arg0_1
2023-01-11T21:41:23.8125924Z     return (buf0, buf1, )
2023-01-11T21:41:23.8125933Z 
2023-01-11T21:41:23.8125940Z 
2023-01-11T21:41:23.8126050Z if __name__ == "__main__":
2023-01-11T21:41:23.8126249Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8126463Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8126791Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8126974Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8126982Z 
2023-01-11T21:41:23.8126989Z 
2023-01-11T21:41:23.8127150Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8127264Z import torch
2023-01-11T21:41:23.8127378Z import random
2023-01-11T21:41:23.8127561Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8127779Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8127791Z 
2023-01-11T21:41:23.8127922Z aten = torch.ops.aten
2023-01-11T21:41:23.8128157Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8128312Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8128320Z 
2023-01-11T21:41:23.8128327Z 
2023-01-11T21:41:23.8128585Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8128974Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8129206Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:23.8129380Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:23.8129565Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:23.8129688Z {
2023-01-11T21:41:23.8129883Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8130006Z     {
2023-01-11T21:41:23.8130155Z         #pragma omp for 
2023-01-11T21:41:23.8130313Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.8130430Z         {
2023-01-11T21:41:23.8130556Z             {
2023-01-11T21:41:23.8130681Z                 {
2023-01-11T21:41:23.8130857Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8131052Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:23.8131249Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:23.8131419Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.8131564Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8131724Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.8131851Z                 }
2023-01-11T21:41:23.8131977Z             }
2023-01-11T21:41:23.8132100Z         }
2023-01-11T21:41:23.8132223Z     }
2023-01-11T21:41:23.8132328Z }
2023-01-11T21:41:23.8132493Z ''')
2023-01-11T21:41:23.8132501Z 
2023-01-11T21:41:23.8132508Z 
2023-01-11T21:41:23.8132690Z async_compile.wait(globals())
2023-01-11T21:41:23.8132834Z del async_compile
2023-01-11T21:41:23.8132842Z 
2023-01-11T21:41:23.8133077Z def call(args):
2023-01-11T21:41:23.8133211Z     arg0_1, = args
2023-01-11T21:41:23.8133350Z     args.clear()
2023-01-11T21:41:23.8133721Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8134066Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8134372Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8134509Z     del arg0_1
2023-01-11T21:41:23.8134659Z     return (buf0, buf1, )
2023-01-11T21:41:23.8134668Z 
2023-01-11T21:41:23.8134675Z 
2023-01-11T21:41:23.8134821Z if __name__ == "__main__":
2023-01-11T21:41:23.8135041Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8135279Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8135645Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8135840Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8135949Z 
2023-01-11T21:41:23.8135973Z 
2023-01-11T21:41:23.8136145Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8136284Z import torch
2023-01-11T21:41:23.8136423Z import random
2023-01-11T21:41:23.8136648Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8136882Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8136889Z 
2023-01-11T21:41:23.8137044Z aten = torch.ops.aten
2023-01-11T21:41:23.8137303Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8137468Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8137476Z 
2023-01-11T21:41:23.8137482Z 
2023-01-11T21:41:23.8137744Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8138137Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8138361Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.8138547Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8138740Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8138861Z {
2023-01-11T21:41:23.8139040Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8139166Z     {
2023-01-11T21:41:23.8139316Z         #pragma omp for 
2023-01-11T21:41:23.8139473Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.8139597Z         {
2023-01-11T21:41:23.8139723Z             {
2023-01-11T21:41:23.8139851Z                 {
2023-01-11T21:41:23.8140008Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8140212Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.8140404Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:23.8140599Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.8140771Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.8140930Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8141089Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.8141207Z                 }
2023-01-11T21:41:23.8141335Z             }
2023-01-11T21:41:23.8141457Z         }
2023-01-11T21:41:23.8141582Z     }
2023-01-11T21:41:23.8141707Z }
2023-01-11T21:41:23.8141876Z ''')
2023-01-11T21:41:23.8141885Z 
2023-01-11T21:41:23.8141892Z 
2023-01-11T21:41:23.8142077Z async_compile.wait(globals())
2023-01-11T21:41:23.8142210Z del async_compile
2023-01-11T21:41:23.8142233Z 
2023-01-11T21:41:23.8142495Z def call(args):
2023-01-11T21:41:23.8142643Z     arg0_1, = args
2023-01-11T21:41:23.8142787Z     args.clear()
2023-01-11T21:41:23.8143162Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8143521Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8143829Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8143967Z     del arg0_1
2023-01-11T21:41:23.8144108Z     return (buf0, buf1, )
2023-01-11T21:41:23.8144116Z 
2023-01-11T21:41:23.8144236Z 
2023-01-11T21:41:23.8144393Z if __name__ == "__main__":
2023-01-11T21:41:23.8144615Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8144855Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8145221Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.8145434Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8145442Z 
2023-01-11T21:41:23.8145448Z 
2023-01-11T21:41:23.8145635Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8145775Z import torch
2023-01-11T21:41:23.8145903Z import random
2023-01-11T21:41:23.8146129Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8146364Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8146372Z 
2023-01-11T21:41:23.8146526Z aten = torch.ops.aten
2023-01-11T21:41:23.8146783Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8146965Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8146978Z 
2023-01-11T21:41:23.8147076Z 
2023-01-11T21:41:23.8147347Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8147728Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8147936Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.8148124Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8148309Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8148432Z {
2023-01-11T21:41:23.8148627Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8148751Z     {
2023-01-11T21:41:23.8148902Z         #pragma omp for 
2023-01-11T21:41:23.8149050Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.8149178Z         {
2023-01-11T21:41:23.8149305Z             {
2023-01-11T21:41:23.8149436Z                 {
2023-01-11T21:41:23.8149610Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8149815Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.8150021Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:23.8150201Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.8150376Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.8150538Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8150700Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.8150831Z                 }
2023-01-11T21:41:23.8150960Z             }
2023-01-11T21:41:23.8151088Z         }
2023-01-11T21:41:23.8151200Z     }
2023-01-11T21:41:23.8151326Z }
2023-01-11T21:41:23.8151496Z ''')
2023-01-11T21:41:23.8151505Z 
2023-01-11T21:41:23.8151512Z 
2023-01-11T21:41:23.8151692Z async_compile.wait(globals())
2023-01-11T21:41:23.8151839Z del async_compile
2023-01-11T21:41:23.8151847Z 
2023-01-11T21:41:23.8151990Z def call(args):
2023-01-11T21:41:23.8152128Z     arg0_1, = args
2023-01-11T21:41:23.8152254Z     args.clear()
2023-01-11T21:41:23.8152626Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8152990Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8153298Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8153435Z     del arg0_1
2023-01-11T21:41:23.8153588Z     return (buf0, buf1, )
2023-01-11T21:41:23.8153596Z 
2023-01-11T21:41:23.8153603Z 
2023-01-11T21:41:23.8153820Z if __name__ == "__main__":
2023-01-11T21:41:23.8154049Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8154274Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8154597Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.8154760Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8154767Z 
2023-01-11T21:41:23.8155178Z [2023-01-11 21:34:03,017] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 207
2023-01-11T21:41:23.8155907Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8156102Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8156516Z [2023-01-11 21:34:03,035] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 208
2023-01-11T21:41:23.8156932Z [2023-01-11 21:34:04,589] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 208
2023-01-11T21:41:23.8156939Z 
2023-01-11T21:41:23.8157084Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8157175Z import torch
2023-01-11T21:41:23.8157284Z import random
2023-01-11T21:41:23.8157465Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8157717Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8157726Z 
2023-01-11T21:41:23.8157845Z aten = torch.ops.aten
2023-01-11T21:41:23.8158052Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8158194Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8158201Z 
2023-01-11T21:41:23.8158208Z 
2023-01-11T21:41:23.8158420Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8158739Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8158926Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8159068Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8159210Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8159301Z {
2023-01-11T21:41:23.8159458Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8159548Z     {
2023-01-11T21:41:23.8159651Z         #pragma omp for 
2023-01-11T21:41:23.8159783Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.8159879Z         {
2023-01-11T21:41:23.8159968Z             {
2023-01-11T21:41:23.8160062Z                 {
2023-01-11T21:41:23.8160203Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8160362Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.8160503Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:23.8160655Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.8160791Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.8160919Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8161039Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.8161135Z                 }
2023-01-11T21:41:23.8161226Z             }
2023-01-11T21:41:23.8161295Z         }
2023-01-11T21:41:23.8161379Z     }
2023-01-11T21:41:23.8161466Z }
2023-01-11T21:41:23.8161595Z ''')
2023-01-11T21:41:23.8161603Z 
2023-01-11T21:41:23.8161608Z 
2023-01-11T21:41:23.8161752Z async_compile.wait(globals())
2023-01-11T21:41:23.8161856Z del async_compile
2023-01-11T21:41:23.8161862Z 
2023-01-11T21:41:23.8161957Z def call(args):
2023-01-11T21:41:23.8162041Z     arg0_1, = args
2023-01-11T21:41:23.8162139Z     args.clear()
2023-01-11T21:41:23.8162440Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8162735Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8162989Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8163085Z     del arg0_1
2023-01-11T21:41:23.8163193Z     return (buf0, buf1, )
2023-01-11T21:41:23.8163202Z 
2023-01-11T21:41:23.8163208Z 
2023-01-11T21:41:23.8163325Z if __name__ == "__main__":
2023-01-11T21:41:23.8163465Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8163654Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8163959Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8164224Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8164231Z 
2023-01-11T21:41:23.8164236Z 
2023-01-11T21:41:23.8164371Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8164471Z import torch
2023-01-11T21:41:23.8164577Z import random
2023-01-11T21:41:23.8164745Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8164904Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8164911Z 
2023-01-11T21:41:23.8165019Z aten = torch.ops.aten
2023-01-11T21:41:23.8165219Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8165354Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8165362Z 
2023-01-11T21:41:23.8165369Z 
2023-01-11T21:41:23.8165578Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8165895Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8166110Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8166270Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8166393Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8166481Z {
2023-01-11T21:41:23.8166632Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8166720Z     {
2023-01-11T21:41:23.8166829Z         #pragma omp for 
2023-01-11T21:41:23.8166950Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.8167020Z         {
2023-01-11T21:41:23.8167114Z             {
2023-01-11T21:41:23.8167205Z                 {
2023-01-11T21:41:23.8167334Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8167492Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.8167647Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:23.8167796Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.8167929Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.8168038Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8168167Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.8168254Z                 }
2023-01-11T21:41:23.8168344Z             }
2023-01-11T21:41:23.8168434Z         }
2023-01-11T21:41:23.8168520Z     }
2023-01-11T21:41:23.8168591Z }
2023-01-11T21:41:23.8168714Z ''')
2023-01-11T21:41:23.8168722Z 
2023-01-11T21:41:23.8168729Z 
2023-01-11T21:41:23.8168856Z async_compile.wait(globals())
2023-01-11T21:41:23.8168962Z del async_compile
2023-01-11T21:41:23.8168970Z 
2023-01-11T21:41:23.8169071Z def call(args):
2023-01-11T21:41:23.8169169Z     arg0_1, = args
2023-01-11T21:41:23.8169266Z     args.clear()
2023-01-11T21:41:23.8169567Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8169840Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8170091Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8170193Z     del arg0_1
2023-01-11T21:41:23.8170309Z     return (buf0, buf1, )
2023-01-11T21:41:23.8170317Z 
2023-01-11T21:41:23.8170323Z 
2023-01-11T21:41:23.8170430Z if __name__ == "__main__":
2023-01-11T21:41:23.8170600Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8170775Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8171064Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8171201Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8171209Z 
2023-01-11T21:41:23.8171304Z ok (15.913s)
2023-01-11T21:41:23.8172062Z   test_fill1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8172331Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8172761Z [2023-01-11 21:34:04,772] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 209
2023-01-11T21:41:23.8173205Z [2023-01-11 21:34:06,296] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 209
2023-01-11T21:41:23.8173213Z 
2023-01-11T21:41:23.8173362Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8173469Z import torch
2023-01-11T21:41:23.8173580Z import random
2023-01-11T21:41:23.8173759Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8173965Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8173974Z 
2023-01-11T21:41:23.8174099Z aten = torch.ops.aten
2023-01-11T21:41:23.8174331Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8174478Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8174487Z 
2023-01-11T21:41:23.8174499Z 
2023-01-11T21:41:23.8174797Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8175146Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8175319Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8175445Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8175532Z {
2023-01-11T21:41:23.8175680Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8175767Z     {
2023-01-11T21:41:23.8175878Z         #pragma omp for 
2023-01-11T21:41:23.8175999Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.8176088Z         {
2023-01-11T21:41:23.8176281Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8176414Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8176507Z         }
2023-01-11T21:41:23.8176641Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8176758Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.8176849Z         {
2023-01-11T21:41:23.8176996Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:41:23.8177104Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8177196Z         }
2023-01-11T21:41:23.8177308Z         #pragma omp for 
2023-01-11T21:41:23.8177424Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.8177515Z         {
2023-01-11T21:41:23.8177711Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8177845Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8177921Z         }
2023-01-11T21:41:23.8178061Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8178180Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.8178270Z         {
2023-01-11T21:41:23.8178413Z             auto tmp0 = static_cast<float>(2);
2023-01-11T21:41:23.8178530Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:23.8178600Z         }
2023-01-11T21:41:23.8178689Z     }
2023-01-11T21:41:23.8178767Z }
2023-01-11T21:41:23.8178890Z ''')
2023-01-11T21:41:23.8178905Z 
2023-01-11T21:41:23.8178911Z 
2023-01-11T21:41:23.8179029Z async_compile.wait(globals())
2023-01-11T21:41:23.8179122Z del async_compile
2023-01-11T21:41:23.8179129Z 
2023-01-11T21:41:23.8179220Z def call(args):
2023-01-11T21:41:23.8179308Z     arg0_1, = args
2023-01-11T21:41:23.8179396Z     args.clear()
2023-01-11T21:41:23.8179702Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8180002Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8180191Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8180308Z     return (buf0, buf1, )
2023-01-11T21:41:23.8180317Z 
2023-01-11T21:41:23.8180324Z 
2023-01-11T21:41:23.8180432Z if __name__ == "__main__":
2023-01-11T21:41:23.8191950Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8192247Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8192606Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8192906Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8192915Z 
2023-01-11T21:41:23.8192997Z ok (1.707s)
2023-01-11T21:41:23.8193794Z   test_fill2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8193988Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8194406Z [2023-01-11 21:34:06,342] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 210
2023-01-11T21:41:23.8194793Z [2023-01-11 21:34:07,897] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 210
2023-01-11T21:41:23.8194891Z 
2023-01-11T21:41:23.8195026Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8195130Z import torch
2023-01-11T21:41:23.8195233Z import random
2023-01-11T21:41:23.8195408Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8195577Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8195600Z 
2023-01-11T21:41:23.8195693Z aten = torch.ops.aten
2023-01-11T21:41:23.8195900Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8196033Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8196040Z 
2023-01-11T21:41:23.8196048Z 
2023-01-11T21:41:23.8196251Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8196564Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8196736Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8196900Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8196996Z {
2023-01-11T21:41:23.8197170Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8197260Z     {
2023-01-11T21:41:23.8197364Z         #pragma omp for 
2023-01-11T21:41:23.8197484Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.8197577Z         {
2023-01-11T21:41:23.8197781Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8197904Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8197992Z         }
2023-01-11T21:41:23.8198130Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8198243Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.8198331Z         {
2023-01-11T21:41:23.8198481Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:41:23.8198593Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8198666Z         }
2023-01-11T21:41:23.8198779Z         #pragma omp for 
2023-01-11T21:41:23.8198900Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.8198993Z         {
2023-01-11T21:41:23.8199200Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(3.0));
2023-01-11T21:41:23.8199337Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8199422Z         }
2023-01-11T21:41:23.8199541Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8199659Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.8199754Z         {
2023-01-11T21:41:23.8199904Z             auto tmp0 = static_cast<float>(3.0);
2023-01-11T21:41:23.8200020Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:23.8200110Z         }
2023-01-11T21:41:23.8200197Z     }
2023-01-11T21:41:23.8200265Z }
2023-01-11T21:41:23.8200392Z ''')
2023-01-11T21:41:23.8200400Z 
2023-01-11T21:41:23.8200407Z 
2023-01-11T21:41:23.8200543Z async_compile.wait(globals())
2023-01-11T21:41:23.8200644Z del async_compile
2023-01-11T21:41:23.8200652Z 
2023-01-11T21:41:23.8200754Z def call(args):
2023-01-11T21:41:23.8200856Z     arg0_1, = args
2023-01-11T21:41:23.8200959Z     args.clear()
2023-01-11T21:41:23.8201260Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8201636Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8201830Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8201938Z     return (buf0, buf1, )
2023-01-11T21:41:23.8201946Z 
2023-01-11T21:41:23.8201953Z 
2023-01-11T21:41:23.8202060Z if __name__ == "__main__":
2023-01-11T21:41:23.8202226Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8202409Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8202712Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8202856Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8202865Z 
2023-01-11T21:41:23.8202958Z ok (1.601s)
2023-01-11T21:41:23.8203715Z   test_flip_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8203902Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8204308Z [2023-01-11 21:34:07,939] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 211
2023-01-11T21:41:23.8204716Z [2023-01-11 21:34:09,533] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 211
2023-01-11T21:41:23.8204724Z 
2023-01-11T21:41:23.8204860Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8204957Z import torch
2023-01-11T21:41:23.8205055Z import random
2023-01-11T21:41:23.8205207Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8205378Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8205386Z 
2023-01-11T21:41:23.8205509Z aten = torch.ops.aten
2023-01-11T21:41:23.8205696Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8205826Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8205834Z 
2023-01-11T21:41:23.8205840Z 
2023-01-11T21:41:23.8206043Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8206345Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8206520Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8206651Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8206785Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8206870Z {
2023-01-11T21:41:23.8207015Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8207103Z     {
2023-01-11T21:41:23.8207212Z         #pragma omp for 
2023-01-11T21:41:23.8207332Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:23.8207405Z         {
2023-01-11T21:41:23.8207504Z             #pragma GCC ivdep
2023-01-11T21:41:23.8207624Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:23.8207713Z             {
2023-01-11T21:41:23.8207805Z                 {
2023-01-11T21:41:23.8207902Z                     {
2023-01-11T21:41:23.8208166Z                         auto tmp0 = in_ptr0[5 + ((-1)*i1) + (6*i0)];
2023-01-11T21:41:23.8208291Z                         out_ptr0[i1 + (6*i0)] = tmp0;
2023-01-11T21:41:23.8208381Z                     }
2023-01-11T21:41:23.8208474Z                 }
2023-01-11T21:41:23.8208573Z             }
2023-01-11T21:41:23.8208660Z         }
2023-01-11T21:41:23.8208794Z         #pragma omp for  collapse(2)
2023-01-11T21:41:23.8208910Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8208985Z         {
2023-01-11T21:41:23.8209106Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:23.8209200Z             {
2023-01-11T21:41:23.8209325Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8209451Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:41:23.8209624Z                 {
2023-01-11T21:41:23.8209704Z                     {
2023-01-11T21:41:23.8209804Z                         {
2023-01-11T21:41:23.8210088Z                             auto tmp0 = in_ptr0[30 + i2 + ((-6)*i1) + (36*i0)];
2023-01-11T21:41:23.8210257Z                             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.8210475Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8210623Z                             out_ptr1[i2 + (6*i1) + (36*i0)] = tmp2;
2023-01-11T21:41:23.8210722Z                         }
2023-01-11T21:41:23.8210812Z                     }
2023-01-11T21:41:23.8210888Z                 }
2023-01-11T21:41:23.8210974Z             }
2023-01-11T21:41:23.8211062Z         }
2023-01-11T21:41:23.8211148Z     }
2023-01-11T21:41:23.8211237Z }
2023-01-11T21:41:23.8211346Z ''')
2023-01-11T21:41:23.8211355Z 
2023-01-11T21:41:23.8211361Z 
2023-01-11T21:41:23.8211487Z async_compile.wait(globals())
2023-01-11T21:41:23.8211576Z del async_compile
2023-01-11T21:41:23.8211681Z 
2023-01-11T21:41:23.8211787Z def call(args):
2023-01-11T21:41:23.8211887Z     arg0_1, = args
2023-01-11T21:41:23.8211987Z     args.clear()
2023-01-11T21:41:23.8212315Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8212630Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8212875Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8212956Z     del arg0_1
2023-01-11T21:41:23.8213066Z     return (buf0, buf1, )
2023-01-11T21:41:23.8213072Z 
2023-01-11T21:41:23.8213077Z 
2023-01-11T21:41:23.8213186Z if __name__ == "__main__":
2023-01-11T21:41:23.8213357Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8213542Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8213865Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8214030Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8214039Z 
2023-01-11T21:41:23.8214132Z ok (1.635s)
2023-01-11T21:41:23.8214853Z   test_fmod_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8215032Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8215434Z [2023-01-11 21:34:09,556] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 212
2023-01-11T21:41:23.8215854Z [2023-01-11 21:34:11,136] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 212
2023-01-11T21:41:23.8215863Z 
2023-01-11T21:41:23.8216011Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8216115Z import torch
2023-01-11T21:41:23.8216219Z import random
2023-01-11T21:41:23.8216398Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8216583Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8216592Z 
2023-01-11T21:41:23.8216689Z aten = torch.ops.aten
2023-01-11T21:41:23.8216893Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8217027Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8217036Z 
2023-01-11T21:41:23.8217043Z 
2023-01-11T21:41:23.8217249Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8217557Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8217731Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8217882Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8218026Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8218229Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8218314Z {
2023-01-11T21:41:23.8218455Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8218546Z     {
2023-01-11T21:41:23.8218656Z         #pragma omp for 
2023-01-11T21:41:23.8218773Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:23.8218867Z         {
2023-01-11T21:41:23.8219055Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8219245Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8219376Z             auto tmp2 = tmp0.fmod(tmp1);
2023-01-11T21:41:23.8219584Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(3.0));
2023-01-11T21:41:23.8219702Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8219831Z             auto tmp5 = tmp4.fmod(tmp1);
2023-01-11T21:41:23.8220029Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(2.0));
2023-01-11T21:41:23.8220287Z             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:41:23.8220410Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8220537Z             tmp7.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8220628Z         }
2023-01-11T21:41:23.8220760Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8220875Z         for(long i0=72; i0<72; i0+=1)
2023-01-11T21:41:23.8220964Z         {
2023-01-11T21:41:23.8221080Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8221180Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8221327Z             auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:41:23.8221478Z             auto tmp3 = static_cast<float>(3.0);
2023-01-11T21:41:23.8221595Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8221739Z             auto tmp5 = std::fmod(tmp4, tmp1);
2023-01-11T21:41:23.8221879Z             auto tmp6 = static_cast<float>(2.0);
2023-01-11T21:41:23.8222070Z             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:41:23.8222168Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8222280Z             out_ptr1[i0] = tmp7;
2023-01-11T21:41:23.8222508Z         }
2023-01-11T21:41:23.8222595Z     }
2023-01-11T21:41:23.8222678Z }
2023-01-11T21:41:23.8222790Z ''')
2023-01-11T21:41:23.8222800Z 
2023-01-11T21:41:23.8222805Z 
2023-01-11T21:41:23.8222936Z async_compile.wait(globals())
2023-01-11T21:41:23.8223025Z del async_compile
2023-01-11T21:41:23.8223032Z 
2023-01-11T21:41:23.8223129Z def call(args):
2023-01-11T21:41:23.8223238Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8223339Z     args.clear()
2023-01-11T21:41:23.8223661Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8223974Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8224239Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8224312Z     del arg0_1
2023-01-11T21:41:23.8224407Z     del arg1_1
2023-01-11T21:41:23.8224529Z     return (buf0, buf1, )
2023-01-11T21:41:23.8224538Z 
2023-01-11T21:41:23.8224545Z 
2023-01-11T21:41:23.8224648Z if __name__ == "__main__":
2023-01-11T21:41:23.8224821Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8225001Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8225325Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8225636Z     arg1_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8225792Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8225816Z 
2023-01-11T21:41:23.8225895Z ok (1.603s)
2023-01-11T21:41:23.8226598Z   test_fmod_zero_dim_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8226912Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8227318Z [2023-01-11 21:34:11,151] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 213
2023-01-11T21:41:23.8227735Z [2023-01-11 21:34:12,788] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 213
2023-01-11T21:41:23.8228394Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8228581Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8229056Z [2023-01-11 21:34:12,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 214
2023-01-11T21:41:23.8229474Z [2023-01-11 21:34:14,752] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 214
2023-01-11T21:41:23.8229483Z 
2023-01-11T21:41:23.8229622Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8229709Z import torch
2023-01-11T21:41:23.8229808Z import random
2023-01-11T21:41:23.8229980Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8230159Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8230168Z 
2023-01-11T21:41:23.8230279Z aten = torch.ops.aten
2023-01-11T21:41:23.8230479Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8230611Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8230617Z 
2023-01-11T21:41:23.8230624Z 
2023-01-11T21:41:23.8230829Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8231129Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8231308Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8231469Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8231617Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8231709Z {
2023-01-11T21:41:23.8231860Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8231950Z     {
2023-01-11T21:41:23.8232041Z         #pragma omp for 
2023-01-11T21:41:23.8232158Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.8232248Z         {
2023-01-11T21:41:23.8232454Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8232633Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:23.8232767Z             auto tmp2 = tmp0.fmod(tmp1);
2023-01-11T21:41:23.8232898Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8232990Z         }
2023-01-11T21:41:23.8233125Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8233240Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.8233332Z         {
2023-01-11T21:41:23.8233455Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8233572Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:23.8233718Z             auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:41:23.8233896Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8233983Z         }
2023-01-11T21:41:23.8234072Z     }
2023-01-11T21:41:23.8234157Z }
2023-01-11T21:41:23.8234287Z ''')
2023-01-11T21:41:23.8234295Z 
2023-01-11T21:41:23.8234300Z 
2023-01-11T21:41:23.8234436Z async_compile.wait(globals())
2023-01-11T21:41:23.8234550Z del async_compile
2023-01-11T21:41:23.8234557Z 
2023-01-11T21:41:23.8234662Z def call(args):
2023-01-11T21:41:23.8234752Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8234854Z     args.clear()
2023-01-11T21:41:23.8235156Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8235491Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8235599Z     del arg0_1
2023-01-11T21:41:23.8235698Z     del arg1_1
2023-01-11T21:41:23.8235798Z     return (buf0, )
2023-01-11T21:41:23.8235806Z 
2023-01-11T21:41:23.8235811Z 
2023-01-11T21:41:23.8235905Z if __name__ == "__main__":
2023-01-11T21:41:23.8236078Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8236271Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8236585Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8236870Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8237036Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8237044Z 
2023-01-11T21:41:23.8237050Z 
2023-01-11T21:41:23.8237184Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8237290Z import torch
2023-01-11T21:41:23.8237377Z import random
2023-01-11T21:41:23.8237612Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8237790Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8237798Z 
2023-01-11T21:41:23.8237913Z aten = torch.ops.aten
2023-01-11T21:41:23.8238116Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8238249Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8238256Z 
2023-01-11T21:41:23.8238262Z 
2023-01-11T21:41:23.8238469Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8238773Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8238934Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8239089Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8239233Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8239326Z {
2023-01-11T21:41:23.8239467Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8239558Z     {
2023-01-11T21:41:23.8239672Z         #pragma omp for 
2023-01-11T21:41:23.8239776Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.8239866Z         {
2023-01-11T21:41:23.8240052Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:23.8240248Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8240384Z             auto tmp2 = tmp0.fmod(tmp1);
2023-01-11T21:41:23.8240528Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8240619Z         }
2023-01-11T21:41:23.8240745Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8240867Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.8240957Z         {
2023-01-11T21:41:23.8241082Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:23.8241205Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8241361Z             auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:41:23.8241476Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8241563Z         }
2023-01-11T21:41:23.8241658Z     }
2023-01-11T21:41:23.8241749Z }
2023-01-11T21:41:23.8241870Z ''')
2023-01-11T21:41:23.8241878Z 
2023-01-11T21:41:23.8241885Z 
2023-01-11T21:41:23.8242020Z async_compile.wait(globals())
2023-01-11T21:41:23.8242130Z del async_compile
2023-01-11T21:41:23.8242137Z 
2023-01-11T21:41:23.8242241Z def call(args):
2023-01-11T21:41:23.8242331Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8242434Z     args.clear()
2023-01-11T21:41:23.8242747Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8243007Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8243110Z     del arg0_1
2023-01-11T21:41:23.8243208Z     del arg1_1
2023-01-11T21:41:23.8243313Z     return (buf0, )
2023-01-11T21:41:23.8243321Z 
2023-01-11T21:41:23.8243327Z 
2023-01-11T21:41:23.8243439Z if __name__ == "__main__":
2023-01-11T21:41:23.8243599Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8243869Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8244171Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8244463Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8244628Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8244636Z 
2023-01-11T21:41:23.8244729Z ok (3.615s)
2023-01-11T21:41:23.8245456Z   test_forced_buffer_realize_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8245646Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8246122Z [2023-01-11 21:34:14,778] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 215
2023-01-11T21:41:23.8246523Z [2023-01-11 21:34:16,352] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 215
2023-01-11T21:41:23.8246548Z 
2023-01-11T21:41:23.8246665Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8246762Z import torch
2023-01-11T21:41:23.8246867Z import random
2023-01-11T21:41:23.8247037Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8247216Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8247223Z 
2023-01-11T21:41:23.8247337Z aten = torch.ops.aten
2023-01-11T21:41:23.8247538Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8247654Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8247662Z 
2023-01-11T21:41:23.8247670Z 
2023-01-11T21:41:23.8247874Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8248190Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8248362Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.8248521Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:23.8248613Z {
2023-01-11T21:41:23.8248748Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8248837Z     {
2023-01-11T21:41:23.8248931Z         #pragma omp for 
2023-01-11T21:41:23.8249052Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.8249143Z         {
2023-01-11T21:41:23.8249338Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8249537Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8249662Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8249782Z             auto tmp3 = tmp2 * tmp1;
2023-01-11T21:41:23.8249907Z             tmp3.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8249999Z         }
2023-01-11T21:41:23.8250134Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8250258Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:23.8250351Z         {
2023-01-11T21:41:23.8250474Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8250615Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.8250725Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8250844Z             auto tmp3 = tmp2 * tmp1;
2023-01-11T21:41:23.8250966Z             in_out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.8251056Z         }
2023-01-11T21:41:23.8251142Z     }
2023-01-11T21:41:23.8251226Z }
2023-01-11T21:41:23.8251331Z ''')
2023-01-11T21:41:23.8251339Z 
2023-01-11T21:41:23.8251360Z 
2023-01-11T21:41:23.8251476Z async_compile.wait(globals())
2023-01-11T21:41:23.8251584Z del async_compile
2023-01-11T21:41:23.8251591Z 
2023-01-11T21:41:23.8251695Z def call(args):
2023-01-11T21:41:23.8251799Z     arg0_1, = args
2023-01-11T21:41:23.8251900Z     args.clear()
2023-01-11T21:41:23.8252197Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8252397Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:23.8252582Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:23.8252681Z     del arg0_1
2023-01-11T21:41:23.8252790Z     return (buf1, )
2023-01-11T21:41:23.8252797Z 
2023-01-11T21:41:23.8252804Z 
2023-01-11T21:41:23.8252915Z if __name__ == "__main__":
2023-01-11T21:41:23.8253078Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8253262Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8253559Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8253723Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8253730Z 
2023-01-11T21:41:23.8253806Z ok (1.600s)
2023-01-11T21:41:23.8254578Z   test_full_like_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8254774Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8255186Z [2023-01-11 21:34:16,386] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 216
2023-01-11T21:41:23.8255585Z [2023-01-11 21:34:17,926] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 216
2023-01-11T21:41:23.8255593Z 
2023-01-11T21:41:23.8255730Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8255832Z import torch
2023-01-11T21:41:23.8255933Z import random
2023-01-11T21:41:23.8256103Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8256265Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8256272Z 
2023-01-11T21:41:23.8256390Z aten = torch.ops.aten
2023-01-11T21:41:23.8256599Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8256735Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8256742Z 
2023-01-11T21:41:23.8256749Z 
2023-01-11T21:41:23.8256955Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8257260Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8257420Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8257506Z {
2023-01-11T21:41:23.8257634Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8257723Z     {
2023-01-11T21:41:23.8257836Z         #pragma omp for 
2023-01-11T21:41:23.8257951Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.8258038Z         {
2023-01-11T21:41:23.8258251Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(7.777));
2023-01-11T21:41:23.8258455Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8258630Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8258764Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8258854Z         }
2023-01-11T21:41:23.8258988Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8259106Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:23.8259194Z         {
2023-01-11T21:41:23.8259348Z             auto tmp0 = static_cast<float>(7.777);
2023-01-11T21:41:23.8259475Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8259662Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8259778Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8259873Z         }
2023-01-11T21:41:23.8259961Z     }
2023-01-11T21:41:23.8260049Z }
2023-01-11T21:41:23.8260148Z ''')
2023-01-11T21:41:23.8260172Z 
2023-01-11T21:41:23.8260180Z 
2023-01-11T21:41:23.8260302Z async_compile.wait(globals())
2023-01-11T21:41:23.8260408Z del async_compile
2023-01-11T21:41:23.8260415Z 
2023-01-11T21:41:23.8260514Z def call(args):
2023-01-11T21:41:23.8260616Z     arg0_1, = args
2023-01-11T21:41:23.8260792Z     args.clear()
2023-01-11T21:41:23.8261089Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8261242Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8261333Z     return (buf0, )
2023-01-11T21:41:23.8261340Z 
2023-01-11T21:41:23.8261360Z 
2023-01-11T21:41:23.8261456Z if __name__ == "__main__":
2023-01-11T21:41:23.8261624Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8261812Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8262114Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8262269Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8262276Z 
2023-01-11T21:41:23.8262507Z ok (1.574s)
2023-01-11T21:41:23.8263331Z   test_fuse_tiled_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8263526Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8263936Z [2023-01-11 21:34:17,944] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 217
2023-01-11T21:41:23.8264337Z [2023-01-11 21:34:19,558] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 217
2023-01-11T21:41:23.8264347Z 
2023-01-11T21:41:23.8264482Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8264585Z import torch
2023-01-11T21:41:23.8264687Z import random
2023-01-11T21:41:23.8264853Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8265042Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8265050Z 
2023-01-11T21:41:23.8265162Z aten = torch.ops.aten
2023-01-11T21:41:23.8265358Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8265495Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8265502Z 
2023-01-11T21:41:23.8265508Z 
2023-01-11T21:41:23.8265717Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8266023Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8266199Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8266356Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8266496Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8266624Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8266745Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8266835Z {
2023-01-11T21:41:23.8266985Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8267073Z     {
2023-01-11T21:41:23.8267182Z         #pragma omp for 
2023-01-11T21:41:23.8267300Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.8267387Z         {
2023-01-11T21:41:23.8267493Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:23.8267585Z             {
2023-01-11T21:41:23.8267776Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:23.8267976Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:23.8268105Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8268253Z                 tmp2.store(out_ptr0 + (8*i1) + (128*i0));
2023-01-11T21:41:23.8268342Z             }
2023-01-11T21:41:23.8268455Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.8268581Z             for(long i1=128; i1<128; i1+=1)
2023-01-11T21:41:23.8268670Z             {
2023-01-11T21:41:23.8268788Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8268908Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:23.8269029Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8269265Z                 out_ptr0[i1 + (128*i0)] = tmp2;
2023-01-11T21:41:23.8269340Z             }
2023-01-11T21:41:23.8269428Z         }
2023-01-11T21:41:23.8269539Z         #pragma omp for 
2023-01-11T21:41:23.8269658Z         for(long i0=0; i0<2048; i0+=1)
2023-01-11T21:41:23.8269751Z         {
2023-01-11T21:41:23.8269950Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:23.8270152Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8270261Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8270394Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8270487Z         }
2023-01-11T21:41:23.8270627Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8270753Z         for(long i0=16384; i0<16384; i0+=1)
2023-01-11T21:41:23.8270845Z         {
2023-01-11T21:41:23.8270973Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:41:23.8271103Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8271322Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8271443Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8271535Z         }
2023-01-11T21:41:23.8271625Z     }
2023-01-11T21:41:23.8271713Z }
2023-01-11T21:41:23.8271856Z ''')
2023-01-11T21:41:23.8271865Z 
2023-01-11T21:41:23.8271872Z 
2023-01-11T21:41:23.8271992Z async_compile.wait(globals())
2023-01-11T21:41:23.8272094Z del async_compile
2023-01-11T21:41:23.8272102Z 
2023-01-11T21:41:23.8272203Z def call(args):
2023-01-11T21:41:23.8272328Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8272436Z     args.clear()
2023-01-11T21:41:23.8272758Z     buf0 = empty_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8273069Z     buf1 = empty_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8273405Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8273495Z     del arg0_1
2023-01-11T21:41:23.8273595Z     del arg1_1
2023-01-11T21:41:23.8273690Z     del arg2_1
2023-01-11T21:41:23.8273879Z     return (buf0, buf1, )
2023-01-11T21:41:23.8273888Z 
2023-01-11T21:41:23.8273895Z 
2023-01-11T21:41:23.8274014Z if __name__ == "__main__":
2023-01-11T21:41:23.8274190Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8274372Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8274673Z     arg0_1 = rand_strided((128, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8274978Z     arg1_1 = rand_strided((1, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8275282Z     arg2_1 = rand_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8275461Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8275469Z 
2023-01-11T21:41:23.8275561Z ok (1.634s)
2023-01-11T21:41:23.8276282Z   test_gather1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8276459Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8276838Z [2023-01-11 21:34:19,587] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 218
2023-01-11T21:41:23.8277223Z [2023-01-11 21:34:21,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 218
2023-01-11T21:41:23.8277231Z 
2023-01-11T21:41:23.8277360Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8277443Z import torch
2023-01-11T21:41:23.8277540Z import random
2023-01-11T21:41:23.8277710Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8277890Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8277973Z 
2023-01-11T21:41:23.8278090Z aten = torch.ops.aten
2023-01-11T21:41:23.8278283Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8278418Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8278426Z 
2023-01-11T21:41:23.8278432Z 
2023-01-11T21:41:23.8278643Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8278934Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8279106Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8279254Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8279396Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8279535Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8279621Z {
2023-01-11T21:41:23.8279772Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8279844Z     {
2023-01-11T21:41:23.8280015Z         #pragma omp for 
2023-01-11T21:41:23.8280136Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:23.8280228Z         {
2023-01-11T21:41:23.8280338Z             #pragma GCC ivdep
2023-01-11T21:41:23.8280459Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:23.8280549Z             {
2023-01-11T21:41:23.8280624Z                 {
2023-01-11T21:41:23.8280720Z                     {
2023-01-11T21:41:23.8280870Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:23.8281023Z                         auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:23.8281157Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8281306Z                         auto tmp3 = in_ptr1[tmp2 + (6*i1)];
2023-01-11T21:41:23.8281440Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:23.8281558Z                         out_ptr1[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:23.8281654Z                     }
2023-01-11T21:41:23.8281746Z                 }
2023-01-11T21:41:23.8281844Z             }
2023-01-11T21:41:23.8281934Z         }
2023-01-11T21:41:23.8282021Z     }
2023-01-11T21:41:23.8282107Z }
2023-01-11T21:41:23.8282214Z ''')
2023-01-11T21:41:23.8282224Z 
2023-01-11T21:41:23.8282230Z 
2023-01-11T21:41:23.8282367Z async_compile.wait(globals())
2023-01-11T21:41:23.8282471Z del async_compile
2023-01-11T21:41:23.8282479Z 
2023-01-11T21:41:23.8282586Z def call(args):
2023-01-11T21:41:23.8282699Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8282804Z     args.clear()
2023-01-11T21:41:23.8283134Z     buf0 = empty_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8283441Z     buf1 = empty_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8283726Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8283826Z     del arg0_1
2023-01-11T21:41:23.8283924Z     del arg1_1
2023-01-11T21:41:23.8284046Z     return (buf0, buf1, )
2023-01-11T21:41:23.8284055Z 
2023-01-11T21:41:23.8284061Z 
2023-01-11T21:41:23.8284170Z if __name__ == "__main__":
2023-01-11T21:41:23.8284335Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8284516Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8284827Z     arg0_1 = rand_strided((1, 1, 10, 6), (60, 60, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8285149Z     arg1_1 = rand_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8285315Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8285324Z 
2023-01-11T21:41:23.8285424Z ok (1.615s)
2023-01-11T21:41:23.8285579Z   test_gather2_cpu (__main__.CpuTests) ... ok (0.001s)
2023-01-11T21:41:23.8286321Z   test_gather_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8286604Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8287019Z [2023-01-11 21:34:21,275] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 219
2023-01-11T21:41:23.8287431Z [2023-01-11 21:34:22,850] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 219
2023-01-11T21:41:23.8287441Z 
2023-01-11T21:41:23.8287579Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8287666Z import torch
2023-01-11T21:41:23.8287770Z import random
2023-01-11T21:41:23.8287939Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8288105Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8288113Z 
2023-01-11T21:41:23.8288218Z aten = torch.ops.aten
2023-01-11T21:41:23.8288475Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8288615Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8288623Z 
2023-01-11T21:41:23.8288630Z 
2023-01-11T21:41:23.8288841Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8289134Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8289306Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8289458Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8289602Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8289689Z {
2023-01-11T21:41:23.8289834Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8289925Z     {
2023-01-11T21:41:23.8290021Z         #pragma omp for 
2023-01-11T21:41:23.8290138Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.8290228Z         {
2023-01-11T21:41:23.8290437Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:23.8290580Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8290669Z         }
2023-01-11T21:41:23.8290811Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8290917Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:41:23.8291009Z         {
2023-01-11T21:41:23.8291153Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:23.8291263Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8291360Z         }
2023-01-11T21:41:23.8291471Z         #pragma omp for 
2023-01-11T21:41:23.8291573Z         for(long i0=0; i0<80; i0+=1)
2023-01-11T21:41:23.8291658Z         {
2023-01-11T21:41:23.8291776Z             #pragma GCC ivdep
2023-01-11T21:41:23.8291898Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:23.8291989Z             {
2023-01-11T21:41:23.8292084Z                 {
2023-01-11T21:41:23.8292179Z                     {
2023-01-11T21:41:23.8292301Z                         auto tmp0 = in_ptr0[80 + i0];
2023-01-11T21:41:23.8292444Z                         auto tmp1 = in_ptr0[i0];
2023-01-11T21:41:23.8292592Z                         auto tmp2 = in_ptr1[i1 + (32*tmp1)];
2023-01-11T21:41:23.8292741Z                         auto tmp3 = in_ptr1[i1 + (32*tmp0)];
2023-01-11T21:41:23.8292969Z                         auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:23.8293121Z                         auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:23.8293258Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:23.8293413Z                         atomic_add(&out_ptr0[i1 + (32*tmp0)], tmp6);
2023-01-11T21:41:23.8293514Z                     }
2023-01-11T21:41:23.8293609Z                 }
2023-01-11T21:41:23.8293702Z             }
2023-01-11T21:41:23.8293793Z         }
2023-01-11T21:41:23.8293881Z     }
2023-01-11T21:41:23.8293964Z }
2023-01-11T21:41:23.8294072Z ''')
2023-01-11T21:41:23.8294081Z 
2023-01-11T21:41:23.8294103Z 
2023-01-11T21:41:23.8294234Z async_compile.wait(globals())
2023-01-11T21:41:23.8294345Z del async_compile
2023-01-11T21:41:23.8294431Z 
2023-01-11T21:41:23.8294542Z def call(args):
2023-01-11T21:41:23.8294653Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8294759Z     args.clear()
2023-01-11T21:41:23.8295070Z     buf0 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8295320Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8295407Z     del arg0_1
2023-01-11T21:41:23.8295503Z     del arg1_1
2023-01-11T21:41:23.8295605Z     return (buf0, )
2023-01-11T21:41:23.8295612Z 
2023-01-11T21:41:23.8295619Z 
2023-01-11T21:41:23.8295729Z if __name__ == "__main__":
2023-01-11T21:41:23.8295896Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8296081Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8296393Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8296678Z     arg1_1 = rand_strided((2, 80), (80, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8296910Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8296919Z 
2023-01-11T21:41:23.8297019Z ok (1.675s)
2023-01-11T21:41:23.8297748Z   test_gelu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8297934Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8298345Z [2023-01-11 21:34:22,893] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 220
2023-01-11T21:41:23.8298752Z [2023-01-11 21:34:24,502] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 220
2023-01-11T21:41:23.8298759Z 
2023-01-11T21:41:23.8298910Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8299015Z import torch
2023-01-11T21:41:23.8299118Z import random
2023-01-11T21:41:23.8299286Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8299473Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8299481Z 
2023-01-11T21:41:23.8299601Z aten = torch.ops.aten
2023-01-11T21:41:23.8299804Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8299942Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8299950Z 
2023-01-11T21:41:23.8299957Z 
2023-01-11T21:41:23.8300163Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8300469Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8300650Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8300776Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8300921Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8301021Z {
2023-01-11T21:41:23.8301165Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8301257Z     {
2023-01-11T21:41:23.8301370Z         #pragma omp for 
2023-01-11T21:41:23.8301489Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.8301571Z         {
2023-01-11T21:41:23.8301784Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8301990Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(0.5));
2023-01-11T21:41:23.8302114Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8302484Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.7071067811865476));
2023-01-11T21:41:23.8302612Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8302736Z             auto tmp5 = tmp4.erf();
2023-01-11T21:41:23.8302935Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8303040Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:23.8303269Z             auto tmp8 = tmp2 * tmp7;
2023-01-11T21:41:23.8303469Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8303596Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:23.8303716Z             auto tmp11 = tmp0 + tmp6;
2023-01-11T21:41:23.8303834Z             auto tmp12 = tmp11 * tmp1;
2023-01-11T21:41:23.8303953Z             auto tmp13 = tmp11 * tmp3;
2023-01-11T21:41:23.8304057Z             auto tmp14 = tmp13.erf();
2023-01-11T21:41:23.8304179Z             auto tmp15 = tmp14 + tmp6;
2023-01-11T21:41:23.8304302Z             auto tmp16 = tmp12 * tmp15;
2023-01-11T21:41:23.8304434Z             tmp10.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8304566Z             tmp16.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8304653Z         }
2023-01-11T21:41:23.8304794Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8304901Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.8304990Z         {
2023-01-11T21:41:23.8305115Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8305372Z             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:23.8305501Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8305665Z             auto tmp3 = static_cast<float>(0.7071067811865476);
2023-01-11T21:41:23.8305791Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8305911Z             auto tmp5 = std::erf(tmp4);
2023-01-11T21:41:23.8306052Z             auto tmp6 = static_cast<float>(1);
2023-01-11T21:41:23.8306176Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:23.8306295Z             auto tmp8 = tmp2 * tmp7;
2023-01-11T21:41:23.8306437Z             auto tmp9 = static_cast<float>(2);
2023-01-11T21:41:23.8306559Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:23.8306676Z             auto tmp11 = tmp0 + tmp6;
2023-01-11T21:41:23.8306782Z             auto tmp12 = tmp11 * tmp1;
2023-01-11T21:41:23.8306901Z             auto tmp13 = tmp11 * tmp3;
2023-01-11T21:41:23.8307040Z             auto tmp14 = std::erf(tmp13);
2023-01-11T21:41:23.8307172Z             auto tmp15 = tmp14 + tmp6;
2023-01-11T21:41:23.8307297Z             auto tmp16 = tmp12 * tmp15;
2023-01-11T21:41:23.8307411Z             out_ptr0[i0] = tmp10;
2023-01-11T21:41:23.8307523Z             out_ptr1[i0] = tmp16;
2023-01-11T21:41:23.8307599Z         }
2023-01-11T21:41:23.8307688Z     }
2023-01-11T21:41:23.8307779Z }
2023-01-11T21:41:23.8307921Z ''')
2023-01-11T21:41:23.8307929Z 
2023-01-11T21:41:23.8307935Z 
2023-01-11T21:41:23.8308065Z async_compile.wait(globals())
2023-01-11T21:41:23.8308170Z del async_compile
2023-01-11T21:41:23.8308177Z 
2023-01-11T21:41:23.8308278Z def call(args):
2023-01-11T21:41:23.8308362Z     arg0_1, = args
2023-01-11T21:41:23.8308462Z     args.clear()
2023-01-11T21:41:23.8308775Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8309077Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8309322Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8309428Z     del arg0_1
2023-01-11T21:41:23.8309541Z     return (buf0, buf1, )
2023-01-11T21:41:23.8309548Z 
2023-01-11T21:41:23.8309555Z 
2023-01-11T21:41:23.8309667Z if __name__ == "__main__":
2023-01-11T21:41:23.8309830Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8310013Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8310324Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8310486Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8310494Z 
2023-01-11T21:41:23.8310591Z ok (1.652s)
2023-01-11T21:41:23.8311332Z   test_glu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8311601Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8312028Z [2023-01-11 21:34:24,544] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 221
2023-01-11T21:41:23.8312451Z [2023-01-11 21:34:26,226] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 221
2023-01-11T21:41:23.8312459Z 
2023-01-11T21:41:23.8312579Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8312677Z import torch
2023-01-11T21:41:23.8312777Z import random
2023-01-11T21:41:23.8312951Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8313135Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8313143Z 
2023-01-11T21:41:23.8313256Z aten = torch.ops.aten
2023-01-11T21:41:23.8313455Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8313591Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8313662Z 
2023-01-11T21:41:23.8313669Z 
2023-01-11T21:41:23.8313943Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8314253Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8314435Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8314581Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8314723Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8314860Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8314945Z {
2023-01-11T21:41:23.8315079Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8315171Z     {
2023-01-11T21:41:23.8315282Z         #pragma omp for 
2023-01-11T21:41:23.8315408Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8315503Z         {
2023-01-11T21:41:23.8315625Z             #pragma GCC ivdep
2023-01-11T21:41:23.8315751Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8315845Z             {
2023-01-11T21:41:23.8315940Z                 {
2023-01-11T21:41:23.8316042Z                     {
2023-01-11T21:41:23.8316194Z                         auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.8316348Z                         auto tmp1 = in_ptr0[4 + i1 + (8*i0)];
2023-01-11T21:41:23.8316602Z                         auto tmp2 = std::exp(-tmp1);
2023-01-11T21:41:23.8316735Z                         auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:41:23.8316858Z                         auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8316994Z                         out_ptr0[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:23.8317091Z                     }
2023-01-11T21:41:23.8317187Z                 }
2023-01-11T21:41:23.8317280Z             }
2023-01-11T21:41:23.8317368Z         }
2023-01-11T21:41:23.8317482Z         #pragma omp for 
2023-01-11T21:41:23.8317589Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8317681Z         {
2023-01-11T21:41:23.8317804Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:23.8317900Z             {
2023-01-11T21:41:23.8318117Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (1024*i0));
2023-01-11T21:41:23.8318334Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 512 + (8*i1) + (1024*i0));
2023-01-11T21:41:23.8318535Z                 auto tmp2 = decltype(tmp1)(1)/(decltype(tmp1)(1) + tmp1.neg().exp());
2023-01-11T21:41:23.8318664Z                 auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:23.8318801Z                 tmp3.store(out_ptr1 + (8*i1) + (512*i0));
2023-01-11T21:41:23.8318887Z             }
2023-01-11T21:41:23.8319021Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.8319150Z             for(long i1=512; i1<512; i1+=1)
2023-01-11T21:41:23.8319242Z             {
2023-01-11T21:41:23.8319384Z                 auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:41:23.8319534Z                 auto tmp1 = in_ptr0[512 + i1 + (1024*i0)];
2023-01-11T21:41:23.8319837Z                 auto tmp2 = std::exp(-tmp1);
2023-01-11T21:41:23.8319963Z                 auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:41:23.8320086Z                 auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8320223Z                 out_ptr1[i1 + (512*i0)] = tmp4;
2023-01-11T21:41:23.8320315Z             }
2023-01-11T21:41:23.8320402Z         }
2023-01-11T21:41:23.8320500Z         #pragma omp for 
2023-01-11T21:41:23.8320622Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.8320714Z         {
2023-01-11T21:41:23.8320837Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8320925Z             {
2023-01-11T21:41:23.8321136Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (64*i0));
2023-01-11T21:41:23.8321346Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 32 + (8*i1) + (64*i0));
2023-01-11T21:41:23.8321543Z                 auto tmp2 = decltype(tmp1)(1)/(decltype(tmp1)(1) + tmp1.neg().exp());
2023-01-11T21:41:23.8321655Z                 auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:23.8321858Z                 tmp3.store(out_ptr2 + (8*i1) + (32*i0));
2023-01-11T21:41:23.8321950Z             }
2023-01-11T21:41:23.8322084Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.8322210Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:23.8322298Z             {
2023-01-11T21:41:23.8322444Z                 auto tmp0 = in_ptr0[i1 + (64*i0)];
2023-01-11T21:41:23.8322575Z                 auto tmp1 = in_ptr0[32 + i1 + (64*i0)];
2023-01-11T21:41:23.8322799Z                 auto tmp2 = std::exp(-tmp1);
2023-01-11T21:41:23.8322930Z                 auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:41:23.8323056Z                 auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8323181Z                 out_ptr2[i1 + (32*i0)] = tmp4;
2023-01-11T21:41:23.8323274Z             }
2023-01-11T21:41:23.8323364Z         }
2023-01-11T21:41:23.8323441Z     }
2023-01-11T21:41:23.8323525Z }
2023-01-11T21:41:23.8323640Z ''')
2023-01-11T21:41:23.8323649Z 
2023-01-11T21:41:23.8323661Z 
2023-01-11T21:41:23.8323795Z async_compile.wait(globals())
2023-01-11T21:41:23.8323899Z del async_compile
2023-01-11T21:41:23.8323907Z 
2023-01-11T21:41:23.8324013Z def call(args):
2023-01-11T21:41:23.8324110Z     arg0_1, = args
2023-01-11T21:41:23.8324201Z     args.clear()
2023-01-11T21:41:23.8324532Z     buf0 = empty_strided((8, 16, 8, 4), (512, 32, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8324855Z     buf1 = empty_strided((8, 8, 8, 8), (512, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8325176Z     buf2 = empty_strided((8, 16, 4, 8), (512, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8325459Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8325562Z     del arg0_1
2023-01-11T21:41:23.8325685Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8325693Z 
2023-01-11T21:41:23.8325699Z 
2023-01-11T21:41:23.8325808Z if __name__ == "__main__":
2023-01-11T21:41:23.8325978Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8326140Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8326472Z     arg0_1 = rand_strided((8, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8326628Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8326635Z 
2023-01-11T21:41:23.8326729Z ok (1.724s)
2023-01-11T21:41:23.8327461Z   test_grid_sampler_2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8327648Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8328062Z [2023-01-11 21:34:27,679] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 222
2023-01-11T21:41:23.8328458Z [2023-01-11 21:34:28,312] torch._inductor.scheduler: [DEBUG] remove_buffer('buf5')
2023-01-11T21:41:23.8328762Z [2023-01-11 21:34:28,312] torch._inductor.scheduler: [DEBUG] remove_buffer('buf3')
2023-01-11T21:41:23.8329051Z [2023-01-11 21:34:28,312] torch._inductor.scheduler: [DEBUG] remove_buffer('buf7')
2023-01-11T21:41:23.8329059Z 
2023-01-11T21:41:23.8329202Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8329308Z import torch
2023-01-11T21:41:23.8329413Z import random
2023-01-11T21:41:23.8329585Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8329766Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8329774Z 
2023-01-11T21:41:23.8329895Z aten = torch.ops.aten
2023-01-11T21:41:23.8330094Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8330216Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8330228Z 
2023-01-11T21:41:23.8330315Z 
2023-01-11T21:41:23.8330539Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8330860Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8331043Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.8331198Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.8331353Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8331512Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8331664Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8331801Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8331950Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.8332099Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.8332249Z                        long* __restrict__ out_ptr4,
2023-01-11T21:41:23.8332395Z                        long* __restrict__ out_ptr5,
2023-01-11T21:41:23.8332541Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:23.8332680Z                        long* __restrict__ out_ptr7,
2023-01-11T21:41:23.8332800Z                        long* __restrict__ out_ptr8,
2023-01-11T21:41:23.8332939Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:23.8333076Z                        long* __restrict__ out_ptr10,
2023-01-11T21:41:23.8333215Z                        long* __restrict__ out_ptr11,
2023-01-11T21:41:23.8333363Z                        float* __restrict__ out_ptr12,
2023-01-11T21:41:23.8333505Z                        long* __restrict__ out_ptr13,
2023-01-11T21:41:23.8333644Z                        long* __restrict__ out_ptr14,
2023-01-11T21:41:23.8333772Z                        float* __restrict__ out_ptr15)
2023-01-11T21:41:23.8333865Z {
2023-01-11T21:41:23.8334016Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8334104Z     {
2023-01-11T21:41:23.8334215Z         #pragma omp for 
2023-01-11T21:41:23.8334352Z         for(long i0=0; i0<495616; i0+=1)
2023-01-11T21:41:23.8334443Z         {
2023-01-11T21:41:23.8334521Z             {
2023-01-11T21:41:23.8334613Z                 {
2023-01-11T21:41:23.8334753Z                     auto tmp0 = in_ptr0[2*i0];
2023-01-11T21:41:23.8334894Z                     auto tmp9 = in_ptr0[1 + (2*i0)];
2023-01-11T21:41:23.8335056Z                     auto tmp1 = static_cast<float>(175.5);
2023-01-11T21:41:23.8335187Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8335321Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:23.8335459Z                     auto tmp4 = std::floor(tmp3);
2023-01-11T21:41:23.8335606Z                     auto tmp5 = static_cast<float>(0);
2023-01-11T21:41:23.8335739Z                     auto tmp6 = tmp4 >= tmp5;
2023-01-11T21:41:23.8335895Z                     auto tmp7 = static_cast<float>(352);
2023-01-11T21:41:23.8336032Z                     auto tmp8 = tmp4 < tmp7;
2023-01-11T21:41:23.8336250Z                     auto tmp10 = tmp9 * tmp1;
2023-01-11T21:41:23.8336381Z                     auto tmp11 = tmp10 + tmp1;
2023-01-11T21:41:23.8336513Z                     auto tmp12 = std::floor(tmp11);
2023-01-11T21:41:23.8336647Z                     auto tmp13 = tmp12 >= tmp5;
2023-01-11T21:41:23.8336782Z                     auto tmp14 = tmp12 < tmp7;
2023-01-11T21:41:23.8336915Z                     auto tmp15 = tmp13 && tmp14;
2023-01-11T21:41:23.8337046Z                     auto tmp16 = tmp8 && tmp15;
2023-01-11T21:41:23.8337182Z                     auto tmp17 = tmp6 && tmp16;
2023-01-11T21:41:23.8337335Z                     auto tmp18 = static_cast<float>(1);
2023-01-11T21:41:23.8337452Z                     auto tmp19 = tmp4 + tmp18;
2023-01-11T21:41:23.8337689Z                     auto tmp20 = tmp19 - tmp3;
2023-01-11T21:41:23.8337823Z                     auto tmp21 = tmp12 + tmp18;
2023-01-11T21:41:23.8338036Z                     auto tmp22 = tmp21 - tmp11;
2023-01-11T21:41:23.8338228Z                     auto tmp23 = tmp20 * tmp22;
2023-01-11T21:41:23.8338377Z                     auto tmp24 = tmp17 ? tmp23 : tmp5;
2023-01-11T21:41:23.8338510Z                     auto tmp25 = tmp19 >= tmp5;
2023-01-11T21:41:23.8338645Z                     auto tmp26 = tmp19 < tmp7;
2023-01-11T21:41:23.8338766Z                     auto tmp27 = tmp26 && tmp15;
2023-01-11T21:41:23.8338906Z                     auto tmp28 = tmp25 && tmp27;
2023-01-11T21:41:23.8339115Z                     auto tmp29 = tmp3 - tmp4;
2023-01-11T21:41:23.8339246Z                     auto tmp30 = tmp29 * tmp22;
2023-01-11T21:41:23.8339391Z                     auto tmp31 = tmp28 ? tmp30 : tmp5;
2023-01-11T21:41:23.8339523Z                     auto tmp32 = tmp21 >= tmp5;
2023-01-11T21:41:23.8339656Z                     auto tmp33 = tmp21 < tmp7;
2023-01-11T21:41:23.8339777Z                     auto tmp34 = tmp32 && tmp33;
2023-01-11T21:41:23.8339913Z                     auto tmp35 = tmp8 && tmp34;
2023-01-11T21:41:23.8340052Z                     auto tmp36 = tmp6 && tmp35;
2023-01-11T21:41:23.8340272Z                     auto tmp37 = tmp11 - tmp12;
2023-01-11T21:41:23.8340401Z                     auto tmp38 = tmp20 * tmp37;
2023-01-11T21:41:23.8340546Z                     auto tmp39 = tmp36 ? tmp38 : tmp5;
2023-01-11T21:41:23.8340679Z                     auto tmp40 = tmp26 && tmp34;
2023-01-11T21:41:23.8340798Z                     auto tmp41 = tmp25 && tmp40;
2023-01-11T21:41:23.8340937Z                     auto tmp42 = tmp29 * tmp37;
2023-01-11T21:41:23.8341081Z                     auto tmp43 = tmp41 ? tmp42 : tmp5;
2023-01-11T21:41:23.8341244Z                     auto tmp44 = static_cast<float>(176.0);
2023-01-11T21:41:23.8341376Z                     auto tmp45 = tmp0 * tmp44;
2023-01-11T21:41:23.8341518Z                     auto tmp46 = tmp45 + tmp1;
2023-01-11T21:41:23.8341677Z                     auto tmp47 = static_cast<float>(0.0);
2023-01-11T21:41:23.8341882Z                     auto tmp48 = (tmp47 != tmp47) ? tmp47 : std::max(tmp46, tmp47);
2023-01-11T21:41:23.8342044Z                     auto tmp49 = static_cast<float>(351.0);
2023-01-11T21:41:23.8342248Z                     auto tmp50 = (tmp49 != tmp49) ? tmp49 : std::min(tmp48, tmp49);
2023-01-11T21:41:23.8342573Z                     auto tmp51 = std::floor(tmp50);
2023-01-11T21:41:23.8342718Z                     auto tmp52 = tmp51 >= tmp5;
2023-01-11T21:41:23.8342856Z                     auto tmp53 = tmp51 < tmp7;
2023-01-11T21:41:23.8342994Z                     auto tmp54 = tmp9 * tmp44;
2023-01-11T21:41:23.8343130Z                     auto tmp55 = tmp54 + tmp1;
2023-01-11T21:41:23.8343303Z                     auto tmp56 = (tmp47 != tmp47) ? tmp47 : std::max(tmp55, tmp47);
2023-01-11T21:41:23.8343482Z                     auto tmp57 = (tmp49 != tmp49) ? tmp49 : std::min(tmp56, tmp49);
2023-01-11T21:41:23.8343628Z                     auto tmp58 = std::floor(tmp57);
2023-01-11T21:41:23.8343765Z                     auto tmp59 = tmp58 >= tmp5;
2023-01-11T21:41:23.8343902Z                     auto tmp60 = tmp58 < tmp7;
2023-01-11T21:41:23.8344143Z                     auto tmp61 = tmp59 && tmp60;
2023-01-11T21:41:23.8344277Z                     auto tmp62 = tmp53 && tmp61;
2023-01-11T21:41:23.8344405Z                     auto tmp63 = tmp52 && tmp62;
2023-01-11T21:41:23.8344543Z                     auto tmp64 = static_cast<long>(tmp51);
2023-01-11T21:41:23.8344692Z                     auto tmp65 = static_cast<long>(0);
2023-01-11T21:41:23.8344842Z                     auto tmp66 = tmp63 ? tmp64 : tmp65;
2023-01-11T21:41:23.8344996Z                     auto tmp67 = static_cast<long>(tmp58);
2023-01-11T21:41:23.8345141Z                     auto tmp68 = tmp63 ? tmp67 : tmp65;
2023-01-11T21:41:23.8345275Z                     auto tmp69 = tmp51 + tmp18;
2023-01-11T21:41:23.8345519Z                     auto tmp70 = tmp69 - tmp50;
2023-01-11T21:41:23.8345637Z                     auto tmp71 = tmp58 + tmp18;
2023-01-11T21:41:23.8345848Z                     auto tmp72 = tmp71 - tmp57;
2023-01-11T21:41:23.8346064Z                     auto tmp73 = tmp70 * tmp72;
2023-01-11T21:41:23.8346207Z                     auto tmp74 = tmp63 ? tmp73 : tmp5;
2023-01-11T21:41:23.8346342Z                     auto tmp75 = tmp69 >= tmp5;
2023-01-11T21:41:23.8346477Z                     auto tmp76 = tmp69 < tmp7;
2023-01-11T21:41:23.8346614Z                     auto tmp77 = tmp76 && tmp61;
2023-01-11T21:41:23.8346752Z                     auto tmp78 = tmp75 && tmp77;
2023-01-11T21:41:23.8346890Z                     auto tmp79 = static_cast<long>(tmp69);
2023-01-11T21:41:23.8347039Z                     auto tmp80 = tmp78 ? tmp79 : tmp65;
2023-01-11T21:41:23.8347183Z                     auto tmp81 = tmp78 ? tmp67 : tmp65;
2023-01-11T21:41:23.8347391Z                     auto tmp82 = tmp50 - tmp51;
2023-01-11T21:41:23.8347518Z                     auto tmp83 = tmp82 * tmp72;
2023-01-11T21:41:23.8347661Z                     auto tmp84 = tmp78 ? tmp83 : tmp5;
2023-01-11T21:41:23.8347792Z                     auto tmp85 = tmp71 >= tmp5;
2023-01-11T21:41:23.8347919Z                     auto tmp86 = tmp71 < tmp7;
2023-01-11T21:41:23.8348050Z                     auto tmp87 = tmp85 && tmp86;
2023-01-11T21:41:23.8348185Z                     auto tmp88 = tmp53 && tmp87;
2023-01-11T21:41:23.8348322Z                     auto tmp89 = tmp52 && tmp88;
2023-01-11T21:41:23.8348468Z                     auto tmp90 = tmp89 ? tmp64 : tmp65;
2023-01-11T21:41:23.8348624Z                     auto tmp91 = static_cast<long>(tmp71);
2023-01-11T21:41:23.8348777Z                     auto tmp92 = tmp89 ? tmp91 : tmp65;
2023-01-11T21:41:23.8348974Z                     auto tmp93 = tmp57 - tmp58;
2023-01-11T21:41:23.8349103Z                     auto tmp94 = tmp70 * tmp93;
2023-01-11T21:41:23.8349245Z                     auto tmp95 = tmp89 ? tmp94 : tmp5;
2023-01-11T21:41:23.8349385Z                     auto tmp96 = tmp76 && tmp87;
2023-01-11T21:41:23.8349522Z                     auto tmp97 = tmp75 && tmp96;
2023-01-11T21:41:23.8349668Z                     auto tmp98 = tmp97 ? tmp79 : tmp65;
2023-01-11T21:41:23.8349818Z                     auto tmp99 = tmp97 ? tmp91 : tmp65;
2023-01-11T21:41:23.8349949Z                     auto tmp100 = tmp82 * tmp93;
2023-01-11T21:41:23.8350080Z                     auto tmp101 = tmp97 ? tmp100 : tmp5;
2023-01-11T21:41:23.8350202Z                     out_ptr0[i0] = tmp24;
2023-01-11T21:41:23.8350322Z                     out_ptr1[i0] = tmp31;
2023-01-11T21:41:23.8350447Z                     out_ptr2[i0] = tmp39;
2023-01-11T21:41:23.8350567Z                     out_ptr3[i0] = tmp43;
2023-01-11T21:41:23.8350693Z                     out_ptr4[i0] = tmp66;
2023-01-11T21:41:23.8350805Z                     out_ptr5[i0] = tmp68;
2023-01-11T21:41:23.8350906Z                     out_ptr6[i0] = tmp74;
2023-01-11T21:41:23.8351025Z                     out_ptr7[i0] = tmp80;
2023-01-11T21:41:23.8351137Z                     out_ptr8[i0] = tmp81;
2023-01-11T21:41:23.8351253Z                     out_ptr9[i0] = tmp84;
2023-01-11T21:41:23.8351379Z                     out_ptr10[i0] = tmp90;
2023-01-11T21:41:23.8351578Z                     out_ptr11[i0] = tmp92;
2023-01-11T21:41:23.8351697Z                     out_ptr12[i0] = tmp95;
2023-01-11T21:41:23.8351801Z                     out_ptr13[i0] = tmp98;
2023-01-11T21:41:23.8351919Z                     out_ptr14[i0] = tmp99;
2023-01-11T21:41:23.8352045Z                     out_ptr15[i0] = tmp101;
2023-01-11T21:41:23.8352140Z                 }
2023-01-11T21:41:23.8352229Z             }
2023-01-11T21:41:23.8352318Z         }
2023-01-11T21:41:23.8352432Z         #pragma omp for 
2023-01-11T21:41:23.8352540Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8352630Z         {
2023-01-11T21:41:23.8352747Z             #pragma GCC ivdep
2023-01-11T21:41:23.8352863Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.8352951Z             {
2023-01-11T21:41:23.8353068Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8353184Z                 for(long i2=0; i2<123904; i2+=1)
2023-01-11T21:41:23.8353277Z                 {
2023-01-11T21:41:23.8353431Z                     {
2023-01-11T21:41:23.8353529Z                         {
2023-01-11T21:41:23.8353683Z                             auto tmp2 = in_ptr0[(2*i2) + (247808*i0)];
2023-01-11T21:41:23.8353915Z                             auto tmp11 = in_ptr0[1 + (2*i2) + (247808*i0)];
2023-01-11T21:41:23.8354073Z                             auto tmp51 = out_ptr0[i2 + (123904*i0)];
2023-01-11T21:41:23.8354213Z                             auto tmp53 = out_ptr1[i2 + (123904*i0)];
2023-01-11T21:41:23.8354360Z                             auto tmp56 = out_ptr2[i2 + (123904*i0)];
2023-01-11T21:41:23.8354509Z                             auto tmp59 = out_ptr3[i2 + (123904*i0)];
2023-01-11T21:41:23.8354670Z                             auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:23.8354830Z                             auto tmp1 = static_cast<long>(i1);
2023-01-11T21:41:23.8354989Z                             auto tmp3 = static_cast<float>(175.5);
2023-01-11T21:41:23.8355135Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.8355274Z                             auto tmp5 = tmp4 + tmp3;
2023-01-11T21:41:23.8355416Z                             auto tmp6 = std::floor(tmp5);
2023-01-11T21:41:23.8355573Z                             auto tmp7 = static_cast<float>(0);
2023-01-11T21:41:23.8355715Z                             auto tmp8 = tmp6 >= tmp7;
2023-01-11T21:41:23.8355869Z                             auto tmp9 = static_cast<float>(352);
2023-01-11T21:41:23.8356013Z                             auto tmp10 = tmp6 < tmp9;
2023-01-11T21:41:23.8356156Z                             auto tmp12 = tmp11 * tmp3;
2023-01-11T21:41:23.8356285Z                             auto tmp13 = tmp12 + tmp3;
2023-01-11T21:41:23.8356435Z                             auto tmp14 = std::floor(tmp13);
2023-01-11T21:41:23.8356559Z                             auto tmp15 = tmp14 >= tmp7;
2023-01-11T21:41:23.8356692Z                             auto tmp16 = tmp14 < tmp9;
2023-01-11T21:41:23.8356848Z                             auto tmp17 = tmp15 && tmp16;
2023-01-11T21:41:23.8356993Z                             auto tmp18 = tmp10 && tmp17;
2023-01-11T21:41:23.8357129Z                             auto tmp19 = tmp8 && tmp18;
2023-01-11T21:41:23.8357292Z                             auto tmp20 = static_cast<long>(tmp14);
2023-01-11T21:41:23.8357447Z                             auto tmp21 = static_cast<long>(0);
2023-01-11T21:41:23.8357586Z                             auto tmp22 = tmp19 ? tmp20 : tmp21;
2023-01-11T21:41:23.8357746Z                             auto tmp23 = static_cast<long>(tmp6);
2023-01-11T21:41:23.8357900Z                             auto tmp24 = tmp19 ? tmp23 : tmp21;
2023-01-11T21:41:23.8358101Z                             auto tmp25 = in_ptr1[tmp24 + (352*tmp22) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8358275Z                             auto tmp26 = static_cast<float>(1);
2023-01-11T21:41:23.8358422Z                             auto tmp27 = tmp6 + tmp26;
2023-01-11T21:41:23.8358646Z                             auto tmp28 = tmp27 >= tmp7;
2023-01-11T21:41:23.8358780Z                             auto tmp29 = tmp27 < tmp9;
2023-01-11T21:41:23.8358903Z                             auto tmp30 = tmp29 && tmp17;
2023-01-11T21:41:23.8359046Z                             auto tmp31 = tmp28 && tmp30;
2023-01-11T21:41:23.8359196Z                             auto tmp32 = tmp31 ? tmp20 : tmp21;
2023-01-11T21:41:23.8359357Z                             auto tmp33 = static_cast<long>(tmp27);
2023-01-11T21:41:23.8359514Z                             auto tmp34 = tmp31 ? tmp33 : tmp21;
2023-01-11T21:41:23.8359704Z                             auto tmp35 = in_ptr1[tmp34 + (352*tmp32) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8359848Z                             auto tmp36 = tmp14 + tmp26;
2023-01-11T21:41:23.8359983Z                             auto tmp37 = tmp36 >= tmp7;
2023-01-11T21:41:23.8360106Z                             auto tmp38 = tmp36 < tmp9;
2023-01-11T21:41:23.8360304Z                             auto tmp39 = tmp37 && tmp38;
2023-01-11T21:41:23.8360446Z                             auto tmp40 = tmp10 && tmp39;
2023-01-11T21:41:23.8360592Z                             auto tmp41 = tmp8 && tmp40;
2023-01-11T21:41:23.8360768Z                             auto tmp42 = static_cast<long>(tmp36);
2023-01-11T21:41:23.8360941Z                             auto tmp43 = tmp41 ? tmp42 : tmp21;
2023-01-11T21:41:23.8361110Z                             auto tmp44 = tmp41 ? tmp23 : tmp21;
2023-01-11T21:41:23.8361302Z                             auto tmp45 = in_ptr1[tmp44 + (352*tmp43) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8361449Z                             auto tmp46 = tmp29 && tmp39;
2023-01-11T21:41:23.8361588Z                             auto tmp47 = tmp28 && tmp46;
2023-01-11T21:41:23.8361745Z                             auto tmp48 = tmp47 ? tmp42 : tmp21;
2023-01-11T21:41:23.8361900Z                             auto tmp49 = tmp47 ? tmp33 : tmp21;
2023-01-11T21:41:23.8362102Z                             auto tmp50 = in_ptr1[tmp49 + (352*tmp48) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8362248Z                             auto tmp52 = tmp25 * tmp51;
2023-01-11T21:41:23.8362390Z                             auto tmp54 = tmp35 * tmp53;
2023-01-11T21:41:23.8362512Z                             auto tmp55 = tmp52 + tmp54;
2023-01-11T21:41:23.8362658Z                             auto tmp57 = tmp45 * tmp56;
2023-01-11T21:41:23.8362800Z                             auto tmp58 = tmp55 + tmp57;
2023-01-11T21:41:23.8362934Z                             auto tmp60 = tmp50 * tmp59;
2023-01-11T21:41:23.8363072Z                             auto tmp61 = tmp58 + tmp60;
2023-01-11T21:41:23.8363233Z                             in_out_ptr0[i2 + (123904*i1) + (371712*i0)] = tmp61;
2023-01-11T21:41:23.8363332Z                         }
2023-01-11T21:41:23.8363413Z                     }
2023-01-11T21:41:23.8363500Z                 }
2023-01-11T21:41:23.8363595Z             }
2023-01-11T21:41:23.8363695Z         }
2023-01-11T21:41:23.8363817Z         #pragma omp for 
2023-01-11T21:41:23.8363936Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8364025Z         {
2023-01-11T21:41:23.8364130Z             #pragma GCC ivdep
2023-01-11T21:41:23.8364250Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.8364349Z             {
2023-01-11T21:41:23.8364462Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8364591Z                 for(long i2=0; i2<123904; i2+=1)
2023-01-11T21:41:23.8364688Z                 {
2023-01-11T21:41:23.8364785Z                     {
2023-01-11T21:41:23.8364871Z                         {
2023-01-11T21:41:23.8365023Z                             auto tmp2 = out_ptr5[i2 + (123904*i0)];
2023-01-11T21:41:23.8365179Z                             auto tmp3 = out_ptr4[i2 + (123904*i0)];
2023-01-11T21:41:23.8365329Z                             auto tmp5 = out_ptr6[i2 + (123904*i0)];
2023-01-11T21:41:23.8365476Z                             auto tmp7 = out_ptr8[i2 + (123904*i0)];
2023-01-11T21:41:23.8365704Z                             auto tmp8 = out_ptr7[i2 + (123904*i0)];
2023-01-11T21:41:23.8365860Z                             auto tmp10 = out_ptr9[i2 + (123904*i0)];
2023-01-11T21:41:23.8365998Z                             auto tmp13 = out_ptr11[i2 + (123904*i0)];
2023-01-11T21:41:23.8366146Z                             auto tmp14 = out_ptr10[i2 + (123904*i0)];
2023-01-11T21:41:23.8366299Z                             auto tmp16 = out_ptr12[i2 + (123904*i0)];
2023-01-11T21:41:23.8366445Z                             auto tmp19 = out_ptr14[i2 + (123904*i0)];
2023-01-11T21:41:23.8366588Z                             auto tmp20 = out_ptr13[i2 + (123904*i0)];
2023-01-11T21:41:23.8366728Z                             auto tmp22 = out_ptr15[i2 + (123904*i0)];
2023-01-11T21:41:23.8366887Z                             auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:23.8367036Z                             auto tmp1 = static_cast<long>(i1);
2023-01-11T21:41:23.8367260Z                             auto tmp4 = in_ptr1[tmp3 + (352*tmp2) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8367406Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:23.8367591Z                             auto tmp9 = in_ptr1[tmp8 + (352*tmp7) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8367732Z                             auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:23.8367872Z                             auto tmp12 = tmp6 + tmp11;
2023-01-11T21:41:23.8368059Z                             auto tmp15 = in_ptr1[tmp14 + (352*tmp13) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8368198Z                             auto tmp17 = tmp15 * tmp16;
2023-01-11T21:41:23.8368338Z                             auto tmp18 = tmp12 + tmp17;
2023-01-11T21:41:23.8368512Z                             auto tmp21 = in_ptr1[tmp20 + (352*tmp19) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:23.8368645Z                             auto tmp23 = tmp21 * tmp22;
2023-01-11T21:41:23.8368788Z                             auto tmp24 = tmp18 + tmp23;
2023-01-11T21:41:23.8368956Z                             in_out_ptr1[i2 + (123904*i1) + (371712*i0)] = tmp24;
2023-01-11T21:41:23.8369054Z                         }
2023-01-11T21:41:23.8369147Z                     }
2023-01-11T21:41:23.8369239Z                 }
2023-01-11T21:41:23.8369315Z             }
2023-01-11T21:41:23.8369402Z         }
2023-01-11T21:41:23.8369490Z     }
2023-01-11T21:41:23.8369577Z }
2023-01-11T21:41:23.8369722Z ''')
2023-01-11T21:41:23.8369732Z 
2023-01-11T21:41:23.8369739Z 
2023-01-11T21:41:23.8369872Z async_compile.wait(globals())
2023-01-11T21:41:23.8369975Z del async_compile
2023-01-11T21:41:23.8369982Z 
2023-01-11T21:41:23.8370069Z def call(args):
2023-01-11T21:41:23.8370179Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8370282Z     args.clear()
2023-01-11T21:41:23.8370622Z     buf0 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8370966Z     buf2 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8371303Z     buf4 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8371630Z     buf6 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8371951Z     buf9 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8372257Z     buf10 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8372577Z     buf11 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8372893Z     buf12 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8373208Z     buf13 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8373534Z     buf14 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8373928Z     buf15 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8374242Z     buf16 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8374565Z     buf17 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8374876Z     buf19 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8375181Z     buf20 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8375491Z     buf21 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8375837Z     buf1 = empty_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8375960Z     buf8 = buf1; del buf1  # reuse
2023-01-11T21:41:23.8376374Z     buf18 = empty_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8376508Z     buf22 = buf18; del buf18  # reuse
2023-01-11T21:41:23.8377385Z     kernel_cpp_0(c_void_p(buf8.data_ptr()), c_void_p(buf22.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf13.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf15.data_ptr()), c_void_p(buf16.data_ptr()), c_void_p(buf17.data_ptr()), c_void_p(buf19.data_ptr()), c_void_p(buf20.data_ptr()), c_void_p(buf21.data_ptr()))
2023-01-11T21:41:23.8377488Z     del arg0_1
2023-01-11T21:41:23.8377587Z     del arg1_1
2023-01-11T21:41:23.8377688Z     return (buf8, buf22, )
2023-01-11T21:41:23.8377697Z 
2023-01-11T21:41:23.8377719Z 
2023-01-11T21:41:23.8377817Z if __name__ == "__main__":
2023-01-11T21:41:23.8377987Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8378180Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8378539Z     arg0_1 = rand_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8378875Z     arg1_1 = rand_strided((4, 352, 352, 2), (247808, 704, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8379055Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8379475Z [2023-01-11 21:34:30,009] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 222
2023-01-11T21:41:23.8379484Z 
2023-01-11T21:41:23.8379580Z ok (3.878s)
2023-01-11T21:41:23.8380273Z   test_hardsigmoid_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8380464Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8380858Z [2023-01-11 21:34:30,157] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 223
2023-01-11T21:41:23.8381267Z [2023-01-11 21:34:31,759] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 223
2023-01-11T21:41:23.8381275Z 
2023-01-11T21:41:23.8381416Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8381523Z import torch
2023-01-11T21:41:23.8381627Z import random
2023-01-11T21:41:23.8381799Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8381983Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8381992Z 
2023-01-11T21:41:23.8382095Z aten = torch.ops.aten
2023-01-11T21:41:23.8382282Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8382556Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8382673Z 
2023-01-11T21:41:23.8382680Z 
2023-01-11T21:41:23.8382896Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8383203Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8383382Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8383531Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8383672Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8383793Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8383882Z {
2023-01-11T21:41:23.8384031Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8384126Z     {
2023-01-11T21:41:23.8384242Z         #pragma omp for 
2023-01-11T21:41:23.8384361Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8384450Z         {
2023-01-11T21:41:23.8384635Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8384944Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.8385074Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8385278Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:41:23.8385439Z             auto tmp4 = at::vec::maximum(tmp2, tmp3);
2023-01-11T21:41:23.8385637Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(6.0));
2023-01-11T21:41:23.8385790Z             auto tmp6 = at::vec::minimum(tmp4, tmp5);
2023-01-11T21:41:23.8385982Z             auto tmp7 = at::vec::Vectorized<float>(static_cast<float>(6));
2023-01-11T21:41:23.8386087Z             auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:23.8386208Z             auto tmp9 = tmp2 + tmp1;
2023-01-11T21:41:23.8386365Z             auto tmp10 = at::vec::maximum(tmp9, tmp3);
2023-01-11T21:41:23.8386528Z             auto tmp11 = at::vec::minimum(tmp10, tmp5);
2023-01-11T21:41:23.8386652Z             auto tmp12 = tmp11 / tmp7;
2023-01-11T21:41:23.8386867Z             auto tmp13 = tmp0 - tmp1;
2023-01-11T21:41:23.8386993Z             auto tmp14 = tmp13 + tmp1;
2023-01-11T21:41:23.8387133Z             auto tmp15 = at::vec::maximum(tmp14, tmp3);
2023-01-11T21:41:23.8387291Z             auto tmp16 = at::vec::minimum(tmp15, tmp5);
2023-01-11T21:41:23.8387416Z             auto tmp17 = tmp16 / tmp7;
2023-01-11T21:41:23.8387549Z             tmp8.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8387684Z             tmp12.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8387818Z             tmp17.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8387913Z         }
2023-01-11T21:41:23.8388038Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8388158Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8388250Z         {
2023-01-11T21:41:23.8388377Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8388521Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.8388643Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8388790Z             auto tmp3 = static_cast<float>(0.0);
2023-01-11T21:41:23.8388980Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::max(tmp2, tmp3);
2023-01-11T21:41:23.8389113Z             auto tmp5 = static_cast<float>(6.0);
2023-01-11T21:41:23.8389298Z             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::min(tmp4, tmp5);
2023-01-11T21:41:23.8389447Z             auto tmp7 = static_cast<float>(6);
2023-01-11T21:41:23.8389575Z             auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:23.8389695Z             auto tmp9 = tmp2 + tmp1;
2023-01-11T21:41:23.8389884Z             auto tmp10 = (tmp3 != tmp3) ? tmp3 : std::max(tmp9, tmp3);
2023-01-11T21:41:23.8390065Z             auto tmp11 = (tmp5 != tmp5) ? tmp5 : std::min(tmp10, tmp5);
2023-01-11T21:41:23.8390175Z             auto tmp12 = tmp11 / tmp7;
2023-01-11T21:41:23.8390378Z             auto tmp13 = tmp0 - tmp1;
2023-01-11T21:41:23.8390502Z             auto tmp14 = tmp13 + tmp1;
2023-01-11T21:41:23.8390681Z             auto tmp15 = (tmp3 != tmp3) ? tmp3 : std::max(tmp14, tmp3);
2023-01-11T21:41:23.8390943Z             auto tmp16 = (tmp5 != tmp5) ? tmp5 : std::min(tmp15, tmp5);
2023-01-11T21:41:23.8391067Z             auto tmp17 = tmp16 / tmp7;
2023-01-11T21:41:23.8391189Z             out_ptr0[i0] = tmp8;
2023-01-11T21:41:23.8391305Z             out_ptr1[i0] = tmp12;
2023-01-11T21:41:23.8391404Z             out_ptr2[i0] = tmp17;
2023-01-11T21:41:23.8391495Z         }
2023-01-11T21:41:23.8391587Z     }
2023-01-11T21:41:23.8391675Z }
2023-01-11T21:41:23.8391799Z ''')
2023-01-11T21:41:23.8391808Z 
2023-01-11T21:41:23.8391814Z 
2023-01-11T21:41:23.8391948Z async_compile.wait(globals())
2023-01-11T21:41:23.8392054Z del async_compile
2023-01-11T21:41:23.8392062Z 
2023-01-11T21:41:23.8392153Z def call(args):
2023-01-11T21:41:23.8392252Z     arg0_1, = args
2023-01-11T21:41:23.8392355Z     args.clear()
2023-01-11T21:41:23.8392656Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8392947Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8393302Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8393585Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8393665Z     del arg0_1
2023-01-11T21:41:23.8393852Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8393862Z 
2023-01-11T21:41:23.8393869Z 
2023-01-11T21:41:23.8393979Z if __name__ == "__main__":
2023-01-11T21:41:23.8394146Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8394326Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8394620Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8394780Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8394787Z 
2023-01-11T21:41:23.8394879Z ok (1.660s)
2023-01-11T21:41:23.8395598Z   test_hardswish_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8395789Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8396178Z [2023-01-11 21:34:31,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 224
2023-01-11T21:41:23.8396591Z [2023-01-11 21:34:33,422] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 224
2023-01-11T21:41:23.8396600Z 
2023-01-11T21:41:23.8396741Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8396842Z import torch
2023-01-11T21:41:23.8396951Z import random
2023-01-11T21:41:23.8397122Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8397303Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8397317Z 
2023-01-11T21:41:23.8397431Z aten = torch.ops.aten
2023-01-11T21:41:23.8397614Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8397750Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8397757Z 
2023-01-11T21:41:23.8397763Z 
2023-01-11T21:41:23.8397968Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8398270Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8398449Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8398593Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8398734Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8398868Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8398937Z {
2023-01-11T21:41:23.8399077Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8399170Z     {
2023-01-11T21:41:23.8399281Z         #pragma omp for 
2023-01-11T21:41:23.8399481Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8399570Z         {
2023-01-11T21:41:23.8399760Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8399960Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.8400085Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8400290Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:41:23.8400454Z             auto tmp4 = at::vec::maximum(tmp2, tmp3);
2023-01-11T21:41:23.8400658Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(6.0));
2023-01-11T21:41:23.8400812Z             auto tmp6 = at::vec::minimum(tmp4, tmp5);
2023-01-11T21:41:23.8400936Z             auto tmp7 = tmp0 * tmp6;
2023-01-11T21:41:23.8401136Z             auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(6));
2023-01-11T21:41:23.8401250Z             auto tmp9 = tmp7 / tmp8;
2023-01-11T21:41:23.8401434Z             auto tmp10 = tmp2 + tmp1;
2023-01-11T21:41:23.8401601Z             auto tmp11 = at::vec::maximum(tmp10, tmp3);
2023-01-11T21:41:23.8401764Z             auto tmp12 = at::vec::minimum(tmp11, tmp5);
2023-01-11T21:41:23.8401887Z             auto tmp13 = tmp2 * tmp12;
2023-01-11T21:41:23.8402007Z             auto tmp14 = tmp13 / tmp8;
2023-01-11T21:41:23.8402200Z             auto tmp15 = tmp0 - tmp1;
2023-01-11T21:41:23.8402308Z             auto tmp16 = tmp15 + tmp1;
2023-01-11T21:41:23.8402459Z             auto tmp17 = at::vec::maximum(tmp16, tmp3);
2023-01-11T21:41:23.8402614Z             auto tmp18 = at::vec::minimum(tmp17, tmp5);
2023-01-11T21:41:23.8402743Z             auto tmp19 = tmp15 * tmp18;
2023-01-11T21:41:23.8402869Z             auto tmp20 = tmp19 / tmp8;
2023-01-11T21:41:23.8403001Z             tmp9.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8403135Z             tmp14.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8403251Z             tmp20.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8403344Z         }
2023-01-11T21:41:23.8403488Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8403611Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8403701Z         {
2023-01-11T21:41:23.8403820Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8403964Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.8404065Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8404205Z             auto tmp3 = static_cast<float>(0.0);
2023-01-11T21:41:23.8404387Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::max(tmp2, tmp3);
2023-01-11T21:41:23.8404533Z             auto tmp5 = static_cast<float>(6.0);
2023-01-11T21:41:23.8404714Z             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::min(tmp4, tmp5);
2023-01-11T21:41:23.8404838Z             auto tmp7 = tmp0 * tmp6;
2023-01-11T21:41:23.8404982Z             auto tmp8 = static_cast<float>(6);
2023-01-11T21:41:23.8405100Z             auto tmp9 = tmp7 / tmp8;
2023-01-11T21:41:23.8405209Z             auto tmp10 = tmp2 + tmp1;
2023-01-11T21:41:23.8405393Z             auto tmp11 = (tmp3 != tmp3) ? tmp3 : std::max(tmp10, tmp3);
2023-01-11T21:41:23.8405573Z             auto tmp12 = (tmp5 != tmp5) ? tmp5 : std::min(tmp11, tmp5);
2023-01-11T21:41:23.8405702Z             auto tmp13 = tmp2 * tmp12;
2023-01-11T21:41:23.8405825Z             auto tmp14 = tmp13 / tmp8;
2023-01-11T21:41:23.8406031Z             auto tmp15 = tmp0 - tmp1;
2023-01-11T21:41:23.8406151Z             auto tmp16 = tmp15 + tmp1;
2023-01-11T21:41:23.8406311Z             auto tmp17 = (tmp3 != tmp3) ? tmp3 : std::max(tmp16, tmp3);
2023-01-11T21:41:23.8406492Z             auto tmp18 = (tmp5 != tmp5) ? tmp5 : std::min(tmp17, tmp5);
2023-01-11T21:41:23.8406612Z             auto tmp19 = tmp15 * tmp18;
2023-01-11T21:41:23.8406732Z             auto tmp20 = tmp19 / tmp8;
2023-01-11T21:41:23.8406845Z             out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.8406958Z             out_ptr1[i0] = tmp14;
2023-01-11T21:41:23.8407071Z             out_ptr2[i0] = tmp20;
2023-01-11T21:41:23.8407217Z         }
2023-01-11T21:41:23.8407312Z     }
2023-01-11T21:41:23.8407399Z }
2023-01-11T21:41:23.8407513Z ''')
2023-01-11T21:41:23.8407523Z 
2023-01-11T21:41:23.8407531Z 
2023-01-11T21:41:23.8407654Z async_compile.wait(globals())
2023-01-11T21:41:23.8407754Z del async_compile
2023-01-11T21:41:23.8407760Z 
2023-01-11T21:41:23.8407856Z def call(args):
2023-01-11T21:41:23.8407947Z     arg0_1, = args
2023-01-11T21:41:23.8408039Z     args.clear()
2023-01-11T21:41:23.8408327Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8408606Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8408885Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8409168Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8409266Z     del arg0_1
2023-01-11T21:41:23.8409384Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8409451Z 
2023-01-11T21:41:23.8409459Z 
2023-01-11T21:41:23.8409574Z if __name__ == "__main__":
2023-01-11T21:41:23.8409730Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8409918Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8410217Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8410373Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8410380Z 
2023-01-11T21:41:23.8410473Z ok (1.693s)
2023-01-11T21:41:23.8411208Z   test_hardtanh_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8411390Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8411802Z [2023-01-11 21:34:33,529] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 225
2023-01-11T21:41:23.8412211Z [2023-01-11 21:34:35,134] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 225
2023-01-11T21:41:23.8412220Z 
2023-01-11T21:41:23.8412347Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8412455Z import torch
2023-01-11T21:41:23.8412558Z import random
2023-01-11T21:41:23.8412729Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8412906Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8412913Z 
2023-01-11T21:41:23.8413031Z aten = torch.ops.aten
2023-01-11T21:41:23.8413230Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8413365Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8413373Z 
2023-01-11T21:41:23.8413380Z 
2023-01-11T21:41:23.8413565Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8413870Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8414043Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8414187Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8414323Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8414460Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8414548Z {
2023-01-11T21:41:23.8414676Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8414767Z     {
2023-01-11T21:41:23.8414877Z         #pragma omp for 
2023-01-11T21:41:23.8414990Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8415082Z         {
2023-01-11T21:41:23.8415281Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8415602Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.0));
2023-01-11T21:41:23.8415762Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:23.8416026Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:41:23.8416186Z             auto tmp4 = at::vec::minimum(tmp2, tmp3);
2023-01-11T21:41:23.8416378Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8416508Z             auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:23.8416664Z             auto tmp7 = at::vec::maximum(tmp6, tmp1);
2023-01-11T21:41:23.8416815Z             auto tmp8 = at::vec::minimum(tmp7, tmp3);
2023-01-11T21:41:23.8417009Z             auto tmp9 = tmp0 - tmp5;
2023-01-11T21:41:23.8417167Z             auto tmp10 = at::vec::maximum(tmp9, tmp1);
2023-01-11T21:41:23.8417292Z             auto tmp11 = at::vec::minimum(tmp10, tmp3);
2023-01-11T21:41:23.8417408Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8417527Z             tmp8.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8417642Z             tmp11.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8417719Z         }
2023-01-11T21:41:23.8417891Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8417999Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8418063Z         {
2023-01-11T21:41:23.8418171Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8418370Z             auto tmp1 = static_cast<float>(-1.0);
2023-01-11T21:41:23.8418528Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:23.8418659Z             auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:23.8418810Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:41:23.8418935Z             auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:23.8419027Z             auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:23.8419182Z             auto tmp7 = (tmp1 != tmp1) ? tmp1 : std::max(tmp6, tmp1);
2023-01-11T21:41:23.8419335Z             auto tmp8 = (tmp3 != tmp3) ? tmp3 : std::min(tmp7, tmp3);
2023-01-11T21:41:23.8419499Z             auto tmp9 = tmp0 - tmp5;
2023-01-11T21:41:23.8419663Z             auto tmp10 = (tmp1 != tmp1) ? tmp1 : std::max(tmp9, tmp1);
2023-01-11T21:41:23.8419819Z             auto tmp11 = (tmp3 != tmp3) ? tmp3 : std::min(tmp10, tmp3);
2023-01-11T21:41:23.8419923Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.8420025Z             out_ptr1[i0] = tmp8;
2023-01-11T21:41:23.8420110Z             out_ptr2[i0] = tmp11;
2023-01-11T21:41:23.8420190Z         }
2023-01-11T21:41:23.8420268Z     }
2023-01-11T21:41:23.8420347Z }
2023-01-11T21:41:23.8420449Z ''')
2023-01-11T21:41:23.8420458Z 
2023-01-11T21:41:23.8420463Z 
2023-01-11T21:41:23.8420576Z async_compile.wait(globals())
2023-01-11T21:41:23.8420670Z del async_compile
2023-01-11T21:41:23.8420677Z 
2023-01-11T21:41:23.8420754Z def call(args):
2023-01-11T21:41:23.8420847Z     arg0_1, = args
2023-01-11T21:41:23.8420939Z     args.clear()
2023-01-11T21:41:23.8421198Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8421460Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8421727Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8421983Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8422069Z     del arg0_1
2023-01-11T21:41:23.8422159Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8422165Z 
2023-01-11T21:41:23.8422171Z 
2023-01-11T21:41:23.8422266Z if __name__ == "__main__":
2023-01-11T21:41:23.8422584Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8422750Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8423011Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8423147Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8423154Z 
2023-01-11T21:41:23.8423238Z ok (1.676s)
2023-01-11T21:41:23.8423872Z   test_horizonal_fusion1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8424137Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8424474Z [2023-01-11 21:34:35,154] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 226
2023-01-11T21:41:23.8424827Z [2023-01-11 21:34:36,729] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 226
2023-01-11T21:41:23.8424833Z 
2023-01-11T21:41:23.8424952Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8425041Z import torch
2023-01-11T21:41:23.8425128Z import random
2023-01-11T21:41:23.8425272Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8425483Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8425494Z 
2023-01-11T21:41:23.8425593Z aten = torch.ops.aten
2023-01-11T21:41:23.8425750Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8425865Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8425871Z 
2023-01-11T21:41:23.8425876Z 
2023-01-11T21:41:23.8426052Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8426319Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8426472Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8426604Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8426733Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8426856Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8426963Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8427081Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8427166Z {
2023-01-11T21:41:23.8427295Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8427373Z     {
2023-01-11T21:41:23.8427471Z         #pragma omp for 
2023-01-11T21:41:23.8427575Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.8427639Z         {
2023-01-11T21:41:23.8427817Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8427985Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8428094Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8428209Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8428288Z         }
2023-01-11T21:41:23.8428408Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8428503Z         for(long i0=2048; i0<2048; i0+=1)
2023-01-11T21:41:23.8428580Z         {
2023-01-11T21:41:23.8428685Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8428790Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8428899Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8429000Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8429079Z         }
2023-01-11T21:41:23.8429161Z         #pragma omp for 
2023-01-11T21:41:23.8429264Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8429343Z         {
2023-01-11T21:41:23.8429444Z             #pragma GCC ivdep
2023-01-11T21:41:23.8429550Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:23.8429631Z             {
2023-01-11T21:41:23.8429742Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:41:23.8429812Z                 {
2023-01-11T21:41:23.8430001Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:23.8430165Z                     auto tmp1 = at::vec::Vectorized<float>(in_ptr2[i1]);
2023-01-11T21:41:23.8430356Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:23.8430545Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8430766Z                     auto tmp4 = tmp3 * tmp1;
2023-01-11T21:41:23.8430905Z                     tmp2.store(out_ptr1 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:23.8431043Z                     tmp4.store(out_ptr2 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:23.8431110Z                 }
2023-01-11T21:41:23.8431233Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.8431344Z                 for(long i2=16; i2<16; i2+=1)
2023-01-11T21:41:23.8431429Z                 {
2023-01-11T21:41:23.8431563Z                     auto tmp0 = in_ptr0[i2 + (16*i1) + (256*i0)];
2023-01-11T21:41:23.8431676Z                     auto tmp1 = in_ptr2[i1];
2023-01-11T21:41:23.8431810Z                     auto tmp3 = in_ptr1[i2 + (16*i1) + (256*i0)];
2023-01-11T21:41:23.8431976Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8432090Z                     auto tmp4 = tmp3 * tmp1;
2023-01-11T21:41:23.8432218Z                     out_ptr1[i2 + (16*i1) + (256*i0)] = tmp2;
2023-01-11T21:41:23.8432393Z                     out_ptr2[i2 + (16*i1) + (256*i0)] = tmp4;
2023-01-11T21:41:23.8432480Z                 }
2023-01-11T21:41:23.8432561Z             }
2023-01-11T21:41:23.8432640Z         }
2023-01-11T21:41:23.8432703Z     }
2023-01-11T21:41:23.8432778Z }
2023-01-11T21:41:23.8432881Z ''')
2023-01-11T21:41:23.8432889Z 
2023-01-11T21:41:23.8432894Z 
2023-01-11T21:41:23.8433015Z async_compile.wait(globals())
2023-01-11T21:41:23.8433109Z del async_compile
2023-01-11T21:41:23.8433116Z 
2023-01-11T21:41:23.8433208Z def call(args):
2023-01-11T21:41:23.8433311Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8433388Z     args.clear()
2023-01-11T21:41:23.8433668Z     buf0 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8434012Z     buf1 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8434280Z     buf2 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8434601Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8434689Z     del arg0_1
2023-01-11T21:41:23.8434776Z     del arg1_1
2023-01-11T21:41:23.8434864Z     del arg2_1
2023-01-11T21:41:23.8434957Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8434965Z 
2023-01-11T21:41:23.8434970Z 
2023-01-11T21:41:23.8435072Z if __name__ == "__main__":
2023-01-11T21:41:23.8435223Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8435380Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8435663Z     arg0_1 = rand_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8435956Z     arg1_1 = rand_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8436253Z     arg2_1 = rand_strided((1, 16, 1), (16, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8436474Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8436484Z 
2023-01-11T21:41:23.8436602Z ok (1.596s)
2023-01-11T21:41:23.8437810Z   test_horizonal_fusion2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8438094Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8438711Z [2023-01-11 21:34:36,751] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 227
2023-01-11T21:41:23.8439296Z [2023-01-11 21:34:38,348] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 227
2023-01-11T21:41:23.8439411Z 
2023-01-11T21:41:23.8439632Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8439786Z import torch
2023-01-11T21:41:23.8439941Z import random
2023-01-11T21:41:23.8440206Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8440476Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8440504Z 
2023-01-11T21:41:23.8440671Z aten = torch.ops.aten
2023-01-11T21:41:23.8440983Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8441198Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8441209Z 
2023-01-11T21:41:23.8441218Z 
2023-01-11T21:41:23.8441545Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8442048Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8442320Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8442547Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8442854Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8443084Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8443304Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8443520Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8443651Z {
2023-01-11T21:41:23.8443864Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8444007Z     {
2023-01-11T21:41:23.8444164Z         #pragma omp for 
2023-01-11T21:41:23.8444350Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.8444482Z         {
2023-01-11T21:41:23.8444789Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8445101Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8445288Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8445496Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8445631Z         }
2023-01-11T21:41:23.8445828Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8446023Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:41:23.8446168Z         {
2023-01-11T21:41:23.8446353Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8446574Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8446757Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8446938Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8447056Z         }
2023-01-11T21:41:23.8447227Z         #pragma omp for 
2023-01-11T21:41:23.8447403Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8447539Z         {
2023-01-11T21:41:23.8447834Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8448145Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8448336Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8448525Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8448667Z         }
2023-01-11T21:41:23.8448882Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8449073Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.8449211Z         {
2023-01-11T21:41:23.8449392Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.8449609Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.8449780Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8449955Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8450088Z         }
2023-01-11T21:41:23.8450260Z         #pragma omp for 
2023-01-11T21:41:23.8450439Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8450576Z         {
2023-01-11T21:41:23.8450863Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:23.8451158Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.8451346Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8451551Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8451689Z         }
2023-01-11T21:41:23.8451900Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8452161Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:23.8452301Z         {
2023-01-11T21:41:23.8452471Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:41:23.8452691Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.8452877Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8453038Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:23.8453172Z         }
2023-01-11T21:41:23.8453302Z     }
2023-01-11T21:41:23.8453418Z }
2023-01-11T21:41:23.8453599Z ''')
2023-01-11T21:41:23.8453609Z 
2023-01-11T21:41:23.8453618Z 
2023-01-11T21:41:23.8453822Z async_compile.wait(globals())
2023-01-11T21:41:23.8453979Z del async_compile
2023-01-11T21:41:23.8453990Z 
2023-01-11T21:41:23.8454145Z def call(args):
2023-01-11T21:41:23.8454325Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8454468Z     args.clear()
2023-01-11T21:41:23.8454938Z     buf0 = empty_strided((8, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8455438Z     buf1 = empty_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8455874Z     buf2 = empty_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8456430Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8456585Z     del arg0_1
2023-01-11T21:41:23.8456734Z     del arg1_1
2023-01-11T21:41:23.8456878Z     del arg2_1
2023-01-11T21:41:23.8457069Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8457079Z 
2023-01-11T21:41:23.8457087Z 
2023-01-11T21:41:23.8457259Z if __name__ == "__main__":
2023-01-11T21:41:23.8457514Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8457796Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8458282Z     arg0_1 = rand_strided((8, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8458740Z     arg1_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8459185Z     arg2_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8459466Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8459476Z 
2023-01-11T21:41:23.8459620Z ok (1.618s)
2023-01-11T21:41:23.8460817Z   test_index1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8461097Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8461714Z [2023-01-11 21:34:38,392] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 228
2023-01-11T21:41:23.8462508Z [2023-01-11 21:34:40,005] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 228
2023-01-11T21:41:23.8463619Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8463900Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8464540Z [2023-01-11 21:34:40,049] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 229
2023-01-11T21:41:23.8465179Z [2023-01-11 21:34:41,645] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 229
2023-01-11T21:41:23.8465191Z 
2023-01-11T21:41:23.8465408Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8465680Z import torch
2023-01-11T21:41:23.8465849Z import random
2023-01-11T21:41:23.8466099Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8466393Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8466403Z 
2023-01-11T21:41:23.8466577Z aten = torch.ops.aten
2023-01-11T21:41:23.8466897Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8467107Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8467117Z 
2023-01-11T21:41:23.8467126Z 
2023-01-11T21:41:23.8467450Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8467953Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8468229Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8468440Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.8468671Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8468893Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8469115Z {
2023-01-11T21:41:23.8469348Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8469487Z     {
2023-01-11T21:41:23.8469655Z         #pragma omp for 
2023-01-11T21:41:23.8469819Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8469955Z         {
2023-01-11T21:41:23.8470132Z             #pragma GCC ivdep
2023-01-11T21:41:23.8470326Z             for(long i1=0; i1<12; i1+=1)
2023-01-11T21:41:23.8470469Z             {
2023-01-11T21:41:23.8470612Z                 {
2023-01-11T21:41:23.8470755Z                     {
2023-01-11T21:41:23.8470949Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8471157Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8471396Z                         auto tmp2 = in_ptr2[i1 + (12*tmp1) + (96*tmp0)];
2023-01-11T21:41:23.8471595Z                         out_ptr0[i1 + (12*i0)] = tmp2;
2023-01-11T21:41:23.8471738Z                     }
2023-01-11T21:41:23.8471885Z                 }
2023-01-11T21:41:23.8472037Z             }
2023-01-11T21:41:23.8472159Z         }
2023-01-11T21:41:23.8472290Z     }
2023-01-11T21:41:23.8472421Z }
2023-01-11T21:41:23.8472600Z ''')
2023-01-11T21:41:23.8472610Z 
2023-01-11T21:41:23.8472619Z 
2023-01-11T21:41:23.8472833Z async_compile.wait(globals())
2023-01-11T21:41:23.8473004Z del async_compile
2023-01-11T21:41:23.8473013Z 
2023-01-11T21:41:23.8473176Z def call(args):
2023-01-11T21:41:23.8473339Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8473495Z     args.clear()
2023-01-11T21:41:23.8474029Z     buf0 = empty_strided((4, 12), (12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8474480Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8474633Z     del arg0_1
2023-01-11T21:41:23.8474782Z     del arg1_1
2023-01-11T21:41:23.8474930Z     del arg2_1
2023-01-11T21:41:23.8475080Z     return (buf0, )
2023-01-11T21:41:23.8475108Z 
2023-01-11T21:41:23.8475122Z 
2023-01-11T21:41:23.8475285Z if __name__ == "__main__":
2023-01-11T21:41:23.8475554Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8475844Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8476320Z     arg0_1 = rand_strided((8, 8, 12), (96, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8476749Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8477187Z     arg2_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8477473Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8477483Z 
2023-01-11T21:41:23.8477492Z 
2023-01-11T21:41:23.8477714Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8477852Z import torch
2023-01-11T21:41:23.8478011Z import random
2023-01-11T21:41:23.8478285Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8478569Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8478664Z 
2023-01-11T21:41:23.8478841Z aten = torch.ops.aten
2023-01-11T21:41:23.8479148Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8479363Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8479373Z 
2023-01-11T21:41:23.8479382Z 
2023-01-11T21:41:23.8479694Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8480172Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8480447Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8480676Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.8480902Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8481124Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8481260Z {
2023-01-11T21:41:23.8481491Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8481609Z     {
2023-01-11T21:41:23.8481779Z         #pragma omp for 
2023-01-11T21:41:23.8482026Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8482166Z         {
2023-01-11T21:41:23.8482350Z             #pragma GCC ivdep
2023-01-11T21:41:23.8482535Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8482676Z             {
2023-01-11T21:41:23.8482838Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8483033Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:23.8483173Z                 {
2023-01-11T21:41:23.8483312Z                     {
2023-01-11T21:41:23.8483457Z                         {
2023-01-11T21:41:23.8483661Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8483846Z                             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8484097Z                             auto tmp2 = in_ptr2[i2 + (12*tmp1) + (96*tmp0)];
2023-01-11T21:41:23.8484314Z                             out_ptr0[i2 + (12*i1) + (48*i0)] = tmp2;
2023-01-11T21:41:23.8484468Z                         }
2023-01-11T21:41:23.8484609Z                     }
2023-01-11T21:41:23.8484755Z                 }
2023-01-11T21:41:23.8484891Z             }
2023-01-11T21:41:23.8485016Z         }
2023-01-11T21:41:23.8485156Z     }
2023-01-11T21:41:23.8485288Z }
2023-01-11T21:41:23.8485472Z ''')
2023-01-11T21:41:23.8485482Z 
2023-01-11T21:41:23.8485491Z 
2023-01-11T21:41:23.8485692Z async_compile.wait(globals())
2023-01-11T21:41:23.8485853Z del async_compile
2023-01-11T21:41:23.8485862Z 
2023-01-11T21:41:23.8486019Z def call(args):
2023-01-11T21:41:23.8486204Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8486342Z     args.clear()
2023-01-11T21:41:23.8486670Z     buf0 = empty_strided((4, 4, 12), (48, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8486913Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8487015Z     del arg0_1
2023-01-11T21:41:23.8487117Z     del arg1_1
2023-01-11T21:41:23.8487219Z     del arg2_1
2023-01-11T21:41:23.8487325Z     return (buf0, )
2023-01-11T21:41:23.8487341Z 
2023-01-11T21:41:23.8487347Z 
2023-01-11T21:41:23.8487436Z if __name__ == "__main__":
2023-01-11T21:41:23.8487603Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8487789Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8488107Z     arg0_1 = rand_strided((8, 8, 12), (96, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8488400Z     arg1_1 = rand_strided((1, 4), (4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8488680Z     arg2_1 = rand_strided((4, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8488866Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8488874Z 
2023-01-11T21:41:23.8488974Z ok (3.296s)
2023-01-11T21:41:23.8489731Z   test_index2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8489991Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8490395Z [2023-01-11 21:34:41,716] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 230
2023-01-11T21:41:23.8490791Z [2023-01-11 21:34:43,324] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 230
2023-01-11T21:41:23.8490800Z 
2023-01-11T21:41:23.8490964Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8491088Z import torch
2023-01-11T21:41:23.8491203Z import random
2023-01-11T21:41:23.8491399Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8491606Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8491614Z 
2023-01-11T21:41:23.8491732Z aten = torch.ops.aten
2023-01-11T21:41:23.8492027Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8492181Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8492189Z 
2023-01-11T21:41:23.8492195Z 
2023-01-11T21:41:23.8492416Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8492735Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8492914Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8493073Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8493220Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8493352Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8493444Z {
2023-01-11T21:41:23.8493589Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8493674Z     {
2023-01-11T21:41:23.8493787Z         #pragma omp for 
2023-01-11T21:41:23.8493909Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8494004Z         {
2023-01-11T21:41:23.8494120Z             #pragma GCC ivdep
2023-01-11T21:41:23.8494241Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:23.8494330Z             {
2023-01-11T21:41:23.8494421Z                 {
2023-01-11T21:41:23.8494518Z                     {
2023-01-11T21:41:23.8494651Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8494799Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:41:23.8494917Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:41:23.8495006Z                     }
2023-01-11T21:41:23.8495087Z                 }
2023-01-11T21:41:23.8495173Z             }
2023-01-11T21:41:23.8495253Z         }
2023-01-11T21:41:23.8495352Z         #pragma omp for 
2023-01-11T21:41:23.8495465Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8495531Z         {
2023-01-11T21:41:23.8495647Z             #pragma GCC ivdep
2023-01-11T21:41:23.8495761Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8495851Z             {
2023-01-11T21:41:23.8495980Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8496095Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:23.8496170Z                 {
2023-01-11T21:41:23.8496268Z                     {
2023-01-11T21:41:23.8496357Z                         {
2023-01-11T21:41:23.8496490Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8496646Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:23.8496798Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:41:23.8496894Z                         }
2023-01-11T21:41:23.8496971Z                     }
2023-01-11T21:41:23.8497066Z                 }
2023-01-11T21:41:23.8497157Z             }
2023-01-11T21:41:23.8497246Z         }
2023-01-11T21:41:23.8497335Z     }
2023-01-11T21:41:23.8497423Z }
2023-01-11T21:41:23.8497555Z ''')
2023-01-11T21:41:23.8497563Z 
2023-01-11T21:41:23.8497568Z 
2023-01-11T21:41:23.8497683Z async_compile.wait(globals())
2023-01-11T21:41:23.8497869Z del async_compile
2023-01-11T21:41:23.8497875Z 
2023-01-11T21:41:23.8497968Z def call(args):
2023-01-11T21:41:23.8498072Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8498171Z     args.clear()
2023-01-11T21:41:23.8498498Z     buf0 = empty_strided((1, 4, 8, 8), (256, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8498809Z     buf1 = empty_strided((8, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8499097Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8499186Z     del arg0_1
2023-01-11T21:41:23.8499281Z     del arg1_1
2023-01-11T21:41:23.8499400Z     return (buf0, buf1, )
2023-01-11T21:41:23.8499410Z 
2023-01-11T21:41:23.8499415Z 
2023-01-11T21:41:23.8499524Z if __name__ == "__main__":
2023-01-11T21:41:23.8499680Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8499858Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8500229Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8500507Z     arg1_1 = rand_strided((1, 4), (4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8500666Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8500675Z 
2023-01-11T21:41:23.8500778Z ok (1.680s)
2023-01-11T21:41:23.8501451Z   test_index3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8501644Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8502039Z [2023-01-11 21:34:43,367] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 231
2023-01-11T21:41:23.8502498Z [2023-01-11 21:34:43,389] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.index
2023-01-11T21:41:23.8502928Z [2023-01-11 21:34:43,392] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 231
2023-01-11T21:41:23.8502937Z 
2023-01-11T21:41:23.8503096Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8503205Z import torch
2023-01-11T21:41:23.8503278Z import random
2023-01-11T21:41:23.8503428Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8503596Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8503604Z 
2023-01-11T21:41:23.8503710Z aten = torch.ops.aten
2023-01-11T21:41:23.8503915Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8504042Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8504048Z 
2023-01-11T21:41:23.8504054Z 
2023-01-11T21:41:23.8504167Z async_compile.wait(globals())
2023-01-11T21:41:23.8504277Z del async_compile
2023-01-11T21:41:23.8504291Z 
2023-01-11T21:41:23.8504368Z def call(args):
2023-01-11T21:41:23.8504483Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8504587Z     args.clear()
2023-01-11T21:41:23.8504775Z     buf0 = aten.index(as_strided(arg0_1, (3, 4, 1, 4, 3), (192, 48, 0, 12, 1)), [None, arg1_1, None, arg2_1])
2023-01-11T21:41:23.8504864Z     del arg0_1
2023-01-11T21:41:23.8504956Z     del arg1_1
2023-01-11T21:41:23.8505044Z     del arg2_1
2023-01-11T21:41:23.8505119Z     buf1 = buf0
2023-01-11T21:41:23.8505260Z     assert_size_stride(buf1, (3, 3, 1, 3), (9, 3, 3, 1))
2023-01-11T21:41:23.8505346Z     del buf0
2023-01-11T21:41:23.8505441Z     return (buf1, )
2023-01-11T21:41:23.8505448Z 
2023-01-11T21:41:23.8505454Z 
2023-01-11T21:41:23.8505548Z if __name__ == "__main__":
2023-01-11T21:41:23.8505694Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8505853Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8506199Z     arg0_1 = rand_strided((3, 4, 4, 4, 3), (192, 48, 12, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8506612Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8506891Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8507088Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8507095Z 
2023-01-11T21:41:23.8507197Z ok (0.068s)
2023-01-11T21:41:23.8507863Z   test_index_put1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8508059Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8508575Z [2023-01-11 21:34:43,790] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 232
2023-01-11T21:41:23.8509013Z [2023-01-11 21:34:45,431] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 232
2023-01-11T21:41:23.8509728Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8509931Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8510333Z [2023-01-11 21:34:46,422] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 233
2023-01-11T21:41:23.8510764Z [2023-01-11 21:34:48,076] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 233
2023-01-11T21:41:23.8510773Z 
2023-01-11T21:41:23.8510916Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8511020Z import torch
2023-01-11T21:41:23.8511120Z import random
2023-01-11T21:41:23.8511297Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8511475Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8511482Z 
2023-01-11T21:41:23.8511587Z aten = torch.ops.aten
2023-01-11T21:41:23.8511750Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8511894Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8511901Z 
2023-01-11T21:41:23.8511909Z 
2023-01-11T21:41:23.8512136Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8512444Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8512625Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8512776Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.8512918Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8513053Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8513167Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8513287Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8513366Z {
2023-01-11T21:41:23.8513493Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8513574Z     {
2023-01-11T21:41:23.8513672Z         #pragma omp for 
2023-01-11T21:41:23.8513855Z         for(long i0=0; i0<1254400; i0+=1)
2023-01-11T21:41:23.8513925Z         {
2023-01-11T21:41:23.8514107Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8514284Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8514396Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8514520Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8514635Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8514719Z         }
2023-01-11T21:41:23.8514928Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8515067Z         for(long i0=10035200; i0<10035200; i0+=1)
2023-01-11T21:41:23.8515161Z         {
2023-01-11T21:41:23.8515278Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8515407Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8515517Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8515621Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8515710Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8515787Z         }
2023-01-11T21:41:23.8515888Z         #pragma omp for 
2023-01-11T21:41:23.8515995Z         for(long i0=0; i0<601; i0+=1)
2023-01-11T21:41:23.8516077Z         {
2023-01-11T21:41:23.8516179Z             #pragma GCC ivdep
2023-01-11T21:41:23.8516295Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.8516362Z             {
2023-01-11T21:41:23.8516458Z                 {
2023-01-11T21:41:23.8516552Z                     {
2023-01-11T21:41:23.8516768Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.8516941Z                         auto tmp1 = in_ptr2[i1 + (12544*i0)];
2023-01-11T21:41:23.8517106Z                         auto tmp2 = static_cast<long>(1);
2023-01-11T21:41:23.8517250Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.8517402Z                         auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:23.8517538Z                         auto tmp5 = tmp1 + tmp4;
2023-01-11T21:41:23.8517692Z                         out_ptr0[i1 + (12544*tmp0)] = tmp1;
2023-01-11T21:41:23.8517830Z                         out_ptr1[i1 + (12544*tmp3)] = tmp5;
2023-01-11T21:41:23.8517921Z                     }
2023-01-11T21:41:23.8518007Z                 }
2023-01-11T21:41:23.8518087Z             }
2023-01-11T21:41:23.8518152Z         }
2023-01-11T21:41:23.8518254Z         #pragma omp for 
2023-01-11T21:41:23.8518377Z         for(long i0=0; i0<1254400; i0+=1)
2023-01-11T21:41:23.8518474Z         {
2023-01-11T21:41:23.8518680Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8518880Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8519004Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8519113Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8519203Z         }
2023-01-11T21:41:23.8519327Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8519455Z         for(long i0=10035200; i0<10035200; i0+=1)
2023-01-11T21:41:23.8519539Z         {
2023-01-11T21:41:23.8519659Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:23.8519788Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8519887Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8519995Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:23.8520087Z         }
2023-01-11T21:41:23.8520169Z     }
2023-01-11T21:41:23.8520258Z }
2023-01-11T21:41:23.8520398Z ''')
2023-01-11T21:41:23.8520408Z 
2023-01-11T21:41:23.8520414Z 
2023-01-11T21:41:23.8520552Z async_compile.wait(globals())
2023-01-11T21:41:23.8520648Z del async_compile
2023-01-11T21:41:23.8520656Z 
2023-01-11T21:41:23.8520757Z def call(args):
2023-01-11T21:41:23.8520879Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8520981Z     args.clear()
2023-01-11T21:41:23.8521324Z     buf0 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8521647Z     buf2 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8521967Z     buf4 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8522304Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.8522397Z     del arg0_1
2023-01-11T21:41:23.8522495Z     del arg1_1
2023-01-11T21:41:23.8522594Z     del arg2_1
2023-01-11T21:41:23.8522792Z     return (buf0, buf4, )
2023-01-11T21:41:23.8522805Z 
2023-01-11T21:41:23.8522811Z 
2023-01-11T21:41:23.8522929Z if __name__ == "__main__":
2023-01-11T21:41:23.8523099Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8523292Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8523646Z     arg0_1 = rand_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8523916Z     arg1_1 = rand_strided((601, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8524237Z     arg2_1 = rand_strided((601, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8524419Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8524426Z 
2023-01-11T21:41:23.8524432Z 
2023-01-11T21:41:23.8524572Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8524675Z import torch
2023-01-11T21:41:23.8524769Z import random
2023-01-11T21:41:23.8524927Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8525196Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8525206Z 
2023-01-11T21:41:23.8525299Z aten = torch.ops.aten
2023-01-11T21:41:23.8525493Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8525624Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8525631Z 
2023-01-11T21:41:23.8525637Z 
2023-01-11T21:41:23.8525846Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8526112Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8526282Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8526439Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.8526597Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8526722Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8526867Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8527008Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8527095Z {
2023-01-11T21:41:23.8527248Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8527340Z     {
2023-01-11T21:41:23.8527459Z         #pragma omp for 
2023-01-11T21:41:23.8527571Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8527663Z         {
2023-01-11T21:41:23.8527847Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8528032Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8528144Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8528268Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8528391Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8528466Z         }
2023-01-11T21:41:23.8528607Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8528730Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:23.8528816Z         {
2023-01-11T21:41:23.8528948Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8529091Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8529217Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8529314Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8529420Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8529515Z         }
2023-01-11T21:41:23.8529621Z         #pragma omp for 
2023-01-11T21:41:23.8529738Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8529826Z         {
2023-01-11T21:41:23.8529933Z             #pragma GCC ivdep
2023-01-11T21:41:23.8530055Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.8530137Z             {
2023-01-11T21:41:23.8530223Z                 {
2023-01-11T21:41:23.8530308Z                     {
2023-01-11T21:41:23.8530445Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.8530583Z                         auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:23.8530720Z                         auto tmp2 = static_cast<long>(1);
2023-01-11T21:41:23.8530953Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.8531108Z                         auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:23.8531250Z                         auto tmp5 = tmp1 + tmp4;
2023-01-11T21:41:23.8531391Z                         out_ptr0[i1 + (8*tmp0)] = tmp1;
2023-01-11T21:41:23.8531541Z                         out_ptr1[i1 + (8*tmp3)] = tmp5;
2023-01-11T21:41:23.8531634Z                     }
2023-01-11T21:41:23.8531726Z                 }
2023-01-11T21:41:23.8531799Z             }
2023-01-11T21:41:23.8531894Z         }
2023-01-11T21:41:23.8531998Z         #pragma omp for 
2023-01-11T21:41:23.8532112Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8532205Z         {
2023-01-11T21:41:23.8532400Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8532605Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8532715Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8532914Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8533001Z         }
2023-01-11T21:41:23.8533134Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8533256Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:23.8533341Z         {
2023-01-11T21:41:23.8533448Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:23.8533582Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8533695Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8533798Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:23.8533884Z         }
2023-01-11T21:41:23.8533973Z     }
2023-01-11T21:41:23.8534063Z }
2023-01-11T21:41:23.8534172Z ''')
2023-01-11T21:41:23.8534179Z 
2023-01-11T21:41:23.8534198Z 
2023-01-11T21:41:23.8534311Z async_compile.wait(globals())
2023-01-11T21:41:23.8534424Z del async_compile
2023-01-11T21:41:23.8534431Z 
2023-01-11T21:41:23.8534530Z def call(args):
2023-01-11T21:41:23.8534638Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8534742Z     args.clear()
2023-01-11T21:41:23.8535058Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8535350Z     buf2 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8535632Z     buf4 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8535977Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.8536075Z     del arg0_1
2023-01-11T21:41:23.8536169Z     del arg1_1
2023-01-11T21:41:23.8536272Z     del arg2_1
2023-01-11T21:41:23.8536378Z     return (buf0, buf4, )
2023-01-11T21:41:23.8536386Z 
2023-01-11T21:41:23.8536392Z 
2023-01-11T21:41:23.8536494Z if __name__ == "__main__":
2023-01-11T21:41:23.8536653Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8536826Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8537153Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8537421Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8537698Z     arg2_1 = rand_strided((4, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8537866Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8537874Z 
2023-01-11T21:41:23.8537963Z ok (4.686s)
2023-01-11T21:41:23.8538633Z   test_index_put2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8538804Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8539273Z [2023-01-11 21:34:48,345] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 234
2023-01-11T21:41:23.8539633Z [2023-01-11 21:34:50,618] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 234
2023-01-11T21:41:23.8539659Z 
2023-01-11T21:41:23.8539791Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8539891Z import torch
2023-01-11T21:41:23.8539984Z import random
2023-01-11T21:41:23.8540153Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8540345Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8540352Z 
2023-01-11T21:41:23.8540468Z aten = torch.ops.aten
2023-01-11T21:41:23.8540673Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8540794Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8540800Z 
2023-01-11T21:41:23.8540822Z 
2023-01-11T21:41:23.8541006Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8541404Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8541591Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8541745Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.8541895Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8542029Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8542122Z {
2023-01-11T21:41:23.8542250Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8542465Z     {
2023-01-11T21:41:23.8542584Z         #pragma omp for 
2023-01-11T21:41:23.8542702Z         for(long i0=0; i0<156800; i0+=1)
2023-01-11T21:41:23.8542786Z         {
2023-01-11T21:41:23.8543001Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8543149Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8543229Z         }
2023-01-11T21:41:23.8543379Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8543525Z         for(long i0=1254400; i0<1254400; i0+=1)
2023-01-11T21:41:23.8543626Z         {
2023-01-11T21:41:23.8543759Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8543882Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8543964Z         }
2023-01-11T21:41:23.8544085Z         #pragma omp for 
2023-01-11T21:41:23.8544216Z         for(long i0=0; i0<600; i0+=1)
2023-01-11T21:41:23.8544315Z         {
2023-01-11T21:41:23.8544439Z             #pragma GCC ivdep
2023-01-11T21:41:23.8544577Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:23.8544679Z             {
2023-01-11T21:41:23.8544766Z                 {
2023-01-11T21:41:23.8544868Z                     {
2023-01-11T21:41:23.8545007Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:23.8545161Z                         auto tmp1 = in_ptr2[i1 + (12544*i0)];
2023-01-11T21:41:23.8545335Z                         atomic_add(&out_ptr0[i1 + (12544*tmp0)], tmp1);
2023-01-11T21:41:23.8545438Z                     }
2023-01-11T21:41:23.8545548Z                 }
2023-01-11T21:41:23.8545619Z             }
2023-01-11T21:41:23.8545707Z         }
2023-01-11T21:41:23.8545801Z     }
2023-01-11T21:41:23.8545894Z }
2023-01-11T21:41:23.8546039Z ''')
2023-01-11T21:41:23.8546049Z 
2023-01-11T21:41:23.8546055Z 
2023-01-11T21:41:23.8546181Z async_compile.wait(globals())
2023-01-11T21:41:23.8546283Z del async_compile
2023-01-11T21:41:23.8546289Z 
2023-01-11T21:41:23.8546373Z def call(args):
2023-01-11T21:41:23.8546492Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8546598Z     args.clear()
2023-01-11T21:41:23.8546946Z     buf0 = empty_strided((100, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8547220Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8547321Z     del arg0_1
2023-01-11T21:41:23.8547416Z     del arg1_1
2023-01-11T21:41:23.8547507Z     del arg2_1
2023-01-11T21:41:23.8547713Z     return (buf0, )
2023-01-11T21:41:23.8547720Z 
2023-01-11T21:41:23.8547726Z 
2023-01-11T21:41:23.8547833Z if __name__ == "__main__":
2023-01-11T21:41:23.8547995Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8548184Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8548528Z     arg0_1 = rand_strided((100, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8548826Z     arg1_1 = rand_strided((600, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8549155Z     arg2_1 = rand_strided((600, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8549333Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8549342Z 
2023-01-11T21:41:23.8549422Z ok (2.728s)
2023-01-11T21:41:23.8550191Z   test_index_put3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8550373Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8550755Z [2023-01-11 21:34:50,902] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 235
2023-01-11T21:41:23.8551136Z [2023-01-11 21:34:52,598] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 235
2023-01-11T21:41:23.8551143Z 
2023-01-11T21:41:23.8551275Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8551373Z import torch
2023-01-11T21:41:23.8551470Z import random
2023-01-11T21:41:23.8551628Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8551774Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8551781Z 
2023-01-11T21:41:23.8551890Z aten = torch.ops.aten
2023-01-11T21:41:23.8552081Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8552205Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8552211Z 
2023-01-11T21:41:23.8552217Z 
2023-01-11T21:41:23.8552405Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8552686Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8552845Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8552986Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8553108Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8553232Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8553310Z {
2023-01-11T21:41:23.8553446Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8553533Z     {
2023-01-11T21:41:23.8553641Z         #pragma omp for 
2023-01-11T21:41:23.8553826Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8553910Z         {
2023-01-11T21:41:23.8554018Z             #pragma GCC ivdep
2023-01-11T21:41:23.8554131Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.8554219Z             {
2023-01-11T21:41:23.8554325Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8554445Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:41:23.8554536Z                 {
2023-01-11T21:41:23.8554617Z                     {
2023-01-11T21:41:23.8554717Z                         {
2023-01-11T21:41:23.8554842Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8554990Z                             auto tmp1 = in_ptr1[i2 + (2*i0)];
2023-01-11T21:41:23.8555140Z                             out_ptr0[i2 + (2*tmp0) + (8*i0)] = tmp1;
2023-01-11T21:41:23.8555233Z                         }
2023-01-11T21:41:23.8555323Z                     }
2023-01-11T21:41:23.8555409Z                 }
2023-01-11T21:41:23.8555502Z             }
2023-01-11T21:41:23.8555588Z         }
2023-01-11T21:41:23.8555792Z         #pragma omp for 
2023-01-11T21:41:23.8555921Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8556017Z         {
2023-01-11T21:41:23.8556201Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8556405Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8556545Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8556680Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8556769Z         }
2023-01-11T21:41:23.8556897Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8557018Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:23.8557101Z         {
2023-01-11T21:41:23.8557198Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8557332Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8557454Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8557568Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8557660Z         }
2023-01-11T21:41:23.8557851Z         #pragma omp for 
2023-01-11T21:41:23.8557963Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8558059Z         {
2023-01-11T21:41:23.8558179Z             #pragma GCC ivdep
2023-01-11T21:41:23.8558300Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.8558393Z             {
2023-01-11T21:41:23.8558506Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8558628Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:41:23.8558703Z                 {
2023-01-11T21:41:23.8558791Z                     {
2023-01-11T21:41:23.8558883Z                         {
2023-01-11T21:41:23.8559018Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8559163Z                             auto tmp3 = in_ptr1[i2 + (2*i0)];
2023-01-11T21:41:23.8559306Z                             auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:23.8559432Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8559568Z                             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:23.8559703Z                             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.8559853Z                             out_ptr1[i2 + (2*tmp2) + (8*i0)] = tmp5;
2023-01-11T21:41:23.8559947Z                         }
2023-01-11T21:41:23.8560042Z                     }
2023-01-11T21:41:23.8560133Z                 }
2023-01-11T21:41:23.8560221Z             }
2023-01-11T21:41:23.8560290Z         }
2023-01-11T21:41:23.8560376Z     }
2023-01-11T21:41:23.8560462Z }
2023-01-11T21:41:23.8560593Z ''')
2023-01-11T21:41:23.8560601Z 
2023-01-11T21:41:23.8560608Z 
2023-01-11T21:41:23.8560739Z async_compile.wait(globals())
2023-01-11T21:41:23.8560842Z del async_compile
2023-01-11T21:41:23.8560849Z 
2023-01-11T21:41:23.8560942Z def call(args):
2023-01-11T21:41:23.8561039Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8561137Z     args.clear()
2023-01-11T21:41:23.8561450Z     buf1 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8561708Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8561795Z     del arg0_1
2023-01-11T21:41:23.8561880Z     del arg1_1
2023-01-11T21:41:23.8561967Z     del arg2_1
2023-01-11T21:41:23.8562043Z     return (buf1, )
2023-01-11T21:41:23.8562067Z 
2023-01-11T21:41:23.8562073Z 
2023-01-11T21:41:23.8562176Z if __name__ == "__main__":
2023-01-11T21:41:23.8562338Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8562525Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8562831Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8563113Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8563398Z     arg2_1 = rand_strided((1024, 1, 2), (2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8563576Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8563670Z 
2023-01-11T21:41:23.8563773Z ok (1.796s)
2023-01-11T21:41:23.8564470Z   test_index_put_as_masked_fill_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8564639Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8565030Z [2023-01-11 21:34:52,652] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 236
2023-01-11T21:41:23.8565409Z [2023-01-11 21:34:54,231] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 236
2023-01-11T21:41:23.8566099Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8566298Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8566690Z [2023-01-11 21:34:54,280] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 237
2023-01-11T21:41:23.8567095Z [2023-01-11 21:34:55,892] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 237
2023-01-11T21:41:23.8567105Z 
2023-01-11T21:41:23.8567249Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8567348Z import torch
2023-01-11T21:41:23.8567452Z import random
2023-01-11T21:41:23.8567598Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8567775Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8567787Z 
2023-01-11T21:41:23.8567904Z aten = torch.ops.aten
2023-01-11T21:41:23.8568106Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8568248Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8568256Z 
2023-01-11T21:41:23.8568262Z 
2023-01-11T21:41:23.8568465Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8568769Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8568945Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.8569090Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8569241Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8569371Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8569457Z {
2023-01-11T21:41:23.8569595Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8569674Z     {
2023-01-11T21:41:23.8569777Z         #pragma omp for 
2023-01-11T21:41:23.8569877Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8569960Z         {
2023-01-11T21:41:23.8570098Z             float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:23.8570278Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:23.8570496Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:23.8570666Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:23.8570843Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:23.8570987Z             auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:41:23.8571113Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8571209Z         }
2023-01-11T21:41:23.8571350Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8571483Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:23.8571574Z         {
2023-01-11T21:41:23.8571692Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8571877Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:23.8571988Z             auto tmp2 = in_ptr2[i0];
2023-01-11T21:41:23.8572117Z             auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:23.8572222Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.8572304Z         }
2023-01-11T21:41:23.8572380Z     }
2023-01-11T21:41:23.8572457Z }
2023-01-11T21:41:23.8572568Z ''')
2023-01-11T21:41:23.8572575Z 
2023-01-11T21:41:23.8572582Z 
2023-01-11T21:41:23.8572709Z async_compile.wait(globals())
2023-01-11T21:41:23.8572808Z del async_compile
2023-01-11T21:41:23.8572816Z 
2023-01-11T21:41:23.8572917Z def call(args):
2023-01-11T21:41:23.8573036Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8573138Z     args.clear()
2023-01-11T21:41:23.8573461Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8573730Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8573830Z     del arg0_1
2023-01-11T21:41:23.8574068Z     del arg1_1
2023-01-11T21:41:23.8574165Z     del arg2_1
2023-01-11T21:41:23.8574273Z     return (buf0, )
2023-01-11T21:41:23.8574281Z 
2023-01-11T21:41:23.8574287Z 
2023-01-11T21:41:23.8574393Z if __name__ == "__main__":
2023-01-11T21:41:23.8574559Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8574752Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8575043Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8575330Z     arg1_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8575601Z     arg2_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8575783Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8575792Z 
2023-01-11T21:41:23.8575799Z 
2023-01-11T21:41:23.8575940Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8576042Z import torch
2023-01-11T21:41:23.8576148Z import random
2023-01-11T21:41:23.8576294Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8576435Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8576456Z 
2023-01-11T21:41:23.8576548Z aten = torch.ops.aten
2023-01-11T21:41:23.8576743Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8576870Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8576877Z 
2023-01-11T21:41:23.8576882Z 
2023-01-11T21:41:23.8577075Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8577351Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8577511Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.8577659Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8577789Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8577921Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8578013Z {
2023-01-11T21:41:23.8578146Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8578243Z     {
2023-01-11T21:41:23.8578356Z         #pragma omp for 
2023-01-11T21:41:23.8578473Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.8578550Z         {
2023-01-11T21:41:23.8578694Z             float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:23.8578881Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:23.8579075Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:23.8579242Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8579404Z             auto tmp2 = at::vec::Vectorized<float>(in_ptr2[0]);
2023-01-11T21:41:23.8579512Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.8579682Z             auto tmp4 = decltype(tmp3)::blendv(tmp1, tmp3, tmp0);
2023-01-11T21:41:23.8579795Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8579976Z         }
2023-01-11T21:41:23.8580115Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8580239Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:23.8580319Z         {
2023-01-11T21:41:23.8580434Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8580552Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8580649Z             auto tmp2 = in_ptr2[0];
2023-01-11T21:41:23.8580756Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.8580876Z             auto tmp4 = tmp0 ? tmp3 : tmp1;
2023-01-11T21:41:23.8580983Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:23.8581065Z         }
2023-01-11T21:41:23.8581151Z     }
2023-01-11T21:41:23.8581242Z }
2023-01-11T21:41:23.8581367Z ''')
2023-01-11T21:41:23.8581377Z 
2023-01-11T21:41:23.8581384Z 
2023-01-11T21:41:23.8581525Z async_compile.wait(globals())
2023-01-11T21:41:23.8581632Z del async_compile
2023-01-11T21:41:23.8581639Z 
2023-01-11T21:41:23.8581735Z def call(args):
2023-01-11T21:41:23.8581930Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8582046Z     args.clear()
2023-01-11T21:41:23.8582510Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8582813Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8582912Z     del arg0_1
2023-01-11T21:41:23.8583010Z     del arg1_1
2023-01-11T21:41:23.8583109Z     del arg2_1
2023-01-11T21:41:23.8583211Z     return (buf0, )
2023-01-11T21:41:23.8583220Z 
2023-01-11T21:41:23.8583226Z 
2023-01-11T21:41:23.8583334Z if __name__ == "__main__":
2023-01-11T21:41:23.8583523Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8583723Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8584053Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8584356Z     arg1_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8584633Z     arg2_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8584804Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8584812Z 
2023-01-11T21:41:23.8584901Z ok (3.291s)
2023-01-11T21:41:23.8585619Z   test_index_put_fallback1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8585804Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8586246Z [2023-01-11 21:34:55,943] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 238
2023-01-11T21:41:23.8586730Z [2023-01-11 21:34:57,738] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 238
2023-01-11T21:41:23.8587478Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8587667Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8588113Z [2023-01-11 21:34:57,787] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 239
2023-01-11T21:41:23.8588570Z [2023-01-11 21:34:57,794] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 239
2023-01-11T21:41:23.8588579Z 
2023-01-11T21:41:23.8588728Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8588840Z import torch
2023-01-11T21:41:23.8588952Z import random
2023-01-11T21:41:23.8589264Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8589447Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8589455Z 
2023-01-11T21:41:23.8589552Z aten = torch.ops.aten
2023-01-11T21:41:23.8589744Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8589876Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8589884Z 
2023-01-11T21:41:23.8589890Z 
2023-01-11T21:41:23.8590101Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8590383Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8590548Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8590688Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8590772Z {
2023-01-11T21:41:23.8590869Z     #pragma GCC ivdep
2023-01-11T21:41:23.8590982Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.8591069Z     {
2023-01-11T21:41:23.8591168Z         {
2023-01-11T21:41:23.8591341Z             {
2023-01-11T21:41:23.8591470Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8591590Z                 out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8591658Z             }
2023-01-11T21:41:23.8591739Z         }
2023-01-11T21:41:23.8591825Z     }
2023-01-11T21:41:23.8591916Z }
2023-01-11T21:41:23.8592036Z ''')
2023-01-11T21:41:23.8592045Z 
2023-01-11T21:41:23.8592050Z 
2023-01-11T21:41:23.8592174Z async_compile.wait(globals())
2023-01-11T21:41:23.8592284Z del async_compile
2023-01-11T21:41:23.8592291Z 
2023-01-11T21:41:23.8592372Z def call(args):
2023-01-11T21:41:23.8592483Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8592583Z     args.clear()
2023-01-11T21:41:23.8592867Z     buf0 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8593068Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8593169Z     del arg0_1
2023-01-11T21:41:23.8593322Z     aten.index_put_(buf0, [arg1_1], arg2_1, False)
2023-01-11T21:41:23.8593409Z     del arg1_1
2023-01-11T21:41:23.8593507Z     del arg2_1
2023-01-11T21:41:23.8593611Z     return (buf0, )
2023-01-11T21:41:23.8593619Z 
2023-01-11T21:41:23.8593626Z 
2023-01-11T21:41:23.8593806Z if __name__ == "__main__":
2023-01-11T21:41:23.8593976Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8594160Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8594450Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8594741Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8595018Z     arg2_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8595201Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8595210Z 
2023-01-11T21:41:23.8595217Z 
2023-01-11T21:41:23.8595358Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8595458Z import torch
2023-01-11T21:41:23.8595567Z import random
2023-01-11T21:41:23.8595733Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8595920Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8595927Z 
2023-01-11T21:41:23.8596041Z aten = torch.ops.aten
2023-01-11T21:41:23.8596211Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8596346Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8596353Z 
2023-01-11T21:41:23.8596359Z 
2023-01-11T21:41:23.8596554Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8596859Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8597027Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8597175Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8597260Z {
2023-01-11T21:41:23.8597356Z     #pragma GCC ivdep
2023-01-11T21:41:23.8597469Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.8597553Z     {
2023-01-11T21:41:23.8597725Z         {
2023-01-11T21:41:23.8597815Z             {
2023-01-11T21:41:23.8597944Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8598067Z                 out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8598143Z             }
2023-01-11T21:41:23.8598228Z         }
2023-01-11T21:41:23.8598315Z     }
2023-01-11T21:41:23.8598404Z }
2023-01-11T21:41:23.8598529Z ''')
2023-01-11T21:41:23.8598537Z 
2023-01-11T21:41:23.8598543Z 
2023-01-11T21:41:23.8598674Z async_compile.wait(globals())
2023-01-11T21:41:23.8598776Z del async_compile
2023-01-11T21:41:23.8598783Z 
2023-01-11T21:41:23.8598864Z def call(args):
2023-01-11T21:41:23.8598981Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8599083Z     args.clear()
2023-01-11T21:41:23.8599371Z     buf0 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8599561Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8599662Z     del arg0_1
2023-01-11T21:41:23.8599884Z     aten.index_put_(buf0, [arg1_1], arg2_1, True)
2023-01-11T21:41:23.8599972Z     del arg1_1
2023-01-11T21:41:23.8600064Z     del arg2_1
2023-01-11T21:41:23.8600161Z     return (buf0, )
2023-01-11T21:41:23.8600169Z 
2023-01-11T21:41:23.8600174Z 
2023-01-11T21:41:23.8600283Z if __name__ == "__main__":
2023-01-11T21:41:23.8600445Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8600635Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8600931Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8601196Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8601455Z     arg2_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8601630Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8601637Z 
2023-01-11T21:41:23.8601726Z ok (1.901s)
2023-01-11T21:41:23.8602464Z   test_index_put_fallback2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8602655Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8603048Z [2023-01-11 21:34:57,846] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 240
2023-01-11T21:41:23.8603432Z [2023-01-11 21:34:59,407] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 240
2023-01-11T21:41:23.8604049Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8604230Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8604603Z [2023-01-11 21:34:59,458] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 241
2023-01-11T21:41:23.8604981Z [2023-01-11 21:34:59,467] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 241
2023-01-11T21:41:23.8604992Z 
2023-01-11T21:41:23.8605110Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8605204Z import torch
2023-01-11T21:41:23.8605298Z import random
2023-01-11T21:41:23.8605459Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8605629Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8605636Z 
2023-01-11T21:41:23.8605741Z aten = torch.ops.aten
2023-01-11T21:41:23.8605929Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8606128Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8606152Z 
2023-01-11T21:41:23.8606158Z 
2023-01-11T21:41:23.8606341Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8606636Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8606798Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8606935Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8607017Z {
2023-01-11T21:41:23.8607162Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8607246Z     {
2023-01-11T21:41:23.8607340Z         #pragma omp for 
2023-01-11T21:41:23.8607460Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:23.8607551Z         {
2023-01-11T21:41:23.8607641Z             {
2023-01-11T21:41:23.8607734Z                 {
2023-01-11T21:41:23.8607863Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8607974Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8608042Z                 }
2023-01-11T21:41:23.8608204Z             }
2023-01-11T21:41:23.8608295Z         }
2023-01-11T21:41:23.8608379Z     }
2023-01-11T21:41:23.8608462Z }
2023-01-11T21:41:23.8608586Z ''')
2023-01-11T21:41:23.8608594Z 
2023-01-11T21:41:23.8608600Z 
2023-01-11T21:41:23.8608729Z async_compile.wait(globals())
2023-01-11T21:41:23.8608814Z del async_compile
2023-01-11T21:41:23.8608821Z 
2023-01-11T21:41:23.8608922Z def call(args):
2023-01-11T21:41:23.8609044Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.8609145Z     args.clear()
2023-01-11T21:41:23.8609437Z     buf0 = empty_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8609615Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8609700Z     del arg0_1
2023-01-11T21:41:23.8609840Z     aten.index_put_(buf0, [None,arg1_1,arg2_1], arg3_1, False)
2023-01-11T21:41:23.8609928Z     del arg1_1
2023-01-11T21:41:23.8610013Z     del arg2_1
2023-01-11T21:41:23.8610097Z     del arg3_1
2023-01-11T21:41:23.8610193Z     return (buf0, )
2023-01-11T21:41:23.8610201Z 
2023-01-11T21:41:23.8610206Z 
2023-01-11T21:41:23.8610304Z if __name__ == "__main__":
2023-01-11T21:41:23.8610455Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8610601Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8610904Z     arg0_1 = rand_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8611163Z     arg1_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8611446Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8611724Z     arg3_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8611901Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.8611909Z 
2023-01-11T21:41:23.8611915Z 
2023-01-11T21:41:23.8612042Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8612134Z import torch
2023-01-11T21:41:23.8612222Z import random
2023-01-11T21:41:23.8612378Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8612546Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8612553Z 
2023-01-11T21:41:23.8612659Z aten = torch.ops.aten
2023-01-11T21:41:23.8612846Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8612979Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8612987Z 
2023-01-11T21:41:23.8612994Z 
2023-01-11T21:41:23.8613193Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8613491Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8613643Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8613778Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8613858Z {
2023-01-11T21:41:23.8613996Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8614081Z     {
2023-01-11T21:41:23.8614194Z         #pragma omp for 
2023-01-11T21:41:23.8614401Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:23.8614478Z         {
2023-01-11T21:41:23.8614560Z             {
2023-01-11T21:41:23.8614648Z                 {
2023-01-11T21:41:23.8614768Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8614878Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:23.8614972Z                 }
2023-01-11T21:41:23.8615062Z             }
2023-01-11T21:41:23.8615134Z         }
2023-01-11T21:41:23.8615224Z     }
2023-01-11T21:41:23.8615310Z }
2023-01-11T21:41:23.8615435Z ''')
2023-01-11T21:41:23.8615445Z 
2023-01-11T21:41:23.8615452Z 
2023-01-11T21:41:23.8615584Z async_compile.wait(globals())
2023-01-11T21:41:23.8615687Z del async_compile
2023-01-11T21:41:23.8615694Z 
2023-01-11T21:41:23.8615790Z def call(args):
2023-01-11T21:41:23.8615893Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.8615988Z     args.clear()
2023-01-11T21:41:23.8616293Z     buf0 = empty_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8616560Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8616661Z     del arg0_1
2023-01-11T21:41:23.8616833Z     aten.index_put_(buf0, [None,arg1_1,arg2_1], arg3_1, True)
2023-01-11T21:41:23.8616923Z     del arg1_1
2023-01-11T21:41:23.8617001Z     del arg2_1
2023-01-11T21:41:23.8617095Z     del arg3_1
2023-01-11T21:41:23.8617198Z     return (buf0, )
2023-01-11T21:41:23.8617206Z 
2023-01-11T21:41:23.8617213Z 
2023-01-11T21:41:23.8617327Z if __name__ == "__main__":
2023-01-11T21:41:23.8617504Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8617688Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8618002Z     arg0_1 = rand_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8618290Z     arg1_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8618550Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8618838Z     arg3_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8619027Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.8619035Z 
2023-01-11T21:41:23.8619129Z ok (1.672s)
2023-01-11T21:41:23.8619857Z   test_index_select_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8620041Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8620436Z [2023-01-11 21:34:59,509] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 242
2023-01-11T21:41:23.8620845Z [2023-01-11 21:35:01,244] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 242
2023-01-11T21:41:23.8621472Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8621654Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8622032Z [2023-01-11 21:35:01,287] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 243
2023-01-11T21:41:23.8622566Z [2023-01-11 21:35:02,956] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 243
2023-01-11T21:41:23.8622576Z 
2023-01-11T21:41:23.8622712Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8622814Z import torch
2023-01-11T21:41:23.8622914Z import random
2023-01-11T21:41:23.8623204Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8623385Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8623393Z 
2023-01-11T21:41:23.8623509Z aten = torch.ops.aten
2023-01-11T21:41:23.8623696Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8623833Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8623840Z 
2023-01-11T21:41:23.8623847Z 
2023-01-11T21:41:23.8624078Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8624369Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8624534Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.8624677Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8624813Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8624941Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8625155Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8625248Z {
2023-01-11T21:41:23.8625390Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8625479Z     {
2023-01-11T21:41:23.8625590Z         #pragma omp for 
2023-01-11T21:41:23.8625707Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8625797Z         {
2023-01-11T21:41:23.8625894Z             #pragma GCC ivdep
2023-01-11T21:41:23.8626009Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:23.8626099Z             {
2023-01-11T21:41:23.8626188Z                 {
2023-01-11T21:41:23.8626279Z                     {
2023-01-11T21:41:23.8626414Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8626560Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:41:23.8626681Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:41:23.8626773Z                     }
2023-01-11T21:41:23.8626857Z                 }
2023-01-11T21:41:23.8626944Z             }
2023-01-11T21:41:23.8627033Z         }
2023-01-11T21:41:23.8627152Z         #pragma omp for 
2023-01-11T21:41:23.8627249Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8627334Z         {
2023-01-11T21:41:23.8627443Z             #pragma GCC ivdep
2023-01-11T21:41:23.8627561Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8627646Z             {
2023-01-11T21:41:23.8627758Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8627878Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:23.8627951Z                 {
2023-01-11T21:41:23.8628043Z                     {
2023-01-11T21:41:23.8628138Z                         {
2023-01-11T21:41:23.8628282Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8628443Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:23.8628596Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:41:23.8628691Z                         }
2023-01-11T21:41:23.8628768Z                     }
2023-01-11T21:41:23.8628873Z                 }
2023-01-11T21:41:23.8628965Z             }
2023-01-11T21:41:23.8629049Z         }
2023-01-11T21:41:23.8629167Z         #pragma omp for 
2023-01-11T21:41:23.8629286Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8629378Z         {
2023-01-11T21:41:23.8629476Z             #pragma GCC ivdep
2023-01-11T21:41:23.8629594Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8629685Z             {
2023-01-11T21:41:23.8629804Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8629928Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:23.8630024Z                 {
2023-01-11T21:41:23.8630121Z                     {
2023-01-11T21:41:23.8630200Z                         {
2023-01-11T21:41:23.8630329Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8630457Z                             auto tmp1 = in_ptr0[i2];
2023-01-11T21:41:23.8630616Z                             auto tmp2 = in_ptr1[tmp1 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:23.8630845Z                             out_ptr2[i2 + (4*i1) + (16*i0)] = tmp2;
2023-01-11T21:41:23.8630943Z                         }
2023-01-11T21:41:23.8631038Z                     }
2023-01-11T21:41:23.8631117Z                 }
2023-01-11T21:41:23.8631206Z             }
2023-01-11T21:41:23.8631290Z         }
2023-01-11T21:41:23.8631383Z     }
2023-01-11T21:41:23.8631469Z }
2023-01-11T21:41:23.8631604Z ''')
2023-01-11T21:41:23.8631614Z 
2023-01-11T21:41:23.8631620Z 
2023-01-11T21:41:23.8631755Z async_compile.wait(globals())
2023-01-11T21:41:23.8631840Z del async_compile
2023-01-11T21:41:23.8631846Z 
2023-01-11T21:41:23.8631945Z def call(args):
2023-01-11T21:41:23.8632054Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8632154Z     args.clear()
2023-01-11T21:41:23.8632465Z     buf0 = empty_strided((4, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8632765Z     buf1 = empty_strided((8, 4, 8), (32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8633156Z     buf2 = empty_strided((8, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8633465Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8633562Z     del arg0_1
2023-01-11T21:41:23.8633661Z     del arg1_1
2023-01-11T21:41:23.8633873Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8633880Z 
2023-01-11T21:41:23.8633887Z 
2023-01-11T21:41:23.8633992Z if __name__ == "__main__":
2023-01-11T21:41:23.8634152Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8634317Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8642662Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8643082Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.8643258Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8643266Z 
2023-01-11T21:41:23.8643280Z 
2023-01-11T21:41:23.8643419Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8643520Z import torch
2023-01-11T21:41:23.8643623Z import random
2023-01-11T21:41:23.8643791Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8643967Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8643976Z 
2023-01-11T21:41:23.8644089Z aten = torch.ops.aten
2023-01-11T21:41:23.8644270Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8644401Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8644409Z 
2023-01-11T21:41:23.8644414Z 
2023-01-11T21:41:23.8644622Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8644917Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8645090Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8645248Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8645393Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8645533Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8645659Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8645746Z {
2023-01-11T21:41:23.8645886Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8645972Z     {
2023-01-11T21:41:23.8646081Z         #pragma omp for 
2023-01-11T21:41:23.8646194Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8646278Z         {
2023-01-11T21:41:23.8646372Z             #pragma GCC ivdep
2023-01-11T21:41:23.8646488Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:23.8646576Z             {
2023-01-11T21:41:23.8646665Z                 {
2023-01-11T21:41:23.8646752Z                     {
2023-01-11T21:41:23.8646880Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8647008Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:41:23.8647145Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:41:23.8647354Z                     }
2023-01-11T21:41:23.8647440Z                 }
2023-01-11T21:41:23.8647526Z             }
2023-01-11T21:41:23.8647609Z         }
2023-01-11T21:41:23.8647713Z         #pragma omp for 
2023-01-11T21:41:23.8647807Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8647890Z         {
2023-01-11T21:41:23.8648000Z             #pragma GCC ivdep
2023-01-11T21:41:23.8648110Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8648198Z             {
2023-01-11T21:41:23.8648306Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8648424Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:23.8648495Z                 {
2023-01-11T21:41:23.8648584Z                     {
2023-01-11T21:41:23.8648676Z                         {
2023-01-11T21:41:23.8648801Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8648952Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:23.8649152Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:41:23.8649252Z                         }
2023-01-11T21:41:23.8649325Z                     }
2023-01-11T21:41:23.8649410Z                 }
2023-01-11T21:41:23.8649492Z             }
2023-01-11T21:41:23.8649577Z         }
2023-01-11T21:41:23.8649682Z         #pragma omp for 
2023-01-11T21:41:23.8649789Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8649874Z         {
2023-01-11T21:41:23.8649964Z             #pragma GCC ivdep
2023-01-11T21:41:23.8650074Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8650158Z             {
2023-01-11T21:41:23.8650269Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8650387Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:23.8650471Z                 {
2023-01-11T21:41:23.8650546Z                     {
2023-01-11T21:41:23.8650639Z                         {
2023-01-11T21:41:23.8650770Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.8650897Z                             auto tmp1 = in_ptr0[i2];
2023-01-11T21:41:23.8651057Z                             auto tmp2 = in_ptr1[tmp1 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:23.8651193Z                             out_ptr2[i2 + (4*i1) + (16*i0)] = tmp2;
2023-01-11T21:41:23.8651284Z                         }
2023-01-11T21:41:23.8651372Z                     }
2023-01-11T21:41:23.8651443Z                 }
2023-01-11T21:41:23.8651528Z             }
2023-01-11T21:41:23.8651613Z         }
2023-01-11T21:41:23.8651700Z     }
2023-01-11T21:41:23.8651785Z }
2023-01-11T21:41:23.8651915Z ''')
2023-01-11T21:41:23.8651924Z 
2023-01-11T21:41:23.8651931Z 
2023-01-11T21:41:23.8652048Z async_compile.wait(globals())
2023-01-11T21:41:23.8652145Z del async_compile
2023-01-11T21:41:23.8652153Z 
2023-01-11T21:41:23.8652249Z def call(args):
2023-01-11T21:41:23.8652347Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8652445Z     args.clear()
2023-01-11T21:41:23.8652763Z     buf0 = empty_strided((4, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8653090Z     buf1 = empty_strided((8, 4, 8), (32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8653386Z     buf2 = empty_strided((8, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8653696Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8653793Z     del arg0_1
2023-01-11T21:41:23.8653884Z     del arg1_1
2023-01-11T21:41:23.8654002Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8654011Z 
2023-01-11T21:41:23.8654017Z 
2023-01-11T21:41:23.8654121Z if __name__ == "__main__":
2023-01-11T21:41:23.8654279Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8654458Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8654766Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8655038Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8655299Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8655307Z 
2023-01-11T21:41:23.8655402Z ok (3.490s)
2023-01-11T21:41:23.8656100Z   test_indirect_load_broadcast_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8656276Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8656665Z [2023-01-11 21:35:03,049] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 244
2023-01-11T21:41:23.8657055Z [2023-01-11 21:35:04,652] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 244
2023-01-11T21:41:23.8657128Z 
2023-01-11T21:41:23.8657265Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8657362Z import torch
2023-01-11T21:41:23.8657443Z import random
2023-01-11T21:41:23.8657601Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8657764Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8657773Z 
2023-01-11T21:41:23.8657880Z aten = torch.ops.aten
2023-01-11T21:41:23.8658065Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8658187Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8658194Z 
2023-01-11T21:41:23.8658201Z 
2023-01-11T21:41:23.8658397Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8658684Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8658833Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8658975Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8659121Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8659258Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8659351Z {
2023-01-11T21:41:23.8659491Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8659580Z     {
2023-01-11T21:41:23.8659676Z         #pragma omp for 
2023-01-11T21:41:23.8659789Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.8659875Z         {
2023-01-11T21:41:23.8659983Z             #pragma GCC ivdep
2023-01-11T21:41:23.8660100Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:41:23.8660187Z             {
2023-01-11T21:41:23.8660279Z                 {
2023-01-11T21:41:23.8660358Z                     {
2023-01-11T21:41:23.8660495Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:23.8660630Z                         auto tmp2 = in_ptr2[i0];
2023-01-11T21:41:23.8660780Z                         auto tmp1 = in_ptr1[i1 + (512*tmp0)];
2023-01-11T21:41:23.8660916Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.8661062Z                         out_ptr0[i1 + (21*i0)] = tmp3;
2023-01-11T21:41:23.8661154Z                     }
2023-01-11T21:41:23.8661231Z                 }
2023-01-11T21:41:23.8661317Z             }
2023-01-11T21:41:23.8661407Z         }
2023-01-11T21:41:23.8661491Z     }
2023-01-11T21:41:23.8661576Z }
2023-01-11T21:41:23.8661700Z ''')
2023-01-11T21:41:23.8661707Z 
2023-01-11T21:41:23.8661713Z 
2023-01-11T21:41:23.8661834Z async_compile.wait(globals())
2023-01-11T21:41:23.8661917Z del async_compile
2023-01-11T21:41:23.8661923Z 
2023-01-11T21:41:23.8662013Z def call(args):
2023-01-11T21:41:23.8662124Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8662218Z     args.clear()
2023-01-11T21:41:23.8662676Z     buf0 = empty_strided((32, 21), (21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8662938Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8663141Z     del arg0_1
2023-01-11T21:41:23.8663219Z     del arg1_1
2023-01-11T21:41:23.8663311Z     del arg2_1
2023-01-11T21:41:23.8663413Z     return (buf0, )
2023-01-11T21:41:23.8663421Z 
2023-01-11T21:41:23.8663427Z 
2023-01-11T21:41:23.8663537Z if __name__ == "__main__":
2023-01-11T21:41:23.8663705Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8663887Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8664182Z     arg0_1 = rand_strided((32, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8664482Z     arg1_1 = rand_strided((9521, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8664753Z     arg2_1 = rand_strided((32, 21), (1, 32), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8664932Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8664940Z 
2023-01-11T21:41:23.8665034Z ok (1.800s)
2023-01-11T21:41:23.8665833Z   test_inf_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8666033Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8666436Z [2023-01-11 21:35:04,786] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 245
2023-01-11T21:41:23.8666832Z [2023-01-11 21:35:06,451] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 245
2023-01-11T21:41:23.8666842Z 
2023-01-11T21:41:23.8666971Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8667068Z import torch
2023-01-11T21:41:23.8667157Z import random
2023-01-11T21:41:23.8667298Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8667480Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8667491Z 
2023-01-11T21:41:23.8667596Z aten = torch.ops.aten
2023-01-11T21:41:23.8667785Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8667907Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8667914Z 
2023-01-11T21:41:23.8667919Z 
2023-01-11T21:41:23.8668123Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8668418Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8668583Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8668703Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8668836Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8668963Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8669055Z {
2023-01-11T21:41:23.8669199Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8669290Z     {
2023-01-11T21:41:23.8669399Z         #pragma omp for 
2023-01-11T21:41:23.8669507Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.8669594Z         {
2023-01-11T21:41:23.8669789Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8670001Z             auto tmp1 = at::vec::Vectorized<float>(std::numeric_limits<float>::infinity());
2023-01-11T21:41:23.8670118Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8670444Z             auto tmp3 = at::vec::Vectorized<float>(-std::numeric_limits<float>::infinity());
2023-01-11T21:41:23.8670559Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.8670657Z             auto tmp5 = tmp0 * tmp3;
2023-01-11T21:41:23.8670782Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8670903Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8671033Z             tmp5.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8671122Z         }
2023-01-11T21:41:23.8671259Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8671375Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:23.8671529Z         {
2023-01-11T21:41:23.8671644Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8671816Z             auto tmp1 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.8671941Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8672286Z             auto tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.8672405Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:23.8672527Z             auto tmp5 = tmp0 * tmp3;
2023-01-11T21:41:23.8672622Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8672739Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.8672851Z             out_ptr2[i0] = tmp5;
2023-01-11T21:41:23.8672940Z         }
2023-01-11T21:41:23.8673031Z     }
2023-01-11T21:41:23.8673110Z }
2023-01-11T21:41:23.8673225Z ''')
2023-01-11T21:41:23.8673233Z 
2023-01-11T21:41:23.8673238Z 
2023-01-11T21:41:23.8673349Z async_compile.wait(globals())
2023-01-11T21:41:23.8673448Z del async_compile
2023-01-11T21:41:23.8673460Z 
2023-01-11T21:41:23.8673625Z def call(args):
2023-01-11T21:41:23.8673792Z     arg0_1, = args
2023-01-11T21:41:23.8673892Z     args.clear()
2023-01-11T21:41:23.8674186Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8674476Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8674738Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8674980Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8675072Z     del arg0_1
2023-01-11T21:41:23.8675189Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:23.8675198Z 
2023-01-11T21:41:23.8675204Z 
2023-01-11T21:41:23.8675309Z if __name__ == "__main__":
2023-01-11T21:41:23.8675460Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8675628Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8675912Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8676055Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8676062Z 
2023-01-11T21:41:23.8676133Z ok (1.695s)
2023-01-11T21:41:23.8676832Z   test_inplace_activations_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8677015Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8677415Z [2023-01-11 21:35:06,603] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 246
2023-01-11T21:41:23.8677820Z [2023-01-11 21:35:08,372] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 246
2023-01-11T21:41:23.8677831Z 
2023-01-11T21:41:23.8677957Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8678048Z import torch
2023-01-11T21:41:23.8678143Z import random
2023-01-11T21:41:23.8678297Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8678442Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8678449Z 
2023-01-11T21:41:23.8678552Z aten = torch.ops.aten
2023-01-11T21:41:23.8678741Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8678872Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8678880Z 
2023-01-11T21:41:23.8678887Z 
2023-01-11T21:41:23.8679079Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8679374Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8679538Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8679677Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8679884Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8680020Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.8680193Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.8680395Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:23.8680596Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:23.8680778Z                        float* __restrict__ out_ptr6)
2023-01-11T21:41:23.8680903Z {
2023-01-11T21:41:23.8681087Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8681212Z     {
2023-01-11T21:41:23.8681365Z         #pragma omp for 
2023-01-11T21:41:23.8681528Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.8681654Z         {
2023-01-11T21:41:23.8681782Z             {
2023-01-11T21:41:23.8681908Z                 {
2023-01-11T21:41:23.8682066Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8682265Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8682523Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8682732Z                     auto tmp3 = static_cast<float>(3);
2023-01-11T21:41:23.8682901Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.8683104Z                     auto tmp5 = static_cast<float>(0.0);
2023-01-11T21:41:23.8683349Z                     auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp4, tmp5);
2023-01-11T21:41:23.8683540Z                     auto tmp7 = static_cast<float>(6.0);
2023-01-11T21:41:23.8683778Z                     auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp6, tmp7);
2023-01-11T21:41:23.8683938Z                     auto tmp9 = tmp2 * tmp8;
2023-01-11T21:41:23.8684122Z                     auto tmp10 = static_cast<float>(6);
2023-01-11T21:41:23.8684285Z                     auto tmp11 = tmp9 / tmp10;
2023-01-11T21:41:23.8684595Z                     auto tmp12 = static_cast<float>(-1.0);
2023-01-11T21:41:23.8684821Z                     auto tmp13 = (tmp12 != tmp12) ? tmp12 : std::max(tmp2, tmp12);
2023-01-11T21:41:23.8685016Z                     auto tmp14 = static_cast<float>(1.0);
2023-01-11T21:41:23.8685237Z                     auto tmp15 = (tmp14 != tmp14) ? tmp14 : std::min(tmp13, tmp14);
2023-01-11T21:41:23.8685426Z                     auto tmp16 = static_cast<float>(0);
2023-01-11T21:41:23.8685588Z                     auto tmp17 = tmp2 > tmp16;
2023-01-11T21:41:23.8685781Z                     auto tmp18 = static_cast<float>(0.01);
2023-01-11T21:41:23.8685949Z                     auto tmp19 = tmp2 * tmp18;
2023-01-11T21:41:23.8686135Z                     auto tmp20 = tmp17 ? tmp2 : tmp19;
2023-01-11T21:41:23.8686420Z                     auto tmp21 = std::exp(-tmp2);
2023-01-11T21:41:23.8686585Z                     auto tmp22 = 1 / (1 + tmp21);
2023-01-11T21:41:23.8686748Z                     auto tmp23 = tmp2 * tmp22;
2023-01-11T21:41:23.8686934Z                     auto tmp24 = std::log1p(tmp2);
2023-01-11T21:41:23.8687130Z                     auto tmp25 = static_cast<bool>(0);
2023-01-11T21:41:23.8687338Z                     auto tmp26 = static_cast<float>(99.0);
2023-01-11T21:41:23.8687526Z                     auto tmp27 = tmp25 ? tmp26 : tmp2;
2023-01-11T21:41:23.8687717Z                     auto tmp28 = static_cast<bool>(1);
2023-01-11T21:41:23.8687904Z                     auto tmp29 = tmp28 ? tmp26 : tmp2;
2023-01-11T21:41:23.8688049Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:23.8688211Z                     out_ptr1[i0] = tmp15;
2023-01-11T21:41:23.8688377Z                     out_ptr2[i0] = tmp20;
2023-01-11T21:41:23.8688534Z                     out_ptr3[i0] = tmp23;
2023-01-11T21:41:23.8688692Z                     out_ptr4[i0] = tmp24;
2023-01-11T21:41:23.8688846Z                     out_ptr5[i0] = tmp27;
2023-01-11T21:41:23.8689002Z                     out_ptr6[i0] = tmp29;
2023-01-11T21:41:23.8689118Z                 }
2023-01-11T21:41:23.8689244Z             }
2023-01-11T21:41:23.8689371Z         }
2023-01-11T21:41:23.8689493Z     }
2023-01-11T21:41:23.8689702Z }
2023-01-11T21:41:23.8689866Z ''')
2023-01-11T21:41:23.8689876Z 
2023-01-11T21:41:23.8689882Z 
2023-01-11T21:41:23.8690064Z async_compile.wait(globals())
2023-01-11T21:41:23.8690195Z del async_compile
2023-01-11T21:41:23.8690217Z 
2023-01-11T21:41:23.8690336Z def call(args):
2023-01-11T21:41:23.8690469Z     arg0_1, = args
2023-01-11T21:41:23.8690605Z     args.clear()
2023-01-11T21:41:23.8690976Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8691339Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8691685Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8692036Z     buf3 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8692362Z     buf4 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8692694Z     buf5 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8693112Z     buf6 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8693647Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:41:23.8693763Z     del arg0_1
2023-01-11T21:41:23.8693955Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, )
2023-01-11T21:41:23.8693965Z 
2023-01-11T21:41:23.8693973Z 
2023-01-11T21:41:23.8694112Z if __name__ == "__main__":
2023-01-11T21:41:23.8694327Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8694547Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8694911Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8695119Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8695127Z 
2023-01-11T21:41:23.8695261Z ok (1.923s)
2023-01-11T21:41:23.8695912Z   test_inplace_add_cpu (__main__.CpuTests) ... [2023-01-11 21:35:08,393] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 247
2023-01-11T21:41:23.8696429Z [2023-01-11 21:35:10,091] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 247
2023-01-11T21:41:23.8696438Z 
2023-01-11T21:41:23.8696618Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8696758Z import torch
2023-01-11T21:41:23.8696873Z import random
2023-01-11T21:41:23.8697040Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8697255Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8697263Z 
2023-01-11T21:41:23.8697413Z aten = torch.ops.aten
2023-01-11T21:41:23.8697660Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8697838Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8697847Z 
2023-01-11T21:41:23.8697854Z 
2023-01-11T21:41:23.8698126Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8698538Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8698777Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8698964Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8699155Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8699277Z {
2023-01-11T21:41:23.8699463Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8699576Z     {
2023-01-11T21:41:23.8699722Z         #pragma omp for 
2023-01-11T21:41:23.8699878Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8699987Z         {
2023-01-11T21:41:23.8700247Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8700504Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8700667Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8700826Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8701040Z         }
2023-01-11T21:41:23.8701226Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8701361Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.8701478Z         {
2023-01-11T21:41:23.8701641Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8701798Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8701958Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8702112Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8702228Z         }
2023-01-11T21:41:23.8702464Z     }
2023-01-11T21:41:23.8702556Z }
2023-01-11T21:41:23.8702722Z ''')
2023-01-11T21:41:23.8702732Z 
2023-01-11T21:41:23.8702739Z 
2023-01-11T21:41:23.8702914Z async_compile.wait(globals())
2023-01-11T21:41:23.8703049Z del async_compile
2023-01-11T21:41:23.8703057Z 
2023-01-11T21:41:23.8703191Z def call(args):
2023-01-11T21:41:23.8703332Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8703451Z     args.clear()
2023-01-11T21:41:23.8703889Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:23.8704025Z     del arg1_1
2023-01-11T21:41:23.8704174Z     return (arg0_1, )
2023-01-11T21:41:23.8704182Z 
2023-01-11T21:41:23.8704189Z 
2023-01-11T21:41:23.8704339Z if __name__ == "__main__":
2023-01-11T21:41:23.8704555Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8704778Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8705118Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8705435Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8705651Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8705659Z 
2023-01-11T21:41:23.8705791Z ok (1.716s)
2023-01-11T21:41:23.8706427Z   test_inplace_mixed_dtype_ops_cpu (__main__.CpuTests) ... [2023-01-11 21:35:10,139] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 248
2023-01-11T21:41:23.8706891Z [2023-01-11 21:35:11,800] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 248
2023-01-11T21:41:23.8706899Z 
2023-01-11T21:41:23.8707044Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8707152Z import torch
2023-01-11T21:41:23.8707255Z import random
2023-01-11T21:41:23.8707411Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8707592Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8707600Z 
2023-01-11T21:41:23.8707743Z aten = torch.ops.aten
2023-01-11T21:41:23.8707983Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8708137Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8708145Z 
2023-01-11T21:41:23.8708150Z 
2023-01-11T21:41:23.8708397Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8708772Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8708981Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.8709164Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8709347Z                        const double* __restrict__ in_ptr1)
2023-01-11T21:41:23.8709459Z {
2023-01-11T21:41:23.8709621Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8709723Z     {
2023-01-11T21:41:23.8709857Z         #pragma omp for 
2023-01-11T21:41:23.8709996Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8710095Z         {
2023-01-11T21:41:23.8710198Z             {
2023-01-11T21:41:23.8710308Z                 {
2023-01-11T21:41:23.8710458Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8710601Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8710772Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:23.8710927Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:23.8711093Z                     auto tmp4 = static_cast<double>(tmp3);
2023-01-11T21:41:23.8711250Z                     auto tmp5 = tmp4 + tmp1;
2023-01-11T21:41:23.8711534Z                     auto tmp6 = static_cast<float>(tmp5);
2023-01-11T21:41:23.8711693Z                     auto tmp7 = static_cast<double>(tmp6);
2023-01-11T21:41:23.8711844Z                     auto tmp8 = tmp7 * tmp1;
2023-01-11T21:41:23.8712036Z                     auto tmp9 = static_cast<float>(tmp8);
2023-01-11T21:41:23.8712203Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.8712319Z                 }
2023-01-11T21:41:23.8712434Z             }
2023-01-11T21:41:23.8712533Z         }
2023-01-11T21:41:23.8712643Z     }
2023-01-11T21:41:23.8712736Z }
2023-01-11T21:41:23.8712881Z ''')
2023-01-11T21:41:23.8712891Z 
2023-01-11T21:41:23.8712898Z 
2023-01-11T21:41:23.8713046Z async_compile.wait(globals())
2023-01-11T21:41:23.8713124Z del async_compile
2023-01-11T21:41:23.8713131Z 
2023-01-11T21:41:23.8713222Z def call(args):
2023-01-11T21:41:23.8713316Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8713405Z     args.clear()
2023-01-11T21:41:23.8713828Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8713980Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:23.8714250Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()))
2023-01-11T21:41:23.8714328Z     del arg0_1
2023-01-11T21:41:23.8714425Z     del arg1_1
2023-01-11T21:41:23.8714533Z     return (buf1, )
2023-01-11T21:41:23.8714541Z 
2023-01-11T21:41:23.8714546Z 
2023-01-11T21:41:23.8714661Z if __name__ == "__main__":
2023-01-11T21:41:23.8714853Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8715063Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8715402Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8715766Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8715960Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8715976Z 
2023-01-11T21:41:23.8716072Z ok (1.709s)
2023-01-11T21:41:23.8716678Z   test_input_mutation1_cpu (__main__.CpuTests) ... [2023-01-11 21:35:11,820] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 249
2023-01-11T21:41:23.8717056Z [2023-01-11 21:35:11,833] torch._inductor.scheduler: [DEBUG] remove_buffer('buf0')
2023-01-11T21:41:23.8717428Z [2023-01-11 21:35:11,835] torch._inductor.scheduler: [DEBUG] remove_buffer('buf0')
2023-01-11T21:41:23.8717878Z [2023-01-11 21:35:13,436] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 249
2023-01-11T21:41:23.8717889Z 
2023-01-11T21:41:23.8718052Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8718159Z import torch
2023-01-11T21:41:23.8718267Z import random
2023-01-11T21:41:23.8718470Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8718691Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8718699Z 
2023-01-11T21:41:23.8718841Z aten = torch.ops.aten
2023-01-11T21:41:23.8719103Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8719256Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8719264Z 
2023-01-11T21:41:23.8719271Z 
2023-01-11T21:41:23.8719503Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8719823Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8720001Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8720169Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.8720332Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:23.8720435Z {
2023-01-11T21:41:23.8720609Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8720698Z     {
2023-01-11T21:41:23.8720833Z         #pragma omp for 
2023-01-11T21:41:23.8720954Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8721070Z         {
2023-01-11T21:41:23.8721311Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8721694Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8721848Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8721990Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:23.8722208Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8722352Z             auto tmp5 = tmp2 + tmp4;
2023-01-11T21:41:23.8722474Z             auto tmp6 = tmp3 / tmp5;
2023-01-11T21:41:23.8722625Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8722764Z             tmp6.store(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8722862Z         }
2023-01-11T21:41:23.8723007Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8723134Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8723232Z         {
2023-01-11T21:41:23.8723357Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:23.8723517Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8723733Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8723889Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:23.8724058Z             auto tmp4 = static_cast<float>(2);
2023-01-11T21:41:23.8724205Z             auto tmp5 = tmp2 + tmp4;
2023-01-11T21:41:23.8724355Z             auto tmp6 = tmp3 / tmp5;
2023-01-11T21:41:23.8724488Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8724637Z             out_ptr2[i0] = tmp6;
2023-01-11T21:41:23.8724755Z         }
2023-01-11T21:41:23.8724869Z     }
2023-01-11T21:41:23.8724985Z }
2023-01-11T21:41:23.8725152Z ''')
2023-01-11T21:41:23.8725165Z 
2023-01-11T21:41:23.8725172Z 
2023-01-11T21:41:23.8725343Z async_compile.wait(globals())
2023-01-11T21:41:23.8725471Z del async_compile
2023-01-11T21:41:23.8725479Z 
2023-01-11T21:41:23.8725601Z def call(args):
2023-01-11T21:41:23.8725729Z     arg0_1, = args
2023-01-11T21:41:23.8725850Z     args.clear()
2023-01-11T21:41:23.8726182Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8726455Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:23.8726558Z     del arg0_1
2023-01-11T21:41:23.8726655Z     return (buf2, )
2023-01-11T21:41:23.8726663Z 
2023-01-11T21:41:23.8726684Z 
2023-01-11T21:41:23.8726783Z if __name__ == "__main__":
2023-01-11T21:41:23.8726966Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8727165Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8727478Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8727664Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8727672Z 
2023-01-11T21:41:23.8727785Z ok (1.636s)
2023-01-11T21:41:23.8728400Z   test_input_mutation2_cpu (__main__.CpuTests) ... [2023-01-11 21:35:13,505] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 250
2023-01-11T21:41:23.8728768Z [2023-01-11 21:35:13,508] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:41:23.8728941Z   target: aten.expand_copy.default
2023-01-11T21:41:23.8729081Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:41:23.8729194Z     Pointwise(
2023-01-11T21:41:23.8729353Z       'cpu',
2023-01-11T21:41:23.8729495Z       torch.float32,
2023-01-11T21:41:23.8729662Z       tmp0 = constant(66.0, torch.float32)
2023-01-11T21:41:23.8729773Z       return tmp0
2023-01-11T21:41:23.8729890Z       ,
2023-01-11T21:41:23.8730019Z       ranges=[1],
2023-01-11T21:41:23.8730217Z       origins={lift_fresh_copy, _tensor_constant0}
2023-01-11T21:41:23.8730328Z     )
2023-01-11T21:41:23.8730423Z   ))
2023-01-11T21:41:23.8730537Z   args[1]: [64]
2023-01-11T21:41:23.8730996Z [2023-01-11 21:35:13,516] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten.expand_copy.default
2023-01-11T21:41:23.8731461Z [2023-01-11 21:35:15,200] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 250
2023-01-11T21:41:23.8731470Z 
2023-01-11T21:41:23.8731743Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8731870Z import torch
2023-01-11T21:41:23.8731987Z import random
2023-01-11T21:41:23.8732195Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8732430Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8732438Z 
2023-01-11T21:41:23.8732595Z aten = torch.ops.aten
2023-01-11T21:41:23.8732841Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8733021Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8733031Z 
2023-01-11T21:41:23.8733038Z 
2023-01-11T21:41:23.8733307Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8733687Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8733918Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8734106Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8734289Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8734492Z {
2023-01-11T21:41:23.8734678Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8734805Z     {
2023-01-11T21:41:23.8734959Z         #pragma omp for 
2023-01-11T21:41:23.8735118Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8735246Z         {
2023-01-11T21:41:23.8735503Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8735759Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8735912Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8736095Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8736225Z         }
2023-01-11T21:41:23.8736413Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8736572Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8736700Z         {
2023-01-11T21:41:23.8736851Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8737042Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8737216Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8737376Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8737502Z         }
2023-01-11T21:41:23.8737660Z         #pragma omp single
2023-01-11T21:41:23.8737787Z         {
2023-01-11T21:41:23.8737901Z             {
2023-01-11T21:41:23.8738031Z                 {
2023-01-11T21:41:23.8738235Z                     auto tmp0 = static_cast<float>(66.0);
2023-01-11T21:41:23.8738397Z                     out_ptr1[0] = tmp0;
2023-01-11T21:41:23.8738527Z                 }
2023-01-11T21:41:23.8738657Z             }
2023-01-11T21:41:23.8738785Z         }
2023-01-11T21:41:23.8738895Z     }
2023-01-11T21:41:23.8739021Z }
2023-01-11T21:41:23.8739196Z ''')
2023-01-11T21:41:23.8739206Z 
2023-01-11T21:41:23.8739212Z 
2023-01-11T21:41:23.8739471Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.8739851Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8740088Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8740280Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8740391Z {
2023-01-11T21:41:23.8740583Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8740711Z     {
2023-01-11T21:41:23.8740866Z         #pragma omp for 
2023-01-11T21:41:23.8741027Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8741156Z         {
2023-01-11T21:41:23.8741416Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8741656Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8741824Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8742001Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8742132Z         }
2023-01-11T21:41:23.8742457Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8742627Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8742754Z         {
2023-01-11T21:41:23.8742905Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8743214Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.8743379Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8743536Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8743663Z         }
2023-01-11T21:41:23.8743788Z     }
2023-01-11T21:41:23.8743913Z }
2023-01-11T21:41:23.8744075Z ''')
2023-01-11T21:41:23.8744084Z 
2023-01-11T21:41:23.8744090Z 
2023-01-11T21:41:23.8744271Z async_compile.wait(globals())
2023-01-11T21:41:23.8744419Z del async_compile
2023-01-11T21:41:23.8744427Z 
2023-01-11T21:41:23.8744571Z def call(args):
2023-01-11T21:41:23.8744724Z     primals_1, = args
2023-01-11T21:41:23.8744868Z     args.clear()
2023-01-11T21:41:23.8745245Z     buf0 = empty_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8745603Z     buf1 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8745910Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8746152Z     del primals_1
2023-01-11T21:41:23.8746399Z     buf2 = torch.ops.aten.expand_copy.default(buf1, [64])
2023-01-11T21:41:23.8746532Z     del buf1
2023-01-11T21:41:23.8746671Z     buf3 = buf2
2023-01-11T21:41:23.8746857Z     assert_size_stride(buf3, (64, ), (1, ))
2023-01-11T21:41:23.8746992Z     del buf2
2023-01-11T21:41:23.8747351Z     buf4 = empty_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8747603Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.8747808Z     return (as_strided(buf3, (1, 64), (64, 1)), buf0, buf4, )
2023-01-11T21:41:23.8747817Z 
2023-01-11T21:41:23.8747825Z 
2023-01-11T21:41:23.8747974Z if __name__ == "__main__":
2023-01-11T21:41:23.8748193Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8748433Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8748816Z     primals_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8749047Z     print_performance(lambda: call([primals_1]))
2023-01-11T21:41:23.8749055Z 
2023-01-11T21:41:23.8749175Z ok (1.765s)
2023-01-11T21:41:23.8749821Z   test_input_mutation3_cpu (__main__.CpuTests) ... [2023-01-11 21:35:15,252] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 251
2023-01-11T21:41:23.8750333Z [2023-01-11 21:35:16,918] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 251
2023-01-11T21:41:23.8750342Z 
2023-01-11T21:41:23.8750529Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8750670Z import torch
2023-01-11T21:41:23.8750813Z import random
2023-01-11T21:41:23.8751038Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8751273Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8751281Z 
2023-01-11T21:41:23.8751424Z aten = torch.ops.aten
2023-01-11T21:41:23.8751684Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8751874Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8751883Z 
2023-01-11T21:41:23.8751890Z 
2023-01-11T21:41:23.8752152Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8752533Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8752764Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8752946Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8753063Z {
2023-01-11T21:41:23.8753228Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8753345Z     {
2023-01-11T21:41:23.8753448Z         #pragma omp for 
2023-01-11T21:41:23.8753565Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8753666Z         {
2023-01-11T21:41:23.8753938Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8754142Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8754253Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8754465Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8754555Z         }
2023-01-11T21:41:23.8754711Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8754825Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8754920Z         {
2023-01-11T21:41:23.8755045Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8755188Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:23.8755316Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8755440Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8755535Z         }
2023-01-11T21:41:23.8755635Z         #pragma omp for 
2023-01-11T21:41:23.8755755Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8755848Z         {
2023-01-11T21:41:23.8756035Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8756239Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8756365Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8756549Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8756630Z         }
2023-01-11T21:41:23.8756754Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8756863Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8756928Z         {
2023-01-11T21:41:23.8757048Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8757189Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.8757306Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8757421Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8757513Z         }
2023-01-11T21:41:23.8757621Z         #pragma omp for 
2023-01-11T21:41:23.8757719Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8757807Z         {
2023-01-11T21:41:23.8758004Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8758205Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:23.8758332Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8758414Z         }
2023-01-11T21:41:23.8758542Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8758633Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8758721Z         {
2023-01-11T21:41:23.8758845Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8759076Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:23.8759196Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:23.8759311Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8759392Z         }
2023-01-11T21:41:23.8759479Z         #pragma omp for 
2023-01-11T21:41:23.8759594Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8759676Z         {
2023-01-11T21:41:23.8759856Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8760036Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:23.8760147Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8760271Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8760337Z         }
2023-01-11T21:41:23.8760462Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8760569Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8760650Z         {
2023-01-11T21:41:23.8760763Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8760895Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:23.8761008Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.8761096Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8761181Z         }
2023-01-11T21:41:23.8761284Z         #pragma omp for 
2023-01-11T21:41:23.8761393Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8761477Z         {
2023-01-11T21:41:23.8761660Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8761836Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:23.8761934Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8762145Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8762229Z         }
2023-01-11T21:41:23.8762360Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8762472Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8762558Z         {
2023-01-11T21:41:23.8762672Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8762784Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:23.8762890Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8762988Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8763066Z         }
2023-01-11T21:41:23.8763163Z         #pragma omp for 
2023-01-11T21:41:23.8763266Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8763342Z         {
2023-01-11T21:41:23.8763510Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8763682Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.8763800Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8763881Z         }
2023-01-11T21:41:23.8764065Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8764180Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8764258Z         {
2023-01-11T21:41:23.8764365Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8764483Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.8764598Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8764690Z         }
2023-01-11T21:41:23.8764774Z     }
2023-01-11T21:41:23.8764857Z }
2023-01-11T21:41:23.8764969Z ''')
2023-01-11T21:41:23.8764993Z 
2023-01-11T21:41:23.8764998Z 
2023-01-11T21:41:23.8765110Z async_compile.wait(globals())
2023-01-11T21:41:23.8765218Z del async_compile
2023-01-11T21:41:23.8765226Z 
2023-01-11T21:41:23.8765321Z def call(args):
2023-01-11T21:41:23.8765413Z     arg0_1, = args
2023-01-11T21:41:23.8765508Z     args.clear()
2023-01-11T21:41:23.8765696Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:23.8765832Z     return (as_strided(arg0_1, (64, ), (1, )), )
2023-01-11T21:41:23.8765849Z 
2023-01-11T21:41:23.8765854Z 
2023-01-11T21:41:23.8765959Z if __name__ == "__main__":
2023-01-11T21:41:23.8766106Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8766274Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8766575Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8766727Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8766734Z 
2023-01-11T21:41:23.8766828Z ok (1.717s)
2023-01-11T21:41:23.8767320Z   test_input_mutation4_cpu (__main__.CpuTests) ... [2023-01-11 21:35:16,934] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 252
2023-01-11T21:41:23.8767688Z [2023-01-11 21:35:18,507] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 252
2023-01-11T21:41:23.8767696Z 
2023-01-11T21:41:23.8767820Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8767894Z import torch
2023-01-11T21:41:23.8767995Z import random
2023-01-11T21:41:23.8768152Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8768312Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8768318Z 
2023-01-11T21:41:23.8768427Z aten = torch.ops.aten
2023-01-11T21:41:23.8768605Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8768724Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8768731Z 
2023-01-11T21:41:23.8768736Z 
2023-01-11T21:41:23.8768922Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8769184Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8769344Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8769476Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8769560Z {
2023-01-11T21:41:23.8769690Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8769770Z     {
2023-01-11T21:41:23.8769962Z         #pragma omp for 
2023-01-11T21:41:23.8770055Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8770134Z         {
2023-01-11T21:41:23.8770315Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8770493Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.8770626Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8770721Z         }
2023-01-11T21:41:23.8770855Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8770954Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.8771038Z         {
2023-01-11T21:41:23.8771154Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:23.8771271Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.8771381Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8771463Z         }
2023-01-11T21:41:23.8771548Z     }
2023-01-11T21:41:23.8771612Z }
2023-01-11T21:41:23.8771733Z ''')
2023-01-11T21:41:23.8771741Z 
2023-01-11T21:41:23.8771747Z 
2023-01-11T21:41:23.8771960Z async_compile.wait(globals())
2023-01-11T21:41:23.8772068Z del async_compile
2023-01-11T21:41:23.8772074Z 
2023-01-11T21:41:23.8772169Z def call(args):
2023-01-11T21:41:23.8772261Z     arg0_1, = args
2023-01-11T21:41:23.8772353Z     args.clear()
2023-01-11T21:41:23.8772523Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:23.8772619Z     return (arg0_1, )
2023-01-11T21:41:23.8772625Z 
2023-01-11T21:41:23.8772631Z 
2023-01-11T21:41:23.8772729Z if __name__ == "__main__":
2023-01-11T21:41:23.8772879Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8773040Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8773331Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8773473Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8773480Z 
2023-01-11T21:41:23.8773567Z ok (1.589s)
2023-01-11T21:41:23.8774253Z   test_invalid_operand_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8774409Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8774784Z [2023-01-11 21:35:19,343] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 253
2023-01-11T21:41:23.8775177Z [2023-01-11 21:35:21,531] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 253
2023-01-11T21:41:23.8775187Z 
2023-01-11T21:41:23.8775319Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8775413Z import torch
2023-01-11T21:41:23.8775514Z import random
2023-01-11T21:41:23.8775677Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8775855Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8775862Z 
2023-01-11T21:41:23.8775952Z aten = torch.ops.aten
2023-01-11T21:41:23.8776135Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8776258Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8776265Z 
2023-01-11T21:41:23.8776270Z 
2023-01-11T21:41:23.8776474Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8776753Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8776910Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.8777052Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.8777188Z                        const long* __restrict__ in_ptr2,
2023-01-11T21:41:23.8777312Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:23.8777443Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8777527Z {
2023-01-11T21:41:23.8777755Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8777841Z     {
2023-01-11T21:41:23.8777948Z         #pragma omp for 
2023-01-11T21:41:23.8778055Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.8778123Z         {
2023-01-11T21:41:23.8778227Z             #pragma GCC ivdep
2023-01-11T21:41:23.8778340Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:23.8778425Z             {
2023-01-11T21:41:23.8778536Z                 #pragma GCC ivdep
2023-01-11T21:41:23.8778663Z                 for(long i2=0; i2<768; i2+=1)
2023-01-11T21:41:23.8778750Z                 {
2023-01-11T21:41:23.8778825Z                     {
2023-01-11T21:41:23.8778910Z                         {
2023-01-11T21:41:23.8779039Z                             auto tmp3 = in_ptr0[i0];
2023-01-11T21:41:23.8779176Z                             auto tmp8 = in_ptr2[i1 + (128*i0)];
2023-01-11T21:41:23.8779316Z                             auto tmp0 = static_cast<int>(i1);
2023-01-11T21:41:23.8779536Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:41:23.8779679Z                             auto tmp2 = tmp0 == tmp1;
2023-01-11T21:41:23.8779804Z                             auto tmp4 = static_cast<int>(1);
2023-01-11T21:41:23.8779929Z                             auto tmp5 = tmp0 >= tmp4;
2023-01-11T21:41:23.8780041Z                             auto tmp6 = 0;
2023-01-11T21:41:23.8780143Z                             if(tmp5)
2023-01-11T21:41:23.8780238Z                             {
2023-01-11T21:41:23.8780530Z                                 auto tmp7 = in_ptr1[(-1) + i1 + (127*i0)];
2023-01-11T21:41:23.8780651Z                                 tmp6 = tmp7;
2023-01-11T21:41:23.8780732Z                             }
2023-01-11T21:41:23.8780883Z                             auto tmp9 = tmp5 ? tmp6 : tmp8;
2023-01-11T21:41:23.8781032Z                             auto tmp10 = tmp2 ? tmp3 : tmp9;
2023-01-11T21:41:23.8781187Z                             auto tmp11 = in_ptr3[i2 + (768*tmp10)];
2023-01-11T21:41:23.8781339Z                             out_ptr0[i2 + (768*i1) + (98304*i0)] = tmp11;
2023-01-11T21:41:23.8781435Z                         }
2023-01-11T21:41:23.8781524Z                     }
2023-01-11T21:41:23.8781625Z                 }
2023-01-11T21:41:23.8781719Z             }
2023-01-11T21:41:23.8781822Z         }
2023-01-11T21:41:23.8781905Z     }
2023-01-11T21:41:23.8781987Z }
2023-01-11T21:41:23.8782102Z ''')
2023-01-11T21:41:23.8782112Z 
2023-01-11T21:41:23.8782117Z 
2023-01-11T21:41:23.8782249Z async_compile.wait(globals())
2023-01-11T21:41:23.8782490Z del async_compile
2023-01-11T21:41:23.8782497Z 
2023-01-11T21:41:23.8782585Z def call(args):
2023-01-11T21:41:23.8782734Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
2023-01-11T21:41:23.8782845Z     args.clear()
2023-01-11T21:41:23.8783166Z     buf0 = empty_strided((8, 128, 768), (98304, 768, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8783458Z     kernel_cpp_0(c_void_p(arg3_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8783553Z     del arg0_1
2023-01-11T21:41:23.8783640Z     del arg2_1
2023-01-11T21:41:23.8783711Z     del arg3_1
2023-01-11T21:41:23.8783803Z     del arg4_1
2023-01-11T21:41:23.8783898Z     return (buf0, )
2023-01-11T21:41:23.8783905Z 
2023-01-11T21:41:23.8783910Z 
2023-01-11T21:41:23.8784010Z if __name__ == "__main__":
2023-01-11T21:41:23.8784165Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8784337Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8784630Z     arg0_1 = rand_strided((50005, 768), (768, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8784901Z     arg1_1 = rand_strided((8, 128), (128, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8785161Z     arg2_1 = rand_strided((8, 127), (127, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8785424Z     arg3_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8785828Z     arg4_1 = rand_strided((8, 128), (128, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.8786024Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1]))
2023-01-11T21:41:23.8786034Z 
2023-01-11T21:41:23.8786142Z ok (3.820s)
2023-01-11T21:41:23.8786841Z   test_isinf2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8787006Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8787374Z [2023-01-11 21:35:22,359] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 254
2023-01-11T21:41:23.8787805Z [2023-01-11 21:35:23,975] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 254
2023-01-11T21:41:23.8787820Z 
2023-01-11T21:41:23.8787946Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8788022Z import torch
2023-01-11T21:41:23.8788113Z import random
2023-01-11T21:41:23.8788264Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8788417Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8788423Z 
2023-01-11T21:41:23.8788525Z aten = torch.ops.aten
2023-01-11T21:41:23.8788700Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8788818Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8788826Z 
2023-01-11T21:41:23.8788832Z 
2023-01-11T21:41:23.8789000Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8789268Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8789429Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8789558Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:23.8789643Z {
2023-01-11T21:41:23.8789769Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8789849Z     {
2023-01-11T21:41:23.8789936Z         #pragma omp for 
2023-01-11T21:41:23.8790042Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:23.8790124Z         {
2023-01-11T21:41:23.8790207Z             {
2023-01-11T21:41:23.8790292Z                 {
2023-01-11T21:41:23.8790411Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8790550Z                     auto tmp1 = static_cast<long>(i0);
2023-01-11T21:41:23.8790670Z                     auto tmp2 = static_cast<long>(2);
2023-01-11T21:41:23.8790786Z                     auto tmp3 = tmp1 < tmp2;
2023-01-11T21:41:23.8790917Z                     auto tmp4 = static_cast<long>(1);
2023-01-11T21:41:23.8791037Z                     auto tmp5 = tmp1 < tmp4;
2023-01-11T21:41:23.8791174Z                     auto tmp6 = static_cast<float>(1.0);
2023-01-11T21:41:23.8791353Z                     auto tmp7 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.8791483Z                     auto tmp8 = tmp5 ? tmp6 : tmp7;
2023-01-11T21:41:23.8791616Z                     auto tmp9 = static_cast<long>(3);
2023-01-11T21:41:23.8791737Z                     auto tmp10 = tmp1 < tmp9;
2023-01-11T21:41:23.8791881Z                     auto tmp11 = static_cast<float>(2.0);
2023-01-11T21:41:23.8792024Z                     auto tmp12 = static_cast<long>(4);
2023-01-11T21:41:23.8792152Z                     auto tmp13 = tmp1 < tmp12;
2023-01-11T21:41:23.8792511Z                     auto tmp14 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.8792706Z                     auto tmp15 = std::numeric_limits<float>::quiet_NaN();
2023-01-11T21:41:23.8792855Z                     auto tmp16 = tmp13 ? tmp14 : tmp15;
2023-01-11T21:41:23.8792974Z                     auto tmp17 = tmp10 ? tmp11 : tmp16;
2023-01-11T21:41:23.8793114Z                     auto tmp18 = tmp3 ? tmp8 : tmp17;
2023-01-11T21:41:23.8793306Z                     auto tmp19 = tmp0 == tmp18;
2023-01-11T21:41:23.8793418Z                     out_ptr0[i0] = tmp19;
2023-01-11T21:41:23.8793502Z                 }
2023-01-11T21:41:23.8793588Z             }
2023-01-11T21:41:23.8793678Z         }
2023-01-11T21:41:23.8793814Z     }
2023-01-11T21:41:23.8793894Z }
2023-01-11T21:41:23.8794006Z ''')
2023-01-11T21:41:23.8794014Z 
2023-01-11T21:41:23.8794019Z 
2023-01-11T21:41:23.8794138Z async_compile.wait(globals())
2023-01-11T21:41:23.8794233Z del async_compile
2023-01-11T21:41:23.8794239Z 
2023-01-11T21:41:23.8794330Z def call(args):
2023-01-11T21:41:23.8794418Z     arg0_1, = args
2023-01-11T21:41:23.8794504Z     args.clear()
2023-01-11T21:41:23.8794792Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8794978Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8795071Z     del arg0_1
2023-01-11T21:41:23.8795170Z     return (buf0, )
2023-01-11T21:41:23.8795177Z 
2023-01-11T21:41:23.8795243Z 
2023-01-11T21:41:23.8795351Z if __name__ == "__main__":
2023-01-11T21:41:23.8795511Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8795682Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8795950Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8796097Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8796103Z 
2023-01-11T21:41:23.8796200Z ok (1.649s)
2023-01-11T21:41:23.8796862Z   test_isinf_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8797039Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8797426Z [2023-01-11 21:35:23,993] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 255
2023-01-11T21:41:23.8797823Z [2023-01-11 21:35:25,555] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 255
2023-01-11T21:41:23.8798456Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8798630Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8799031Z [2023-01-11 21:35:25,571] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 256
2023-01-11T21:41:23.8799446Z [2023-01-11 21:35:27,131] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 256
2023-01-11T21:41:23.8799459Z 
2023-01-11T21:41:23.8799583Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8799686Z import torch
2023-01-11T21:41:23.8799787Z import random
2023-01-11T21:41:23.8799954Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8800135Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8800142Z 
2023-01-11T21:41:23.8800259Z aten = torch.ops.aten
2023-01-11T21:41:23.8800464Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8800591Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8800613Z 
2023-01-11T21:41:23.8800619Z 
2023-01-11T21:41:23.8800812Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8801115Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8801290Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8801432Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:23.8801647Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:23.8801734Z {
2023-01-11T21:41:23.8801880Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8801958Z     {
2023-01-11T21:41:23.8802069Z         #pragma omp for 
2023-01-11T21:41:23.8802186Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:23.8802274Z         {
2023-01-11T21:41:23.8802363Z             {
2023-01-11T21:41:23.8802452Z                 {
2023-01-11T21:41:23.8802583Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8802720Z                     auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:23.8802861Z                     auto tmp2 = std::isnan(tmp0);
2023-01-11T21:41:23.8802977Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8803093Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8803182Z                 }
2023-01-11T21:41:23.8803270Z             }
2023-01-11T21:41:23.8803361Z         }
2023-01-11T21:41:23.8803433Z     }
2023-01-11T21:41:23.8803525Z }
2023-01-11T21:41:23.8803700Z ''')
2023-01-11T21:41:23.8803710Z 
2023-01-11T21:41:23.8803716Z 
2023-01-11T21:41:23.8803851Z async_compile.wait(globals())
2023-01-11T21:41:23.8803955Z del async_compile
2023-01-11T21:41:23.8803962Z 
2023-01-11T21:41:23.8804065Z def call(args):
2023-01-11T21:41:23.8804161Z     arg0_1, = args
2023-01-11T21:41:23.8804247Z     args.clear()
2023-01-11T21:41:23.8804528Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8804807Z     buf1 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8805042Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8805142Z     del arg0_1
2023-01-11T21:41:23.8805249Z     return (buf0, buf1, )
2023-01-11T21:41:23.8805257Z 
2023-01-11T21:41:23.8805263Z 
2023-01-11T21:41:23.8805369Z if __name__ == "__main__":
2023-01-11T21:41:23.8805530Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8805699Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8805992Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8806145Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8806152Z 
2023-01-11T21:41:23.8806158Z 
2023-01-11T21:41:23.8806291Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8806387Z import torch
2023-01-11T21:41:23.8806486Z import random
2023-01-11T21:41:23.8806647Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8806818Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8806825Z 
2023-01-11T21:41:23.8806920Z aten = torch.ops.aten
2023-01-11T21:41:23.8807109Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8807237Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8807244Z 
2023-01-11T21:41:23.8807250Z 
2023-01-11T21:41:23.8807442Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8807741Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8807921Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:23.8808058Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:23.8808195Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:23.8808264Z {
2023-01-11T21:41:23.8808402Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8808487Z     {
2023-01-11T21:41:23.8808596Z         #pragma omp for 
2023-01-11T21:41:23.8808714Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:23.8808799Z         {
2023-01-11T21:41:23.8808869Z             {
2023-01-11T21:41:23.8808960Z                 {
2023-01-11T21:41:23.8809089Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8809234Z                     auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:23.8809373Z                     auto tmp2 = std::isnan(tmp0);
2023-01-11T21:41:23.8809493Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8809684Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:41:23.8809759Z                 }
2023-01-11T21:41:23.8809850Z             }
2023-01-11T21:41:23.8809933Z         }
2023-01-11T21:41:23.8810017Z     }
2023-01-11T21:41:23.8810098Z }
2023-01-11T21:41:23.8810215Z ''')
2023-01-11T21:41:23.8810224Z 
2023-01-11T21:41:23.8810229Z 
2023-01-11T21:41:23.8810357Z async_compile.wait(globals())
2023-01-11T21:41:23.8810444Z del async_compile
2023-01-11T21:41:23.8810465Z 
2023-01-11T21:41:23.8810546Z def call(args):
2023-01-11T21:41:23.8810642Z     arg0_1, = args
2023-01-11T21:41:23.8810745Z     args.clear()
2023-01-11T21:41:23.8811025Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8811297Z     buf1 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8811533Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8811631Z     del arg0_1
2023-01-11T21:41:23.8811724Z     return (buf0, buf1, )
2023-01-11T21:41:23.8811789Z 
2023-01-11T21:41:23.8811797Z 
2023-01-11T21:41:23.8811905Z if __name__ == "__main__":
2023-01-11T21:41:23.8812068Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8812242Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8812529Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.8812684Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8812692Z 
2023-01-11T21:41:23.8812783Z ok (3.173s)
2023-01-11T21:41:23.8813286Z   test_kernel_names_cpu (__main__.CpuTests) ... [2023-01-11 21:35:27,170] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 257
2023-01-11T21:41:23.8813684Z [2023-01-11 21:35:28,791] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 257
2023-01-11T21:41:23.8813708Z 
2023-01-11T21:41:23.8813827Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8813925Z import torch
2023-01-11T21:41:23.8814034Z import random
2023-01-11T21:41:23.8814201Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8814378Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8814384Z 
2023-01-11T21:41:23.8814496Z aten = torch.ops.aten
2023-01-11T21:41:23.8814695Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8814810Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8814817Z 
2023-01-11T21:41:23.8814823Z 
2023-01-11T21:41:23.8815012Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8815316Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8815489Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8815626Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.8815712Z {
2023-01-11T21:41:23.8815850Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8815932Z     {
2023-01-11T21:41:23.8816028Z         #pragma omp for 
2023-01-11T21:41:23.8816145Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.8816239Z         {
2023-01-11T21:41:23.8816433Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8816627Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8816746Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8816876Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8816947Z         }
2023-01-11T21:41:23.8817080Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8817195Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:23.8817280Z         {
2023-01-11T21:41:23.8817396Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8817535Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:23.8817651Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.8817745Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8817831Z         }
2023-01-11T21:41:23.8817987Z     }
2023-01-11T21:41:23.8818075Z }
2023-01-11T21:41:23.8818192Z ''')
2023-01-11T21:41:23.8818201Z 
2023-01-11T21:41:23.8818207Z 
2023-01-11T21:41:23.8818339Z async_compile.wait(globals())
2023-01-11T21:41:23.8818441Z del async_compile
2023-01-11T21:41:23.8818448Z 
2023-01-11T21:41:23.8818531Z def call(args):
2023-01-11T21:41:23.8818626Z     arg0_1, = args
2023-01-11T21:41:23.8818727Z     args.clear()
2023-01-11T21:41:23.8819011Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8819204Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.8819300Z     del arg0_1
2023-01-11T21:41:23.8819397Z     return (buf0, )
2023-01-11T21:41:23.8819403Z 
2023-01-11T21:41:23.8819408Z 
2023-01-11T21:41:23.8819499Z if __name__ == "__main__":
2023-01-11T21:41:23.8819663Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8819842Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8820192Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8820356Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8820364Z 
2023-01-11T21:41:23.8820456Z ok (1.642s)
2023-01-11T21:41:23.8821154Z   test_kwargs_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8821335Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8821731Z [2023-01-11 21:35:28,832] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 258
2023-01-11T21:41:23.8822047Z [2023-01-11 21:35:28,834] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:41:23.8822207Z   target: aten._histogramdd_bin_edges.default
2023-01-11T21:41:23.8822476Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:41:23.8822844Z     InputBuffer(name='arg0_1', layout=FixedLayout('cpu', torch.float32, size=[4, 2], stride=[2, 1]))
2023-01-11T21:41:23.8822933Z   ))
2023-01-11T21:41:23.8823028Z   args[1]: [3, 3]
2023-01-11T21:41:23.8823243Z   kwargs: {'weight': TensorBox(StorageBox(
2023-01-11T21:41:23.8823591Z     InputBuffer(name='arg1_1', layout=FixedLayout('cpu', torch.float32, size=[4], stride=[1]))
2023-01-11T21:41:23.8823662Z   ))}
2023-01-11T21:41:23.8824074Z [2023-01-11 21:35:28,852] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten._histogramdd_bin_edges.default
2023-01-11T21:41:23.8824399Z [2023-01-11 21:35:28,855] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:41:23.8824555Z   target: aten._histogramdd_from_bin_cts.default
2023-01-11T21:41:23.8824684Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:41:23.8825046Z     InputBuffer(name='arg0_1', layout=FixedLayout('cpu', torch.float32, size=[4, 2], stride=[2, 1]))
2023-01-11T21:41:23.8825130Z   ))
2023-01-11T21:41:23.8825208Z   args[1]: [3, 3]
2023-01-11T21:41:23.8825415Z   kwargs: {'weight': TensorBox(StorageBox(
2023-01-11T21:41:23.8825750Z     InputBuffer(name='arg1_1', layout=FixedLayout('cpu', torch.float32, size=[4], stride=[1]))
2023-01-11T21:41:23.8825834Z   ))}
2023-01-11T21:41:23.8826255Z [2023-01-11 21:35:28,872] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten._histogramdd_from_bin_cts.default
2023-01-11T21:41:23.8826645Z [2023-01-11 21:35:28,876] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 258
2023-01-11T21:41:23.8826653Z 
2023-01-11T21:41:23.8826786Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8826889Z import torch
2023-01-11T21:41:23.8826970Z import random
2023-01-11T21:41:23.8827133Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8827305Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8827417Z 
2023-01-11T21:41:23.8827524Z aten = torch.ops.aten
2023-01-11T21:41:23.8827713Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8827842Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8827850Z 
2023-01-11T21:41:23.8827857Z 
2023-01-11T21:41:23.8827980Z async_compile.wait(globals())
2023-01-11T21:41:23.8828083Z del async_compile
2023-01-11T21:41:23.8828090Z 
2023-01-11T21:41:23.8828175Z def call(args):
2023-01-11T21:41:23.8828283Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8828384Z     args.clear()
2023-01-11T21:41:23.8828600Z     buf0 = torch.ops.aten._histogramdd_bin_edges.default(arg0_1, [3, 3], weight=arg1_1)
2023-01-11T21:41:23.8828697Z     buf1 = buf0[0]
2023-01-11T21:41:23.8828826Z     assert_size_stride(buf1, (4, ), (1, ))
2023-01-11T21:41:23.8828925Z     buf2 = buf0[1]
2023-01-11T21:41:23.8829037Z     assert_size_stride(buf2, (4, ), (1, ))
2023-01-11T21:41:23.8829125Z     del buf0
2023-01-11T21:41:23.8829429Z     buf3 = torch.ops.aten._histogramdd_from_bin_cts.default(arg0_1, [3, 3], weight=arg1_1)
2023-01-11T21:41:23.8829531Z     del arg0_1
2023-01-11T21:41:23.8829622Z     del arg1_1
2023-01-11T21:41:23.8829718Z     buf4 = buf3
2023-01-11T21:41:23.8829854Z     assert_size_stride(buf4, (3, 3), (3, 1))
2023-01-11T21:41:23.8829927Z     del buf3
2023-01-11T21:41:23.8830039Z     return (buf4, buf1, buf2, )
2023-01-11T21:41:23.8830047Z 
2023-01-11T21:41:23.8830053Z 
2023-01-11T21:41:23.8830158Z if __name__ == "__main__":
2023-01-11T21:41:23.8830324Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8830500Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8830796Z     arg0_1 = rand_strided((4, 2), (2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8831077Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8831228Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8831250Z 
2023-01-11T21:41:23.8831331Z ok (0.086s)
2023-01-11T21:41:23.8832093Z   test_l1_loss_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8832282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8832739Z [2023-01-11 21:35:28,903] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 259
2023-01-11T21:41:23.8833166Z [2023-01-11 21:35:30,568] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 259
2023-01-11T21:41:23.8833175Z 
2023-01-11T21:41:23.8833329Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8833445Z import torch
2023-01-11T21:41:23.8833553Z import random
2023-01-11T21:41:23.8833818Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8833997Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8834005Z 
2023-01-11T21:41:23.8834128Z aten = torch.ops.aten
2023-01-11T21:41:23.8834340Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8834478Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8834486Z 
2023-01-11T21:41:23.8834492Z 
2023-01-11T21:41:23.8834723Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8835072Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8835256Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.8835418Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.8835568Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8835741Z                        const float* __restrict__ in_ptr1)
2023-01-11T21:41:23.8835843Z {
2023-01-11T21:41:23.8835984Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:23.8836212Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:23.8836307Z     {
2023-01-11T21:41:23.8836633Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.8836742Z         float tmp4 = 0;
2023-01-11T21:41:23.8836931Z         auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:23.8837033Z         float tmp6 = 0;
2023-01-11T21:41:23.8837194Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:23.8837364Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8837467Z         {
2023-01-11T21:41:23.8837696Z             #pragma omp for reduction(+:tmp4_vec) reduction(+:tmp6_vec) 
2023-01-11T21:41:23.8837818Z             for(long i0=0; i0<192; i0+=1)
2023-01-11T21:41:23.8837924Z             {
2023-01-11T21:41:23.8838155Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8838445Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.8838687Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8838826Z                 auto tmp3 = tmp2.abs();
2023-01-11T21:41:23.8838978Z                 auto tmp5 = tmp2 * tmp2;
2023-01-11T21:41:23.8839109Z                 tmp4_vec += tmp3;
2023-01-11T21:41:23.8839227Z                 tmp6_vec += tmp5;
2023-01-11T21:41:23.8839334Z             }
2023-01-11T21:41:23.8839657Z             tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:23.8839947Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:23.8840162Z             #pragma omp for simd simdlen(4) reduction(+:tmp4) reduction(+:tmp6) 
2023-01-11T21:41:23.8840288Z             for(long i0=1536; i0<1536; i0+=1)
2023-01-11T21:41:23.8840378Z             {
2023-01-11T21:41:23.8840520Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8840633Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.8840834Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8840969Z                 auto tmp3 = std::abs(tmp2);
2023-01-11T21:41:23.8841105Z                 auto tmp5 = tmp2 * tmp2;
2023-01-11T21:41:23.8841209Z                 tmp4 += tmp3;
2023-01-11T21:41:23.8841311Z                 tmp6 += tmp5;
2023-01-11T21:41:23.8841399Z             }
2023-01-11T21:41:23.8841468Z         }
2023-01-11T21:41:23.8841574Z         out_ptr0[0] = tmp4;
2023-01-11T21:41:23.8841682Z         out_ptr1[0] = tmp6;
2023-01-11T21:41:23.8841765Z     }
2023-01-11T21:41:23.8841850Z     {
2023-01-11T21:41:23.8841934Z         {
2023-01-11T21:41:23.8842036Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:23.8842194Z             auto tmp1 = static_cast<float>(1536);
2023-01-11T21:41:23.8842311Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.8842431Z             in_out_ptr0[0] = tmp2;
2023-01-11T21:41:23.8842523Z         }
2023-01-11T21:41:23.8842605Z     }
2023-01-11T21:41:23.8842687Z     {
2023-01-11T21:41:23.8842760Z         {
2023-01-11T21:41:23.8842877Z             auto tmp0 = out_ptr1[0];
2023-01-11T21:41:23.8843024Z             auto tmp1 = static_cast<float>(1536);
2023-01-11T21:41:23.8843151Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.8843269Z             in_out_ptr1[0] = tmp2;
2023-01-11T21:41:23.8843363Z         }
2023-01-11T21:41:23.8843451Z     }
2023-01-11T21:41:23.8843519Z }
2023-01-11T21:41:23.8843639Z ''')
2023-01-11T21:41:23.8843647Z 
2023-01-11T21:41:23.8843654Z 
2023-01-11T21:41:23.8843787Z async_compile.wait(globals())
2023-01-11T21:41:23.8843897Z del async_compile
2023-01-11T21:41:23.8843905Z 
2023-01-11T21:41:23.8844009Z def call(args):
2023-01-11T21:41:23.8844124Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.8844223Z     args.clear()
2023-01-11T21:41:23.8844494Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8844864Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8844998Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:23.8845133Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:23.8845454Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()))
2023-01-11T21:41:23.8845552Z     del arg0_1
2023-01-11T21:41:23.8845646Z     del arg1_1
2023-01-11T21:41:23.8845741Z     return (buf2, buf3, )
2023-01-11T21:41:23.8845764Z 
2023-01-11T21:41:23.8845769Z 
2023-01-11T21:41:23.8845865Z if __name__ == "__main__":
2023-01-11T21:41:23.8846034Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8846212Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8846545Z     arg0_1 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8846946Z     arg1_1 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8847127Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.8847135Z 
2023-01-11T21:41:23.8847237Z ok (1.693s)
2023-01-11T21:41:23.8848038Z   test_layer_norm_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8848241Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8848660Z [2023-01-11 21:35:30,641] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 260
2023-01-11T21:41:23.8849080Z [2023-01-11 21:35:32,826] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 260
2023-01-11T21:41:23.8849097Z 
2023-01-11T21:41:23.8849233Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8849339Z import torch
2023-01-11T21:41:23.8849445Z import random
2023-01-11T21:41:23.8849627Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8849820Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8849828Z 
2023-01-11T21:41:23.8849952Z aten = torch.ops.aten
2023-01-11T21:41:23.8850151Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8850292Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8850300Z 
2023-01-11T21:41:23.8850308Z 
2023-01-11T21:41:23.8850517Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8850836Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8851019Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.8851187Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.8851356Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8851514Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.8851657Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.8851811Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8851955Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:23.8852049Z {
2023-01-11T21:41:23.8852179Z     auto out_ptr2 = in_out_ptr0;
2023-01-11T21:41:23.8852309Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:23.8852467Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8852547Z     {
2023-01-11T21:41:23.8852660Z         #pragma omp for 
2023-01-11T21:41:23.8852789Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8852877Z         {
2023-01-11T21:41:23.8852961Z             {
2023-01-11T21:41:23.8853254Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.8853375Z                 float tmp1 = 0;
2023-01-11T21:41:23.8853626Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:23.8853763Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8853863Z                 {
2023-01-11T21:41:23.8854085Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:23.8854204Z                     tmp1_vec += tmp0;
2023-01-11T21:41:23.8854304Z                 }
2023-01-11T21:41:23.8854613Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:23.8854811Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:23.8854930Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:23.8855019Z                 {
2023-01-11T21:41:23.8855158Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:23.8855263Z                     tmp1 += tmp0;
2023-01-11T21:41:23.8855448Z                 }
2023-01-11T21:41:23.8855572Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8855661Z             }
2023-01-11T21:41:23.8855739Z         }
2023-01-11T21:41:23.8855840Z         #pragma omp for 
2023-01-11T21:41:23.8855950Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8856037Z         {
2023-01-11T21:41:23.8856121Z             {
2023-01-11T21:41:23.8856397Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.8856502Z                 float tmp6 = 0;
2023-01-11T21:41:23.8856651Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:23.8856760Z                 float tmp7 = 0;
2023-01-11T21:41:23.8856935Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:23.8857060Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8857152Z                 {
2023-01-11T21:41:23.8857380Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:23.8857571Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:23.8857761Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:41:23.8857877Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:23.8858079Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:23.8858199Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:23.8858317Z                     tmp6_vec += tmp5;
2023-01-11T21:41:23.8858427Z                     tmp7_vec += tmp0;
2023-01-11T21:41:23.8858519Z                 }
2023-01-11T21:41:23.8858815Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:23.8859073Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:23.8859273Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:23.8859403Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:23.8859495Z                 {
2023-01-11T21:41:23.8859641Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:23.8859781Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:23.8859942Z                     auto tmp2 = static_cast<float>(32);
2023-01-11T21:41:23.8860076Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:23.8860288Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:23.8860393Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:23.8860504Z                     tmp6 += tmp5;
2023-01-11T21:41:23.8860633Z                     tmp7 += tmp0;
2023-01-11T21:41:23.8860738Z                 }
2023-01-11T21:41:23.8860870Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:23.8860997Z                 out_ptr2[i0] = tmp7;
2023-01-11T21:41:23.8861163Z             }
2023-01-11T21:41:23.8861231Z         }
2023-01-11T21:41:23.8861332Z         #pragma omp for 
2023-01-11T21:41:23.8861439Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8861522Z         {
2023-01-11T21:41:23.8861702Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:23.8861880Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:41:23.8861993Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.8862106Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8862189Z         }
2023-01-11T21:41:23.8862472Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8862591Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.8862671Z         {
2023-01-11T21:41:23.8862783Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:23.8862916Z             auto tmp1 = static_cast<float>(32);
2023-01-11T21:41:23.8863012Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.8863205Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8863290Z         }
2023-01-11T21:41:23.8863392Z         #pragma omp for 
2023-01-11T21:41:23.8863499Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8863580Z         {
2023-01-11T21:41:23.8863744Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8863923Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:41:23.8864031Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.8864332Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:23.8864441Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.8864552Z             auto tmp5 = tmp4.rsqrt();
2023-01-11T21:41:23.8864677Z             tmp5.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:23.8864760Z         }
2023-01-11T21:41:23.8864872Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8864976Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.8865061Z         {
2023-01-11T21:41:23.8865171Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:23.8865303Z             auto tmp1 = static_cast<float>(32);
2023-01-11T21:41:23.8865412Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.8865615Z             auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:41:23.8865707Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.8865830Z             auto tmp5 = 1 / std::sqrt(tmp4);
2023-01-11T21:41:23.8865938Z             in_out_ptr1[i0] = tmp5;
2023-01-11T21:41:23.8866018Z         }
2023-01-11T21:41:23.8866118Z         #pragma omp for 
2023-01-11T21:41:23.8866220Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8866304Z         {
2023-01-11T21:41:23.8866396Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.8866478Z             {
2023-01-11T21:41:23.8866665Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:23.8866835Z                 auto tmp1 = at::vec::Vectorized<float>(in_out_ptr0[i0]);
2023-01-11T21:41:23.8867010Z                 auto tmp3 = at::vec::Vectorized<float>(in_out_ptr1[i0]);
2023-01-11T21:41:23.8867187Z                 auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:23.8867361Z                 auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i1);
2023-01-11T21:41:23.8867542Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8867642Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.8867755Z                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:23.8867868Z                 auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:23.8868037Z                 auto tmp9 = at::vec::clamp_min(tmp8, decltype(tmp8)(0));
2023-01-11T21:41:23.8868171Z                 tmp9.store(out_ptr3 + (8*i1) + (32*i0));
2023-01-11T21:41:23.8868257Z             }
2023-01-11T21:41:23.8868377Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.8868477Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:23.8868644Z             {
2023-01-11T21:41:23.8868775Z                 auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:23.8868897Z                 auto tmp1 = in_out_ptr0[i0];
2023-01-11T21:41:23.8869018Z                 auto tmp3 = in_out_ptr1[i0];
2023-01-11T21:41:23.8869132Z                 auto tmp5 = in_ptr1[i1];
2023-01-11T21:41:23.8869243Z                 auto tmp7 = in_ptr2[i1];
2023-01-11T21:41:23.8869408Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.8869522Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.8869633Z                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:23.8869748Z                 auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:23.8869869Z                 auto tmp9 = tmp8 * (tmp8>0);
2023-01-11T21:41:23.8869992Z                 out_ptr3[i1 + (32*i0)] = tmp9;
2023-01-11T21:41:23.8870075Z             }
2023-01-11T21:41:23.8870144Z         }
2023-01-11T21:41:23.8870225Z     }
2023-01-11T21:41:23.8870306Z }
2023-01-11T21:41:23.8870415Z ''')
2023-01-11T21:41:23.8870427Z 
2023-01-11T21:41:23.8870480Z 
2023-01-11T21:41:23.8870604Z async_compile.wait(globals())
2023-01-11T21:41:23.8870701Z del async_compile
2023-01-11T21:41:23.8870708Z 
2023-01-11T21:41:23.8870804Z def call(args):
2023-01-11T21:41:23.8870928Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.8871026Z     args.clear()
2023-01-11T21:41:23.8871318Z     buf0 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8871587Z     buf1 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8871853Z     buf2 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8871999Z     buf3 = as_strided(buf2, (16, 1), (1, 1)); del buf2  # reuse
2023-01-11T21:41:23.8872146Z     buf4 = as_strided(buf1, (16, 1), (1, 1)); del buf1  # reuse
2023-01-11T21:41:23.8872417Z     buf5 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8872780Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:23.8872956Z     return (buf5, primals_1, primals_2, primals_3, buf3, buf4, )
2023-01-11T21:41:23.8872962Z 
2023-01-11T21:41:23.8872968Z 
2023-01-11T21:41:23.8873068Z if __name__ == "__main__":
2023-01-11T21:41:23.8873219Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8873384Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8873663Z     primals_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8874013Z     primals_2 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8874298Z     primals_3 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8874479Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.8874486Z 
2023-01-11T21:41:23.8874561Z ok (2.258s)
2023-01-11T21:41:23.8875223Z   test_leaky_relu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8875393Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8875755Z [2023-01-11 21:35:32,865] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 261
2023-01-11T21:41:23.8876121Z [2023-01-11 21:35:34,654] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 261
2023-01-11T21:41:23.8876128Z 
2023-01-11T21:41:23.8876253Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8876345Z import torch
2023-01-11T21:41:23.8876437Z import random
2023-01-11T21:41:23.8876661Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8876809Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8876816Z 
2023-01-11T21:41:23.8876920Z aten = torch.ops.aten
2023-01-11T21:41:23.8877098Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8877223Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8877230Z 
2023-01-11T21:41:23.8877235Z 
2023-01-11T21:41:23.8877420Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8877692Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8877849Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8877984Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8878096Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8878174Z {
2023-01-11T21:41:23.8878303Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8878384Z     {
2023-01-11T21:41:23.8878534Z         #pragma omp for 
2023-01-11T21:41:23.8878644Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.8878725Z         {
2023-01-11T21:41:23.8878794Z             {
2023-01-11T21:41:23.8878877Z                 {
2023-01-11T21:41:23.8878997Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8879130Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:23.8879247Z                     auto tmp2 = tmp0 > tmp1;
2023-01-11T21:41:23.8879386Z                     auto tmp3 = static_cast<float>(0.2);
2023-01-11T21:41:23.8879504Z                     auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:23.8879617Z                     auto tmp5 = tmp2 ? tmp0 : tmp4;
2023-01-11T21:41:23.8879749Z                     auto tmp6 = static_cast<float>(2);
2023-01-11T21:41:23.8879866Z                     auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:23.8879998Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:41:23.8880114Z                     auto tmp9 = tmp0 + tmp8;
2023-01-11T21:41:23.8880237Z                     auto tmp10 = tmp9 > tmp1;
2023-01-11T21:41:23.8880377Z                     auto tmp11 = static_cast<float>(0.01);
2023-01-11T21:41:23.8880480Z                     auto tmp12 = tmp9 * tmp11;
2023-01-11T21:41:23.8880611Z                     auto tmp13 = tmp10 ? tmp9 : tmp12;
2023-01-11T21:41:23.8880719Z                     out_ptr0[i0] = tmp7;
2023-01-11T21:41:23.8880829Z                     out_ptr1[i0] = tmp13;
2023-01-11T21:41:23.8880913Z                 }
2023-01-11T21:41:23.8880998Z             }
2023-01-11T21:41:23.8881079Z         }
2023-01-11T21:41:23.8881142Z     }
2023-01-11T21:41:23.8881221Z }
2023-01-11T21:41:23.8881331Z ''')
2023-01-11T21:41:23.8881338Z 
2023-01-11T21:41:23.8881343Z 
2023-01-11T21:41:23.8881461Z async_compile.wait(globals())
2023-01-11T21:41:23.8881556Z del async_compile
2023-01-11T21:41:23.8881561Z 
2023-01-11T21:41:23.8881652Z def call(args):
2023-01-11T21:41:23.8881740Z     arg0_1, = args
2023-01-11T21:41:23.8881820Z     args.clear()
2023-01-11T21:41:23.8882098Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8882366Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8882586Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8882676Z     del arg0_1
2023-01-11T21:41:23.8882778Z     return (buf0, buf1, )
2023-01-11T21:41:23.8882785Z 
2023-01-11T21:41:23.8882791Z 
2023-01-11T21:41:23.8882888Z if __name__ == "__main__":
2023-01-11T21:41:23.8883040Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8883193Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8883476Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8883626Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8883632Z 
2023-01-11T21:41:23.8883721Z ok (1.828s)
2023-01-11T21:41:23.8884480Z   test_lgamma_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8884671Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8885135Z [2023-01-11 21:35:34,688] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 262
2023-01-11T21:41:23.8885545Z [2023-01-11 21:35:36,287] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 262
2023-01-11T21:41:23.8885553Z 
2023-01-11T21:41:23.8885683Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8885778Z import torch
2023-01-11T21:41:23.8885860Z import random
2023-01-11T21:41:23.8886026Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8886290Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8886299Z 
2023-01-11T21:41:23.8886425Z aten = torch.ops.aten
2023-01-11T21:41:23.8886649Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8886791Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8886798Z 
2023-01-11T21:41:23.8886806Z 
2023-01-11T21:41:23.8887032Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8887397Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8887619Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.8887804Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.8887982Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.8888092Z {
2023-01-11T21:41:23.8888274Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8888382Z     {
2023-01-11T21:41:23.8888504Z         #pragma omp for 
2023-01-11T21:41:23.8888662Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:23.8888776Z         {
2023-01-11T21:41:23.8889030Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.8889186Z             auto tmp1 = tmp0.lgamma();
2023-01-11T21:41:23.8889434Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.8889583Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.8889816Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.8889925Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:23.8890053Z             auto tmp6 = tmp5.cos();
2023-01-11T21:41:23.8890187Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.8890312Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.8890407Z         }
2023-01-11T21:41:23.8890537Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8890648Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:23.8890734Z         {
2023-01-11T21:41:23.8890870Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.8891031Z             auto tmp1 = std::lgamma(tmp0);
2023-01-11T21:41:23.8891199Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:23.8891340Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:23.8891508Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:23.8891643Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:23.8891777Z             auto tmp6 = std::cos(tmp5);
2023-01-11T21:41:23.8891888Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.8891998Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:23.8892084Z         }
2023-01-11T21:41:23.8892171Z     }
2023-01-11T21:41:23.8892255Z }
2023-01-11T21:41:23.8892382Z ''')
2023-01-11T21:41:23.8892390Z 
2023-01-11T21:41:23.8892395Z 
2023-01-11T21:41:23.8892507Z async_compile.wait(globals())
2023-01-11T21:41:23.8892608Z del async_compile
2023-01-11T21:41:23.8892615Z 
2023-01-11T21:41:23.8892711Z def call(args):
2023-01-11T21:41:23.8892883Z     arg0_1, = args
2023-01-11T21:41:23.8892984Z     args.clear()
2023-01-11T21:41:23.8893283Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8893582Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8893810Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8893903Z     del arg0_1
2023-01-11T21:41:23.8894012Z     return (buf0, buf1, )
2023-01-11T21:41:23.8894020Z 
2023-01-11T21:41:23.8894026Z 
2023-01-11T21:41:23.8894132Z if __name__ == "__main__":
2023-01-11T21:41:23.8894297Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8894473Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8894771Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8894930Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.8894937Z 
2023-01-11T21:41:23.8895081Z ok (1.633s)
2023-01-11T21:41:23.8895802Z   test_linear1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8895986Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8896382Z [2023-01-11 21:35:36,340] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 263
2023-01-11T21:41:23.8896789Z [2023-01-11 21:35:36,348] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 263
2023-01-11T21:41:23.8896797Z 
2023-01-11T21:41:23.8896934Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8897035Z import torch
2023-01-11T21:41:23.8897136Z import random
2023-01-11T21:41:23.8897312Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8897469Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8897491Z 
2023-01-11T21:41:23.8897584Z aten = torch.ops.aten
2023-01-11T21:41:23.8897778Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8897908Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8897915Z 
2023-01-11T21:41:23.8897922Z 
2023-01-11T21:41:23.8898118Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8898423Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8898590Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.8898674Z {
2023-01-11T21:41:23.8898800Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8898884Z     {
2023-01-11T21:41:23.8898994Z         #pragma omp for 
2023-01-11T21:41:23.8899108Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.8899195Z         {
2023-01-11T21:41:23.8899407Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8899597Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:23.8899731Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8899802Z         }
2023-01-11T21:41:23.8899937Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8900052Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:23.8900143Z         {
2023-01-11T21:41:23.8900273Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.8900478Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:23.8900581Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:23.8900700Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.8900785Z         }
2023-01-11T21:41:23.8900870Z     }
2023-01-11T21:41:23.8900951Z }
2023-01-11T21:41:23.8901064Z ''')
2023-01-11T21:41:23.8901073Z 
2023-01-11T21:41:23.8901078Z 
2023-01-11T21:41:23.8901205Z async_compile.wait(globals())
2023-01-11T21:41:23.8901371Z del async_compile
2023-01-11T21:41:23.8901392Z 
2023-01-11T21:41:23.8901478Z def call(args):
2023-01-11T21:41:23.8901620Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.8901721Z     args.clear()
2023-01-11T21:41:23.8902017Z     buf0 = empty_strided((2, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8902254Z     aten.addmm.out(primals_2, primals_3, as_strided(primals_1, (8, 16), (1, 8)), beta=1, alpha=1, out=buf0)
2023-01-11T21:41:23.8902491Z     del primals_1
2023-01-11T21:41:23.8902594Z     del primals_2
2023-01-11T21:41:23.8902699Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:23.8902848Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8902977Z     return (buf1, primals_3, buf1, )
2023-01-11T21:41:23.8902985Z 
2023-01-11T21:41:23.8902992Z 
2023-01-11T21:41:23.8903101Z if __name__ == "__main__":
2023-01-11T21:41:23.8903266Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8903535Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8903849Z     primals_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8904152Z     primals_2 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8904434Z     primals_3 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8904636Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.8904643Z 
2023-01-11T21:41:23.8904737Z ok (0.061s)
2023-01-11T21:41:23.8905454Z   test_linear2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8905640Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8906041Z [2023-01-11 21:35:36,557] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 264
2023-01-11T21:41:23.8906450Z [2023-01-11 21:35:36,603] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 264
2023-01-11T21:41:23.8906458Z 
2023-01-11T21:41:23.8906594Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8906698Z import torch
2023-01-11T21:41:23.8906783Z import random
2023-01-11T21:41:23.8906950Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8907129Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8907137Z 
2023-01-11T21:41:23.8907256Z aten = torch.ops.aten
2023-01-11T21:41:23.8907452Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8907586Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8907594Z 
2023-01-11T21:41:23.8907600Z 
2023-01-11T21:41:23.8907801Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.8908108Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8908260Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.8908347Z {
2023-01-11T21:41:23.8908488Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8908574Z     {
2023-01-11T21:41:23.8908683Z         #pragma omp for 
2023-01-11T21:41:23.8908800Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8908887Z         {
2023-01-11T21:41:23.8909079Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8909264Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.8909401Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8909489Z         }
2023-01-11T21:41:23.8909628Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8909749Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.8909835Z         {
2023-01-11T21:41:23.8910056Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.8910185Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.8910303Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8910392Z         }
2023-01-11T21:41:23.8910477Z     }
2023-01-11T21:41:23.8910563Z }
2023-01-11T21:41:23.8910685Z ''')
2023-01-11T21:41:23.8910692Z 
2023-01-11T21:41:23.8910700Z 
2023-01-11T21:41:23.8910882Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.8911186Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8911357Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.8911442Z {
2023-01-11T21:41:23.8911585Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8911669Z     {
2023-01-11T21:41:23.8911780Z         #pragma omp for 
2023-01-11T21:41:23.8911881Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8911969Z         {
2023-01-11T21:41:23.8912242Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8912438Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.8912571Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8912664Z         }
2023-01-11T21:41:23.8912800Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8912903Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.8912992Z         {
2023-01-11T21:41:23.8913119Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.8913245Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.8913363Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8913452Z         }
2023-01-11T21:41:23.8913540Z     }
2023-01-11T21:41:23.8913613Z }
2023-01-11T21:41:23.8913804Z ''')
2023-01-11T21:41:23.8913813Z 
2023-01-11T21:41:23.8913819Z 
2023-01-11T21:41:23.8914020Z kernel_cpp_2 = async_compile.cpp('''
2023-01-11T21:41:23.8914321Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8914503Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:23.8914589Z {
2023-01-11T21:41:23.8914732Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8914819Z     {
2023-01-11T21:41:23.8914914Z         #pragma omp for 
2023-01-11T21:41:23.8915032Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.8915123Z         {
2023-01-11T21:41:23.8915322Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8915503Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:23.8915643Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.8915732Z         }
2023-01-11T21:41:23.8915860Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.8915976Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:23.8916063Z         {
2023-01-11T21:41:23.8916191Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.8916317Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.8916440Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8916524Z         }
2023-01-11T21:41:23.8916600Z     }
2023-01-11T21:41:23.8916685Z }
2023-01-11T21:41:23.8916806Z ''')
2023-01-11T21:41:23.8916814Z 
2023-01-11T21:41:23.8916820Z 
2023-01-11T21:41:23.8917021Z kernel_cpp_3 = async_compile.cpp('''
2023-01-11T21:41:23.8917324Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.8917496Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.8917636Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:23.8917703Z {
2023-01-11T21:41:23.8917849Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.8917935Z     {
2023-01-11T21:41:23.8918044Z         #pragma omp for 
2023-01-11T21:41:23.8918162Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.8918249Z         {
2023-01-11T21:41:23.8918342Z             {
2023-01-11T21:41:23.8918420Z                 {
2023-01-11T21:41:23.8918641Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:23.8918778Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:23.8918932Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:41:23.8919066Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:41:23.8919198Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.8919323Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.8919401Z                 }
2023-01-11T21:41:23.8919493Z             }
2023-01-11T21:41:23.8919581Z         }
2023-01-11T21:41:23.8919666Z     }
2023-01-11T21:41:23.8919747Z }
2023-01-11T21:41:23.8919865Z ''')
2023-01-11T21:41:23.8919874Z 
2023-01-11T21:41:23.8919881Z 
2023-01-11T21:41:23.8920015Z async_compile.wait(globals())
2023-01-11T21:41:23.8920106Z del async_compile
2023-01-11T21:41:23.8920112Z 
2023-01-11T21:41:23.8920214Z def call(args):
2023-01-11T21:41:23.8920474Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
2023-01-11T21:41:23.8920644Z     args.clear()
2023-01-11T21:41:23.8920950Z     buf0 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8921194Z     aten.addmm.out(primals_2, primals_9, as_strided(primals_1, (8, 8), (1, 8)), beta=1, alpha=1, out=buf0)
2023-01-11T21:41:23.8921298Z     del primals_1
2023-01-11T21:41:23.8921383Z     del primals_2
2023-01-11T21:41:23.8921501Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:23.8921647Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.8921947Z     buf2 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8922181Z     aten.addmm.out(primals_4, buf1, as_strided(primals_3, (8, 8), (1, 8)), beta=1, alpha=1, out=buf2)
2023-01-11T21:41:23.8922284Z     del primals_4
2023-01-11T21:41:23.8922403Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:23.8922536Z     kernel_cpp_1(c_void_p(buf3.data_ptr()))
2023-01-11T21:41:23.8922836Z     buf4 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8923066Z     aten.addmm.out(primals_6, buf3, as_strided(primals_5, (8, 8), (1, 8)), beta=1, alpha=1, out=buf4)
2023-01-11T21:41:23.8923167Z     del primals_6
2023-01-11T21:41:23.8923285Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:41:23.8923430Z     kernel_cpp_2(c_void_p(buf5.data_ptr()))
2023-01-11T21:41:23.8923720Z     buf6 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8923947Z     aten.addmm.out(primals_8, buf5, as_strided(primals_7, (8, 8), (1, 8)), beta=1, alpha=1, out=buf6)
2023-01-11T21:41:23.8924037Z     del primals_8
2023-01-11T21:41:23.8924156Z     buf7 = buf6; del buf6  # reuse
2023-01-11T21:41:23.8924438Z     buf8 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.8924628Z     kernel_cpp_3(c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:23.8924927Z     return (buf7, primals_9, buf1, buf3, buf5, buf8, as_strided(primals_7, (8, 8), (8, 1)), as_strided(primals_5, (8, 8), (8, 1)), as_strided(primals_3, (8, 8), (8, 1)), )
2023-01-11T21:41:23.8924940Z 
2023-01-11T21:41:23.8924946Z 
2023-01-11T21:41:23.8925052Z if __name__ == "__main__":
2023-01-11T21:41:23.8925212Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8925389Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8925684Z     primals_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8925986Z     primals_2 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8926294Z     primals_3 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8926590Z     primals_4 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8926886Z     primals_5 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8927187Z     primals_6 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8927560Z     primals_7 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8927853Z     primals_8 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8928138Z     primals_9 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.8928453Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]))
2023-01-11T21:41:23.8928462Z 
2023-01-11T21:41:23.8928554Z ok (0.258s)
2023-01-11T21:41:23.8929287Z   test_linear_binary_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8929530Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8929946Z [2023-01-11 21:35:36,705] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 265
2023-01-11T21:41:23.8930366Z [2023-01-11 21:35:36,707] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 265
2023-01-11T21:41:23.8931029Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8931209Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8931608Z [2023-01-11 21:35:36,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 266
2023-01-11T21:41:23.8932016Z [2023-01-11 21:35:36,741] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 266
2023-01-11T21:41:23.8932666Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8932847Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8933228Z [2023-01-11 21:35:36,825] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 267
2023-01-11T21:41:23.8933641Z [2023-01-11 21:35:36,828] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 267
2023-01-11T21:41:23.8934302Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8934489Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8934873Z [2023-01-11 21:35:36,856] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 268
2023-01-11T21:41:23.8935272Z [2023-01-11 21:35:36,858] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 268
2023-01-11T21:41:23.8935943Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8936132Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8936608Z [2023-01-11 21:35:36,944] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 269
2023-01-11T21:41:23.8937012Z [2023-01-11 21:35:36,946] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 269
2023-01-11T21:41:23.8937683Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8937869Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8938251Z [2023-01-11 21:35:36,977] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 270
2023-01-11T21:41:23.8938697Z [2023-01-11 21:35:36,979] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 270
2023-01-11T21:41:23.8939361Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8939547Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8939939Z [2023-01-11 21:35:37,068] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 271
2023-01-11T21:41:23.8940349Z [2023-01-11 21:35:37,070] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 271
2023-01-11T21:41:23.8941001Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8941187Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8941576Z [2023-01-11 21:35:37,101] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 272
2023-01-11T21:41:23.8941585Z 
2023-01-11T21:41:23.8941720Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8941819Z import torch
2023-01-11T21:41:23.8941905Z import random
2023-01-11T21:41:23.8942075Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8942250Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8942257Z 
2023-01-11T21:41:23.8942547Z aten = torch.ops.aten
2023-01-11T21:41:23.8942746Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8942880Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8942897Z 
2023-01-11T21:41:23.8942903Z 
2023-01-11T21:41:23.8943032Z async_compile.wait(globals())
2023-01-11T21:41:23.8943120Z del async_compile
2023-01-11T21:41:23.8943142Z 
2023-01-11T21:41:23.8943227Z def call(args):
2023-01-11T21:41:23.8943357Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.8943460Z     args.clear()
2023-01-11T21:41:23.8943811Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.8943906Z     del arg0_1
2023-01-11T21:41:23.8943996Z     del arg1_1
2023-01-11T21:41:23.8944075Z     del arg2_1
2023-01-11T21:41:23.8944163Z     del arg3_1
2023-01-11T21:41:23.8944263Z     return (buf0, )
2023-01-11T21:41:23.8944271Z 
2023-01-11T21:41:23.8944277Z 
2023-01-11T21:41:23.8944382Z if __name__ == "__main__":
2023-01-11T21:41:23.8944552Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8944735Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8945160Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8945463Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8945765Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8946079Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8946271Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.8946280Z 
2023-01-11T21:41:23.8946286Z 
2023-01-11T21:41:23.8946421Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8946524Z import torch
2023-01-11T21:41:23.8946629Z import random
2023-01-11T21:41:23.8946803Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8946984Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8946992Z 
2023-01-11T21:41:23.8947091Z aten = torch.ops.aten
2023-01-11T21:41:23.8947378Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8947519Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8947526Z 
2023-01-11T21:41:23.8947532Z 
2023-01-11T21:41:23.8947663Z async_compile.wait(globals())
2023-01-11T21:41:23.8947767Z del async_compile
2023-01-11T21:41:23.8947774Z 
2023-01-11T21:41:23.8947875Z def call(args):
2023-01-11T21:41:23.8947991Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8948095Z     args.clear()
2023-01-11T21:41:23.8948429Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.8948523Z     del arg0_1
2023-01-11T21:41:23.8948618Z     del arg1_1
2023-01-11T21:41:23.8948711Z     del arg2_1
2023-01-11T21:41:23.8948814Z     return (buf0, )
2023-01-11T21:41:23.8948821Z 
2023-01-11T21:41:23.8948828Z 
2023-01-11T21:41:23.8948934Z if __name__ == "__main__":
2023-01-11T21:41:23.8949098Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8949260Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8949579Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8949894Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8950211Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8950388Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8950397Z 
2023-01-11T21:41:23.8950403Z 
2023-01-11T21:41:23.8950543Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8950640Z import torch
2023-01-11T21:41:23.8950740Z import random
2023-01-11T21:41:23.8950895Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8951067Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8951074Z 
2023-01-11T21:41:23.8951184Z aten = torch.ops.aten
2023-01-11T21:41:23.8951379Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8951507Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8951522Z 
2023-01-11T21:41:23.8951528Z 
2023-01-11T21:41:23.8951656Z async_compile.wait(globals())
2023-01-11T21:41:23.8951756Z del async_compile
2023-01-11T21:41:23.8951763Z 
2023-01-11T21:41:23.8951862Z def call(args):
2023-01-11T21:41:23.8951972Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.8952069Z     args.clear()
2023-01-11T21:41:23.8952417Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.8952516Z     del arg0_1
2023-01-11T21:41:23.8952609Z     del arg1_1
2023-01-11T21:41:23.8952709Z     del arg2_1
2023-01-11T21:41:23.8952802Z     del arg3_1
2023-01-11T21:41:23.8952885Z     return (buf0, )
2023-01-11T21:41:23.8952893Z 
2023-01-11T21:41:23.8952914Z 
2023-01-11T21:41:23.8953006Z if __name__ == "__main__":
2023-01-11T21:41:23.8953169Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8953345Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8953792Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8954114Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8954455Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8954795Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8954991Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.8955016Z 
2023-01-11T21:41:23.8955023Z 
2023-01-11T21:41:23.8955164Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8955280Z import torch
2023-01-11T21:41:23.8955393Z import random
2023-01-11T21:41:23.8955590Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8955801Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8955808Z 
2023-01-11T21:41:23.8955937Z aten = torch.ops.aten
2023-01-11T21:41:23.8956225Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8956366Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8956389Z 
2023-01-11T21:41:23.8956396Z 
2023-01-11T21:41:23.8956527Z async_compile.wait(globals())
2023-01-11T21:41:23.8956646Z del async_compile
2023-01-11T21:41:23.8956654Z 
2023-01-11T21:41:23.8956761Z def call(args):
2023-01-11T21:41:23.8956894Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8957001Z     args.clear()
2023-01-11T21:41:23.8957396Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.8957505Z     del arg0_1
2023-01-11T21:41:23.8957597Z     del arg1_1
2023-01-11T21:41:23.8957708Z     del arg2_1
2023-01-11T21:41:23.8957823Z     return (buf0, )
2023-01-11T21:41:23.8957830Z 
2023-01-11T21:41:23.8957837Z 
2023-01-11T21:41:23.8957957Z if __name__ == "__main__":
2023-01-11T21:41:23.8958146Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8958353Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8958713Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8959041Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8959384Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8959586Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8959594Z 
2023-01-11T21:41:23.8959601Z 
2023-01-11T21:41:23.8959762Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8959875Z import torch
2023-01-11T21:41:23.8959990Z import random
2023-01-11T21:41:23.8960184Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8960389Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8960397Z 
2023-01-11T21:41:23.8960513Z aten = torch.ops.aten
2023-01-11T21:41:23.8960742Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8960898Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8960914Z 
2023-01-11T21:41:23.8960922Z 
2023-01-11T21:41:23.8961072Z async_compile.wait(globals())
2023-01-11T21:41:23.8961193Z del async_compile
2023-01-11T21:41:23.8961200Z 
2023-01-11T21:41:23.8961316Z def call(args):
2023-01-11T21:41:23.8961457Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.8961572Z     args.clear()
2023-01-11T21:41:23.8961947Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.8962053Z     del arg0_1
2023-01-11T21:41:23.8962154Z     del arg1_1
2023-01-11T21:41:23.8962258Z     del arg2_1
2023-01-11T21:41:23.8962368Z     del arg3_1
2023-01-11T21:41:23.8962485Z     return (buf0, )
2023-01-11T21:41:23.8962493Z 
2023-01-11T21:41:23.8962500Z 
2023-01-11T21:41:23.8962625Z if __name__ == "__main__":
2023-01-11T21:41:23.8962798Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8963000Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8963360Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8963759Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8964123Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8964482Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8964703Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.8964712Z 
2023-01-11T21:41:23.8964720Z 
2023-01-11T21:41:23.8964879Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8964995Z import torch
2023-01-11T21:41:23.8965096Z import random
2023-01-11T21:41:23.8965287Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8965492Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8965500Z 
2023-01-11T21:41:23.8965628Z aten = torch.ops.aten
2023-01-11T21:41:23.8965900Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8966058Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8966066Z 
2023-01-11T21:41:23.8966074Z 
2023-01-11T21:41:23.8966222Z async_compile.wait(globals())
2023-01-11T21:41:23.8966340Z del async_compile
2023-01-11T21:41:23.8966349Z 
2023-01-11T21:41:23.8966446Z def call(args):
2023-01-11T21:41:23.8966577Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8966693Z     args.clear()
2023-01-11T21:41:23.8967092Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.8967200Z     del arg0_1
2023-01-11T21:41:23.8967309Z     del arg1_1
2023-01-11T21:41:23.8967416Z     del arg2_1
2023-01-11T21:41:23.8967521Z     return (buf0, )
2023-01-11T21:41:23.8967529Z 
2023-01-11T21:41:23.8967537Z 
2023-01-11T21:41:23.8967659Z if __name__ == "__main__":
2023-01-11T21:41:23.8967850Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8968058Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8968418Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8968774Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8969136Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8969339Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8969347Z 
2023-01-11T21:41:23.8969354Z 
2023-01-11T21:41:23.8969494Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8969605Z import torch
2023-01-11T21:41:23.8969724Z import random
2023-01-11T21:41:23.8969916Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8970123Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8970131Z 
2023-01-11T21:41:23.8970262Z aten = torch.ops.aten
2023-01-11T21:41:23.8970487Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8970626Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8970656Z 
2023-01-11T21:41:23.8970663Z 
2023-01-11T21:41:23.8970797Z async_compile.wait(globals())
2023-01-11T21:41:23.8970918Z del async_compile
2023-01-11T21:41:23.8970925Z 
2023-01-11T21:41:23.8971043Z def call(args):
2023-01-11T21:41:23.8971189Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.8971308Z     args.clear()
2023-01-11T21:41:23.8971716Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.8971824Z     del arg0_1
2023-01-11T21:41:23.8971917Z     del arg1_1
2023-01-11T21:41:23.8972025Z     del arg2_1
2023-01-11T21:41:23.8972128Z     del arg3_1
2023-01-11T21:41:23.8972244Z     return (buf0, )
2023-01-11T21:41:23.8972252Z 
2023-01-11T21:41:23.8972259Z 
2023-01-11T21:41:23.8972384Z if __name__ == "__main__":
2023-01-11T21:41:23.8972579Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8972787Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8973197Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8973519Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8973858Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8974199Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8974420Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.8974427Z 
2023-01-11T21:41:23.8974899Z [2023-01-11 21:35:37,103] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 272
2023-01-11T21:41:23.8975730Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8975947Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8976407Z [2023-01-11 21:35:37,190] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 273
2023-01-11T21:41:23.8976878Z [2023-01-11 21:35:37,193] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 273
2023-01-11T21:41:23.8977644Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8977831Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8978288Z [2023-01-11 21:35:37,224] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 274
2023-01-11T21:41:23.8978758Z [2023-01-11 21:35:37,226] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 274
2023-01-11T21:41:23.8979524Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8979731Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8980176Z [2023-01-11 21:35:37,321] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 275
2023-01-11T21:41:23.8980637Z [2023-01-11 21:35:37,323] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 275
2023-01-11T21:41:23.8981397Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8981610Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8982055Z [2023-01-11 21:35:37,353] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 276
2023-01-11T21:41:23.8982655Z [2023-01-11 21:35:37,356] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 276
2023-01-11T21:41:23.8983421Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8983717Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8984162Z [2023-01-11 21:35:37,443] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 277
2023-01-11T21:41:23.8984630Z [2023-01-11 21:35:37,445] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 277
2023-01-11T21:41:23.8985405Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8985613Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8986114Z [2023-01-11 21:35:37,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 278
2023-01-11T21:41:23.8986591Z [2023-01-11 21:35:37,478] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 278
2023-01-11T21:41:23.8987359Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.8987576Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.8988034Z [2023-01-11 21:35:37,564] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 279
2023-01-11T21:41:23.8988045Z 
2023-01-11T21:41:23.8988203Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8988302Z import torch
2023-01-11T21:41:23.8988416Z import random
2023-01-11T21:41:23.8988609Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8988825Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8988834Z 
2023-01-11T21:41:23.8988963Z aten = torch.ops.aten
2023-01-11T21:41:23.8989193Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8989348Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8989357Z 
2023-01-11T21:41:23.8989364Z 
2023-01-11T21:41:23.8989511Z async_compile.wait(globals())
2023-01-11T21:41:23.8989614Z del async_compile
2023-01-11T21:41:23.8989637Z 
2023-01-11T21:41:23.8989742Z def call(args):
2023-01-11T21:41:23.8989879Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8989992Z     args.clear()
2023-01-11T21:41:23.8990388Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.8990499Z     del arg0_1
2023-01-11T21:41:23.8990608Z     del arg1_1
2023-01-11T21:41:23.8990700Z     del arg2_1
2023-01-11T21:41:23.8990816Z     return (buf0, )
2023-01-11T21:41:23.8990830Z 
2023-01-11T21:41:23.8990840Z 
2023-01-11T21:41:23.8990967Z if __name__ == "__main__":
2023-01-11T21:41:23.8991161Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8991373Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8991725Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8992070Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8992411Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8992601Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.8992623Z 
2023-01-11T21:41:23.8992630Z 
2023-01-11T21:41:23.8992768Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8992879Z import torch
2023-01-11T21:41:23.8992989Z import random
2023-01-11T21:41:23.8993182Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8993390Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8993494Z 
2023-01-11T21:41:23.8993619Z aten = torch.ops.aten
2023-01-11T21:41:23.8993908Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8994044Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8994053Z 
2023-01-11T21:41:23.8994075Z 
2023-01-11T21:41:23.8994208Z async_compile.wait(globals())
2023-01-11T21:41:23.8994331Z del async_compile
2023-01-11T21:41:23.8994338Z 
2023-01-11T21:41:23.8994456Z def call(args):
2023-01-11T21:41:23.8994600Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.8994716Z     args.clear()
2023-01-11T21:41:23.8995117Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.8995226Z     del arg0_1
2023-01-11T21:41:23.8995318Z     del arg1_1
2023-01-11T21:41:23.8995423Z     del arg2_1
2023-01-11T21:41:23.8995521Z     del arg3_1
2023-01-11T21:41:23.8995633Z     return (buf0, )
2023-01-11T21:41:23.8995641Z 
2023-01-11T21:41:23.8995648Z 
2023-01-11T21:41:23.8995821Z if __name__ == "__main__":
2023-01-11T21:41:23.8996017Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.8996224Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.8996559Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8996901Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8997258Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8997619Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.8997836Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.8997845Z 
2023-01-11T21:41:23.8997852Z 
2023-01-11T21:41:23.8998004Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.8998117Z import torch
2023-01-11T21:41:23.8998233Z import random
2023-01-11T21:41:23.8998417Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.8998627Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.8998634Z 
2023-01-11T21:41:23.8998760Z aten = torch.ops.aten
2023-01-11T21:41:23.8998984Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.8999134Z async_compile = AsyncCompile()
2023-01-11T21:41:23.8999143Z 
2023-01-11T21:41:23.8999151Z 
2023-01-11T21:41:23.8999302Z async_compile.wait(globals())
2023-01-11T21:41:23.8999425Z del async_compile
2023-01-11T21:41:23.8999433Z 
2023-01-11T21:41:23.8999554Z def call(args):
2023-01-11T21:41:23.8999674Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.8999789Z     args.clear()
2023-01-11T21:41:23.9000176Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.9000285Z     del arg0_1
2023-01-11T21:41:23.9000391Z     del arg1_1
2023-01-11T21:41:23.9000494Z     del arg2_1
2023-01-11T21:41:23.9000610Z     return (buf0, )
2023-01-11T21:41:23.9000623Z 
2023-01-11T21:41:23.9000634Z 
2023-01-11T21:41:23.9000755Z if __name__ == "__main__":
2023-01-11T21:41:23.9000929Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9001135Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9001481Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9001843Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9002197Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9002397Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9002405Z 
2023-01-11T21:41:23.9002411Z 
2023-01-11T21:41:23.9002570Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9002687Z import torch
2023-01-11T21:41:23.9002790Z import random
2023-01-11T21:41:23.9002986Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9003196Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9003256Z 
2023-01-11T21:41:23.9003385Z aten = torch.ops.aten
2023-01-11T21:41:23.9003617Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9003765Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9003771Z 
2023-01-11T21:41:23.9003777Z 
2023-01-11T21:41:23.9003922Z async_compile.wait(globals())
2023-01-11T21:41:23.9004040Z del async_compile
2023-01-11T21:41:23.9004048Z 
2023-01-11T21:41:23.9004150Z def call(args):
2023-01-11T21:41:23.9004295Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9004411Z     args.clear()
2023-01-11T21:41:23.9004811Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.9004908Z     del arg0_1
2023-01-11T21:41:23.9005018Z     del arg1_1
2023-01-11T21:41:23.9005119Z     del arg2_1
2023-01-11T21:41:23.9005210Z     del arg3_1
2023-01-11T21:41:23.9005324Z     return (buf0, )
2023-01-11T21:41:23.9005331Z 
2023-01-11T21:41:23.9005343Z 
2023-01-11T21:41:23.9005506Z if __name__ == "__main__":
2023-01-11T21:41:23.9005697Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9005901Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9006255Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9006591Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9006918Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9007263Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9007481Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9007490Z 
2023-01-11T21:41:23.9007497Z 
2023-01-11T21:41:23.9007654Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9007765Z import torch
2023-01-11T21:41:23.9007885Z import random
2023-01-11T21:41:23.9008085Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9008295Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9008304Z 
2023-01-11T21:41:23.9008415Z aten = torch.ops.aten
2023-01-11T21:41:23.9008638Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9008788Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9008795Z 
2023-01-11T21:41:23.9008802Z 
2023-01-11T21:41:23.9008948Z async_compile.wait(globals())
2023-01-11T21:41:23.9009062Z del async_compile
2023-01-11T21:41:23.9009070Z 
2023-01-11T21:41:23.9009180Z def call(args):
2023-01-11T21:41:23.9009311Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9009432Z     args.clear()
2023-01-11T21:41:23.9009807Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.9009913Z     del arg0_1
2023-01-11T21:41:23.9010020Z     del arg1_1
2023-01-11T21:41:23.9010125Z     del arg2_1
2023-01-11T21:41:23.9010237Z     return (buf0, )
2023-01-11T21:41:23.9010244Z 
2023-01-11T21:41:23.9010258Z 
2023-01-11T21:41:23.9010379Z if __name__ == "__main__":
2023-01-11T21:41:23.9010571Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9010776Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9011113Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9011453Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9011785Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9011991Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9011999Z 
2023-01-11T21:41:23.9012006Z 
2023-01-11T21:41:23.9012162Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9012277Z import torch
2023-01-11T21:41:23.9012391Z import random
2023-01-11T21:41:23.9012580Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9012774Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9012838Z 
2023-01-11T21:41:23.9012964Z aten = torch.ops.aten
2023-01-11T21:41:23.9013194Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9013341Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9013350Z 
2023-01-11T21:41:23.9013357Z 
2023-01-11T21:41:23.9013506Z async_compile.wait(globals())
2023-01-11T21:41:23.9013627Z del async_compile
2023-01-11T21:41:23.9013634Z 
2023-01-11T21:41:23.9013745Z def call(args):
2023-01-11T21:41:23.9013891Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9013991Z     args.clear()
2023-01-11T21:41:23.9014397Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.9014506Z     del arg0_1
2023-01-11T21:41:23.9014615Z     del arg1_1
2023-01-11T21:41:23.9014717Z     del arg2_1
2023-01-11T21:41:23.9014816Z     del arg3_1
2023-01-11T21:41:23.9014913Z     return (buf0, )
2023-01-11T21:41:23.9014934Z 
2023-01-11T21:41:23.9014941Z 
2023-01-11T21:41:23.9015114Z if __name__ == "__main__":
2023-01-11T21:41:23.9015306Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9015512Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9015857Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9016195Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9016552Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9016901Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9017118Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9017127Z 
2023-01-11T21:41:23.9017135Z 
2023-01-11T21:41:23.9017277Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9017389Z import torch
2023-01-11T21:41:23.9017508Z import random
2023-01-11T21:41:23.9017704Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9017916Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9017924Z 
2023-01-11T21:41:23.9018053Z aten = torch.ops.aten
2023-01-11T21:41:23.9018279Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9018414Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9018437Z 
2023-01-11T21:41:23.9018444Z 
2023-01-11T21:41:23.9018578Z async_compile.wait(globals())
2023-01-11T21:41:23.9018691Z del async_compile
2023-01-11T21:41:23.9018698Z 
2023-01-11T21:41:23.9018811Z def call(args):
2023-01-11T21:41:23.9018937Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9019053Z     args.clear()
2023-01-11T21:41:23.9019455Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.9019567Z     del arg0_1
2023-01-11T21:41:23.9019664Z     del arg1_1
2023-01-11T21:41:23.9019772Z     del arg2_1
2023-01-11T21:41:23.9019894Z     return (buf0, )
2023-01-11T21:41:23.9019901Z 
2023-01-11T21:41:23.9019912Z 
2023-01-11T21:41:23.9020037Z if __name__ == "__main__":
2023-01-11T21:41:23.9020228Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9020430Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9020778Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9021139Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9021483Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9021692Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9021700Z 
2023-01-11T21:41:23.9022171Z [2023-01-11 21:35:37,566] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 279
2023-01-11T21:41:23.9023073Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9023371Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9023827Z [2023-01-11 21:35:37,595] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 280
2023-01-11T21:41:23.9024289Z [2023-01-11 21:35:37,597] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 280
2023-01-11T21:41:23.9025045Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9025305Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9025765Z [2023-01-11 21:35:37,688] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 281
2023-01-11T21:41:23.9026235Z [2023-01-11 21:35:37,691] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 281
2023-01-11T21:41:23.9026997Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9027209Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9027667Z [2023-01-11 21:35:37,724] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 282
2023-01-11T21:41:23.9028135Z [2023-01-11 21:35:37,727] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 282
2023-01-11T21:41:23.9028915Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9029123Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9029586Z [2023-01-11 21:35:37,812] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 283
2023-01-11T21:41:23.9030053Z [2023-01-11 21:35:37,815] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 283
2023-01-11T21:41:23.9030830Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9031039Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9031489Z [2023-01-11 21:35:37,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 284
2023-01-11T21:41:23.9031953Z [2023-01-11 21:35:37,847] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 284
2023-01-11T21:41:23.9032740Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9032942Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9033440Z [2023-01-11 21:35:37,934] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 285
2023-01-11T21:41:23.9033968Z [2023-01-11 21:35:37,936] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 285
2023-01-11T21:41:23.9034721Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9034925Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9035376Z [2023-01-11 21:35:37,967] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 286
2023-01-11T21:41:23.9035386Z 
2023-01-11T21:41:23.9035589Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9035703Z import torch
2023-01-11T21:41:23.9035816Z import random
2023-01-11T21:41:23.9035997Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9036202Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9036211Z 
2023-01-11T21:41:23.9036334Z aten = torch.ops.aten
2023-01-11T21:41:23.9036559Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9036710Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9036718Z 
2023-01-11T21:41:23.9036726Z 
2023-01-11T21:41:23.9036872Z async_compile.wait(globals())
2023-01-11T21:41:23.9036986Z del async_compile
2023-01-11T21:41:23.9036994Z 
2023-01-11T21:41:23.9037108Z def call(args):
2023-01-11T21:41:23.9037238Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9037352Z     args.clear()
2023-01-11T21:41:23.9037751Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:23.9037866Z     del arg0_1
2023-01-11T21:41:23.9037980Z     del arg1_1
2023-01-11T21:41:23.9038085Z     del arg2_1
2023-01-11T21:41:23.9038192Z     del arg3_1
2023-01-11T21:41:23.9038297Z     return (buf0, )
2023-01-11T21:41:23.9038304Z 
2023-01-11T21:41:23.9038322Z 
2023-01-11T21:41:23.9038429Z if __name__ == "__main__":
2023-01-11T21:41:23.9038616Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9038822Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9039173Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9039517Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9039855Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9040192Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9040390Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9040417Z 
2023-01-11T21:41:23.9040429Z 
2023-01-11T21:41:23.9040569Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9040683Z import torch
2023-01-11T21:41:23.9040797Z import random
2023-01-11T21:41:23.9040996Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9041204Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9041212Z 
2023-01-11T21:41:23.9041344Z aten = torch.ops.aten
2023-01-11T21:41:23.9041571Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9041711Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9041718Z 
2023-01-11T21:41:23.9041739Z 
2023-01-11T21:41:23.9041869Z async_compile.wait(globals())
2023-01-11T21:41:23.9041987Z del async_compile
2023-01-11T21:41:23.9041994Z 
2023-01-11T21:41:23.9042108Z def call(args):
2023-01-11T21:41:23.9042239Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9042356Z     args.clear()
2023-01-11T21:41:23.9042754Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:23.9042921Z     del arg0_1
2023-01-11T21:41:23.9043014Z     del arg1_1
2023-01-11T21:41:23.9043117Z     del arg2_1
2023-01-11T21:41:23.9043233Z     return (buf0, )
2023-01-11T21:41:23.9043241Z 
2023-01-11T21:41:23.9043248Z 
2023-01-11T21:41:23.9043368Z if __name__ == "__main__":
2023-01-11T21:41:23.9043558Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9043767Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9044115Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9044440Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9044781Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9044986Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9044993Z 
2023-01-11T21:41:23.9045000Z 
2023-01-11T21:41:23.9045157Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9045315Z import torch
2023-01-11T21:41:23.9045433Z import random
2023-01-11T21:41:23.9045630Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9045832Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9045840Z 
2023-01-11T21:41:23.9045954Z aten = torch.ops.aten
2023-01-11T21:41:23.9046183Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9046331Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9046340Z 
2023-01-11T21:41:23.9046347Z 
2023-01-11T21:41:23.9046494Z async_compile.wait(globals())
2023-01-11T21:41:23.9046610Z del async_compile
2023-01-11T21:41:23.9046618Z 
2023-01-11T21:41:23.9046732Z def call(args):
2023-01-11T21:41:23.9046878Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9046993Z     args.clear()
2023-01-11T21:41:23.9047383Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:23.9047495Z     del arg0_1
2023-01-11T21:41:23.9047611Z     del arg1_1
2023-01-11T21:41:23.9047719Z     del arg2_1
2023-01-11T21:41:23.9047825Z     del arg3_1
2023-01-11T21:41:23.9047942Z     return (buf0, )
2023-01-11T21:41:23.9047950Z 
2023-01-11T21:41:23.9047957Z 
2023-01-11T21:41:23.9048080Z if __name__ == "__main__":
2023-01-11T21:41:23.9048261Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9048463Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9048813Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9049144Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9049495Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9049855Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9050073Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9050085Z 
2023-01-11T21:41:23.9050096Z 
2023-01-11T21:41:23.9050251Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9050366Z import torch
2023-01-11T21:41:23.9050469Z import random
2023-01-11T21:41:23.9050660Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9050860Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9050868Z 
2023-01-11T21:41:23.9050992Z aten = torch.ops.aten
2023-01-11T21:41:23.9051214Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9051366Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9051374Z 
2023-01-11T21:41:23.9051380Z 
2023-01-11T21:41:23.9051525Z async_compile.wait(globals())
2023-01-11T21:41:23.9051629Z del async_compile
2023-01-11T21:41:23.9051651Z 
2023-01-11T21:41:23.9051749Z def call(args):
2023-01-11T21:41:23.9051880Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9051998Z     args.clear()
2023-01-11T21:41:23.9052399Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:23.9052561Z     del arg0_1
2023-01-11T21:41:23.9052667Z     del arg1_1
2023-01-11T21:41:23.9052763Z     del arg2_1
2023-01-11T21:41:23.9052877Z     return (buf0, )
2023-01-11T21:41:23.9052885Z 
2023-01-11T21:41:23.9052892Z 
2023-01-11T21:41:23.9053016Z if __name__ == "__main__":
2023-01-11T21:41:23.9053205Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9053411Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9053764Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9054121Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9054475Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9054667Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9054688Z 
2023-01-11T21:41:23.9054695Z 
2023-01-11T21:41:23.9054887Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9055005Z import torch
2023-01-11T21:41:23.9055116Z import random
2023-01-11T21:41:23.9055307Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9055514Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9055522Z 
2023-01-11T21:41:23.9055652Z aten = torch.ops.aten
2023-01-11T21:41:23.9055881Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9056019Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9056041Z 
2023-01-11T21:41:23.9056048Z 
2023-01-11T21:41:23.9056180Z async_compile.wait(globals())
2023-01-11T21:41:23.9056298Z del async_compile
2023-01-11T21:41:23.9056305Z 
2023-01-11T21:41:23.9056424Z def call(args):
2023-01-11T21:41:23.9056567Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9056680Z     args.clear()
2023-01-11T21:41:23.9057071Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:23.9057179Z     del arg0_1
2023-01-11T21:41:23.9057271Z     del arg1_1
2023-01-11T21:41:23.9057377Z     del arg2_1
2023-01-11T21:41:23.9057482Z     del arg3_1
2023-01-11T21:41:23.9057598Z     return (buf0, )
2023-01-11T21:41:23.9057606Z 
2023-01-11T21:41:23.9057613Z 
2023-01-11T21:41:23.9057739Z if __name__ == "__main__":
2023-01-11T21:41:23.9057929Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9058139Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9058481Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9058822Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9059158Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9059486Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9059699Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9059710Z 
2023-01-11T21:41:23.9059721Z 
2023-01-11T21:41:23.9059879Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9059993Z import torch
2023-01-11T21:41:23.9060109Z import random
2023-01-11T21:41:23.9060284Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9060483Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9060491Z 
2023-01-11T21:41:23.9060615Z aten = torch.ops.aten
2023-01-11T21:41:23.9060839Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9060983Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9060991Z 
2023-01-11T21:41:23.9060999Z 
2023-01-11T21:41:23.9061145Z async_compile.wait(globals())
2023-01-11T21:41:23.9061263Z del async_compile
2023-01-11T21:41:23.9061270Z 
2023-01-11T21:41:23.9061377Z def call(args):
2023-01-11T21:41:23.9061493Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9061610Z     args.clear()
2023-01-11T21:41:23.9061997Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:23.9062160Z     del arg0_1
2023-01-11T21:41:23.9062273Z     del arg1_1
2023-01-11T21:41:23.9062508Z     del arg2_1
2023-01-11T21:41:23.9062620Z     return (buf0, )
2023-01-11T21:41:23.9062629Z 
2023-01-11T21:41:23.9062636Z 
2023-01-11T21:41:23.9062759Z if __name__ == "__main__":
2023-01-11T21:41:23.9062933Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9063135Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9063488Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9063826Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9064168Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9064372Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9064379Z 
2023-01-11T21:41:23.9064386Z 
2023-01-11T21:41:23.9064542Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9064738Z import torch
2023-01-11T21:41:23.9064834Z import random
2023-01-11T21:41:23.9065024Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9065227Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9065235Z 
2023-01-11T21:41:23.9065361Z aten = torch.ops.aten
2023-01-11T21:41:23.9065583Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9065738Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9065745Z 
2023-01-11T21:41:23.9065752Z 
2023-01-11T21:41:23.9065897Z async_compile.wait(globals())
2023-01-11T21:41:23.9066012Z del async_compile
2023-01-11T21:41:23.9066020Z 
2023-01-11T21:41:23.9066117Z def call(args):
2023-01-11T21:41:23.9066261Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9066376Z     args.clear()
2023-01-11T21:41:23.9066778Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:23.9066891Z     del arg0_1
2023-01-11T21:41:23.9067006Z     del arg1_1
2023-01-11T21:41:23.9067110Z     del arg2_1
2023-01-11T21:41:23.9067204Z     del arg3_1
2023-01-11T21:41:23.9067321Z     return (buf0, )
2023-01-11T21:41:23.9067329Z 
2023-01-11T21:41:23.9067337Z 
2023-01-11T21:41:23.9067465Z if __name__ == "__main__":
2023-01-11T21:41:23.9067655Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9067860Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9068219Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9068556Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9068914Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9069251Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9069468Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9069480Z 
2023-01-11T21:41:23.9069957Z [2023-01-11 21:35:37,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 286
2023-01-11T21:41:23.9070725Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9070931Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9071377Z [2023-01-11 21:35:38,053] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 287
2023-01-11T21:41:23.9071832Z [2023-01-11 21:35:38,055] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 287
2023-01-11T21:41:23.9072591Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9072870Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9073325Z [2023-01-11 21:35:38,084] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 288
2023-01-11T21:41:23.9073849Z [2023-01-11 21:35:38,085] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 288
2023-01-11T21:41:23.9074643Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9074863Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9075324Z [2023-01-11 21:35:38,200] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 289
2023-01-11T21:41:23.9075791Z [2023-01-11 21:35:38,204] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 289
2023-01-11T21:41:23.9076541Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9076749Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9077196Z [2023-01-11 21:35:38,238] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 290
2023-01-11T21:41:23.9077667Z [2023-01-11 21:35:38,240] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 290
2023-01-11T21:41:23.9078433Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9078639Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9079080Z [2023-01-11 21:35:38,325] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 291
2023-01-11T21:41:23.9079544Z [2023-01-11 21:35:38,327] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 291
2023-01-11T21:41:23.9080286Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9080502Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9080957Z [2023-01-11 21:35:38,355] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 292
2023-01-11T21:41:23.9081416Z [2023-01-11 21:35:38,356] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 292
2023-01-11T21:41:23.9081426Z 
2023-01-11T21:41:23.9081584Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9081702Z import torch
2023-01-11T21:41:23.9081815Z import random
2023-01-11T21:41:23.9082007Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9082208Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9082261Z 
2023-01-11T21:41:23.9082377Z aten = torch.ops.aten
2023-01-11T21:41:23.9082600Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9082751Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9082759Z 
2023-01-11T21:41:23.9082766Z 
2023-01-11T21:41:23.9082917Z async_compile.wait(globals())
2023-01-11T21:41:23.9083039Z del async_compile
2023-01-11T21:41:23.9083047Z 
2023-01-11T21:41:23.9083162Z def call(args):
2023-01-11T21:41:23.9083291Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9083405Z     args.clear()
2023-01-11T21:41:23.9083786Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:23.9083895Z     del arg0_1
2023-01-11T21:41:23.9084008Z     del arg1_1
2023-01-11T21:41:23.9084116Z     del arg2_1
2023-01-11T21:41:23.9084231Z     return (buf0, )
2023-01-11T21:41:23.9084239Z 
2023-01-11T21:41:23.9084246Z 
2023-01-11T21:41:23.9084370Z if __name__ == "__main__":
2023-01-11T21:41:23.9084620Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9084817Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9085164Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9085521Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9085867Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9086069Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9086077Z 
2023-01-11T21:41:23.9086083Z 
2023-01-11T21:41:23.9086236Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9086351Z import torch
2023-01-11T21:41:23.9086465Z import random
2023-01-11T21:41:23.9086642Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9086841Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9086847Z 
2023-01-11T21:41:23.9086947Z aten = torch.ops.aten
2023-01-11T21:41:23.9087130Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9087251Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9087258Z 
2023-01-11T21:41:23.9087264Z 
2023-01-11T21:41:23.9087381Z async_compile.wait(globals())
2023-01-11T21:41:23.9087473Z del async_compile
2023-01-11T21:41:23.9087479Z 
2023-01-11T21:41:23.9087571Z def call(args):
2023-01-11T21:41:23.9087671Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9087765Z     args.clear()
2023-01-11T21:41:23.9088085Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:23.9088173Z     del arg0_1
2023-01-11T21:41:23.9088261Z     del arg1_1
2023-01-11T21:41:23.9088348Z     del arg2_1
2023-01-11T21:41:23.9088430Z     del arg3_1
2023-01-11T21:41:23.9088507Z     return (buf0, )
2023-01-11T21:41:23.9088513Z 
2023-01-11T21:41:23.9088518Z 
2023-01-11T21:41:23.9088616Z if __name__ == "__main__":
2023-01-11T21:41:23.9088767Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9088939Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9089220Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9089481Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9089755Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9090019Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9090175Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9090183Z 
2023-01-11T21:41:23.9090204Z 
2023-01-11T21:41:23.9090313Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9090403Z import torch
2023-01-11T21:41:23.9090494Z import random
2023-01-11T21:41:23.9090646Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9090807Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9090877Z 
2023-01-11T21:41:23.9090983Z aten = torch.ops.aten
2023-01-11T21:41:23.9091163Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9091267Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9091273Z 
2023-01-11T21:41:23.9091292Z 
2023-01-11T21:41:23.9091392Z async_compile.wait(globals())
2023-01-11T21:41:23.9091483Z del async_compile
2023-01-11T21:41:23.9091489Z 
2023-01-11T21:41:23.9091581Z def call(args):
2023-01-11T21:41:23.9091684Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9091775Z     args.clear()
2023-01-11T21:41:23.9092089Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:23.9092176Z     del arg0_1
2023-01-11T21:41:23.9092252Z     del arg1_1
2023-01-11T21:41:23.9092336Z     del arg2_1
2023-01-11T21:41:23.9092428Z     return (buf0, )
2023-01-11T21:41:23.9092434Z 
2023-01-11T21:41:23.9092439Z 
2023-01-11T21:41:23.9092536Z if __name__ == "__main__":
2023-01-11T21:41:23.9092726Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9092892Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9093163Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9093415Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9093688Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9093850Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9093856Z 
2023-01-11T21:41:23.9093862Z 
2023-01-11T21:41:23.9093988Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9094077Z import torch
2023-01-11T21:41:23.9094170Z import random
2023-01-11T21:41:23.9094321Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9094477Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9094483Z 
2023-01-11T21:41:23.9094569Z aten = torch.ops.aten
2023-01-11T21:41:23.9094751Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9094871Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9094878Z 
2023-01-11T21:41:23.9094883Z 
2023-01-11T21:41:23.9094997Z async_compile.wait(globals())
2023-01-11T21:41:23.9095092Z del async_compile
2023-01-11T21:41:23.9095098Z 
2023-01-11T21:41:23.9095187Z def call(args):
2023-01-11T21:41:23.9095302Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9095394Z     args.clear()
2023-01-11T21:41:23.9095691Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:23.9095777Z     del arg0_1
2023-01-11T21:41:23.9095865Z     del arg1_1
2023-01-11T21:41:23.9095952Z     del arg2_1
2023-01-11T21:41:23.9096038Z     del arg3_1
2023-01-11T21:41:23.9096126Z     return (buf0, )
2023-01-11T21:41:23.9096132Z 
2023-01-11T21:41:23.9096138Z 
2023-01-11T21:41:23.9096235Z if __name__ == "__main__":
2023-01-11T21:41:23.9096370Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9096537Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9096810Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9097079Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9097383Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9097701Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9097907Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9097916Z 
2023-01-11T21:41:23.9097923Z 
2023-01-11T21:41:23.9098061Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9098146Z import torch
2023-01-11T21:41:23.9098250Z import random
2023-01-11T21:41:23.9098433Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9098615Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9098691Z 
2023-01-11T21:41:23.9098813Z aten = torch.ops.aten
2023-01-11T21:41:23.9099023Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9099157Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9099165Z 
2023-01-11T21:41:23.9099171Z 
2023-01-11T21:41:23.9099300Z async_compile.wait(globals())
2023-01-11T21:41:23.9099391Z del async_compile
2023-01-11T21:41:23.9099413Z 
2023-01-11T21:41:23.9099499Z def call(args):
2023-01-11T21:41:23.9099615Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9099717Z     args.clear()
2023-01-11T21:41:23.9100073Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:23.9100171Z     del arg0_1
2023-01-11T21:41:23.9100267Z     del arg1_1
2023-01-11T21:41:23.9100350Z     del arg2_1
2023-01-11T21:41:23.9100452Z     return (buf0, )
2023-01-11T21:41:23.9100459Z 
2023-01-11T21:41:23.9100465Z 
2023-01-11T21:41:23.9100576Z if __name__ == "__main__":
2023-01-11T21:41:23.9100806Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9100999Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9101308Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9101630Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9101935Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9102107Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9102130Z 
2023-01-11T21:41:23.9102135Z 
2023-01-11T21:41:23.9102258Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9102494Z import torch
2023-01-11T21:41:23.9102604Z import random
2023-01-11T21:41:23.9102789Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9102975Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9102983Z 
2023-01-11T21:41:23.9103100Z aten = torch.ops.aten
2023-01-11T21:41:23.9103315Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9103437Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9103459Z 
2023-01-11T21:41:23.9103465Z 
2023-01-11T21:41:23.9103589Z async_compile.wait(globals())
2023-01-11T21:41:23.9103695Z del async_compile
2023-01-11T21:41:23.9103703Z 
2023-01-11T21:41:23.9103801Z def call(args):
2023-01-11T21:41:23.9103931Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9104040Z     args.clear()
2023-01-11T21:41:23.9104400Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:23.9104506Z     del arg0_1
2023-01-11T21:41:23.9104594Z     del arg1_1
2023-01-11T21:41:23.9104699Z     del arg2_1
2023-01-11T21:41:23.9104800Z     del arg3_1
2023-01-11T21:41:23.9104909Z     return (buf0, )
2023-01-11T21:41:23.9104917Z 
2023-01-11T21:41:23.9104924Z 
2023-01-11T21:41:23.9105031Z if __name__ == "__main__":
2023-01-11T21:41:23.9105209Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9105416Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9105736Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9106048Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9106363Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9106665Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9106860Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9106867Z 
2023-01-11T21:41:23.9106874Z 
2023-01-11T21:41:23.9107010Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9107115Z import torch
2023-01-11T21:41:23.9107217Z import random
2023-01-11T21:41:23.9107373Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9107561Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9107681Z 
2023-01-11T21:41:23.9107810Z aten = torch.ops.aten
2023-01-11T21:41:23.9108018Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9108162Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9108169Z 
2023-01-11T21:41:23.9108175Z 
2023-01-11T21:41:23.9108320Z async_compile.wait(globals())
2023-01-11T21:41:23.9108430Z del async_compile
2023-01-11T21:41:23.9108438Z 
2023-01-11T21:41:23.9108549Z def call(args):
2023-01-11T21:41:23.9108661Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9108780Z     args.clear()
2023-01-11T21:41:23.9109163Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:23.9109269Z     del arg0_1
2023-01-11T21:41:23.9109370Z     del arg1_1
2023-01-11T21:41:23.9109475Z     del arg2_1
2023-01-11T21:41:23.9109588Z     return (buf0, )
2023-01-11T21:41:23.9109595Z 
2023-01-11T21:41:23.9109601Z 
2023-01-11T21:41:23.9109719Z if __name__ == "__main__":
2023-01-11T21:41:23.9109959Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9110165Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9110504Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9110834Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9111160Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9111357Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9111366Z 
2023-01-11T21:41:23.9111475Z ok (1.750s)
2023-01-11T21:41:23.9112293Z   test_linear_packed_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9112496Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9112926Z [2023-01-11 21:35:38,391] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 293
2023-01-11T21:41:23.9113373Z [2023-01-11 21:35:38,410] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 293
2023-01-11T21:41:23.9114200Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9114401Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9114844Z [2023-01-11 21:35:38,437] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 294
2023-01-11T21:41:23.9115305Z [2023-01-11 21:35:38,449] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 294
2023-01-11T21:41:23.9116075Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9116282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9116736Z [2023-01-11 21:35:38,482] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 295
2023-01-11T21:41:23.9117195Z [2023-01-11 21:35:38,500] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 295
2023-01-11T21:41:23.9117964Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9118273Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9118723Z [2023-01-11 21:35:38,525] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 296
2023-01-11T21:41:23.9119184Z [2023-01-11 21:35:38,537] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 296
2023-01-11T21:41:23.9119194Z 
2023-01-11T21:41:23.9119349Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9119461Z import torch
2023-01-11T21:41:23.9119574Z import random
2023-01-11T21:41:23.9119762Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9119967Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9119978Z 
2023-01-11T21:41:23.9120177Z aten = torch.ops.aten
2023-01-11T21:41:23.9120394Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9120543Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9120551Z 
2023-01-11T21:41:23.9120559Z 
2023-01-11T21:41:23.9120707Z async_compile.wait(globals())
2023-01-11T21:41:23.9120824Z del async_compile
2023-01-11T21:41:23.9120832Z 
2023-01-11T21:41:23.9120948Z def call(args):
2023-01-11T21:41:23.9121089Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9121197Z     args.clear()
2023-01-11T21:41:23.9121383Z     buf0 = torch.ops.mkl._mkl_linear(arg3_1, arg2_1, arg0_1, arg1_1, 6)
2023-01-11T21:41:23.9121490Z     del arg0_1
2023-01-11T21:41:23.9121597Z     del arg1_1
2023-01-11T21:41:23.9121706Z     del arg2_1
2023-01-11T21:41:23.9121808Z     del arg3_1
2023-01-11T21:41:23.9121923Z     return (buf0, )
2023-01-11T21:41:23.9121930Z 
2023-01-11T21:41:23.9121937Z 
2023-01-11T21:41:23.9122055Z if __name__ == "__main__":
2023-01-11T21:41:23.9122253Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9122445Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9122813Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9123142Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9123479Z     arg2_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9123826Z     arg3_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9124036Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9124044Z 
2023-01-11T21:41:23.9124051Z 
2023-01-11T21:41:23.9124213Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9124328Z import torch
2023-01-11T21:41:23.9124429Z import random
2023-01-11T21:41:23.9124622Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9124833Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9124846Z 
2023-01-11T21:41:23.9124976Z aten = torch.ops.aten
2023-01-11T21:41:23.9134111Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9134313Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9134321Z 
2023-01-11T21:41:23.9134327Z 
2023-01-11T21:41:23.9134459Z async_compile.wait(globals())
2023-01-11T21:41:23.9134558Z del async_compile
2023-01-11T21:41:23.9134565Z 
2023-01-11T21:41:23.9134645Z def call(args):
2023-01-11T21:41:23.9134759Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9134859Z     args.clear()
2023-01-11T21:41:23.9135035Z     buf0 = torch.ops.mkl._mkl_linear(arg2_1, arg1_1, arg0_1, None, 6)
2023-01-11T21:41:23.9135127Z     del arg0_1
2023-01-11T21:41:23.9135218Z     del arg1_1
2023-01-11T21:41:23.9135312Z     del arg2_1
2023-01-11T21:41:23.9135405Z     return (buf0, )
2023-01-11T21:41:23.9135414Z 
2023-01-11T21:41:23.9135420Z 
2023-01-11T21:41:23.9135531Z if __name__ == "__main__":
2023-01-11T21:41:23.9135825Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9136025Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9136387Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9136692Z     arg1_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9137002Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9137184Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9137192Z 
2023-01-11T21:41:23.9137198Z 
2023-01-11T21:41:23.9137316Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9137415Z import torch
2023-01-11T21:41:23.9137514Z import random
2023-01-11T21:41:23.9137680Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9137854Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9137862Z 
2023-01-11T21:41:23.9137971Z aten = torch.ops.aten
2023-01-11T21:41:23.9138246Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9138401Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9138409Z 
2023-01-11T21:41:23.9138415Z 
2023-01-11T21:41:23.9138528Z async_compile.wait(globals())
2023-01-11T21:41:23.9138634Z del async_compile
2023-01-11T21:41:23.9138642Z 
2023-01-11T21:41:23.9138747Z def call(args):
2023-01-11T21:41:23.9138872Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:23.9138969Z     args.clear()
2023-01-11T21:41:23.9139149Z     buf0 = torch.ops.mkl._mkl_linear(arg3_1, arg2_1, arg0_1, arg1_1, 2)
2023-01-11T21:41:23.9139240Z     del arg0_1
2023-01-11T21:41:23.9139319Z     del arg1_1
2023-01-11T21:41:23.9139414Z     del arg2_1
2023-01-11T21:41:23.9139508Z     del arg3_1
2023-01-11T21:41:23.9139608Z     return (buf0, )
2023-01-11T21:41:23.9139615Z 
2023-01-11T21:41:23.9139621Z 
2023-01-11T21:41:23.9139729Z if __name__ == "__main__":
2023-01-11T21:41:23.9139899Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9140086Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9140398Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9140681Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9140988Z     arg2_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9141279Z     arg3_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9141467Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:23.9141474Z 
2023-01-11T21:41:23.9141480Z 
2023-01-11T21:41:23.9141605Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9141706Z import torch
2023-01-11T21:41:23.9141810Z import random
2023-01-11T21:41:23.9141982Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9142144Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9142155Z 
2023-01-11T21:41:23.9142273Z aten = torch.ops.aten
2023-01-11T21:41:23.9142626Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9142759Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9142767Z 
2023-01-11T21:41:23.9142773Z 
2023-01-11T21:41:23.9142904Z async_compile.wait(globals())
2023-01-11T21:41:23.9152995Z del async_compile
2023-01-11T21:41:23.9153006Z 
2023-01-11T21:41:23.9153109Z def call(args):
2023-01-11T21:41:23.9153227Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9153311Z     args.clear()
2023-01-11T21:41:23.9153496Z     buf0 = torch.ops.mkl._mkl_linear(arg2_1, arg1_1, arg0_1, None, 2)
2023-01-11T21:41:23.9153591Z     del arg0_1
2023-01-11T21:41:23.9153684Z     del arg1_1
2023-01-11T21:41:23.9153848Z     del arg2_1
2023-01-11T21:41:23.9153951Z     return (buf0, )
2023-01-11T21:41:23.9153959Z 
2023-01-11T21:41:23.9153966Z 
2023-01-11T21:41:23.9154072Z if __name__ == "__main__":
2023-01-11T21:41:23.9154230Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9154534Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9154874Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9155181Z     arg1_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9155475Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9155649Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9155656Z 
2023-01-11T21:41:23.9155751Z ok (0.180s)
2023-01-11T21:41:23.9156447Z   test_linear_unary_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9156741Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9157119Z [2023-01-11 21:35:38,620] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 297
2023-01-11T21:41:23.9157506Z [2023-01-11 21:35:38,623] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 297
2023-01-11T21:41:23.9158150Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9158325Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9158712Z [2023-01-11 21:35:38,651] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 298
2023-01-11T21:41:23.9159113Z [2023-01-11 21:35:38,653] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 298
2023-01-11T21:41:23.9159744Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9159925Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9160318Z [2023-01-11 21:35:38,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 299
2023-01-11T21:41:23.9160720Z [2023-01-11 21:35:38,742] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 299
2023-01-11T21:41:23.9161354Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9161541Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9161921Z [2023-01-11 21:35:38,770] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 300
2023-01-11T21:41:23.9162301Z [2023-01-11 21:35:38,772] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 300
2023-01-11T21:41:23.9162937Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9163219Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9163602Z [2023-01-11 21:35:38,858] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 301
2023-01-11T21:41:23.9163994Z [2023-01-11 21:35:38,860] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 301
2023-01-11T21:41:23.9164618Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9164794Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9165181Z [2023-01-11 21:35:38,888] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 302
2023-01-11T21:41:23.9165630Z [2023-01-11 21:35:38,891] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 302
2023-01-11T21:41:23.9166287Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9166472Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9166860Z [2023-01-11 21:35:38,973] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 303
2023-01-11T21:41:23.9167243Z [2023-01-11 21:35:38,976] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 303
2023-01-11T21:41:23.9167879Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9168064Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9168460Z [2023-01-11 21:35:39,003] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 304
2023-01-11T21:41:23.9168846Z [2023-01-11 21:35:39,005] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 304
2023-01-11T21:41:23.9169508Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9169694Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9170080Z [2023-01-11 21:35:39,090] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 305
2023-01-11T21:41:23.9170089Z 
2023-01-11T21:41:23.9170219Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9170318Z import torch
2023-01-11T21:41:23.9170403Z import random
2023-01-11T21:41:23.9170564Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9170732Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9170740Z 
2023-01-11T21:41:23.9170849Z aten = torch.ops.aten
2023-01-11T21:41:23.9171049Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9171182Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9171190Z 
2023-01-11T21:41:23.9171197Z 
2023-01-11T21:41:23.9171327Z async_compile.wait(globals())
2023-01-11T21:41:23.9171435Z del async_compile
2023-01-11T21:41:23.9171508Z 
2023-01-11T21:41:23.9171597Z def call(args):
2023-01-11T21:41:23.9171711Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9171812Z     args.clear()
2023-01-11T21:41:23.9172142Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'relu', [], '')
2023-01-11T21:41:23.9172237Z     del arg0_1
2023-01-11T21:41:23.9172330Z     del arg1_1
2023-01-11T21:41:23.9172420Z     del arg2_1
2023-01-11T21:41:23.9172505Z     return (buf0, )
2023-01-11T21:41:23.9172513Z 
2023-01-11T21:41:23.9172535Z 
2023-01-11T21:41:23.9172626Z if __name__ == "__main__":
2023-01-11T21:41:23.9172785Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9172963Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9173260Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9173545Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9173898Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9174079Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9174086Z 
2023-01-11T21:41:23.9174092Z 
2023-01-11T21:41:23.9174228Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9174316Z import torch
2023-01-11T21:41:23.9174419Z import random
2023-01-11T21:41:23.9174581Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9174759Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9174766Z 
2023-01-11T21:41:23.9174879Z aten = torch.ops.aten
2023-01-11T21:41:23.9175077Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9175206Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9175213Z 
2023-01-11T21:41:23.9175218Z 
2023-01-11T21:41:23.9175327Z async_compile.wait(globals())
2023-01-11T21:41:23.9175431Z del async_compile
2023-01-11T21:41:23.9175438Z 
2023-01-11T21:41:23.9175535Z def call(args):
2023-01-11T21:41:23.9175642Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9175753Z     args.clear()
2023-01-11T21:41:23.9176091Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'relu', [], '')
2023-01-11T21:41:23.9176193Z     del arg0_1
2023-01-11T21:41:23.9176274Z     del arg1_1
2023-01-11T21:41:23.9176380Z     return (buf0, )
2023-01-11T21:41:23.9176387Z 
2023-01-11T21:41:23.9176393Z 
2023-01-11T21:41:23.9176507Z if __name__ == "__main__":
2023-01-11T21:41:23.9176674Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9176847Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9177155Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9177468Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9177638Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9177646Z 
2023-01-11T21:41:23.9177652Z 
2023-01-11T21:41:23.9177789Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9177880Z import torch
2023-01-11T21:41:23.9177982Z import random
2023-01-11T21:41:23.9178157Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9178336Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9178343Z 
2023-01-11T21:41:23.9178459Z aten = torch.ops.aten
2023-01-11T21:41:23.9178657Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9178787Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9178794Z 
2023-01-11T21:41:23.9178800Z 
2023-01-11T21:41:23.9178929Z async_compile.wait(globals())
2023-01-11T21:41:23.9179018Z del async_compile
2023-01-11T21:41:23.9179025Z 
2023-01-11T21:41:23.9179129Z def call(args):
2023-01-11T21:41:23.9179250Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9179350Z     args.clear()
2023-01-11T21:41:23.9179677Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'relu', [], '')
2023-01-11T21:41:23.9179777Z     del arg0_1
2023-01-11T21:41:23.9179874Z     del arg1_1
2023-01-11T21:41:23.9180025Z     del arg2_1
2023-01-11T21:41:23.9180123Z     return (buf0, )
2023-01-11T21:41:23.9180130Z 
2023-01-11T21:41:23.9180135Z 
2023-01-11T21:41:23.9180245Z if __name__ == "__main__":
2023-01-11T21:41:23.9180413Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9180604Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9180929Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9181235Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9181552Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9181730Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9181738Z 
2023-01-11T21:41:23.9181745Z 
2023-01-11T21:41:23.9181886Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9181986Z import torch
2023-01-11T21:41:23.9182094Z import random
2023-01-11T21:41:23.9182606Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9182806Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9182813Z 
2023-01-11T21:41:23.9182936Z aten = torch.ops.aten
2023-01-11T21:41:23.9183133Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9183271Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9183278Z 
2023-01-11T21:41:23.9183285Z 
2023-01-11T21:41:23.9183421Z async_compile.wait(globals())
2023-01-11T21:41:23.9183533Z del async_compile
2023-01-11T21:41:23.9183540Z 
2023-01-11T21:41:23.9183646Z def call(args):
2023-01-11T21:41:23.9183767Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9183878Z     args.clear()
2023-01-11T21:41:23.9184242Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'relu', [], '')
2023-01-11T21:41:23.9184327Z     del arg0_1
2023-01-11T21:41:23.9184427Z     del arg1_1
2023-01-11T21:41:23.9184536Z     return (buf0, )
2023-01-11T21:41:23.9184544Z 
2023-01-11T21:41:23.9184555Z 
2023-01-11T21:41:23.9184670Z if __name__ == "__main__":
2023-01-11T21:41:23.9184838Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9185027Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9185352Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9185674Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9185839Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9185846Z 
2023-01-11T21:41:23.9185866Z 
2023-01-11T21:41:23.9185992Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9186099Z import torch
2023-01-11T21:41:23.9186208Z import random
2023-01-11T21:41:23.9186385Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9186574Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9186582Z 
2023-01-11T21:41:23.9186695Z aten = torch.ops.aten
2023-01-11T21:41:23.9186910Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9187041Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9187050Z 
2023-01-11T21:41:23.9187057Z 
2023-01-11T21:41:23.9187192Z async_compile.wait(globals())
2023-01-11T21:41:23.9187301Z del async_compile
2023-01-11T21:41:23.9187308Z 
2023-01-11T21:41:23.9187418Z def call(args):
2023-01-11T21:41:23.9187539Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9187638Z     args.clear()
2023-01-11T21:41:23.9187999Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'sigmoid', [], '')
2023-01-11T21:41:23.9188083Z     del arg0_1
2023-01-11T21:41:23.9188185Z     del arg1_1
2023-01-11T21:41:23.9188285Z     del arg2_1
2023-01-11T21:41:23.9188399Z     return (buf0, )
2023-01-11T21:41:23.9188406Z 
2023-01-11T21:41:23.9188411Z 
2023-01-11T21:41:23.9188523Z if __name__ == "__main__":
2023-01-11T21:41:23.9188705Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9188902Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9189371Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9189678Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9190023Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9190222Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9190231Z 
2023-01-11T21:41:23.9190238Z 
2023-01-11T21:41:23.9190407Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9190517Z import torch
2023-01-11T21:41:23.9190631Z import random
2023-01-11T21:41:23.9190813Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9191008Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9191017Z 
2023-01-11T21:41:23.9191125Z aten = torch.ops.aten
2023-01-11T21:41:23.9191338Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9191492Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9191600Z 
2023-01-11T21:41:23.9191609Z 
2023-01-11T21:41:23.9191756Z async_compile.wait(globals())
2023-01-11T21:41:23.9191878Z del async_compile
2023-01-11T21:41:23.9191886Z 
2023-01-11T21:41:23.9191994Z def call(args):
2023-01-11T21:41:23.9192105Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9192213Z     args.clear()
2023-01-11T21:41:23.9192550Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'sigmoid', [], '')
2023-01-11T21:41:23.9192657Z     del arg0_1
2023-01-11T21:41:23.9192759Z     del arg1_1
2023-01-11T21:41:23.9192864Z     return (buf0, )
2023-01-11T21:41:23.9192872Z 
2023-01-11T21:41:23.9192879Z 
2023-01-11T21:41:23.9192998Z if __name__ == "__main__":
2023-01-11T21:41:23.9193178Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9193374Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9193690Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9194120Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9194304Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9194312Z 
2023-01-11T21:41:23.9194318Z 
2023-01-11T21:41:23.9194469Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9194576Z import torch
2023-01-11T21:41:23.9194687Z import random
2023-01-11T21:41:23.9194868Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9195064Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9195071Z 
2023-01-11T21:41:23.9195182Z aten = torch.ops.aten
2023-01-11T21:41:23.9195409Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9195565Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9195572Z 
2023-01-11T21:41:23.9195579Z 
2023-01-11T21:41:23.9195721Z async_compile.wait(globals())
2023-01-11T21:41:23.9195839Z del async_compile
2023-01-11T21:41:23.9195847Z 
2023-01-11T21:41:23.9195997Z def call(args):
2023-01-11T21:41:23.9196130Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9196242Z     args.clear()
2023-01-11T21:41:23.9196603Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'sigmoid', [], '')
2023-01-11T21:41:23.9196710Z     del arg0_1
2023-01-11T21:41:23.9196814Z     del arg1_1
2023-01-11T21:41:23.9196913Z     del arg2_1
2023-01-11T21:41:23.9197019Z     return (buf0, )
2023-01-11T21:41:23.9197026Z 
2023-01-11T21:41:23.9197033Z 
2023-01-11T21:41:23.9197152Z if __name__ == "__main__":
2023-01-11T21:41:23.9197325Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9197506Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9197846Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9198180Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9198515Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9198811Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9198820Z 
2023-01-11T21:41:23.9198828Z 
2023-01-11T21:41:23.9198984Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9199099Z import torch
2023-01-11T21:41:23.9199217Z import random
2023-01-11T21:41:23.9199391Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9199597Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9199606Z 
2023-01-11T21:41:23.9199727Z aten = torch.ops.aten
2023-01-11T21:41:23.9199921Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9200055Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9200061Z 
2023-01-11T21:41:23.9200068Z 
2023-01-11T21:41:23.9200198Z async_compile.wait(globals())
2023-01-11T21:41:23.9200305Z del async_compile
2023-01-11T21:41:23.9200312Z 
2023-01-11T21:41:23.9200411Z def call(args):
2023-01-11T21:41:23.9200504Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9200602Z     args.clear()
2023-01-11T21:41:23.9201043Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'sigmoid', [], '')
2023-01-11T21:41:23.9201141Z     del arg0_1
2023-01-11T21:41:23.9201236Z     del arg1_1
2023-01-11T21:41:23.9201338Z     return (buf0, )
2023-01-11T21:41:23.9201345Z 
2023-01-11T21:41:23.9201351Z 
2023-01-11T21:41:23.9201458Z if __name__ == "__main__":
2023-01-11T21:41:23.9201618Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9201785Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9202091Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9202398Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9202566Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9202575Z 
2023-01-11T21:41:23.9202994Z [2023-01-11 21:35:39,092] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 305
2023-01-11T21:41:23.9203662Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9203848Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9204248Z [2023-01-11 21:35:39,123] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 306
2023-01-11T21:41:23.9204660Z [2023-01-11 21:35:39,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 306
2023-01-11T21:41:23.9205321Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9205493Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9205877Z [2023-01-11 21:35:39,210] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 307
2023-01-11T21:41:23.9206280Z [2023-01-11 21:35:39,212] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 307
2023-01-11T21:41:23.9206928Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9207108Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9207581Z [2023-01-11 21:35:39,240] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 308
2023-01-11T21:41:23.9207988Z [2023-01-11 21:35:39,243] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 308
2023-01-11T21:41:23.9208622Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9208803Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9209191Z [2023-01-11 21:35:39,356] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 309
2023-01-11T21:41:23.9209595Z [2023-01-11 21:35:39,359] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 309
2023-01-11T21:41:23.9210313Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9210493Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9210870Z [2023-01-11 21:35:39,418] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 310
2023-01-11T21:41:23.9211267Z [2023-01-11 21:35:39,420] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 310
2023-01-11T21:41:23.9211923Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9212099Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9212480Z [2023-01-11 21:35:39,538] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 311
2023-01-11T21:41:23.9212879Z [2023-01-11 21:35:39,541] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 311
2023-01-11T21:41:23.9213513Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9213688Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9214080Z [2023-01-11 21:35:39,624] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 312
2023-01-11T21:41:23.9214476Z [2023-01-11 21:35:39,627] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 312
2023-01-11T21:41:23.9215117Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9215296Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9215662Z [2023-01-11 21:35:39,875] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 313
2023-01-11T21:41:23.9215672Z 
2023-01-11T21:41:23.9215800Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9215896Z import torch
2023-01-11T21:41:23.9216069Z import random
2023-01-11T21:41:23.9216234Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9216407Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9216415Z 
2023-01-11T21:41:23.9216526Z aten = torch.ops.aten
2023-01-11T21:41:23.9216705Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9216834Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9216840Z 
2023-01-11T21:41:23.9216848Z 
2023-01-11T21:41:23.9216977Z async_compile.wait(globals())
2023-01-11T21:41:23.9217084Z del async_compile
2023-01-11T21:41:23.9217090Z 
2023-01-11T21:41:23.9217187Z def call(args):
2023-01-11T21:41:23.9217307Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9217407Z     args.clear()
2023-01-11T21:41:23.9217730Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'tanh', [], '')
2023-01-11T21:41:23.9217812Z     del arg0_1
2023-01-11T21:41:23.9217903Z     del arg1_1
2023-01-11T21:41:23.9217996Z     del arg2_1
2023-01-11T21:41:23.9218150Z     return (buf0, )
2023-01-11T21:41:23.9218158Z 
2023-01-11T21:41:23.9218164Z 
2023-01-11T21:41:23.9218273Z if __name__ == "__main__":
2023-01-11T21:41:23.9218437Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9218616Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9218918Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9219194Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9219502Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9219679Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9219686Z 
2023-01-11T21:41:23.9219692Z 
2023-01-11T21:41:23.9219826Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9219926Z import torch
2023-01-11T21:41:23.9220028Z import random
2023-01-11T21:41:23.9220189Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9220352Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9220371Z 
2023-01-11T21:41:23.9220467Z aten = torch.ops.aten
2023-01-11T21:41:23.9220662Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9220790Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9220798Z 
2023-01-11T21:41:23.9220804Z 
2023-01-11T21:41:23.9220933Z async_compile.wait(globals())
2023-01-11T21:41:23.9221035Z del async_compile
2023-01-11T21:41:23.9221041Z 
2023-01-11T21:41:23.9221140Z def call(args):
2023-01-11T21:41:23.9221248Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9221333Z     args.clear()
2023-01-11T21:41:23.9221654Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'tanh', [], '')
2023-01-11T21:41:23.9221747Z     del arg0_1
2023-01-11T21:41:23.9221841Z     del arg1_1
2023-01-11T21:41:23.9221943Z     return (buf0, )
2023-01-11T21:41:23.9221949Z 
2023-01-11T21:41:23.9221954Z 
2023-01-11T21:41:23.9222064Z if __name__ == "__main__":
2023-01-11T21:41:23.9222228Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9222547Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9222841Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9223146Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9223311Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9223319Z 
2023-01-11T21:41:23.9223325Z 
2023-01-11T21:41:23.9223458Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9223556Z import torch
2023-01-11T21:41:23.9223655Z import random
2023-01-11T21:41:23.9223815Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9223986Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9223993Z 
2023-01-11T21:41:23.9224088Z aten = torch.ops.aten
2023-01-11T21:41:23.9224282Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9224519Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9224526Z 
2023-01-11T21:41:23.9224532Z 
2023-01-11T21:41:23.9224661Z async_compile.wait(globals())
2023-01-11T21:41:23.9224766Z del async_compile
2023-01-11T21:41:23.9224773Z 
2023-01-11T21:41:23.9224873Z def call(args):
2023-01-11T21:41:23.9224986Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9225082Z     args.clear()
2023-01-11T21:41:23.9225391Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'tanh', [], '')
2023-01-11T21:41:23.9225484Z     del arg0_1
2023-01-11T21:41:23.9225581Z     del arg1_1
2023-01-11T21:41:23.9225672Z     del arg2_1
2023-01-11T21:41:23.9225771Z     return (buf0, )
2023-01-11T21:41:23.9225778Z 
2023-01-11T21:41:23.9225784Z 
2023-01-11T21:41:23.9225891Z if __name__ == "__main__":
2023-01-11T21:41:23.9226053Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9226215Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9226585Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9226878Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9227170Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9227348Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9227356Z 
2023-01-11T21:41:23.9227362Z 
2023-01-11T21:41:23.9227494Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9227593Z import torch
2023-01-11T21:41:23.9227694Z import random
2023-01-11T21:41:23.9227842Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9228012Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9228019Z 
2023-01-11T21:41:23.9228126Z aten = torch.ops.aten
2023-01-11T21:41:23.9228317Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9228445Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9228455Z 
2023-01-11T21:41:23.9228465Z 
2023-01-11T21:41:23.9228590Z async_compile.wait(globals())
2023-01-11T21:41:23.9228690Z del async_compile
2023-01-11T21:41:23.9228697Z 
2023-01-11T21:41:23.9228794Z def call(args):
2023-01-11T21:41:23.9228886Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9228981Z     args.clear()
2023-01-11T21:41:23.9229299Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'tanh', [], '')
2023-01-11T21:41:23.9229393Z     del arg0_1
2023-01-11T21:41:23.9229493Z     del arg1_1
2023-01-11T21:41:23.9229597Z     return (buf0, )
2023-01-11T21:41:23.9229605Z 
2023-01-11T21:41:23.9229610Z 
2023-01-11T21:41:23.9229715Z if __name__ == "__main__":
2023-01-11T21:41:23.9229860Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9230037Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9230333Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9230630Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9230796Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9230804Z 
2023-01-11T21:41:23.9230810Z 
2023-01-11T21:41:23.9230939Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9231041Z import torch
2023-01-11T21:41:23.9231139Z import random
2023-01-11T21:41:23.9231287Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9231458Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9231465Z 
2023-01-11T21:41:23.9231573Z aten = torch.ops.aten
2023-01-11T21:41:23.9231764Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9231892Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9231899Z 
2023-01-11T21:41:23.9231905Z 
2023-01-11T21:41:23.9232028Z async_compile.wait(globals())
2023-01-11T21:41:23.9232133Z del async_compile
2023-01-11T21:41:23.9232140Z 
2023-01-11T21:41:23.9232239Z def call(args):
2023-01-11T21:41:23.9232338Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9232519Z     args.clear()
2023-01-11T21:41:23.9232863Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardswish', [], '')
2023-01-11T21:41:23.9232955Z     del arg0_1
2023-01-11T21:41:23.9233048Z     del arg1_1
2023-01-11T21:41:23.9233141Z     del arg2_1
2023-01-11T21:41:23.9233240Z     return (buf0, )
2023-01-11T21:41:23.9233246Z 
2023-01-11T21:41:23.9233252Z 
2023-01-11T21:41:23.9233359Z if __name__ == "__main__":
2023-01-11T21:41:23.9233508Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9233679Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9234077Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9234367Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9234672Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9234914Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9234927Z 
2023-01-11T21:41:23.9234934Z 
2023-01-11T21:41:23.9235071Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9235170Z import torch
2023-01-11T21:41:23.9235256Z import random
2023-01-11T21:41:23.9235418Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9235588Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9235595Z 
2023-01-11T21:41:23.9235706Z aten = torch.ops.aten
2023-01-11T21:41:23.9235896Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9236025Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9236033Z 
2023-01-11T21:41:23.9236039Z 
2023-01-11T21:41:23.9236166Z async_compile.wait(globals())
2023-01-11T21:41:23.9236251Z del async_compile
2023-01-11T21:41:23.9236273Z 
2023-01-11T21:41:23.9236353Z def call(args):
2023-01-11T21:41:23.9236455Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9236554Z     args.clear()
2023-01-11T21:41:23.9236889Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardswish', [], '')
2023-01-11T21:41:23.9236988Z     del arg0_1
2023-01-11T21:41:23.9237085Z     del arg1_1
2023-01-11T21:41:23.9237171Z     return (buf0, )
2023-01-11T21:41:23.9237193Z 
2023-01-11T21:41:23.9237199Z 
2023-01-11T21:41:23.9237291Z if __name__ == "__main__":
2023-01-11T21:41:23.9237455Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9237631Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9237927Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9238228Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9238390Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9238398Z 
2023-01-11T21:41:23.9238403Z 
2023-01-11T21:41:23.9238533Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9238628Z import torch
2023-01-11T21:41:23.9238710Z import random
2023-01-11T21:41:23.9238879Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9239050Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9239056Z 
2023-01-11T21:41:23.9239168Z aten = torch.ops.aten
2023-01-11T21:41:23.9239356Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9239484Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9239491Z 
2023-01-11T21:41:23.9239496Z 
2023-01-11T21:41:23.9239621Z async_compile.wait(globals())
2023-01-11T21:41:23.9239722Z del async_compile
2023-01-11T21:41:23.9239729Z 
2023-01-11T21:41:23.9239812Z def call(args):
2023-01-11T21:41:23.9239925Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9240022Z     args.clear()
2023-01-11T21:41:23.9240358Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardswish', [], '')
2023-01-11T21:41:23.9240456Z     del arg0_1
2023-01-11T21:41:23.9240551Z     del arg1_1
2023-01-11T21:41:23.9240647Z     del arg2_1
2023-01-11T21:41:23.9240806Z     return (buf0, )
2023-01-11T21:41:23.9240816Z 
2023-01-11T21:41:23.9240822Z 
2023-01-11T21:41:23.9240926Z if __name__ == "__main__":
2023-01-11T21:41:23.9241093Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9241268Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9241564Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9241855Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9242149Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9242321Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9242329Z 
2023-01-11T21:41:23.9242334Z 
2023-01-11T21:41:23.9242453Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9242549Z import torch
2023-01-11T21:41:23.9242645Z import random
2023-01-11T21:41:23.9242806Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9243042Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9243050Z 
2023-01-11T21:41:23.9243161Z aten = torch.ops.aten
2023-01-11T21:41:23.9243356Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9243485Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9243491Z 
2023-01-11T21:41:23.9243497Z 
2023-01-11T21:41:23.9243609Z async_compile.wait(globals())
2023-01-11T21:41:23.9243709Z del async_compile
2023-01-11T21:41:23.9243715Z 
2023-01-11T21:41:23.9243813Z def call(args):
2023-01-11T21:41:23.9243917Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9244016Z     args.clear()
2023-01-11T21:41:23.9244348Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardswish', [], '')
2023-01-11T21:41:23.9244444Z     del arg0_1
2023-01-11T21:41:23.9244521Z     del arg1_1
2023-01-11T21:41:23.9244621Z     return (buf0, )
2023-01-11T21:41:23.9244628Z 
2023-01-11T21:41:23.9244633Z 
2023-01-11T21:41:23.9244735Z if __name__ == "__main__":
2023-01-11T21:41:23.9244903Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9245074Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9245366Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9245664Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9245832Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9245838Z 
2023-01-11T21:41:23.9246228Z [2023-01-11 21:35:39,878] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 313
2023-01-11T21:41:23.9246873Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9247061Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9247452Z [2023-01-11 21:35:39,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 314
2023-01-11T21:41:23.9247851Z [2023-01-11 21:35:39,910] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 314
2023-01-11T21:41:23.9248505Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9248688Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9249086Z [2023-01-11 21:35:39,994] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 315
2023-01-11T21:41:23.9249559Z [2023-01-11 21:35:39,997] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 315
2023-01-11T21:41:23.9250208Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9250388Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9250842Z [2023-01-11 21:35:40,025] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 316
2023-01-11T21:41:23.9251310Z [2023-01-11 21:35:40,027] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 316
2023-01-11T21:41:23.9252158Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9252382Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9252852Z [2023-01-11 21:35:40,114] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 317
2023-01-11T21:41:23.9253346Z [2023-01-11 21:35:40,117] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 317
2023-01-11T21:41:23.9254164Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9254398Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9254863Z [2023-01-11 21:35:40,148] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 318
2023-01-11T21:41:23.9255350Z [2023-01-11 21:35:40,150] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 318
2023-01-11T21:41:23.9256164Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9256382Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9256857Z [2023-01-11 21:35:40,236] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 319
2023-01-11T21:41:23.9257346Z [2023-01-11 21:35:40,239] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 319
2023-01-11T21:41:23.9258129Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9258348Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9258829Z [2023-01-11 21:35:40,268] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 320
2023-01-11T21:41:23.9259314Z [2023-01-11 21:35:40,271] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 320
2023-01-11T21:41:23.9260126Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9260395Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9260867Z [2023-01-11 21:35:40,356] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 321
2023-01-11T21:41:23.9260877Z 
2023-01-11T21:41:23.9261045Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9261167Z import torch
2023-01-11T21:41:23.9261282Z import random
2023-01-11T21:41:23.9261471Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9261688Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9261696Z 
2023-01-11T21:41:23.9261831Z aten = torch.ops.aten
2023-01-11T21:41:23.9262072Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9262294Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9262303Z 
2023-01-11T21:41:23.9262448Z 
2023-01-11T21:41:23.9262609Z async_compile.wait(globals())
2023-01-11T21:41:23.9262727Z del async_compile
2023-01-11T21:41:23.9262735Z 
2023-01-11T21:41:23.9262860Z def call(args):
2023-01-11T21:41:23.9262981Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9263101Z     args.clear()
2023-01-11T21:41:23.9263517Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'leaky_relu', [0.1], '')
2023-01-11T21:41:23.9263635Z     del arg0_1
2023-01-11T21:41:23.9263752Z     del arg1_1
2023-01-11T21:41:23.9263862Z     del arg2_1
2023-01-11T21:41:23.9263983Z     return (buf0, )
2023-01-11T21:41:23.9263992Z 
2023-01-11T21:41:23.9263999Z 
2023-01-11T21:41:23.9264111Z if __name__ == "__main__":
2023-01-11T21:41:23.9264312Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9264534Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9264913Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9265262Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9265631Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9265841Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9265849Z 
2023-01-11T21:41:23.9265857Z 
2023-01-11T21:41:23.9266017Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9266124Z import torch
2023-01-11T21:41:23.9266247Z import random
2023-01-11T21:41:23.9266453Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9266675Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9266683Z 
2023-01-11T21:41:23.9266822Z aten = torch.ops.aten
2023-01-11T21:41:23.9267064Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9267225Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9267233Z 
2023-01-11T21:41:23.9267245Z 
2023-01-11T21:41:23.9267400Z async_compile.wait(globals())
2023-01-11T21:41:23.9267513Z del async_compile
2023-01-11T21:41:23.9267521Z 
2023-01-11T21:41:23.9267639Z def call(args):
2023-01-11T21:41:23.9267766Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9267885Z     args.clear()
2023-01-11T21:41:23.9268295Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'leaky_relu', [0.1], '')
2023-01-11T21:41:23.9268411Z     del arg0_1
2023-01-11T21:41:23.9268523Z     del arg1_1
2023-01-11T21:41:23.9268633Z     return (buf0, )
2023-01-11T21:41:23.9268641Z 
2023-01-11T21:41:23.9268663Z 
2023-01-11T21:41:23.9268776Z if __name__ == "__main__":
2023-01-11T21:41:23.9268971Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9269181Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9269547Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9269926Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9270213Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9270222Z 
2023-01-11T21:41:23.9270229Z 
2023-01-11T21:41:23.9270394Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9270510Z import torch
2023-01-11T21:41:23.9270619Z import random
2023-01-11T21:41:23.9270818Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9271030Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9271038Z 
2023-01-11T21:41:23.9271169Z aten = torch.ops.aten
2023-01-11T21:41:23.9271403Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9271568Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9271577Z 
2023-01-11T21:41:23.9271584Z 
2023-01-11T21:41:23.9271738Z async_compile.wait(globals())
2023-01-11T21:41:23.9271847Z del async_compile
2023-01-11T21:41:23.9271869Z 
2023-01-11T21:41:23.9271978Z def call(args):
2023-01-11T21:41:23.9272116Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9272295Z     args.clear()
2023-01-11T21:41:23.9272719Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'leaky_relu', [0.1], '')
2023-01-11T21:41:23.9272839Z     del arg0_1
2023-01-11T21:41:23.9272955Z     del arg1_1
2023-01-11T21:41:23.9273054Z     del arg2_1
2023-01-11T21:41:23.9273180Z     return (buf0, )
2023-01-11T21:41:23.9273187Z 
2023-01-11T21:41:23.9273193Z 
2023-01-11T21:41:23.9273324Z if __name__ == "__main__":
2023-01-11T21:41:23.9273521Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9273803Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9274165Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9274514Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9274872Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9275076Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9275103Z 
2023-01-11T21:41:23.9275111Z 
2023-01-11T21:41:23.9275262Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9275382Z import torch
2023-01-11T21:41:23.9275503Z import random
2023-01-11T21:41:23.9275705Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9275919Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9275928Z 
2023-01-11T21:41:23.9276064Z aten = torch.ops.aten
2023-01-11T21:41:23.9276295Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9276437Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9276460Z 
2023-01-11T21:41:23.9276468Z 
2023-01-11T21:41:23.9276608Z async_compile.wait(globals())
2023-01-11T21:41:23.9276730Z del async_compile
2023-01-11T21:41:23.9276738Z 
2023-01-11T21:41:23.9276862Z def call(args):
2023-01-11T21:41:23.9276993Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9277115Z     args.clear()
2023-01-11T21:41:23.9277530Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'leaky_relu', [0.1], '')
2023-01-11T21:41:23.9277653Z     del arg0_1
2023-01-11T21:41:23.9277752Z     del arg1_1
2023-01-11T21:41:23.9277876Z     return (buf0, )
2023-01-11T21:41:23.9277884Z 
2023-01-11T21:41:23.9277891Z 
2023-01-11T21:41:23.9278022Z if __name__ == "__main__":
2023-01-11T21:41:23.9278223Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9278442Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9278801Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9279163Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9279360Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9279369Z 
2023-01-11T21:41:23.9279377Z 
2023-01-11T21:41:23.9279523Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9279647Z import torch
2023-01-11T21:41:23.9279765Z import random
2023-01-11T21:41:23.9280022Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9280241Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9280249Z 
2023-01-11T21:41:23.9280387Z aten = torch.ops.aten
2023-01-11T21:41:23.9280627Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9280781Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9280789Z 
2023-01-11T21:41:23.9280797Z 
2023-01-11T21:41:23.9280932Z async_compile.wait(globals())
2023-01-11T21:41:23.9281060Z del async_compile
2023-01-11T21:41:23.9281068Z 
2023-01-11T21:41:23.9281187Z def call(args):
2023-01-11T21:41:23.9281329Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9281447Z     args.clear()
2023-01-11T21:41:23.9281879Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:23.9282000Z     del arg0_1
2023-01-11T21:41:23.9282097Z     del arg1_1
2023-01-11T21:41:23.9282211Z     del arg2_1
2023-01-11T21:41:23.9282331Z     return (buf0, )
2023-01-11T21:41:23.9282380Z 
2023-01-11T21:41:23.9282388Z 
2023-01-11T21:41:23.9282522Z if __name__ == "__main__":
2023-01-11T21:41:23.9282724Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9282939Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9283311Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9283669Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9284025Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9284241Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9284249Z 
2023-01-11T21:41:23.9284257Z 
2023-01-11T21:41:23.9284426Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9284547Z import torch
2023-01-11T21:41:23.9284670Z import random
2023-01-11T21:41:23.9284875Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9285103Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9285112Z 
2023-01-11T21:41:23.9285243Z aten = torch.ops.aten
2023-01-11T21:41:23.9285470Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9285630Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9285639Z 
2023-01-11T21:41:23.9285646Z 
2023-01-11T21:41:23.9285803Z async_compile.wait(globals())
2023-01-11T21:41:23.9285928Z del async_compile
2023-01-11T21:41:23.9285936Z 
2023-01-11T21:41:23.9286058Z def call(args):
2023-01-11T21:41:23.9286189Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9286312Z     args.clear()
2023-01-11T21:41:23.9286730Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:23.9286827Z     del arg0_1
2023-01-11T21:41:23.9286942Z     del arg1_1
2023-01-11T21:41:23.9287066Z     return (buf0, )
2023-01-11T21:41:23.9287074Z 
2023-01-11T21:41:23.9287082Z 
2023-01-11T21:41:23.9287217Z if __name__ == "__main__":
2023-01-11T21:41:23.9287426Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9287643Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9288014Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9288374Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9288576Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9288584Z 
2023-01-11T21:41:23.9288591Z 
2023-01-11T21:41:23.9288757Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9288876Z import torch
2023-01-11T21:41:23.9288997Z import random
2023-01-11T21:41:23.9289201Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9289414Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9289422Z 
2023-01-11T21:41:23.9289553Z aten = torch.ops.aten
2023-01-11T21:41:23.9289774Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9289981Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9289990Z 
2023-01-11T21:41:23.9289997Z 
2023-01-11T21:41:23.9290149Z async_compile.wait(globals())
2023-01-11T21:41:23.9290275Z del async_compile
2023-01-11T21:41:23.9290283Z 
2023-01-11T21:41:23.9290399Z def call(args):
2023-01-11T21:41:23.9290544Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9290665Z     args.clear()
2023-01-11T21:41:23.9291092Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:23.9291191Z     del arg0_1
2023-01-11T21:41:23.9291309Z     del arg1_1
2023-01-11T21:41:23.9291422Z     del arg2_1
2023-01-11T21:41:23.9291546Z     return (buf0, )
2023-01-11T21:41:23.9291552Z 
2023-01-11T21:41:23.9291558Z 
2023-01-11T21:41:23.9291691Z if __name__ == "__main__":
2023-01-11T21:41:23.9291889Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9292108Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9292498Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9292858Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9293213Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9293427Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9293436Z 
2023-01-11T21:41:23.9293442Z 
2023-01-11T21:41:23.9293607Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9293728Z import torch
2023-01-11T21:41:23.9293849Z import random
2023-01-11T21:41:23.9294046Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9294238Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9294262Z 
2023-01-11T21:41:23.9294383Z aten = torch.ops.aten
2023-01-11T21:41:23.9294619Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9294774Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9294782Z 
2023-01-11T21:41:23.9294793Z 
2023-01-11T21:41:23.9294950Z async_compile.wait(globals())
2023-01-11T21:41:23.9295078Z del async_compile
2023-01-11T21:41:23.9295086Z 
2023-01-11T21:41:23.9295204Z def call(args):
2023-01-11T21:41:23.9295333Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9295432Z     args.clear()
2023-01-11T21:41:23.9295858Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:23.9295973Z     del arg0_1
2023-01-11T21:41:23.9296087Z     del arg1_1
2023-01-11T21:41:23.9296202Z     return (buf0, )
2023-01-11T21:41:23.9296210Z 
2023-01-11T21:41:23.9296217Z 
2023-01-11T21:41:23.9296348Z if __name__ == "__main__":
2023-01-11T21:41:23.9296545Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9296761Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9297110Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9297474Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9297679Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9297688Z 
2023-01-11T21:41:23.9298175Z [2023-01-11 21:35:40,359] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 321
2023-01-11T21:41:23.9298985Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9299205Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9299674Z [2023-01-11 21:35:40,390] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 322
2023-01-11T21:41:23.9300165Z [2023-01-11 21:35:40,392] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 322
2023-01-11T21:41:23.9301062Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9301279Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9301762Z [2023-01-11 21:35:40,479] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 323
2023-01-11T21:41:23.9302239Z [2023-01-11 21:35:40,482] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 323
2023-01-11T21:41:23.9303235Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9303465Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9303946Z [2023-01-11 21:35:40,509] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 324
2023-01-11T21:41:23.9304434Z [2023-01-11 21:35:40,512] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 324
2023-01-11T21:41:23.9305239Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9305460Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9305941Z [2023-01-11 21:35:40,599] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 325
2023-01-11T21:41:23.9306422Z [2023-01-11 21:35:40,602] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 325
2023-01-11T21:41:23.9307221Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9307441Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9307916Z [2023-01-11 21:35:40,635] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 326
2023-01-11T21:41:23.9308396Z [2023-01-11 21:35:40,637] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 326
2023-01-11T21:41:23.9309218Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9309435Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9309914Z [2023-01-11 21:35:40,726] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 327
2023-01-11T21:41:23.9310406Z [2023-01-11 21:35:40,729] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 327
2023-01-11T21:41:23.9311220Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9311493Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9311979Z [2023-01-11 21:35:40,761] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 328
2023-01-11T21:41:23.9312468Z [2023-01-11 21:35:40,764] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 328
2023-01-11T21:41:23.9313273Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9313580Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9314154Z [2023-01-11 21:35:40,856] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 329
2023-01-11T21:41:23.9314166Z 
2023-01-11T21:41:23.9314310Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9314430Z import torch
2023-01-11T21:41:23.9314555Z import random
2023-01-11T21:41:23.9314762Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9314978Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9314986Z 
2023-01-11T21:41:23.9315123Z aten = torch.ops.aten
2023-01-11T21:41:23.9315360Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9315510Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9315533Z 
2023-01-11T21:41:23.9315541Z 
2023-01-11T21:41:23.9315681Z async_compile.wait(globals())
2023-01-11T21:41:23.9315806Z del async_compile
2023-01-11T21:41:23.9315815Z 
2023-01-11T21:41:23.9315933Z def call(args):
2023-01-11T21:41:23.9316075Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9316206Z     args.clear()
2023-01-11T21:41:23.9316605Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'none')
2023-01-11T21:41:23.9316721Z     del arg0_1
2023-01-11T21:41:23.9316813Z     del arg1_1
2023-01-11T21:41:23.9316928Z     del arg2_1
2023-01-11T21:41:23.9317052Z     return (buf0, )
2023-01-11T21:41:23.9317060Z 
2023-01-11T21:41:23.9317067Z 
2023-01-11T21:41:23.9317199Z if __name__ == "__main__":
2023-01-11T21:41:23.9317399Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9317611Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9317983Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9318339Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9318701Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9318921Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9318934Z 
2023-01-11T21:41:23.9318942Z 
2023-01-11T21:41:23.9319106Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9319227Z import torch
2023-01-11T21:41:23.9319347Z import random
2023-01-11T21:41:23.9319545Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9319764Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9319772Z 
2023-01-11T21:41:23.9319908Z aten = torch.ops.aten
2023-01-11T21:41:23.9320133Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9320289Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9320297Z 
2023-01-11T21:41:23.9320305Z 
2023-01-11T21:41:23.9320458Z async_compile.wait(globals())
2023-01-11T21:41:23.9320585Z del async_compile
2023-01-11T21:41:23.9320594Z 
2023-01-11T21:41:23.9320719Z def call(args):
2023-01-11T21:41:23.9320850Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9320961Z     args.clear()
2023-01-11T21:41:23.9321350Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'none')
2023-01-11T21:41:23.9321515Z     del arg0_1
2023-01-11T21:41:23.9321633Z     del arg1_1
2023-01-11T21:41:23.9321759Z     return (buf0, )
2023-01-11T21:41:23.9321767Z 
2023-01-11T21:41:23.9321775Z 
2023-01-11T21:41:23.9321899Z if __name__ == "__main__":
2023-01-11T21:41:23.9322100Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9322319Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9322687Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9323051Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9323252Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9323261Z 
2023-01-11T21:41:23.9323268Z 
2023-01-11T21:41:23.9323426Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9323544Z import torch
2023-01-11T21:41:23.9323667Z import random
2023-01-11T21:41:23.9323911Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9324128Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9324136Z 
2023-01-11T21:41:23.9324271Z aten = torch.ops.aten
2023-01-11T21:41:23.9324499Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9324656Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9324664Z 
2023-01-11T21:41:23.9324672Z 
2023-01-11T21:41:23.9324819Z async_compile.wait(globals())
2023-01-11T21:41:23.9324946Z del async_compile
2023-01-11T21:41:23.9324954Z 
2023-01-11T21:41:23.9325074Z def call(args):
2023-01-11T21:41:23.9325217Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9325338Z     args.clear()
2023-01-11T21:41:23.9325738Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'none')
2023-01-11T21:41:23.9325840Z     del arg0_1
2023-01-11T21:41:23.9325950Z     del arg1_1
2023-01-11T21:41:23.9326065Z     del arg2_1
2023-01-11T21:41:23.9326186Z     return (buf0, )
2023-01-11T21:41:23.9326203Z 
2023-01-11T21:41:23.9326211Z 
2023-01-11T21:41:23.9326346Z if __name__ == "__main__":
2023-01-11T21:41:23.9326543Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9326763Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9327116Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9327466Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9327830Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9328045Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9328053Z 
2023-01-11T21:41:23.9328060Z 
2023-01-11T21:41:23.9328229Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9328348Z import torch
2023-01-11T21:41:23.9328467Z import random
2023-01-11T21:41:23.9328673Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9328881Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9328889Z 
2023-01-11T21:41:23.9329028Z aten = torch.ops.aten
2023-01-11T21:41:23.9329265Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9329428Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9329437Z 
2023-01-11T21:41:23.9329444Z 
2023-01-11T21:41:23.9329599Z async_compile.wait(globals())
2023-01-11T21:41:23.9329731Z del async_compile
2023-01-11T21:41:23.9329739Z 
2023-01-11T21:41:23.9329862Z def call(args):
2023-01-11T21:41:23.9329990Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9330089Z     args.clear()
2023-01-11T21:41:23.9330481Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'none')
2023-01-11T21:41:23.9330594Z     del arg0_1
2023-01-11T21:41:23.9330712Z     del arg1_1
2023-01-11T21:41:23.9330831Z     return (buf0, )
2023-01-11T21:41:23.9330839Z 
2023-01-11T21:41:23.9330846Z 
2023-01-11T21:41:23.9330978Z if __name__ == "__main__":
2023-01-11T21:41:23.9331229Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9331430Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9331791Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9332157Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9332357Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9332365Z 
2023-01-11T21:41:23.9332373Z 
2023-01-11T21:41:23.9332536Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9332655Z import torch
2023-01-11T21:41:23.9332774Z import random
2023-01-11T21:41:23.9332982Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9333178Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9333199Z 
2023-01-11T21:41:23.9333321Z aten = torch.ops.aten
2023-01-11T21:41:23.9333554Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9333770Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9333784Z 
2023-01-11T21:41:23.9333791Z 
2023-01-11T21:41:23.9333942Z async_compile.wait(globals())
2023-01-11T21:41:23.9334072Z del async_compile
2023-01-11T21:41:23.9334081Z 
2023-01-11T21:41:23.9334197Z def call(args):
2023-01-11T21:41:23.9334335Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9334437Z     args.clear()
2023-01-11T21:41:23.9334839Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'tanh')
2023-01-11T21:41:23.9334956Z     del arg0_1
2023-01-11T21:41:23.9335067Z     del arg1_1
2023-01-11T21:41:23.9335184Z     del arg2_1
2023-01-11T21:41:23.9335308Z     return (buf0, )
2023-01-11T21:41:23.9335316Z 
2023-01-11T21:41:23.9335324Z 
2023-01-11T21:41:23.9335454Z if __name__ == "__main__":
2023-01-11T21:41:23.9335653Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9335851Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9336219Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9336569Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9336942Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9337159Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9337167Z 
2023-01-11T21:41:23.9337175Z 
2023-01-11T21:41:23.9337339Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9337457Z import torch
2023-01-11T21:41:23.9337580Z import random
2023-01-11T21:41:23.9337764Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9337979Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9337987Z 
2023-01-11T21:41:23.9338123Z aten = torch.ops.aten
2023-01-11T21:41:23.9338360Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9338522Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9338530Z 
2023-01-11T21:41:23.9338541Z 
2023-01-11T21:41:23.9338705Z async_compile.wait(globals())
2023-01-11T21:41:23.9338834Z del async_compile
2023-01-11T21:41:23.9338842Z 
2023-01-11T21:41:23.9338966Z def call(args):
2023-01-11T21:41:23.9339080Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9339200Z     args.clear()
2023-01-11T21:41:23.9339601Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'tanh')
2023-01-11T21:41:23.9339718Z     del arg0_1
2023-01-11T21:41:23.9339834Z     del arg1_1
2023-01-11T21:41:23.9339950Z     return (buf0, )
2023-01-11T21:41:23.9339958Z 
2023-01-11T21:41:23.9339965Z 
2023-01-11T21:41:23.9340093Z if __name__ == "__main__":
2023-01-11T21:41:23.9340274Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9340492Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9340858Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9341234Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9341475Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9341484Z 
2023-01-11T21:41:23.9341491Z 
2023-01-11T21:41:23.9341654Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9341774Z import torch
2023-01-11T21:41:23.9341896Z import random
2023-01-11T21:41:23.9342083Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9342296Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9342304Z 
2023-01-11T21:41:23.9342543Z aten = torch.ops.aten
2023-01-11T21:41:23.9342781Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9342940Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9342949Z 
2023-01-11T21:41:23.9342955Z 
2023-01-11T21:41:23.9343111Z async_compile.wait(globals())
2023-01-11T21:41:23.9343236Z del async_compile
2023-01-11T21:41:23.9343244Z 
2023-01-11T21:41:23.9343366Z def call(args):
2023-01-11T21:41:23.9343482Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9343671Z     args.clear()
2023-01-11T21:41:23.9344080Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'tanh')
2023-01-11T21:41:23.9344192Z     del arg0_1
2023-01-11T21:41:23.9344310Z     del arg1_1
2023-01-11T21:41:23.9344424Z     del arg2_1
2023-01-11T21:41:23.9344546Z     return (buf0, )
2023-01-11T21:41:23.9344554Z 
2023-01-11T21:41:23.9344562Z 
2023-01-11T21:41:23.9344668Z if __name__ == "__main__":
2023-01-11T21:41:23.9344868Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9345085Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9345447Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9345799Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9346155Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9346368Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9346385Z 
2023-01-11T21:41:23.9346393Z 
2023-01-11T21:41:23.9346555Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9346661Z import torch
2023-01-11T21:41:23.9346782Z import random
2023-01-11T21:41:23.9346989Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9347206Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9347215Z 
2023-01-11T21:41:23.9347355Z aten = torch.ops.aten
2023-01-11T21:41:23.9347586Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9347743Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9347751Z 
2023-01-11T21:41:23.9347758Z 
2023-01-11T21:41:23.9347914Z async_compile.wait(globals())
2023-01-11T21:41:23.9348021Z del async_compile
2023-01-11T21:41:23.9348029Z 
2023-01-11T21:41:23.9348147Z def call(args):
2023-01-11T21:41:23.9348271Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9348393Z     args.clear()
2023-01-11T21:41:23.9348788Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'tanh')
2023-01-11T21:41:23.9348906Z     del arg0_1
2023-01-11T21:41:23.9349024Z     del arg1_1
2023-01-11T21:41:23.9349128Z     return (buf0, )
2023-01-11T21:41:23.9349135Z 
2023-01-11T21:41:23.9349158Z 
2023-01-11T21:41:23.9349272Z if __name__ == "__main__":
2023-01-11T21:41:23.9349469Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9349687Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9350054Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9350412Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9350612Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9350620Z 
2023-01-11T21:41:23.9351112Z [2023-01-11 21:35:40,859] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 329
2023-01-11T21:41:23.9351912Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9352195Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9352654Z [2023-01-11 21:35:40,891] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 330
2023-01-11T21:41:23.9353148Z [2023-01-11 21:35:40,894] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 330
2023-01-11T21:41:23.9354060Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9354287Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9354760Z [2023-01-11 21:35:40,980] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 331
2023-01-11T21:41:23.9355249Z [2023-01-11 21:35:40,983] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 331
2023-01-11T21:41:23.9356041Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9356262Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9356745Z [2023-01-11 21:35:41,013] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 332
2023-01-11T21:41:23.9357236Z [2023-01-11 21:35:41,016] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 332
2023-01-11T21:41:23.9358048Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9358266Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9358722Z [2023-01-11 21:35:41,128] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 333
2023-01-11T21:41:23.9359193Z [2023-01-11 21:35:41,130] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 333
2023-01-11T21:41:23.9359992Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9360215Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9360680Z [2023-01-11 21:35:41,186] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 334
2023-01-11T21:41:23.9361164Z [2023-01-11 21:35:41,189] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 334
2023-01-11T21:41:23.9361969Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9362236Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9362706Z [2023-01-11 21:35:41,313] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 335
2023-01-11T21:41:23.9363193Z [2023-01-11 21:35:41,316] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 335
2023-01-11T21:41:23.9364003Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9364228Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9364724Z [2023-01-11 21:35:41,391] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 336
2023-01-11T21:41:23.9365218Z [2023-01-11 21:35:41,393] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 336
2023-01-11T21:41:23.9365227Z 
2023-01-11T21:41:23.9365388Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9365510Z import torch
2023-01-11T21:41:23.9365628Z import random
2023-01-11T21:41:23.9365832Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9366051Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9366060Z 
2023-01-11T21:41:23.9366199Z aten = torch.ops.aten
2023-01-11T21:41:23.9366424Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9366581Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9366589Z 
2023-01-11T21:41:23.9366597Z 
2023-01-11T21:41:23.9366749Z async_compile.wait(globals())
2023-01-11T21:41:23.9366876Z del async_compile
2023-01-11T21:41:23.9366883Z 
2023-01-11T21:41:23.9366998Z def call(args):
2023-01-11T21:41:23.9367151Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9367270Z     args.clear()
2023-01-11T21:41:23.9367691Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [0, 6], '')
2023-01-11T21:41:23.9367797Z     del arg0_1
2023-01-11T21:41:23.9367913Z     del arg1_1
2023-01-11T21:41:23.9368026Z     del arg2_1
2023-01-11T21:41:23.9368150Z     return (buf0, )
2023-01-11T21:41:23.9368158Z 
2023-01-11T21:41:23.9368165Z 
2023-01-11T21:41:23.9368295Z if __name__ == "__main__":
2023-01-11T21:41:23.9368492Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9368711Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9369059Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9369413Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9369780Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9370006Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9370014Z 
2023-01-11T21:41:23.9370021Z 
2023-01-11T21:41:23.9370188Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9370303Z import torch
2023-01-11T21:41:23.9370428Z import random
2023-01-11T21:41:23.9370623Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9370818Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9370826Z 
2023-01-11T21:41:23.9370960Z aten = torch.ops.aten
2023-01-11T21:41:23.9371197Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9371359Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9371368Z 
2023-01-11T21:41:23.9371375Z 
2023-01-11T21:41:23.9371525Z async_compile.wait(globals())
2023-01-11T21:41:23.9371651Z del async_compile
2023-01-11T21:41:23.9371659Z 
2023-01-11T21:41:23.9371783Z def call(args):
2023-01-11T21:41:23.9371910Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9372017Z     args.clear()
2023-01-11T21:41:23.9372476Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [0, 6], '')
2023-01-11T21:41:23.9372588Z     del arg0_1
2023-01-11T21:41:23.9372702Z     del arg1_1
2023-01-11T21:41:23.9372826Z     return (buf0, )
2023-01-11T21:41:23.9372834Z 
2023-01-11T21:41:23.9372841Z 
2023-01-11T21:41:23.9372974Z if __name__ == "__main__":
2023-01-11T21:41:23.9373168Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9373370Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9373728Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9374107Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9374306Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9374314Z 
2023-01-11T21:41:23.9374321Z 
2023-01-11T21:41:23.9374481Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9374604Z import torch
2023-01-11T21:41:23.9374731Z import random
2023-01-11T21:41:23.9374974Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9375171Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9375195Z 
2023-01-11T21:41:23.9375311Z aten = torch.ops.aten
2023-01-11T21:41:23.9375550Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9375711Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9375718Z 
2023-01-11T21:41:23.9375725Z 
2023-01-11T21:41:23.9375885Z async_compile.wait(globals())
2023-01-11T21:41:23.9376013Z del async_compile
2023-01-11T21:41:23.9376021Z 
2023-01-11T21:41:23.9376141Z def call(args):
2023-01-11T21:41:23.9376280Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9376387Z     args.clear()
2023-01-11T21:41:23.9376798Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [0, 6], '')
2023-01-11T21:41:23.9376917Z     del arg0_1
2023-01-11T21:41:23.9377030Z     del arg1_1
2023-01-11T21:41:23.9377149Z     del arg2_1
2023-01-11T21:41:23.9377279Z     return (buf0, )
2023-01-11T21:41:23.9377288Z 
2023-01-11T21:41:23.9377295Z 
2023-01-11T21:41:23.9377424Z if __name__ == "__main__":
2023-01-11T21:41:23.9377627Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9377832Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9378182Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9378533Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9378883Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9379095Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9379103Z 
2023-01-11T21:41:23.9379111Z 
2023-01-11T21:41:23.9379280Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9379398Z import torch
2023-01-11T21:41:23.9379519Z import random
2023-01-11T21:41:23.9379706Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9379924Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9379933Z 
2023-01-11T21:41:23.9380068Z aten = torch.ops.aten
2023-01-11T21:41:23.9380305Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9380463Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9380472Z 
2023-01-11T21:41:23.9380479Z 
2023-01-11T21:41:23.9380637Z async_compile.wait(globals())
2023-01-11T21:41:23.9380758Z del async_compile
2023-01-11T21:41:23.9380766Z 
2023-01-11T21:41:23.9380884Z def call(args):
2023-01-11T21:41:23.9380991Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9381111Z     args.clear()
2023-01-11T21:41:23.9381511Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [0, 6], '')
2023-01-11T21:41:23.9381624Z     del arg0_1
2023-01-11T21:41:23.9381741Z     del arg1_1
2023-01-11T21:41:23.9381860Z     return (buf0, )
2023-01-11T21:41:23.9381868Z 
2023-01-11T21:41:23.9381875Z 
2023-01-11T21:41:23.9382003Z if __name__ == "__main__":
2023-01-11T21:41:23.9382233Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9382566Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9382928Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9383287Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9383481Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9383489Z 
2023-01-11T21:41:23.9383497Z 
2023-01-11T21:41:23.9383661Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9383786Z import torch
2023-01-11T21:41:23.9383899Z import random
2023-01-11T21:41:23.9384086Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9384297Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9384306Z 
2023-01-11T21:41:23.9384439Z aten = torch.ops.aten
2023-01-11T21:41:23.9384677Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9384912Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9384922Z 
2023-01-11T21:41:23.9384929Z 
2023-01-11T21:41:23.9385086Z async_compile.wait(globals())
2023-01-11T21:41:23.9385213Z del async_compile
2023-01-11T21:41:23.9385221Z 
2023-01-11T21:41:23.9385339Z def call(args):
2023-01-11T21:41:23.9385467Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9385585Z     args.clear()
2023-01-11T21:41:23.9385978Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'swish', [], '')
2023-01-11T21:41:23.9386092Z     del arg0_1
2023-01-11T21:41:23.9386203Z     del arg1_1
2023-01-11T21:41:23.9386315Z     del arg2_1
2023-01-11T21:41:23.9386434Z     return (buf0, )
2023-01-11T21:41:23.9386442Z 
2023-01-11T21:41:23.9386447Z 
2023-01-11T21:41:23.9386559Z if __name__ == "__main__":
2023-01-11T21:41:23.9386748Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9386956Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9387328Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9387679Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9388056Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9388266Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9388274Z 
2023-01-11T21:41:23.9388282Z 
2023-01-11T21:41:23.9388446Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9388552Z import torch
2023-01-11T21:41:23.9388665Z import random
2023-01-11T21:41:23.9388860Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9389079Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9389086Z 
2023-01-11T21:41:23.9389225Z aten = torch.ops.aten
2023-01-11T21:41:23.9389462Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9389620Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9389632Z 
2023-01-11T21:41:23.9389644Z 
2023-01-11T21:41:23.9389799Z async_compile.wait(globals())
2023-01-11T21:41:23.9389911Z del async_compile
2023-01-11T21:41:23.9389919Z 
2023-01-11T21:41:23.9390032Z def call(args):
2023-01-11T21:41:23.9390163Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9390277Z     args.clear()
2023-01-11T21:41:23.9390670Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'swish', [], '')
2023-01-11T21:41:23.9390777Z     del arg0_1
2023-01-11T21:41:23.9390892Z     del arg1_1
2023-01-11T21:41:23.9390990Z     return (buf0, )
2023-01-11T21:41:23.9390998Z 
2023-01-11T21:41:23.9391018Z 
2023-01-11T21:41:23.9391134Z if __name__ == "__main__":
2023-01-11T21:41:23.9391327Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9391538Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9391894Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9392270Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9392531Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9392539Z 
2023-01-11T21:41:23.9392547Z 
2023-01-11T21:41:23.9392706Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9392808Z import torch
2023-01-11T21:41:23.9392929Z import random
2023-01-11T21:41:23.9393129Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9393340Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9393349Z 
2023-01-11T21:41:23.9393491Z aten = torch.ops.aten
2023-01-11T21:41:23.9393785Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9393940Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9393948Z 
2023-01-11T21:41:23.9393956Z 
2023-01-11T21:41:23.9394109Z async_compile.wait(globals())
2023-01-11T21:41:23.9394219Z del async_compile
2023-01-11T21:41:23.9394243Z 
2023-01-11T21:41:23.9394344Z def call(args):
2023-01-11T21:41:23.9394486Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9394654Z     args.clear()
2023-01-11T21:41:23.9395042Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'swish', [], '')
2023-01-11T21:41:23.9395163Z     del arg0_1
2023-01-11T21:41:23.9395276Z     del arg1_1
2023-01-11T21:41:23.9395376Z     del arg2_1
2023-01-11T21:41:23.9395494Z     return (buf0, )
2023-01-11T21:41:23.9395503Z 
2023-01-11T21:41:23.9395510Z 
2023-01-11T21:41:23.9395640Z if __name__ == "__main__":
2023-01-11T21:41:23.9395830Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9396043Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9396403Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9396758Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9397115Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9397321Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9397345Z 
2023-01-11T21:41:23.9397352Z 
2023-01-11T21:41:23.9397504Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9397619Z import torch
2023-01-11T21:41:23.9397740Z import random
2023-01-11T21:41:23.9397940Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9398148Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9398156Z 
2023-01-11T21:41:23.9398287Z aten = torch.ops.aten
2023-01-11T21:41:23.9398528Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9398670Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9398679Z 
2023-01-11T21:41:23.9398699Z 
2023-01-11T21:41:23.9398833Z async_compile.wait(globals())
2023-01-11T21:41:23.9398956Z del async_compile
2023-01-11T21:41:23.9398965Z 
2023-01-11T21:41:23.9399081Z def call(args):
2023-01-11T21:41:23.9399206Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9399322Z     args.clear()
2023-01-11T21:41:23.9399715Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'swish', [], '')
2023-01-11T21:41:23.9399824Z     del arg0_1
2023-01-11T21:41:23.9399920Z     del arg1_1
2023-01-11T21:41:23.9400037Z     return (buf0, )
2023-01-11T21:41:23.9400045Z 
2023-01-11T21:41:23.9400052Z 
2023-01-11T21:41:23.9400182Z if __name__ == "__main__":
2023-01-11T21:41:23.9400378Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9400588Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9400948Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9401303Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:23.9401484Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9401507Z 
2023-01-11T21:41:23.9401604Z ok (2.862s)
2023-01-11T21:41:23.9402497Z   test_linspace1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9402768Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9403243Z [2023-01-11 21:35:41,455] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 337
2023-01-11T21:41:23.9403731Z [2023-01-11 21:35:43,118] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 337
2023-01-11T21:41:23.9403740Z 
2023-01-11T21:41:23.9403907Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9404027Z import torch
2023-01-11T21:41:23.9404154Z import random
2023-01-11T21:41:23.9404355Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9404546Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9404554Z 
2023-01-11T21:41:23.9404752Z aten = torch.ops.aten
2023-01-11T21:41:23.9404987Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9405151Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9405159Z 
2023-01-11T21:41:23.9405167Z 
2023-01-11T21:41:23.9405405Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9405775Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9405982Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9406154Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9406242Z {
2023-01-11T21:41:23.9406412Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9406517Z     {
2023-01-11T21:41:23.9406644Z         #pragma omp for 
2023-01-11T21:41:23.9406786Z         for(long i0=0; i0<7; i0+=1)
2023-01-11T21:41:23.9406891Z         {
2023-01-11T21:41:23.9406986Z             {
2023-01-11T21:41:23.9407096Z                 {
2023-01-11T21:41:23.9407263Z                     auto tmp4 = in_ptr0[i0];
2023-01-11T21:41:23.9407448Z                     auto tmp0 = static_cast<float>(0.125);
2023-01-11T21:41:23.9407631Z                     auto tmp1 = static_cast<float>(i0);
2023-01-11T21:41:23.9407791Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.9407943Z                     auto tmp3 = tmp2 + tmp0;
2023-01-11T21:41:23.9408092Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.9408224Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:41:23.9408336Z                 }
2023-01-11T21:41:23.9408449Z             }
2023-01-11T21:41:23.9408550Z         }
2023-01-11T21:41:23.9408656Z     }
2023-01-11T21:41:23.9408754Z }
2023-01-11T21:41:23.9408880Z ''')
2023-01-11T21:41:23.9408889Z 
2023-01-11T21:41:23.9408911Z 
2023-01-11T21:41:23.9409055Z async_compile.wait(globals())
2023-01-11T21:41:23.9409182Z del async_compile
2023-01-11T21:41:23.9409191Z 
2023-01-11T21:41:23.9409317Z def call(args):
2023-01-11T21:41:23.9409432Z     arg0_1, = args
2023-01-11T21:41:23.9409564Z     args.clear()
2023-01-11T21:41:23.9409914Z     buf0 = empty_strided((1, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9410152Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9410252Z     del arg0_1
2023-01-11T21:41:23.9410374Z     return (buf0, )
2023-01-11T21:41:23.9410383Z 
2023-01-11T21:41:23.9410391Z 
2023-01-11T21:41:23.9410525Z if __name__ == "__main__":
2023-01-11T21:41:23.9410728Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9410943Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9411297Z     arg0_1 = rand_strided((1, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9411487Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9411496Z 
2023-01-11T21:41:23.9411608Z ok (1.719s)
2023-01-11T21:41:23.9412478Z   test_linspace2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9412739Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9413217Z [2023-01-11 21:35:43,150] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 338
2023-01-11T21:41:23.9413716Z [2023-01-11 21:35:44,698] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 338
2023-01-11T21:41:23.9413724Z 
2023-01-11T21:41:23.9413894Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9414017Z import torch
2023-01-11T21:41:23.9414132Z import random
2023-01-11T21:41:23.9414336Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9414549Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9414598Z 
2023-01-11T21:41:23.9414721Z aten = torch.ops.aten
2023-01-11T21:41:23.9414963Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9415121Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9415129Z 
2023-01-11T21:41:23.9415136Z 
2023-01-11T21:41:23.9415375Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9415740Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9415947Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9416123Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9416218Z {
2023-01-11T21:41:23.9416311Z     {
2023-01-11T21:41:23.9416413Z         {
2023-01-11T21:41:23.9416563Z             auto tmp4 = in_ptr0[0];
2023-01-11T21:41:23.9416735Z             auto tmp0 = static_cast<float>(0.0);
2023-01-11T21:41:23.9416907Z             auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:23.9417057Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.9417190Z             auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:23.9417330Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.9417464Z             out_ptr0[0] = tmp5;
2023-01-11T21:41:23.9417566Z         }
2023-01-11T21:41:23.9417671Z     }
2023-01-11T21:41:23.9417774Z }
2023-01-11T21:41:23.9417910Z ''')
2023-01-11T21:41:23.9417918Z 
2023-01-11T21:41:23.9417926Z 
2023-01-11T21:41:23.9418066Z async_compile.wait(globals())
2023-01-11T21:41:23.9418195Z del async_compile
2023-01-11T21:41:23.9418203Z 
2023-01-11T21:41:23.9418326Z def call(args):
2023-01-11T21:41:23.9418447Z     arg0_1, = args
2023-01-11T21:41:23.9418566Z     args.clear()
2023-01-11T21:41:23.9418926Z     buf0 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9419164Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9419278Z     del arg0_1
2023-01-11T21:41:23.9419383Z     return (buf0, )
2023-01-11T21:41:23.9419392Z 
2023-01-11T21:41:23.9419403Z 
2023-01-11T21:41:23.9419538Z if __name__ == "__main__":
2023-01-11T21:41:23.9419736Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9419955Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9420309Z     arg0_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9420494Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9420502Z 
2023-01-11T21:41:23.9420614Z ok (1.580s)
2023-01-11T21:41:23.9421499Z   test_linspace3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9421711Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9422227Z [2023-01-11 21:35:44,727] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 339
2023-01-11T21:41:23.9422839Z [2023-01-11 21:35:44,729] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 339
2023-01-11T21:41:23.9422848Z 
2023-01-11T21:41:23.9423013Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9423124Z import torch
2023-01-11T21:41:23.9423243Z import random
2023-01-11T21:41:23.9423440Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9423647Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9423655Z 
2023-01-11T21:41:23.9423795Z aten = torch.ops.aten
2023-01-11T21:41:23.9424020Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9424176Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9424185Z 
2023-01-11T21:41:23.9424193Z 
2023-01-11T21:41:23.9424351Z async_compile.wait(globals())
2023-01-11T21:41:23.9424479Z del async_compile
2023-01-11T21:41:23.9424557Z 
2023-01-11T21:41:23.9424682Z def call(args):
2023-01-11T21:41:23.9425027Z     buf0 = empty_strided((0, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9425151Z     return (buf0, )
2023-01-11T21:41:23.9425160Z 
2023-01-11T21:41:23.9425167Z 
2023-01-11T21:41:23.9425296Z if __name__ == "__main__":
2023-01-11T21:41:23.9425480Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9425699Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9425870Z     print_performance(lambda: call([]))
2023-01-11T21:41:23.9425878Z 
2023-01-11T21:41:23.9425991Z ok (0.031s)
2023-01-11T21:41:23.9426618Z   test_list_clearing_cpu (__main__.CpuTests) ... [2023-01-11 21:35:44,758] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:23.9427108Z [2023-01-11 21:35:46,376] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:23.9427117Z 
2023-01-11T21:41:23.9427292Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9427408Z import torch
2023-01-11T21:41:23.9427512Z import random
2023-01-11T21:41:23.9427709Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9427915Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9427924Z 
2023-01-11T21:41:23.9428054Z aten = torch.ops.aten
2023-01-11T21:41:23.9428285Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9428448Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9428456Z 
2023-01-11T21:41:23.9428463Z 
2023-01-11T21:41:23.9428702Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9429067Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9429263Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9429443Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9429616Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9429727Z {
2023-01-11T21:41:23.9429900Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9430007Z     {
2023-01-11T21:41:23.9430140Z         #pragma omp for 
2023-01-11T21:41:23.9430271Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:23.9430375Z         {
2023-01-11T21:41:23.9430625Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9430857Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.9431003Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9431162Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9431272Z         }
2023-01-11T21:41:23.9431438Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9431558Z         for(long i0=24; i0<25; i0+=1)
2023-01-11T21:41:23.9431663Z         {
2023-01-11T21:41:23.9431804Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9431948Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.9432157Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9432292Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9432385Z         }
2023-01-11T21:41:23.9432484Z     }
2023-01-11T21:41:23.9432587Z }
2023-01-11T21:41:23.9432724Z ''')
2023-01-11T21:41:23.9432732Z 
2023-01-11T21:41:23.9432740Z 
2023-01-11T21:41:23.9432897Z async_compile.wait(globals())
2023-01-11T21:41:23.9433019Z del async_compile
2023-01-11T21:41:23.9433028Z 
2023-01-11T21:41:23.9433150Z def call(args):
2023-01-11T21:41:23.9433264Z     x_1, y_1 = args
2023-01-11T21:41:23.9433368Z     args.clear()
2023-01-11T21:41:23.9433771Z     buf0 = empty_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9434047Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(y_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9434159Z     del x_1
2023-01-11T21:41:23.9434267Z     del y_1
2023-01-11T21:41:23.9434613Z     buf1 = empty_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9434812Z     aten.mm.out(buf0, buf0, out=buf1)
2023-01-11T21:41:23.9434910Z     return (buf1, )
2023-01-11T21:41:23.9434919Z 
2023-01-11T21:41:23.9434926Z 
2023-01-11T21:41:23.9435056Z if __name__ == "__main__":
2023-01-11T21:41:23.9435248Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9435459Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9435803Z     x_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9436131Z     y_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9436325Z     print_performance(lambda: call([x_1, y_1]))
2023-01-11T21:41:23.9436332Z 
2023-01-11T21:41:23.9436443Z ok (1.647s)
2023-01-11T21:41:23.9437303Z   test_log1p_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9437533Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9438011Z [2023-01-11 21:35:46,394] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 340
2023-01-11T21:41:23.9438502Z [2023-01-11 21:35:48,014] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 340
2023-01-11T21:41:23.9439299Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9439514Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9440003Z [2023-01-11 21:35:48,032] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 341
2023-01-11T21:41:23.9440486Z [2023-01-11 21:35:49,654] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 341
2023-01-11T21:41:23.9441295Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9441515Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9441984Z [2023-01-11 21:35:49,672] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 342
2023-01-11T21:41:23.9442467Z [2023-01-11 21:35:51,319] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 342
2023-01-11T21:41:23.9443299Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9443520Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9443995Z [2023-01-11 21:35:51,346] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 343
2023-01-11T21:41:23.9444480Z [2023-01-11 21:35:52,923] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 343
2023-01-11T21:41:23.9445317Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9445548Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9446018Z [2023-01-11 21:35:52,941] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 344
2023-01-11T21:41:23.9446028Z 
2023-01-11T21:41:23.9446197Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9446316Z import torch
2023-01-11T21:41:23.9446434Z import random
2023-01-11T21:41:23.9446624Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9446840Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9446848Z 
2023-01-11T21:41:23.9446981Z aten = torch.ops.aten
2023-01-11T21:41:23.9447218Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9447379Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9447387Z 
2023-01-11T21:41:23.9447395Z 
2023-01-11T21:41:23.9447639Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9448018Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9448229Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:23.9448380Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:23.9448543Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:23.9448653Z {
2023-01-11T21:41:23.9448823Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9448926Z     {
2023-01-11T21:41:23.9449051Z         #pragma omp for 
2023-01-11T21:41:23.9449190Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.9449284Z         {
2023-01-11T21:41:23.9449390Z             {
2023-01-11T21:41:23.9449500Z                 {
2023-01-11T21:41:23.9449693Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:23.9449870Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:23.9450048Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:23.9450203Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9450334Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9450478Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9450584Z                 }
2023-01-11T21:41:23.9450695Z             }
2023-01-11T21:41:23.9450801Z         }
2023-01-11T21:41:23.9450907Z     }
2023-01-11T21:41:23.9451015Z }
2023-01-11T21:41:23.9451135Z ''')
2023-01-11T21:41:23.9451143Z 
2023-01-11T21:41:23.9451151Z 
2023-01-11T21:41:23.9451304Z async_compile.wait(globals())
2023-01-11T21:41:23.9451431Z del async_compile
2023-01-11T21:41:23.9451439Z 
2023-01-11T21:41:23.9451560Z def call(args):
2023-01-11T21:41:23.9451679Z     arg0_1, = args
2023-01-11T21:41:23.9451799Z     args.clear()
2023-01-11T21:41:23.9452150Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.9452479Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.9452811Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9452928Z     del arg0_1
2023-01-11T21:41:23.9453059Z     return (buf0, buf1, )
2023-01-11T21:41:23.9453066Z 
2023-01-11T21:41:23.9453072Z 
2023-01-11T21:41:23.9453199Z if __name__ == "__main__":
2023-01-11T21:41:23.9453395Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9453613Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9453970Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.9454141Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9454166Z 
2023-01-11T21:41:23.9454174Z 
2023-01-11T21:41:23.9454319Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9454441Z import torch
2023-01-11T21:41:23.9454564Z import random
2023-01-11T21:41:23.9454766Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9455051Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9455064Z 
2023-01-11T21:41:23.9455197Z aten = torch.ops.aten
2023-01-11T21:41:23.9455431Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9455575Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9455582Z 
2023-01-11T21:41:23.9455604Z 
2023-01-11T21:41:23.9455820Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9456169Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9456361Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:23.9456519Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:23.9456674Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:23.9456776Z {
2023-01-11T21:41:23.9456939Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9457021Z     {
2023-01-11T21:41:23.9457154Z         #pragma omp for 
2023-01-11T21:41:23.9457288Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.9457397Z         {
2023-01-11T21:41:23.9457497Z             {
2023-01-11T21:41:23.9457602Z                 {
2023-01-11T21:41:23.9457785Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:23.9457955Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:23.9458134Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:23.9458284Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9458424Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9458563Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9458670Z                 }
2023-01-11T21:41:23.9458757Z             }
2023-01-11T21:41:23.9458864Z         }
2023-01-11T21:41:23.9458962Z     }
2023-01-11T21:41:23.9459064Z }
2023-01-11T21:41:23.9459197Z ''')
2023-01-11T21:41:23.9459204Z 
2023-01-11T21:41:23.9459210Z 
2023-01-11T21:41:23.9459363Z async_compile.wait(globals())
2023-01-11T21:41:23.9459483Z del async_compile
2023-01-11T21:41:23.9459495Z 
2023-01-11T21:41:23.9459612Z def call(args):
2023-01-11T21:41:23.9459707Z     arg0_1, = args
2023-01-11T21:41:23.9459814Z     args.clear()
2023-01-11T21:41:23.9460146Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.9460488Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.9460771Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9460881Z     del arg0_1
2023-01-11T21:41:23.9460995Z     return (buf0, buf1, )
2023-01-11T21:41:23.9461003Z 
2023-01-11T21:41:23.9461010Z 
2023-01-11T21:41:23.9461118Z if __name__ == "__main__":
2023-01-11T21:41:23.9461305Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9461502Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9461837Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:23.9462016Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9462079Z 
2023-01-11T21:41:23.9462087Z 
2023-01-11T21:41:23.9462241Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9462472Z import torch
2023-01-11T21:41:23.9462594Z import random
2023-01-11T21:41:23.9462772Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9462980Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9462988Z 
2023-01-11T21:41:23.9463115Z aten = torch.ops.aten
2023-01-11T21:41:23.9463347Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9463497Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9463505Z 
2023-01-11T21:41:23.9463512Z 
2023-01-11T21:41:23.9463739Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9464095Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9464299Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9464448Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9464684Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9464778Z {
2023-01-11T21:41:23.9464948Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9465053Z     {
2023-01-11T21:41:23.9465185Z         #pragma omp for 
2023-01-11T21:41:23.9465316Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9465403Z         {
2023-01-11T21:41:23.9465632Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9465783Z             auto tmp1 = tmp0.log1p();
2023-01-11T21:41:23.9466012Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.9466152Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9466307Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9466454Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.9466538Z         }
2023-01-11T21:41:23.9466693Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9466826Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.9466939Z         {
2023-01-11T21:41:23.9467080Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9467232Z             auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:23.9467402Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:23.9467522Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9467651Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9467780Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9467875Z         }
2023-01-11T21:41:23.9467968Z     }
2023-01-11T21:41:23.9468064Z }
2023-01-11T21:41:23.9468188Z ''')
2023-01-11T21:41:23.9468197Z 
2023-01-11T21:41:23.9468204Z 
2023-01-11T21:41:23.9468335Z async_compile.wait(globals())
2023-01-11T21:41:23.9468456Z del async_compile
2023-01-11T21:41:23.9468465Z 
2023-01-11T21:41:23.9468578Z def call(args):
2023-01-11T21:41:23.9468689Z     arg0_1, = args
2023-01-11T21:41:23.9468806Z     args.clear()
2023-01-11T21:41:23.9469141Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9469485Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9469768Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9469865Z     del arg0_1
2023-01-11T21:41:23.9469988Z     return (buf0, buf1, )
2023-01-11T21:41:23.9469995Z 
2023-01-11T21:41:23.9470002Z 
2023-01-11T21:41:23.9470124Z if __name__ == "__main__":
2023-01-11T21:41:23.9470319Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9470529Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9470874Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9471055Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9471061Z 
2023-01-11T21:41:23.9471069Z 
2023-01-11T21:41:23.9471231Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9471327Z import torch
2023-01-11T21:41:23.9471431Z import random
2023-01-11T21:41:23.9471709Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9471914Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9471922Z 
2023-01-11T21:41:23.9472050Z aten = torch.ops.aten
2023-01-11T21:41:23.9472284Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9472440Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9472448Z 
2023-01-11T21:41:23.9472455Z 
2023-01-11T21:41:23.9472687Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9473027Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9473229Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9473386Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9473552Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9473652Z {
2023-01-11T21:41:23.9473875Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9473989Z     {
2023-01-11T21:41:23.9474165Z         #pragma omp for 
2023-01-11T21:41:23.9474295Z         for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:23.9474400Z         {
2023-01-11T21:41:23.9474625Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9474769Z             auto tmp1 = tmp0.log1p();
2023-01-11T21:41:23.9474990Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.9475131Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9475267Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9475418Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.9475519Z         }
2023-01-11T21:41:23.9475681Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9475817Z         for(long i0=200; i0<201; i0+=1)
2023-01-11T21:41:23.9475917Z         {
2023-01-11T21:41:23.9476058Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9476199Z             auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:23.9476367Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:23.9476508Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9476641Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9476768Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9476872Z         }
2023-01-11T21:41:23.9476975Z     }
2023-01-11T21:41:23.9477060Z }
2023-01-11T21:41:23.9477201Z ''')
2023-01-11T21:41:23.9477211Z 
2023-01-11T21:41:23.9477218Z 
2023-01-11T21:41:23.9477366Z async_compile.wait(globals())
2023-01-11T21:41:23.9477485Z del async_compile
2023-01-11T21:41:23.9477493Z 
2023-01-11T21:41:23.9477611Z def call(args):
2023-01-11T21:41:23.9477723Z     arg0_1, = args
2023-01-11T21:41:23.9477837Z     args.clear()
2023-01-11T21:41:23.9478171Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9478497Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9478779Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9478891Z     del arg0_1
2023-01-11T21:41:23.9479018Z     return (buf0, buf1, )
2023-01-11T21:41:23.9479025Z 
2023-01-11T21:41:23.9479032Z 
2023-01-11T21:41:23.9479161Z if __name__ == "__main__":
2023-01-11T21:41:23.9479353Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9479568Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9479891Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9480071Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9480077Z 
2023-01-11T21:41:23.9480548Z [2023-01-11 21:35:54,650] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 344
2023-01-11T21:41:23.9481321Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9481577Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9482027Z [2023-01-11 21:35:54,668] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 345
2023-01-11T21:41:23.9482494Z [2023-01-11 21:35:56,303] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 345
2023-01-11T21:41:23.9483256Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9483461Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9483961Z [2023-01-11 21:35:56,322] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 346
2023-01-11T21:41:23.9484421Z [2023-01-11 21:35:57,885] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 346
2023-01-11T21:41:23.9485169Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9485358Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9485796Z [2023-01-11 21:35:57,902] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 347
2023-01-11T21:41:23.9486251Z [2023-01-11 21:35:59,533] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 347
2023-01-11T21:41:23.9486999Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9487209Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9487650Z [2023-01-11 21:35:59,556] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 348
2023-01-11T21:41:23.9487658Z 
2023-01-11T21:41:23.9487811Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9487930Z import torch
2023-01-11T21:41:23.9488045Z import random
2023-01-11T21:41:23.9488223Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9488426Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9488434Z 
2023-01-11T21:41:23.9488567Z aten = torch.ops.aten
2023-01-11T21:41:23.9488789Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9488941Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9488950Z 
2023-01-11T21:41:23.9488957Z 
2023-01-11T21:41:23.9489185Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9489538Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9489744Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:23.9489906Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:23.9490055Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:23.9490152Z {
2023-01-11T21:41:23.9490311Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9490411Z     {
2023-01-11T21:41:23.9490533Z         #pragma omp for 
2023-01-11T21:41:23.9490667Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.9490754Z         {
2023-01-11T21:41:23.9490852Z             {
2023-01-11T21:41:23.9491004Z                 {
2023-01-11T21:41:23.9491152Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9491320Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:23.9491487Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:23.9491637Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9491755Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9491893Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9491998Z                 }
2023-01-11T21:41:23.9492098Z             }
2023-01-11T21:41:23.9492195Z         }
2023-01-11T21:41:23.9492288Z     }
2023-01-11T21:41:23.9492385Z }
2023-01-11T21:41:23.9492505Z ''')
2023-01-11T21:41:23.9492513Z 
2023-01-11T21:41:23.9492521Z 
2023-01-11T21:41:23.9492669Z async_compile.wait(globals())
2023-01-11T21:41:23.9492786Z del async_compile
2023-01-11T21:41:23.9492794Z 
2023-01-11T21:41:23.9492909Z def call(args):
2023-01-11T21:41:23.9493024Z     arg0_1, = args
2023-01-11T21:41:23.9493177Z     args.clear()
2023-01-11T21:41:23.9493510Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9493816Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9494087Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9494196Z     del arg0_1
2023-01-11T21:41:23.9494323Z     return (buf0, buf1, )
2023-01-11T21:41:23.9494331Z 
2023-01-11T21:41:23.9494340Z 
2023-01-11T21:41:23.9494460Z if __name__ == "__main__":
2023-01-11T21:41:23.9494643Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9494852Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9495184Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9495353Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9495377Z 
2023-01-11T21:41:23.9495383Z 
2023-01-11T21:41:23.9495528Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9495638Z import torch
2023-01-11T21:41:23.9495753Z import random
2023-01-11T21:41:23.9495938Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9496141Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9496147Z 
2023-01-11T21:41:23.9496276Z aten = torch.ops.aten
2023-01-11T21:41:23.9496502Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9496637Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9496645Z 
2023-01-11T21:41:23.9496667Z 
2023-01-11T21:41:23.9496883Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9497238Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9497442Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:23.9497606Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:23.9497768Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:23.9497874Z {
2023-01-11T21:41:23.9498035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9498118Z     {
2023-01-11T21:41:23.9498246Z         #pragma omp for 
2023-01-11T21:41:23.9498388Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.9498497Z         {
2023-01-11T21:41:23.9498602Z             {
2023-01-11T21:41:23.9498706Z                 {
2023-01-11T21:41:23.9498841Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9499006Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:23.9499184Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:23.9499332Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9499474Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9499603Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9499709Z                 }
2023-01-11T21:41:23.9499810Z             }
2023-01-11T21:41:23.9499898Z         }
2023-01-11T21:41:23.9500001Z     }
2023-01-11T21:41:23.9500142Z }
2023-01-11T21:41:23.9500278Z ''')
2023-01-11T21:41:23.9500286Z 
2023-01-11T21:41:23.9500294Z 
2023-01-11T21:41:23.9500443Z async_compile.wait(globals())
2023-01-11T21:41:23.9500564Z del async_compile
2023-01-11T21:41:23.9500572Z 
2023-01-11T21:41:23.9500689Z def call(args):
2023-01-11T21:41:23.9500790Z     arg0_1, = args
2023-01-11T21:41:23.9500905Z     args.clear()
2023-01-11T21:41:23.9501232Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9501574Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9501854Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9501966Z     del arg0_1
2023-01-11T21:41:23.9502095Z     return (buf0, buf1, )
2023-01-11T21:41:23.9502104Z 
2023-01-11T21:41:23.9502111Z 
2023-01-11T21:41:23.9502240Z if __name__ == "__main__":
2023-01-11T21:41:23.9502567Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9502861Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9503214Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9503395Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9503403Z 
2023-01-11T21:41:23.9503411Z 
2023-01-11T21:41:23.9503564Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9503677Z import torch
2023-01-11T21:41:23.9503796Z import random
2023-01-11T21:41:23.9503983Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9504191Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9504200Z 
2023-01-11T21:41:23.9504332Z aten = torch.ops.aten
2023-01-11T21:41:23.9504561Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9504719Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9504727Z 
2023-01-11T21:41:23.9504735Z 
2023-01-11T21:41:23.9504976Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9505340Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9505537Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.9505682Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9505847Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9505946Z {
2023-01-11T21:41:23.9506115Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9506223Z     {
2023-01-11T21:41:23.9506358Z         #pragma omp for 
2023-01-11T21:41:23.9506497Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.9506589Z         {
2023-01-11T21:41:23.9506691Z             {
2023-01-11T21:41:23.9506800Z                 {
2023-01-11T21:41:23.9506952Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9507131Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.9507302Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:23.9507478Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.9507622Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.9507763Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9507900Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.9508007Z                 }
2023-01-11T21:41:23.9508116Z             }
2023-01-11T21:41:23.9508216Z         }
2023-01-11T21:41:23.9508315Z     }
2023-01-11T21:41:23.9508399Z }
2023-01-11T21:41:23.9508533Z ''')
2023-01-11T21:41:23.9508541Z 
2023-01-11T21:41:23.9508549Z 
2023-01-11T21:41:23.9508704Z async_compile.wait(globals())
2023-01-11T21:41:23.9508827Z del async_compile
2023-01-11T21:41:23.9508835Z 
2023-01-11T21:41:23.9508954Z def call(args):
2023-01-11T21:41:23.9509071Z     arg0_1, = args
2023-01-11T21:41:23.9509189Z     args.clear()
2023-01-11T21:41:23.9509514Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9509847Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9510199Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9510320Z     del arg0_1
2023-01-11T21:41:23.9510455Z     return (buf0, buf1, )
2023-01-11T21:41:23.9510463Z 
2023-01-11T21:41:23.9510471Z 
2023-01-11T21:41:23.9510603Z if __name__ == "__main__":
2023-01-11T21:41:23.9510802Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9511011Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9511340Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.9511531Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9511539Z 
2023-01-11T21:41:23.9511546Z 
2023-01-11T21:41:23.9511702Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9511826Z import torch
2023-01-11T21:41:23.9511948Z import random
2023-01-11T21:41:23.9512152Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9512363Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9512420Z 
2023-01-11T21:41:23.9512548Z aten = torch.ops.aten
2023-01-11T21:41:23.9512765Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9512916Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9512925Z 
2023-01-11T21:41:23.9512932Z 
2023-01-11T21:41:23.9513162Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9513528Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9513788Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:23.9513952Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9514114Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9514211Z {
2023-01-11T21:41:23.9514369Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9514468Z     {
2023-01-11T21:41:23.9514592Z         #pragma omp for 
2023-01-11T21:41:23.9514733Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.9514841Z         {
2023-01-11T21:41:23.9514945Z             {
2023-01-11T21:41:23.9515038Z                 {
2023-01-11T21:41:23.9515190Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9515370Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.9515540Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:23.9515717Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.9515861Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.9515994Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9516133Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.9516226Z                 }
2023-01-11T21:41:23.9516331Z             }
2023-01-11T21:41:23.9516434Z         }
2023-01-11T21:41:23.9516537Z     }
2023-01-11T21:41:23.9516630Z }
2023-01-11T21:41:23.9516756Z ''')
2023-01-11T21:41:23.9516764Z 
2023-01-11T21:41:23.9516771Z 
2023-01-11T21:41:23.9516908Z async_compile.wait(globals())
2023-01-11T21:41:23.9517025Z del async_compile
2023-01-11T21:41:23.9517042Z 
2023-01-11T21:41:23.9517156Z def call(args):
2023-01-11T21:41:23.9517277Z     arg0_1, = args
2023-01-11T21:41:23.9517394Z     args.clear()
2023-01-11T21:41:23.9517744Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9518090Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9518369Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9518471Z     del arg0_1
2023-01-11T21:41:23.9518606Z     return (buf0, buf1, )
2023-01-11T21:41:23.9518614Z 
2023-01-11T21:41:23.9518622Z 
2023-01-11T21:41:23.9518750Z if __name__ == "__main__":
2023-01-11T21:41:23.9518950Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9519165Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9519506Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:23.9519745Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9519754Z 
2023-01-11T21:41:23.9520243Z [2023-01-11 21:36:01,293] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 348
2023-01-11T21:41:23.9521020Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9521219Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9521682Z [2023-01-11 21:36:01,310] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 349
2023-01-11T21:41:23.9522151Z [2023-01-11 21:36:02,916] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 349
2023-01-11T21:41:23.9522165Z 
2023-01-11T21:41:23.9522381Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9522501Z import torch
2023-01-11T21:41:23.9522621Z import random
2023-01-11T21:41:23.9522819Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9523034Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9523042Z 
2023-01-11T21:41:23.9523160Z aten = torch.ops.aten
2023-01-11T21:41:23.9523400Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9523554Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9523562Z 
2023-01-11T21:41:23.9523569Z 
2023-01-11T21:41:23.9523807Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9524162Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9524363Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9524531Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9524700Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9524785Z {
2023-01-11T21:41:23.9524956Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9525058Z     {
2023-01-11T21:41:23.9525194Z         #pragma omp for 
2023-01-11T21:41:23.9525334Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.9525431Z         {
2023-01-11T21:41:23.9525535Z             {
2023-01-11T21:41:23.9525635Z                 {
2023-01-11T21:41:23.9525792Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9525976Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.9526147Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:23.9526322Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.9526474Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.9526614Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9526735Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.9526845Z                 }
2023-01-11T21:41:23.9526950Z             }
2023-01-11T21:41:23.9527061Z         }
2023-01-11T21:41:23.9527162Z     }
2023-01-11T21:41:23.9527265Z }
2023-01-11T21:41:23.9527382Z ''')
2023-01-11T21:41:23.9527406Z 
2023-01-11T21:41:23.9527414Z 
2023-01-11T21:41:23.9527549Z async_compile.wait(globals())
2023-01-11T21:41:23.9527667Z del async_compile
2023-01-11T21:41:23.9527675Z 
2023-01-11T21:41:23.9527788Z def call(args):
2023-01-11T21:41:23.9527905Z     arg0_1, = args
2023-01-11T21:41:23.9528026Z     args.clear()
2023-01-11T21:41:23.9528368Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9528706Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9528971Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9529091Z     del arg0_1
2023-01-11T21:41:23.9529220Z     return (buf0, buf1, )
2023-01-11T21:41:23.9529229Z 
2023-01-11T21:41:23.9529236Z 
2023-01-11T21:41:23.9529362Z if __name__ == "__main__":
2023-01-11T21:41:23.9529613Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9529829Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9530169Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9530356Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9530366Z 
2023-01-11T21:41:23.9530373Z 
2023-01-11T21:41:23.9530525Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9530626Z import torch
2023-01-11T21:41:23.9530751Z import random
2023-01-11T21:41:23.9530946Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9531157Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9531166Z 
2023-01-11T21:41:23.9531294Z aten = torch.ops.aten
2023-01-11T21:41:23.9531527Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9531685Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9531692Z 
2023-01-11T21:41:23.9531702Z 
2023-01-11T21:41:23.9531968Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9532329Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9532532Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9532700Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9532865Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9532971Z {
2023-01-11T21:41:23.9533138Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9533227Z     {
2023-01-11T21:41:23.9533354Z         #pragma omp for 
2023-01-11T21:41:23.9533498Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:23.9533600Z         {
2023-01-11T21:41:23.9533711Z             {
2023-01-11T21:41:23.9533818Z                 {
2023-01-11T21:41:23.9533968Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9534132Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:23.9534305Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:23.9534484Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:23.9534635Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:23.9534777Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9534917Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.9535029Z                 }
2023-01-11T21:41:23.9535113Z             }
2023-01-11T21:41:23.9535215Z         }
2023-01-11T21:41:23.9535319Z     }
2023-01-11T21:41:23.9535420Z }
2023-01-11T21:41:23.9535561Z ''')
2023-01-11T21:41:23.9535570Z 
2023-01-11T21:41:23.9535577Z 
2023-01-11T21:41:23.9535730Z async_compile.wait(globals())
2023-01-11T21:41:23.9535854Z del async_compile
2023-01-11T21:41:23.9535862Z 
2023-01-11T21:41:23.9535965Z def call(args):
2023-01-11T21:41:23.9536084Z     arg0_1, = args
2023-01-11T21:41:23.9536206Z     args.clear()
2023-01-11T21:41:23.9536553Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9536896Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9537179Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9537296Z     del arg0_1
2023-01-11T21:41:23.9537426Z     return (buf0, buf1, )
2023-01-11T21:41:23.9537435Z 
2023-01-11T21:41:23.9537442Z 
2023-01-11T21:41:23.9537555Z if __name__ == "__main__":
2023-01-11T21:41:23.9537753Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9537954Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9538292Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9538475Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9538483Z 
2023-01-11T21:41:23.9538595Z ok (16.541s)
2023-01-11T21:41:23.9539429Z   test_log2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9539691Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9540158Z [2023-01-11 21:36:02,938] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 350
2023-01-11T21:41:23.9540625Z [2023-01-11 21:36:04,580] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 350
2023-01-11T21:41:23.9540649Z 
2023-01-11T21:41:23.9540796Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9540914Z import torch
2023-01-11T21:41:23.9541034Z import random
2023-01-11T21:41:23.9541238Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9541450Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9541457Z 
2023-01-11T21:41:23.9541657Z aten = torch.ops.aten
2023-01-11T21:41:23.9541891Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9542034Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9542042Z 
2023-01-11T21:41:23.9542065Z 
2023-01-11T21:41:23.9542278Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9542766Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9542967Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9543132Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9543296Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9543399Z {
2023-01-11T21:41:23.9543567Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9543655Z     {
2023-01-11T21:41:23.9543780Z         #pragma omp for 
2023-01-11T21:41:23.9543918Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9544025Z         {
2023-01-11T21:41:23.9544263Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9544404Z             auto tmp1 = tmp0.log();
2023-01-11T21:41:23.9544645Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1.4426950408889634));
2023-01-11T21:41:23.9544779Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9545011Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:23.9545154Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:23.9545295Z             auto tmp6 = tmp5.log();
2023-01-11T21:41:23.9545439Z             auto tmp7 = tmp6 * tmp2;
2023-01-11T21:41:23.9545665Z             auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:23.9545886Z             auto tmp9 = tmp7 - tmp8;
2023-01-11T21:41:23.9546028Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9546182Z             tmp9.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.9546286Z         }
2023-01-11T21:41:23.9546445Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9546595Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.9546701Z         {
2023-01-11T21:41:23.9546846Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9546978Z             auto tmp1 = std::log(tmp0);
2023-01-11T21:41:23.9547164Z             auto tmp2 = static_cast<float>(1.4426950408889634);
2023-01-11T21:41:23.9547309Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9547478Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:23.9547621Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:23.9547775Z             auto tmp6 = std::log(tmp5);
2023-01-11T21:41:23.9547917Z             auto tmp7 = tmp6 * tmp2;
2023-01-11T21:41:23.9548074Z             auto tmp8 = static_cast<float>(2);
2023-01-11T21:41:23.9548294Z             auto tmp9 = tmp7 - tmp8;
2023-01-11T21:41:23.9548426Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.9548561Z             out_ptr1[i0] = tmp9;
2023-01-11T21:41:23.9548674Z         }
2023-01-11T21:41:23.9548853Z     }
2023-01-11T21:41:23.9548964Z }
2023-01-11T21:41:23.9549084Z ''')
2023-01-11T21:41:23.9549093Z 
2023-01-11T21:41:23.9549116Z 
2023-01-11T21:41:23.9549258Z async_compile.wait(globals())
2023-01-11T21:41:23.9549380Z del async_compile
2023-01-11T21:41:23.9549388Z 
2023-01-11T21:41:23.9549506Z def call(args):
2023-01-11T21:41:23.9549618Z     arg0_1, = args
2023-01-11T21:41:23.9549738Z     args.clear()
2023-01-11T21:41:23.9550077Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9550420Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9550691Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9550807Z     del arg0_1
2023-01-11T21:41:23.9550940Z     return (buf0, buf1, )
2023-01-11T21:41:23.9550948Z 
2023-01-11T21:41:23.9550955Z 
2023-01-11T21:41:23.9551084Z if __name__ == "__main__":
2023-01-11T21:41:23.9551279Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9551564Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9551906Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9552089Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9552098Z 
2023-01-11T21:41:23.9552196Z ok (1.683s)
2023-01-11T21:41:23.9553037Z   test_log_fp64_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9553255Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9553791Z [2023-01-11 21:36:04,666] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 351
2023-01-11T21:41:23.9554283Z [2023-01-11 21:36:06,524] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 351
2023-01-11T21:41:23.9554292Z 
2023-01-11T21:41:23.9554447Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9554569Z import torch
2023-01-11T21:41:23.9554685Z import random
2023-01-11T21:41:23.9554885Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9555081Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9555090Z 
2023-01-11T21:41:23.9555215Z aten = torch.ops.aten
2023-01-11T21:41:23.9555452Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9555612Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9555620Z 
2023-01-11T21:41:23.9555627Z 
2023-01-11T21:41:23.9555859Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9556222Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9556445Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:23.9556622Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:23.9556767Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:23.9556869Z {
2023-01-11T21:41:23.9557039Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9557142Z     {
2023-01-11T21:41:23.9557267Z         #pragma omp for 
2023-01-11T21:41:23.9557406Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.9557512Z         {
2023-01-11T21:41:23.9557601Z             {
2023-01-11T21:41:23.9557708Z                 {
2023-01-11T21:41:23.9557862Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9558034Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:41:23.9558223Z                     auto tmp2 = static_cast<double>(1.4426950408889634);
2023-01-11T21:41:23.9558379Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:23.9558515Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9558640Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9558804Z                 }
2023-01-11T21:41:23.9558917Z             }
2023-01-11T21:41:23.9559024Z         }
2023-01-11T21:41:23.9559129Z     }
2023-01-11T21:41:23.9559225Z }
2023-01-11T21:41:23.9559348Z ''')
2023-01-11T21:41:23.9559356Z 
2023-01-11T21:41:23.9559378Z 
2023-01-11T21:41:23.9559516Z async_compile.wait(globals())
2023-01-11T21:41:23.9559636Z del async_compile
2023-01-11T21:41:23.9559644Z 
2023-01-11T21:41:23.9559768Z def call(args):
2023-01-11T21:41:23.9559889Z     arg0_1, = args
2023-01-11T21:41:23.9560008Z     args.clear()
2023-01-11T21:41:23.9560353Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9560703Z     buf1 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9560966Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9561085Z     del arg0_1
2023-01-11T21:41:23.9561215Z     return (buf0, buf1, )
2023-01-11T21:41:23.9561268Z 
2023-01-11T21:41:23.9561276Z 
2023-01-11T21:41:23.9561404Z if __name__ == "__main__":
2023-01-11T21:41:23.9561599Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9561808Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9562152Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:23.9562336Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9562343Z 
2023-01-11T21:41:23.9562441Z ok (1.919s)
2023-01-11T21:41:23.9563301Z   test_log_softmax_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9563516Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9563986Z [2023-01-11 21:36:06,573] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 352
2023-01-11T21:41:23.9563995Z 
2023-01-11T21:41:23.9564155Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9564273Z import torch
2023-01-11T21:41:23.9564393Z import random
2023-01-11T21:41:23.9564590Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9564803Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9564811Z 
2023-01-11T21:41:23.9564931Z aten = torch.ops.aten
2023-01-11T21:41:23.9565161Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9565318Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9565325Z 
2023-01-11T21:41:23.9565333Z 
2023-01-11T21:41:23.9565564Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9565930Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9566142Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9566317Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9566485Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9566636Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.9566791Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.9566943Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:23.9567099Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:23.9567255Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:23.9567419Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:23.9567578Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:23.9567723Z                        float* __restrict__ out_ptr8)
2023-01-11T21:41:23.9567828Z {
2023-01-11T21:41:23.9567997Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9568139Z     {
2023-01-11T21:41:23.9568273Z         #pragma omp for 
2023-01-11T21:41:23.9568415Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9568524Z         {
2023-01-11T21:41:23.9568607Z             {
2023-01-11T21:41:23.9569303Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:23.9569705Z                 float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9569908Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:23.9570054Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9570159Z                 {
2023-01-11T21:41:23.9570395Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9570644Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9570842Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9571030Z                     tmp3_vec = at::vec::maximum(tmp3_vec, tmp2);
2023-01-11T21:41:23.9571130Z                 }
2023-01-11T21:41:23.9571471Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp3_vec);
2023-01-11T21:41:23.9571657Z                 #pragma omp simd simdlen(4)  reduction(max:tmp3)
2023-01-11T21:41:23.9571792Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9571890Z                 {
2023-01-11T21:41:23.9572034Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.9572188Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:23.9572314Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9572463Z                     tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:41:23.9572565Z                 }
2023-01-11T21:41:23.9572687Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:23.9572788Z             }
2023-01-11T21:41:23.9572876Z         }
2023-01-11T21:41:23.9572972Z         #pragma omp for 
2023-01-11T21:41:23.9573095Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9573189Z         {
2023-01-11T21:41:23.9573283Z             {
2023-01-11T21:41:23.9573589Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.9573709Z                 float tmp6 = 0;
2023-01-11T21:41:23.9573896Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:23.9574546Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:23.9574886Z                 float tmp7 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9575056Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:23.9575193Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9575289Z                 {
2023-01-11T21:41:23.9575502Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9575718Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9575913Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:23.9576049Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9576258Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:23.9576385Z                     auto tmp5 = tmp4.exp();
2023-01-11T21:41:23.9576504Z                     tmp6_vec += tmp5;
2023-01-11T21:41:23.9576686Z                     tmp7_vec = at::vec::maximum(tmp7_vec, tmp1);
2023-01-11T21:41:23.9576778Z                 }
2023-01-11T21:41:23.9577088Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:23.9577513Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp7_vec);
2023-01-11T21:41:23.9577730Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(max:tmp7)
2023-01-11T21:41:23.9577862Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9577941Z                 {
2023-01-11T21:41:23.9578085Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.9578234Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:23.9578371Z                     auto tmp3 = out_ptr0[i0];
2023-01-11T21:41:23.9578508Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9578723Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:23.9578875Z                     auto tmp5 = std::exp(tmp4);
2023-01-11T21:41:23.9578988Z                     tmp6 += tmp5;
2023-01-11T21:41:23.9579119Z                     tmp7 = std::max(tmp7, tmp1);
2023-01-11T21:41:23.9579280Z                 }
2023-01-11T21:41:23.9579396Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:23.9579515Z                 out_ptr2[i0] = tmp7;
2023-01-11T21:41:23.9579606Z             }
2023-01-11T21:41:23.9579693Z         }
2023-01-11T21:41:23.9579801Z         #pragma omp for 
2023-01-11T21:41:23.9579923Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9580011Z         {
2023-01-11T21:41:23.9580101Z             {
2023-01-11T21:41:23.9580193Z                 {
2023-01-11T21:41:23.9580527Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9580656Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.9580735Z                     {
2023-01-11T21:41:23.9580829Z                         {
2023-01-11T21:41:23.9580966Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:23.9581112Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:23.9581211Z                         }
2023-01-11T21:41:23.9581306Z                     }
2023-01-11T21:41:23.9581420Z                     out_ptr3[i0] = tmp1;
2023-01-11T21:41:23.9581489Z                 }
2023-01-11T21:41:23.9581579Z             }
2023-01-11T21:41:23.9581659Z         }
2023-01-11T21:41:23.9581767Z         #pragma omp for 
2023-01-11T21:41:23.9581877Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9581957Z         {
2023-01-11T21:41:23.9582036Z             {
2023-01-11T21:41:23.9582108Z                 {
2023-01-11T21:41:23.9582215Z                     float tmp4 = 0;
2023-01-11T21:41:23.9582495Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.9582589Z                     {
2023-01-11T21:41:23.9582680Z                         {
2023-01-11T21:41:23.9582816Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:23.9582946Z                             auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:23.9583160Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.9583319Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:23.9583434Z                             tmp4 += tmp3;
2023-01-11T21:41:23.9583524Z                         }
2023-01-11T21:41:23.9583613Z                     }
2023-01-11T21:41:23.9583725Z                     out_ptr4[i0] = tmp4;
2023-01-11T21:41:23.9583818Z                 }
2023-01-11T21:41:23.9583892Z             }
2023-01-11T21:41:23.9583972Z         }
2023-01-11T21:41:23.9584077Z         #pragma omp for 
2023-01-11T21:41:23.9584192Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9584278Z         {
2023-01-11T21:41:23.9584363Z             {
2023-01-11T21:41:23.9584634Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.9584735Z                 float tmp4 = 0;
2023-01-11T21:41:23.9584910Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:23.9585040Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9585248Z                 {
2023-01-11T21:41:23.9585457Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9585640Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:23.9585858Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.9585990Z                     auto tmp3 = tmp2.exp();
2023-01-11T21:41:23.9586095Z                     tmp4_vec += tmp3;
2023-01-11T21:41:23.9586192Z                 }
2023-01-11T21:41:23.9586480Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:23.9586664Z                 #pragma omp simd simdlen(4)  reduction(+:tmp4)
2023-01-11T21:41:23.9586790Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9586883Z                 {
2023-01-11T21:41:23.9587021Z                     auto tmp0 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:23.9587225Z                     auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:23.9587439Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.9587578Z                     auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:23.9587689Z                     tmp4 += tmp3;
2023-01-11T21:41:23.9587779Z                 }
2023-01-11T21:41:23.9587891Z                 out_ptr5[i0] = tmp4;
2023-01-11T21:41:23.9587974Z             }
2023-01-11T21:41:23.9588045Z         }
2023-01-11T21:41:23.9588155Z         #pragma omp for 
2023-01-11T21:41:23.9588266Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9588360Z         {
2023-01-11T21:41:23.9588472Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9588560Z             {
2023-01-11T21:41:23.9588766Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9588957Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9589143Z                 auto tmp3 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:23.9589317Z                 auto tmp5 = at::vec::Vectorized<float>(out_ptr1[i0]);
2023-01-11T21:41:23.9589510Z                 auto tmp8 = at::vec::Vectorized<float>::loadu(out_ptr3 + 8*i1);
2023-01-11T21:41:23.9589706Z                 auto tmp10 = at::vec::Vectorized<float>::loadu(out_ptr4 + 8*i1);
2023-01-11T21:41:23.9589890Z                 auto tmp13 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:23.9590084Z                 auto tmp15 = at::vec::Vectorized<float>(out_ptr5[i0]);
2023-01-11T21:41:23.9590220Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9590432Z                 auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:23.9590537Z                 auto tmp6 = tmp5.log();
2023-01-11T21:41:23.9590729Z                 auto tmp7 = tmp4 - tmp6;
2023-01-11T21:41:23.9590927Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:23.9591069Z                 auto tmp11 = tmp10.log();
2023-01-11T21:41:23.9591294Z                 auto tmp12 = tmp9 - tmp11;
2023-01-11T21:41:23.9591508Z                 auto tmp14 = tmp1 - tmp13;
2023-01-11T21:41:23.9591639Z                 auto tmp16 = tmp15.log();
2023-01-11T21:41:23.9591827Z                 auto tmp17 = tmp14 - tmp16;
2023-01-11T21:41:23.9591982Z                 tmp7.store(out_ptr6 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9592139Z                 tmp12.store(out_ptr7 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9592303Z                 tmp17.store(out_ptr8 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9592409Z             }
2023-01-11T21:41:23.9592562Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.9592695Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9592782Z             {
2023-01-11T21:41:23.9592936Z                 auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.9593095Z                 auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:23.9593236Z                 auto tmp3 = out_ptr0[i0];
2023-01-11T21:41:23.9593391Z                 auto tmp5 = out_ptr1[i0];
2023-01-11T21:41:23.9593614Z                 auto tmp8 = out_ptr3[i1];
2023-01-11T21:41:23.9593823Z                 auto tmp10 = out_ptr4[i1];
2023-01-11T21:41:23.9593955Z                 auto tmp13 = out_ptr2[i0];
2023-01-11T21:41:23.9594091Z                 auto tmp15 = out_ptr5[i0];
2023-01-11T21:41:23.9594232Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9594458Z                 auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:23.9594614Z                 auto tmp6 = std::log(tmp5);
2023-01-11T21:41:23.9594820Z                 auto tmp7 = tmp4 - tmp6;
2023-01-11T21:41:23.9595024Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:23.9595159Z                 auto tmp11 = std::log(tmp10);
2023-01-11T21:41:23.9595367Z                 auto tmp12 = tmp9 - tmp11;
2023-01-11T21:41:23.9595582Z                 auto tmp14 = tmp1 - tmp13;
2023-01-11T21:41:23.9595731Z                 auto tmp16 = std::log(tmp15);
2023-01-11T21:41:23.9595943Z                 auto tmp17 = tmp14 - tmp16;
2023-01-11T21:41:23.9596160Z                 out_ptr6[i1 + (8*i0)] = tmp7;
2023-01-11T21:41:23.9596308Z                 out_ptr7[i1 + (8*i0)] = tmp12;
2023-01-11T21:41:23.9596452Z                 out_ptr8[i1 + (8*i0)] = tmp17;
2023-01-11T21:41:23.9596542Z             }
2023-01-11T21:41:23.9596645Z         }
2023-01-11T21:41:23.9596729Z     }
2023-01-11T21:41:23.9596826Z }
2023-01-11T21:41:23.9596954Z ''')
2023-01-11T21:41:23.9596964Z 
2023-01-11T21:41:23.9596970Z 
2023-01-11T21:41:23.9597115Z async_compile.wait(globals())
2023-01-11T21:41:23.9597237Z del async_compile
2023-01-11T21:41:23.9597246Z 
2023-01-11T21:41:23.9597357Z def call(args):
2023-01-11T21:41:23.9597484Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9597602Z     args.clear()
2023-01-11T21:41:23.9597944Z     buf0 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9598286Z     buf1 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9598626Z     buf6 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9598966Z     buf3 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9599279Z     buf4 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9599605Z     buf7 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9599942Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9600274Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9600606Z     buf8 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9601221Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:23.9601350Z     del arg0_1
2023-01-11T21:41:23.9601459Z     del arg1_1
2023-01-11T21:41:23.9601579Z     return (buf2, buf5, buf8, )
2023-01-11T21:41:23.9601603Z 
2023-01-11T21:41:23.9601610Z 
2023-01-11T21:41:23.9601717Z if __name__ == "__main__":
2023-01-11T21:41:23.9601914Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9602122Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9602460Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9602804Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9603006Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9603480Z [2023-01-11 21:36:08,277] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 352
2023-01-11T21:41:23.9603490Z 
2023-01-11T21:41:23.9603590Z ok (1.753s)
2023-01-11T21:41:23.9604434Z   test_logsumexp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9604759Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9605220Z [2023-01-11 21:36:08,328] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 353
2023-01-11T21:41:23.9605701Z [2023-01-11 21:36:10,028] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 353
2023-01-11T21:41:23.9605711Z 
2023-01-11T21:41:23.9605871Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9605993Z import torch
2023-01-11T21:41:23.9606115Z import random
2023-01-11T21:41:23.9606322Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9606593Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9606603Z 
2023-01-11T21:41:23.9606716Z aten = torch.ops.aten
2023-01-11T21:41:23.9606954Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9607111Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9607120Z 
2023-01-11T21:41:23.9607128Z 
2023-01-11T21:41:23.9607365Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9607730Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9607941Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.9608117Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.9608297Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9608446Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:23.9608610Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:23.9608709Z {
2023-01-11T21:41:23.9608863Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:23.9609004Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:23.9609172Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9609270Z     {
2023-01-11T21:41:23.9609385Z         #pragma omp for 
2023-01-11T21:41:23.9609523Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9609623Z         {
2023-01-11T21:41:23.9609734Z             {
2023-01-11T21:41:23.9610408Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:23.9610790Z                 float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9611003Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:23.9611151Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9611248Z                 {
2023-01-11T21:41:23.9611497Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9611700Z                     tmp1_vec = at::vec::maximum(tmp1_vec, tmp0);
2023-01-11T21:41:23.9611815Z                 }
2023-01-11T21:41:23.9612187Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp1_vec);
2023-01-11T21:41:23.9612392Z                 #pragma omp simd simdlen(4)  reduction(max:tmp1)
2023-01-11T21:41:23.9612536Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9612639Z                 {
2023-01-11T21:41:23.9612787Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.9612945Z                     tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:23.9613046Z                 }
2023-01-11T21:41:23.9613162Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:23.9613256Z             }
2023-01-11T21:41:23.9613355Z         }
2023-01-11T21:41:23.9613488Z         #pragma omp for 
2023-01-11T21:41:23.9613607Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9613771Z         {
2023-01-11T21:41:23.9613873Z             {
2023-01-11T21:41:23.9614199Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.9614338Z                 float tmp4 = 0;
2023-01-11T21:41:23.9614545Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:23.9614692Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9614776Z                 {
2023-01-11T21:41:23.9615021Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9615238Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:23.9615481Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.9615636Z                     auto tmp3 = tmp2.exp();
2023-01-11T21:41:23.9615774Z                     tmp4_vec += tmp3;
2023-01-11T21:41:23.9615881Z                 }
2023-01-11T21:41:23.9616266Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:23.9616456Z                 #pragma omp simd simdlen(4)  reduction(+:tmp4)
2023-01-11T21:41:23.9616599Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9616700Z                 {
2023-01-11T21:41:23.9616863Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.9617017Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:23.9617262Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.9617595Z                     auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:23.9617728Z                     tmp4 += tmp3;
2023-01-11T21:41:23.9617823Z                 }
2023-01-11T21:41:23.9617961Z                 out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.9618070Z             }
2023-01-11T21:41:23.9618174Z         }
2023-01-11T21:41:23.9618303Z         #pragma omp for 
2023-01-11T21:41:23.9618450Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9618545Z         {
2023-01-11T21:41:23.9618645Z             {
2023-01-11T21:41:23.9618744Z                 {
2023-01-11T21:41:23.9618896Z                     auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:23.9619047Z                     auto tmp2 = out_ptr0[i0];
2023-01-11T21:41:23.9619208Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:41:23.9619368Z                     auto tmp3 = std::abs(tmp2);
2023-01-11T21:41:23.9619562Z                     auto tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9619713Z                     auto tmp5 = tmp3 == tmp4;
2023-01-11T21:41:23.9619890Z                     auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:23.9620050Z                     auto tmp7 = tmp5 ? tmp6 : tmp2;
2023-01-11T21:41:23.9620201Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:41:23.9620341Z                     in_out_ptr0[i0] = tmp8;
2023-01-11T21:41:23.9620451Z                 }
2023-01-11T21:41:23.9620550Z             }
2023-01-11T21:41:23.9620649Z         }
2023-01-11T21:41:23.9620769Z         #pragma omp for 
2023-01-11T21:41:23.9620906Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9621015Z         {
2023-01-11T21:41:23.9621115Z             {
2023-01-11T21:41:23.9621217Z                 {
2023-01-11T21:41:23.9621589Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9621745Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.9621856Z                     {
2023-01-11T21:41:23.9621959Z                         {
2023-01-11T21:41:23.9622115Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:23.9622289Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:23.9622534Z                         }
2023-01-11T21:41:23.9622636Z                     }
2023-01-11T21:41:23.9622780Z                     out_ptr2[i0] = tmp1;
2023-01-11T21:41:23.9622883Z                 }
2023-01-11T21:41:23.9623087Z             }
2023-01-11T21:41:23.9623186Z         }
2023-01-11T21:41:23.9623312Z         #pragma omp for 
2023-01-11T21:41:23.9623441Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9623531Z         {
2023-01-11T21:41:23.9623634Z             {
2023-01-11T21:41:23.9623735Z                 {
2023-01-11T21:41:23.9623866Z                     float tmp4 = 0;
2023-01-11T21:41:23.9624023Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:23.9624137Z                     {
2023-01-11T21:41:23.9624252Z                         {
2023-01-11T21:41:23.9624409Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:23.9624574Z                             auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:23.9624830Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.9625002Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:23.9625140Z                             tmp4 += tmp3;
2023-01-11T21:41:23.9625255Z                         }
2023-01-11T21:41:23.9625421Z                     }
2023-01-11T21:41:23.9625552Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:23.9625655Z                 }
2023-01-11T21:41:23.9625758Z             }
2023-01-11T21:41:23.9625866Z         }
2023-01-11T21:41:23.9626001Z         #pragma omp for 
2023-01-11T21:41:23.9626133Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9626221Z         {
2023-01-11T21:41:23.9626322Z             {
2023-01-11T21:41:23.9626435Z                 {
2023-01-11T21:41:23.9626590Z                     auto tmp0 = out_ptr3[i0];
2023-01-11T21:41:23.9626743Z                     auto tmp2 = out_ptr2[i0];
2023-01-11T21:41:23.9626906Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:41:23.9627068Z                     auto tmp3 = std::abs(tmp2);
2023-01-11T21:41:23.9627285Z                     auto tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9627424Z                     auto tmp5 = tmp3 == tmp4;
2023-01-11T21:41:23.9627612Z                     auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:23.9627780Z                     auto tmp7 = tmp5 ? tmp6 : tmp2;
2023-01-11T21:41:23.9627928Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:41:23.9628104Z                     auto tmp9 = static_cast<float>(2);
2023-01-11T21:41:23.9628347Z                     auto tmp10 = tmp8 - tmp9;
2023-01-11T21:41:23.9628492Z                     in_out_ptr1[i0] = tmp10;
2023-01-11T21:41:23.9628583Z                 }
2023-01-11T21:41:23.9628687Z             }
2023-01-11T21:41:23.9628797Z         }
2023-01-11T21:41:23.9628903Z     }
2023-01-11T21:41:23.9629002Z }
2023-01-11T21:41:23.9629132Z ''')
2023-01-11T21:41:23.9629142Z 
2023-01-11T21:41:23.9629149Z 
2023-01-11T21:41:23.9629306Z async_compile.wait(globals())
2023-01-11T21:41:23.9629412Z del async_compile
2023-01-11T21:41:23.9629420Z 
2023-01-11T21:41:23.9629540Z def call(args):
2023-01-11T21:41:23.9629659Z     arg0_1, = args
2023-01-11T21:41:23.9629777Z     args.clear()
2023-01-11T21:41:23.9630116Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9630441Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9630618Z     buf2 = as_strided(buf0, (8, ), (1, )); del buf0  # reuse
2023-01-11T21:41:23.9630936Z     buf3 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9631261Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9631442Z     buf5 = as_strided(buf3, (8, ), (1, )); del buf3  # reuse
2023-01-11T21:41:23.9631809Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:23.9631924Z     del arg0_1
2023-01-11T21:41:23.9632056Z     return (buf2, buf5, )
2023-01-11T21:41:23.9632063Z 
2023-01-11T21:41:23.9632070Z 
2023-01-11T21:41:23.9632201Z if __name__ == "__main__":
2023-01-11T21:41:23.9632403Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9632657Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9633003Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9633189Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9633197Z 
2023-01-11T21:41:23.9633312Z ok (1.776s)
2023-01-11T21:41:23.9634222Z   test_long_tensor_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9634441Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9634915Z [2023-01-11 21:36:10,079] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 354
2023-01-11T21:41:23.9635450Z [2023-01-11 21:36:11,749] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 354
2023-01-11T21:41:23.9635462Z 
2023-01-11T21:41:23.9635616Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9635738Z import torch
2023-01-11T21:41:23.9635844Z import random
2023-01-11T21:41:23.9636046Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9636253Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9636261Z 
2023-01-11T21:41:23.9636390Z aten = torch.ops.aten
2023-01-11T21:41:23.9636626Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9636785Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9636793Z 
2023-01-11T21:41:23.9636799Z 
2023-01-11T21:41:23.9637029Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9637395Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9637584Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9637752Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:23.9637917Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.9638017Z {
2023-01-11T21:41:23.9638184Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9638290Z     {
2023-01-11T21:41:23.9638416Z         #pragma omp for 
2023-01-11T21:41:23.9638535Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:23.9638634Z         {
2023-01-11T21:41:23.9638744Z             {
2023-01-11T21:41:23.9638852Z                 {
2023-01-11T21:41:23.9639003Z                     auto tmp1 = in_ptr0[i0];
2023-01-11T21:41:23.9639178Z                     auto tmp0 = static_cast<long>(294);
2023-01-11T21:41:23.9639411Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:23.9639570Z                     auto tmp3 = static_cast<long>(295);
2023-01-11T21:41:23.9639723Z                     auto tmp4 = tmp3 + tmp1;
2023-01-11T21:41:23.9639861Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9640009Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:23.9640115Z                 }
2023-01-11T21:41:23.9640222Z             }
2023-01-11T21:41:23.9640302Z         }
2023-01-11T21:41:23.9640408Z     }
2023-01-11T21:41:23.9640508Z }
2023-01-11T21:41:23.9640642Z ''')
2023-01-11T21:41:23.9640651Z 
2023-01-11T21:41:23.9640658Z 
2023-01-11T21:41:23.9640812Z async_compile.wait(globals())
2023-01-11T21:41:23.9640934Z del async_compile
2023-01-11T21:41:23.9640942Z 
2023-01-11T21:41:23.9641061Z def call(args):
2023-01-11T21:41:23.9641173Z     arg0_1, = args
2023-01-11T21:41:23.9641279Z     args.clear()
2023-01-11T21:41:23.9641613Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9641938Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9642221Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9642384Z     del arg0_1
2023-01-11T21:41:23.9642520Z     return (buf0, buf1, )
2023-01-11T21:41:23.9642528Z 
2023-01-11T21:41:23.9642535Z 
2023-01-11T21:41:23.9642666Z if __name__ == "__main__":
2023-01-11T21:41:23.9642843Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9643057Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9643397Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9643583Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9643590Z 
2023-01-11T21:41:23.9643699Z ok (1.697s)
2023-01-11T21:41:23.9644300Z   test_lowmem_dropout1_cpu (__main__.CpuTests) ... [2023-01-11 21:36:11,779] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 355
2023-01-11T21:41:23.9644723Z [2023-01-11 21:36:11,786] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 355
2023-01-11T21:41:23.9645168Z [2023-01-11 21:36:11,788] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 355
2023-01-11T21:41:23.9645591Z [2023-01-11 21:36:11,796] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 355
2023-01-11T21:41:23.9645976Z [2023-01-11 21:36:11,951] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 356
2023-01-11T21:41:23.9646368Z [2023-01-11 21:36:11,952] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.9646773Z [2023-01-11 21:36:11,961] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 356
2023-01-11T21:41:23.9647164Z [2023-01-11 21:36:11,964] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 356
2023-01-11T21:41:23.9647172Z 
2023-01-11T21:41:23.9647307Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9647413Z import torch
2023-01-11T21:41:23.9647516Z import random
2023-01-11T21:41:23.9647682Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9647853Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9647873Z 
2023-01-11T21:41:23.9647971Z aten = torch.ops.aten
2023-01-11T21:41:23.9648167Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9648303Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9648311Z 
2023-01-11T21:41:23.9648317Z 
2023-01-11T21:41:23.9648513Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9648811Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9648981Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9649131Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9649261Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9649348Z {
2023-01-11T21:41:23.9649491Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9649582Z     {
2023-01-11T21:41:23.9649691Z         #pragma omp for 
2023-01-11T21:41:23.9649813Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:23.9649904Z         {
2023-01-11T21:41:23.9650098Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9650284Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.9650412Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.9650551Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9650641Z         }
2023-01-11T21:41:23.9650780Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9650916Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:23.9650992Z         {
2023-01-11T21:41:23.9651112Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9651233Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.9651346Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.9651459Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9651545Z         }
2023-01-11T21:41:23.9651633Z     }
2023-01-11T21:41:23.9651777Z }
2023-01-11T21:41:23.9651910Z ''')
2023-01-11T21:41:23.9651918Z 
2023-01-11T21:41:23.9651924Z 
2023-01-11T21:41:23.9652054Z async_compile.wait(globals())
2023-01-11T21:41:23.9652168Z del async_compile
2023-01-11T21:41:23.9652174Z 
2023-01-11T21:41:23.9652275Z def call(args):
2023-01-11T21:41:23.9652403Z     primals_1, primals_2 = args
2023-01-11T21:41:23.9652506Z     args.clear()
2023-01-11T21:41:23.9652803Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9653049Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9653148Z     del primals_2
2023-01-11T21:41:23.9653275Z     return (buf0, primals_1, )
2023-01-11T21:41:23.9653282Z 
2023-01-11T21:41:23.9653288Z 
2023-01-11T21:41:23.9653399Z if __name__ == "__main__":
2023-01-11T21:41:23.9653573Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9653751Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9654128Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9654439Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9654606Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:23.9654613Z 
2023-01-11T21:41:23.9654619Z 
2023-01-11T21:41:23.9654751Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9654846Z import torch
2023-01-11T21:41:23.9654941Z import random
2023-01-11T21:41:23.9655106Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9655274Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9655282Z 
2023-01-11T21:41:23.9655392Z aten = torch.ops.aten
2023-01-11T21:41:23.9655570Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9655698Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9655706Z 
2023-01-11T21:41:23.9655711Z 
2023-01-11T21:41:23.9655914Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9656225Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9656401Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9656553Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9656694Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9656782Z {
2023-01-11T21:41:23.9656906Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9656993Z     {
2023-01-11T21:41:23.9657103Z         #pragma omp for 
2023-01-11T21:41:23.9657222Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:23.9657314Z         {
2023-01-11T21:41:23.9657507Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9657701Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.9657826Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.9657942Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9658037Z         }
2023-01-11T21:41:23.9658170Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9658303Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:23.9658394Z         {
2023-01-11T21:41:23.9658514Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9658617Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.9658732Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:23.9658845Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9658937Z         }
2023-01-11T21:41:23.9659023Z     }
2023-01-11T21:41:23.9659108Z }
2023-01-11T21:41:23.9659229Z ''')
2023-01-11T21:41:23.9659236Z 
2023-01-11T21:41:23.9659243Z 
2023-01-11T21:41:23.9659355Z async_compile.wait(globals())
2023-01-11T21:41:23.9659460Z del async_compile
2023-01-11T21:41:23.9659468Z 
2023-01-11T21:41:23.9659570Z def call(args):
2023-01-11T21:41:23.9659695Z     primals_1, tangents_1 = args
2023-01-11T21:41:23.9659802Z     args.clear()
2023-01-11T21:41:23.9660187Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9660452Z     kernel_cpp_0(c_void_p(tangents_1.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9660556Z     del primals_1
2023-01-11T21:41:23.9660640Z     del tangents_1
2023-01-11T21:41:23.9660747Z     return (None, buf0, )
2023-01-11T21:41:23.9660754Z 
2023-01-11T21:41:23.9660760Z 
2023-01-11T21:41:23.9660863Z if __name__ == "__main__":
2023-01-11T21:41:23.9661037Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9661232Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9661537Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9661861Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9662066Z     print_performance(lambda: call([primals_1, tangents_1]))
2023-01-11T21:41:23.9662074Z 
2023-01-11T21:41:23.9662081Z 
2023-01-11T21:41:23.9662271Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9671514Z import torch
2023-01-11T21:41:23.9671637Z import random
2023-01-11T21:41:23.9671812Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9671991Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9671999Z 
2023-01-11T21:41:23.9672115Z aten = torch.ops.aten
2023-01-11T21:41:23.9672313Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9672432Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9672678Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.9672687Z 
2023-01-11T21:41:23.9672693Z 
2023-01-11T21:41:23.9672929Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9673230Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9673399Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:23.9673559Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9673708Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.9673925Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9674020Z {
2023-01-11T21:41:23.9674150Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9674235Z     {
2023-01-11T21:41:23.9674346Z         #pragma omp for 
2023-01-11T21:41:23.9674466Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:23.9674553Z         {
2023-01-11T21:41:23.9674639Z             {
2023-01-11T21:41:23.9674714Z                 {
2023-01-11T21:41:23.9674834Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.9674966Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.9675094Z                     auto tmp7 = in_ptr2[i0];
2023-01-11T21:41:23.9675239Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.9675440Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.9675599Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:23.9675714Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.9675865Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.9675989Z                     auto tmp8 = tmp6 * tmp7;
2023-01-11T21:41:23.9676115Z                     auto tmp9 = tmp5 * tmp8;
2023-01-11T21:41:23.9676280Z                     auto tmp10 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:23.9676406Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:23.9676524Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:23.9676625Z                 }
2023-01-11T21:41:23.9676722Z             }
2023-01-11T21:41:23.9676819Z         }
2023-01-11T21:41:23.9676925Z     }
2023-01-11T21:41:23.9677030Z }
2023-01-11T21:41:23.9677161Z ''')
2023-01-11T21:41:23.9677168Z 
2023-01-11T21:41:23.9677174Z 
2023-01-11T21:41:23.9677307Z async_compile.wait(globals())
2023-01-11T21:41:23.9677538Z del async_compile
2023-01-11T21:41:23.9677549Z 
2023-01-11T21:41:23.9677629Z def call(args):
2023-01-11T21:41:23.9677755Z     primals_1, primals_2 = args
2023-01-11T21:41:23.9677852Z     args.clear()
2023-01-11T21:41:23.9678048Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.9678418Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9678780Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9678905Z     del primals_2
2023-01-11T21:41:23.9679088Z     return (buf0, primals_1, seed_cpu_None.clone(), )
2023-01-11T21:41:23.9679112Z 
2023-01-11T21:41:23.9679120Z 
2023-01-11T21:41:23.9679231Z if __name__ == "__main__":
2023-01-11T21:41:23.9679432Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9679649Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9680089Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9680472Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9680842Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9681069Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:23.9681077Z 
2023-01-11T21:41:23.9681586Z [2023-01-11 21:36:11,974] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 356
2023-01-11T21:41:23.9681595Z 
2023-01-11T21:41:23.9681745Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9681863Z import torch
2023-01-11T21:41:23.9681990Z import random
2023-01-11T21:41:23.9682196Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9682416Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9682424Z 
2023-01-11T21:41:23.9682556Z aten = torch.ops.aten
2023-01-11T21:41:23.9682798Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9682947Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9682971Z 
2023-01-11T21:41:23.9682978Z 
2023-01-11T21:41:23.9683205Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9683587Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9683797Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9683982Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9684164Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:23.9684338Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9684438Z {
2023-01-11T21:41:23.9684599Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9684700Z     {
2023-01-11T21:41:23.9684836Z         #pragma omp for 
2023-01-11T21:41:23.9684981Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:23.9685091Z         {
2023-01-11T21:41:23.9685211Z             {
2023-01-11T21:41:23.9685322Z                 {
2023-01-11T21:41:23.9685465Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:23.9685594Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.9685723Z                     auto tmp10 = in_ptr2[i0];
2023-01-11T21:41:23.9685864Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.9686061Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.9686211Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:23.9686340Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.9686479Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.9686612Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.9686778Z                     auto tmp8 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:23.9686903Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.9687035Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:23.9687207Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:23.9687299Z                 }
2023-01-11T21:41:23.9687369Z             }
2023-01-11T21:41:23.9687460Z         }
2023-01-11T21:41:23.9687548Z     }
2023-01-11T21:41:23.9687636Z }
2023-01-11T21:41:23.9687757Z ''')
2023-01-11T21:41:23.9687766Z 
2023-01-11T21:41:23.9687772Z 
2023-01-11T21:41:23.9687905Z async_compile.wait(globals())
2023-01-11T21:41:23.9688011Z del async_compile
2023-01-11T21:41:23.9688019Z 
2023-01-11T21:41:23.9688122Z def call(args):
2023-01-11T21:41:23.9688266Z     primals_1, philox_seed_like, tangents_1 = args
2023-01-11T21:41:23.9688368Z     args.clear()
2023-01-11T21:41:23.9688667Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9689022Z     kernel_cpp_0(c_void_p(philox_seed_like.data_ptr()), c_void_p(tangents_1.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9689141Z     del philox_seed_like
2023-01-11T21:41:23.9689293Z     del primals_1
2023-01-11T21:41:23.9689392Z     del tangents_1
2023-01-11T21:41:23.9689483Z     return (None, buf0, )
2023-01-11T21:41:23.9689490Z 
2023-01-11T21:41:23.9689497Z 
2023-01-11T21:41:23.9689606Z if __name__ == "__main__":
2023-01-11T21:41:23.9689770Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9689946Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9690255Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9690561Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9690865Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9691080Z     print_performance(lambda: call([primals_1, philox_seed_like, tangents_1]))
2023-01-11T21:41:23.9691086Z 
2023-01-11T21:41:23.9691165Z ok (0.230s)
2023-01-11T21:41:23.9691691Z   test_lowmem_dropout2_cpu (__main__.CpuTests) ... [2023-01-11 21:36:12,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 357
2023-01-11T21:41:23.9692111Z [2023-01-11 21:36:12,222] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:23.9692552Z [2023-01-11 21:36:13,866] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 357
2023-01-11T21:41:23.9692938Z [2023-01-11 21:36:13,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 357
2023-01-11T21:41:23.9693330Z [2023-01-11 21:36:15,598] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 357
2023-01-11T21:41:23.9693338Z 
2023-01-11T21:41:23.9693463Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9693555Z import torch
2023-01-11T21:41:23.9693653Z import random
2023-01-11T21:41:23.9693803Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9693978Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9693989Z 
2023-01-11T21:41:23.9694102Z aten = torch.ops.aten
2023-01-11T21:41:23.9694293Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9694423Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9694656Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:23.9694664Z 
2023-01-11T21:41:23.9694671Z 
2023-01-11T21:41:23.9694871Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9695188Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9695375Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.9695516Z                        const long* __restrict__ seed0)
2023-01-11T21:41:23.9695600Z {
2023-01-11T21:41:23.9695735Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9695821Z     {
2023-01-11T21:41:23.9695930Z         #pragma omp for 
2023-01-11T21:41:23.9696048Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.9696200Z         {
2023-01-11T21:41:23.9696285Z             {
2023-01-11T21:41:23.9696378Z                 {
2023-01-11T21:41:23.9696498Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.9696634Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:41:23.9696775Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.9696969Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.9697103Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:23.9697226Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.9697373Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.9697499Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.9697645Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:23.9697769Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.9697944Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.9698025Z                 }
2023-01-11T21:41:23.9698112Z             }
2023-01-11T21:41:23.9698201Z         }
2023-01-11T21:41:23.9698288Z     }
2023-01-11T21:41:23.9698370Z }
2023-01-11T21:41:23.9698490Z ''')
2023-01-11T21:41:23.9698497Z 
2023-01-11T21:41:23.9698503Z 
2023-01-11T21:41:23.9698698Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.9698974Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9699138Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.9699278Z                        const long* __restrict__ seed0)
2023-01-11T21:41:23.9699359Z {
2023-01-11T21:41:23.9699495Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9699581Z     {
2023-01-11T21:41:23.9699682Z         #pragma omp for 
2023-01-11T21:41:23.9699782Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.9699863Z         {
2023-01-11T21:41:23.9699950Z             {
2023-01-11T21:41:23.9700047Z                 {
2023-01-11T21:41:23.9700163Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:23.9700293Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:41:23.9700440Z                     auto tmp1 = static_cast<int>(256 + i0);
2023-01-11T21:41:23.9700620Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.9700766Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:23.9700890Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.9701032Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.9701158Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.9701298Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:23.9701417Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.9701523Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.9701606Z                 }
2023-01-11T21:41:23.9701701Z             }
2023-01-11T21:41:23.9701790Z         }
2023-01-11T21:41:23.9701874Z     }
2023-01-11T21:41:23.9701957Z }
2023-01-11T21:41:23.9702073Z ''')
2023-01-11T21:41:23.9702081Z 
2023-01-11T21:41:23.9702086Z 
2023-01-11T21:41:23.9702199Z async_compile.wait(globals())
2023-01-11T21:41:23.9702298Z del async_compile
2023-01-11T21:41:23.9702305Z 
2023-01-11T21:41:23.9702546Z def call(args):
2023-01-11T21:41:23.9702694Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:23.9702794Z     args.clear()
2023-01-11T21:41:23.9702983Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:23.9703286Z     buf0 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9703472Z     aten.mm.out(primals_3, as_strided(primals_1, (32, 32), (1, 32)), out=buf0)
2023-01-11T21:41:23.9703560Z     del primals_1
2023-01-11T21:41:23.9703677Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:23.9703884Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(seed_cpu_None.data_ptr()))
2023-01-11T21:41:23.9704273Z     buf2 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9704445Z     aten.mm.out(buf1, as_strided(primals_2, (32, 32), (1, 32)), out=buf2)
2023-01-11T21:41:23.9704560Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:23.9704762Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(seed_cpu_None.data_ptr()))
2023-01-11T21:41:23.9704970Z     return (buf3, primals_3, seed_cpu_None.clone(), buf1, as_strided(primals_2, (32, 32), (32, 1)), )
2023-01-11T21:41:23.9704994Z 
2023-01-11T21:41:23.9705000Z 
2023-01-11T21:41:23.9705086Z if __name__ == "__main__":
2023-01-11T21:41:23.9705242Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9705409Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9705692Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9705988Z     primals_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9706361Z     primals_2 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9706654Z     primals_3 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9706848Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:23.9706855Z 
2023-01-11T21:41:23.9706861Z 
2023-01-11T21:41:23.9706976Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9707068Z import torch
2023-01-11T21:41:23.9707163Z import random
2023-01-11T21:41:23.9707321Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9707482Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9707489Z 
2023-01-11T21:41:23.9707594Z aten = torch.ops.aten
2023-01-11T21:41:23.9707772Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9707880Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9707900Z 
2023-01-11T21:41:23.9707906Z 
2023-01-11T21:41:23.9708086Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9708372Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9708538Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9708685Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9708826Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9708912Z {
2023-01-11T21:41:23.9709056Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9709129Z     {
2023-01-11T21:41:23.9709233Z         #pragma omp for 
2023-01-11T21:41:23.9709348Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.9709432Z         {
2023-01-11T21:41:23.9709520Z             {
2023-01-11T21:41:23.9709611Z                 {
2023-01-11T21:41:23.9709738Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:23.9709855Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:23.9710007Z                     auto tmp1 = static_cast<int>(256 + i0);
2023-01-11T21:41:23.9710221Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.9710370Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:23.9710498Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.9710651Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.9710780Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.9710915Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:23.9711040Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.9711157Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.9711247Z                 }
2023-01-11T21:41:23.9711336Z             }
2023-01-11T21:41:23.9711422Z         }
2023-01-11T21:41:23.9711506Z     }
2023-01-11T21:41:23.9711574Z }
2023-01-11T21:41:23.9711696Z ''')
2023-01-11T21:41:23.9711704Z 
2023-01-11T21:41:23.9711712Z 
2023-01-11T21:41:23.9711915Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:23.9712288Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9712454Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.9712602Z                        const long* __restrict__ in_ptr0)
2023-01-11T21:41:23.9712686Z {
2023-01-11T21:41:23.9712810Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9712895Z     {
2023-01-11T21:41:23.9713002Z         #pragma omp for 
2023-01-11T21:41:23.9713113Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:23.9713199Z         {
2023-01-11T21:41:23.9713286Z             {
2023-01-11T21:41:23.9713376Z                 {
2023-01-11T21:41:23.9713489Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:23.9713621Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:41:23.9713840Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:23.9714039Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:23.9714245Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:23.9714375Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:23.9714529Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:23.9714643Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:23.9714793Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:23.9714918Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:23.9715045Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:23.9715135Z                 }
2023-01-11T21:41:23.9715224Z             }
2023-01-11T21:41:23.9715312Z         }
2023-01-11T21:41:23.9715383Z     }
2023-01-11T21:41:23.9715463Z }
2023-01-11T21:41:23.9715590Z ''')
2023-01-11T21:41:23.9715598Z 
2023-01-11T21:41:23.9715605Z 
2023-01-11T21:41:23.9715740Z async_compile.wait(globals())
2023-01-11T21:41:23.9715845Z del async_compile
2023-01-11T21:41:23.9715852Z 
2023-01-11T21:41:23.9715960Z def call(args):
2023-01-11T21:41:23.9716159Z     primals_3, philox_seed_like, mul_1, permute_4, tangents_1 = args
2023-01-11T21:41:23.9716260Z     args.clear()
2023-01-11T21:41:23.9716550Z     buf0 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9716826Z     kernel_cpp_0(c_void_p(philox_seed_like.data_ptr()), c_void_p(tangents_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9716935Z     del tangents_1
2023-01-11T21:41:23.9717238Z     buf1 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9717409Z     aten.mm.out(as_strided(buf0, (32, 8), (1, 32)), mul_1, out=buf1)
2023-01-11T21:41:23.9717502Z     del mul_1
2023-01-11T21:41:23.9717793Z     buf2 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9717925Z     aten.mm.out(buf0, permute_4, out=buf2)
2023-01-11T21:41:23.9718015Z     del buf0
2023-01-11T21:41:23.9718119Z     del permute_4
2023-01-11T21:41:23.9718240Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:23.9718462Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(philox_seed_like.data_ptr()))
2023-01-11T21:41:23.9718578Z     del philox_seed_like
2023-01-11T21:41:23.9718883Z     buf4 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9719057Z     aten.mm.out(as_strided(buf3, (32, 8), (1, 32)), primals_3, out=buf4)
2023-01-11T21:41:23.9719149Z     del buf3
2023-01-11T21:41:23.9719251Z     del primals_3
2023-01-11T21:41:23.9719444Z     return (as_strided(buf4, (32, 32), (32, 1)), as_strided(buf1, (32, 32), (32, 1)), None, )
2023-01-11T21:41:23.9719451Z 
2023-01-11T21:41:23.9719456Z 
2023-01-11T21:41:23.9719565Z if __name__ == "__main__":
2023-01-11T21:41:23.9719742Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9719938Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9720272Z     primals_3 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9720671Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9720986Z     mul_1 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9721320Z     permute_4 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9721643Z     tangents_1 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9721912Z     print_performance(lambda: call([primals_3, philox_seed_like, mul_1, permute_4, tangents_1]))
2023-01-11T21:41:23.9721921Z 
2023-01-11T21:41:23.9722020Z ok (3.617s)
2023-01-11T21:41:23.9722802Z   test_masked_fill_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9723068Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9723504Z [2023-01-11 21:36:15,637] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 358
2023-01-11T21:41:23.9723945Z [2023-01-11 21:36:17,255] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 358
2023-01-11T21:41:23.9723954Z 
2023-01-11T21:41:23.9724084Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9724189Z import torch
2023-01-11T21:41:23.9724296Z import random
2023-01-11T21:41:23.9724478Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9724669Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9724677Z 
2023-01-11T21:41:23.9724798Z aten = torch.ops.aten
2023-01-11T21:41:23.9725011Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9725137Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9725158Z 
2023-01-11T21:41:23.9725169Z 
2023-01-11T21:41:23.9725372Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9725699Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9725882Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.9726042Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9726189Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9726336Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9726423Z {
2023-01-11T21:41:23.9726558Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9726649Z     {
2023-01-11T21:41:23.9726766Z         #pragma omp for 
2023-01-11T21:41:23.9726889Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.9726984Z         {
2023-01-11T21:41:23.9727103Z             #pragma GCC ivdep
2023-01-11T21:41:23.9727228Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:23.9727305Z             {
2023-01-11T21:41:23.9727405Z                 {
2023-01-11T21:41:23.9727502Z                     {
2023-01-11T21:41:23.9727647Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.9727802Z                         auto tmp2 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:23.9728090Z                         auto tmp1 = static_cast<float>(-10000.0);
2023-01-11T21:41:23.9728246Z                         auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:23.9728395Z                         auto tmp4 = static_cast<float>(2);
2023-01-11T21:41:23.9728541Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:23.9728679Z                         auto tmp6 = tmp0 == 0;
2023-01-11T21:41:23.9728851Z                         auto tmp7 = static_cast<float>(667.0);
2023-01-11T21:41:23.9729018Z                         auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:23.9729158Z                         auto tmp9 = tmp2 / tmp8;
2023-01-11T21:41:23.9729313Z                         auto tmp10 = tmp6 ? tmp7 : tmp9;
2023-01-11T21:41:23.9729525Z                         out_ptr0[i1 + (16*i0)] = tmp5;
2023-01-11T21:41:23.9729671Z                         out_ptr1[i1 + (16*i0)] = tmp10;
2023-01-11T21:41:23.9729768Z                     }
2023-01-11T21:41:23.9729866Z                 }
2023-01-11T21:41:23.9729963Z             }
2023-01-11T21:41:23.9730056Z         }
2023-01-11T21:41:23.9730146Z     }
2023-01-11T21:41:23.9730223Z }
2023-01-11T21:41:23.9730351Z ''')
2023-01-11T21:41:23.9730360Z 
2023-01-11T21:41:23.9730367Z 
2023-01-11T21:41:23.9730510Z async_compile.wait(globals())
2023-01-11T21:41:23.9730620Z del async_compile
2023-01-11T21:41:23.9730628Z 
2023-01-11T21:41:23.9730735Z def call(args):
2023-01-11T21:41:23.9730848Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9730956Z     args.clear()
2023-01-11T21:41:23.9731267Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9731587Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9731958Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9732064Z     del arg0_1
2023-01-11T21:41:23.9732167Z     del arg1_1
2023-01-11T21:41:23.9732285Z     return (buf0, buf1, )
2023-01-11T21:41:23.9732293Z 
2023-01-11T21:41:23.9732299Z 
2023-01-11T21:41:23.9732415Z if __name__ == "__main__":
2023-01-11T21:41:23.9732596Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9732774Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9733090Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.9733413Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9733593Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9733602Z 
2023-01-11T21:41:23.9733701Z ok (1.658s)
2023-01-11T21:41:23.9734281Z   test_masked_fill_promotion_cpu (__main__.CpuTests) ... [2023-01-11 21:36:17,280] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 359
2023-01-11T21:41:23.9734719Z [2023-01-11 21:36:18,807] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 359
2023-01-11T21:41:23.9735135Z [2023-01-11 21:36:18,830] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 360
2023-01-11T21:41:23.9735569Z [2023-01-11 21:36:20,397] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 360
2023-01-11T21:41:23.9735576Z 
2023-01-11T21:41:23.9735703Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9735808Z import torch
2023-01-11T21:41:23.9735909Z import random
2023-01-11T21:41:23.9736090Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9736281Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9736289Z 
2023-01-11T21:41:23.9736410Z aten = torch.ops.aten
2023-01-11T21:41:23.9736628Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9736774Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9736782Z 
2023-01-11T21:41:23.9736789Z 
2023-01-11T21:41:23.9736994Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9737329Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9737515Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.9737680Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9737834Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9737926Z {
2023-01-11T21:41:23.9738078Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9738153Z     {
2023-01-11T21:41:23.9738269Z         #pragma omp for 
2023-01-11T21:41:23.9738396Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.9738492Z         {
2023-01-11T21:41:23.9738618Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:23.9738717Z             {
2023-01-11T21:41:23.9738965Z                 float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:23.9739145Z                 flag_to_float(in_ptr0 + 8*i1, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:23.9739384Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:23.9739607Z                 auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (16*i0));
2023-01-11T21:41:23.9739824Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3.5));
2023-01-11T21:41:23.9740027Z                 auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:41:23.9740187Z                 tmp3.store(out_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:23.9740281Z             }
2023-01-11T21:41:23.9740425Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.9740542Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:23.9740639Z             {
2023-01-11T21:41:23.9740773Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.9741001Z                 auto tmp2 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:23.9741169Z                 auto tmp1 = static_cast<float>(3.5);
2023-01-11T21:41:23.9741317Z                 auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:23.9741455Z                 out_ptr0[i1 + (16*i0)] = tmp3;
2023-01-11T21:41:23.9741537Z             }
2023-01-11T21:41:23.9741629Z         }
2023-01-11T21:41:23.9741725Z     }
2023-01-11T21:41:23.9741818Z }
2023-01-11T21:41:23.9741955Z ''')
2023-01-11T21:41:23.9741964Z 
2023-01-11T21:41:23.9741970Z 
2023-01-11T21:41:23.9742114Z async_compile.wait(globals())
2023-01-11T21:41:23.9742222Z del async_compile
2023-01-11T21:41:23.9742230Z 
2023-01-11T21:41:23.9742550Z def call(args):
2023-01-11T21:41:23.9742670Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9742777Z     args.clear()
2023-01-11T21:41:23.9743111Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9743366Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9743474Z     del arg0_1
2023-01-11T21:41:23.9743572Z     del arg1_1
2023-01-11T21:41:23.9743665Z     return (buf0, )
2023-01-11T21:41:23.9743687Z 
2023-01-11T21:41:23.9743693Z 
2023-01-11T21:41:23.9743790Z if __name__ == "__main__":
2023-01-11T21:41:23.9743968Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9744158Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9744476Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.9744804Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9744983Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9744992Z 
2023-01-11T21:41:23.9744998Z 
2023-01-11T21:41:23.9745146Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9745252Z import torch
2023-01-11T21:41:23.9745344Z import random
2023-01-11T21:41:23.9745525Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9745724Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9745733Z 
2023-01-11T21:41:23.9745853Z aten = torch.ops.aten
2023-01-11T21:41:23.9746067Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9746207Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9746214Z 
2023-01-11T21:41:23.9746221Z 
2023-01-11T21:41:23.9746440Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9746774Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9746948Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:23.9747107Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:23.9747258Z                        long* __restrict__ out_ptr0)
2023-01-11T21:41:23.9747351Z {
2023-01-11T21:41:23.9747505Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9747598Z     {
2023-01-11T21:41:23.9747714Z         #pragma omp for 
2023-01-11T21:41:23.9747943Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.9748040Z         {
2023-01-11T21:41:23.9748165Z             #pragma GCC ivdep
2023-01-11T21:41:23.9748300Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:23.9748396Z             {
2023-01-11T21:41:23.9748492Z                 {
2023-01-11T21:41:23.9748583Z                     {
2023-01-11T21:41:23.9748727Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:23.9748882Z                         auto tmp3 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:23.9749052Z                         auto tmp1 = static_cast<float>(3.5);
2023-01-11T21:41:23.9749222Z                         auto tmp2 = static_cast<long>(tmp1);
2023-01-11T21:41:23.9749377Z                         auto tmp4 = tmp0 ? tmp2 : tmp3;
2023-01-11T21:41:23.9749524Z                         out_ptr0[i1 + (16*i0)] = tmp4;
2023-01-11T21:41:23.9749625Z                     }
2023-01-11T21:41:23.9749705Z                 }
2023-01-11T21:41:23.9749802Z             }
2023-01-11T21:41:23.9750014Z         }
2023-01-11T21:41:23.9750113Z     }
2023-01-11T21:41:23.9750204Z }
2023-01-11T21:41:23.9750338Z ''')
2023-01-11T21:41:23.9750346Z 
2023-01-11T21:41:23.9750353Z 
2023-01-11T21:41:23.9750494Z async_compile.wait(globals())
2023-01-11T21:41:23.9750591Z del async_compile
2023-01-11T21:41:23.9750599Z 
2023-01-11T21:41:23.9750705Z def call(args):
2023-01-11T21:41:23.9750819Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9750927Z     args.clear()
2023-01-11T21:41:23.9751250Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9751509Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9751608Z     del arg0_1
2023-01-11T21:41:23.9751694Z     del arg1_1
2023-01-11T21:41:23.9751799Z     return (buf0, )
2023-01-11T21:41:23.9751807Z 
2023-01-11T21:41:23.9751813Z 
2023-01-11T21:41:23.9751927Z if __name__ == "__main__":
2023-01-11T21:41:23.9752105Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9752301Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9752624Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:23.9752942Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9753117Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9753125Z 
2023-01-11T21:41:23.9753211Z ok (3.141s)
2023-01-11T21:41:23.9754064Z   test_max_min_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9754266Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9754707Z [2023-01-11 21:36:20,422] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 361
2023-01-11T21:41:23.9755152Z [2023-01-11 21:36:22,068] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 361
2023-01-11T21:41:23.9755864Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9756059Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9756486Z [2023-01-11 21:36:22,085] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 362
2023-01-11T21:41:23.9756934Z [2023-01-11 21:36:22,095] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 362
2023-01-11T21:41:23.9757025Z 
2023-01-11T21:41:23.9757175Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9757282Z import torch
2023-01-11T21:41:23.9757375Z import random
2023-01-11T21:41:23.9757555Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9757748Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9757756Z 
2023-01-11T21:41:23.9757879Z aten = torch.ops.aten
2023-01-11T21:41:23.9758092Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9758234Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9758241Z 
2023-01-11T21:41:23.9758248Z 
2023-01-11T21:41:23.9758467Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9758800Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9758973Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9759134Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9759351Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9759503Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9759594Z {
2023-01-11T21:41:23.9759747Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9759839Z     {
2023-01-11T21:41:23.9759943Z         #pragma omp for 
2023-01-11T21:41:23.9760067Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.9760161Z         {
2023-01-11T21:41:23.9760374Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9760580Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.9760751Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:23.9760920Z             auto tmp3 = at::vec::minimum(tmp0, tmp1);
2023-01-11T21:41:23.9761049Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9761188Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.9761280Z         }
2023-01-11T21:41:23.9761430Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9761562Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:23.9761657Z         {
2023-01-11T21:41:23.9761789Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9761906Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.9762102Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:23.9762295Z             auto tmp3 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:41:23.9762421Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9762540Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9762635Z         }
2023-01-11T21:41:23.9762728Z     }
2023-01-11T21:41:23.9762808Z }
2023-01-11T21:41:23.9762943Z ''')
2023-01-11T21:41:23.9762953Z 
2023-01-11T21:41:23.9762960Z 
2023-01-11T21:41:23.9763101Z async_compile.wait(globals())
2023-01-11T21:41:23.9763216Z del async_compile
2023-01-11T21:41:23.9763223Z 
2023-01-11T21:41:23.9763332Z def call(args):
2023-01-11T21:41:23.9763446Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9763557Z     args.clear()
2023-01-11T21:41:23.9763880Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9764183Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9764498Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9764603Z     del arg0_1
2023-01-11T21:41:23.9764707Z     del arg1_1
2023-01-11T21:41:23.9764828Z     return (buf0, buf1, )
2023-01-11T21:41:23.9764835Z 
2023-01-11T21:41:23.9764842Z 
2023-01-11T21:41:23.9764958Z if __name__ == "__main__":
2023-01-11T21:41:23.9765144Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9765347Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9765660Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9765976Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9766247Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9766254Z 
2023-01-11T21:41:23.9766261Z 
2023-01-11T21:41:23.9766409Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9766518Z import torch
2023-01-11T21:41:23.9766628Z import random
2023-01-11T21:41:23.9766808Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9766986Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9767007Z 
2023-01-11T21:41:23.9767113Z aten = torch.ops.aten
2023-01-11T21:41:23.9767323Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9767467Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9767475Z 
2023-01-11T21:41:23.9767482Z 
2023-01-11T21:41:23.9767704Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9768032Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9768221Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9768456Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9768621Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9768764Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:23.9768862Z {
2023-01-11T21:41:23.9769020Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9769116Z     {
2023-01-11T21:41:23.9769239Z         #pragma omp for 
2023-01-11T21:41:23.9769367Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.9769450Z         {
2023-01-11T21:41:23.9769668Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9769883Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:23.9770052Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:23.9770219Z             auto tmp3 = at::vec::minimum(tmp0, tmp1);
2023-01-11T21:41:23.9770362Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:23.9770510Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:23.9770605Z         }
2023-01-11T21:41:23.9770736Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9770862Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:23.9770955Z         {
2023-01-11T21:41:23.9771087Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9771216Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:23.9771405Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:23.9771595Z             auto tmp3 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:41:23.9771706Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9771826Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:23.9771924Z         }
2023-01-11T21:41:23.9772019Z     }
2023-01-11T21:41:23.9772112Z }
2023-01-11T21:41:23.9772250Z ''')
2023-01-11T21:41:23.9772260Z 
2023-01-11T21:41:23.9772266Z 
2023-01-11T21:41:23.9772407Z async_compile.wait(globals())
2023-01-11T21:41:23.9772503Z del async_compile
2023-01-11T21:41:23.9772532Z 
2023-01-11T21:41:23.9772625Z def call(args):
2023-01-11T21:41:23.9772740Z     arg0_1, arg1_1 = args
2023-01-11T21:41:23.9772855Z     args.clear()
2023-01-11T21:41:23.9773179Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9773499Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9773809Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9773915Z     del arg0_1
2023-01-11T21:41:23.9774003Z     del arg1_1
2023-01-11T21:41:23.9774125Z     return (buf0, buf1, )
2023-01-11T21:41:23.9774132Z 
2023-01-11T21:41:23.9774140Z 
2023-01-11T21:41:23.9774261Z if __name__ == "__main__":
2023-01-11T21:41:23.9774444Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9774643Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9774973Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9775371Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9775541Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:23.9775563Z 
2023-01-11T21:41:23.9775646Z ok (1.699s)
2023-01-11T21:41:23.9776466Z   test_max_pool2d1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9776679Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9777144Z [2023-01-11 21:36:22,117] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 363
2023-01-11T21:41:23.9777673Z [2023-01-11 21:36:23,787] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 363
2023-01-11T21:41:23.9777688Z 
2023-01-11T21:41:23.9777841Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9777956Z import torch
2023-01-11T21:41:23.9778068Z import random
2023-01-11T21:41:23.9778258Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9778444Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9778451Z 
2023-01-11T21:41:23.9778574Z aten = torch.ops.aten
2023-01-11T21:41:23.9778797Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9778948Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9778955Z 
2023-01-11T21:41:23.9778962Z 
2023-01-11T21:41:23.9779189Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9779533Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9779731Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9779896Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9780038Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.9780130Z {
2023-01-11T21:41:23.9780288Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9780388Z     {
2023-01-11T21:41:23.9780510Z         #pragma omp for 
2023-01-11T21:41:23.9780638Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9780736Z         {
2023-01-11T21:41:23.9780847Z             #pragma GCC ivdep
2023-01-11T21:41:23.9780975Z             for(long i1=0; i1<7; i1+=1)
2023-01-11T21:41:23.9781075Z             {
2023-01-11T21:41:23.9781203Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9781344Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:41:23.9781444Z                 {
2023-01-11T21:41:23.9781537Z                     {
2023-01-11T21:41:23.9781644Z                         {
2023-01-11T21:41:23.9781821Z                             auto tmp0 = in_ptr0[(2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9782009Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9782187Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9782523Z                             auto tmp5 = in_ptr0[16 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9782705Z                             auto tmp7 = in_ptr0[17 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9782873Z                             auto tmp9 = in_ptr0[18 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9783033Z                             auto tmp11 = in_ptr0[32 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9783206Z                             auto tmp13 = in_ptr0[33 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9783374Z                             auto tmp15 = in_ptr0[34 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:23.9783584Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:23.9783891Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:23.9784082Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:23.9784274Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:23.9784474Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:23.9784683Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:23.9784887Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:23.9785074Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:23.9785256Z                             auto tmp17 = static_cast<long>((2*i2) + (32*i1));
2023-01-11T21:41:23.9785438Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9785660Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:23.9785823Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:23.9786006Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9786149Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:23.9786305Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:23.9786475Z                             auto tmp24 = static_cast<long>(16 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9786618Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:23.9786780Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:23.9786964Z                             auto tmp27 = static_cast<long>(17 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9787108Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:23.9787264Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:23.9787453Z                             auto tmp30 = static_cast<long>(18 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9787584Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:23.9787743Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:23.9787923Z                             auto tmp33 = static_cast<long>(32 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9788074Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:23.9788233Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:23.9788416Z                             auto tmp36 = static_cast<long>(33 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9788561Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:23.9788718Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:23.9788883Z                             auto tmp39 = static_cast<long>(34 + (2*i2) + (32*i1));
2023-01-11T21:41:23.9789032Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:23.9789198Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:23.9789356Z                             out_ptr0[i2 + (7*i1) + (49*i0)] = tmp16;
2023-01-11T21:41:23.9789514Z                             out_ptr1[i2 + (7*i1) + (49*i0)] = tmp41;
2023-01-11T21:41:23.9789618Z                         }
2023-01-11T21:41:23.9789717Z                     }
2023-01-11T21:41:23.9789797Z                 }
2023-01-11T21:41:23.9789890Z             }
2023-01-11T21:41:23.9789983Z         }
2023-01-11T21:41:23.9790076Z     }
2023-01-11T21:41:23.9790166Z }
2023-01-11T21:41:23.9790313Z ''')
2023-01-11T21:41:23.9790324Z 
2023-01-11T21:41:23.9790330Z 
2023-01-11T21:41:23.9790474Z async_compile.wait(globals())
2023-01-11T21:41:23.9790569Z del async_compile
2023-01-11T21:41:23.9790590Z 
2023-01-11T21:41:23.9790681Z def call(args):
2023-01-11T21:41:23.9790787Z     arg0_1, = args
2023-01-11T21:41:23.9790890Z     args.clear()
2023-01-11T21:41:23.9791320Z     buf0 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9791670Z     buf1 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9791935Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9792037Z     del arg0_1
2023-01-11T21:41:23.9792142Z     return (buf0, buf1, )
2023-01-11T21:41:23.9792150Z 
2023-01-11T21:41:23.9792157Z 
2023-01-11T21:41:23.9792273Z if __name__ == "__main__":
2023-01-11T21:41:23.9792458Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9792657Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9793023Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9793193Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9793202Z 
2023-01-11T21:41:23.9793302Z ok (1.691s)
2023-01-11T21:41:23.9794257Z   test_max_pool2d2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9794465Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9794896Z [2023-01-11 21:36:23,862] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 364
2023-01-11T21:41:23.9795340Z [2023-01-11 21:36:25,525] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 364
2023-01-11T21:41:23.9795348Z 
2023-01-11T21:41:23.9795495Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9795604Z import torch
2023-01-11T21:41:23.9795712Z import random
2023-01-11T21:41:23.9795899Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9796101Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9796110Z 
2023-01-11T21:41:23.9796234Z aten = torch.ops.aten
2023-01-11T21:41:23.9796437Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9796578Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9796585Z 
2023-01-11T21:41:23.9796592Z 
2023-01-11T21:41:23.9796814Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9797148Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9797333Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9797488Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9797636Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.9797729Z {
2023-01-11T21:41:23.9797869Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9797959Z     {
2023-01-11T21:41:23.9798075Z         #pragma omp for 
2023-01-11T21:41:23.9798208Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.9798304Z         {
2023-01-11T21:41:23.9798425Z             #pragma GCC ivdep
2023-01-11T21:41:23.9798553Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:41:23.9798636Z             {
2023-01-11T21:41:23.9798760Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9798896Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:23.9798996Z                 {
2023-01-11T21:41:23.9799101Z                     {
2023-01-11T21:41:23.9799204Z                         {
2023-01-11T21:41:23.9799365Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9799543Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9799717Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9799893Z                             auto tmp5 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9800154Z                             auto tmp7 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9800325Z                             auto tmp9 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9800504Z                             auto tmp11 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9800679Z                             auto tmp13 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9800854Z                             auto tmp15 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:23.9801045Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:23.9801238Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:23.9801427Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:23.9801616Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:23.9801899Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:23.9802107Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:23.9802308Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:23.9802509Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:23.9802689Z                             auto tmp17 = static_cast<long>((2*i2) + (110*i1));
2023-01-11T21:41:23.9802858Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9803006Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:23.9803163Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:23.9803350Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9803502Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:23.9803662Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:23.9803849Z                             auto tmp24 = static_cast<long>(55 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9803994Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:23.9804139Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:23.9804322Z                             auto tmp27 = static_cast<long>(56 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9804468Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:23.9804625Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:23.9804809Z                             auto tmp30 = static_cast<long>(57 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9804953Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:23.9805108Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:23.9805299Z                             auto tmp33 = static_cast<long>(110 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9805434Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:23.9805591Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:23.9805775Z                             auto tmp36 = static_cast<long>(111 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9805923Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:23.9806074Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:23.9806254Z                             auto tmp39 = static_cast<long>(112 + (2*i2) + (110*i1));
2023-01-11T21:41:23.9806399Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:23.9806541Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:23.9806702Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp16;
2023-01-11T21:41:23.9806849Z                             out_ptr1[i2 + (27*i1) + (729*i0)] = tmp41;
2023-01-11T21:41:23.9807031Z                         }
2023-01-11T21:41:23.9807128Z                     }
2023-01-11T21:41:23.9807218Z                 }
2023-01-11T21:41:23.9807310Z             }
2023-01-11T21:41:23.9807380Z         }
2023-01-11T21:41:23.9807460Z     }
2023-01-11T21:41:23.9807543Z }
2023-01-11T21:41:23.9807678Z ''')
2023-01-11T21:41:23.9807687Z 
2023-01-11T21:41:23.9807693Z 
2023-01-11T21:41:23.9807829Z async_compile.wait(globals())
2023-01-11T21:41:23.9807936Z del async_compile
2023-01-11T21:41:23.9807943Z 
2023-01-11T21:41:23.9808043Z def call(args):
2023-01-11T21:41:23.9808143Z     arg0_1, = args
2023-01-11T21:41:23.9808226Z     args.clear()
2023-01-11T21:41:23.9808568Z     buf0 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9808896Z     buf1 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9809201Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9809304Z     del arg0_1
2023-01-11T21:41:23.9809414Z     return (buf0, buf1, )
2023-01-11T21:41:23.9809421Z 
2023-01-11T21:41:23.9809427Z 
2023-01-11T21:41:23.9809536Z if __name__ == "__main__":
2023-01-11T21:41:23.9809704Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9809864Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9810209Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9810365Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9810372Z 
2023-01-11T21:41:23.9810467Z ok (1.800s)
2023-01-11T21:41:23.9811184Z   test_max_pool2d3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9811378Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9811789Z [2023-01-11 21:36:25,610] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 365
2023-01-11T21:41:23.9811796Z 
2023-01-11T21:41:23.9811931Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9812034Z import torch
2023-01-11T21:41:23.9812130Z import random
2023-01-11T21:41:23.9812310Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9812514Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9812523Z 
2023-01-11T21:41:23.9812646Z aten = torch.ops.aten
2023-01-11T21:41:23.9812870Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9813019Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9813028Z 
2023-01-11T21:41:23.9813034Z 
2023-01-11T21:41:23.9813260Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9813571Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9813738Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9813883Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9814033Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.9814124Z {
2023-01-11T21:41:23.9814271Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9814364Z     {
2023-01-11T21:41:23.9814482Z         #pragma omp for 
2023-01-11T21:41:23.9814594Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:23.9814687Z         {
2023-01-11T21:41:23.9814816Z             #pragma GCC ivdep
2023-01-11T21:41:23.9814943Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:23.9815042Z             {
2023-01-11T21:41:23.9815137Z                 {
2023-01-11T21:41:23.9815234Z                     {
2023-01-11T21:41:23.9815586Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:23.9815748Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:23.9815894Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:23.9816050Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:23.9816196Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:23.9816326Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:23.9816615Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:23.9816749Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:23.9816881Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:23.9817014Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:23.9817150Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:23.9817520Z                         float tmp11 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9817702Z                         if(tmp10)
2023-01-11T21:41:23.9817801Z                         {
2023-01-11T21:41:23.9818073Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9818204Z                             tmp11 = tmp12;
2023-01-11T21:41:23.9818310Z                         }
2023-01-11T21:41:23.9818481Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:23.9818629Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:23.9818775Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:23.9818920Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:23.9819063Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:23.9819418Z                         float tmp18 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9819533Z                         if(tmp17)
2023-01-11T21:41:23.9819636Z                         {
2023-01-11T21:41:23.9819919Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9820051Z                             tmp18 = tmp19;
2023-01-11T21:41:23.9820152Z                         }
2023-01-11T21:41:23.9820355Z                         auto tmp20 = (tmp11 != tmp11) ? tmp11 : std::max(tmp18, tmp11);
2023-01-11T21:41:23.9820516Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:23.9820661Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:23.9820800Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:23.9820936Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:23.9821077Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:23.9821435Z                         float tmp26 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9821548Z                         if(tmp25)
2023-01-11T21:41:23.9821650Z                         {
2023-01-11T21:41:23.9821913Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9822043Z                             tmp26 = tmp27;
2023-01-11T21:41:23.9822148Z                         }
2023-01-11T21:41:23.9822492Z                         auto tmp28 = (tmp20 != tmp20) ? tmp20 : std::max(tmp26, tmp20);
2023-01-11T21:41:23.9822665Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:23.9822810Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:23.9822955Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:23.9823084Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:23.9823227Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:23.9823593Z                         float tmp34 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9823708Z                         if(tmp33)
2023-01-11T21:41:23.9823810Z                         {
2023-01-11T21:41:23.9824085Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9824316Z                             tmp34 = tmp35;
2023-01-11T21:41:23.9824420Z                         }
2023-01-11T21:41:23.9824606Z                         auto tmp36 = (tmp28 != tmp28) ? tmp28 : std::max(tmp34, tmp28);
2023-01-11T21:41:23.9824748Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:23.9825098Z                         float tmp38 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9825207Z                         if(tmp37)
2023-01-11T21:41:23.9825304Z                         {
2023-01-11T21:41:23.9825457Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:23.9825582Z                             tmp38 = tmp39;
2023-01-11T21:41:23.9825671Z                         }
2023-01-11T21:41:23.9825866Z                         auto tmp40 = (tmp36 != tmp36) ? tmp36 : std::max(tmp38, tmp36);
2023-01-11T21:41:23.9825995Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:23.9826371Z                         float tmp42 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9826474Z                         if(tmp41)
2023-01-11T21:41:23.9826563Z                         {
2023-01-11T21:41:23.9826715Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9826824Z                             tmp42 = tmp43;
2023-01-11T21:41:23.9826901Z                         }
2023-01-11T21:41:23.9827073Z                         auto tmp44 = (tmp40 != tmp40) ? tmp40 : std::max(tmp42, tmp40);
2023-01-11T21:41:23.9827220Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:23.9827342Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:23.9827461Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:23.9827590Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:23.9827716Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:23.9828025Z                         float tmp50 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9828134Z                         if(tmp49)
2023-01-11T21:41:23.9828227Z                         {
2023-01-11T21:41:23.9828379Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9828495Z                             tmp50 = tmp51;
2023-01-11T21:41:23.9828588Z                         }
2023-01-11T21:41:23.9828772Z                         auto tmp52 = (tmp44 != tmp44) ? tmp44 : std::max(tmp50, tmp44);
2023-01-11T21:41:23.9828906Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:23.9829228Z                         float tmp54 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9829336Z                         if(tmp53)
2023-01-11T21:41:23.9829431Z                         {
2023-01-11T21:41:23.9829591Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9829713Z                             tmp54 = tmp55;
2023-01-11T21:41:23.9829818Z                         }
2023-01-11T21:41:23.9830022Z                         auto tmp56 = (tmp52 != tmp52) ? tmp52 : std::max(tmp54, tmp52);
2023-01-11T21:41:23.9830166Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:23.9830513Z                         float tmp58 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9830627Z                         if(tmp57)
2023-01-11T21:41:23.9830729Z                         {
2023-01-11T21:41:23.9830895Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9831018Z                             tmp58 = tmp59;
2023-01-11T21:41:23.9831117Z                         }
2023-01-11T21:41:23.9831315Z                         auto tmp60 = (tmp56 != tmp56) ? tmp56 : std::max(tmp58, tmp56);
2023-01-11T21:41:23.9831654Z                         float tmp61 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9831766Z                         if(tmp10)
2023-01-11T21:41:23.9831868Z                         {
2023-01-11T21:41:23.9832147Z                             auto tmp62 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9832355Z                             tmp61 = tmp62;
2023-01-11T21:41:23.9832461Z                         }
2023-01-11T21:41:23.9832756Z                         auto tmp63 = static_cast<long>((-9) + (2*i1) + (16*i0));
2023-01-11T21:41:23.9833111Z                         float tmp64 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9833211Z                         if(tmp17)
2023-01-11T21:41:23.9833314Z                         {
2023-01-11T21:41:23.9833591Z                             auto tmp65 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9833718Z                             tmp64 = tmp65;
2023-01-11T21:41:23.9833917Z                         }
2023-01-11T21:41:23.9834219Z                         auto tmp66 = static_cast<long>((-8) + (2*i1) + (16*i0));
2023-01-11T21:41:23.9834364Z                         auto tmp67 = tmp64 > tmp61;
2023-01-11T21:41:23.9834508Z                         auto tmp68 = tmp67 ? tmp66 : tmp63;
2023-01-11T21:41:23.9834783Z                         auto tmp69 = (tmp61 != tmp61) ? tmp61 : std::max(tmp64, tmp61);
2023-01-11T21:41:23.9835150Z                         float tmp70 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9835265Z                         if(tmp25)
2023-01-11T21:41:23.9835371Z                         {
2023-01-11T21:41:23.9835647Z                             auto tmp71 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9835773Z                             tmp70 = tmp71;
2023-01-11T21:41:23.9835875Z                         }
2023-01-11T21:41:23.9836154Z                         auto tmp72 = static_cast<long>((-7) + (2*i1) + (16*i0));
2023-01-11T21:41:23.9836296Z                         auto tmp73 = tmp70 > tmp69;
2023-01-11T21:41:23.9836456Z                         auto tmp74 = tmp73 ? tmp72 : tmp68;
2023-01-11T21:41:23.9836661Z                         auto tmp75 = (tmp69 != tmp69) ? tmp69 : std::max(tmp70, tmp69);
2023-01-11T21:41:23.9837011Z                         float tmp76 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9837122Z                         if(tmp33)
2023-01-11T21:41:23.9837225Z                         {
2023-01-11T21:41:23.9837488Z                             auto tmp77 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9837615Z                             tmp76 = tmp77;
2023-01-11T21:41:23.9837718Z                         }
2023-01-11T21:41:23.9838013Z                         auto tmp78 = static_cast<long>((-1) + (2*i1) + (16*i0));
2023-01-11T21:41:23.9838154Z                         auto tmp79 = tmp76 > tmp75;
2023-01-11T21:41:23.9838303Z                         auto tmp80 = tmp79 ? tmp78 : tmp74;
2023-01-11T21:41:23.9838500Z                         auto tmp81 = (tmp75 != tmp75) ? tmp75 : std::max(tmp76, tmp75);
2023-01-11T21:41:23.9838855Z                         float tmp82 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9838953Z                         if(tmp37)
2023-01-11T21:41:23.9839058Z                         {
2023-01-11T21:41:23.9839228Z                             auto tmp83 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:23.9839350Z                             tmp82 = tmp83;
2023-01-11T21:41:23.9839451Z                         }
2023-01-11T21:41:23.9839628Z                         auto tmp84 = static_cast<long>((2*i1) + (16*i0));
2023-01-11T21:41:23.9839769Z                         auto tmp85 = tmp82 > tmp81;
2023-01-11T21:41:23.9839912Z                         auto tmp86 = tmp85 ? tmp84 : tmp80;
2023-01-11T21:41:23.9840111Z                         auto tmp87 = (tmp81 != tmp81) ? tmp81 : std::max(tmp82, tmp81);
2023-01-11T21:41:23.9840466Z                         float tmp88 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9840577Z                         if(tmp41)
2023-01-11T21:41:23.9840680Z                         {
2023-01-11T21:41:23.9840839Z                             auto tmp89 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9840966Z                             tmp88 = tmp89;
2023-01-11T21:41:23.9841144Z                         }
2023-01-11T21:41:23.9841309Z                         auto tmp90 = static_cast<long>(1 + (2*i1) + (16*i0));
2023-01-11T21:41:23.9841448Z                         auto tmp91 = tmp88 > tmp87;
2023-01-11T21:41:23.9841604Z                         auto tmp92 = tmp91 ? tmp90 : tmp86;
2023-01-11T21:41:23.9841806Z                         auto tmp93 = (tmp87 != tmp87) ? tmp87 : std::max(tmp88, tmp87);
2023-01-11T21:41:23.9842168Z                         float tmp94 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9842285Z                         if(tmp49)
2023-01-11T21:41:23.9842386Z                         {
2023-01-11T21:41:23.9842549Z                             auto tmp95 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9842664Z                             tmp94 = tmp95;
2023-01-11T21:41:23.9842761Z                         }
2023-01-11T21:41:23.9842940Z                         auto tmp96 = static_cast<long>(7 + (2*i1) + (16*i0));
2023-01-11T21:41:23.9843149Z                         auto tmp97 = tmp94 > tmp93;
2023-01-11T21:41:23.9843308Z                         auto tmp98 = tmp97 ? tmp96 : tmp92;
2023-01-11T21:41:23.9843501Z                         auto tmp99 = (tmp93 != tmp93) ? tmp93 : std::max(tmp94, tmp93);
2023-01-11T21:41:23.9843863Z                         float tmp100 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9843970Z                         if(tmp53)
2023-01-11T21:41:23.9844055Z                         {
2023-01-11T21:41:23.9844219Z                             auto tmp101 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9844349Z                             tmp100 = tmp101;
2023-01-11T21:41:23.9844451Z                         }
2023-01-11T21:41:23.9844633Z                         auto tmp102 = static_cast<long>(8 + (2*i1) + (16*i0));
2023-01-11T21:41:23.9844784Z                         auto tmp103 = tmp100 > tmp99;
2023-01-11T21:41:23.9844946Z                         auto tmp104 = tmp103 ? tmp102 : tmp98;
2023-01-11T21:41:23.9845146Z                         auto tmp105 = (tmp99 != tmp99) ? tmp99 : std::max(tmp100, tmp99);
2023-01-11T21:41:23.9845511Z                         float tmp106 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:23.9845629Z                         if(tmp57)
2023-01-11T21:41:23.9845730Z                         {
2023-01-11T21:41:23.9845895Z                             auto tmp107 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:23.9846024Z                             tmp106 = tmp107;
2023-01-11T21:41:23.9846125Z                         }
2023-01-11T21:41:23.9846302Z                         auto tmp108 = static_cast<long>(9 + (2*i1) + (16*i0));
2023-01-11T21:41:23.9846433Z                         auto tmp109 = tmp106 > tmp105;
2023-01-11T21:41:23.9846592Z                         auto tmp110 = tmp109 ? tmp108 : tmp104;
2023-01-11T21:41:23.9846800Z                         auto tmp111 = (tmp105 != tmp105) ? tmp105 : std::max(tmp106, tmp105);
2023-01-11T21:41:23.9846943Z                         out_ptr0[i1 + (4*i0)] = tmp60;
2023-01-11T21:41:23.9847095Z                         out_ptr1[i1 + (4*i0)] = tmp110;
2023-01-11T21:41:23.9847197Z                     }
2023-01-11T21:41:23.9847294Z                 }
2023-01-11T21:41:23.9847374Z             }
2023-01-11T21:41:23.9847470Z         }
2023-01-11T21:41:23.9847562Z     }
2023-01-11T21:41:23.9847650Z }
2023-01-11T21:41:23.9847776Z ''')
2023-01-11T21:41:23.9847787Z 
2023-01-11T21:41:23.9847793Z 
2023-01-11T21:41:23.9847934Z async_compile.wait(globals())
2023-01-11T21:41:23.9848042Z del async_compile
2023-01-11T21:41:23.9848049Z 
2023-01-11T21:41:23.9848141Z def call(args):
2023-01-11T21:41:23.9848244Z     arg0_1, = args
2023-01-11T21:41:23.9848352Z     args.clear()
2023-01-11T21:41:23.9848700Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9849035Z     buf1 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9849302Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9849476Z     del arg0_1
2023-01-11T21:41:23.9849594Z     return (buf0, buf1, )
2023-01-11T21:41:23.9849604Z 
2023-01-11T21:41:23.9849610Z 
2023-01-11T21:41:23.9849711Z if __name__ == "__main__":
2023-01-11T21:41:23.9849889Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9850083Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9850433Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9850597Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9851037Z [2023-01-11 21:36:27,324] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 365
2023-01-11T21:41:23.9851045Z 
2023-01-11T21:41:23.9851147Z ok (1.737s)
2023-01-11T21:41:23.9851957Z   test_max_pool2d4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9852155Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9852539Z [2023-01-11 21:36:27,364] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 366
2023-01-11T21:41:23.9852951Z [2023-01-11 21:36:29,060] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 366
2023-01-11T21:41:23.9852959Z 
2023-01-11T21:41:23.9853102Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9853209Z import torch
2023-01-11T21:41:23.9853318Z import random
2023-01-11T21:41:23.9853488Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9853683Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9853691Z 
2023-01-11T21:41:23.9853827Z aten = torch.ops.aten
2023-01-11T21:41:23.9854038Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9854185Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9854194Z 
2023-01-11T21:41:23.9854202Z 
2023-01-11T21:41:23.9854426Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9854758Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9854928Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9855070Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9855223Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.9855311Z {
2023-01-11T21:41:23.9855442Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9855531Z     {
2023-01-11T21:41:23.9855645Z         #pragma omp for 
2023-01-11T21:41:23.9855770Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:23.9855865Z         {
2023-01-11T21:41:23.9855996Z             #pragma GCC ivdep
2023-01-11T21:41:23.9856116Z             for(long i1=0; i1<55; i1+=1)
2023-01-11T21:41:23.9856187Z             {
2023-01-11T21:41:23.9856301Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9856434Z                 for(long i2=0; i2<55; i2+=1)
2023-01-11T21:41:23.9856534Z                 {
2023-01-11T21:41:23.9856637Z                     {
2023-01-11T21:41:23.9856738Z                         {
2023-01-11T21:41:23.9856894Z                             auto tmp0 = in_ptr0[(2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9857068Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9857236Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9857404Z                             auto tmp5 = in_ptr0[111 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9857571Z                             auto tmp7 = in_ptr0[112 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9857815Z                             auto tmp9 = in_ptr0[113 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9857979Z                             auto tmp11 = in_ptr0[222 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9858146Z                             auto tmp13 = in_ptr0[223 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9858315Z                             auto tmp15 = in_ptr0[224 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:23.9858495Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:23.9858678Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:23.9858860Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:23.9859042Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:23.9859221Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:23.9859490Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:23.9859686Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:23.9859874Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:23.9860045Z                             auto tmp17 = static_cast<long>((2*i2) + (222*i1));
2023-01-11T21:41:23.9860216Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9860367Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:23.9860529Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:23.9860708Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9860850Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:23.9861007Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:23.9861199Z                             auto tmp24 = static_cast<long>(111 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9861339Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:23.9861484Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:23.9861671Z                             auto tmp27 = static_cast<long>(112 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9861819Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:23.9861976Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:23.9862158Z                             auto tmp30 = static_cast<long>(113 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9862304Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:23.9862624Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:23.9862812Z                             auto tmp33 = static_cast<long>(222 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9862957Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:23.9863115Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:23.9863298Z                             auto tmp36 = static_cast<long>(223 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9863447Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:23.9863605Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:23.9863786Z                             auto tmp39 = static_cast<long>(224 + (2*i2) + (222*i1));
2023-01-11T21:41:23.9863935Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:23.9864090Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:23.9864237Z                             out_ptr0[i2 + (55*i1) + (3025*i0)] = tmp16;
2023-01-11T21:41:23.9864397Z                             out_ptr1[i2 + (55*i1) + (3025*i0)] = tmp41;
2023-01-11T21:41:23.9864500Z                         }
2023-01-11T21:41:23.9864719Z                     }
2023-01-11T21:41:23.9864815Z                 }
2023-01-11T21:41:23.9864910Z             }
2023-01-11T21:41:23.9864990Z         }
2023-01-11T21:41:23.9865083Z     }
2023-01-11T21:41:23.9865172Z }
2023-01-11T21:41:23.9865320Z ''')
2023-01-11T21:41:23.9865330Z 
2023-01-11T21:41:23.9865336Z 
2023-01-11T21:41:23.9865477Z async_compile.wait(globals())
2023-01-11T21:41:23.9865589Z del async_compile
2023-01-11T21:41:23.9865596Z 
2023-01-11T21:41:23.9865701Z def call(args):
2023-01-11T21:41:23.9865803Z     arg0_1, = args
2023-01-11T21:41:23.9865893Z     args.clear()
2023-01-11T21:41:23.9866256Z     buf0 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9866607Z     buf1 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9866865Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9866970Z     del arg0_1
2023-01-11T21:41:23.9867181Z     return (buf0, buf1, )
2023-01-11T21:41:23.9867190Z 
2023-01-11T21:41:23.9867197Z 
2023-01-11T21:41:23.9867316Z if __name__ == "__main__":
2023-01-11T21:41:23.9867494Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9867676Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9868053Z     arg0_1 = rand_strided((2, 8, 111, 111), (98568, 12321, 111, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9868222Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9868230Z 
2023-01-11T21:41:23.9868327Z ok (1.738s)
2023-01-11T21:41:23.9869098Z   test_max_pool2d5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9869305Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9869747Z [2023-01-11 21:36:29,133] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 367
2023-01-11T21:41:23.9870199Z [2023-01-11 21:36:30,823] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 367
2023-01-11T21:41:23.9870208Z 
2023-01-11T21:41:23.9870356Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9870450Z import torch
2023-01-11T21:41:23.9870553Z import random
2023-01-11T21:41:23.9870738Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9870927Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9870935Z 
2023-01-11T21:41:23.9871059Z aten = torch.ops.aten
2023-01-11T21:41:23.9871269Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9871410Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9871418Z 
2023-01-11T21:41:23.9871429Z 
2023-01-11T21:41:23.9871653Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9871966Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9872156Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9872311Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:23.9872457Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:23.9872548Z {
2023-01-11T21:41:23.9872701Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9872794Z     {
2023-01-11T21:41:23.9872894Z         #pragma omp for 
2023-01-11T21:41:23.9873020Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:23.9873113Z         {
2023-01-11T21:41:23.9873232Z             #pragma GCC ivdep
2023-01-11T21:41:23.9873360Z             for(long i1=0; i1<18; i1+=1)
2023-01-11T21:41:23.9873459Z             {
2023-01-11T21:41:23.9873581Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9873704Z                 for(long i2=0; i2<18; i2+=1)
2023-01-11T21:41:23.9873958Z                 {
2023-01-11T21:41:23.9874060Z                     {
2023-01-11T21:41:23.9874162Z                         {
2023-01-11T21:41:23.9874337Z                             auto tmp0 = in_ptr0[(3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9874514Z                             auto tmp1 = in_ptr0[1 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9874687Z                             auto tmp3 = in_ptr0[2 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9874851Z                             auto tmp5 = in_ptr0[55 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9875021Z                             auto tmp7 = in_ptr0[56 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9875189Z                             auto tmp9 = in_ptr0[57 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9875366Z                             auto tmp11 = in_ptr0[110 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9875598Z                             auto tmp13 = in_ptr0[111 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9875784Z                             auto tmp15 = in_ptr0[112 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:23.9875993Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:23.9876186Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:23.9876373Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:23.9876546Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:23.9876740Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:23.9876935Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:23.9877130Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:23.9877329Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:23.9877506Z                             auto tmp17 = static_cast<long>((3*i2) + (165*i1));
2023-01-11T21:41:23.9877680Z                             auto tmp18 = static_cast<long>(1 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9877824Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:23.9877981Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:23.9878149Z                             auto tmp21 = static_cast<long>(2 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9878291Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:23.9878441Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:23.9878611Z                             auto tmp24 = static_cast<long>(55 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9878748Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:23.9878903Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:23.9879076Z                             auto tmp27 = static_cast<long>(56 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9879206Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:23.9879352Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:23.9879521Z                             auto tmp30 = static_cast<long>(57 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9879662Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:23.9879807Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:23.9879981Z                             auto tmp33 = static_cast<long>(110 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9880122Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:23.9880267Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:23.9880426Z                             auto tmp36 = static_cast<long>(111 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9880694Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:23.9880844Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:23.9881016Z                             auto tmp39 = static_cast<long>(112 + (3*i2) + (165*i1));
2023-01-11T21:41:23.9881151Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:23.9881294Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:23.9881440Z                             out_ptr0[i2 + (18*i1) + (324*i0)] = tmp16;
2023-01-11T21:41:23.9881589Z                             out_ptr1[i2 + (18*i1) + (324*i0)] = tmp41;
2023-01-11T21:41:23.9881672Z                         }
2023-01-11T21:41:23.9881767Z                     }
2023-01-11T21:41:23.9881870Z                 }
2023-01-11T21:41:23.9881967Z             }
2023-01-11T21:41:23.9882050Z         }
2023-01-11T21:41:23.9882141Z     }
2023-01-11T21:41:23.9882225Z }
2023-01-11T21:41:23.9882346Z ''')
2023-01-11T21:41:23.9882358Z 
2023-01-11T21:41:23.9882425Z 
2023-01-11T21:41:23.9882557Z async_compile.wait(globals())
2023-01-11T21:41:23.9882659Z del async_compile
2023-01-11T21:41:23.9882666Z 
2023-01-11T21:41:23.9882763Z def call(args):
2023-01-11T21:41:23.9882867Z     arg0_1, = args
2023-01-11T21:41:23.9882965Z     args.clear()
2023-01-11T21:41:23.9883312Z     buf0 = empty_strided((16, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9883632Z     buf1 = empty_strided((16, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9883880Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:23.9883975Z     del arg0_1
2023-01-11T21:41:23.9884081Z     return (buf0, buf1, )
2023-01-11T21:41:23.9884089Z 
2023-01-11T21:41:23.9884094Z 
2023-01-11T21:41:23.9884197Z if __name__ == "__main__":
2023-01-11T21:41:23.9884361Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9884545Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9884881Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9885023Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9885043Z 
2023-01-11T21:41:23.9885123Z ok (1.817s)
2023-01-11T21:41:23.9885831Z   test_max_pool2d6_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9886013Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9886428Z [2023-01-11 21:36:30,960] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 368
2023-01-11T21:41:23.9886816Z [2023-01-11 21:36:30,966] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.max_pool2d_with_indices
2023-01-11T21:41:23.9887214Z [2023-01-11 21:36:30,968] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 368
2023-01-11T21:41:23.9887222Z 
2023-01-11T21:41:23.9887360Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9887460Z import torch
2023-01-11T21:41:23.9887562Z import random
2023-01-11T21:41:23.9887720Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9887896Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9887903Z 
2023-01-11T21:41:23.9888012Z aten = torch.ops.aten
2023-01-11T21:41:23.9888208Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9888336Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9888342Z 
2023-01-11T21:41:23.9888349Z 
2023-01-11T21:41:23.9888475Z async_compile.wait(globals())
2023-01-11T21:41:23.9888570Z del async_compile
2023-01-11T21:41:23.9888653Z 
2023-01-11T21:41:23.9888757Z def call(args):
2023-01-11T21:41:23.9888846Z     arg0_1, = args
2023-01-11T21:41:23.9888943Z     args.clear()
2023-01-11T21:41:23.9889124Z     buf0 = aten.max_pool2d_with_indices(arg0_1, [13, 13], [13, 13], [0, 0], 1, False)
2023-01-11T21:41:23.9889220Z     del arg0_1
2023-01-11T21:41:23.9889316Z     buf1 = buf0[0]
2023-01-11T21:41:23.9889474Z     assert_size_stride(buf1, (16, 64, 4, 4), (1024, 16, 4, 1))
2023-01-11T21:41:23.9889574Z     buf2 = buf0[1]
2023-01-11T21:41:23.9889718Z     assert_size_stride(buf2, (16, 64, 4, 4), (1024, 16, 4, 1))
2023-01-11T21:41:23.9889805Z     del buf0
2023-01-11T21:41:23.9889918Z     return (buf1, buf2, )
2023-01-11T21:41:23.9889924Z 
2023-01-11T21:41:23.9889931Z 
2023-01-11T21:41:23.9890040Z if __name__ == "__main__":
2023-01-11T21:41:23.9890202Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9890385Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9890817Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9890972Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:23.9890993Z 
2023-01-11T21:41:23.9891075Z ok (0.129s)
2023-01-11T21:41:23.9891842Z   test_max_pool2d_with_indices_backward2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9892027Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9892439Z [2023-01-11 21:36:31,034] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 369
2023-01-11T21:41:23.9892853Z [2023-01-11 21:36:32,688] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 369
2023-01-11T21:41:23.9892866Z 
2023-01-11T21:41:23.9892994Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9893097Z import torch
2023-01-11T21:41:23.9893197Z import random
2023-01-11T21:41:23.9893360Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9893520Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9893528Z 
2023-01-11T21:41:23.9893634Z aten = torch.ops.aten
2023-01-11T21:41:23.9893833Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9893968Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9893975Z 
2023-01-11T21:41:23.9893980Z 
2023-01-11T21:41:23.9894199Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9894531Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9894717Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9894879Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9895031Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9895121Z {
2023-01-11T21:41:23.9895276Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9895367Z     {
2023-01-11T21:41:23.9895490Z         #pragma omp for 
2023-01-11T21:41:23.9895617Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9895709Z         {
2023-01-11T21:41:23.9895818Z             #pragma GCC ivdep
2023-01-11T21:41:23.9895948Z             for(long i1=0; i1<40; i1+=1)
2023-01-11T21:41:23.9896043Z             {
2023-01-11T21:41:23.9896166Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9896300Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:41:23.9896397Z                 {
2023-01-11T21:41:23.9896482Z                     {
2023-01-11T21:41:23.9896584Z                         {
2023-01-11T21:41:23.9896757Z                             auto tmp0 = static_cast<int>(i2 + (56*i1));
2023-01-11T21:41:23.9896932Z                             auto tmp1 = static_cast<int>((i1 / 2));
2023-01-11T21:41:23.9897187Z                             auto tmp2 = static_cast<int>((i2 / 2));
2023-01-11T21:41:23.9897365Z                             auto tmp3 = static_cast<int>(1 + (((1 + i1) / 2)));
2023-01-11T21:41:23.9897543Z                             auto tmp4 = static_cast<int>(1 + (((1 + i2) / 2)));
2023-01-11T21:41:23.9897703Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:23.9897895Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:23.9898088Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:23.9898253Z                             auto tmp8 = static_cast<int>(21);
2023-01-11T21:41:23.9898450Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:23.9898612Z                             auto tmp10 = static_cast<int>(29);
2023-01-11T21:41:23.9898869Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:41:23.9899027Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:41:23.9899171Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:41:23.9899319Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:41:23.9899550Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:41:23.9899741Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:41:23.9899968Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:41:23.9900157Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:41:23.9900325Z                             auto tmp19 = in_ptr0[tmp18 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:23.9900503Z                             auto tmp20 = in_ptr1[tmp18 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:23.9900644Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:41:23.9900811Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:23.9900944Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:41:23.9901082Z                             auto tmp24 = tmp7 + tmp14;
2023-01-11T21:41:23.9901272Z                             auto tmp25 = (tmp17 != tmp17) ? tmp17 : std::min(tmp24, tmp17);
2023-01-11T21:41:23.9901442Z                             auto tmp26 = in_ptr0[tmp25 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:23.9901607Z                             auto tmp27 = in_ptr1[tmp25 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:23.9901742Z                             auto tmp28 = tmp26 == tmp0;
2023-01-11T21:41:23.9901882Z                             auto tmp29 = tmp12 < tmp9;
2023-01-11T21:41:23.9902019Z                             auto tmp30 = tmp24 < tmp11;
2023-01-11T21:41:23.9902171Z                             auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:23.9902460Z                             auto tmp32 = tmp31 & tmp28;
2023-01-11T21:41:23.9902626Z                             auto tmp33 = tmp23 + tmp27;
2023-01-11T21:41:23.9902792Z                             auto tmp34 = tmp32 ? tmp33 : tmp23;
2023-01-11T21:41:23.9902942Z                             auto tmp35 = tmp6 + tmp14;
2023-01-11T21:41:23.9903155Z                             auto tmp36 = (tmp15 != tmp15) ? tmp15 : std::min(tmp35, tmp15);
2023-01-11T21:41:23.9903340Z                             auto tmp37 = in_ptr0[tmp18 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:23.9903511Z                             auto tmp38 = in_ptr1[tmp18 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:23.9903661Z                             auto tmp39 = tmp37 == tmp0;
2023-01-11T21:41:23.9903812Z                             auto tmp40 = tmp35 < tmp9;
2023-01-11T21:41:23.9903960Z                             auto tmp41 = tmp13 < tmp11;
2023-01-11T21:41:23.9904107Z                             auto tmp42 = tmp40 & tmp41;
2023-01-11T21:41:23.9904253Z                             auto tmp43 = tmp42 & tmp39;
2023-01-11T21:41:23.9904519Z                             auto tmp44 = tmp34 + tmp38;
2023-01-11T21:41:23.9904677Z                             auto tmp45 = tmp43 ? tmp44 : tmp34;
2023-01-11T21:41:23.9904845Z                             auto tmp46 = in_ptr0[tmp25 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:23.9905022Z                             auto tmp47 = in_ptr1[tmp25 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:23.9905171Z                             auto tmp48 = tmp46 == tmp0;
2023-01-11T21:41:23.9905312Z                             auto tmp49 = tmp40 & tmp30;
2023-01-11T21:41:23.9905458Z                             auto tmp50 = tmp49 & tmp48;
2023-01-11T21:41:23.9905602Z                             auto tmp51 = tmp45 + tmp47;
2023-01-11T21:41:23.9905761Z                             auto tmp52 = tmp50 ? tmp51 : tmp45;
2023-01-11T21:41:23.9905905Z                             out_ptr0[i2 + (56*i1) + (2240*i0)] = tmp52;
2023-01-11T21:41:23.9906009Z                         }
2023-01-11T21:41:23.9906188Z                     }
2023-01-11T21:41:23.9906296Z                 }
2023-01-11T21:41:23.9906391Z             }
2023-01-11T21:41:23.9906486Z         }
2023-01-11T21:41:23.9906580Z     }
2023-01-11T21:41:23.9906656Z }
2023-01-11T21:41:23.9906793Z ''')
2023-01-11T21:41:23.9906802Z 
2023-01-11T21:41:23.9906810Z 
2023-01-11T21:41:23.9906950Z async_compile.wait(globals())
2023-01-11T21:41:23.9907063Z del async_compile
2023-01-11T21:41:23.9907070Z 
2023-01-11T21:41:23.9907176Z def call(args):
2023-01-11T21:41:23.9907294Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9907399Z     args.clear()
2023-01-11T21:41:23.9907787Z     buf0 = empty_strided((2, 4, 40, 56), (8960, 2240, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9908046Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9908154Z     del arg0_1
2023-01-11T21:41:23.9908254Z     del arg2_1
2023-01-11T21:41:23.9908364Z     return (buf0, )
2023-01-11T21:41:23.9908377Z 
2023-01-11T21:41:23.9908388Z 
2023-01-11T21:41:23.9908503Z if __name__ == "__main__":
2023-01-11T21:41:23.9908683Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9908879Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9909229Z     arg0_1 = rand_strided((2, 4, 21, 29), (2436, 609, 29, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9909585Z     arg1_1 = rand_strided((2, 4, 40, 56), (8960, 2240, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9909937Z     arg2_1 = rand_strided((2, 4, 21, 29), (2436, 609, 29, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9910126Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9910135Z 
2023-01-11T21:41:23.9910234Z ok (1.680s)
2023-01-11T21:41:23.9911058Z   test_max_pool2d_with_indices_backward3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9911256Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9911695Z [2023-01-11 21:36:32,953] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 370
2023-01-11T21:41:23.9912133Z [2023-01-11 21:36:34,567] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 370
2023-01-11T21:41:23.9912140Z 
2023-01-11T21:41:23.9912278Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9912364Z import torch
2023-01-11T21:41:23.9912465Z import random
2023-01-11T21:41:23.9912639Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9912815Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9912822Z 
2023-01-11T21:41:23.9912933Z aten = torch.ops.aten
2023-01-11T21:41:23.9913209Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9913343Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9913352Z 
2023-01-11T21:41:23.9913358Z 
2023-01-11T21:41:23.9913560Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9913956Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9914147Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9914314Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9914472Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9914566Z {
2023-01-11T21:41:23.9914725Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9914818Z     {
2023-01-11T21:41:23.9914917Z         #pragma omp for 
2023-01-11T21:41:23.9915045Z         for(long i0=0; i0<8192; i0+=1)
2023-01-11T21:41:23.9915141Z         {
2023-01-11T21:41:23.9915265Z             #pragma GCC ivdep
2023-01-11T21:41:23.9915490Z             for(long i1=0; i1<37; i1+=1)
2023-01-11T21:41:23.9915591Z             {
2023-01-11T21:41:23.9915717Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9915841Z                 for(long i2=0; i2<38; i2+=1)
2023-01-11T21:41:23.9915939Z                 {
2023-01-11T21:41:23.9916041Z                     {
2023-01-11T21:41:23.9916140Z                         {
2023-01-11T21:41:23.9916316Z                             auto tmp0 = static_cast<int>(i2 + (38*i1));
2023-01-11T21:41:23.9916494Z                             auto tmp1 = static_cast<int>(((1 + i1) / 2));
2023-01-11T21:41:23.9916670Z                             auto tmp2 = static_cast<int>(((1 + i2) / 2));
2023-01-11T21:41:23.9916831Z                             auto tmp3 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:23.9917002Z                             auto tmp4 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:23.9917166Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:23.9917389Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:23.9917589Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:23.9917756Z                             auto tmp8 = static_cast<int>(19);
2023-01-11T21:41:23.9917957Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:23.9918158Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::min(tmp4, tmp8);
2023-01-11T21:41:23.9918295Z                             auto tmp11 = tmp6 + tmp5;
2023-01-11T21:41:23.9918415Z                             auto tmp12 = tmp7 + tmp5;
2023-01-11T21:41:23.9918568Z                             auto tmp13 = static_cast<int>(1);
2023-01-11T21:41:23.9918812Z                             auto tmp14 = tmp9 - tmp13;
2023-01-11T21:41:23.9919007Z                             auto tmp15 = (tmp14 != tmp14) ? tmp14 : std::min(tmp11, tmp14);
2023-01-11T21:41:23.9919241Z                             auto tmp16 = tmp10 - tmp13;
2023-01-11T21:41:23.9919435Z                             auto tmp17 = (tmp16 != tmp16) ? tmp16 : std::min(tmp12, tmp16);
2023-01-11T21:41:23.9919609Z                             auto tmp18 = in_ptr0[tmp17 + (19*tmp15) + (361*i0)];
2023-01-11T21:41:23.9919781Z                             auto tmp19 = in_ptr1[tmp17 + (19*tmp15) + (361*i0)];
2023-01-11T21:41:23.9919912Z                             auto tmp20 = tmp18 == tmp0;
2023-01-11T21:41:23.9920077Z                             auto tmp21 = static_cast<float>(0.0);
2023-01-11T21:41:23.9920234Z                             auto tmp22 = tmp20 ? tmp19 : tmp21;
2023-01-11T21:41:23.9920382Z                             out_ptr0[i2 + (38*i1) + (1406*i0)] = tmp22;
2023-01-11T21:41:23.9920480Z                         }
2023-01-11T21:41:23.9920577Z                     }
2023-01-11T21:41:23.9920672Z                 }
2023-01-11T21:41:23.9920757Z             }
2023-01-11T21:41:23.9920857Z         }
2023-01-11T21:41:23.9921031Z     }
2023-01-11T21:41:23.9921136Z }
2023-01-11T21:41:23.9921279Z ''')
2023-01-11T21:41:23.9921288Z 
2023-01-11T21:41:23.9921296Z 
2023-01-11T21:41:23.9921443Z async_compile.wait(globals())
2023-01-11T21:41:23.9921555Z del async_compile
2023-01-11T21:41:23.9921562Z 
2023-01-11T21:41:23.9921657Z def call(args):
2023-01-11T21:41:23.9921784Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9921894Z     args.clear()
2023-01-11T21:41:23.9922278Z     buf0 = empty_strided((32, 256, 37, 38), (359936, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9922541Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9922648Z     del arg0_1
2023-01-11T21:41:23.9922748Z     del arg2_1
2023-01-11T21:41:23.9922846Z     return (buf0, )
2023-01-11T21:41:23.9922854Z 
2023-01-11T21:41:23.9922875Z 
2023-01-11T21:41:23.9922979Z if __name__ == "__main__":
2023-01-11T21:41:23.9923163Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9923440Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9923833Z     arg0_1 = rand_strided((32, 256, 19, 19), (92416, 361, 19, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9924207Z     arg1_1 = rand_strided((32, 256, 37, 38), (359936, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9924575Z     arg2_1 = rand_strided((32, 256, 19, 19), (92416, 361, 19, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9924756Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9924762Z 
2023-01-11T21:41:23.9924848Z ok (2.439s)
2023-01-11T21:41:23.9925541Z   test_max_pool2d_with_indices_backward4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9925722Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9926106Z [2023-01-11 21:36:35,151] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 371
2023-01-11T21:41:23.9926113Z 
2023-01-11T21:41:23.9926250Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9926352Z import torch
2023-01-11T21:41:23.9926460Z import random
2023-01-11T21:41:23.9926648Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9926845Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9926852Z 
2023-01-11T21:41:23.9926972Z aten = torch.ops.aten
2023-01-11T21:41:23.9927169Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9927316Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9927325Z 
2023-01-11T21:41:23.9927331Z 
2023-01-11T21:41:23.9927558Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9927899Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9928085Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9928255Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9928411Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9928502Z {
2023-01-11T21:41:23.9928642Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9928738Z     {
2023-01-11T21:41:23.9928858Z         #pragma omp for 
2023-01-11T21:41:23.9928988Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:23.9929084Z         {
2023-01-11T21:41:23.9929207Z             #pragma GCC ivdep
2023-01-11T21:41:23.9929321Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:23.9929413Z             {
2023-01-11T21:41:23.9929539Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9929676Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:23.9929773Z                 {
2023-01-11T21:41:23.9929962Z                     {
2023-01-11T21:41:23.9930066Z                         {
2023-01-11T21:41:23.9930230Z                             auto tmp0 = static_cast<int>(i2 + (4*i1));
2023-01-11T21:41:23.9930522Z                             auto tmp1 = static_cast<int>((-2) + i1);
2023-01-11T21:41:23.9930783Z                             auto tmp2 = static_cast<int>((-2) + i2);
2023-01-11T21:41:23.9930935Z                             auto tmp3 = static_cast<int>(3 + i1);
2023-01-11T21:41:23.9931094Z                             auto tmp4 = static_cast<int>(3 + i2);
2023-01-11T21:41:23.9931248Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:23.9931444Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:23.9931622Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:23.9931758Z                             auto tmp8 = static_cast<int>(3);
2023-01-11T21:41:23.9932017Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:23.9932173Z                             auto tmp10 = static_cast<int>(4);
2023-01-11T21:41:23.9932365Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:41:23.9932503Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:41:23.9932642Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:41:23.9932789Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:41:23.9933038Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:41:23.9933236Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:41:23.9933479Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:41:23.9933686Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:41:23.9933872Z                             auto tmp19 = in_ptr0[tmp18 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9934061Z                             auto tmp20 = in_ptr1[tmp18 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9934209Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:41:23.9934387Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:23.9934549Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:41:23.9934689Z                             auto tmp24 = tmp7 + tmp14;
2023-01-11T21:41:23.9934896Z                             auto tmp25 = (tmp17 != tmp17) ? tmp17 : std::min(tmp24, tmp17);
2023-01-11T21:41:23.9935078Z                             auto tmp26 = in_ptr0[tmp25 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9935265Z                             auto tmp27 = in_ptr1[tmp25 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9935411Z                             auto tmp28 = tmp26 == tmp0;
2023-01-11T21:41:23.9935555Z                             auto tmp29 = tmp12 < tmp9;
2023-01-11T21:41:23.9935713Z                             auto tmp30 = tmp24 < tmp11;
2023-01-11T21:41:23.9935858Z                             auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:23.9935988Z                             auto tmp32 = tmp31 & tmp28;
2023-01-11T21:41:23.9936133Z                             auto tmp33 = tmp23 + tmp27;
2023-01-11T21:41:23.9936295Z                             auto tmp34 = tmp32 ? tmp33 : tmp23;
2023-01-11T21:41:23.9936462Z                             auto tmp35 = static_cast<int>(2);
2023-01-11T21:41:23.9936608Z                             auto tmp36 = tmp7 + tmp35;
2023-01-11T21:41:23.9936812Z                             auto tmp37 = (tmp17 != tmp17) ? tmp17 : std::min(tmp36, tmp17);
2023-01-11T21:41:23.9936984Z                             auto tmp38 = in_ptr0[tmp37 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9937150Z                             auto tmp39 = in_ptr1[tmp37 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9937273Z                             auto tmp40 = tmp38 == tmp0;
2023-01-11T21:41:23.9937520Z                             auto tmp41 = tmp36 < tmp11;
2023-01-11T21:41:23.9937656Z                             auto tmp42 = tmp29 & tmp41;
2023-01-11T21:41:23.9937795Z                             auto tmp43 = tmp42 & tmp40;
2023-01-11T21:41:23.9937932Z                             auto tmp44 = tmp34 + tmp39;
2023-01-11T21:41:23.9938075Z                             auto tmp45 = tmp43 ? tmp44 : tmp34;
2023-01-11T21:41:23.9938204Z                             auto tmp46 = tmp7 + tmp8;
2023-01-11T21:41:23.9938386Z                             auto tmp47 = (tmp17 != tmp17) ? tmp17 : std::min(tmp46, tmp17);
2023-01-11T21:41:23.9938539Z                             auto tmp48 = in_ptr0[tmp47 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9938696Z                             auto tmp49 = in_ptr1[tmp47 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9938831Z                             auto tmp50 = tmp48 == tmp0;
2023-01-11T21:41:23.9938970Z                             auto tmp51 = tmp46 < tmp11;
2023-01-11T21:41:23.9939175Z                             auto tmp52 = tmp29 & tmp51;
2023-01-11T21:41:23.9939316Z                             auto tmp53 = tmp52 & tmp50;
2023-01-11T21:41:23.9939452Z                             auto tmp54 = tmp45 + tmp49;
2023-01-11T21:41:23.9939587Z                             auto tmp55 = tmp53 ? tmp54 : tmp45;
2023-01-11T21:41:23.9939731Z                             auto tmp56 = tmp7 + tmp10;
2023-01-11T21:41:23.9939924Z                             auto tmp57 = (tmp17 != tmp17) ? tmp17 : std::min(tmp56, tmp17);
2023-01-11T21:41:23.9940101Z                             auto tmp58 = in_ptr0[tmp57 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9940262Z                             auto tmp59 = in_ptr1[tmp57 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:23.9940391Z                             auto tmp60 = tmp58 == tmp0;
2023-01-11T21:41:23.9940527Z                             auto tmp61 = tmp56 < tmp11;
2023-01-11T21:41:23.9940664Z                             auto tmp62 = tmp29 & tmp61;
2023-01-11T21:41:23.9940798Z                             auto tmp63 = tmp62 & tmp60;
2023-01-11T21:41:23.9940942Z                             auto tmp64 = tmp55 + tmp59;
2023-01-11T21:41:23.9941102Z                             auto tmp65 = tmp63 ? tmp64 : tmp55;
2023-01-11T21:41:23.9941240Z                             auto tmp66 = tmp6 + tmp14;
2023-01-11T21:41:23.9941430Z                             auto tmp67 = (tmp15 != tmp15) ? tmp15 : std::min(tmp66, tmp15);
2023-01-11T21:41:23.9941585Z                             auto tmp68 = in_ptr0[tmp18 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9941740Z                             auto tmp69 = in_ptr1[tmp18 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9941871Z                             auto tmp70 = tmp68 == tmp0;
2023-01-11T21:41:23.9941987Z                             auto tmp71 = tmp66 < tmp9;
2023-01-11T21:41:23.9942116Z                             auto tmp72 = tmp13 < tmp11;
2023-01-11T21:41:23.9942244Z                             auto tmp73 = tmp71 & tmp72;
2023-01-11T21:41:23.9942547Z                             auto tmp74 = tmp73 & tmp70;
2023-01-11T21:41:23.9942684Z                             auto tmp75 = tmp65 + tmp69;
2023-01-11T21:41:23.9942825Z                             auto tmp76 = tmp74 ? tmp75 : tmp65;
2023-01-11T21:41:23.9942985Z                             auto tmp77 = in_ptr0[tmp25 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9943145Z                             auto tmp78 = in_ptr1[tmp25 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9943264Z                             auto tmp79 = tmp77 == tmp0;
2023-01-11T21:41:23.9943394Z                             auto tmp80 = tmp71 & tmp30;
2023-01-11T21:41:23.9943526Z                             auto tmp81 = tmp80 & tmp79;
2023-01-11T21:41:23.9943652Z                             auto tmp82 = tmp76 + tmp78;
2023-01-11T21:41:23.9943789Z                             auto tmp83 = tmp81 ? tmp82 : tmp76;
2023-01-11T21:41:23.9943950Z                             auto tmp84 = in_ptr0[tmp37 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9944234Z                             auto tmp85 = in_ptr1[tmp37 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9944352Z                             auto tmp86 = tmp84 == tmp0;
2023-01-11T21:41:23.9944484Z                             auto tmp87 = tmp71 & tmp41;
2023-01-11T21:41:23.9944611Z                             auto tmp88 = tmp87 & tmp86;
2023-01-11T21:41:23.9944738Z                             auto tmp89 = tmp83 + tmp85;
2023-01-11T21:41:23.9944880Z                             auto tmp90 = tmp88 ? tmp89 : tmp83;
2023-01-11T21:41:23.9945038Z                             auto tmp91 = in_ptr0[tmp47 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9945197Z                             auto tmp92 = in_ptr1[tmp47 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9945326Z                             auto tmp93 = tmp91 == tmp0;
2023-01-11T21:41:23.9945439Z                             auto tmp94 = tmp71 & tmp51;
2023-01-11T21:41:23.9945566Z                             auto tmp95 = tmp94 & tmp93;
2023-01-11T21:41:23.9945772Z                             auto tmp96 = tmp90 + tmp92;
2023-01-11T21:41:23.9945923Z                             auto tmp97 = tmp95 ? tmp96 : tmp90;
2023-01-11T21:41:23.9946085Z                             auto tmp98 = in_ptr0[tmp57 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9946251Z                             auto tmp99 = in_ptr1[tmp57 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:23.9946388Z                             auto tmp100 = tmp98 == tmp0;
2023-01-11T21:41:23.9946511Z                             auto tmp101 = tmp71 & tmp61;
2023-01-11T21:41:23.9946651Z                             auto tmp102 = tmp101 & tmp100;
2023-01-11T21:41:23.9946781Z                             auto tmp103 = tmp97 + tmp99;
2023-01-11T21:41:23.9946926Z                             auto tmp104 = tmp102 ? tmp103 : tmp97;
2023-01-11T21:41:23.9947055Z                             auto tmp105 = tmp6 + tmp35;
2023-01-11T21:41:23.9947250Z                             auto tmp106 = (tmp15 != tmp15) ? tmp15 : std::min(tmp105, tmp15);
2023-01-11T21:41:23.9947422Z                             auto tmp107 = in_ptr0[tmp18 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9947587Z                             auto tmp108 = in_ptr1[tmp18 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9947711Z                             auto tmp109 = tmp107 == tmp0;
2023-01-11T21:41:23.9947849Z                             auto tmp110 = tmp105 < tmp9;
2023-01-11T21:41:23.9947986Z                             auto tmp111 = tmp110 & tmp72;
2023-01-11T21:41:23.9948127Z                             auto tmp112 = tmp111 & tmp109;
2023-01-11T21:41:23.9948263Z                             auto tmp113 = tmp104 + tmp108;
2023-01-11T21:41:23.9948413Z                             auto tmp114 = tmp112 ? tmp113 : tmp104;
2023-01-11T21:41:23.9948577Z                             auto tmp115 = in_ptr0[tmp25 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9948741Z                             auto tmp116 = in_ptr1[tmp25 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9948864Z                             auto tmp117 = tmp115 == tmp0;
2023-01-11T21:41:23.9948999Z                             auto tmp118 = tmp110 & tmp30;
2023-01-11T21:41:23.9949139Z                             auto tmp119 = tmp118 & tmp117;
2023-01-11T21:41:23.9949272Z                             auto tmp120 = tmp114 + tmp116;
2023-01-11T21:41:23.9949420Z                             auto tmp121 = tmp119 ? tmp120 : tmp114;
2023-01-11T21:41:23.9949583Z                             auto tmp122 = in_ptr0[tmp37 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9949742Z                             auto tmp123 = in_ptr1[tmp37 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9949872Z                             auto tmp124 = tmp122 == tmp0;
2023-01-11T21:41:23.9949993Z                             auto tmp125 = tmp110 & tmp41;
2023-01-11T21:41:23.9950128Z                             auto tmp126 = tmp125 & tmp124;
2023-01-11T21:41:23.9950267Z                             auto tmp127 = tmp121 + tmp123;
2023-01-11T21:41:23.9950418Z                             auto tmp128 = tmp126 ? tmp127 : tmp121;
2023-01-11T21:41:23.9950660Z                             auto tmp129 = in_ptr0[tmp47 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9950817Z                             auto tmp130 = in_ptr1[tmp47 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9950953Z                             auto tmp131 = tmp129 == tmp0;
2023-01-11T21:41:23.9951071Z                             auto tmp132 = tmp110 & tmp51;
2023-01-11T21:41:23.9951208Z                             auto tmp133 = tmp132 & tmp131;
2023-01-11T21:41:23.9951340Z                             auto tmp134 = tmp128 + tmp130;
2023-01-11T21:41:23.9951485Z                             auto tmp135 = tmp133 ? tmp134 : tmp128;
2023-01-11T21:41:23.9951647Z                             auto tmp136 = in_ptr0[tmp57 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9951804Z                             auto tmp137 = in_ptr1[tmp57 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:23.9951938Z                             auto tmp138 = tmp136 == tmp0;
2023-01-11T21:41:23.9952137Z                             auto tmp139 = tmp110 & tmp61;
2023-01-11T21:41:23.9952266Z                             auto tmp140 = tmp139 & tmp138;
2023-01-11T21:41:23.9952405Z                             auto tmp141 = tmp135 + tmp137;
2023-01-11T21:41:23.9952553Z                             auto tmp142 = tmp140 ? tmp141 : tmp135;
2023-01-11T21:41:23.9952692Z                             auto tmp143 = tmp6 + tmp8;
2023-01-11T21:41:23.9952882Z                             auto tmp144 = (tmp15 != tmp15) ? tmp15 : std::min(tmp143, tmp15);
2023-01-11T21:41:23.9953041Z                             auto tmp145 = in_ptr0[tmp18 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9953203Z                             auto tmp146 = in_ptr1[tmp18 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9953340Z                             auto tmp147 = tmp145 == tmp0;
2023-01-11T21:41:23.9953455Z                             auto tmp148 = tmp143 < tmp9;
2023-01-11T21:41:23.9953591Z                             auto tmp149 = tmp148 & tmp72;
2023-01-11T21:41:23.9953812Z                             auto tmp150 = tmp149 & tmp147;
2023-01-11T21:41:23.9953953Z                             auto tmp151 = tmp142 + tmp146;
2023-01-11T21:41:23.9954103Z                             auto tmp152 = tmp150 ? tmp151 : tmp142;
2023-01-11T21:41:23.9954263Z                             auto tmp153 = in_ptr0[tmp25 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9954426Z                             auto tmp154 = in_ptr1[tmp25 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9954566Z                             auto tmp155 = tmp153 == tmp0;
2023-01-11T21:41:23.9954683Z                             auto tmp156 = tmp148 & tmp30;
2023-01-11T21:41:23.9954823Z                             auto tmp157 = tmp156 & tmp155;
2023-01-11T21:41:23.9954963Z                             auto tmp158 = tmp152 + tmp154;
2023-01-11T21:41:23.9955113Z                             auto tmp159 = tmp157 ? tmp158 : tmp152;
2023-01-11T21:41:23.9955275Z                             auto tmp160 = in_ptr0[tmp37 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9955442Z                             auto tmp161 = in_ptr1[tmp37 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9955578Z                             auto tmp162 = tmp160 == tmp0;
2023-01-11T21:41:23.9955698Z                             auto tmp163 = tmp148 & tmp41;
2023-01-11T21:41:23.9955838Z                             auto tmp164 = tmp163 & tmp162;
2023-01-11T21:41:23.9955971Z                             auto tmp165 = tmp159 + tmp161;
2023-01-11T21:41:23.9956128Z                             auto tmp166 = tmp164 ? tmp165 : tmp159;
2023-01-11T21:41:23.9956297Z                             auto tmp167 = in_ptr0[tmp47 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9956462Z                             auto tmp168 = in_ptr1[tmp47 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9956603Z                             auto tmp169 = tmp167 == tmp0;
2023-01-11T21:41:23.9956744Z                             auto tmp170 = tmp148 & tmp51;
2023-01-11T21:41:23.9956951Z                             auto tmp171 = tmp170 & tmp169;
2023-01-11T21:41:23.9957092Z                             auto tmp172 = tmp166 + tmp168;
2023-01-11T21:41:23.9957255Z                             auto tmp173 = tmp171 ? tmp172 : tmp166;
2023-01-11T21:41:23.9957426Z                             auto tmp174 = in_ptr0[tmp57 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9957596Z                             auto tmp175 = in_ptr1[tmp57 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:23.9957745Z                             auto tmp176 = tmp174 == tmp0;
2023-01-11T21:41:23.9957892Z                             auto tmp177 = tmp148 & tmp61;
2023-01-11T21:41:23.9958045Z                             auto tmp178 = tmp177 & tmp176;
2023-01-11T21:41:23.9958182Z                             auto tmp179 = tmp173 + tmp175;
2023-01-11T21:41:23.9958348Z                             auto tmp180 = tmp178 ? tmp179 : tmp173;
2023-01-11T21:41:23.9958508Z                             auto tmp181 = tmp6 + tmp10;
2023-01-11T21:41:23.9958775Z                             auto tmp182 = (tmp15 != tmp15) ? tmp15 : std::min(tmp181, tmp15);
2023-01-11T21:41:23.9958951Z                             auto tmp183 = in_ptr0[tmp18 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9959139Z                             auto tmp184 = in_ptr1[tmp18 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9959293Z                             auto tmp185 = tmp183 == tmp0;
2023-01-11T21:41:23.9959443Z                             auto tmp186 = tmp181 < tmp9;
2023-01-11T21:41:23.9959578Z                             auto tmp187 = tmp186 & tmp72;
2023-01-11T21:41:23.9959731Z                             auto tmp188 = tmp187 & tmp185;
2023-01-11T21:41:23.9959887Z                             auto tmp189 = tmp180 + tmp184;
2023-01-11T21:41:23.9960048Z                             auto tmp190 = tmp188 ? tmp189 : tmp180;
2023-01-11T21:41:23.9960227Z                             auto tmp191 = in_ptr0[tmp25 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9960416Z                             auto tmp192 = in_ptr1[tmp25 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9960563Z                             auto tmp193 = tmp191 == tmp0;
2023-01-11T21:41:23.9960691Z                             auto tmp194 = tmp186 & tmp30;
2023-01-11T21:41:23.9960842Z                             auto tmp195 = tmp194 & tmp193;
2023-01-11T21:41:23.9960980Z                             auto tmp196 = tmp190 + tmp192;
2023-01-11T21:41:23.9961126Z                             auto tmp197 = tmp195 ? tmp196 : tmp190;
2023-01-11T21:41:23.9961304Z                             auto tmp198 = in_ptr0[tmp37 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9961485Z                             auto tmp199 = in_ptr1[tmp37 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9961636Z                             auto tmp200 = tmp198 == tmp0;
2023-01-11T21:41:23.9961773Z                             auto tmp201 = tmp186 & tmp41;
2023-01-11T21:41:23.9961900Z                             auto tmp202 = tmp201 & tmp200;
2023-01-11T21:41:23.9962037Z                             auto tmp203 = tmp197 + tmp199;
2023-01-11T21:41:23.9962190Z                             auto tmp204 = tmp202 ? tmp203 : tmp197;
2023-01-11T21:41:23.9962364Z                             auto tmp205 = in_ptr0[tmp47 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9962537Z                             auto tmp206 = in_ptr1[tmp47 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9962678Z                             auto tmp207 = tmp205 == tmp0;
2023-01-11T21:41:23.9962845Z                             auto tmp208 = tmp186 & tmp51;
2023-01-11T21:41:23.9963002Z                             auto tmp209 = tmp208 & tmp207;
2023-01-11T21:41:23.9963155Z                             auto tmp210 = tmp204 + tmp206;
2023-01-11T21:41:23.9963339Z                             auto tmp211 = tmp209 ? tmp210 : tmp204;
2023-01-11T21:41:23.9963544Z                             auto tmp212 = in_ptr0[tmp57 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9963721Z                             auto tmp213 = in_ptr1[tmp57 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:23.9963943Z                             auto tmp214 = tmp212 == tmp0;
2023-01-11T21:41:23.9964115Z                             auto tmp215 = tmp186 & tmp61;
2023-01-11T21:41:23.9964265Z                             auto tmp216 = tmp215 & tmp214;
2023-01-11T21:41:23.9964403Z                             auto tmp217 = tmp211 + tmp213;
2023-01-11T21:41:23.9964565Z                             auto tmp218 = tmp216 ? tmp217 : tmp211;
2023-01-11T21:41:23.9964714Z                             out_ptr0[i2 + (4*i1) + (12*i0)] = tmp218;
2023-01-11T21:41:23.9964814Z                         }
2023-01-11T21:41:23.9964914Z                     }
2023-01-11T21:41:23.9965016Z                 }
2023-01-11T21:41:23.9965115Z             }
2023-01-11T21:41:23.9965192Z         }
2023-01-11T21:41:23.9965290Z     }
2023-01-11T21:41:23.9965388Z }
2023-01-11T21:41:23.9965558Z ''')
2023-01-11T21:41:23.9965567Z 
2023-01-11T21:41:23.9965574Z 
2023-01-11T21:41:23.9965725Z async_compile.wait(globals())
2023-01-11T21:41:23.9965838Z del async_compile
2023-01-11T21:41:23.9965936Z 
2023-01-11T21:41:23.9966046Z def call(args):
2023-01-11T21:41:23.9966173Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9966273Z     args.clear()
2023-01-11T21:41:23.9966640Z     buf0 = empty_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9966909Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9967021Z     del arg0_1
2023-01-11T21:41:23.9967130Z     del arg2_1
2023-01-11T21:41:23.9967245Z     return (buf0, )
2023-01-11T21:41:23.9967252Z 
2023-01-11T21:41:23.9967258Z 
2023-01-11T21:41:23.9967380Z if __name__ == "__main__":
2023-01-11T21:41:23.9967557Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9967773Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9968125Z     arg0_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9968490Z     arg1_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9968829Z     arg2_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9969030Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9969492Z [2023-01-11 21:36:36,967] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 371
2023-01-11T21:41:23.9969502Z 
2023-01-11T21:41:23.9969604Z ok (1.850s)
2023-01-11T21:41:23.9970446Z   test_max_pool2d_with_indices_backward5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9970662Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9971072Z [2023-01-11 21:36:37,003] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 372
2023-01-11T21:41:23.9971472Z [2023-01-11 21:36:37,018] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.max_pool2d_with_indices_backward
2023-01-11T21:41:23.9971875Z [2023-01-11 21:36:37,021] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 372
2023-01-11T21:41:23.9971884Z 
2023-01-11T21:41:23.9972033Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9972143Z import torch
2023-01-11T21:41:23.9972254Z import random
2023-01-11T21:41:23.9972432Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9972637Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9972644Z 
2023-01-11T21:41:23.9972765Z aten = torch.ops.aten
2023-01-11T21:41:23.9973007Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9973248Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9973256Z 
2023-01-11T21:41:23.9973262Z 
2023-01-11T21:41:23.9973411Z async_compile.wait(globals())
2023-01-11T21:41:23.9973526Z del async_compile
2023-01-11T21:41:23.9973534Z 
2023-01-11T21:41:23.9973646Z def call(args):
2023-01-11T21:41:23.9973779Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9973901Z     args.clear()
2023-01-11T21:41:23.9974132Z     buf0 = aten.max_pool2d_with_indices_backward(arg0_1, arg1_1, [13, 13], [1, 1], [2, 2], [1, 1], False, arg2_1)
2023-01-11T21:41:23.9974238Z     del arg0_1
2023-01-11T21:41:23.9974333Z     del arg1_1
2023-01-11T21:41:23.9974425Z     del arg2_1
2023-01-11T21:41:23.9974521Z     buf1 = buf0
2023-01-11T21:41:23.9974680Z     assert_size_stride(buf1, (2, 64, 20, 20), (25600, 400, 20, 1))
2023-01-11T21:41:23.9974774Z     del buf0
2023-01-11T21:41:23.9974857Z     return (buf1, )
2023-01-11T21:41:23.9974864Z 
2023-01-11T21:41:23.9974870Z 
2023-01-11T21:41:23.9974980Z if __name__ == "__main__":
2023-01-11T21:41:23.9975209Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9975392Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9975754Z     arg0_1 = rand_strided((2, 64, 12, 12), (9216, 144, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9976103Z     arg1_1 = rand_strided((2, 64, 20, 20), (25600, 400, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9976440Z     arg2_1 = rand_strided((2, 64, 12, 12), (9216, 144, 12, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9976631Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9976641Z 
2023-01-11T21:41:23.9976727Z ok (0.045s)
2023-01-11T21:41:23.9977565Z   test_max_pool2d_with_indices_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9977774Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9978227Z [2023-01-11 21:36:37,047] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 373
2023-01-11T21:41:23.9978680Z [2023-01-11 21:36:38,621] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 373
2023-01-11T21:41:23.9978689Z 
2023-01-11T21:41:23.9978844Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9978956Z import torch
2023-01-11T21:41:23.9979070Z import random
2023-01-11T21:41:23.9979260Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9979443Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9979468Z 
2023-01-11T21:41:23.9979575Z aten = torch.ops.aten
2023-01-11T21:41:23.9979798Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9979958Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9979966Z 
2023-01-11T21:41:23.9979974Z 
2023-01-11T21:41:23.9980198Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9980541Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9980728Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:23.9980885Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:23.9981019Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:23.9981110Z {
2023-01-11T21:41:23.9981250Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9981344Z     {
2023-01-11T21:41:23.9981464Z         #pragma omp for 
2023-01-11T21:41:23.9981601Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9981695Z         {
2023-01-11T21:41:23.9981808Z             #pragma GCC ivdep
2023-01-11T21:41:23.9981950Z             for(long i1=0; i1<18; i1+=1)
2023-01-11T21:41:23.9982058Z             {
2023-01-11T21:41:23.9982305Z                 #pragma GCC ivdep
2023-01-11T21:41:23.9982757Z                 for(long i2=0; i2<14; i2+=1)
2023-01-11T21:41:23.9982865Z                 {
2023-01-11T21:41:23.9982934Z                     {
2023-01-11T21:41:23.9982990Z                         {
2023-01-11T21:41:23.9983111Z                             auto tmp0 = static_cast<int>(i2 + (14*i1));
2023-01-11T21:41:23.9983222Z                             auto tmp1 = static_cast<int>((i1 / 2));
2023-01-11T21:41:23.9983329Z                             auto tmp2 = static_cast<int>((i2 / 2));
2023-01-11T21:41:23.9983442Z                             auto tmp3 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:23.9983554Z                             auto tmp4 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:23.9983659Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:23.9983798Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:23.9984024Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:23.9984134Z                             auto tmp8 = static_cast<int>(9);
2023-01-11T21:41:23.9984261Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:23.9984367Z                             auto tmp10 = static_cast<int>(7);
2023-01-11T21:41:23.9984498Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:41:23.9984597Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:41:23.9984691Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:41:23.9984796Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:41:23.9984960Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:41:23.9985089Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:41:23.9985238Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:41:23.9985373Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:41:23.9985494Z                             auto tmp19 = in_ptr0[tmp18 + (7*tmp16) + (63*i0)];
2023-01-11T21:41:23.9985612Z                             auto tmp20 = in_ptr1[tmp18 + (7*tmp16) + (63*i0)];
2023-01-11T21:41:23.9985710Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:41:23.9985821Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:23.9985912Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:41:23.9986019Z                             out_ptr0[i2 + (14*i1) + (252*i0)] = tmp23;
2023-01-11T21:41:23.9986087Z                         }
2023-01-11T21:41:23.9986155Z                     }
2023-01-11T21:41:23.9986220Z                 }
2023-01-11T21:41:23.9986286Z             }
2023-01-11T21:41:23.9986348Z         }
2023-01-11T21:41:23.9986395Z     }
2023-01-11T21:41:23.9986458Z }
2023-01-11T21:41:23.9986540Z ''')
2023-01-11T21:41:23.9986547Z 
2023-01-11T21:41:23.9986551Z 
2023-01-11T21:41:23.9986643Z async_compile.wait(globals())
2023-01-11T21:41:23.9986715Z del async_compile
2023-01-11T21:41:23.9986720Z 
2023-01-11T21:41:23.9986790Z def call(args):
2023-01-11T21:41:23.9986874Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:23.9986932Z     args.clear()
2023-01-11T21:41:23.9987154Z     buf0 = empty_strided((2, 4, 18, 14), (1008, 252, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9987320Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:23.9987389Z     del arg0_1
2023-01-11T21:41:23.9987454Z     del arg2_1
2023-01-11T21:41:23.9987526Z     return (buf0, )
2023-01-11T21:41:23.9987531Z 
2023-01-11T21:41:23.9987535Z 
2023-01-11T21:41:23.9987613Z if __name__ == "__main__":
2023-01-11T21:41:23.9987730Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:23.9987844Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:23.9988103Z     arg0_1 = rand_strided((2, 4, 9, 7), (252, 63, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9988320Z     arg1_1 = rand_strided((2, 4, 18, 14), (1008, 252, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:23.9988532Z     arg2_1 = rand_strided((2, 4, 9, 7), (252, 63, 7, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:23.9988652Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:23.9988656Z 
2023-01-11T21:41:23.9988724Z ok (1.598s)
2023-01-11T21:41:23.9989188Z   test_mean_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:23.9989341Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:23.9989608Z [2023-01-11 21:36:38,644] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 374
2023-01-11T21:41:23.9989863Z [2023-01-11 21:36:40,337] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 374
2023-01-11T21:41:23.9989882Z 
2023-01-11T21:41:23.9989962Z from ctypes import c_void_p, c_long
2023-01-11T21:41:23.9990031Z import torch
2023-01-11T21:41:23.9990101Z import random
2023-01-11T21:41:23.9990217Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:23.9990335Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:23.9990340Z 
2023-01-11T21:41:23.9990417Z aten = torch.ops.aten
2023-01-11T21:41:23.9990552Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:23.9990630Z async_compile = AsyncCompile()
2023-01-11T21:41:23.9990635Z 
2023-01-11T21:41:23.9990650Z 
2023-01-11T21:41:23.9990772Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:23.9990982Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:23.9991097Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:23.9991198Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:23.9991301Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:23.9991398Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:23.9991492Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:23.9991541Z {
2023-01-11T21:41:23.9991625Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:23.9991706Z     auto out_ptr0 = in_out_ptr1;
2023-01-11T21:41:23.9991766Z     {
2023-01-11T21:41:23.9991950Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.9992022Z         float tmp1 = 0;
2023-01-11T21:41:23.9992135Z         auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:23.9992229Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9992291Z         {
2023-01-11T21:41:23.9992396Z             #pragma omp for reduction(+:tmp1_vec) 
2023-01-11T21:41:23.9992478Z             for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9992541Z             {
2023-01-11T21:41:23.9992674Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:23.9992753Z                 tmp1_vec += tmp0;
2023-01-11T21:41:23.9992803Z             }
2023-01-11T21:41:23.9992997Z             tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:23.9993119Z             #pragma omp for simd simdlen(4) reduction(+:tmp1) 
2023-01-11T21:41:23.9993203Z             for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:23.9993264Z             {
2023-01-11T21:41:23.9993351Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:23.9993423Z                 tmp1 += tmp0;
2023-01-11T21:41:23.9993516Z             }
2023-01-11T21:41:23.9993565Z         }
2023-01-11T21:41:23.9993641Z         out_ptr0[0] = tmp1;
2023-01-11T21:41:23.9993702Z     }
2023-01-11T21:41:23.9993867Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:23.9993930Z     {
2023-01-11T21:41:23.9994005Z         #pragma omp for 
2023-01-11T21:41:23.9994073Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:23.9994134Z         {
2023-01-11T21:41:23.9994198Z             {
2023-01-11T21:41:23.9994387Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:23.9994466Z                 float tmp1 = 0;
2023-01-11T21:41:23.9994587Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:23.9994676Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9994741Z                 {
2023-01-11T21:41:23.9994899Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9994985Z                     tmp1_vec += tmp0;
2023-01-11T21:41:23.9995049Z                 }
2023-01-11T21:41:23.9995246Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:23.9995362Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:23.9995449Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9995512Z                 {
2023-01-11T21:41:23.9995609Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:23.9995673Z                     tmp1 += tmp0;
2023-01-11T21:41:23.9995736Z                 }
2023-01-11T21:41:23.9995815Z                 out_ptr1[i0] = tmp1;
2023-01-11T21:41:23.9995877Z             }
2023-01-11T21:41:23.9995938Z         }
2023-01-11T21:41:23.9996013Z         #pragma omp for 
2023-01-11T21:41:23.9996080Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:23.9996139Z         {
2023-01-11T21:41:23.9996273Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:23.9996406Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:23.9996491Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.9996583Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:23.9996645Z         }
2023-01-11T21:41:23.9996739Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:23.9996806Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:23.9996865Z         {
2023-01-11T21:41:23.9996950Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:23.9997047Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:23.9997128Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:23.9997206Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:23.9997267Z         }
2023-01-11T21:41:23.9997329Z         #pragma omp for 
2023-01-11T21:41:23.9997407Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:23.9997470Z         {
2023-01-11T21:41:23.9997552Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:23.9997613Z             {
2023-01-11T21:41:23.9997751Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:23.9997890Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8 + (8*i1) + (32*i0));
2023-01-11T21:41:23.9998017Z                 auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16 + (8*i1) + (32*i0));
2023-01-11T21:41:23.9998155Z                 auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr0 + 24 + (8*i1) + (32*i0));
2023-01-11T21:41:23.9998241Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9998326Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.9998409Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:23.9998541Z                 auto tmp7 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:23.9998623Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:23.9998755Z                 tmp8.store(out_ptr2 + (8*i0) + (8*i1));
2023-01-11T21:41:23.9998806Z             }
2023-01-11T21:41:23.9998893Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:23.9998972Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:23.9999033Z             {
2023-01-11T21:41:23.9999129Z                 auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:23.9999228Z                 auto tmp1 = in_ptr0[8 + i1 + (32*i0)];
2023-01-11T21:41:23.9999325Z                 auto tmp3 = in_ptr0[16 + i1 + (32*i0)];
2023-01-11T21:41:23.9999409Z                 auto tmp5 = in_ptr0[24 + i1 + (32*i0)];
2023-01-11T21:41:23.9999494Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:23.9999578Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:23.9999661Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:23.9999760Z                 auto tmp7 = static_cast<float>(4);
2023-01-11T21:41:23.9999845Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:23.9999967Z                 out_ptr2[i1 + (8*i0)] = tmp8;
2023-01-11T21:41:24.0000021Z             }
2023-01-11T21:41:24.0000081Z         }
2023-01-11T21:41:24.0000156Z         #pragma omp for 
2023-01-11T21:41:24.0000236Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0000297Z         {
2023-01-11T21:41:24.0000428Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0000561Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 32 + (8*i0));
2023-01-11T21:41:24.0000633Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0000761Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0000844Z             auto tmp4 = tmp2 / tmp3;
2023-01-11T21:41:24.0000932Z             tmp4.store(out_ptr3 + 8*i0);
2023-01-11T21:41:24.0000993Z         }
2023-01-11T21:41:24.0001086Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0001168Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:24.0001216Z         {
2023-01-11T21:41:24.0001302Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0001388Z             auto tmp1 = in_ptr0[32 + i0];
2023-01-11T21:41:24.0001471Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0001569Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:24.0001649Z             auto tmp4 = tmp2 / tmp3;
2023-01-11T21:41:24.0001726Z             out_ptr3[i0] = tmp4;
2023-01-11T21:41:24.0001776Z         }
2023-01-11T21:41:24.0001852Z         #pragma omp single
2023-01-11T21:41:24.0001913Z         {
2023-01-11T21:41:24.0001973Z             {
2023-01-11T21:41:24.0002036Z                 {
2023-01-11T21:41:24.0002126Z                     auto tmp0 = out_ptr0[0];
2023-01-11T21:41:24.0002227Z                     auto tmp1 = static_cast<float>(64);
2023-01-11T21:41:24.0002304Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0002390Z                     in_out_ptr1[0] = tmp2;
2023-01-11T21:41:24.0002454Z                 }
2023-01-11T21:41:24.0002517Z             }
2023-01-11T21:41:24.0002584Z         }
2023-01-11T21:41:24.0002645Z     }
2023-01-11T21:41:24.0002692Z }
2023-01-11T21:41:24.0002780Z ''')
2023-01-11T21:41:24.0002786Z 
2023-01-11T21:41:24.0002790Z 
2023-01-11T21:41:24.0002880Z async_compile.wait(globals())
2023-01-11T21:41:24.0002952Z del async_compile
2023-01-11T21:41:24.0002956Z 
2023-01-11T21:41:24.0003026Z def call(args):
2023-01-11T21:41:24.0003094Z     arg0_1, = args
2023-01-11T21:41:24.0003163Z     args.clear()
2023-01-11T21:41:24.0003351Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0003540Z     buf1 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0003623Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0003830Z     buf3 = empty_strided((1, 2, 1, 8), (16, 8, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0004021Z     buf4 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0004104Z     buf5 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0004347Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:24.0004414Z     del arg0_1
2023-01-11T21:41:24.0004498Z     return (buf5, buf2, buf3, buf4, )
2023-01-11T21:41:24.0004503Z 
2023-01-11T21:41:24.0004509Z 
2023-01-11T21:41:24.0004572Z if __name__ == "__main__":
2023-01-11T21:41:24.0004685Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0004807Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0005017Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0005123Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0005128Z 
2023-01-11T21:41:24.0005192Z ok (1.716s)
2023-01-11T21:41:24.0005700Z   test_min_max_reduction_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0005828Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0006093Z [2023-01-11 21:36:40,364] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 375
2023-01-11T21:41:24.0006347Z [2023-01-11 21:36:41,985] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 375
2023-01-11T21:41:24.0006364Z 
2023-01-11T21:41:24.0006443Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0006511Z import torch
2023-01-11T21:41:24.0006580Z import random
2023-01-11T21:41:24.0006692Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0006812Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0006817Z 
2023-01-11T21:41:24.0006899Z aten = torch.ops.aten
2023-01-11T21:41:24.0007033Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0007111Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0007115Z 
2023-01-11T21:41:24.0007132Z 
2023-01-11T21:41:24.0007253Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0007457Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0007575Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0007678Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0007776Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0007870Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0007963Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0008012Z {
2023-01-11T21:41:24.0008072Z     {
2023-01-11T21:41:24.0008441Z         #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:24.0008656Z         float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0008772Z         auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0009014Z         #pragma omp declare reduction(min:at::vec::Vectorized<float>:omp_out = at::vec::minimum(omp_out, omp_in)) initializer(omp_priv={{std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:24.0009134Z         float tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0009248Z         auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:24.0009440Z         float tmp7 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0009541Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:24.0009642Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0009705Z         {
2023-01-11T21:41:24.0009871Z             #pragma omp for reduction(max:tmp3_vec) reduction(min:tmp4_vec) reduction(max:tmp7_vec) 
2023-01-11T21:41:24.0009987Z             for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0010051Z             {
2023-01-11T21:41:24.0010184Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0010318Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0010392Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0010519Z                 auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0010602Z                 auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:24.0010715Z                 tmp3_vec = at::vec::maximum(tmp3_vec, tmp2);
2023-01-11T21:41:24.0010828Z                 tmp4_vec = at::vec::minimum(tmp4_vec, tmp2);
2023-01-11T21:41:24.0010935Z                 tmp7_vec = at::vec::maximum(tmp7_vec, tmp6);
2023-01-11T21:41:24.0010996Z             }
2023-01-11T21:41:24.0011234Z             tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp3_vec);
2023-01-11T21:41:24.0011443Z             tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::minimum(x, y);}, tmp4_vec);
2023-01-11T21:41:24.0011647Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp7_vec);
2023-01-11T21:41:24.0011803Z             #pragma omp for simd simdlen(4) reduction(max:tmp3) reduction(min:tmp4) reduction(max:tmp7) 
2023-01-11T21:41:24.0011887Z             for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0011951Z             {
2023-01-11T21:41:24.0012036Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0012120Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.0012206Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0012307Z                 auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:24.0012384Z                 auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:24.0012478Z                 tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:41:24.0012596Z                 tmp4 = std::min(tmp4, tmp2);
2023-01-11T21:41:24.0012726Z                 tmp7 = std::max(tmp7, tmp6);
2023-01-11T21:41:24.0012814Z             }
2023-01-11T21:41:24.0012888Z         }
2023-01-11T21:41:24.0012965Z         out_ptr0[0] = tmp3;
2023-01-11T21:41:24.0013027Z         out_ptr1[0] = tmp4;
2023-01-11T21:41:24.0013099Z         out_ptr2[0] = tmp7;
2023-01-11T21:41:24.0013160Z     }
2023-01-11T21:41:24.0013218Z }
2023-01-11T21:41:24.0013300Z ''')
2023-01-11T21:41:24.0013305Z 
2023-01-11T21:41:24.0013309Z 
2023-01-11T21:41:24.0013399Z async_compile.wait(globals())
2023-01-11T21:41:24.0013471Z del async_compile
2023-01-11T21:41:24.0013475Z 
2023-01-11T21:41:24.0013532Z def call(args):
2023-01-11T21:41:24.0013608Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0013679Z     args.clear()
2023-01-11T21:41:24.0013875Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0014056Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0014252Z     buf2 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0014466Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0014536Z     del arg0_1
2023-01-11T21:41:24.0014590Z     del arg1_1
2023-01-11T21:41:24.0014674Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0014679Z 
2023-01-11T21:41:24.0014683Z 
2023-01-11T21:41:24.0014761Z if __name__ == "__main__":
2023-01-11T21:41:24.0014877Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0015002Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0015199Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0015440Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0015556Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0015561Z 
2023-01-11T21:41:24.0015615Z ok (1.656s)
2023-01-11T21:41:24.0016099Z   test_misaligned_address_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0016225Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0016490Z [2023-01-11 21:36:42,019] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 376
2023-01-11T21:41:24.0016925Z [2023-01-11 21:36:43,567] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 376
2023-01-11T21:41:24.0016940Z 
2023-01-11T21:41:24.0017076Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0017152Z import torch
2023-01-11T21:41:24.0017224Z import random
2023-01-11T21:41:24.0017339Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0017447Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0017452Z 
2023-01-11T21:41:24.0017531Z aten = torch.ops.aten
2023-01-11T21:41:24.0017665Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0017758Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0017763Z 
2023-01-11T21:41:24.0017767Z 
2023-01-11T21:41:24.0017906Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0018110Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0018231Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:24.0018336Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0018427Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0018488Z {
2023-01-11T21:41:24.0018551Z     {
2023-01-11T21:41:24.0018615Z         {
2023-01-11T21:41:24.0018700Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.0018788Z             auto tmp1 = in_ptr1[tmp0];
2023-01-11T21:41:24.0018872Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:24.0018923Z         }
2023-01-11T21:41:24.0018985Z     }
2023-01-11T21:41:24.0019047Z }
2023-01-11T21:41:24.0019126Z ''')
2023-01-11T21:41:24.0019131Z 
2023-01-11T21:41:24.0019135Z 
2023-01-11T21:41:24.0019224Z async_compile.wait(globals())
2023-01-11T21:41:24.0019298Z del async_compile
2023-01-11T21:41:24.0019302Z 
2023-01-11T21:41:24.0019374Z def call(args):
2023-01-11T21:41:24.0019435Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0019507Z     args.clear()
2023-01-11T21:41:24.0019703Z     buf0 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0019868Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0019937Z     del arg0_1
2023-01-11T21:41:24.0020004Z     del arg1_1
2023-01-11T21:41:24.0020106Z     return (buf0, )
2023-01-11T21:41:24.0020112Z 
2023-01-11T21:41:24.0020117Z 
2023-01-11T21:41:24.0020215Z if __name__ == "__main__":
2023-01-11T21:41:24.0020366Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0020533Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0020832Z     arg0_1 = rand_strided((1, 1000), (1000, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0021120Z     arg1_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0021238Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0021243Z 
2023-01-11T21:41:24.0021310Z ok (1.574s)
2023-01-11T21:41:24.0021780Z   test_mm_views_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0021961Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0022230Z [2023-01-11 21:36:43,587] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 377
2023-01-11T21:41:24.0022620Z [2023-01-11 21:36:43,589] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 377
2023-01-11T21:41:24.0022626Z 
2023-01-11T21:41:24.0022721Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0022790Z import torch
2023-01-11T21:41:24.0022860Z import random
2023-01-11T21:41:24.0022977Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0023098Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0023106Z 
2023-01-11T21:41:24.0023244Z aten = torch.ops.aten
2023-01-11T21:41:24.0023367Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0023458Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0023462Z 
2023-01-11T21:41:24.0023467Z 
2023-01-11T21:41:24.0023556Z async_compile.wait(globals())
2023-01-11T21:41:24.0023628Z del async_compile
2023-01-11T21:41:24.0023633Z 
2023-01-11T21:41:24.0023702Z def call(args):
2023-01-11T21:41:24.0023777Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0023848Z     args.clear()
2023-01-11T21:41:24.0024050Z     buf0 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0024159Z     aten.mm.out(arg0_1, as_strided(arg1_1, (32, 32), (32, 1)), out=buf0)
2023-01-11T21:41:24.0024226Z     del arg0_1
2023-01-11T21:41:24.0024295Z     del arg1_1
2023-01-11T21:41:24.0024366Z     return (buf0, )
2023-01-11T21:41:24.0024371Z 
2023-01-11T21:41:24.0024375Z 
2023-01-11T21:41:24.0024451Z if __name__ == "__main__":
2023-01-11T21:41:24.0024569Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0024692Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0024895Z     arg0_1 = rand_strided((32, 32), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0025091Z     arg1_1 = rand_strided((32, 1, 32), (32, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0025207Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0025212Z 
2023-01-11T21:41:24.0025280Z ok (0.023s)
2023-01-11T21:41:24.0025753Z   test_move_arange_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0025881Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0026143Z [2023-01-11 21:36:43,622] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 378
2023-01-11T21:41:24.0026407Z [2023-01-11 21:36:45,226] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 378
2023-01-11T21:41:24.0026413Z 
2023-01-11T21:41:24.0026505Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0026572Z import torch
2023-01-11T21:41:24.0026631Z import random
2023-01-11T21:41:24.0026742Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0026860Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0026865Z 
2023-01-11T21:41:24.0026941Z aten = torch.ops.aten
2023-01-11T21:41:24.0027072Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0027163Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0027168Z 
2023-01-11T21:41:24.0027172Z 
2023-01-11T21:41:24.0027303Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0027595Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0027758Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0027893Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0027970Z {
2023-01-11T21:41:24.0028072Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0028134Z     {
2023-01-11T21:41:24.0028211Z         #pragma omp for 
2023-01-11T21:41:24.0028291Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0028341Z         {
2023-01-11T21:41:24.0028407Z             {
2023-01-11T21:41:24.0028470Z                 {
2023-01-11T21:41:24.0028563Z                     auto tmp2 = in_ptr0[i0];
2023-01-11T21:41:24.0028665Z                     auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:24.0028770Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.0028861Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0028968Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0029033Z                 }
2023-01-11T21:41:24.0029098Z             }
2023-01-11T21:41:24.0029158Z         }
2023-01-11T21:41:24.0029219Z     }
2023-01-11T21:41:24.0029281Z }
2023-01-11T21:41:24.0029352Z ''')
2023-01-11T21:41:24.0029368Z 
2023-01-11T21:41:24.0029373Z 
2023-01-11T21:41:24.0029450Z async_compile.wait(globals())
2023-01-11T21:41:24.0029519Z del async_compile
2023-01-11T21:41:24.0029524Z 
2023-01-11T21:41:24.0029593Z def call(args):
2023-01-11T21:41:24.0029660Z     arg0_1, = args
2023-01-11T21:41:24.0029729Z     args.clear()
2023-01-11T21:41:24.0029924Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0030055Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0030112Z     del arg0_1
2023-01-11T21:41:24.0030180Z     return (buf0, )
2023-01-11T21:41:24.0030184Z 
2023-01-11T21:41:24.0030188Z 
2023-01-11T21:41:24.0030261Z if __name__ == "__main__":
2023-01-11T21:41:24.0030378Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0030500Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0030693Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0030799Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0030804Z 
2023-01-11T21:41:24.0030869Z ok (1.669s)
2023-01-11T21:41:24.0030993Z   test_multi_device_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:24.0031153Z   test_multi_gpu_device_cpu (__main__.CpuTests) ... skip: requires multiple cuda devices (0.001s)
2023-01-11T21:41:24.0031296Z   test_multilayer_low_prec_cpu (__main__.CpuTests) ... skip: requires CUDA (0.001s)
2023-01-11T21:41:24.0031439Z   test_nan_to_num_cpu (__main__.CpuTests) ... skip: Skipping due to op bugs (0.001s)
2023-01-11T21:41:24.0031902Z   test_narrow_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0032029Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0032292Z [2023-01-11 21:36:45,308] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 379
2023-01-11T21:41:24.0032557Z [2023-01-11 21:36:46,840] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 379
2023-01-11T21:41:24.0032562Z 
2023-01-11T21:41:24.0032654Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0032721Z import torch
2023-01-11T21:41:24.0032778Z import random
2023-01-11T21:41:24.0032892Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0033011Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0033043Z 
2023-01-11T21:41:24.0033124Z aten = torch.ops.aten
2023-01-11T21:41:24.0033256Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0033346Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0033351Z 
2023-01-11T21:41:24.0033355Z 
2023-01-11T21:41:24.0033488Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0033690Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0033854Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0033954Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0034014Z {
2023-01-11T21:41:24.0034114Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0034176Z     {
2023-01-11T21:41:24.0034251Z         #pragma omp for 
2023-01-11T21:41:24.0034319Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:24.0034381Z         {
2023-01-11T21:41:24.0034560Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 640 + (8*i0));
2023-01-11T21:41:24.0034696Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0034782Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0034912Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0034996Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0035085Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0035134Z         }
2023-01-11T21:41:24.0035225Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0035310Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:41:24.0035371Z         {
2023-01-11T21:41:24.0035460Z             auto tmp0 = in_ptr0[640 + i0];
2023-01-11T21:41:24.0035558Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:24.0035642Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0035725Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:24.0035807Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0035891Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0035953Z         }
2023-01-11T21:41:24.0036013Z     }
2023-01-11T21:41:24.0036072Z }
2023-01-11T21:41:24.0036152Z ''')
2023-01-11T21:41:24.0036157Z 
2023-01-11T21:41:24.0036162Z 
2023-01-11T21:41:24.0036237Z async_compile.wait(globals())
2023-01-11T21:41:24.0036308Z del async_compile
2023-01-11T21:41:24.0036313Z 
2023-01-11T21:41:24.0036385Z def call(args):
2023-01-11T21:41:24.0036453Z     arg0_1, = args
2023-01-11T21:41:24.0036523Z     args.clear()
2023-01-11T21:41:24.0036725Z     buf0 = empty_strided((16, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0036855Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0036950Z     return (as_strided(arg0_1, (64, 16), (64, 1), 10), buf0, )
2023-01-11T21:41:24.0036968Z 
2023-01-11T21:41:24.0036972Z 
2023-01-11T21:41:24.0037035Z if __name__ == "__main__":
2023-01-11T21:41:24.0037145Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0037271Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0037468Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0037574Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0037579Z 
2023-01-11T21:41:24.0037645Z ok (1.577s)
2023-01-11T21:41:24.0038123Z   test_new_empty_strided_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0038248Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0038507Z [2023-01-11 21:36:46,886] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 380
2023-01-11T21:41:24.0038791Z [2023-01-11 21:36:48,742] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 380
2023-01-11T21:41:24.0038808Z 
2023-01-11T21:41:24.0038888Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0038957Z import torch
2023-01-11T21:41:24.0039027Z import random
2023-01-11T21:41:24.0039142Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0039260Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0039265Z 
2023-01-11T21:41:24.0039341Z aten = torch.ops.aten
2023-01-11T21:41:24.0039472Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0039549Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0039554Z 
2023-01-11T21:41:24.0039559Z 
2023-01-11T21:41:24.0039690Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0039892Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0040004Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0040095Z {
2023-01-11T21:41:24.0040192Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0040252Z     {
2023-01-11T21:41:24.0040316Z         #pragma omp for 
2023-01-11T21:41:24.0040397Z         for(long i0=0; i0<2048; i0+=1)
2023-01-11T21:41:24.0040458Z         {
2023-01-11T21:41:24.0040592Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(123));
2023-01-11T21:41:24.0040681Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0040741Z         }
2023-01-11T21:41:24.0040833Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0040909Z         for(long i0=16384; i0<16384; i0+=1)
2023-01-11T21:41:24.0040969Z         {
2023-01-11T21:41:24.0041068Z             auto tmp0 = static_cast<float>(123);
2023-01-11T21:41:24.0041147Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0041207Z         }
2023-01-11T21:41:24.0041266Z     }
2023-01-11T21:41:24.0041324Z }
2023-01-11T21:41:24.0041390Z ''')
2023-01-11T21:41:24.0041397Z 
2023-01-11T21:41:24.0041404Z 
2023-01-11T21:41:24.0041490Z async_compile.wait(globals())
2023-01-11T21:41:24.0041560Z del async_compile
2023-01-11T21:41:24.0041565Z 
2023-01-11T21:41:24.0041634Z def call(args):
2023-01-11T21:41:24.0041700Z     arg0_1, = args
2023-01-11T21:41:24.0041768Z     args.clear()
2023-01-11T21:41:24.0041985Z     buf0 = empty_strided((1, 128, 128), (16384, 128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0042084Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0042142Z     return (buf0, )
2023-01-11T21:41:24.0042147Z 
2023-01-11T21:41:24.0042151Z 
2023-01-11T21:41:24.0042225Z if __name__ == "__main__":
2023-01-11T21:41:24.0042338Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0042459Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0042651Z     arg0_1 = rand_strided((55, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0042757Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0042765Z 
2023-01-11T21:41:24.0042831Z ok (1.903s)
2023-01-11T21:41:24.0043296Z   test_new_ones_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0043420Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0043668Z [2023-01-11 21:36:48,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 381
2023-01-11T21:41:24.0043929Z [2023-01-11 21:36:51,103] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 381
2023-01-11T21:41:24.0043934Z 
2023-01-11T21:41:24.0044027Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0044094Z import torch
2023-01-11T21:41:24.0044192Z import random
2023-01-11T21:41:24.0044308Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0044428Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0044433Z 
2023-01-11T21:41:24.0044509Z aten = torch.ops.aten
2023-01-11T21:41:24.0044627Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0044716Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0044721Z 
2023-01-11T21:41:24.0044725Z 
2023-01-11T21:41:24.0044857Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0045060Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0045173Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0045268Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0045328Z {
2023-01-11T21:41:24.0045377Z     {
2023-01-11T21:41:24.0045438Z         {
2023-01-11T21:41:24.0045537Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:41:24.0045644Z             out_ptr0[0] = tmp0;
2023-01-11T21:41:24.0045706Z         }
2023-01-11T21:41:24.0045765Z     }
2023-01-11T21:41:24.0045823Z     {
2023-01-11T21:41:24.0045871Z         {
2023-01-11T21:41:24.0045966Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:24.0046042Z             out_ptr1[0] = tmp0;
2023-01-11T21:41:24.0046103Z         }
2023-01-11T21:41:24.0046162Z     }
2023-01-11T21:41:24.0046219Z }
2023-01-11T21:41:24.0046285Z ''')
2023-01-11T21:41:24.0046301Z 
2023-01-11T21:41:24.0046306Z 
2023-01-11T21:41:24.0046382Z async_compile.wait(globals())
2023-01-11T21:41:24.0046452Z del async_compile
2023-01-11T21:41:24.0046457Z 
2023-01-11T21:41:24.0046524Z def call(args):
2023-01-11T21:41:24.0046592Z     arg0_1, = args
2023-01-11T21:41:24.0046659Z     args.clear()
2023-01-11T21:41:24.0046844Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0047022Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0047146Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0047222Z     return (buf0, buf1, )
2023-01-11T21:41:24.0047227Z 
2023-01-11T21:41:24.0047231Z 
2023-01-11T21:41:24.0047306Z if __name__ == "__main__":
2023-01-11T21:41:24.0047416Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0047536Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0047728Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0047835Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0047840Z 
2023-01-11T21:41:24.0047906Z ok (2.359s)
2023-01-11T21:41:24.0048379Z   test_nll_loss_forward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0048493Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0048752Z [2023-01-11 21:36:51,185] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 382
2023-01-11T21:41:24.0049017Z [2023-01-11 21:36:53,639] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 382
2023-01-11T21:41:24.0049022Z 
2023-01-11T21:41:24.0049119Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0049189Z import torch
2023-01-11T21:41:24.0049260Z import random
2023-01-11T21:41:24.0049375Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0049493Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0049498Z 
2023-01-11T21:41:24.0049563Z aten = torch.ops.aten
2023-01-11T21:41:24.0049699Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0049791Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0049828Z 
2023-01-11T21:41:24.0049832Z 
2023-01-11T21:41:24.0049967Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0050174Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0050292Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0050399Z                        const long* __restrict__ in_ptr0,
2023-01-11T21:41:24.0050505Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0050594Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0050655Z {
2023-01-11T21:41:24.0050741Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:24.0050803Z     {
2023-01-11T21:41:24.0050868Z         {
2023-01-11T21:41:24.0050944Z             float tmp3 = 0;
2023-01-11T21:41:24.0051051Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0051103Z             {
2023-01-11T21:41:24.0051207Z                 #pragma omp for reduction(+:tmp3) 
2023-01-11T21:41:24.0051327Z                 for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:24.0051394Z                 {
2023-01-11T21:41:24.0051461Z                     {
2023-01-11T21:41:24.0051557Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0051663Z                         auto tmp1 = in_ptr1[tmp0 + (5*i0)];
2023-01-11T21:41:24.0051780Z                         auto tmp2 = -tmp1;
2023-01-11T21:41:24.0051859Z                         tmp3 += tmp2;
2023-01-11T21:41:24.0051926Z                     }
2023-01-11T21:41:24.0051992Z                 }
2023-01-11T21:41:24.0052055Z             }
2023-01-11T21:41:24.0052133Z             out_ptr0[0] = tmp3;
2023-01-11T21:41:24.0052197Z         }
2023-01-11T21:41:24.0052244Z     }
2023-01-11T21:41:24.0052304Z     {
2023-01-11T21:41:24.0052367Z         {
2023-01-11T21:41:24.0052453Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:24.0052553Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:41:24.0052638Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0052709Z             in_out_ptr0[0] = tmp2;
2023-01-11T21:41:24.0052771Z         }
2023-01-11T21:41:24.0052832Z     }
2023-01-11T21:41:24.0052892Z     {
2023-01-11T21:41:24.0052954Z         {
2023-01-11T21:41:24.0053056Z             auto tmp0 = static_cast<float>(5.0);
2023-01-11T21:41:24.0053134Z             out_ptr1[0] = tmp0;
2023-01-11T21:41:24.0053184Z         }
2023-01-11T21:41:24.0053245Z     }
2023-01-11T21:41:24.0053306Z }
2023-01-11T21:41:24.0053384Z ''')
2023-01-11T21:41:24.0053389Z 
2023-01-11T21:41:24.0053394Z 
2023-01-11T21:41:24.0053483Z async_compile.wait(globals())
2023-01-11T21:41:24.0053555Z del async_compile
2023-01-11T21:41:24.0053560Z 
2023-01-11T21:41:24.0053629Z def call(args):
2023-01-11T21:41:24.0053690Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0053759Z     args.clear()
2023-01-11T21:41:24.0053943Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0054028Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0054210Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0054399Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0054466Z     del arg0_1
2023-01-11T21:41:24.0054519Z     del arg1_1
2023-01-11T21:41:24.0054593Z     return (buf1, buf2, )
2023-01-11T21:41:24.0054598Z 
2023-01-11T21:41:24.0054602Z 
2023-01-11T21:41:24.0054679Z if __name__ == "__main__":
2023-01-11T21:41:24.0054791Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0054913Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0055106Z     arg0_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0055295Z     arg1_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0055409Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0055414Z 
2023-01-11T21:41:24.0055479Z ok (2.536s)
2023-01-11T21:41:24.0055985Z   test_no_mega_fusion_during_lowering_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0056112Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0056372Z [2023-01-11 21:36:53,938] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 383
2023-01-11T21:41:24.0056377Z 
2023-01-11T21:41:24.0056471Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0056538Z import torch
2023-01-11T21:41:24.0056607Z import random
2023-01-11T21:41:24.0056722Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0056840Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0056874Z 
2023-01-11T21:41:24.0056951Z aten = torch.ops.aten
2023-01-11T21:41:24.0057069Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0057158Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0057162Z 
2023-01-11T21:41:24.0057167Z 
2023-01-11T21:41:24.0057299Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0057502Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0057616Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0057719Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0057822Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0057921Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0058011Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:24.0058109Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:24.0058214Z                        const float* __restrict__ in_ptr5,
2023-01-11T21:41:24.0058311Z                        const float* __restrict__ in_ptr6,
2023-01-11T21:41:24.0058410Z                        const float* __restrict__ in_ptr7,
2023-01-11T21:41:24.0058506Z                        const float* __restrict__ in_ptr8,
2023-01-11T21:41:24.0058602Z                        const float* __restrict__ in_ptr9,
2023-01-11T21:41:24.0058694Z                        const float* __restrict__ in_ptr10,
2023-01-11T21:41:24.0058795Z                        const float* __restrict__ in_ptr11,
2023-01-11T21:41:24.0058894Z                        const float* __restrict__ in_ptr12,
2023-01-11T21:41:24.0058994Z                        const float* __restrict__ in_ptr13,
2023-01-11T21:41:24.0059093Z                        const float* __restrict__ in_ptr14,
2023-01-11T21:41:24.0059192Z                        const float* __restrict__ in_ptr15,
2023-01-11T21:41:24.0059292Z                        const float* __restrict__ in_ptr16,
2023-01-11T21:41:24.0059394Z                        const float* __restrict__ in_ptr17,
2023-01-11T21:41:24.0059480Z                        const float* __restrict__ in_ptr18,
2023-01-11T21:41:24.0059577Z                        const float* __restrict__ in_ptr19,
2023-01-11T21:41:24.0059676Z                        const float* __restrict__ in_ptr20,
2023-01-11T21:41:24.0059774Z                        const float* __restrict__ in_ptr21,
2023-01-11T21:41:24.0059872Z                        const float* __restrict__ in_ptr22,
2023-01-11T21:41:24.0059969Z                        const float* __restrict__ in_ptr23,
2023-01-11T21:41:24.0060068Z                        const float* __restrict__ in_ptr24,
2023-01-11T21:41:24.0060155Z                        const float* __restrict__ in_ptr25,
2023-01-11T21:41:24.0060251Z                        const float* __restrict__ in_ptr26,
2023-01-11T21:41:24.0060348Z                        const float* __restrict__ in_ptr27,
2023-01-11T21:41:24.0060448Z                        const float* __restrict__ in_ptr28,
2023-01-11T21:41:24.0060588Z                        const float* __restrict__ in_ptr29,
2023-01-11T21:41:24.0060685Z                        const float* __restrict__ in_ptr30,
2023-01-11T21:41:24.0060781Z                        const float* __restrict__ in_ptr31,
2023-01-11T21:41:24.0060877Z                        const float* __restrict__ in_ptr32,
2023-01-11T21:41:24.0060961Z                        const float* __restrict__ in_ptr33,
2023-01-11T21:41:24.0061058Z                        const float* __restrict__ in_ptr34,
2023-01-11T21:41:24.0061154Z                        const float* __restrict__ in_ptr35,
2023-01-11T21:41:24.0061256Z                        const float* __restrict__ in_ptr36,
2023-01-11T21:41:24.0061353Z                        const float* __restrict__ in_ptr37,
2023-01-11T21:41:24.0061450Z                        const float* __restrict__ in_ptr38,
2023-01-11T21:41:24.0061546Z                        const float* __restrict__ in_ptr39,
2023-01-11T21:41:24.0061663Z                        const float* __restrict__ in_ptr40,
2023-01-11T21:41:24.0061761Z                        const float* __restrict__ in_ptr41,
2023-01-11T21:41:24.0061859Z                        const float* __restrict__ in_ptr42,
2023-01-11T21:41:24.0061957Z                        const float* __restrict__ in_ptr43,
2023-01-11T21:41:24.0062056Z                        const float* __restrict__ in_ptr44,
2023-01-11T21:41:24.0062154Z                        const float* __restrict__ in_ptr45,
2023-01-11T21:41:24.0062251Z                        const float* __restrict__ in_ptr46,
2023-01-11T21:41:24.0062495Z                        const float* __restrict__ in_ptr47,
2023-01-11T21:41:24.0062618Z                        const float* __restrict__ in_ptr48,
2023-01-11T21:41:24.0062717Z                        const float* __restrict__ in_ptr49)
2023-01-11T21:41:24.0062777Z {
2023-01-11T21:41:24.0062864Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:24.0062960Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0063029Z     {
2023-01-11T21:41:24.0063106Z         #pragma omp for 
2023-01-11T21:41:24.0063174Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0063237Z         {
2023-01-11T21:41:24.0063373Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0063503Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0063629Z             auto tmp4 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:24.0063754Z             auto tmp6 = at::vec::Vectorized<float>::loadu(in_ptr3 + 8*i0);
2023-01-11T21:41:24.0063877Z             auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr4 + 8*i0);
2023-01-11T21:41:24.0064006Z             auto tmp10 = at::vec::Vectorized<float>::loadu(in_ptr5 + 8*i0);
2023-01-11T21:41:24.0064121Z             auto tmp12 = at::vec::Vectorized<float>::loadu(in_ptr6 + 8*i0);
2023-01-11T21:41:24.0064244Z             auto tmp14 = at::vec::Vectorized<float>::loadu(in_ptr7 + 8*i0);
2023-01-11T21:41:24.0064391Z             auto tmp16 = at::vec::Vectorized<float>::loadu(in_ptr8 + 8*i0);
2023-01-11T21:41:24.0064514Z             auto tmp1 = tmp0 + tmp0;
2023-01-11T21:41:24.0064599Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0064678Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:24.0064757Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:24.0064836Z             auto tmp9 = tmp7 + tmp8;
2023-01-11T21:41:24.0064908Z             auto tmp11 = tmp9 + tmp10;
2023-01-11T21:41:24.0064995Z             auto tmp13 = tmp11 + tmp12;
2023-01-11T21:41:24.0065079Z             auto tmp15 = tmp13 + tmp14;
2023-01-11T21:41:24.0065161Z             auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:24.0065249Z             tmp17.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0065310Z         }
2023-01-11T21:41:24.0065401Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0065470Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0065530Z         {
2023-01-11T21:41:24.0065683Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0065764Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:24.0065844Z             auto tmp4 = in_ptr2[i0];
2023-01-11T21:41:24.0065924Z             auto tmp6 = in_ptr3[i0];
2023-01-11T21:41:24.0065990Z             auto tmp8 = in_ptr4[i0];
2023-01-11T21:41:24.0066073Z             auto tmp10 = in_ptr5[i0];
2023-01-11T21:41:24.0066155Z             auto tmp12 = in_ptr6[i0];
2023-01-11T21:41:24.0066236Z             auto tmp14 = in_ptr7[i0];
2023-01-11T21:41:24.0066317Z             auto tmp16 = in_ptr8[i0];
2023-01-11T21:41:24.0066398Z             auto tmp1 = tmp0 + tmp0;
2023-01-11T21:41:24.0066477Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0066544Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:24.0066622Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:24.0066701Z             auto tmp9 = tmp7 + tmp8;
2023-01-11T21:41:24.0066786Z             auto tmp11 = tmp9 + tmp10;
2023-01-11T21:41:24.0066907Z             auto tmp13 = tmp11 + tmp12;
2023-01-11T21:41:24.0066994Z             auto tmp15 = tmp13 + tmp14;
2023-01-11T21:41:24.0067074Z             auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:24.0067143Z             out_ptr0[i0] = tmp17;
2023-01-11T21:41:24.0067205Z         }
2023-01-11T21:41:24.0067280Z         #pragma omp for 
2023-01-11T21:41:24.0067359Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0067423Z         {
2023-01-11T21:41:24.0067552Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0067680Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr9 + 8*i0);
2023-01-11T21:41:24.0067804Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr10 + 8*i0);
2023-01-11T21:41:24.0067919Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr11 + 8*i0);
2023-01-11T21:41:24.0068041Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr12 + 8*i0);
2023-01-11T21:41:24.0068166Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr13 + 8*i0);
2023-01-11T21:41:24.0068298Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr14 + 8*i0);
2023-01-11T21:41:24.0068428Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr15 + 8*i0);
2023-01-11T21:41:24.0068556Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr16 + 8*i0);
2023-01-11T21:41:24.0068640Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0068722Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0068790Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0068869Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0068951Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0069034Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0069118Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0069201Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0069297Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0069346Z         }
2023-01-11T21:41:24.0069442Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0069522Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0069583Z         {
2023-01-11T21:41:24.0069665Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:24.0069747Z             auto tmp1 = in_ptr9[i0];
2023-01-11T21:41:24.0069829Z             auto tmp3 = in_ptr10[i0];
2023-01-11T21:41:24.0069898Z             auto tmp5 = in_ptr11[i0];
2023-01-11T21:41:24.0069979Z             auto tmp7 = in_ptr12[i0];
2023-01-11T21:41:24.0070059Z             auto tmp9 = in_ptr13[i0];
2023-01-11T21:41:24.0070143Z             auto tmp11 = in_ptr14[i0];
2023-01-11T21:41:24.0070228Z             auto tmp13 = in_ptr15[i0];
2023-01-11T21:41:24.0070313Z             auto tmp15 = in_ptr16[i0];
2023-01-11T21:41:24.0070394Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0070463Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0070542Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0070622Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0070736Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0070819Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0070902Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0070987Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0071058Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:24.0071118Z         }
2023-01-11T21:41:24.0071193Z         #pragma omp for 
2023-01-11T21:41:24.0076741Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0076823Z         {
2023-01-11T21:41:24.0076977Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0077114Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr17 + 8*i0);
2023-01-11T21:41:24.0077230Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr18 + 8*i0);
2023-01-11T21:41:24.0077356Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr19 + 8*i0);
2023-01-11T21:41:24.0077550Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr20 + 8*i0);
2023-01-11T21:41:24.0077678Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr21 + 8*i0);
2023-01-11T21:41:24.0077810Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr22 + 8*i0);
2023-01-11T21:41:24.0077940Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr23 + 8*i0);
2023-01-11T21:41:24.0078066Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr24 + 8*i0);
2023-01-11T21:41:24.0078151Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0078234Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0078302Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0078384Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0078467Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0078553Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0078639Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0078722Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0078825Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0078875Z         }
2023-01-11T21:41:24.0078970Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0079052Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0079115Z         {
2023-01-11T21:41:24.0079201Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:24.0079286Z             auto tmp1 = in_ptr17[i0];
2023-01-11T21:41:24.0079368Z             auto tmp3 = in_ptr18[i0];
2023-01-11T21:41:24.0079438Z             auto tmp5 = in_ptr19[i0];
2023-01-11T21:41:24.0079520Z             auto tmp7 = in_ptr20[i0];
2023-01-11T21:41:24.0079600Z             auto tmp9 = in_ptr21[i0];
2023-01-11T21:41:24.0079685Z             auto tmp11 = in_ptr22[i0];
2023-01-11T21:41:24.0079768Z             auto tmp13 = in_ptr23[i0];
2023-01-11T21:41:24.0079849Z             auto tmp15 = in_ptr24[i0];
2023-01-11T21:41:24.0079933Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0080002Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0080088Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0080168Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0080249Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0080335Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0080419Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0080502Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0080571Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:24.0080634Z         }
2023-01-11T21:41:24.0080710Z         #pragma omp for 
2023-01-11T21:41:24.0080791Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0080853Z         {
2023-01-11T21:41:24.0080989Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0081120Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr25 + 8*i0);
2023-01-11T21:41:24.0081236Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr26 + 8*i0);
2023-01-11T21:41:24.0081364Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr27 + 8*i0);
2023-01-11T21:41:24.0081527Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr28 + 8*i0);
2023-01-11T21:41:24.0081650Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr29 + 8*i0);
2023-01-11T21:41:24.0081780Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr30 + 8*i0);
2023-01-11T21:41:24.0081911Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr31 + 8*i0);
2023-01-11T21:41:24.0082038Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr32 + 8*i0);
2023-01-11T21:41:24.0082124Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0082208Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0082277Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0082358Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0082443Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0082529Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0082613Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0082729Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0082830Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0082880Z         }
2023-01-11T21:41:24.0082974Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0083056Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0083119Z         {
2023-01-11T21:41:24.0083207Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:24.0083294Z             auto tmp1 = in_ptr25[i0];
2023-01-11T21:41:24.0083364Z             auto tmp3 = in_ptr26[i0];
2023-01-11T21:41:24.0083446Z             auto tmp5 = in_ptr27[i0];
2023-01-11T21:41:24.0083529Z             auto tmp7 = in_ptr28[i0];
2023-01-11T21:41:24.0083610Z             auto tmp9 = in_ptr29[i0];
2023-01-11T21:41:24.0083697Z             auto tmp11 = in_ptr30[i0];
2023-01-11T21:41:24.0083785Z             auto tmp13 = in_ptr31[i0];
2023-01-11T21:41:24.0083871Z             auto tmp15 = in_ptr32[i0];
2023-01-11T21:41:24.0083944Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0084029Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0084111Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0084193Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0084277Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0084362Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0084446Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0084518Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0084602Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:24.0084668Z         }
2023-01-11T21:41:24.0084745Z         #pragma omp for 
2023-01-11T21:41:24.0084827Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0084889Z         {
2023-01-11T21:41:24.0085026Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0085142Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr33 + 8*i0);
2023-01-11T21:41:24.0085273Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr34 + 8*i0);
2023-01-11T21:41:24.0085401Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr35 + 8*i0);
2023-01-11T21:41:24.0085526Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr36 + 8*i0);
2023-01-11T21:41:24.0085649Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr37 + 8*i0);
2023-01-11T21:41:24.0085781Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr38 + 8*i0);
2023-01-11T21:41:24.0085912Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr39 + 8*i0);
2023-01-11T21:41:24.0086040Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr40 + 8*i0);
2023-01-11T21:41:24.0086126Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0086196Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0086283Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0086365Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0086447Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0086563Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0086646Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0086733Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0086817Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0086880Z         }
2023-01-11T21:41:24.0086976Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0087057Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0087120Z         {
2023-01-11T21:41:24.0087208Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:24.0087291Z             auto tmp1 = in_ptr33[i0];
2023-01-11T21:41:24.0087361Z             auto tmp3 = in_ptr34[i0];
2023-01-11T21:41:24.0087444Z             auto tmp5 = in_ptr35[i0];
2023-01-11T21:41:24.0087526Z             auto tmp7 = in_ptr36[i0];
2023-01-11T21:41:24.0087609Z             auto tmp9 = in_ptr37[i0];
2023-01-11T21:41:24.0087693Z             auto tmp11 = in_ptr38[i0];
2023-01-11T21:41:24.0087779Z             auto tmp13 = in_ptr39[i0];
2023-01-11T21:41:24.0087890Z             auto tmp15 = in_ptr40[i0];
2023-01-11T21:41:24.0087962Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0088044Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0088127Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0088208Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0088288Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0088370Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0088455Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0088528Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0088608Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:24.0088671Z         }
2023-01-11T21:41:24.0088746Z         #pragma omp for 
2023-01-11T21:41:24.0088827Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0088891Z         {
2023-01-11T21:41:24.0089026Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0089144Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr41 + 8*i0);
2023-01-11T21:41:24.0089273Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr42 + 8*i0);
2023-01-11T21:41:24.0089396Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr43 + 8*i0);
2023-01-11T21:41:24.0089518Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr44 + 8*i0);
2023-01-11T21:41:24.0089639Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr45 + 8*i0);
2023-01-11T21:41:24.0089768Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr46 + 8*i0);
2023-01-11T21:41:24.0089894Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr47 + 8*i0);
2023-01-11T21:41:24.0090021Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr48 + 8*i0);
2023-01-11T21:41:24.0090104Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0090174Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0090255Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0090340Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0090422Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0090504Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0090587Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0090669Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0090754Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0090815Z         }
2023-01-11T21:41:24.0090908Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0090988Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0091052Z         {
2023-01-11T21:41:24.0091139Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:24.0091224Z             auto tmp1 = in_ptr41[i0];
2023-01-11T21:41:24.0091294Z             auto tmp3 = in_ptr42[i0];
2023-01-11T21:41:24.0091374Z             auto tmp5 = in_ptr43[i0];
2023-01-11T21:41:24.0091454Z             auto tmp7 = in_ptr44[i0];
2023-01-11T21:41:24.0091533Z             auto tmp9 = in_ptr45[i0];
2023-01-11T21:41:24.0091646Z             auto tmp11 = in_ptr46[i0];
2023-01-11T21:41:24.0091728Z             auto tmp13 = in_ptr47[i0];
2023-01-11T21:41:24.0091811Z             auto tmp15 = in_ptr48[i0];
2023-01-11T21:41:24.0091882Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0091962Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0092041Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0092120Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0092201Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:24.0092284Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:24.0092366Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:24.0092436Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:24.0092518Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:24.0092577Z         }
2023-01-11T21:41:24.0092650Z         #pragma omp for 
2023-01-11T21:41:24.0092729Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0092789Z         {
2023-01-11T21:41:24.0092952Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0093069Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr49 + 8*i0);
2023-01-11T21:41:24.0093150Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0093244Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0093305Z         }
2023-01-11T21:41:24.0093396Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0093475Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0093533Z         {
2023-01-11T21:41:24.0093609Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:24.0093689Z             auto tmp1 = in_ptr49[i0];
2023-01-11T21:41:24.0093771Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0093852Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0093912Z         }
2023-01-11T21:41:24.0093972Z     }
2023-01-11T21:41:24.0094020Z }
2023-01-11T21:41:24.0094128Z ''')
2023-01-11T21:41:24.0094135Z 
2023-01-11T21:41:24.0094140Z 
2023-01-11T21:41:24.0094235Z async_compile.wait(globals())
2023-01-11T21:41:24.0094311Z del async_compile
2023-01-11T21:41:24.0094316Z 
2023-01-11T21:41:24.0094386Z def call(args):
2023-01-11T21:41:24.0094716Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1 = args
2023-01-11T21:41:24.0094788Z     args.clear()
2023-01-11T21:41:24.0094993Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0095077Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0095146Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0095224Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:24.0095304Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:24.0095387Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:41:24.0095466Z     buf6 = buf5; del buf5  # reuse
2023-01-11T21:41:24.0096712Z     kernel_cpp_0(c_void_p(buf6.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg5_1.data_ptr()), c_void_p(arg6_1.data_ptr()), c_void_p(arg7_1.data_ptr()), c_void_p(arg8_1.data_ptr()), c_void_p(arg9_1.data_ptr()), c_void_p(arg10_1.data_ptr()), c_void_p(arg11_1.data_ptr()), c_void_p(arg12_1.data_ptr()), c_void_p(arg13_1.data_ptr()), c_void_p(arg14_1.data_ptr()), c_void_p(arg15_1.data_ptr()), c_void_p(arg16_1.data_ptr()), c_void_p(arg17_1.data_ptr()), c_void_p(arg18_1.data_ptr()), c_void_p(arg19_1.data_ptr()), c_void_p(arg20_1.data_ptr()), c_void_p(arg21_1.data_ptr()), c_void_p(arg22_1.data_ptr()), c_void_p(arg23_1.data_ptr()), c_void_p(arg24_1.data_ptr()), c_void_p(arg25_1.data_ptr()), c_void_p(arg26_1.data_ptr()), c_void_p(arg27_1.data_ptr()), c_void_p(arg28_1.data_ptr()), c_void_p(arg29_1.data_ptr()), c_void_p(arg30_1.data_ptr()), c_void_p(arg31_1.data_ptr()), c_void_p(arg32_1.data_ptr()), c_void_p(arg33_1.data_ptr()), c_void_p(arg34_1.data_ptr()), c_void_p(arg35_1.data_ptr()), c_void_p(arg36_1.data_ptr()), c_void_p(arg37_1.data_ptr()), c_void_p(arg38_1.data_ptr()), c_void_p(arg39_1.data_ptr()), c_void_p(arg40_1.data_ptr()), c_void_p(arg41_1.data_ptr()), c_void_p(arg42_1.data_ptr()), c_void_p(arg43_1.data_ptr()), c_void_p(arg44_1.data_ptr()), c_void_p(arg45_1.data_ptr()), c_void_p(arg46_1.data_ptr()), c_void_p(arg47_1.data_ptr()), c_void_p(arg48_1.data_ptr()), c_void_p(arg49_1.data_ptr()))
2023-01-11T21:41:24.0096817Z     del arg0_1
2023-01-11T21:41:24.0096882Z     del arg10_1
2023-01-11T21:41:24.0096946Z     del arg11_1
2023-01-11T21:41:24.0097009Z     del arg12_1
2023-01-11T21:41:24.0097072Z     del arg13_1
2023-01-11T21:41:24.0097136Z     del arg14_1
2023-01-11T21:41:24.0097189Z     del arg15_1
2023-01-11T21:41:24.0097281Z     del arg16_1
2023-01-11T21:41:24.0097347Z     del arg17_1
2023-01-11T21:41:24.0097410Z     del arg18_1
2023-01-11T21:41:24.0097473Z     del arg19_1
2023-01-11T21:41:24.0097537Z     del arg1_1
2023-01-11T21:41:24.0097589Z     del arg20_1
2023-01-11T21:41:24.0097653Z     del arg21_1
2023-01-11T21:41:24.0097718Z     del arg22_1
2023-01-11T21:41:24.0097783Z     del arg23_1
2023-01-11T21:41:24.0097848Z     del arg24_1
2023-01-11T21:41:24.0097913Z     del arg25_1
2023-01-11T21:41:24.0097977Z     del arg26_1
2023-01-11T21:41:24.0098029Z     del arg27_1
2023-01-11T21:41:24.0098095Z     del arg28_1
2023-01-11T21:41:24.0098162Z     del arg29_1
2023-01-11T21:41:24.0098228Z     del arg2_1
2023-01-11T21:41:24.0098293Z     del arg30_1
2023-01-11T21:41:24.0098356Z     del arg31_1
2023-01-11T21:41:24.0098407Z     del arg32_1
2023-01-11T21:41:24.0098470Z     del arg33_1
2023-01-11T21:41:24.0098534Z     del arg34_1
2023-01-11T21:41:24.0098599Z     del arg35_1
2023-01-11T21:41:24.0098662Z     del arg36_1
2023-01-11T21:41:24.0098733Z     del arg37_1
2023-01-11T21:41:24.0098797Z     del arg38_1
2023-01-11T21:41:24.0098850Z     del arg39_1
2023-01-11T21:41:24.0098914Z     del arg3_1
2023-01-11T21:41:24.0098979Z     del arg40_1
2023-01-11T21:41:24.0099042Z     del arg41_1
2023-01-11T21:41:24.0099104Z     del arg42_1
2023-01-11T21:41:24.0099169Z     del arg43_1
2023-01-11T21:41:24.0099221Z     del arg44_1
2023-01-11T21:41:24.0099284Z     del arg45_1
2023-01-11T21:41:24.0099347Z     del arg46_1
2023-01-11T21:41:24.0099409Z     del arg47_1
2023-01-11T21:41:24.0099472Z     del arg48_1
2023-01-11T21:41:24.0099536Z     del arg49_1
2023-01-11T21:41:24.0099600Z     del arg4_1
2023-01-11T21:41:24.0099652Z     del arg5_1
2023-01-11T21:41:24.0099716Z     del arg6_1
2023-01-11T21:41:24.0099783Z     del arg7_1
2023-01-11T21:41:24.0099845Z     del arg8_1
2023-01-11T21:41:24.0099910Z     del arg9_1
2023-01-11T21:41:24.0099979Z     return (buf6, )
2023-01-11T21:41:24.0099984Z 
2023-01-11T21:41:24.0099988Z 
2023-01-11T21:41:24.0100065Z if __name__ == "__main__":
2023-01-11T21:41:24.0100170Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0100293Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0100495Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0100687Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0100873Z     arg2_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0101058Z     arg3_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0101240Z     arg4_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0101423Z     arg5_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0101594Z     arg6_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0101777Z     arg7_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0102004Z     arg8_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0102187Z     arg9_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0102546Z     arg10_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0102793Z     arg11_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0102983Z     arg12_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0103159Z     arg13_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0103343Z     arg14_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0103527Z     arg15_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0103711Z     arg16_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0103960Z     arg17_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0104147Z     arg18_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0104331Z     arg19_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0104517Z     arg20_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0104689Z     arg21_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0104871Z     arg22_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0105053Z     arg23_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0105236Z     arg24_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0105422Z     arg25_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0105605Z     arg26_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0105794Z     arg27_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0105976Z     arg28_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0106148Z     arg29_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0106329Z     arg30_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0106511Z     arg31_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0106693Z     arg32_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0106875Z     arg33_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0107057Z     arg34_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0107241Z     arg35_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0107423Z     arg36_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0107599Z     arg37_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0107784Z     arg38_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0107964Z     arg39_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0108147Z     arg40_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0108329Z     arg41_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0108512Z     arg42_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0108698Z     arg43_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0108879Z     arg44_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0109050Z     arg45_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0109235Z     arg46_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0109468Z     arg47_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0109652Z     arg48_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0109834Z     arg49_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0110196Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1]))
2023-01-11T21:41:24.0110473Z [2023-01-11 21:36:55,820] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 383
2023-01-11T21:41:24.0110482Z 
2023-01-11T21:41:24.0110587Z --> 7
2023-01-11T21:41:24.0110654Z ok (2.187s)
2023-01-11T21:41:24.0111130Z   test_no_op_reduction_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0111255Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0111504Z [2023-01-11 21:36:55,847] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 384
2023-01-11T21:41:24.0111766Z [2023-01-11 21:36:57,494] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 384
2023-01-11T21:41:24.0111773Z 
2023-01-11T21:41:24.0111865Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0111938Z import torch
2023-01-11T21:41:24.0112008Z import random
2023-01-11T21:41:24.0112123Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0112241Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0112246Z 
2023-01-11T21:41:24.0112322Z aten = torch.ops.aten
2023-01-11T21:41:24.0112443Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0112532Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0112538Z 
2023-01-11T21:41:24.0112542Z 
2023-01-11T21:41:24.0112677Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0112878Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0112996Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0113096Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0113192Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0113251Z {
2023-01-11T21:41:24.0113341Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0113401Z     {
2023-01-11T21:41:24.0113476Z         #pragma omp for 
2023-01-11T21:41:24.0113557Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.0113618Z         {
2023-01-11T21:41:24.0113814Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0113949Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0114021Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0114113Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0114201Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0114264Z         }
2023-01-11T21:41:24.0114357Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0114438Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:24.0114501Z         {
2023-01-11T21:41:24.0114571Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0114670Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0114789Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0114871Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0114950Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:24.0115012Z         }
2023-01-11T21:41:24.0115073Z     }
2023-01-11T21:41:24.0115120Z }
2023-01-11T21:41:24.0115204Z ''')
2023-01-11T21:41:24.0115210Z 
2023-01-11T21:41:24.0115214Z 
2023-01-11T21:41:24.0115305Z async_compile.wait(globals())
2023-01-11T21:41:24.0115379Z del async_compile
2023-01-11T21:41:24.0115383Z 
2023-01-11T21:41:24.0115455Z def call(args):
2023-01-11T21:41:24.0115525Z     arg0_1, = args
2023-01-11T21:41:24.0115598Z     args.clear()
2023-01-11T21:41:24.0115783Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0115986Z     buf1 = empty_strided((8, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0116151Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0116223Z     del arg0_1
2023-01-11T21:41:24.0116329Z     return (buf0, buf1, )
2023-01-11T21:41:24.0116335Z 
2023-01-11T21:41:24.0116340Z 
2023-01-11T21:41:24.0116417Z if __name__ == "__main__":
2023-01-11T21:41:24.0116530Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0116655Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0116847Z     arg0_1 = rand_strided((8, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0116955Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0116960Z 
2023-01-11T21:41:24.0117028Z ok (1.668s)
2023-01-11T21:41:24.0117365Z   test_output_strides_cpu (__main__.CpuTests) ... [2023-01-11 21:36:57,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 385
2023-01-11T21:41:24.0117633Z [2023-01-11 21:36:59,139] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 385
2023-01-11T21:41:24.0117890Z [2023-01-11 21:36:59,151] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 386
2023-01-11T21:41:24.0118158Z [2023-01-11 21:36:59,153] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 386
2023-01-11T21:41:24.0118412Z [2023-01-11 21:36:59,192] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 387
2023-01-11T21:41:24.0118674Z [2023-01-11 21:36:59,196] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 387
2023-01-11T21:41:24.0119106Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:3148: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0119210Z   self.assertEqual(inp.storage(), out.storage())
2023-01-11T21:41:24.0119876Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0119971Z   device=typed_storage.device,
2023-01-11T21:41:24.0119976Z 
2023-01-11T21:41:24.0120069Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0120139Z import torch
2023-01-11T21:41:24.0120211Z import random
2023-01-11T21:41:24.0120327Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0120450Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0120455Z 
2023-01-11T21:41:24.0120535Z aten = torch.ops.aten
2023-01-11T21:41:24.0120657Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0120748Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0120753Z 
2023-01-11T21:41:24.0120788Z 
2023-01-11T21:41:24.0120928Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0121136Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0121254Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0121354Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0121417Z {
2023-01-11T21:41:24.0121503Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0121563Z     {
2023-01-11T21:41:24.0121640Z         #pragma omp for 
2023-01-11T21:41:24.0121724Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0121787Z         {
2023-01-11T21:41:24.0121864Z             #pragma GCC ivdep
2023-01-11T21:41:24.0121946Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:24.0121998Z             {
2023-01-11T21:41:24.0122080Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0122169Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:24.0122237Z                 {
2023-01-11T21:41:24.0122334Z                     {
2023-01-11T21:41:24.0122401Z                         {
2023-01-11T21:41:24.0122511Z                             auto tmp0 = in_ptr0[i1 + (16*i2) + (64*i0)];
2023-01-11T21:41:24.0122602Z                             out_ptr0[i2 + (4*i1) + (64*i0)] = tmp0;
2023-01-11T21:41:24.0122670Z                         }
2023-01-11T21:41:24.0122739Z                     }
2023-01-11T21:41:24.0122801Z                 }
2023-01-11T21:41:24.0122864Z             }
2023-01-11T21:41:24.0122923Z         }
2023-01-11T21:41:24.0122981Z     }
2023-01-11T21:41:24.0123026Z }
2023-01-11T21:41:24.0123104Z ''')
2023-01-11T21:41:24.0123110Z 
2023-01-11T21:41:24.0123114Z 
2023-01-11T21:41:24.0123201Z async_compile.wait(globals())
2023-01-11T21:41:24.0123272Z del async_compile
2023-01-11T21:41:24.0123276Z 
2023-01-11T21:41:24.0123344Z def call(args):
2023-01-11T21:41:24.0123411Z     arg0_1, = args
2023-01-11T21:41:24.0123479Z     args.clear()
2023-01-11T21:41:24.0123680Z     buf0 = empty_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0123813Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0123880Z     del arg0_1
2023-01-11T21:41:24.0123953Z     return (buf0, )
2023-01-11T21:41:24.0123957Z 
2023-01-11T21:41:24.0123961Z 
2023-01-11T21:41:24.0124035Z if __name__ == "__main__":
2023-01-11T21:41:24.0124146Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0124267Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0124477Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0124572Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0124577Z 
2023-01-11T21:41:24.0124592Z 
2023-01-11T21:41:24.0124672Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0124742Z import torch
2023-01-11T21:41:24.0124813Z import random
2023-01-11T21:41:24.0124926Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0125050Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0125055Z 
2023-01-11T21:41:24.0125131Z aten = torch.ops.aten
2023-01-11T21:41:24.0125262Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0125340Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0125344Z 
2023-01-11T21:41:24.0125349Z 
2023-01-11T21:41:24.0125433Z async_compile.wait(globals())
2023-01-11T21:41:24.0125504Z del async_compile
2023-01-11T21:41:24.0125508Z 
2023-01-11T21:41:24.0125577Z def call(args):
2023-01-11T21:41:24.0125643Z     arg0_1, = args
2023-01-11T21:41:24.0125711Z     args.clear()
2023-01-11T21:41:24.0125807Z     return (as_strided(arg0_1, (64, 4), (4, 1)), )
2023-01-11T21:41:24.0125812Z 
2023-01-11T21:41:24.0125816Z 
2023-01-11T21:41:24.0125887Z if __name__ == "__main__":
2023-01-11T21:41:24.0125983Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0126106Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0126346Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0126454Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0126458Z 
2023-01-11T21:41:24.0126462Z 
2023-01-11T21:41:24.0126553Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0126620Z import torch
2023-01-11T21:41:24.0126689Z import random
2023-01-11T21:41:24.0126802Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0126908Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0126913Z 
2023-01-11T21:41:24.0126989Z aten = torch.ops.aten
2023-01-11T21:41:24.0127120Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0127210Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0127214Z 
2023-01-11T21:41:24.0127218Z 
2023-01-11T21:41:24.0127303Z async_compile.wait(globals())
2023-01-11T21:41:24.0127372Z del async_compile
2023-01-11T21:41:24.0127376Z 
2023-01-11T21:41:24.0127445Z def call(args):
2023-01-11T21:41:24.0127539Z     arg0_1, = args
2023-01-11T21:41:24.0127596Z     args.clear()
2023-01-11T21:41:24.0127699Z     return (as_strided(arg0_1, (4, 4, 1), (4, 16, 0), 3), )
2023-01-11T21:41:24.0127704Z 
2023-01-11T21:41:24.0127708Z 
2023-01-11T21:41:24.0127778Z if __name__ == "__main__":
2023-01-11T21:41:24.0127887Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0128005Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0128216Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0128323Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0128327Z 
2023-01-11T21:41:24.0128389Z ok (1.701s)
2023-01-11T21:41:24.0128848Z   test_permute_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0128977Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0129238Z [2023-01-11 21:36:59,218] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 388
2023-01-11T21:41:24.0129504Z [2023-01-11 21:37:01,158] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 388
2023-01-11T21:41:24.0129509Z 
2023-01-11T21:41:24.0129601Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0129668Z import torch
2023-01-11T21:41:24.0129735Z import random
2023-01-11T21:41:24.0129846Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0129963Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0129967Z 
2023-01-11T21:41:24.0130032Z aten = torch.ops.aten
2023-01-11T21:41:24.0130162Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0130256Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0130261Z 
2023-01-11T21:41:24.0130266Z 
2023-01-11T21:41:24.0130399Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0130601Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0130718Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0130817Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0130911Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0130958Z {
2023-01-11T21:41:24.0131056Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0131116Z     {
2023-01-11T21:41:24.0131192Z         #pragma omp for 
2023-01-11T21:41:24.0131272Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0131332Z         {
2023-01-11T21:41:24.0131464Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0131587Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0131703Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0131830Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0131913Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0131994Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0132082Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0132170Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0132221Z         }
2023-01-11T21:41:24.0132312Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0132393Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:24.0132454Z         {
2023-01-11T21:41:24.0132535Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0132631Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0132712Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0132796Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:24.0132905Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0132991Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0133068Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0133144Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:24.0133205Z         }
2023-01-11T21:41:24.0133265Z     }
2023-01-11T21:41:24.0133311Z }
2023-01-11T21:41:24.0133389Z ''')
2023-01-11T21:41:24.0133394Z 
2023-01-11T21:41:24.0133399Z 
2023-01-11T21:41:24.0133490Z async_compile.wait(globals())
2023-01-11T21:41:24.0133561Z del async_compile
2023-01-11T21:41:24.0133566Z 
2023-01-11T21:41:24.0133635Z def call(args):
2023-01-11T21:41:24.0133703Z     arg0_1, = args
2023-01-11T21:41:24.0133772Z     args.clear()
2023-01-11T21:41:24.0133977Z     buf0 = empty_strided((2, 2, 2, 2, 2), (4, 8, 1, 16, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0134190Z     buf1 = empty_strided((2, 2, 2, 2, 2), (4, 8, 1, 16, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0134356Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0134425Z     del arg0_1
2023-01-11T21:41:24.0134501Z     return (buf0, buf1, )
2023-01-11T21:41:24.0134506Z 
2023-01-11T21:41:24.0134510Z 
2023-01-11T21:41:24.0134583Z if __name__ == "__main__":
2023-01-11T21:41:24.0134696Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0134817Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0135021Z     arg0_1 = rand_strided((2, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0135126Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0135131Z 
2023-01-11T21:41:24.0135195Z ok (1.963s)
2023-01-11T21:41:24.0135660Z   test_pow1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0135788Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0136048Z [2023-01-11 21:37:01,317] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 389
2023-01-11T21:41:24.0136311Z [2023-01-11 21:37:03,472] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 389
2023-01-11T21:41:24.0136316Z 
2023-01-11T21:41:24.0136412Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0136478Z import torch
2023-01-11T21:41:24.0136534Z import random
2023-01-11T21:41:24.0136646Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0136765Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0136770Z 
2023-01-11T21:41:24.0136845Z aten = torch.ops.aten
2023-01-11T21:41:24.0136977Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0137100Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0137105Z 
2023-01-11T21:41:24.0137109Z 
2023-01-11T21:41:24.0137243Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0137446Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0137560Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0137646Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0137740Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0137833Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0137925Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.0138013Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:24.0138102Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:24.0138191Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:24.0138269Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:24.0138390Z                        float* __restrict__ out_ptr8,
2023-01-11T21:41:24.0138482Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:24.0138578Z                        float* __restrict__ out_ptr10,
2023-01-11T21:41:24.0138674Z                        float* __restrict__ out_ptr11,
2023-01-11T21:41:24.0138767Z                        float* __restrict__ out_ptr12,
2023-01-11T21:41:24.0138860Z                        float* __restrict__ out_ptr13,
2023-01-11T21:41:24.0138941Z                        float* __restrict__ out_ptr14,
2023-01-11T21:41:24.0139034Z                        float* __restrict__ out_ptr15)
2023-01-11T21:41:24.0139094Z {
2023-01-11T21:41:24.0139191Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0139252Z     {
2023-01-11T21:41:24.0139328Z         #pragma omp for 
2023-01-11T21:41:24.0139409Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0139459Z         {
2023-01-11T21:41:24.0139590Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0139688Z             auto tmp1 = tmp0.reciprocal();
2023-01-11T21:41:24.0139771Z             auto tmp2 = tmp1 * tmp1;
2023-01-11T21:41:24.0139849Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:24.0139928Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:24.0140006Z             auto tmp5 = tmp2 * tmp1;
2023-01-11T21:41:24.0140073Z             auto tmp6 = tmp5 * tmp5;
2023-01-11T21:41:24.0140150Z             auto tmp7 = tmp6 * tmp1;
2023-01-11T21:41:24.0140227Z             auto tmp8 = tmp3 * tmp1;
2023-01-11T21:41:24.0140356Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0140436Z             auto tmp10 = tmp0 * tmp0;
2023-01-11T21:41:24.0140519Z             auto tmp11 = tmp10 * tmp0;
2023-01-11T21:41:24.0140602Z             auto tmp12 = tmp10 * tmp10;
2023-01-11T21:41:24.0140674Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:24.0140755Z             auto tmp14 = tmp11 * tmp11;
2023-01-11T21:41:24.0140840Z             auto tmp15 = tmp14 * tmp0;
2023-01-11T21:41:24.0140923Z             auto tmp16 = tmp12 * tmp12;
2023-01-11T21:41:24.0141011Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0141101Z             tmp7.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0141187Z             tmp6.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0141262Z             tmp8.store(out_ptr3 + 8*i0);
2023-01-11T21:41:24.0141347Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:24.0141430Z             tmp5.store(out_ptr5 + 8*i0);
2023-01-11T21:41:24.0141515Z             tmp2.store(out_ptr6 + 8*i0);
2023-01-11T21:41:24.0141597Z             tmp1.store(out_ptr7 + 8*i0);
2023-01-11T21:41:24.0141680Z             tmp9.store(out_ptr8 + 8*i0);
2023-01-11T21:41:24.0141769Z             tmp10.store(out_ptr9 + 8*i0);
2023-01-11T21:41:24.0141847Z             tmp11.store(out_ptr10 + 8*i0);
2023-01-11T21:41:24.0141936Z             tmp12.store(out_ptr11 + 8*i0);
2023-01-11T21:41:24.0142022Z             tmp13.store(out_ptr12 + 8*i0);
2023-01-11T21:41:24.0142156Z             tmp14.store(out_ptr13 + 8*i0);
2023-01-11T21:41:24.0142243Z             tmp15.store(out_ptr14 + 8*i0);
2023-01-11T21:41:24.0142455Z             tmp16.store(out_ptr15 + 8*i0);
2023-01-11T21:41:24.0142547Z         }
2023-01-11T21:41:24.0142628Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0142712Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0142772Z         {
2023-01-11T21:41:24.0142856Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0142935Z             auto tmp1 = 1 / tmp0;
2023-01-11T21:41:24.0143018Z             auto tmp2 = tmp1 * tmp1;
2023-01-11T21:41:24.0143099Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:24.0143167Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:24.0143246Z             auto tmp5 = tmp2 * tmp1;
2023-01-11T21:41:24.0143327Z             auto tmp6 = tmp5 * tmp5;
2023-01-11T21:41:24.0143405Z             auto tmp7 = tmp6 * tmp1;
2023-01-11T21:41:24.0143484Z             auto tmp8 = tmp3 * tmp1;
2023-01-11T21:41:24.0143641Z             auto tmp9 = static_cast<float>(1);
2023-01-11T21:41:24.0143725Z             auto tmp10 = tmp0 * tmp0;
2023-01-11T21:41:24.0143797Z             auto tmp11 = tmp10 * tmp0;
2023-01-11T21:41:24.0143880Z             auto tmp12 = tmp10 * tmp10;
2023-01-11T21:41:24.0143963Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:24.0144044Z             auto tmp14 = tmp11 * tmp11;
2023-01-11T21:41:24.0144124Z             auto tmp15 = tmp14 * tmp0;
2023-01-11T21:41:24.0144205Z             auto tmp16 = tmp12 * tmp12;
2023-01-11T21:41:24.0144285Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0144349Z             out_ptr1[i0] = tmp7;
2023-01-11T21:41:24.0144426Z             out_ptr2[i0] = tmp6;
2023-01-11T21:41:24.0144504Z             out_ptr3[i0] = tmp8;
2023-01-11T21:41:24.0144579Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:24.0144655Z             out_ptr5[i0] = tmp5;
2023-01-11T21:41:24.0144731Z             out_ptr6[i0] = tmp2;
2023-01-11T21:41:24.0144811Z             out_ptr7[i0] = tmp1;
2023-01-11T21:41:24.0144874Z             out_ptr8[i0] = tmp9;
2023-01-11T21:41:24.0144952Z             out_ptr9[i0] = tmp10;
2023-01-11T21:41:24.0145033Z             out_ptr10[i0] = tmp11;
2023-01-11T21:41:24.0145114Z             out_ptr11[i0] = tmp12;
2023-01-11T21:41:24.0145194Z             out_ptr12[i0] = tmp13;
2023-01-11T21:41:24.0145273Z             out_ptr13[i0] = tmp14;
2023-01-11T21:41:24.0145350Z             out_ptr14[i0] = tmp15;
2023-01-11T21:41:24.0145413Z             out_ptr15[i0] = tmp16;
2023-01-11T21:41:24.0145478Z         }
2023-01-11T21:41:24.0145542Z     }
2023-01-11T21:41:24.0145603Z }
2023-01-11T21:41:24.0145698Z ''')
2023-01-11T21:41:24.0145703Z 
2023-01-11T21:41:24.0145708Z 
2023-01-11T21:41:24.0145801Z async_compile.wait(globals())
2023-01-11T21:41:24.0145875Z del async_compile
2023-01-11T21:41:24.0145880Z 
2023-01-11T21:41:24.0145939Z def call(args):
2023-01-11T21:41:24.0146009Z     arg0_1, = args
2023-01-11T21:41:24.0146083Z     args.clear()
2023-01-11T21:41:24.0146294Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0146493Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0146693Z     buf2 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0146886Z     buf3 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0147066Z     buf4 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0147261Z     buf5 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0147453Z     buf6 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0147647Z     buf7 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0147840Z     buf8 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0148034Z     buf9 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0148273Z     buf10 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0148471Z     buf11 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0148669Z     buf12 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0148853Z     buf13 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0149047Z     buf14 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0149240Z     buf15 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0149771Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf13.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf15.data_ptr()))
2023-01-11T21:41:24.0149941Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, arg0_1, buf9, buf10, buf11, buf12, buf13, buf14, buf15, )
2023-01-11T21:41:24.0149946Z 
2023-01-11T21:41:24.0149950Z 
2023-01-11T21:41:24.0150027Z if __name__ == "__main__":
2023-01-11T21:41:24.0150141Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0150265Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0150463Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0150571Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0150576Z 
2023-01-11T21:41:24.0150629Z ok (2.325s)
2023-01-11T21:41:24.0151096Z   test_pow2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0151225Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0151490Z [2023-01-11 21:37:03,532] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 390
2023-01-11T21:41:24.0151754Z [2023-01-11 21:37:06,418] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 390
2023-01-11T21:41:24.0151760Z 
2023-01-11T21:41:24.0151854Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0151928Z import torch
2023-01-11T21:41:24.0151998Z import random
2023-01-11T21:41:24.0152114Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0152222Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0152231Z 
2023-01-11T21:41:24.0152310Z aten = torch.ops.aten
2023-01-11T21:41:24.0152443Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0152535Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0152540Z 
2023-01-11T21:41:24.0152545Z 
2023-01-11T21:41:24.0152678Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0152882Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0153000Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0153103Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0153188Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0153250Z {
2023-01-11T21:41:24.0153347Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0153409Z     {
2023-01-11T21:41:24.0153487Z         #pragma omp for 
2023-01-11T21:41:24.0153566Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0153627Z         {
2023-01-11T21:41:24.0153839Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0153977Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1000));
2023-01-11T21:41:24.0154065Z             auto tmp2 = tmp0.pow(tmp1);
2023-01-11T21:41:24.0154150Z             auto tmp3 = tmp1.pow(tmp0);
2023-01-11T21:41:24.0154241Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0154328Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0154390Z         }
2023-01-11T21:41:24.0154471Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0154551Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0154612Z         {
2023-01-11T21:41:24.0154694Z             auto tmp1 = in_ptr0[i0];
2023-01-11T21:41:24.0154793Z             auto tmp0 = static_cast<float>(1000);
2023-01-11T21:41:24.0154893Z             auto tmp2 = std::pow(tmp0, tmp1);
2023-01-11T21:41:24.0154992Z             auto tmp3 = std::pow(tmp1, tmp0);
2023-01-11T21:41:24.0155095Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0155175Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:24.0155236Z         }
2023-01-11T21:41:24.0155296Z     }
2023-01-11T21:41:24.0155354Z }
2023-01-11T21:41:24.0155434Z ''')
2023-01-11T21:41:24.0155440Z 
2023-01-11T21:41:24.0155444Z 
2023-01-11T21:41:24.0155531Z async_compile.wait(globals())
2023-01-11T21:41:24.0155590Z del async_compile
2023-01-11T21:41:24.0155595Z 
2023-01-11T21:41:24.0155666Z def call(args):
2023-01-11T21:41:24.0155732Z     arg0_1, = args
2023-01-11T21:41:24.0155801Z     args.clear()
2023-01-11T21:41:24.0156001Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0156199Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0156362Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0156426Z     del arg0_1
2023-01-11T21:41:24.0156490Z     return (buf0, buf1, )
2023-01-11T21:41:24.0156499Z 
2023-01-11T21:41:24.0156504Z 
2023-01-11T21:41:24.0156577Z if __name__ == "__main__":
2023-01-11T21:41:24.0156688Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0156809Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0157009Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0157113Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0157117Z 
2023-01-11T21:41:24.0157183Z ok (2.942s)
2023-01-11T21:41:24.0157501Z   test_pow3_cpu (__main__.CpuTests) ... [2023-01-11 21:37:06,456] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 391
2023-01-11T21:41:24.0157757Z [2023-01-11 21:37:09,163] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 391
2023-01-11T21:41:24.0157774Z 
2023-01-11T21:41:24.0157855Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0157923Z import torch
2023-01-11T21:41:24.0157995Z import random
2023-01-11T21:41:24.0158109Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0158228Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0158233Z 
2023-01-11T21:41:24.0158308Z aten = torch.ops.aten
2023-01-11T21:41:24.0158437Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0158516Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0158520Z 
2023-01-11T21:41:24.0158524Z 
2023-01-11T21:41:24.0158655Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0158861Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0158978Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0159079Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0159138Z {
2023-01-11T21:41:24.0159199Z     {
2023-01-11T21:41:24.0159248Z         {
2023-01-11T21:41:24.0159328Z             auto tmp1 = in_ptr0[0];
2023-01-11T21:41:24.0159475Z             auto tmp0 = static_cast<float>(0.12300000339746475);
2023-01-11T21:41:24.0159557Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0159648Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:24.0159725Z             out_ptr0[0] = tmp3;
2023-01-11T21:41:24.0159786Z         }
2023-01-11T21:41:24.0159834Z     }
2023-01-11T21:41:24.0159891Z }
2023-01-11T21:41:24.0159968Z ''')
2023-01-11T21:41:24.0159973Z 
2023-01-11T21:41:24.0159977Z 
2023-01-11T21:41:24.0160063Z async_compile.wait(globals())
2023-01-11T21:41:24.0160135Z del async_compile
2023-01-11T21:41:24.0160140Z 
2023-01-11T21:41:24.0160208Z def call(args):
2023-01-11T21:41:24.0160274Z     arg0_1, = args
2023-01-11T21:41:24.0160333Z     args.clear()
2023-01-11T21:41:24.0160517Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0160647Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0160714Z     del arg0_1
2023-01-11T21:41:24.0160783Z     return (buf0, )
2023-01-11T21:41:24.0160819Z 
2023-01-11T21:41:24.0160824Z 
2023-01-11T21:41:24.0160898Z if __name__ == "__main__":
2023-01-11T21:41:24.0161010Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0161132Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0161307Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0161413Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0161418Z 
2023-01-11T21:41:24.0161486Z ok (2.738s)
2023-01-11T21:41:24.0161827Z   test_profiler_mark_wrapper_call_cpu (__main__.CpuTests) ... STAGE:2023-01-11 21:37:09 1454:1454 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:41:24.0162084Z [2023-01-11 21:37:09,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 392
2023-01-11T21:41:24.0162348Z [2023-01-11 21:37:09,184] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 392
2023-01-11T21:41:24.0162602Z STAGE:2023-01-11 21:37:09 1454:1454 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:41:24.0162857Z STAGE:2023-01-11 21:37:09 1454:1454 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:41:24.0162863Z 
2023-01-11T21:41:24.0162956Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0163012Z import torch
2023-01-11T21:41:24.0163080Z import random
2023-01-11T21:41:24.0163193Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0163311Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0163316Z 
2023-01-11T21:41:24.0163393Z aten = torch.ops.aten
2023-01-11T21:41:24.0163522Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0163613Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0163617Z 
2023-01-11T21:41:24.0163622Z 
2023-01-11T21:41:24.0163753Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0163945Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0164065Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0164166Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0164263Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0164322Z {
2023-01-11T21:41:24.0164418Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0164479Z     {
2023-01-11T21:41:24.0164542Z         #pragma omp for 
2023-01-11T21:41:24.0164623Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.0164683Z         {
2023-01-11T21:41:24.0164814Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0164942Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0165025Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0165116Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0165164Z         }
2023-01-11T21:41:24.0165258Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0165370Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:24.0165429Z         {
2023-01-11T21:41:24.0165509Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0165588Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.0165670Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0165737Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0165796Z         }
2023-01-11T21:41:24.0165855Z     }
2023-01-11T21:41:24.0165912Z }
2023-01-11T21:41:24.0165990Z ''')
2023-01-11T21:41:24.0165995Z 
2023-01-11T21:41:24.0166000Z 
2023-01-11T21:41:24.0166085Z async_compile.wait(globals())
2023-01-11T21:41:24.0166157Z del async_compile
2023-01-11T21:41:24.0166161Z 
2023-01-11T21:41:24.0166218Z def call(args):
2023-01-11T21:41:24.0166289Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0166355Z     args.clear()
2023-01-11T21:41:24.0166468Z     from torch.profiler import record_function
2023-01-11T21:41:24.0166629Z     with record_function('inductor_wrapper_call'):
2023-01-11T21:41:24.0166862Z         buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0167025Z         kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0167081Z         del arg0_1
2023-01-11T21:41:24.0167145Z         del arg1_1
2023-01-11T21:41:24.0167214Z         return (buf0, )
2023-01-11T21:41:24.0167220Z 
2023-01-11T21:41:24.0167224Z 
2023-01-11T21:41:24.0167298Z if __name__ == "__main__":
2023-01-11T21:41:24.0167410Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0167531Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0167728Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0167920Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0168022Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0168038Z 
2023-01-11T21:41:24.0168095Z ok (0.022s)
2023-01-11T21:41:24.0168446Z   test_rand_like_deterministic_cpu (__main__.CpuTests) ... [2023-01-11 21:37:09,265] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 393
2023-01-11T21:41:24.0168704Z [2023-01-11 21:37:09,265] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:24.0168969Z [2023-01-11 21:37:10,851] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 393
2023-01-11T21:41:24.0168974Z 
2023-01-11T21:41:24.0169067Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0169135Z import torch
2023-01-11T21:41:24.0169205Z import random
2023-01-11T21:41:24.0169316Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0169422Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0169427Z 
2023-01-11T21:41:24.0169504Z aten = torch.ops.aten
2023-01-11T21:41:24.0169635Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0169731Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0169889Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:24.0169894Z 
2023-01-11T21:41:24.0169899Z 
2023-01-11T21:41:24.0170031Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0170236Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0170348Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:24.0170436Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0170530Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0170586Z {
2023-01-11T21:41:24.0170683Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0170743Z     {
2023-01-11T21:41:24.0170817Z         #pragma omp for 
2023-01-11T21:41:24.0170899Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:24.0170949Z         {
2023-01-11T21:41:24.0171011Z             {
2023-01-11T21:41:24.0171107Z                 {
2023-01-11T21:41:24.0171192Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:24.0171292Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:24.0171427Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:24.0171532Z                     auto tmp3 = static_cast<int>(1024 + i0);
2023-01-11T21:41:24.0171654Z                     auto tmp4 = static_cast<float>(normalized_rand_cpu(tmp0, tmp3));;
2023-01-11T21:41:24.0171740Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0171820Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:24.0171884Z                 }
2023-01-11T21:41:24.0171945Z             }
2023-01-11T21:41:24.0172003Z         }
2023-01-11T21:41:24.0172060Z     }
2023-01-11T21:41:24.0172106Z }
2023-01-11T21:41:24.0172183Z ''')
2023-01-11T21:41:24.0172188Z 
2023-01-11T21:41:24.0172192Z 
2023-01-11T21:41:24.0172281Z async_compile.wait(globals())
2023-01-11T21:41:24.0172384Z del async_compile
2023-01-11T21:41:24.0172389Z 
2023-01-11T21:41:24.0172459Z def call(args):
2023-01-11T21:41:24.0172525Z     arg0_1, = args
2023-01-11T21:41:24.0172593Z     args.clear()
2023-01-11T21:41:24.0172712Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:24.0172906Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0173098Z     buf1 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0173270Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0173346Z     return (buf0, buf1, )
2023-01-11T21:41:24.0173352Z 
2023-01-11T21:41:24.0173357Z 
2023-01-11T21:41:24.0173432Z if __name__ == "__main__":
2023-01-11T21:41:24.0173546Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0173666Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0173850Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0174047Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0174151Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0174156Z 
2023-01-11T21:41:24.0174219Z ok (1.666s)
2023-01-11T21:41:24.0174685Z   test_reduction1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0174810Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0175070Z [2023-01-11 21:37:10,874] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 394
2023-01-11T21:41:24.0175339Z [2023-01-11 21:37:12,403] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 394
2023-01-11T21:41:24.0175346Z 
2023-01-11T21:41:24.0175436Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0175505Z import torch
2023-01-11T21:41:24.0175561Z import random
2023-01-11T21:41:24.0175674Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0175795Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0175799Z 
2023-01-11T21:41:24.0175876Z aten = torch.ops.aten
2023-01-11T21:41:24.0176007Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0176097Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0176102Z 
2023-01-11T21:41:24.0176106Z 
2023-01-11T21:41:24.0176237Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0176429Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0176547Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0176676Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0176772Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0176864Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0176957Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:24.0177048Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:24.0177107Z {
2023-01-11T21:41:24.0177155Z     {
2023-01-11T21:41:24.0177215Z         {
2023-01-11T21:41:24.0177290Z             float tmp1 = 0;
2023-01-11T21:41:24.0177508Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0177631Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0177747Z             struct IndexValue_9 {size_t index; float value;};
2023-01-11T21:41:24.0177966Z             IndexValue_9 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0178118Z             #pragma omp declare reduction(argmax : struct IndexValue_9 :\
2023-01-11T21:41:24.0178268Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0178411Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0178643Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0178760Z             struct IndexValue_10 {size_t index; float value;};
2023-01-11T21:41:24.0178891Z             IndexValue_10 tmp5{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0179028Z             #pragma omp declare reduction(argmin : struct IndexValue_10 :\
2023-01-11T21:41:24.0179173Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0179317Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0179443Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0179530Z             for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:24.0179592Z             {
2023-01-11T21:41:24.0179656Z                 {
2023-01-11T21:41:24.0179746Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0179825Z                     tmp1 += tmp0;
2023-01-11T21:41:24.0179926Z                     tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:24.0180009Z                     tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:24.0180102Z                     if (tmp4.value < tmp0) {
2023-01-11T21:41:24.0180211Z                         tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:24.0180280Z                     }
2023-01-11T21:41:24.0180370Z                     if (tmp5.value > tmp0) {
2023-01-11T21:41:24.0180475Z                         tmp5.index = i0; tmp5.value = tmp0;
2023-01-11T21:41:24.0180542Z                     }
2023-01-11T21:41:24.0180593Z                 }
2023-01-11T21:41:24.0180656Z             }
2023-01-11T21:41:24.0180735Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:24.0180815Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:24.0180892Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:24.0180976Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:24.0181058Z             out_ptr4[0] = tmp5.index;
2023-01-11T21:41:24.0181107Z         }
2023-01-11T21:41:24.0181168Z     }
2023-01-11T21:41:24.0181230Z }
2023-01-11T21:41:24.0181310Z ''')
2023-01-11T21:41:24.0181315Z 
2023-01-11T21:41:24.0181320Z 
2023-01-11T21:41:24.0181409Z async_compile.wait(globals())
2023-01-11T21:41:24.0181480Z del async_compile
2023-01-11T21:41:24.0181485Z 
2023-01-11T21:41:24.0181555Z def call(args):
2023-01-11T21:41:24.0181609Z     arg0_1, = args
2023-01-11T21:41:24.0181679Z     args.clear()
2023-01-11T21:41:24.0181866Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0182046Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0182223Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0182614Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0182796Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0183035Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:24.0183090Z     del arg0_1
2023-01-11T21:41:24.0183188Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:24.0183193Z 
2023-01-11T21:41:24.0183197Z 
2023-01-11T21:41:24.0183274Z if __name__ == "__main__":
2023-01-11T21:41:24.0183391Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0183513Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0183707Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0183815Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0183822Z 
2023-01-11T21:41:24.0183947Z ok (1.552s)
2023-01-11T21:41:24.0184418Z   test_reduction2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0184530Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0184797Z [2023-01-11 21:37:12,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 395
2023-01-11T21:41:24.0185066Z [2023-01-11 21:37:14,022] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 395
2023-01-11T21:41:24.0185072Z 
2023-01-11T21:41:24.0185165Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0185234Z import torch
2023-01-11T21:41:24.0185306Z import random
2023-01-11T21:41:24.0185424Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0185545Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0185551Z 
2023-01-11T21:41:24.0185616Z aten = torch.ops.aten
2023-01-11T21:41:24.0185747Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0185838Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0185843Z 
2023-01-11T21:41:24.0185847Z 
2023-01-11T21:41:24.0185980Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0186189Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0186305Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0186404Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0186501Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0186582Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0186682Z                        long* __restrict__ out_ptr3)
2023-01-11T21:41:24.0186744Z {
2023-01-11T21:41:24.0186801Z     {
2023-01-11T21:41:24.0186860Z         {
2023-01-11T21:41:24.0186932Z             float tmp1 = 0;
2023-01-11T21:41:24.0187135Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0187265Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0187409Z             struct IndexValue_11 {size_t index; float value;};
2023-01-11T21:41:24.0187580Z             IndexValue_11 tmp4{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0187763Z             #pragma omp declare reduction(argmin : struct IndexValue_11 :\
2023-01-11T21:41:24.0187960Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0188158Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0188352Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0188562Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0188636Z             {
2023-01-11T21:41:24.0188886Z                 #pragma omp for reduction(+:tmp1) reduction(max:tmp2) reduction(min:tmp3) reduction(argmin:tmp4) 
2023-01-11T21:41:24.0188998Z                 for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0189079Z                 {
2023-01-11T21:41:24.0189169Z                     {
2023-01-11T21:41:24.0189300Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0189410Z                         tmp1 += tmp0;
2023-01-11T21:41:24.0189549Z                         tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:24.0189671Z                         tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:24.0189808Z                         if (tmp4.value > tmp0) {
2023-01-11T21:41:24.0189954Z                             tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:24.0190048Z                         }
2023-01-11T21:41:24.0190191Z                     }
2023-01-11T21:41:24.0190280Z                 }
2023-01-11T21:41:24.0190368Z             }
2023-01-11T21:41:24.0190463Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:24.0190562Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:24.0190668Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:24.0190779Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:24.0190867Z         }
2023-01-11T21:41:24.0190948Z     }
2023-01-11T21:41:24.0191019Z }
2023-01-11T21:41:24.0191148Z ''')
2023-01-11T21:41:24.0191156Z 
2023-01-11T21:41:24.0191161Z 
2023-01-11T21:41:24.0191293Z async_compile.wait(globals())
2023-01-11T21:41:24.0191393Z del async_compile
2023-01-11T21:41:24.0191400Z 
2023-01-11T21:41:24.0191504Z def call(args):
2023-01-11T21:41:24.0191612Z     arg0_1, = args
2023-01-11T21:41:24.0191720Z     args.clear()
2023-01-11T21:41:24.0192007Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0192285Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0192579Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0192853Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0193159Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:24.0193274Z     del arg0_1
2023-01-11T21:41:24.0193392Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:24.0193400Z 
2023-01-11T21:41:24.0193405Z 
2023-01-11T21:41:24.0193516Z if __name__ == "__main__":
2023-01-11T21:41:24.0193637Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0193815Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0194017Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0194124Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0194129Z 
2023-01-11T21:41:24.0194195Z ok (1.619s)
2023-01-11T21:41:24.0194670Z   test_reduction3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0194797Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0195063Z [2023-01-11 21:37:14,042] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 396
2023-01-11T21:41:24.0195329Z [2023-01-11 21:37:15,583] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 396
2023-01-11T21:41:24.0195335Z 
2023-01-11T21:41:24.0195430Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0195515Z import torch
2023-01-11T21:41:24.0195610Z import random
2023-01-11T21:41:24.0195860Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0196035Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0196043Z 
2023-01-11T21:41:24.0196160Z aten = torch.ops.aten
2023-01-11T21:41:24.0196344Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0196470Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0196479Z 
2023-01-11T21:41:24.0196486Z 
2023-01-11T21:41:24.0196700Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0196898Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0197019Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0197118Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0197213Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0197305Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0197398Z                        long* __restrict__ out_ptr3)
2023-01-11T21:41:24.0197514Z {
2023-01-11T21:41:24.0197564Z     {
2023-01-11T21:41:24.0197624Z         {
2023-01-11T21:41:24.0197696Z             float tmp1 = 0;
2023-01-11T21:41:24.0197918Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0198038Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0198156Z             struct IndexValue_12 {size_t index; float value;};
2023-01-11T21:41:24.0198378Z             IndexValue_12 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0198519Z             #pragma omp declare reduction(argmax : struct IndexValue_12 :\
2023-01-11T21:41:24.0198655Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0198795Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0199026Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0199137Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0199201Z             {
2023-01-11T21:41:24.0199375Z                 #pragma omp for reduction(+:tmp1) reduction(max:tmp2) reduction(min:tmp3) reduction(argmax:tmp4) 
2023-01-11T21:41:24.0199463Z                 for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0199528Z                 {
2023-01-11T21:41:24.0199581Z                     {
2023-01-11T21:41:24.0199675Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0199754Z                         tmp1 += tmp0;
2023-01-11T21:41:24.0199853Z                         tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:24.0199948Z                         tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:24.0200038Z                         if (tmp4.value < tmp0) {
2023-01-11T21:41:24.0200144Z                             tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:24.0200199Z                         }
2023-01-11T21:41:24.0200260Z                     }
2023-01-11T21:41:24.0200328Z                 }
2023-01-11T21:41:24.0200388Z             }
2023-01-11T21:41:24.0200464Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:24.0200542Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:24.0200614Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:24.0200686Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:24.0200745Z         }
2023-01-11T21:41:24.0200803Z     }
2023-01-11T21:41:24.0200858Z }
2023-01-11T21:41:24.0200936Z ''')
2023-01-11T21:41:24.0200941Z 
2023-01-11T21:41:24.0200945Z 
2023-01-11T21:41:24.0201034Z async_compile.wait(globals())
2023-01-11T21:41:24.0201102Z del async_compile
2023-01-11T21:41:24.0201107Z 
2023-01-11T21:41:24.0201163Z def call(args):
2023-01-11T21:41:24.0201229Z     arg0_1, = args
2023-01-11T21:41:24.0201298Z     args.clear()
2023-01-11T21:41:24.0201484Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0201664Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0201873Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0202048Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0202249Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:24.0202315Z     del arg0_1
2023-01-11T21:41:24.0202400Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:24.0202406Z 
2023-01-11T21:41:24.0202410Z 
2023-01-11T21:41:24.0202483Z if __name__ == "__main__":
2023-01-11T21:41:24.0202595Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0202715Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0202906Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0203012Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0203017Z 
2023-01-11T21:41:24.0203080Z ok (1.561s)
2023-01-11T21:41:24.0203330Z   test_reduction4_cpu (__main__.CpuTests) ... skip: Non-deterministic CPU results (0.001s)
2023-01-11T21:41:24.0203818Z   test_reflection_pad2d_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0203944Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0204206Z [2023-01-11 21:37:15,606] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 397
2023-01-11T21:41:24.0204472Z [2023-01-11 21:37:17,171] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 397
2023-01-11T21:41:24.0204900Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0205024Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0205283Z [2023-01-11 21:37:17,192] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 398
2023-01-11T21:41:24.0205548Z [2023-01-11 21:37:18,808] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 398
2023-01-11T21:41:24.0205972Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0206099Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0206353Z [2023-01-11 21:37:18,831] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 399
2023-01-11T21:41:24.0206358Z 
2023-01-11T21:41:24.0206439Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0206509Z import torch
2023-01-11T21:41:24.0206579Z import random
2023-01-11T21:41:24.0206691Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0206809Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0206814Z 
2023-01-11T21:41:24.0206890Z aten = torch.ops.aten
2023-01-11T21:41:24.0207021Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0207099Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0207115Z 
2023-01-11T21:41:24.0207119Z 
2023-01-11T21:41:24.0207239Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0207442Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0207600Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0207697Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0207756Z {
2023-01-11T21:41:24.0207850Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0207911Z     {
2023-01-11T21:41:24.0207975Z         #pragma omp for 
2023-01-11T21:41:24.0208053Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0208114Z         {
2023-01-11T21:41:24.0208187Z             #pragma GCC ivdep
2023-01-11T21:41:24.0208270Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0208332Z             {
2023-01-11T21:41:24.0208393Z                 {
2023-01-11T21:41:24.0208446Z                     {
2023-01-11T21:41:24.0208548Z                         auto tmp0 = static_cast<int>(i0);
2023-01-11T21:41:24.0208648Z                         auto tmp1 = static_cast<int>(i1);
2023-01-11T21:41:24.0208778Z                         auto tmp2 = in_ptr0[tmp1 + (8*tmp0)];
2023-01-11T21:41:24.0208873Z                         out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:24.0208938Z                     }
2023-01-11T21:41:24.0208998Z                 }
2023-01-11T21:41:24.0209048Z             }
2023-01-11T21:41:24.0209107Z         }
2023-01-11T21:41:24.0209164Z     }
2023-01-11T21:41:24.0209220Z }
2023-01-11T21:41:24.0209296Z ''')
2023-01-11T21:41:24.0209301Z 
2023-01-11T21:41:24.0209305Z 
2023-01-11T21:41:24.0209390Z async_compile.wait(globals())
2023-01-11T21:41:24.0209459Z del async_compile
2023-01-11T21:41:24.0209464Z 
2023-01-11T21:41:24.0209519Z def call(args):
2023-01-11T21:41:24.0209591Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0209659Z     args.clear()
2023-01-11T21:41:24.0209868Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0210001Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0210067Z     del arg0_1
2023-01-11T21:41:24.0210139Z     return (buf0, )
2023-01-11T21:41:24.0210144Z 
2023-01-11T21:41:24.0210148Z 
2023-01-11T21:41:24.0210211Z if __name__ == "__main__":
2023-01-11T21:41:24.0210323Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0210445Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0210658Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0210865Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0210976Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0210981Z 
2023-01-11T21:41:24.0210985Z 
2023-01-11T21:41:24.0211075Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0211147Z import torch
2023-01-11T21:41:24.0211205Z import random
2023-01-11T21:41:24.0211318Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0211435Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0211442Z 
2023-01-11T21:41:24.0211521Z aten = torch.ops.aten
2023-01-11T21:41:24.0211652Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0211741Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0211746Z 
2023-01-11T21:41:24.0211751Z 
2023-01-11T21:41:24.0211883Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0212085Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0212193Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0212291Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0212350Z {
2023-01-11T21:41:24.0212446Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0212503Z     {
2023-01-11T21:41:24.0212578Z         #pragma omp for 
2023-01-11T21:41:24.0212655Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0212708Z         {
2023-01-11T21:41:24.0212787Z             #pragma GCC ivdep
2023-01-11T21:41:24.0212898Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0212961Z             {
2023-01-11T21:41:24.0213025Z                 {
2023-01-11T21:41:24.0213089Z                     {
2023-01-11T21:41:24.0213193Z                         auto tmp0 = static_cast<int>(1 + i0);
2023-01-11T21:41:24.0213288Z                         auto tmp1 = static_cast<int>(1 + i1);
2023-01-11T21:41:24.0213393Z                         auto tmp2 = in_ptr0[tmp1 + (10*tmp0)];
2023-01-11T21:41:24.0213492Z                         auto tmp3 = static_cast<int>(i1);
2023-01-11T21:41:24.0213581Z                         auto tmp4 = tmp3 >= 1;
2023-01-11T21:41:24.0213670Z                         auto tmp5 = tmp3 <= 1;
2023-01-11T21:41:24.0213762Z                         auto tmp6 = tmp4 & tmp5;
2023-01-11T21:41:24.0213845Z                         float tmp7 = 0.0;
2023-01-11T21:41:24.0213907Z                         if(tmp6)
2023-01-11T21:41:24.0213971Z                         {
2023-01-11T21:41:24.0214110Z                             auto tmp8 = static_cast<int>(1 + i0);
2023-01-11T21:41:24.0214288Z                             auto tmp9 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:24.0214395Z                             auto tmp10 = in_ptr0[tmp9 + (10*tmp8)];
2023-01-11T21:41:24.0214474Z                             tmp7 = tmp10;
2023-01-11T21:41:24.0214539Z                         }
2023-01-11T21:41:24.0214621Z                         auto tmp11 = tmp2 + tmp7;
2023-01-11T21:41:24.0214710Z                         auto tmp12 = tmp3 >= 6;
2023-01-11T21:41:24.0214795Z                         auto tmp13 = tmp3 <= 6;
2023-01-11T21:41:24.0214888Z                         auto tmp14 = tmp12 & tmp13;
2023-01-11T21:41:24.0214972Z                         float tmp15 = 0.0;
2023-01-11T21:41:24.0215046Z                         if(tmp14)
2023-01-11T21:41:24.0215111Z                         {
2023-01-11T21:41:24.0215215Z                             auto tmp16 = static_cast<int>(1 + i0);
2023-01-11T21:41:24.0215382Z                             auto tmp17 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:24.0215492Z                             auto tmp18 = in_ptr0[tmp17 + (10*tmp16)];
2023-01-11T21:41:24.0215576Z                             tmp15 = tmp18;
2023-01-11T21:41:24.0215640Z                         }
2023-01-11T21:41:24.0215735Z                         auto tmp19 = tmp11 + tmp15;
2023-01-11T21:41:24.0215837Z                         auto tmp20 = static_cast<int>(i0);
2023-01-11T21:41:24.0215929Z                         auto tmp21 = tmp20 >= 1;
2023-01-11T21:41:24.0216008Z                         auto tmp22 = tmp20 <= 1;
2023-01-11T21:41:24.0216100Z                         auto tmp23 = tmp21 & tmp22;
2023-01-11T21:41:24.0216183Z                         float tmp24 = 0.0;
2023-01-11T21:41:24.0216259Z                         if(tmp23)
2023-01-11T21:41:24.0216328Z                         {
2023-01-11T21:41:24.0216503Z                             auto tmp25 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:41:24.0216612Z                             auto tmp26 = static_cast<int>(1 + i1);
2023-01-11T21:41:24.0216710Z                             auto tmp27 = in_ptr0[tmp26 + (10*tmp25)];
2023-01-11T21:41:24.0216792Z                             tmp24 = tmp27;
2023-01-11T21:41:24.0216859Z                         }
2023-01-11T21:41:24.0216951Z                         auto tmp28 = tmp19 + tmp24;
2023-01-11T21:41:24.0217042Z                         auto tmp29 = tmp20 >= 6;
2023-01-11T21:41:24.0217134Z                         auto tmp30 = tmp20 <= 6;
2023-01-11T21:41:24.0217231Z                         auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:24.0217301Z                         float tmp32 = 0.0;
2023-01-11T21:41:24.0217377Z                         if(tmp31)
2023-01-11T21:41:24.0217444Z                         {
2023-01-11T21:41:24.0217623Z                             auto tmp33 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:41:24.0217731Z                             auto tmp34 = static_cast<int>(1 + i1);
2023-01-11T21:41:24.0217841Z                             auto tmp35 = in_ptr0[tmp34 + (10*tmp33)];
2023-01-11T21:41:24.0217950Z                             tmp32 = tmp35;
2023-01-11T21:41:24.0218016Z                         }
2023-01-11T21:41:24.0218097Z                         auto tmp36 = tmp28 + tmp32;
2023-01-11T21:41:24.0218190Z                         auto tmp37 = tmp23 & tmp6;
2023-01-11T21:41:24.0218273Z                         float tmp38 = 0.0;
2023-01-11T21:41:24.0218348Z                         if(tmp37)
2023-01-11T21:41:24.0218415Z                         {
2023-01-11T21:41:24.0218590Z                             auto tmp39 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:41:24.0218765Z                             auto tmp40 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:24.0218862Z                             auto tmp41 = in_ptr0[tmp40 + (10*tmp39)];
2023-01-11T21:41:24.0218943Z                             tmp38 = tmp41;
2023-01-11T21:41:24.0219009Z                         }
2023-01-11T21:41:24.0219103Z                         auto tmp42 = tmp36 + tmp38;
2023-01-11T21:41:24.0219223Z                         auto tmp43 = tmp23 & tmp14;
2023-01-11T21:41:24.0219309Z                         float tmp44 = 0.0;
2023-01-11T21:41:24.0219384Z                         if(tmp43)
2023-01-11T21:41:24.0219439Z                         {
2023-01-11T21:41:24.0219614Z                             auto tmp45 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:41:24.0219793Z                             auto tmp46 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:24.0219898Z                             auto tmp47 = in_ptr0[tmp46 + (10*tmp45)];
2023-01-11T21:41:24.0219979Z                             tmp44 = tmp47;
2023-01-11T21:41:24.0220046Z                         }
2023-01-11T21:41:24.0220137Z                         auto tmp48 = tmp42 + tmp44;
2023-01-11T21:41:24.0220217Z                         auto tmp49 = tmp31 & tmp6;
2023-01-11T21:41:24.0220295Z                         float tmp50 = 0.0;
2023-01-11T21:41:24.0220367Z                         if(tmp49)
2023-01-11T21:41:24.0220439Z                         {
2023-01-11T21:41:24.0220616Z                             auto tmp51 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:41:24.0220788Z                             auto tmp52 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:24.0220892Z                             auto tmp53 = in_ptr0[tmp52 + (10*tmp51)];
2023-01-11T21:41:24.0220973Z                             tmp50 = tmp53;
2023-01-11T21:41:24.0221027Z                         }
2023-01-11T21:41:24.0221118Z                         auto tmp54 = tmp48 + tmp50;
2023-01-11T21:41:24.0221209Z                         auto tmp55 = tmp31 & tmp14;
2023-01-11T21:41:24.0221292Z                         float tmp56 = 0.0;
2023-01-11T21:41:24.0221363Z                         if(tmp55)
2023-01-11T21:41:24.0221427Z                         {
2023-01-11T21:41:24.0221599Z                             auto tmp57 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:41:24.0221761Z                             auto tmp58 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:24.0221872Z                             auto tmp59 = in_ptr0[tmp58 + (10*tmp57)];
2023-01-11T21:41:24.0221952Z                             tmp56 = tmp59;
2023-01-11T21:41:24.0222015Z                         }
2023-01-11T21:41:24.0222105Z                         auto tmp60 = tmp54 + tmp56;
2023-01-11T21:41:24.0222199Z                         out_ptr0[i1 + (8*i0)] = tmp60;
2023-01-11T21:41:24.0222265Z                     }
2023-01-11T21:41:24.0222316Z                 }
2023-01-11T21:41:24.0222542Z             }
2023-01-11T21:41:24.0222624Z         }
2023-01-11T21:41:24.0222686Z     }
2023-01-11T21:41:24.0222746Z }
2023-01-11T21:41:24.0222827Z ''')
2023-01-11T21:41:24.0222833Z 
2023-01-11T21:41:24.0222837Z 
2023-01-11T21:41:24.0222930Z async_compile.wait(globals())
2023-01-11T21:41:24.0222988Z del async_compile
2023-01-11T21:41:24.0222993Z 
2023-01-11T21:41:24.0223062Z def call(args):
2023-01-11T21:41:24.0223135Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0223205Z     args.clear()
2023-01-11T21:41:24.0223502Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0223636Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0223703Z     del arg0_1
2023-01-11T21:41:24.0223759Z     return (buf0, )
2023-01-11T21:41:24.0223780Z 
2023-01-11T21:41:24.0223784Z 
2023-01-11T21:41:24.0223845Z if __name__ == "__main__":
2023-01-11T21:41:24.0223955Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0224073Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0224294Z     arg0_1 = rand_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0224505Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0224616Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0224622Z 
2023-01-11T21:41:24.0224932Z [2023-01-11 21:37:20,418] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 399
2023-01-11T21:41:24.0224941Z 
2023-01-11T21:41:24.0225031Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0225087Z import torch
2023-01-11T21:41:24.0225156Z import random
2023-01-11T21:41:24.0225265Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0225382Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0225386Z 
2023-01-11T21:41:24.0225461Z aten = torch.ops.aten
2023-01-11T21:41:24.0225590Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0225679Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0225684Z 
2023-01-11T21:41:24.0225688Z 
2023-01-11T21:41:24.0225818Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0226008Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0226119Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0226220Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0226281Z {
2023-01-11T21:41:24.0226375Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0226435Z     {
2023-01-11T21:41:24.0226507Z         #pragma omp for 
2023-01-11T21:41:24.0226575Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0226636Z         {
2023-01-11T21:41:24.0226713Z             #pragma GCC ivdep
2023-01-11T21:41:24.0226793Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0226854Z             {
2023-01-11T21:41:24.0226917Z                 {
2023-01-11T21:41:24.0226984Z                     {
2023-01-11T21:41:24.0227078Z                         auto tmp0 = static_cast<int>(3 + i0);
2023-01-11T21:41:24.0227181Z                         auto tmp1 = static_cast<int>(1 + i1);
2023-01-11T21:41:24.0227284Z                         auto tmp2 = in_ptr0[tmp1 + (11*tmp0)];
2023-01-11T21:41:24.0227384Z                         auto tmp3 = static_cast<int>(i1);
2023-01-11T21:41:24.0227472Z                         auto tmp4 = tmp3 >= 1;
2023-01-11T21:41:24.0227565Z                         auto tmp5 = tmp3 <= 1;
2023-01-11T21:41:24.0227657Z                         auto tmp6 = tmp4 & tmp5;
2023-01-11T21:41:24.0227729Z                         float tmp7 = 0.0;
2023-01-11T21:41:24.0227799Z                         if(tmp6)
2023-01-11T21:41:24.0227863Z                         {
2023-01-11T21:41:24.0227967Z                             auto tmp8 = static_cast<int>(3 + i0);
2023-01-11T21:41:24.0228144Z                             auto tmp9 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:24.0228249Z                             auto tmp10 = in_ptr0[tmp9 + (11*tmp8)];
2023-01-11T21:41:24.0228328Z                             tmp7 = tmp10;
2023-01-11T21:41:24.0228383Z                         }
2023-01-11T21:41:24.0228474Z                         auto tmp11 = tmp2 + tmp7;
2023-01-11T21:41:24.0228562Z                         auto tmp12 = tmp3 >= 5;
2023-01-11T21:41:24.0228647Z                         auto tmp13 = tmp3 <= 6;
2023-01-11T21:41:24.0228776Z                         auto tmp14 = tmp12 & tmp13;
2023-01-11T21:41:24.0228858Z                         float tmp15 = 0.0;
2023-01-11T21:41:24.0228932Z                         if(tmp14)
2023-01-11T21:41:24.0228986Z                         {
2023-01-11T21:41:24.0229091Z                             auto tmp16 = static_cast<int>(3 + i0);
2023-01-11T21:41:24.0229268Z                             auto tmp17 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:24.0229377Z                             auto tmp18 = in_ptr0[tmp17 + (11*tmp16)];
2023-01-11T21:41:24.0229454Z                             tmp15 = tmp18;
2023-01-11T21:41:24.0229517Z                         }
2023-01-11T21:41:24.0229606Z                         auto tmp19 = tmp11 + tmp15;
2023-01-11T21:41:24.0229710Z                         auto tmp20 = static_cast<int>(i0);
2023-01-11T21:41:24.0229789Z                         auto tmp21 = tmp20 >= 1;
2023-01-11T21:41:24.0229877Z                         auto tmp22 = tmp20 <= 3;
2023-01-11T21:41:24.0229999Z                         auto tmp23 = tmp21 & tmp22;
2023-01-11T21:41:24.0230082Z                         float tmp24 = 0.0;
2023-01-11T21:41:24.0230154Z                         if(tmp23)
2023-01-11T21:41:24.0230219Z                         {
2023-01-11T21:41:24.0230394Z                             auto tmp25 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:41:24.0230490Z                             auto tmp26 = static_cast<int>(1 + i1);
2023-01-11T21:41:24.0230598Z                             auto tmp27 = in_ptr0[tmp26 + (11*tmp25)];
2023-01-11T21:41:24.0230677Z                             tmp24 = tmp27;
2023-01-11T21:41:24.0230745Z                         }
2023-01-11T21:41:24.0230835Z                         auto tmp28 = tmp19 + tmp24;
2023-01-11T21:41:24.0230924Z                         auto tmp29 = tmp20 >= 3;
2023-01-11T21:41:24.0231013Z                         auto tmp30 = tmp20 <= 6;
2023-01-11T21:41:24.0231094Z                         auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:24.0231178Z                         float tmp32 = 0.0;
2023-01-11T21:41:24.0231251Z                         if(tmp31)
2023-01-11T21:41:24.0231315Z                         {
2023-01-11T21:41:24.0231489Z                             auto tmp33 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:41:24.0231593Z                             auto tmp34 = static_cast<int>(1 + i1);
2023-01-11T21:41:24.0231700Z                             auto tmp35 = in_ptr0[tmp34 + (11*tmp33)];
2023-01-11T21:41:24.0231768Z                             tmp32 = tmp35;
2023-01-11T21:41:24.0231832Z                         }
2023-01-11T21:41:24.0231920Z                         auto tmp36 = tmp28 + tmp32;
2023-01-11T21:41:24.0232013Z                         auto tmp37 = tmp23 & tmp6;
2023-01-11T21:41:24.0232093Z                         float tmp38 = 0.0;
2023-01-11T21:41:24.0232163Z                         if(tmp37)
2023-01-11T21:41:24.0232226Z                         {
2023-01-11T21:41:24.0232386Z                             auto tmp39 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:41:24.0232558Z                             auto tmp40 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:24.0232666Z                             auto tmp41 = in_ptr0[tmp40 + (11*tmp39)];
2023-01-11T21:41:24.0232744Z                             tmp38 = tmp41;
2023-01-11T21:41:24.0232808Z                         }
2023-01-11T21:41:24.0232898Z                         auto tmp42 = tmp36 + tmp38;
2023-01-11T21:41:24.0232986Z                         auto tmp43 = tmp23 & tmp14;
2023-01-11T21:41:24.0233065Z                         float tmp44 = 0.0;
2023-01-11T21:41:24.0233127Z                         if(tmp43)
2023-01-11T21:41:24.0233189Z                         {
2023-01-11T21:41:24.0233359Z                             auto tmp45 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:41:24.0233538Z                             auto tmp46 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:24.0233644Z                             auto tmp47 = in_ptr0[tmp46 + (11*tmp45)];
2023-01-11T21:41:24.0233821Z                             tmp44 = tmp47;
2023-01-11T21:41:24.0233890Z                         }
2023-01-11T21:41:24.0233969Z                         auto tmp48 = tmp42 + tmp44;
2023-01-11T21:41:24.0234058Z                         auto tmp49 = tmp31 & tmp6;
2023-01-11T21:41:24.0234138Z                         float tmp50 = 0.0;
2023-01-11T21:41:24.0234211Z                         if(tmp49)
2023-01-11T21:41:24.0234275Z                         {
2023-01-11T21:41:24.0234452Z                             auto tmp51 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:41:24.0234624Z                             auto tmp52 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:24.0234719Z                             auto tmp53 = in_ptr0[tmp52 + (11*tmp51)];
2023-01-11T21:41:24.0234797Z                             tmp50 = tmp53;
2023-01-11T21:41:24.0234862Z                         }
2023-01-11T21:41:24.0234955Z                         auto tmp54 = tmp48 + tmp50;
2023-01-11T21:41:24.0235075Z                         auto tmp55 = tmp31 & tmp14;
2023-01-11T21:41:24.0235159Z                         float tmp56 = 0.0;
2023-01-11T21:41:24.0235230Z                         if(tmp55)
2023-01-11T21:41:24.0235294Z                         {
2023-01-11T21:41:24.0235455Z                             auto tmp57 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:41:24.0235626Z                             auto tmp58 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:24.0235735Z                             auto tmp59 = in_ptr0[tmp58 + (11*tmp57)];
2023-01-11T21:41:24.0235813Z                             tmp56 = tmp59;
2023-01-11T21:41:24.0235877Z                         }
2023-01-11T21:41:24.0235964Z                         auto tmp60 = tmp54 + tmp56;
2023-01-11T21:41:24.0236058Z                         out_ptr0[i1 + (8*i0)] = tmp60;
2023-01-11T21:41:24.0236112Z                     }
2023-01-11T21:41:24.0236173Z                 }
2023-01-11T21:41:24.0236233Z             }
2023-01-11T21:41:24.0236293Z         }
2023-01-11T21:41:24.0236354Z     }
2023-01-11T21:41:24.0236413Z }
2023-01-11T21:41:24.0236478Z ''')
2023-01-11T21:41:24.0236494Z 
2023-01-11T21:41:24.0236499Z 
2023-01-11T21:41:24.0236575Z async_compile.wait(globals())
2023-01-11T21:41:24.0236643Z del async_compile
2023-01-11T21:41:24.0236649Z 
2023-01-11T21:41:24.0236713Z def call(args):
2023-01-11T21:41:24.0236789Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0236855Z     args.clear()
2023-01-11T21:41:24.0237069Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0237198Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0237253Z     del arg0_1
2023-01-11T21:41:24.0237320Z     return (buf0, )
2023-01-11T21:41:24.0237325Z 
2023-01-11T21:41:24.0237330Z 
2023-01-11T21:41:24.0237403Z if __name__ == "__main__":
2023-01-11T21:41:24.0237510Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0237630Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0237851Z     arg0_1 = rand_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0238060Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0238172Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0238176Z 
2023-01-11T21:41:24.0238230Z ok (4.834s)
2023-01-11T21:41:24.0238703Z   test_reflection_pad2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0238827Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0239093Z [2023-01-11 21:37:20,448] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 400
2023-01-11T21:41:24.0239386Z [2023-01-11 21:37:22,080] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 400
2023-01-11T21:41:24.0239392Z 
2023-01-11T21:41:24.0239481Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0239547Z import torch
2023-01-11T21:41:24.0239614Z import random
2023-01-11T21:41:24.0239724Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0239830Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0239843Z 
2023-01-11T21:41:24.0239907Z aten = torch.ops.aten
2023-01-11T21:41:24.0240036Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0240124Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0240129Z 
2023-01-11T21:41:24.0240133Z 
2023-01-11T21:41:24.0240261Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0240464Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0240607Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0240704Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0240787Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0240843Z {
2023-01-11T21:41:24.0240935Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0240994Z     {
2023-01-11T21:41:24.0241068Z         #pragma omp for 
2023-01-11T21:41:24.0241149Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.0241208Z         {
2023-01-11T21:41:24.0241274Z             #pragma GCC ivdep
2023-01-11T21:41:24.0241355Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.0241415Z             {
2023-01-11T21:41:24.0241475Z                 {
2023-01-11T21:41:24.0241539Z                     {
2023-01-11T21:41:24.0241640Z                         auto tmp0 = static_cast<int>(7);
2023-01-11T21:41:24.0241740Z                         auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:24.0241829Z                         auto tmp2 = static_cast<int>(1);
2023-01-11T21:41:24.0241971Z                         auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:24.0242074Z                         auto tmp4 = std::abs(tmp3);
2023-01-11T21:41:24.0242208Z                         auto tmp5 = tmp0 - tmp4;
2023-01-11T21:41:24.0242303Z                         auto tmp6 = std::abs(tmp5);
2023-01-11T21:41:24.0242437Z                         auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:24.0242536Z                         auto tmp8 = static_cast<int>(i1);
2023-01-11T21:41:24.0242661Z                         auto tmp9 = tmp8 - tmp2;
2023-01-11T21:41:24.0242757Z                         auto tmp10 = std::abs(tmp9);
2023-01-11T21:41:24.0242895Z                         auto tmp11 = tmp0 - tmp10;
2023-01-11T21:41:24.0242991Z                         auto tmp12 = std::abs(tmp11);
2023-01-11T21:41:24.0243131Z                         auto tmp13 = tmp0 - tmp12;
2023-01-11T21:41:24.0243231Z                         auto tmp14 = in_ptr0[tmp13 + (8*tmp7)];
2023-01-11T21:41:24.0243329Z                         out_ptr0[i1 + (10*i0)] = tmp14;
2023-01-11T21:41:24.0243392Z                     }
2023-01-11T21:41:24.0243443Z                 }
2023-01-11T21:41:24.0243503Z             }
2023-01-11T21:41:24.0243562Z         }
2023-01-11T21:41:24.0243636Z         #pragma omp for 
2023-01-11T21:41:24.0243713Z         for(long i0=0; i0<15; i0+=1)
2023-01-11T21:41:24.0243772Z         {
2023-01-11T21:41:24.0243837Z             #pragma GCC ivdep
2023-01-11T21:41:24.0243917Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:41:24.0243979Z             {
2023-01-11T21:41:24.0244039Z                 {
2023-01-11T21:41:24.0244101Z                     {
2023-01-11T21:41:24.0244200Z                         auto tmp0 = static_cast<int>(7);
2023-01-11T21:41:24.0244299Z                         auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:24.0244387Z                         auto tmp2 = static_cast<int>(3);
2023-01-11T21:41:24.0244524Z                         auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:24.0244649Z                         auto tmp4 = std::abs(tmp3);
2023-01-11T21:41:24.0244784Z                         auto tmp5 = tmp0 - tmp4;
2023-01-11T21:41:24.0244878Z                         auto tmp6 = std::abs(tmp5);
2023-01-11T21:41:24.0245012Z                         auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:24.0245107Z                         auto tmp8 = static_cast<int>(i1);
2023-01-11T21:41:24.0245206Z                         auto tmp9 = static_cast<int>(1);
2023-01-11T21:41:24.0245332Z                         auto tmp10 = tmp8 - tmp9;
2023-01-11T21:41:24.0245429Z                         auto tmp11 = std::abs(tmp10);
2023-01-11T21:41:24.0245570Z                         auto tmp12 = tmp0 - tmp11;
2023-01-11T21:41:24.0245666Z                         auto tmp13 = std::abs(tmp12);
2023-01-11T21:41:24.0245803Z                         auto tmp14 = tmp0 - tmp13;
2023-01-11T21:41:24.0245906Z                         auto tmp15 = in_ptr0[tmp14 + (8*tmp7)];
2023-01-11T21:41:24.0246025Z                         out_ptr1[i1 + (11*i0)] = tmp15;
2023-01-11T21:41:24.0246082Z                     }
2023-01-11T21:41:24.0246145Z                 }
2023-01-11T21:41:24.0246208Z             }
2023-01-11T21:41:24.0246270Z         }
2023-01-11T21:41:24.0246333Z     }
2023-01-11T21:41:24.0246392Z }
2023-01-11T21:41:24.0246470Z ''')
2023-01-11T21:41:24.0246475Z 
2023-01-11T21:41:24.0246479Z 
2023-01-11T21:41:24.0246556Z async_compile.wait(globals())
2023-01-11T21:41:24.0246627Z del async_compile
2023-01-11T21:41:24.0246632Z 
2023-01-11T21:41:24.0246702Z def call(args):
2023-01-11T21:41:24.0246769Z     arg0_1, = args
2023-01-11T21:41:24.0246839Z     args.clear()
2023-01-11T21:41:24.0247055Z     buf0 = empty_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0247267Z     buf1 = empty_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0247432Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0247492Z     del arg0_1
2023-01-11T21:41:24.0247569Z     return (buf0, buf1, )
2023-01-11T21:41:24.0247574Z 
2023-01-11T21:41:24.0247578Z 
2023-01-11T21:41:24.0247654Z if __name__ == "__main__":
2023-01-11T21:41:24.0247767Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0247888Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0248096Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0248202Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0248207Z 
2023-01-11T21:41:24.0248271Z ok (1.661s)
2023-01-11T21:41:24.0248722Z   test_relu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0248851Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0249110Z [2023-01-11 21:37:22,103] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 401
2023-01-11T21:41:24.0249374Z [2023-01-11 21:37:22,116] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 401
2023-01-11T21:41:24.0249379Z 
2023-01-11T21:41:24.0249471Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0249540Z import torch
2023-01-11T21:41:24.0249609Z import random
2023-01-11T21:41:24.0249720Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0249839Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0249844Z 
2023-01-11T21:41:24.0249907Z aten = torch.ops.aten
2023-01-11T21:41:24.0250038Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0250130Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0250168Z 
2023-01-11T21:41:24.0250172Z 
2023-01-11T21:41:24.0250305Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0250505Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0250620Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0250724Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0250824Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0250906Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0250969Z {
2023-01-11T21:41:24.0251066Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0251126Z     {
2023-01-11T21:41:24.0251200Z         #pragma omp for 
2023-01-11T21:41:24.0251280Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0251339Z         {
2023-01-11T21:41:24.0251459Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0251613Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0251740Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:24.0251819Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.0251937Z             auto tmp4 = at::vec::clamp_min(tmp3, decltype(tmp3)(0));
2023-01-11T21:41:24.0252065Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:24.0252147Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:24.0252235Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0252311Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0252369Z         }
2023-01-11T21:41:24.0252460Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0252535Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0252593Z         {
2023-01-11T21:41:24.0252671Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0252739Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:24.0252826Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:24.0252905Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.0252990Z             auto tmp4 = tmp3 * (tmp3>0);
2023-01-11T21:41:24.0253088Z             auto tmp5 = static_cast<float>(10);
2023-01-11T21:41:24.0253165Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:24.0253239Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0253303Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.0253364Z         }
2023-01-11T21:41:24.0253422Z     }
2023-01-11T21:41:24.0253481Z }
2023-01-11T21:41:24.0253557Z ''')
2023-01-11T21:41:24.0253562Z 
2023-01-11T21:41:24.0253566Z 
2023-01-11T21:41:24.0253651Z async_compile.wait(globals())
2023-01-11T21:41:24.0253718Z del async_compile
2023-01-11T21:41:24.0253723Z 
2023-01-11T21:41:24.0253786Z def call(args):
2023-01-11T21:41:24.0253847Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0253913Z     args.clear()
2023-01-11T21:41:24.0254104Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0254297Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0254483Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0254554Z     del arg0_1
2023-01-11T21:41:24.0254616Z     del arg1_1
2023-01-11T21:41:24.0254678Z     return (buf0, buf1, )
2023-01-11T21:41:24.0254682Z 
2023-01-11T21:41:24.0254686Z 
2023-01-11T21:41:24.0254756Z if __name__ == "__main__":
2023-01-11T21:41:24.0254866Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0254984Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0255179Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0255368Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0255479Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0255484Z 
2023-01-11T21:41:24.0255583Z ok (0.036s)
2023-01-11T21:41:24.0256054Z   test_remainder_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0256167Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0256424Z [2023-01-11 21:37:22,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 402
2023-01-11T21:41:24.0256690Z [2023-01-11 21:37:23,673] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 402
2023-01-11T21:41:24.0256695Z 
2023-01-11T21:41:24.0256782Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0256850Z import torch
2023-01-11T21:41:24.0256914Z import random
2023-01-11T21:41:24.0257059Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0257175Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0257180Z 
2023-01-11T21:41:24.0257244Z aten = torch.ops.aten
2023-01-11T21:41:24.0257375Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0257462Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0257466Z 
2023-01-11T21:41:24.0257471Z 
2023-01-11T21:41:24.0257601Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0257801Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0257912Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0258015Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0258108Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0258191Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0258281Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0258341Z {
2023-01-11T21:41:24.0258433Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0258488Z     {
2023-01-11T21:41:24.0258560Z         #pragma omp for 
2023-01-11T21:41:24.0258638Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:24.0258687Z         {
2023-01-11T21:41:24.0258746Z             {
2023-01-11T21:41:24.0258809Z                 {
2023-01-11T21:41:24.0258896Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0258983Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.0259075Z                     auto tmp2 = mod(tmp0, tmp1);
2023-01-11T21:41:24.0259159Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:24.0259266Z                     auto tmp4 = ((tmp2 != 0) & ((tmp2 < 0) != (tmp1 < 0))) ? tmp3 : tmp2;
2023-01-11T21:41:24.0259365Z                     auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:24.0259452Z                     auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:24.0259585Z                     auto tmp7 = tmp1 - tmp5;
2023-01-11T21:41:24.0259676Z                     auto tmp8 = mod(tmp6, tmp7);
2023-01-11T21:41:24.0259759Z                     auto tmp9 = tmp8 + tmp7;
2023-01-11T21:41:24.0259883Z                     auto tmp10 = ((tmp8 != 0) & ((tmp8 < 0) != (tmp7 < 0))) ? tmp9 : tmp8;
2023-01-11T21:41:24.0260008Z                     auto tmp11 = tmp0 - tmp5;
2023-01-11T21:41:24.0260094Z                     auto tmp12 = tmp1 + tmp5;
2023-01-11T21:41:24.0260186Z                     auto tmp13 = mod(tmp11, tmp12);
2023-01-11T21:41:24.0260274Z                     auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:24.0260394Z                     auto tmp15 = ((tmp13 != 0) & ((tmp13 < 0) != (tmp12 < 0))) ? tmp14 : tmp13;
2023-01-11T21:41:24.0260474Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0260554Z                     out_ptr1[i0] = tmp10;
2023-01-11T21:41:24.0260634Z                     out_ptr2[i0] = tmp15;
2023-01-11T21:41:24.0260685Z                 }
2023-01-11T21:41:24.0260789Z             }
2023-01-11T21:41:24.0260849Z         }
2023-01-11T21:41:24.0260909Z     }
2023-01-11T21:41:24.0260966Z }
2023-01-11T21:41:24.0261041Z ''')
2023-01-11T21:41:24.0261046Z 
2023-01-11T21:41:24.0261050Z 
2023-01-11T21:41:24.0261136Z async_compile.wait(globals())
2023-01-11T21:41:24.0261194Z del async_compile
2023-01-11T21:41:24.0261199Z 
2023-01-11T21:41:24.0261272Z def call(args):
2023-01-11T21:41:24.0261342Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0261410Z     args.clear()
2023-01-11T21:41:24.0261601Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0261792Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0261975Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0262178Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0262274Z     del arg0_1
2023-01-11T21:41:24.0262473Z     del arg1_1
2023-01-11T21:41:24.0262594Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0262600Z 
2023-01-11T21:41:24.0262604Z 
2023-01-11T21:41:24.0262680Z if __name__ == "__main__":
2023-01-11T21:41:24.0262794Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0262915Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0263113Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0263293Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0263403Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0263408Z 
2023-01-11T21:41:24.0263469Z ok (1.558s)
2023-01-11T21:41:24.0263940Z   test_repeat_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0264070Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0264328Z [2023-01-11 21:37:23,693] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 403
2023-01-11T21:41:24.0264592Z [2023-01-11 21:37:25,455] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 403
2023-01-11T21:41:24.0264597Z 
2023-01-11T21:41:24.0264686Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0264749Z import torch
2023-01-11T21:41:24.0264805Z import random
2023-01-11T21:41:24.0264918Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0265034Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0265040Z 
2023-01-11T21:41:24.0265116Z aten = torch.ops.aten
2023-01-11T21:41:24.0265249Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0265339Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0265344Z 
2023-01-11T21:41:24.0265348Z 
2023-01-11T21:41:24.0265479Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0265677Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0265781Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0265874Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0265971Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0266062Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0266120Z {
2023-01-11T21:41:24.0266215Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0266271Z     {
2023-01-11T21:41:24.0266348Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0266423Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0266481Z         {
2023-01-11T21:41:24.0266617Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0266681Z             {
2023-01-11T21:41:24.0266761Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0266846Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:24.0266897Z                 {
2023-01-11T21:41:24.0266982Z                     for(long i3=0; i3<1; i3+=1)
2023-01-11T21:41:24.0267045Z                     {
2023-01-11T21:41:24.0267198Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i3) + (8*(i2 % 4)) + (32*(i1 % 2)));
2023-01-11T21:41:24.0267317Z                         tmp0.store(out_ptr0 + (8*i2) + (8*i3) + (96*i1) + (384*i0));
2023-01-11T21:41:24.0267380Z                     }
2023-01-11T21:41:24.0267476Z                     #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0267562Z                     for(long i3=8; i3<8; i3+=1)
2023-01-11T21:41:24.0267615Z                     {
2023-01-11T21:41:24.0267725Z                         auto tmp0 = in_ptr0[i3 + (8*(i2 % 4)) + (32*(i1 % 2))];
2023-01-11T21:41:24.0267865Z                         out_ptr0[i3 + (8*i2) + (96*i1) + (384*i0)] = tmp0;
2023-01-11T21:41:24.0267928Z                     }
2023-01-11T21:41:24.0267991Z                 }
2023-01-11T21:41:24.0268052Z             }
2023-01-11T21:41:24.0268110Z         }
2023-01-11T21:41:24.0268173Z         #pragma omp for 
2023-01-11T21:41:24.0268250Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0268310Z         {
2023-01-11T21:41:24.0268391Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0268451Z             {
2023-01-11T21:41:24.0268582Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:24.0268680Z                 tmp0.store(out_ptr1 + (8*i1) + (64*i0));
2023-01-11T21:41:24.0268730Z             }
2023-01-11T21:41:24.0268820Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0268900Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:41:24.0268960Z             {
2023-01-11T21:41:24.0269045Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.0269136Z                 out_ptr1[i1 + (64*i0)] = tmp0;
2023-01-11T21:41:24.0269186Z             }
2023-01-11T21:41:24.0269245Z         }
2023-01-11T21:41:24.0269319Z         #pragma omp for 
2023-01-11T21:41:24.0269399Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0269459Z         {
2023-01-11T21:41:24.0269536Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0269598Z             {
2023-01-11T21:41:24.0269719Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:24.0269820Z                 tmp0.store(out_ptr2 + (8*i1) + (64*i0));
2023-01-11T21:41:24.0269882Z             }
2023-01-11T21:41:24.0269971Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0270051Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:41:24.0270110Z             {
2023-01-11T21:41:24.0270194Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.0270272Z                 out_ptr2[i1 + (64*i0)] = tmp0;
2023-01-11T21:41:24.0270335Z             }
2023-01-11T21:41:24.0270392Z         }
2023-01-11T21:41:24.0270451Z     }
2023-01-11T21:41:24.0270510Z }
2023-01-11T21:41:24.0270586Z ''')
2023-01-11T21:41:24.0270592Z 
2023-01-11T21:41:24.0270596Z 
2023-01-11T21:41:24.0270682Z async_compile.wait(globals())
2023-01-11T21:41:24.0270740Z del async_compile
2023-01-11T21:41:24.0270754Z 
2023-01-11T21:41:24.0270812Z def call(args):
2023-01-11T21:41:24.0270875Z     arg0_1, = args
2023-01-11T21:41:24.0270942Z     args.clear()
2023-01-11T21:41:24.0271159Z     buf0 = empty_strided((2, 4, 12, 8), (384, 96, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0271367Z     buf1 = empty_strided((8, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0271589Z     buf2 = empty_strided((2, 1, 1, 2, 4, 8), (64, 64, 64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0271775Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0271860Z     del arg0_1
2023-01-11T21:41:24.0271940Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0271945Z 
2023-01-11T21:41:24.0271949Z 
2023-01-11T21:41:24.0272021Z if __name__ == "__main__":
2023-01-11T21:41:24.0272131Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0272250Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0272460Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0272562Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0272567Z 
2023-01-11T21:41:24.0272631Z ok (1.781s)
2023-01-11T21:41:24.0273122Z   test_roi_align_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0273238Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0273497Z [2023-01-11 21:37:27,326] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 404
2023-01-11T21:41:24.0273814Z [2023-01-11 21:37:27,743] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.torchvision.roi_align
2023-01-11T21:41:24.0274081Z [2023-01-11 21:37:27,745] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 404
2023-01-11T21:41:24.0274086Z 
2023-01-11T21:41:24.0274176Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0274242Z import torch
2023-01-11T21:41:24.0274310Z import random
2023-01-11T21:41:24.0274421Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0274527Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0274542Z 
2023-01-11T21:41:24.0274607Z aten = torch.ops.aten
2023-01-11T21:41:24.0274737Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0274828Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0274833Z 
2023-01-11T21:41:24.0274837Z 
2023-01-11T21:41:24.0274923Z async_compile.wait(globals())
2023-01-11T21:41:24.0274992Z del async_compile
2023-01-11T21:41:24.0274997Z 
2023-01-11T21:41:24.0275065Z def call(args):
2023-01-11T21:41:24.0275134Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0275191Z     args.clear()
2023-01-11T21:41:24.0275331Z     buf0 = torch.ops.torchvision.roi_align(arg0_1, arg1_1, 0.25, 7, 7, 2, False)
2023-01-11T21:41:24.0275394Z     del arg0_1
2023-01-11T21:41:24.0275458Z     del arg1_1
2023-01-11T21:41:24.0275524Z     buf1 = buf0
2023-01-11T21:41:24.0275630Z     assert_size_stride(buf1, (2292, 256, 7, 7), (12544, 49, 7, 1))
2023-01-11T21:41:24.0275693Z     del buf0
2023-01-11T21:41:24.0275751Z     return (buf1, )
2023-01-11T21:41:24.0275756Z 
2023-01-11T21:41:24.0275768Z 
2023-01-11T21:41:24.0275833Z if __name__ == "__main__":
2023-01-11T21:41:24.0275941Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0276060Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0276294Z     arg0_1 = rand_strided((4, 256, 296, 304), (23035904, 89984, 304, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0276493Z     arg1_1 = rand_strided((2292, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0276604Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0276608Z 
2023-01-11T21:41:24.0276670Z ok (5.252s)
2023-01-11T21:41:24.0277128Z   test_roll_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0277277Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0277535Z [2023-01-11 21:37:30,740] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 405
2023-01-11T21:41:24.0277797Z [2023-01-11 21:37:32,471] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 405
2023-01-11T21:41:24.0277802Z 
2023-01-11T21:41:24.0277893Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0277959Z import torch
2023-01-11T21:41:24.0278030Z import random
2023-01-11T21:41:24.0278146Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0278266Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0278271Z 
2023-01-11T21:41:24.0278336Z aten = torch.ops.aten
2023-01-11T21:41:24.0278469Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0278563Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0278567Z 
2023-01-11T21:41:24.0278572Z 
2023-01-11T21:41:24.0278733Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0278937Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0279053Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0279153Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0279250Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0279298Z {
2023-01-11T21:41:24.0279395Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0279456Z     {
2023-01-11T21:41:24.0279546Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0279627Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0279690Z         {
2023-01-11T21:41:24.0279772Z             for(long i1=0; i1<56; i1+=1)
2023-01-11T21:41:24.0279821Z             {
2023-01-11T21:41:24.0279900Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0279988Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:41:24.0280055Z                 {
2023-01-11T21:41:24.0280149Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:41:24.0280214Z                     {
2023-01-11T21:41:24.0280379Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i3) + (16*((46 + i2) % 56)) + (896*((3 + i1) % 56)) + (50176*i0));
2023-01-11T21:41:24.0280497Z                         tmp0.store(out_ptr0 + (8*i3) + (16*i2) + (896*i1) + (50176*i0));
2023-01-11T21:41:24.0280550Z                     }
2023-01-11T21:41:24.0280647Z                     #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0280738Z                     for(long i3=16; i3<16; i3+=1)
2023-01-11T21:41:24.0280804Z                     {
2023-01-11T21:41:24.0280929Z                         auto tmp0 = in_ptr0[i3 + (16*((46 + i2) % 56)) + (896*((3 + i1) % 56)) + (50176*i0)];
2023-01-11T21:41:24.0281038Z                         out_ptr0[i3 + (16*i2) + (896*i1) + (50176*i0)] = tmp0;
2023-01-11T21:41:24.0281102Z                     }
2023-01-11T21:41:24.0281154Z                 }
2023-01-11T21:41:24.0281223Z             }
2023-01-11T21:41:24.0281286Z         }
2023-01-11T21:41:24.0281376Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0281457Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0281518Z         {
2023-01-11T21:41:24.0281600Z             for(long i1=0; i1<56; i1+=1)
2023-01-11T21:41:24.0281650Z             {
2023-01-11T21:41:24.0281731Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0281821Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:41:24.0281885Z                 {
2023-01-11T21:41:24.0281966Z                     #pragma GCC ivdep
2023-01-11T21:41:24.0282055Z                     for(long i3=0; i3<16; i3+=1)
2023-01-11T21:41:24.0282108Z                     {
2023-01-11T21:41:24.0282175Z                         {
2023-01-11T21:41:24.0282244Z                             {
2023-01-11T21:41:24.0282367Z                                 auto tmp0 = in_ptr0[(100347 + i3 + (16*i2) + (896*i1) + (50176*i0)) % 100352];
2023-01-11T21:41:24.0282516Z                                 out_ptr1[i3 + (16*i2) + (896*i1) + (50176*i0)] = tmp0;
2023-01-11T21:41:24.0282584Z                             }
2023-01-11T21:41:24.0282650Z                         }
2023-01-11T21:41:24.0282712Z                     }
2023-01-11T21:41:24.0282763Z                 }
2023-01-11T21:41:24.0282824Z             }
2023-01-11T21:41:24.0282880Z         }
2023-01-11T21:41:24.0282940Z     }
2023-01-11T21:41:24.0282999Z }
2023-01-11T21:41:24.0283075Z ''')
2023-01-11T21:41:24.0283080Z 
2023-01-11T21:41:24.0283084Z 
2023-01-11T21:41:24.0283170Z async_compile.wait(globals())
2023-01-11T21:41:24.0283229Z del async_compile
2023-01-11T21:41:24.0283233Z 
2023-01-11T21:41:24.0283301Z def call(args):
2023-01-11T21:41:24.0283370Z     arg0_1, = args
2023-01-11T21:41:24.0283438Z     args.clear()
2023-01-11T21:41:24.0283658Z     buf0 = empty_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0283903Z     buf1 = empty_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0284069Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0284124Z     del arg0_1
2023-01-11T21:41:24.0284197Z     return (buf0, buf1, )
2023-01-11T21:41:24.0284203Z 
2023-01-11T21:41:24.0284207Z 
2023-01-11T21:41:24.0284280Z if __name__ == "__main__":
2023-01-11T21:41:24.0284393Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0284513Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0284734Z     arg0_1 = rand_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0284837Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0284842Z 
2023-01-11T21:41:24.0284910Z ok (1.767s)
2023-01-11T21:41:24.0285387Z   test_round_correctness_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0285502Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0285759Z [2023-01-11 21:37:32,487] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 406
2023-01-11T21:41:24.0286021Z [2023-01-11 21:37:34,001] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 406
2023-01-11T21:41:24.0286027Z 
2023-01-11T21:41:24.0286117Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0286183Z import torch
2023-01-11T21:41:24.0286251Z import random
2023-01-11T21:41:24.0286364Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0286481Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0286486Z 
2023-01-11T21:41:24.0286554Z aten = torch.ops.aten
2023-01-11T21:41:24.0286684Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0286772Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0286777Z 
2023-01-11T21:41:24.0286781Z 
2023-01-11T21:41:24.0286913Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0287117Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0287235Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.0287333Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.0287389Z {
2023-01-11T21:41:24.0287473Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0287527Z     {
2023-01-11T21:41:24.0287601Z         #pragma omp for 
2023-01-11T21:41:24.0287686Z         for(long i0=0; i0<200; i0+=1)
2023-01-11T21:41:24.0287746Z         {
2023-01-11T21:41:24.0287806Z             {
2023-01-11T21:41:24.0287868Z                 {
2023-01-11T21:41:24.0287981Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0288085Z                     auto tmp1 = std::nearbyint(tmp0);
2023-01-11T21:41:24.0288165Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0288228Z                 }
2023-01-11T21:41:24.0288288Z             }
2023-01-11T21:41:24.0288346Z         }
2023-01-11T21:41:24.0288402Z     }
2023-01-11T21:41:24.0288448Z }
2023-01-11T21:41:24.0288525Z ''')
2023-01-11T21:41:24.0288531Z 
2023-01-11T21:41:24.0288535Z 
2023-01-11T21:41:24.0288620Z async_compile.wait(globals())
2023-01-11T21:41:24.0288688Z del async_compile
2023-01-11T21:41:24.0288692Z 
2023-01-11T21:41:24.0288761Z def call(args):
2023-01-11T21:41:24.0288827Z     arg0_1, = args
2023-01-11T21:41:24.0288896Z     args.clear()
2023-01-11T21:41:24.0289079Z     buf0 = empty_strided((200, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.0289210Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0289275Z     del arg0_1
2023-01-11T21:41:24.0289371Z     return (buf0, )
2023-01-11T21:41:24.0289376Z 
2023-01-11T21:41:24.0289380Z 
2023-01-11T21:41:24.0289455Z if __name__ == "__main__":
2023-01-11T21:41:24.0289569Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0289689Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0289881Z     arg0_1 = rand_strided((200, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.0289976Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0289980Z 
2023-01-11T21:41:24.0290043Z ok (1.527s)
2023-01-11T21:41:24.0290502Z   test_round_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0290632Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0290893Z [2023-01-11 21:37:34,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 407
2023-01-11T21:41:24.0291159Z [2023-01-11 21:37:35,575] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 407
2023-01-11T21:41:24.0291165Z 
2023-01-11T21:41:24.0291256Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0291322Z import torch
2023-01-11T21:41:24.0291388Z import random
2023-01-11T21:41:24.0291490Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0291607Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0291612Z 
2023-01-11T21:41:24.0291687Z aten = torch.ops.aten
2023-01-11T21:41:24.0291819Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0291907Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0291912Z 
2023-01-11T21:41:24.0291916Z 
2023-01-11T21:41:24.0292051Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0292254Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0292370Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0292460Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0292556Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0292651Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0292742Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0292800Z {
2023-01-11T21:41:24.0292892Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0292952Z     {
2023-01-11T21:41:24.0293016Z         #pragma omp for 
2023-01-11T21:41:24.0293094Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0293155Z         {
2023-01-11T21:41:24.0293286Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0293371Z             auto tmp1 = tmp0.round();
2023-01-11T21:41:24.0293537Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0));
2023-01-11T21:41:24.0293618Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:24.0293690Z             auto tmp4 = tmp3.round();
2023-01-11T21:41:24.0293825Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(0.01));
2023-01-11T21:41:24.0293905Z             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.0293993Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0294079Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0294139Z         }
2023-01-11T21:41:24.0294229Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0294307Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0294356Z         {
2023-01-11T21:41:24.0294436Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0294533Z             auto tmp1 = std::nearbyint(tmp0);
2023-01-11T21:41:24.0294632Z             auto tmp2 = static_cast<float>(100.0);
2023-01-11T21:41:24.0294739Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:24.0294835Z             auto tmp4 = std::nearbyint(tmp3);
2023-01-11T21:41:24.0294932Z             auto tmp5 = static_cast<float>(0.01);
2023-01-11T21:41:24.0295002Z             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.0295078Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0295155Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.0295214Z         }
2023-01-11T21:41:24.0295287Z         #pragma omp for 
2023-01-11T21:41:24.0295365Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0295413Z         {
2023-01-11T21:41:24.0295542Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0295672Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0295752Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0295834Z             auto tmp3 = tmp2.round();
2023-01-11T21:41:24.0295921Z             tmp3.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0295982Z         }
2023-01-11T21:41:24.0296073Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0296141Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0296201Z         {
2023-01-11T21:41:24.0296280Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0296375Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0296454Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0296551Z             auto tmp3 = std::nearbyint(tmp2);
2023-01-11T21:41:24.0296626Z             out_ptr2[i0] = tmp3;
2023-01-11T21:41:24.0296675Z         }
2023-01-11T21:41:24.0296733Z     }
2023-01-11T21:41:24.0296788Z }
2023-01-11T21:41:24.0296864Z ''')
2023-01-11T21:41:24.0296869Z 
2023-01-11T21:41:24.0296874Z 
2023-01-11T21:41:24.0296960Z async_compile.wait(globals())
2023-01-11T21:41:24.0297028Z del async_compile
2023-01-11T21:41:24.0297034Z 
2023-01-11T21:41:24.0297101Z def call(args):
2023-01-11T21:41:24.0297162Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0297229Z     args.clear()
2023-01-11T21:41:24.0297427Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0297620Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0297805Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0298015Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0298082Z     del arg0_1
2023-01-11T21:41:24.0298135Z     del arg1_1
2023-01-11T21:41:24.0298215Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0298219Z 
2023-01-11T21:41:24.0298223Z 
2023-01-11T21:41:24.0298297Z if __name__ == "__main__":
2023-01-11T21:41:24.0298407Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0298526Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0298718Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0298956Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0299067Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0299072Z 
2023-01-11T21:41:24.0299136Z ok (1.574s)
2023-01-11T21:41:24.0299583Z   test_rsqrt_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0299706Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0299966Z [2023-01-11 21:37:35,596] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 408
2023-01-11T21:41:24.0300256Z [2023-01-11 21:37:37,160] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 408
2023-01-11T21:41:24.0300264Z 
2023-01-11T21:41:24.0300358Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0300425Z import torch
2023-01-11T21:41:24.0300493Z import random
2023-01-11T21:41:24.0300605Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0300725Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0300730Z 
2023-01-11T21:41:24.0300793Z aten = torch.ops.aten
2023-01-11T21:41:24.0300928Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0301015Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0301020Z 
2023-01-11T21:41:24.0301024Z 
2023-01-11T21:41:24.0301153Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0301356Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0301472Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0301571Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0301665Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0301713Z {
2023-01-11T21:41:24.0301809Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0301867Z     {
2023-01-11T21:41:24.0301940Z         #pragma omp for 
2023-01-11T21:41:24.0302017Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0302074Z         {
2023-01-11T21:41:24.0302207Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0302280Z             auto tmp1 = tmp0.rsqrt();
2023-01-11T21:41:24.0302576Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0302664Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.0302747Z             auto tmp4 = tmp3.rsqrt();
2023-01-11T21:41:24.0302874Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0303001Z             auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:24.0303092Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0303171Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0303231Z         }
2023-01-11T21:41:24.0303323Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0303403Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0303463Z         {
2023-01-11T21:41:24.0303544Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0303638Z             auto tmp1 = 1 / std::sqrt(tmp0);
2023-01-11T21:41:24.0303723Z             auto tmp2 = static_cast<float>(1);
2023-01-11T21:41:24.0303806Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.0303899Z             auto tmp4 = 1 / std::sqrt(tmp3);
2023-01-11T21:41:24.0303995Z             auto tmp5 = static_cast<float>(2);
2023-01-11T21:41:24.0304116Z             auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:24.0304193Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0304268Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.0304316Z         }
2023-01-11T21:41:24.0304375Z     }
2023-01-11T21:41:24.0304489Z }
2023-01-11T21:41:24.0304568Z ''')
2023-01-11T21:41:24.0304574Z 
2023-01-11T21:41:24.0304578Z 
2023-01-11T21:41:24.0304666Z async_compile.wait(globals())
2023-01-11T21:41:24.0304738Z del async_compile
2023-01-11T21:41:24.0304742Z 
2023-01-11T21:41:24.0304810Z def call(args):
2023-01-11T21:41:24.0304865Z     arg0_1, = args
2023-01-11T21:41:24.0304933Z     args.clear()
2023-01-11T21:41:24.0305125Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0305311Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0305475Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0305543Z     del arg0_1
2023-01-11T21:41:24.0305621Z     return (buf0, buf1, )
2023-01-11T21:41:24.0305626Z 
2023-01-11T21:41:24.0305630Z 
2023-01-11T21:41:24.0305704Z if __name__ == "__main__":
2023-01-11T21:41:24.0305804Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0305964Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0306159Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0306264Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0306268Z 
2023-01-11T21:41:24.0306331Z ok (1.585s)
2023-01-11T21:41:24.0306796Z   test_scatter1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0306918Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0307174Z [2023-01-11 21:37:37,183] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 409
2023-01-11T21:41:24.0307440Z [2023-01-11 21:37:38,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 409
2023-01-11T21:41:24.0307446Z 
2023-01-11T21:41:24.0307539Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0307594Z import torch
2023-01-11T21:41:24.0307659Z import random
2023-01-11T21:41:24.0307770Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0307887Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0307892Z 
2023-01-11T21:41:24.0307967Z aten = torch.ops.aten
2023-01-11T21:41:24.0308098Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0308187Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0308192Z 
2023-01-11T21:41:24.0308196Z 
2023-01-11T21:41:24.0308315Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0308517Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0308632Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0308743Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0308846Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0308942Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0309001Z {
2023-01-11T21:41:24.0309096Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0309144Z     {
2023-01-11T21:41:24.0309218Z         #pragma omp for 
2023-01-11T21:41:24.0309300Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:24.0309363Z         {
2023-01-11T21:41:24.0309427Z             {
2023-01-11T21:41:24.0309490Z                 {
2023-01-11T21:41:24.0309567Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0309648Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0309711Z                 }
2023-01-11T21:41:24.0309773Z             }
2023-01-11T21:41:24.0309837Z         }
2023-01-11T21:41:24.0309914Z         #pragma omp single
2023-01-11T21:41:24.0309975Z         {
2023-01-11T21:41:24.0310056Z             {
2023-01-11T21:41:24.0310117Z                 {
2023-01-11T21:41:24.0310207Z                     auto tmp0 = in_ptr1[0];
2023-01-11T21:41:24.0310296Z                     auto tmp1 = in_ptr2[0];
2023-01-11T21:41:24.0310383Z                     out_ptr0[tmp0] = tmp1;
2023-01-11T21:41:24.0310446Z                 }
2023-01-11T21:41:24.0310509Z             }
2023-01-11T21:41:24.0310557Z         }
2023-01-11T21:41:24.0310617Z     }
2023-01-11T21:41:24.0310676Z }
2023-01-11T21:41:24.0310754Z ''')
2023-01-11T21:41:24.0310759Z 
2023-01-11T21:41:24.0310763Z 
2023-01-11T21:41:24.0310850Z async_compile.wait(globals())
2023-01-11T21:41:24.0310921Z del async_compile
2023-01-11T21:41:24.0310926Z 
2023-01-11T21:41:24.0310995Z def call(args):
2023-01-11T21:41:24.0311063Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0311134Z     args.clear()
2023-01-11T21:41:24.0311329Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0311546Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0311616Z     del arg0_1
2023-01-11T21:41:24.0311681Z     del arg1_1
2023-01-11T21:41:24.0311746Z     del arg2_1
2023-01-11T21:41:24.0311804Z     return (buf0, )
2023-01-11T21:41:24.0311809Z 
2023-01-11T21:41:24.0311813Z 
2023-01-11T21:41:24.0311889Z if __name__ == "__main__":
2023-01-11T21:41:24.0312001Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0312123Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0312318Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0312514Z     arg1_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0312699Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0312820Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0312825Z 
2023-01-11T21:41:24.0312882Z ok (1.573s)
2023-01-11T21:41:24.0313348Z   test_scatter2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0313475Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0313789Z [2023-01-11 21:37:38,755] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 410
2023-01-11T21:41:24.0314059Z [2023-01-11 21:37:40,299] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 410
2023-01-11T21:41:24.0314064Z 
2023-01-11T21:41:24.0314154Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0314225Z import torch
2023-01-11T21:41:24.0314296Z import random
2023-01-11T21:41:24.0314412Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0314520Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0314525Z 
2023-01-11T21:41:24.0314599Z aten = torch.ops.aten
2023-01-11T21:41:24.0314729Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0314816Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0314821Z 
2023-01-11T21:41:24.0314825Z 
2023-01-11T21:41:24.0314953Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0315158Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0315275Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0315379Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0315470Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0315566Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0315626Z {
2023-01-11T21:41:24.0315760Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0315817Z     {
2023-01-11T21:41:24.0315893Z         #pragma omp for 
2023-01-11T21:41:24.0315972Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:24.0316021Z         {
2023-01-11T21:41:24.0316150Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0316238Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0316302Z         }
2023-01-11T21:41:24.0316393Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0316478Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:41:24.0316539Z         {
2023-01-11T21:41:24.0316608Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0316684Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0316746Z         }
2023-01-11T21:41:24.0316818Z         #pragma omp for 
2023-01-11T21:41:24.0316896Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:24.0316954Z         {
2023-01-11T21:41:24.0317029Z             #pragma GCC ivdep
2023-01-11T21:41:24.0317130Z             for(long i1=0; i1<512; i1+=1)
2023-01-11T21:41:24.0317193Z             {
2023-01-11T21:41:24.0317257Z                 {
2023-01-11T21:41:24.0317322Z                     {
2023-01-11T21:41:24.0317423Z                         auto tmp0 = in_ptr1[i1 + (512*i0)];
2023-01-11T21:41:24.0317522Z                         auto tmp1 = in_ptr2[i1 + (512*i0)];
2023-01-11T21:41:24.0317633Z                         atomic_add(&out_ptr0[i1 + (512*tmp0)], tmp1);
2023-01-11T21:41:24.0317686Z                     }
2023-01-11T21:41:24.0317746Z                 }
2023-01-11T21:41:24.0317809Z             }
2023-01-11T21:41:24.0317866Z         }
2023-01-11T21:41:24.0317923Z     }
2023-01-11T21:41:24.0317983Z }
2023-01-11T21:41:24.0318048Z ''')
2023-01-11T21:41:24.0318053Z 
2023-01-11T21:41:24.0318069Z 
2023-01-11T21:41:24.0318145Z async_compile.wait(globals())
2023-01-11T21:41:24.0318213Z del async_compile
2023-01-11T21:41:24.0318217Z 
2023-01-11T21:41:24.0318283Z def call(args):
2023-01-11T21:41:24.0318363Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0318429Z     args.clear()
2023-01-11T21:41:24.0318627Z     buf0 = empty_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0318812Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0318867Z     del arg0_1
2023-01-11T21:41:24.0318929Z     del arg1_1
2023-01-11T21:41:24.0318991Z     del arg2_1
2023-01-11T21:41:24.0319058Z     return (buf0, )
2023-01-11T21:41:24.0319063Z 
2023-01-11T21:41:24.0319067Z 
2023-01-11T21:41:24.0319139Z if __name__ == "__main__":
2023-01-11T21:41:24.0319251Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0319371Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0319571Z     arg0_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0319760Z     arg1_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0319959Z     arg2_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0320077Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0320082Z 
2023-01-11T21:41:24.0320143Z ok (1.569s)
2023-01-11T21:41:24.0320611Z   test_scatter3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0320737Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0320997Z [2023-01-11 21:37:40,323] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 411
2023-01-11T21:41:24.0321264Z [2023-01-11 21:37:41,901] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 411
2023-01-11T21:41:24.0321303Z 
2023-01-11T21:41:24.0321393Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0321450Z import torch
2023-01-11T21:41:24.0321519Z import random
2023-01-11T21:41:24.0321634Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0321752Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0321757Z 
2023-01-11T21:41:24.0321831Z aten = torch.ops.aten
2023-01-11T21:41:24.0321960Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0322051Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0322056Z 
2023-01-11T21:41:24.0322060Z 
2023-01-11T21:41:24.0322190Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0322382Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0322498Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0322628Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0322726Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0322785Z {
2023-01-11T21:41:24.0322877Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0322935Z     {
2023-01-11T21:41:24.0322998Z         #pragma omp for 
2023-01-11T21:41:24.0323075Z         for(long i0=0; i0<235; i0+=1)
2023-01-11T21:41:24.0323135Z         {
2023-01-11T21:41:24.0323266Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0323354Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0323415Z         }
2023-01-11T21:41:24.0323505Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0323577Z         for(long i0=1880; i0<1885; i0+=1)
2023-01-11T21:41:24.0323635Z         {
2023-01-11T21:41:24.0323714Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0323791Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0323851Z         }
2023-01-11T21:41:24.0323933Z         #pragma omp for 
2023-01-11T21:41:24.0324015Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0324063Z         {
2023-01-11T21:41:24.0324123Z             {
2023-01-11T21:41:24.0324184Z                 {
2023-01-11T21:41:24.0324270Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0324372Z                     auto tmp1 = static_cast<float>(0.8);
2023-01-11T21:41:24.0324472Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:41:24.0324536Z                 }
2023-01-11T21:41:24.0324587Z             }
2023-01-11T21:41:24.0324644Z         }
2023-01-11T21:41:24.0324702Z     }
2023-01-11T21:41:24.0324758Z }
2023-01-11T21:41:24.0324833Z ''')
2023-01-11T21:41:24.0324838Z 
2023-01-11T21:41:24.0324842Z 
2023-01-11T21:41:24.0324929Z async_compile.wait(globals())
2023-01-11T21:41:24.0324996Z del async_compile
2023-01-11T21:41:24.0325001Z 
2023-01-11T21:41:24.0325057Z def call(args):
2023-01-11T21:41:24.0325130Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0325204Z     args.clear()
2023-01-11T21:41:24.0325412Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0325572Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0325637Z     del arg0_1
2023-01-11T21:41:24.0325701Z     del arg1_1
2023-01-11T21:41:24.0325758Z     return (buf0, )
2023-01-11T21:41:24.0325763Z 
2023-01-11T21:41:24.0325779Z 
2023-01-11T21:41:24.0325840Z if __name__ == "__main__":
2023-01-11T21:41:24.0325950Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0326069Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0326280Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0326477Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0326588Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0326622Z 
2023-01-11T21:41:24.0326688Z ok (1.600s)
2023-01-11T21:41:24.0327152Z   test_scatter4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0327266Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0327526Z [2023-01-11 21:37:41,921] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 412
2023-01-11T21:41:24.0327791Z [2023-01-11 21:37:43,501] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 412
2023-01-11T21:41:24.0327796Z 
2023-01-11T21:41:24.0327886Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0327952Z import torch
2023-01-11T21:41:24.0328022Z import random
2023-01-11T21:41:24.0328162Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0328282Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0328286Z 
2023-01-11T21:41:24.0328351Z aten = torch.ops.aten
2023-01-11T21:41:24.0328478Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0328566Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0328572Z 
2023-01-11T21:41:24.0328576Z 
2023-01-11T21:41:24.0328707Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0328908Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0329025Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0329128Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0329228Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0329317Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0329377Z {
2023-01-11T21:41:24.0329473Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0329531Z     {
2023-01-11T21:41:24.0329607Z         #pragma omp for 
2023-01-11T21:41:24.0329687Z         for(long i0=0; i0<24304; i0+=1)
2023-01-11T21:41:24.0329747Z         {
2023-01-11T21:41:24.0329867Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0329955Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0330016Z         }
2023-01-11T21:41:24.0330107Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0330194Z         for(long i0=194432; i0<194432; i0+=1)
2023-01-11T21:41:24.0330255Z         {
2023-01-11T21:41:24.0330333Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0330400Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0330460Z         }
2023-01-11T21:41:24.0330533Z         #pragma omp for 
2023-01-11T21:41:24.0330613Z         for(long i0=0; i0<992; i0+=1)
2023-01-11T21:41:24.0330672Z         {
2023-01-11T21:41:24.0330733Z             {
2023-01-11T21:41:24.0330800Z                 {
2023-01-11T21:41:24.0330879Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0330966Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:24.0331062Z                     out_ptr0[i0 + (992*tmp0)] = tmp1;
2023-01-11T21:41:24.0331127Z                 }
2023-01-11T21:41:24.0331190Z             }
2023-01-11T21:41:24.0331250Z         }
2023-01-11T21:41:24.0331300Z     }
2023-01-11T21:41:24.0331358Z }
2023-01-11T21:41:24.0331434Z ''')
2023-01-11T21:41:24.0331440Z 
2023-01-11T21:41:24.0331444Z 
2023-01-11T21:41:24.0331529Z async_compile.wait(globals())
2023-01-11T21:41:24.0331599Z del async_compile
2023-01-11T21:41:24.0331604Z 
2023-01-11T21:41:24.0331673Z def call(args):
2023-01-11T21:41:24.0331753Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0331819Z     args.clear()
2023-01-11T21:41:24.0332011Z     buf0 = empty_strided((196, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0332231Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0332299Z     del arg0_1
2023-01-11T21:41:24.0332363Z     del arg1_1
2023-01-11T21:41:24.0332426Z     del arg2_1
2023-01-11T21:41:24.0332494Z     return (buf0, )
2023-01-11T21:41:24.0332499Z 
2023-01-11T21:41:24.0332503Z 
2023-01-11T21:41:24.0332576Z if __name__ == "__main__":
2023-01-11T21:41:24.0332678Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0332798Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0333004Z     arg0_1 = rand_strided((196, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0333200Z     arg1_1 = rand_strided((1, 992), (992, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0333398Z     arg2_1 = rand_strided((1, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0333517Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0333525Z 
2023-01-11T21:41:24.0333614Z ok (1.603s)
2023-01-11T21:41:24.0333764Z   test_scatter_add1_cpu (__main__.CpuTests) ... skip: Flaky test, needs debugging (0.000s)
2023-01-11T21:41:24.0334227Z   test_scatter_add2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0334353Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0334602Z [2023-01-11 21:37:43,527] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 413
2023-01-11T21:41:24.0334864Z [2023-01-11 21:37:45,114] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 413
2023-01-11T21:41:24.0334873Z 
2023-01-11T21:41:24.0334964Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0335032Z import torch
2023-01-11T21:41:24.0335099Z import random
2023-01-11T21:41:24.0335211Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0335330Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0335334Z 
2023-01-11T21:41:24.0335409Z aten = torch.ops.aten
2023-01-11T21:41:24.0335528Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0335617Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0335622Z 
2023-01-11T21:41:24.0335626Z 
2023-01-11T21:41:24.0335756Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0335956Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0336073Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0336174Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0336277Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0336376Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0336424Z {
2023-01-11T21:41:24.0336516Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0336577Z     {
2023-01-11T21:41:24.0336651Z         #pragma omp for 
2023-01-11T21:41:24.0336727Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:24.0336787Z         {
2023-01-11T21:41:24.0336837Z             {
2023-01-11T21:41:24.0336898Z                 {
2023-01-11T21:41:24.0336986Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0337067Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0337128Z                 }
2023-01-11T21:41:24.0337188Z             }
2023-01-11T21:41:24.0337248Z         }
2023-01-11T21:41:24.0337310Z         #pragma omp for 
2023-01-11T21:41:24.0337388Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0337448Z         {
2023-01-11T21:41:24.0337524Z             #pragma GCC ivdep
2023-01-11T21:41:24.0337603Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0337705Z             {
2023-01-11T21:41:24.0337766Z                 {
2023-01-11T21:41:24.0337819Z                     {
2023-01-11T21:41:24.0337916Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:41:24.0338014Z                         auto tmp1 = in_ptr2[i1 + (3*i0)];
2023-01-11T21:41:24.0338124Z                         atomic_add(&out_ptr0[i1 + (3*tmp0)], tmp1);
2023-01-11T21:41:24.0338187Z                     }
2023-01-11T21:41:24.0338247Z                 }
2023-01-11T21:41:24.0338305Z             }
2023-01-11T21:41:24.0338355Z         }
2023-01-11T21:41:24.0338411Z     }
2023-01-11T21:41:24.0338467Z }
2023-01-11T21:41:24.0338548Z ''')
2023-01-11T21:41:24.0338554Z 
2023-01-11T21:41:24.0338558Z 
2023-01-11T21:41:24.0338646Z async_compile.wait(globals())
2023-01-11T21:41:24.0338718Z del async_compile
2023-01-11T21:41:24.0338723Z 
2023-01-11T21:41:24.0338792Z def call(args):
2023-01-11T21:41:24.0338859Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0338962Z     args.clear()
2023-01-11T21:41:24.0339158Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0339346Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0339414Z     del arg0_1
2023-01-11T21:41:24.0339482Z     del arg1_1
2023-01-11T21:41:24.0339545Z     del arg2_1
2023-01-11T21:41:24.0339605Z     return (buf0, )
2023-01-11T21:41:24.0339609Z 
2023-01-11T21:41:24.0339613Z 
2023-01-11T21:41:24.0339688Z if __name__ == "__main__":
2023-01-11T21:41:24.0339802Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0339922Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0340119Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0340311Z     arg1_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0340504Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0340629Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0340634Z 
2023-01-11T21:41:24.0340689Z ok (1.609s)
2023-01-11T21:41:24.0341158Z   test_scatter_add3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0341283Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0341542Z [2023-01-11 21:37:45,136] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 414
2023-01-11T21:41:24.0341806Z [2023-01-11 21:37:46,666] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 414
2023-01-11T21:41:24.0341815Z 
2023-01-11T21:41:24.0341908Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0341977Z import torch
2023-01-11T21:41:24.0342047Z import random
2023-01-11T21:41:24.0342158Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0342265Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0342270Z 
2023-01-11T21:41:24.0342480Z aten = torch.ops.aten
2023-01-11T21:41:24.0342629Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0342721Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0342726Z 
2023-01-11T21:41:24.0342730Z 
2023-01-11T21:41:24.0342867Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0343071Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0343189Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0343294Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0343445Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0343544Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0343605Z {
2023-01-11T21:41:24.0343701Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0343761Z     {
2023-01-11T21:41:24.0343840Z         #pragma omp for 
2023-01-11T21:41:24.0343921Z         for(long i0=0; i0<235; i0+=1)
2023-01-11T21:41:24.0343970Z         {
2023-01-11T21:41:24.0344105Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0344194Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0344255Z         }
2023-01-11T21:41:24.0344347Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0344432Z         for(long i0=1880; i0<1885; i0+=1)
2023-01-11T21:41:24.0344492Z         {
2023-01-11T21:41:24.0344562Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0344642Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0344703Z         }
2023-01-11T21:41:24.0344819Z         #pragma omp for 
2023-01-11T21:41:24.0344901Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0344958Z         {
2023-01-11T21:41:24.0345021Z             {
2023-01-11T21:41:24.0345072Z                 {
2023-01-11T21:41:24.0345164Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0345254Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:24.0345355Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:41:24.0345418Z                 }
2023-01-11T21:41:24.0345480Z             }
2023-01-11T21:41:24.0345528Z         }
2023-01-11T21:41:24.0345592Z     }
2023-01-11T21:41:24.0345647Z }
2023-01-11T21:41:24.0345724Z ''')
2023-01-11T21:41:24.0345729Z 
2023-01-11T21:41:24.0345734Z 
2023-01-11T21:41:24.0345818Z async_compile.wait(globals())
2023-01-11T21:41:24.0345886Z del async_compile
2023-01-11T21:41:24.0345891Z 
2023-01-11T21:41:24.0345957Z def call(args):
2023-01-11T21:41:24.0346035Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0346096Z     args.clear()
2023-01-11T21:41:24.0346303Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0346490Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0346557Z     del arg0_1
2023-01-11T21:41:24.0346624Z     del arg1_1
2023-01-11T21:41:24.0346688Z     del arg2_1
2023-01-11T21:41:24.0346758Z     return (buf0, )
2023-01-11T21:41:24.0346763Z 
2023-01-11T21:41:24.0346767Z 
2023-01-11T21:41:24.0346827Z if __name__ == "__main__":
2023-01-11T21:41:24.0346935Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0347053Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0347260Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0347462Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0347668Z     arg2_1 = rand_strided((1, 1, 10), (10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0347789Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0347794Z 
2023-01-11T21:41:24.0347857Z ok (1.552s)
2023-01-11T21:41:24.0348334Z   test_scatter_reduce1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0348457Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0348653Z [W TensorAdvancedIndexing.cpp:1739] Warning: scatter_reduce() is in beta and the API may change at any time. (function operator())
2023-01-11T21:41:24.0348914Z [2023-01-11 21:37:46,688] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 415
2023-01-11T21:41:24.0349212Z [2023-01-11 21:37:46,700] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 415
2023-01-11T21:41:24.0349217Z 
2023-01-11T21:41:24.0349306Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0349374Z import torch
2023-01-11T21:41:24.0349441Z import random
2023-01-11T21:41:24.0349551Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0349667Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0349672Z 
2023-01-11T21:41:24.0349736Z aten = torch.ops.aten
2023-01-11T21:41:24.0349866Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0349957Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0349962Z 
2023-01-11T21:41:24.0349966Z 
2023-01-11T21:41:24.0350097Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0350302Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0350449Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0350549Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0350652Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0350739Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0350799Z {
2023-01-11T21:41:24.0350893Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0350952Z     {
2023-01-11T21:41:24.0351027Z         #pragma omp for 
2023-01-11T21:41:24.0351106Z         for(long i0=0; i0<235; i0+=1)
2023-01-11T21:41:24.0351164Z         {
2023-01-11T21:41:24.0351284Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0351372Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0351431Z         }
2023-01-11T21:41:24.0351522Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0351605Z         for(long i0=1880; i0<1885; i0+=1)
2023-01-11T21:41:24.0351667Z         {
2023-01-11T21:41:24.0351750Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0351817Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0351876Z         }
2023-01-11T21:41:24.0351949Z         #pragma omp for 
2023-01-11T21:41:24.0352028Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0352085Z         {
2023-01-11T21:41:24.0352144Z             {
2023-01-11T21:41:24.0352194Z                 {
2023-01-11T21:41:24.0352278Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0352368Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:24.0352469Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:41:24.0352530Z                 }
2023-01-11T21:41:24.0352592Z             }
2023-01-11T21:41:24.0352650Z         }
2023-01-11T21:41:24.0352697Z     }
2023-01-11T21:41:24.0352753Z }
2023-01-11T21:41:24.0352827Z ''')
2023-01-11T21:41:24.0352832Z 
2023-01-11T21:41:24.0352836Z 
2023-01-11T21:41:24.0352923Z async_compile.wait(globals())
2023-01-11T21:41:24.0352996Z del async_compile
2023-01-11T21:41:24.0353001Z 
2023-01-11T21:41:24.0353067Z def call(args):
2023-01-11T21:41:24.0353145Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0353202Z     args.clear()
2023-01-11T21:41:24.0353406Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0353588Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0353654Z     del arg0_1
2023-01-11T21:41:24.0353716Z     del arg1_1
2023-01-11T21:41:24.0353836Z     del arg2_1
2023-01-11T21:41:24.0353907Z     return (buf0, )
2023-01-11T21:41:24.0353912Z 
2023-01-11T21:41:24.0353916Z 
2023-01-11T21:41:24.0353989Z if __name__ == "__main__":
2023-01-11T21:41:24.0354091Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0354212Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0354423Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0354659Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0354859Z     arg2_1 = rand_strided((1, 1, 10), (10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0354979Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0354984Z 
2023-01-11T21:41:24.0355050Z ok (0.034s)
2023-01-11T21:41:24.0355522Z   test_scatter_reduce2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0355644Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0355919Z [2023-01-11 21:37:46,721] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 416
2023-01-11T21:41:24.0356186Z [2023-01-11 21:37:48,322] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 416
2023-01-11T21:41:24.0356191Z 
2023-01-11T21:41:24.0356282Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0356351Z import torch
2023-01-11T21:41:24.0356420Z import random
2023-01-11T21:41:24.0356533Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0356649Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0356655Z 
2023-01-11T21:41:24.0356729Z aten = torch.ops.aten
2023-01-11T21:41:24.0356848Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0356938Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0356943Z 
2023-01-11T21:41:24.0356948Z 
2023-01-11T21:41:24.0357080Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0357283Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0357405Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0357506Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:24.0357607Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0357705Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0357753Z {
2023-01-11T21:41:24.0357846Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0357904Z     {
2023-01-11T21:41:24.0357978Z         #pragma omp for 
2023-01-11T21:41:24.0358055Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:24.0358114Z         {
2023-01-11T21:41:24.0358175Z             {
2023-01-11T21:41:24.0358225Z                 {
2023-01-11T21:41:24.0358315Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0358392Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0358454Z                 }
2023-01-11T21:41:24.0358514Z             }
2023-01-11T21:41:24.0358576Z         }
2023-01-11T21:41:24.0358641Z         #pragma omp for 
2023-01-11T21:41:24.0358718Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0358780Z         {
2023-01-11T21:41:24.0358857Z             #pragma GCC ivdep
2023-01-11T21:41:24.0358936Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0358996Z             {
2023-01-11T21:41:24.0359056Z                 {
2023-01-11T21:41:24.0359108Z                     {
2023-01-11T21:41:24.0359205Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:41:24.0359308Z                         auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:24.0359404Z                         out_ptr0[i1 + (3*tmp0)] = tmp1;
2023-01-11T21:41:24.0359467Z                     }
2023-01-11T21:41:24.0359528Z                 }
2023-01-11T21:41:24.0359590Z             }
2023-01-11T21:41:24.0359638Z         }
2023-01-11T21:41:24.0359709Z         #pragma omp for 
2023-01-11T21:41:24.0359785Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0359875Z         {
2023-01-11T21:41:24.0359954Z             #pragma GCC ivdep
2023-01-11T21:41:24.0360032Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0360081Z             {
2023-01-11T21:41:24.0360141Z                 {
2023-01-11T21:41:24.0360203Z                     {
2023-01-11T21:41:24.0360298Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:41:24.0360394Z                         auto tmp1 = in_ptr2[i1 + (3*i0)];
2023-01-11T21:41:24.0360503Z                         atomic_add(&out_ptr0[i1 + (3*tmp0)], tmp1);
2023-01-11T21:41:24.0360566Z                     }
2023-01-11T21:41:24.0360616Z                 }
2023-01-11T21:41:24.0360675Z             }
2023-01-11T21:41:24.0360734Z         }
2023-01-11T21:41:24.0360794Z     }
2023-01-11T21:41:24.0360852Z }
2023-01-11T21:41:24.0360927Z ''')
2023-01-11T21:41:24.0360932Z 
2023-01-11T21:41:24.0360936Z 
2023-01-11T21:41:24.0361021Z async_compile.wait(globals())
2023-01-11T21:41:24.0361080Z del async_compile
2023-01-11T21:41:24.0361102Z 
2023-01-11T21:41:24.0361185Z def call(args):
2023-01-11T21:41:24.0361263Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0361332Z     args.clear()
2023-01-11T21:41:24.0361526Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0361713Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0361778Z     del arg0_1
2023-01-11T21:41:24.0361843Z     del arg1_1
2023-01-11T21:41:24.0361895Z     del arg2_1
2023-01-11T21:41:24.0361961Z     return (buf0, )
2023-01-11T21:41:24.0361967Z 
2023-01-11T21:41:24.0361971Z 
2023-01-11T21:41:24.0362043Z if __name__ == "__main__":
2023-01-11T21:41:24.0362155Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0367553Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0367796Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0367995Z     arg1_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0368180Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0368329Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0368337Z 
2023-01-11T21:41:24.0368429Z ok (1.637s)
2023-01-11T21:41:24.0368952Z   test_scheduler_vertical_fusion1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0369080Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0369349Z [2023-01-11 21:37:48,462] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 417
2023-01-11T21:41:24.0369621Z [2023-01-11 21:37:50,100] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 417
2023-01-11T21:41:24.0369627Z 
2023-01-11T21:41:24.0369716Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0369785Z import torch
2023-01-11T21:41:24.0369854Z import random
2023-01-11T21:41:24.0369955Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0370073Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0370078Z 
2023-01-11T21:41:24.0370157Z aten = torch.ops.aten
2023-01-11T21:41:24.0370289Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0370380Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0370386Z 
2023-01-11T21:41:24.0370390Z 
2023-01-11T21:41:24.0370523Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0370727Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0370846Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0371006Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:24.0371110Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0371213Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0371311Z                        const float* __restrict__ in_ptr2)
2023-01-11T21:41:24.0371371Z {
2023-01-11T21:41:24.0371456Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:24.0371553Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0371601Z     {
2023-01-11T21:41:24.0371676Z         #pragma omp for 
2023-01-11T21:41:24.0371760Z         for(long i0=0; i0<135252; i0+=1)
2023-01-11T21:41:24.0371823Z         {
2023-01-11T21:41:24.0371962Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0372090Z             auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0372347Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.061519070296458e-11));
2023-01-11T21:41:24.0372435Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0372645Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(-1.988366587925593e-08));
2023-01-11T21:41:24.0372728Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0372806Z             auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:24.0373024Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(-3.087032500374211e-07));
2023-01-11T21:41:24.0373104Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:24.0373320Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1.55093272922008e-10));
2023-01-11T21:41:24.0373403Z             auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:24.0373485Z             auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:24.0373572Z             auto tmp12 = tmp11.reciprocal();
2023-01-11T21:41:24.0373703Z             auto tmp13 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:41:24.0373794Z             auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:24.0373889Z             tmp11.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0373980Z             tmp14.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0374042Z         }
2023-01-11T21:41:24.0374137Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0374216Z         for(long i0=1082016; i0<1082016; i0+=1)
2023-01-11T21:41:24.0374278Z         {
2023-01-11T21:41:24.0374360Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0374441Z             auto tmp8 = in_ptr1[i0];
2023-01-11T21:41:24.0374614Z             auto tmp1 = static_cast<float>(-1.061519070296458e-11);
2023-01-11T21:41:24.0374696Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0374866Z             auto tmp3 = static_cast<float>(-1.988366587925593e-08);
2023-01-11T21:41:24.0374936Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0375017Z             auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:24.0375184Z             auto tmp6 = static_cast<float>(-3.087032500374211e-07);
2023-01-11T21:41:24.0375270Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:24.0375436Z             auto tmp9 = static_cast<float>(1.55093272922008e-10);
2023-01-11T21:41:24.0375548Z             auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:24.0375665Z             auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:24.0375733Z             auto tmp12 = 1 / tmp11;
2023-01-11T21:41:24.0375832Z             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:24.0375916Z             auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:24.0375996Z             in_out_ptr0[i0] = tmp11;
2023-01-11T21:41:24.0376074Z             out_ptr1[i0] = tmp14;
2023-01-11T21:41:24.0376136Z         }
2023-01-11T21:41:24.0376211Z         #pragma omp for 
2023-01-11T21:41:24.0376279Z         for(long i0=0; i0<41616; i0+=1)
2023-01-11T21:41:24.0376340Z         {
2023-01-11T21:41:24.0376421Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0376483Z             {
2023-01-11T21:41:24.0376631Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:24.0376799Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i1);
2023-01-11T21:41:24.0376887Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0376981Z                 tmp2.store(in_out_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:24.0377048Z             }
2023-01-11T21:41:24.0377139Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0377223Z             for(long i1=24; i1<26; i1+=1)
2023-01-11T21:41:24.0377287Z             {
2023-01-11T21:41:24.0377388Z                 auto tmp0 = in_out_ptr0[i1 + (26*i0)];
2023-01-11T21:41:24.0377473Z                 auto tmp1 = in_ptr2[i1];
2023-01-11T21:41:24.0377547Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0377641Z                 in_out_ptr0[i1 + (26*i0)] = tmp2;
2023-01-11T21:41:24.0377705Z             }
2023-01-11T21:41:24.0377765Z         }
2023-01-11T21:41:24.0377840Z         #pragma omp for 
2023-01-11T21:41:24.0377950Z         for(long i0=0; i0<135252; i0+=1)
2023-01-11T21:41:24.0378012Z         {
2023-01-11T21:41:24.0378132Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0378267Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0378349Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0378446Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:24.0378506Z         }
2023-01-11T21:41:24.0378599Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0378688Z         for(long i0=1082016; i0<1082016; i0+=1)
2023-01-11T21:41:24.0378738Z         {
2023-01-11T21:41:24.0378822Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:24.0378907Z             auto tmp1 = in_out_ptr0[i0];
2023-01-11T21:41:24.0378997Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0379115Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:24.0379202Z         }
2023-01-11T21:41:24.0379285Z     }
2023-01-11T21:41:24.0379340Z }
2023-01-11T21:41:24.0379464Z ''')
2023-01-11T21:41:24.0379473Z 
2023-01-11T21:41:24.0379480Z 
2023-01-11T21:41:24.0379575Z async_compile.wait(globals())
2023-01-11T21:41:24.0379646Z del async_compile
2023-01-11T21:41:24.0379651Z 
2023-01-11T21:41:24.0379720Z def call(args):
2023-01-11T21:41:24.0379796Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0379864Z     args.clear()
2023-01-11T21:41:24.0380081Z     buf0 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0380154Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0380366Z     buf2 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0380447Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0380524Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:41:24.0380737Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:41:24.0380804Z     del arg0_1
2023-01-11T21:41:24.0380870Z     del arg1_1
2023-01-11T21:41:24.0380924Z     del arg2_1
2023-01-11T21:41:24.0380992Z     return (buf4, )
2023-01-11T21:41:24.0380998Z 
2023-01-11T21:41:24.0381002Z 
2023-01-11T21:41:24.0381074Z if __name__ == "__main__":
2023-01-11T21:41:24.0381186Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0381306Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0381517Z     arg0_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0381726Z     arg1_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0381919Z     arg2_1 = rand_strided((26, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0382027Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0382043Z 
2023-01-11T21:41:24.0382096Z ok (1.802s)
2023-01-11T21:41:24.0382773Z   test_select_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0382999Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0383265Z [2023-01-11 21:37:50,172] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 418
2023-01-11T21:41:24.0383530Z [2023-01-11 21:37:51,986] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 418
2023-01-11T21:41:24.0383535Z 
2023-01-11T21:41:24.0383627Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0383696Z import torch
2023-01-11T21:41:24.0383765Z import random
2023-01-11T21:41:24.0383866Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0384023Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0384029Z 
2023-01-11T21:41:24.0384106Z aten = torch.ops.aten
2023-01-11T21:41:24.0384239Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0384330Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0384335Z 
2023-01-11T21:41:24.0384339Z 
2023-01-11T21:41:24.0384475Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0384678Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0384794Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0384892Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0384981Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0385077Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0385171Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0385229Z {
2023-01-11T21:41:24.0385327Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0385387Z     {
2023-01-11T21:41:24.0385453Z         #pragma omp for 
2023-01-11T21:41:24.0385534Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0385595Z         {
2023-01-11T21:41:24.0385676Z             #pragma GCC ivdep
2023-01-11T21:41:24.0385760Z             for(long i1=0; i1<197; i1+=1)
2023-01-11T21:41:24.0385822Z             {
2023-01-11T21:41:24.0385900Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0385976Z                 for(long i2=0; i2<38; i2+=1)
2023-01-11T21:41:24.0386040Z                 {
2023-01-11T21:41:24.0386103Z                     {
2023-01-11T21:41:24.0386167Z                         {
2023-01-11T21:41:24.0386270Z                             auto tmp3 = in_ptr0[i2 + (38*i0)];
2023-01-11T21:41:24.0386379Z                             auto tmp4 = in_ptr1[i2 + (38*i1) + (7486*i0)];
2023-01-11T21:41:24.0386484Z                             auto tmp0 = static_cast<int>(i1);
2023-01-11T21:41:24.0386579Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:41:24.0386672Z                             auto tmp2 = tmp0 == tmp1;
2023-01-11T21:41:24.0386769Z                             auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:41:24.0386871Z                             out_ptr0[i2 + (38*i1) + (7486*i0)] = tmp5;
2023-01-11T21:41:24.0386937Z                         }
2023-01-11T21:41:24.0387000Z                     }
2023-01-11T21:41:24.0387061Z                 }
2023-01-11T21:41:24.0387111Z             }
2023-01-11T21:41:24.0387170Z         }
2023-01-11T21:41:24.0387242Z         #pragma omp for 
2023-01-11T21:41:24.0387320Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0387380Z         {
2023-01-11T21:41:24.0387454Z             #pragma GCC ivdep
2023-01-11T21:41:24.0387539Z             for(long i1=0; i1<7486; i1+=1)
2023-01-11T21:41:24.0387589Z             {
2023-01-11T21:41:24.0387650Z                 {
2023-01-11T21:41:24.0387712Z                     {
2023-01-11T21:41:24.0387838Z                         auto tmp3 = in_ptr2[i1];
2023-01-11T21:41:24.0387940Z                         auto tmp4 = in_ptr1[i1 + (7486*i0)];
2023-01-11T21:41:24.0388042Z                         auto tmp0 = static_cast<int>(i0);
2023-01-11T21:41:24.0388141Z                         auto tmp1 = static_cast<int>(1);
2023-01-11T21:41:24.0388222Z                         auto tmp2 = tmp0 == tmp1;
2023-01-11T21:41:24.0388318Z                         auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:41:24.0388412Z                         out_ptr1[i1 + (7486*i0)] = tmp5;
2023-01-11T21:41:24.0388473Z                     }
2023-01-11T21:41:24.0388538Z                 }
2023-01-11T21:41:24.0388599Z             }
2023-01-11T21:41:24.0388659Z         }
2023-01-11T21:41:24.0388707Z     }
2023-01-11T21:41:24.0388765Z }
2023-01-11T21:41:24.0388842Z ''')
2023-01-11T21:41:24.0388847Z 
2023-01-11T21:41:24.0388852Z 
2023-01-11T21:41:24.0388939Z async_compile.wait(globals())
2023-01-11T21:41:24.0389009Z del async_compile
2023-01-11T21:41:24.0389016Z 
2023-01-11T21:41:24.0389111Z def call(args):
2023-01-11T21:41:24.0389192Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.0389250Z     args.clear()
2023-01-11T21:41:24.0389461Z     buf0 = empty_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0389670Z     buf1 = empty_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0389882Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0389945Z     del arg0_1
2023-01-11T21:41:24.0390012Z     del arg1_1
2023-01-11T21:41:24.0390075Z     del arg2_1
2023-01-11T21:41:24.0390148Z     return (buf0, buf1, )
2023-01-11T21:41:24.0390153Z 
2023-01-11T21:41:24.0390158Z 
2023-01-11T21:41:24.0390220Z if __name__ == "__main__":
2023-01-11T21:41:24.0390330Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0390452Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0390662Z     arg0_1 = rand_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0390858Z     arg1_1 = rand_strided((8, 38), (38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0391055Z     arg2_1 = rand_strided((197, 38), (38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0391174Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.0391179Z 
2023-01-11T21:41:24.0391242Z ok (1.850s)
2023-01-11T21:41:24.0391705Z   test_sgn_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0391820Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0392081Z [2023-01-11 21:37:52,012] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 419
2023-01-11T21:41:24.0392345Z [2023-01-11 21:37:53,676] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 419
2023-01-11T21:41:24.0392351Z 
2023-01-11T21:41:24.0392442Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0392512Z import torch
2023-01-11T21:41:24.0392577Z import random
2023-01-11T21:41:24.0392689Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0392807Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0392812Z 
2023-01-11T21:41:24.0392878Z aten = torch.ops.aten
2023-01-11T21:41:24.0393012Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0393102Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0393107Z 
2023-01-11T21:41:24.0393111Z 
2023-01-11T21:41:24.0393242Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0393479Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0393599Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0393695Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0393849Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0393898Z {
2023-01-11T21:41:24.0393994Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0394055Z     {
2023-01-11T21:41:24.0394130Z         #pragma omp for 
2023-01-11T21:41:24.0394209Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:24.0394269Z         {
2023-01-11T21:41:24.0394405Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0394562Z             auto tmp1 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), decltype(tmp0)(0) < tmp0);
2023-01-11T21:41:24.0394759Z             auto tmp2 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), tmp0 < decltype(tmp0)(0));
2023-01-11T21:41:24.0394893Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:24.0395023Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0395107Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:24.0395273Z             auto tmp6 = decltype(tmp5)::blendv(decltype(tmp5)(0), decltype(tmp5)(1), decltype(tmp5)(0) < tmp5);
2023-01-11T21:41:24.0395436Z             auto tmp7 = decltype(tmp5)::blendv(decltype(tmp5)(0), decltype(tmp5)(1), tmp5 < decltype(tmp5)(0));
2023-01-11T21:41:24.0395558Z             auto tmp8 = tmp6 - tmp7;
2023-01-11T21:41:24.0395679Z             auto tmp9 = tmp8 - tmp4;
2023-01-11T21:41:24.0395757Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0395845Z             tmp9.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0395904Z         }
2023-01-11T21:41:24.0395994Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0396074Z         for(long i0=40; i0<41; i0+=1)
2023-01-11T21:41:24.0396137Z         {
2023-01-11T21:41:24.0396220Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0396293Z             auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:41:24.0396377Z             auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:41:24.0396497Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:24.0396594Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:24.0396676Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:24.0396755Z             auto tmp6 = tmp5 > 0 ? 1 : 0;
2023-01-11T21:41:24.0396836Z             auto tmp7 = tmp5 < 0 ? 1 : 0;
2023-01-11T21:41:24.0396946Z             auto tmp8 = tmp6 - tmp7;
2023-01-11T21:41:24.0397068Z             auto tmp9 = tmp8 - tmp4;
2023-01-11T21:41:24.0397147Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0397222Z             out_ptr1[i0] = tmp9;
2023-01-11T21:41:24.0397282Z         }
2023-01-11T21:41:24.0397339Z     }
2023-01-11T21:41:24.0397396Z }
2023-01-11T21:41:24.0397462Z ''')
2023-01-11T21:41:24.0397467Z 
2023-01-11T21:41:24.0397474Z 
2023-01-11T21:41:24.0397565Z async_compile.wait(globals())
2023-01-11T21:41:24.0397641Z del async_compile
2023-01-11T21:41:24.0397646Z 
2023-01-11T21:41:24.0397716Z def call(args):
2023-01-11T21:41:24.0397784Z     arg0_1, = args
2023-01-11T21:41:24.0397850Z     args.clear()
2023-01-11T21:41:24.0398044Z     buf0 = empty_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0398223Z     buf1 = empty_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0398384Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0398449Z     del arg0_1
2023-01-11T21:41:24.0398521Z     return (buf0, buf1, )
2023-01-11T21:41:24.0398527Z 
2023-01-11T21:41:24.0398531Z 
2023-01-11T21:41:24.0398606Z if __name__ == "__main__":
2023-01-11T21:41:24.0398718Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0398837Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0399061Z     arg0_1 = rand_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0399158Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0399163Z 
2023-01-11T21:41:24.0399228Z ok (1.698s)
2023-01-11T21:41:24.0399704Z   test_sgn_extremal_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0399829Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0400088Z [2023-01-11 21:37:53,702] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 420
2023-01-11T21:41:24.0400355Z [2023-01-11 21:37:55,243] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 420
2023-01-11T21:41:24.0400389Z 
2023-01-11T21:41:24.0400480Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0400548Z import torch
2023-01-11T21:41:24.0400616Z import random
2023-01-11T21:41:24.0400716Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0400836Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0400841Z 
2023-01-11T21:41:24.0400916Z aten = torch.ops.aten
2023-01-11T21:41:24.0401048Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0401137Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0401141Z 
2023-01-11T21:41:24.0401146Z 
2023-01-11T21:41:24.0401278Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0401480Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0401595Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0401682Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0401742Z {
2023-01-11T21:41:24.0401842Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0401902Z     {
2023-01-11T21:41:24.0401975Z         #pragma omp for 
2023-01-11T21:41:24.0402054Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0402115Z         {
2023-01-11T21:41:24.0402165Z             {
2023-01-11T21:41:24.0402225Z                 {
2023-01-11T21:41:24.0402319Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0402408Z                     auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:41:24.0402495Z                     auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:41:24.0402626Z                     auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:24.0402708Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0402759Z                 }
2023-01-11T21:41:24.0402821Z             }
2023-01-11T21:41:24.0402883Z         }
2023-01-11T21:41:24.0402945Z     }
2023-01-11T21:41:24.0403002Z }
2023-01-11T21:41:24.0403078Z ''')
2023-01-11T21:41:24.0403083Z 
2023-01-11T21:41:24.0403090Z 
2023-01-11T21:41:24.0403179Z async_compile.wait(globals())
2023-01-11T21:41:24.0403238Z del async_compile
2023-01-11T21:41:24.0403243Z 
2023-01-11T21:41:24.0403310Z def call(args):
2023-01-11T21:41:24.0403375Z     arg0_1, = args
2023-01-11T21:41:24.0403442Z     args.clear()
2023-01-11T21:41:24.0403630Z     buf0 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0403761Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0403826Z     del arg0_1
2023-01-11T21:41:24.0403883Z     return (buf0, )
2023-01-11T21:41:24.0403888Z 
2023-01-11T21:41:24.0403901Z 
2023-01-11T21:41:24.0403963Z if __name__ == "__main__":
2023-01-11T21:41:24.0404074Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0404194Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0404382Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0404486Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0404523Z 
2023-01-11T21:41:24.0404586Z ok (1.556s)
2023-01-11T21:41:24.0405066Z   test_shape_prop_torch_ones_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0405190Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0405437Z [2023-01-11 21:37:55,689] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 421
2023-01-11T21:41:24.0405704Z [2023-01-11 21:37:57,210] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 421
2023-01-11T21:41:24.0405709Z 
2023-01-11T21:41:24.0405801Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0405899Z import torch
2023-01-11T21:41:24.0405970Z import random
2023-01-11T21:41:24.0406087Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0406206Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0406211Z 
2023-01-11T21:41:24.0406288Z aten = torch.ops.aten
2023-01-11T21:41:24.0406408Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0406499Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0406503Z 
2023-01-11T21:41:24.0406508Z 
2023-01-11T21:41:24.0406643Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0406848Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0406966Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0407067Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0407128Z {
2023-01-11T21:41:24.0407225Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0407273Z     {
2023-01-11T21:41:24.0407356Z         #pragma omp for 
2023-01-11T21:41:24.0407438Z         for(long i0=0; i0<3145728; i0+=1)
2023-01-11T21:41:24.0407500Z         {
2023-01-11T21:41:24.0407630Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0407762Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0407845Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0407922Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0407985Z         }
2023-01-11T21:41:24.0408077Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0408166Z         for(long i0=25165824; i0<25165824; i0+=1)
2023-01-11T21:41:24.0408226Z         {
2023-01-11T21:41:24.0408309Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0408406Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0408476Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0408554Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0408615Z         }
2023-01-11T21:41:24.0408675Z     }
2023-01-11T21:41:24.0408731Z }
2023-01-11T21:41:24.0408806Z ''')
2023-01-11T21:41:24.0408812Z 
2023-01-11T21:41:24.0408816Z 
2023-01-11T21:41:24.0408903Z async_compile.wait(globals())
2023-01-11T21:41:24.0408961Z del async_compile
2023-01-11T21:41:24.0408966Z 
2023-01-11T21:41:24.0409036Z def call(args):
2023-01-11T21:41:24.0409104Z     arg0_1, = args
2023-01-11T21:41:24.0409174Z     args.clear()
2023-01-11T21:41:24.0409406Z     buf0 = empty_strided((8, 12, 512, 512), (3145728, 262144, 512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0409538Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0409604Z     del arg0_1
2023-01-11T21:41:24.0409661Z     return (buf0, )
2023-01-11T21:41:24.0409675Z 
2023-01-11T21:41:24.0409679Z 
2023-01-11T21:41:24.0409740Z if __name__ == "__main__":
2023-01-11T21:41:24.0409851Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0409973Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0410235Z     arg0_1 = rand_strided((8, 12, 512, 512), (3145728, 262144, 512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0410342Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0410347Z 
2023-01-11T21:41:24.0410410Z ok (3.163s)
2023-01-11T21:41:24.0410878Z   test_sigmoid_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0411004Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0411263Z [2023-01-11 21:37:58,427] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 422
2023-01-11T21:41:24.0411540Z [2023-01-11 21:37:59,995] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 422
2023-01-11T21:41:24.0411561Z 
2023-01-11T21:41:24.0411644Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0411712Z import torch
2023-01-11T21:41:24.0411781Z import random
2023-01-11T21:41:24.0411891Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0412005Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0412009Z 
2023-01-11T21:41:24.0412085Z aten = torch.ops.aten
2023-01-11T21:41:24.0412218Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0412296Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0412301Z 
2023-01-11T21:41:24.0412305Z 
2023-01-11T21:41:24.0412440Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0412639Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0412755Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0412859Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0412958Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0413053Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0413112Z {
2023-01-11T21:41:24.0413197Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0413256Z     {
2023-01-11T21:41:24.0413330Z         #pragma omp for 
2023-01-11T21:41:24.0413407Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0413467Z         {
2023-01-11T21:41:24.0413600Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0413728Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0413849Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:24.0413931Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.0414058Z             auto tmp4 = decltype(tmp3)(1)/(decltype(tmp3)(1) + tmp3.neg().exp());
2023-01-11T21:41:24.0414151Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0414241Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0414301Z         }
2023-01-11T21:41:24.0414393Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0414462Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0414521Z         {
2023-01-11T21:41:24.0414602Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0414678Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.0414808Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:24.0414887Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:24.0414971Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:24.0415090Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:24.0415170Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:24.0415246Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0415321Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.0415379Z         }
2023-01-11T21:41:24.0415463Z     }
2023-01-11T21:41:24.0415522Z }
2023-01-11T21:41:24.0415586Z ''')
2023-01-11T21:41:24.0415592Z 
2023-01-11T21:41:24.0415596Z 
2023-01-11T21:41:24.0415682Z async_compile.wait(globals())
2023-01-11T21:41:24.0415751Z del async_compile
2023-01-11T21:41:24.0415757Z 
2023-01-11T21:41:24.0415823Z def call(args):
2023-01-11T21:41:24.0415894Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0415962Z     args.clear()
2023-01-11T21:41:24.0416155Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0416335Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0416523Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0416588Z     del arg0_1
2023-01-11T21:41:24.0416656Z     del arg1_1
2023-01-11T21:41:24.0416730Z     return (buf0, buf1, )
2023-01-11T21:41:24.0416734Z 
2023-01-11T21:41:24.0416739Z 
2023-01-11T21:41:24.0416841Z if __name__ == "__main__":
2023-01-11T21:41:24.0416953Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0417076Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0417260Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0417452Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0417563Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0417568Z 
2023-01-11T21:41:24.0417632Z ok (1.591s)
2023-01-11T21:41:24.0418096Z   test_signbit_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0418221Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0418480Z [2023-01-11 21:38:00,034] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 423
2023-01-11T21:41:24.0418741Z [2023-01-11 21:38:01,773] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 423
2023-01-11T21:41:24.0418746Z 
2023-01-11T21:41:24.0418838Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0418903Z import torch
2023-01-11T21:41:24.0418960Z import random
2023-01-11T21:41:24.0419071Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0419187Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0419191Z 
2023-01-11T21:41:24.0419269Z aten = torch.ops.aten
2023-01-11T21:41:24.0419400Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0419489Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0419495Z 
2023-01-11T21:41:24.0419499Z 
2023-01-11T21:41:24.0419627Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0419834Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0419938Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0420033Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:24.0420126Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:24.0420184Z {
2023-01-11T21:41:24.0420280Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0420338Z     {
2023-01-11T21:41:24.0420411Z         #pragma omp for 
2023-01-11T21:41:24.0420480Z         for(long i0=0; i0<72; i0+=1)
2023-01-11T21:41:24.0420540Z         {
2023-01-11T21:41:24.0420600Z             {
2023-01-11T21:41:24.0420661Z                 {
2023-01-11T21:41:24.0420750Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0420851Z                     auto tmp1 = std::signbit(tmp0);
2023-01-11T21:41:24.0420961Z                     auto tmp2 = -tmp0;
2023-01-11T21:41:24.0421103Z                     auto tmp3 = std::signbit(tmp2);
2023-01-11T21:41:24.0421189Z                     auto tmp4 = tmp3 == 0;
2023-01-11T21:41:24.0421291Z                     auto tmp5 = static_cast<long>(tmp4);
2023-01-11T21:41:24.0421392Z                     auto tmp6 = static_cast<long>(1);
2023-01-11T21:41:24.0421479Z                     auto tmp7 = tmp5 & tmp6;
2023-01-11T21:41:24.0421559Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0421638Z                     out_ptr1[i0] = tmp7;
2023-01-11T21:41:24.0421689Z                 }
2023-01-11T21:41:24.0421752Z             }
2023-01-11T21:41:24.0421812Z         }
2023-01-11T21:41:24.0421869Z     }
2023-01-11T21:41:24.0421927Z }
2023-01-11T21:41:24.0422004Z ''')
2023-01-11T21:41:24.0422009Z 
2023-01-11T21:41:24.0422013Z 
2023-01-11T21:41:24.0422098Z async_compile.wait(globals())
2023-01-11T21:41:24.0422158Z del async_compile
2023-01-11T21:41:24.0422162Z 
2023-01-11T21:41:24.0422229Z def call(args):
2023-01-11T21:41:24.0422294Z     arg0_1, = args
2023-01-11T21:41:24.0422576Z     args.clear()
2023-01-11T21:41:24.0422796Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.0423000Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0423163Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0423216Z     del arg0_1
2023-01-11T21:41:24.0423291Z     return (buf0, buf1, )
2023-01-11T21:41:24.0423296Z 
2023-01-11T21:41:24.0423300Z 
2023-01-11T21:41:24.0423373Z if __name__ == "__main__":
2023-01-11T21:41:24.0423485Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0423606Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0423815Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0423921Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0423928Z 
2023-01-11T21:41:24.0423995Z ok (1.777s)
2023-01-11T21:41:24.0424458Z   test_silu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0424570Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0424833Z [2023-01-11 21:38:01,795] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 424
2023-01-11T21:41:24.0425095Z [2023-01-11 21:38:01,805] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 424
2023-01-11T21:41:24.0425100Z 
2023-01-11T21:41:24.0425190Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0425257Z import torch
2023-01-11T21:41:24.0425322Z import random
2023-01-11T21:41:24.0425439Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0425557Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0425562Z 
2023-01-11T21:41:24.0425625Z aten = torch.ops.aten
2023-01-11T21:41:24.0425755Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0425843Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0425848Z 
2023-01-11T21:41:24.0425852Z 
2023-01-11T21:41:24.0425983Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0426185Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0426299Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0426397Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0426456Z {
2023-01-11T21:41:24.0426539Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0426598Z     {
2023-01-11T21:41:24.0426673Z         #pragma omp for 
2023-01-11T21:41:24.0426797Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0426858Z         {
2023-01-11T21:41:24.0426992Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0427124Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:24.0427196Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0427285Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0427345Z         }
2023-01-11T21:41:24.0427438Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0427519Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0427576Z         {
2023-01-11T21:41:24.0427656Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0427777Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:24.0427856Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:24.0427935Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:24.0428013Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0428074Z         }
2023-01-11T21:41:24.0428164Z     }
2023-01-11T21:41:24.0428223Z }
2023-01-11T21:41:24.0428287Z ''')
2023-01-11T21:41:24.0428292Z 
2023-01-11T21:41:24.0428296Z 
2023-01-11T21:41:24.0428380Z async_compile.wait(globals())
2023-01-11T21:41:24.0428449Z del async_compile
2023-01-11T21:41:24.0428454Z 
2023-01-11T21:41:24.0428520Z def call(args):
2023-01-11T21:41:24.0428587Z     arg0_1, = args
2023-01-11T21:41:24.0428655Z     args.clear()
2023-01-11T21:41:24.0428847Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0428966Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0429029Z     del arg0_1
2023-01-11T21:41:24.0429094Z     return (buf0, )
2023-01-11T21:41:24.0429099Z 
2023-01-11T21:41:24.0429103Z 
2023-01-11T21:41:24.0429174Z if __name__ == "__main__":
2023-01-11T21:41:24.0429284Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0429404Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0429601Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0429707Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0429711Z 
2023-01-11T21:41:24.0429765Z ok (0.030s)
2023-01-11T21:41:24.0430235Z   test_simplify_loops_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0430359Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0430617Z [2023-01-11 21:38:01,818] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 425
2023-01-11T21:41:24.0430880Z [2023-01-11 21:38:03,396] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 425
2023-01-11T21:41:24.0430888Z 
2023-01-11T21:41:24.0430979Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0431045Z import torch
2023-01-11T21:41:24.0431112Z import random
2023-01-11T21:41:24.0431223Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0431331Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0431346Z 
2023-01-11T21:41:24.0431410Z aten = torch.ops.aten
2023-01-11T21:41:24.0431540Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0431627Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0431632Z 
2023-01-11T21:41:24.0431636Z 
2023-01-11T21:41:24.0431767Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0431968Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0432083Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0432181Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0432300Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0432359Z {
2023-01-11T21:41:24.0432453Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0432513Z     {
2023-01-11T21:41:24.0432601Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0432680Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:24.0432741Z         {
2023-01-11T21:41:24.0432810Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0432870Z             {
2023-01-11T21:41:24.0432955Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:41:24.0433017Z                 {
2023-01-11T21:41:24.0433162Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (30*i1) + (120*i0));
2023-01-11T21:41:24.0433307Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i2) + (30*i0) + (180*i1));
2023-01-11T21:41:24.0433399Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0433531Z                     tmp2.store(out_ptr0 + (8*i2) + (30*i1) + (120*i0));
2023-01-11T21:41:24.0433587Z                 }
2023-01-11T21:41:24.0433680Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0433847Z                 for(long i2=24; i2<30; i2+=1)
2023-01-11T21:41:24.0433913Z                 {
2023-01-11T21:41:24.0434021Z                     auto tmp0 = in_ptr0[i2 + (30*i1) + (120*i0)];
2023-01-11T21:41:24.0434122Z                     auto tmp1 = in_ptr1[i2 + (30*i0) + (180*i1)];
2023-01-11T21:41:24.0434211Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0434300Z                     out_ptr0[i2 + (30*i1) + (120*i0)] = tmp2;
2023-01-11T21:41:24.0434361Z                 }
2023-01-11T21:41:24.0434421Z             }
2023-01-11T21:41:24.0434482Z         }
2023-01-11T21:41:24.0434541Z     }
2023-01-11T21:41:24.0434600Z }
2023-01-11T21:41:24.0434669Z ''')
2023-01-11T21:41:24.0434686Z 
2023-01-11T21:41:24.0434690Z 
2023-01-11T21:41:24.0434766Z async_compile.wait(globals())
2023-01-11T21:41:24.0434839Z del async_compile
2023-01-11T21:41:24.0434844Z 
2023-01-11T21:41:24.0434909Z def call(args):
2023-01-11T21:41:24.0434982Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0435050Z     args.clear()
2023-01-11T21:41:24.0435274Z     buf0 = empty_strided((2, 3, 4, 5, 6), (360, 120, 30, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0435431Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0435485Z     del arg0_1
2023-01-11T21:41:24.0435548Z     del arg1_1
2023-01-11T21:41:24.0435615Z     return (buf0, )
2023-01-11T21:41:24.0435620Z 
2023-01-11T21:41:24.0435624Z 
2023-01-11T21:41:24.0435696Z if __name__ == "__main__":
2023-01-11T21:41:24.0435805Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0435927Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0436150Z     arg0_1 = rand_strided((2, 3, 4, 5, 6), (360, 120, 30, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0436371Z     arg1_1 = rand_strided((2, 3, 4, 5, 6), (90, 30, 180, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0436474Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0436488Z 
2023-01-11T21:41:24.0436542Z ok (1.593s)
2023-01-11T21:41:24.0436999Z   test_sin_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0437120Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0437386Z [2023-01-11 21:38:03,430] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 426
2023-01-11T21:41:24.0437652Z [2023-01-11 21:38:05,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 426
2023-01-11T21:41:24.0437696Z 
2023-01-11T21:41:24.0437789Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0437858Z import torch
2023-01-11T21:41:24.0437926Z import random
2023-01-11T21:41:24.0438028Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0438147Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0438152Z 
2023-01-11T21:41:24.0438228Z aten = torch.ops.aten
2023-01-11T21:41:24.0438360Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0438451Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0438456Z 
2023-01-11T21:41:24.0438460Z 
2023-01-11T21:41:24.0438592Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0438794Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0438912Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0439012Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0439129Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0439190Z {
2023-01-11T21:41:24.0439286Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0439346Z     {
2023-01-11T21:41:24.0439417Z         #pragma omp for 
2023-01-11T21:41:24.0439498Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0439547Z         {
2023-01-11T21:41:24.0439680Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0439763Z             auto tmp1 = tmp0.sin();
2023-01-11T21:41:24.0439894Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0439975Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0440103Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0440185Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:24.0440264Z             auto tmp6 = tmp5.sin();
2023-01-11T21:41:24.0440341Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0440433Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0440496Z         }
2023-01-11T21:41:24.0440588Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0440670Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0440728Z         {
2023-01-11T21:41:24.0440809Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0440884Z             auto tmp1 = std::sin(tmp0);
2023-01-11T21:41:24.0440980Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:24.0441060Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0441156Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:24.0441239Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:24.0441325Z             auto tmp6 = std::sin(tmp5);
2023-01-11T21:41:24.0441403Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0441467Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.0441530Z         }
2023-01-11T21:41:24.0441590Z     }
2023-01-11T21:41:24.0441651Z }
2023-01-11T21:41:24.0441731Z ''')
2023-01-11T21:41:24.0441736Z 
2023-01-11T21:41:24.0441740Z 
2023-01-11T21:41:24.0441826Z async_compile.wait(globals())
2023-01-11T21:41:24.0441897Z del async_compile
2023-01-11T21:41:24.0441902Z 
2023-01-11T21:41:24.0441958Z def call(args):
2023-01-11T21:41:24.0442027Z     arg0_1, = args
2023-01-11T21:41:24.0442097Z     args.clear()
2023-01-11T21:41:24.0442298Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0442493Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0442657Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0442722Z     del arg0_1
2023-01-11T21:41:24.0442786Z     return (buf0, buf1, )
2023-01-11T21:41:24.0442804Z 
2023-01-11T21:41:24.0442808Z 
2023-01-11T21:41:24.0442870Z if __name__ == "__main__":
2023-01-11T21:41:24.0442982Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0443134Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0443330Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0443436Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0443440Z 
2023-01-11T21:41:24.0443500Z ok (1.604s)
2023-01-11T21:41:24.0443973Z   test_sizehint_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0444097Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0444358Z [2023-01-11 21:38:05,161] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 427
2023-01-11T21:41:24.0444639Z [2023-01-11 21:38:06,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 427
2023-01-11T21:41:24.0444657Z 
2023-01-11T21:41:24.0444740Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0444805Z import torch
2023-01-11T21:41:24.0444870Z import random
2023-01-11T21:41:24.0444982Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0445099Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0445104Z 
2023-01-11T21:41:24.0445182Z aten = torch.ops.aten
2023-01-11T21:41:24.0445312Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0445390Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0445395Z 
2023-01-11T21:41:24.0445399Z 
2023-01-11T21:41:24.0445529Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0445729Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0445845Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0445948Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0446006Z {
2023-01-11T21:41:24.0446100Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0446148Z     {
2023-01-11T21:41:24.0446234Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0446312Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0446371Z         {
2023-01-11T21:41:24.0446454Z             for(long i1=0; i1<384; i1+=1)
2023-01-11T21:41:24.0446514Z             {
2023-01-11T21:41:24.0446592Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0446672Z                 for(long i2=0; i2<196; i2+=1)
2023-01-11T21:41:24.0446732Z                 {
2023-01-11T21:41:24.0446794Z                     {
2023-01-11T21:41:24.0446858Z                         {
2023-01-11T21:41:24.0446969Z                             auto tmp0 = static_cast<long>(4*(i2 / 14));
2023-01-11T21:41:24.0447080Z                             auto tmp1 = static_cast<long>((i1 / 4) % 4);
2023-01-11T21:41:24.0447177Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0447275Z                             auto tmp3 = static_cast<long>(4*(i2 % 14));
2023-01-11T21:41:24.0447383Z                             auto tmp4 = static_cast<long>(i1 % 4);
2023-01-11T21:41:24.0447472Z                             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:24.0447594Z                             auto tmp6 = in_ptr0[tmp5 + (56*tmp2) + (3136*(i1 / 16)) + (75264*i0)];
2023-01-11T21:41:24.0447694Z                             out_ptr0[i2 + (196*i1) + (75264*i0)] = tmp6;
2023-01-11T21:41:24.0447759Z                         }
2023-01-11T21:41:24.0447822Z                     }
2023-01-11T21:41:24.0447882Z                 }
2023-01-11T21:41:24.0447932Z             }
2023-01-11T21:41:24.0447990Z         }
2023-01-11T21:41:24.0448049Z     }
2023-01-11T21:41:24.0448104Z }
2023-01-11T21:41:24.0448180Z ''')
2023-01-11T21:41:24.0448185Z 
2023-01-11T21:41:24.0448190Z 
2023-01-11T21:41:24.0448276Z async_compile.wait(globals())
2023-01-11T21:41:24.0448405Z del async_compile
2023-01-11T21:41:24.0448410Z 
2023-01-11T21:41:24.0448467Z def call(args):
2023-01-11T21:41:24.0448532Z     arg0_1, = args
2023-01-11T21:41:24.0448601Z     args.clear()
2023-01-11T21:41:24.0448816Z     buf0 = empty_strided((2, 384, 196), (75264, 196, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0448944Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0449008Z     del arg0_1
2023-01-11T21:41:24.0449076Z     return (buf0, )
2023-01-11T21:41:24.0449081Z 
2023-01-11T21:41:24.0449085Z 
2023-01-11T21:41:24.0449146Z if __name__ == "__main__":
2023-01-11T21:41:24.0449257Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0449376Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0449598Z     arg0_1 = rand_strided((2, 24, 56, 56), (75264, 3136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0449705Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0449712Z 
2023-01-11T21:41:24.0449804Z ok (1.742s)
2023-01-11T21:41:24.0450273Z   test_slice1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0450398Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0450657Z [2023-01-11 21:38:06,786] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 428
2023-01-11T21:41:24.0450907Z [2023-01-11 21:38:08,341] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 428
2023-01-11T21:41:24.0450922Z 
2023-01-11T21:41:24.0451002Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0451067Z import torch
2023-01-11T21:41:24.0451138Z import random
2023-01-11T21:41:24.0451249Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0451366Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0451371Z 
2023-01-11T21:41:24.0451443Z aten = torch.ops.aten
2023-01-11T21:41:24.0451572Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0451651Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0451655Z 
2023-01-11T21:41:24.0451667Z 
2023-01-11T21:41:24.0451786Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0451982Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0452099Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0452196Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0452286Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0452342Z {
2023-01-11T21:41:24.0452436Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0452488Z     {
2023-01-11T21:41:24.0452576Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0452653Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0452712Z         {
2023-01-11T21:41:24.0452796Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.0452857Z             {
2023-01-11T21:41:24.0452919Z                 {
2023-01-11T21:41:24.0452972Z                     {
2023-01-11T21:41:24.0453075Z                         auto tmp0 = in_ptr0[(2*i1) + (40*i0)];
2023-01-11T21:41:24.0453177Z                         auto tmp1 = in_ptr0[20 + (2*i1) + (40*i0)];
2023-01-11T21:41:24.0453267Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0453369Z                         auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:24.0453461Z                         auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:24.0453551Z                         auto tmp5 = tmp1 + tmp3;
2023-01-11T21:41:24.0453630Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0453760Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.0453853Z                         out_ptr1[i1 + (10*i0)] = tmp6;
2023-01-11T21:41:24.0453917Z                     }
2023-01-11T21:41:24.0453977Z                 }
2023-01-11T21:41:24.0454038Z             }
2023-01-11T21:41:24.0454100Z         }
2023-01-11T21:41:24.0454148Z     }
2023-01-11T21:41:24.0454204Z }
2023-01-11T21:41:24.0454281Z ''')
2023-01-11T21:41:24.0454286Z 
2023-01-11T21:41:24.0454290Z 
2023-01-11T21:41:24.0454376Z async_compile.wait(globals())
2023-01-11T21:41:24.0454444Z del async_compile
2023-01-11T21:41:24.0454449Z 
2023-01-11T21:41:24.0454516Z def call(args):
2023-01-11T21:41:24.0454582Z     arg0_1, = args
2023-01-11T21:41:24.0454638Z     args.clear()
2023-01-11T21:41:24.0454834Z     buf0 = empty_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0455027Z     buf1 = empty_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0455213Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0455281Z     del arg0_1
2023-01-11T21:41:24.0455354Z     return (buf0, buf1, )
2023-01-11T21:41:24.0455359Z 
2023-01-11T21:41:24.0455363Z 
2023-01-11T21:41:24.0455438Z if __name__ == "__main__":
2023-01-11T21:41:24.0455547Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0455656Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0455858Z     arg0_1 = rand_strided((2, 20, 2), (40, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0455961Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0455966Z 
2023-01-11T21:41:24.0456029Z ok (1.599s)
2023-01-11T21:41:24.0456491Z   test_slice2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0456616Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0456871Z [2023-01-11 21:38:08,385] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 429
2023-01-11T21:41:24.0457132Z [2023-01-11 21:38:09,973] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 429
2023-01-11T21:41:24.0457137Z 
2023-01-11T21:41:24.0457226Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0457281Z import torch
2023-01-11T21:41:24.0457350Z import random
2023-01-11T21:41:24.0457459Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0457580Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0457585Z 
2023-01-11T21:41:24.0457660Z aten = torch.ops.aten
2023-01-11T21:41:24.0457790Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0457881Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0457886Z 
2023-01-11T21:41:24.0457891Z 
2023-01-11T21:41:24.0458022Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0458214Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0458331Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0458432Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0458527Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0458587Z {
2023-01-11T21:41:24.0458682Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0458740Z     {
2023-01-11T21:41:24.0458804Z         #pragma omp for 
2023-01-11T21:41:24.0458883Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.0458943Z         {
2023-01-11T21:41:24.0459006Z             {
2023-01-11T21:41:24.0459070Z                 {
2023-01-11T21:41:24.0459166Z                     auto tmp0 = in_ptr0[1 + (4*i0)];
2023-01-11T21:41:24.0459304Z                     auto tmp1 = in_ptr0[42 + (4*i0)];
2023-01-11T21:41:24.0459383Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0459485Z                     auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:24.0459573Z                     auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:24.0459672Z                     auto tmp5 = static_cast<float>(2);
2023-01-11T21:41:24.0459758Z                     auto tmp6 = tmp1 + tmp5;
2023-01-11T21:41:24.0459845Z                     auto tmp7 = tmp4 + tmp6;
2023-01-11T21:41:24.0459926Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0459996Z                     out_ptr1[i0] = tmp7;
2023-01-11T21:41:24.0460059Z                 }
2023-01-11T21:41:24.0460119Z             }
2023-01-11T21:41:24.0460178Z         }
2023-01-11T21:41:24.0460236Z     }
2023-01-11T21:41:24.0460295Z }
2023-01-11T21:41:24.0460361Z ''')
2023-01-11T21:41:24.0460376Z 
2023-01-11T21:41:24.0460380Z 
2023-01-11T21:41:24.0460456Z async_compile.wait(globals())
2023-01-11T21:41:24.0460557Z del async_compile
2023-01-11T21:41:24.0460562Z 
2023-01-11T21:41:24.0460632Z def call(args):
2023-01-11T21:41:24.0460697Z     arg0_1, = args
2023-01-11T21:41:24.0460765Z     args.clear()
2023-01-11T21:41:24.0460963Z     buf0 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0461158Z     buf1 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0461307Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0461374Z     del arg0_1
2023-01-11T21:41:24.0461449Z     return (buf0, buf1, )
2023-01-11T21:41:24.0461454Z 
2023-01-11T21:41:24.0461458Z 
2023-01-11T21:41:24.0461533Z if __name__ == "__main__":
2023-01-11T21:41:24.0461644Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0461764Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0461970Z     arg0_1 = rand_strided((2, 20, 2), (40, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0462075Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0462080Z 
2023-01-11T21:41:24.0462132Z ok (1.632s)
2023-01-11T21:41:24.0462788Z   test_slice_mutation1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0462917Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0463178Z [2023-01-11 21:38:10,023] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 430
2023-01-11T21:41:24.0463439Z [2023-01-11 21:38:11,596] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 430
2023-01-11T21:41:24.0463448Z 
2023-01-11T21:41:24.0463541Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0463610Z import torch
2023-01-11T21:41:24.0463679Z import random
2023-01-11T21:41:24.0463793Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0463900Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0463916Z 
2023-01-11T21:41:24.0463980Z aten = torch.ops.aten
2023-01-11T21:41:24.0464109Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0464199Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0464204Z 
2023-01-11T21:41:24.0464208Z 
2023-01-11T21:41:24.0464339Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0464542Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0464656Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0464749Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0464840Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0464983Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:24.0465038Z {
2023-01-11T21:41:24.0465135Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0465196Z     {
2023-01-11T21:41:24.0465272Z         #pragma omp for 
2023-01-11T21:41:24.0465356Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0465404Z         {
2023-01-11T21:41:24.0465540Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:24.0465671Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0465752Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0465841Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0465925Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0465986Z         }
2023-01-11T21:41:24.0466080Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0466148Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0466211Z         {
2023-01-11T21:41:24.0466342Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:24.0466438Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0466519Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0466596Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0466660Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:24.0466719Z         }
2023-01-11T21:41:24.0466790Z         #pragma omp for 
2023-01-11T21:41:24.0466868Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0466926Z         {
2023-01-11T21:41:24.0466987Z             {
2023-01-11T21:41:24.0467046Z                 {
2023-01-11T21:41:24.0467140Z                     auto tmp0 = static_cast<float>(3.0);
2023-01-11T21:41:24.0467227Z                     out_ptr0[3 + (8*i0)] = tmp0;
2023-01-11T21:41:24.0467289Z                 }
2023-01-11T21:41:24.0467349Z             }
2023-01-11T21:41:24.0467407Z         }
2023-01-11T21:41:24.0467481Z         #pragma omp for 
2023-01-11T21:41:24.0467566Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0467616Z         {
2023-01-11T21:41:24.0467745Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0467834Z             tmp0.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0467895Z         }
2023-01-11T21:41:24.0467984Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0468063Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0468125Z         {
2023-01-11T21:41:24.0468195Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:24.0468273Z             out_ptr2[i0] = tmp0;
2023-01-11T21:41:24.0468334Z         }
2023-01-11T21:41:24.0468408Z         #pragma omp for 
2023-01-11T21:41:24.0468488Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.0468549Z         {
2023-01-11T21:41:24.0468683Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(4.0));
2023-01-11T21:41:24.0468768Z             tmp0.store(out_ptr0 + 32 + (8*i0));
2023-01-11T21:41:24.0468828Z         }
2023-01-11T21:41:24.0468926Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0469004Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:24.0469064Z         {
2023-01-11T21:41:24.0469164Z             auto tmp0 = static_cast<float>(4.0);
2023-01-11T21:41:24.0469246Z             out_ptr0[32 + i0] = tmp0;
2023-01-11T21:41:24.0469295Z         }
2023-01-11T21:41:24.0469367Z         #pragma omp for 
2023-01-11T21:41:24.0469445Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0469505Z         {
2023-01-11T21:41:24.0469635Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0469769Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0469854Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0469929Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:24.0469990Z         }
2023-01-11T21:41:24.0470081Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0470161Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0470257Z         {
2023-01-11T21:41:24.0470339Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:24.0470433Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0470504Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0470579Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:24.0470639Z         }
2023-01-11T21:41:24.0470699Z     }
2023-01-11T21:41:24.0470756Z }
2023-01-11T21:41:24.0470836Z ''')
2023-01-11T21:41:24.0470841Z 
2023-01-11T21:41:24.0470845Z 
2023-01-11T21:41:24.0470928Z async_compile.wait(globals())
2023-01-11T21:41:24.0470986Z del async_compile
2023-01-11T21:41:24.0470992Z 
2023-01-11T21:41:24.0471058Z def call(args):
2023-01-11T21:41:24.0471125Z     arg0_1, = args
2023-01-11T21:41:24.0471191Z     args.clear()
2023-01-11T21:41:24.0471386Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0471575Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0471797Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0471971Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0472159Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:24.0472244Z     return (buf0, buf1, buf3, buf5, )
2023-01-11T21:41:24.0472249Z 
2023-01-11T21:41:24.0472253Z 
2023-01-11T21:41:24.0472329Z if __name__ == "__main__":
2023-01-11T21:41:24.0472440Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0472560Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0472753Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0472860Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0472864Z 
2023-01-11T21:41:24.0472929Z ok (1.624s)
2023-01-11T21:41:24.0473254Z   test_slice_mutation2_cpu (__main__.CpuTests) ... [2023-01-11 21:38:11,633] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 431
2023-01-11T21:41:24.0473525Z [2023-01-11 21:38:13,244] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 431
2023-01-11T21:41:24.0473530Z 
2023-01-11T21:41:24.0473620Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0473688Z import torch
2023-01-11T21:41:24.0473813Z import random
2023-01-11T21:41:24.0473927Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0474045Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0474050Z 
2023-01-11T21:41:24.0474124Z aten = torch.ops.aten
2023-01-11T21:41:24.0474244Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0474331Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0474336Z 
2023-01-11T21:41:24.0474340Z 
2023-01-11T21:41:24.0474471Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0474674Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0474792Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0474887Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0474981Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0475072Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0475121Z {
2023-01-11T21:41:24.0475212Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0475269Z     {
2023-01-11T21:41:24.0475342Z         #pragma omp for 
2023-01-11T21:41:24.0475420Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0475480Z         {
2023-01-11T21:41:24.0475603Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 20 + (8*i0));
2023-01-11T21:41:24.0475733Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0475815Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0475899Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0475993Z         }
2023-01-11T21:41:24.0476081Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0476193Z         for(long i0=16; i0<20; i0+=1)
2023-01-11T21:41:24.0476275Z         {
2023-01-11T21:41:24.0476357Z             auto tmp0 = in_ptr0[20 + i0];
2023-01-11T21:41:24.0476453Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0476532Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0476608Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0476667Z         }
2023-01-11T21:41:24.0476737Z         #pragma omp for 
2023-01-11T21:41:24.0476804Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0476862Z         {
2023-01-11T21:41:24.0476989Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0477088Z             tmp0.store(out_ptr1 + 20 + (8*i0));
2023-01-11T21:41:24.0477147Z         }
2023-01-11T21:41:24.0477238Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0477347Z         for(long i0=16; i0<20; i0+=1)
2023-01-11T21:41:24.0477399Z         {
2023-01-11T21:41:24.0477478Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:24.0477558Z             out_ptr1[20 + i0] = tmp0;
2023-01-11T21:41:24.0477614Z         }
2023-01-11T21:41:24.0477681Z         #pragma omp for 
2023-01-11T21:41:24.0477757Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.0477814Z         {
2023-01-11T21:41:24.0477936Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 1 + (8*i0));
2023-01-11T21:41:24.0478064Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0478147Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0478230Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0478284Z         }
2023-01-11T21:41:24.0478372Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0478450Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:24.0478497Z         {
2023-01-11T21:41:24.0478581Z             auto tmp0 = out_ptr1[1 + i0];
2023-01-11T21:41:24.0478680Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:24.0478759Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0478833Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:24.0478891Z         }
2023-01-11T21:41:24.0478962Z         #pragma omp for 
2023-01-11T21:41:24.0479028Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.0479088Z         {
2023-01-11T21:41:24.0479211Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0479299Z             tmp0.store(out_ptr1 + 2 + (8*i0));
2023-01-11T21:41:24.0479360Z         }
2023-01-11T21:41:24.0479446Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0479520Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:24.0479567Z         {
2023-01-11T21:41:24.0479647Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:24.0479721Z             out_ptr1[2 + i0] = tmp0;
2023-01-11T21:41:24.0479782Z         }
2023-01-11T21:41:24.0479839Z     }
2023-01-11T21:41:24.0479899Z }
2023-01-11T21:41:24.0479982Z ''')
2023-01-11T21:41:24.0479988Z 
2023-01-11T21:41:24.0479992Z 
2023-01-11T21:41:24.0480068Z async_compile.wait(globals())
2023-01-11T21:41:24.0480139Z del async_compile
2023-01-11T21:41:24.0480144Z 
2023-01-11T21:41:24.0480215Z def call(args):
2023-01-11T21:41:24.0480281Z     arg0_1, = args
2023-01-11T21:41:24.0480347Z     args.clear()
2023-01-11T21:41:24.0480545Z     buf0 = empty_strided((1, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0480736Z     buf2 = empty_strided((1, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0480919Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0480974Z     del arg0_1
2023-01-11T21:41:24.0481034Z     return ()
2023-01-11T21:41:24.0481039Z 
2023-01-11T21:41:24.0481043Z 
2023-01-11T21:41:24.0481114Z if __name__ == "__main__":
2023-01-11T21:41:24.0481227Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0481381Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0481577Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0481682Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0481686Z 
2023-01-11T21:41:24.0481749Z ok (1.646s)
2023-01-11T21:41:24.0482210Z   test_slice_scatter2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0482333Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0482592Z [2023-01-11 21:38:13,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 432
2023-01-11T21:41:24.0482888Z [2023-01-11 21:38:14,840] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 432
2023-01-11T21:41:24.0482894Z 
2023-01-11T21:41:24.0482986Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0483053Z import torch
2023-01-11T21:41:24.0483120Z import random
2023-01-11T21:41:24.0483233Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0483348Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0483353Z 
2023-01-11T21:41:24.0483417Z aten = torch.ops.aten
2023-01-11T21:41:24.0483547Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0483634Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0483640Z 
2023-01-11T21:41:24.0483644Z 
2023-01-11T21:41:24.0483778Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0483982Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0484100Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0484197Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0484256Z {
2023-01-11T21:41:24.0484339Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0484397Z     {
2023-01-11T21:41:24.0484469Z         #pragma omp for 
2023-01-11T21:41:24.0484549Z         for(long i0=0; i0<75648; i0+=1)
2023-01-11T21:41:24.0484609Z         {
2023-01-11T21:41:24.0484739Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0484826Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0484874Z         }
2023-01-11T21:41:24.0484964Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0485053Z         for(long i0=605184; i0<605184; i0+=1)
2023-01-11T21:41:24.0485111Z         {
2023-01-11T21:41:24.0485190Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0485267Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:24.0485316Z         }
2023-01-11T21:41:24.0485372Z     }
2023-01-11T21:41:24.0485431Z }
2023-01-11T21:41:24.0485509Z ''')
2023-01-11T21:41:24.0485514Z 
2023-01-11T21:41:24.0485518Z 
2023-01-11T21:41:24.0485605Z async_compile.wait(globals())
2023-01-11T21:41:24.0485673Z del async_compile
2023-01-11T21:41:24.0485678Z 
2023-01-11T21:41:24.0485744Z def call(args):
2023-01-11T21:41:24.0485815Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0485873Z     args.clear()
2023-01-11T21:41:24.0486087Z     buf0 = empty_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0486212Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0486277Z     del arg1_1
2023-01-11T21:41:24.0486346Z     return (buf0, )
2023-01-11T21:41:24.0486351Z 
2023-01-11T21:41:24.0486355Z 
2023-01-11T21:41:24.0486428Z if __name__ == "__main__":
2023-01-11T21:41:24.0486538Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0486650Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0486894Z     arg0_1 = rand_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0487103Z     arg1_1 = rand_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0487216Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0487221Z 
2023-01-11T21:41:24.0487281Z ok (1.611s)
2023-01-11T21:41:24.0487747Z   test_slice_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0487871Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0488128Z [2023-01-11 21:38:14,890] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 433
2023-01-11T21:41:24.0488429Z [2023-01-11 21:38:16,491] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 433
2023-01-11T21:41:24.0488435Z 
2023-01-11T21:41:24.0488528Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0488584Z import torch
2023-01-11T21:41:24.0488650Z import random
2023-01-11T21:41:24.0488763Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0488880Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0488885Z 
2023-01-11T21:41:24.0488959Z aten = torch.ops.aten
2023-01-11T21:41:24.0489089Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0489177Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0489182Z 
2023-01-11T21:41:24.0489187Z 
2023-01-11T21:41:24.0489319Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0489513Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0489631Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0489734Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0489832Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0489924Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0489981Z {
2023-01-11T21:41:24.0490075Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0490123Z     {
2023-01-11T21:41:24.0490193Z         #pragma omp for 
2023-01-11T21:41:24.0490273Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0490332Z         {
2023-01-11T21:41:24.0490410Z             #pragma GCC ivdep
2023-01-11T21:41:24.0490494Z             for(long i1=0; i1<100; i1+=1)
2023-01-11T21:41:24.0490556Z             {
2023-01-11T21:41:24.0490608Z                 {
2023-01-11T21:41:24.0490667Z                     {
2023-01-11T21:41:24.0490769Z                         auto tmp8 = in_ptr1[i1 + (100*i0)];
2023-01-11T21:41:24.0490869Z                         auto tmp0 = static_cast<int>(i1);
2023-01-11T21:41:24.0490972Z                         auto tmp1 = static_cast<int>(10);
2023-01-11T21:41:24.0491063Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:24.0491158Z                         auto tmp3 = static_cast<int>(90);
2023-01-11T21:41:24.0491238Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:24.0491329Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:24.0491409Z                         float tmp6 = 0.0;
2023-01-11T21:41:24.0491482Z                         if(tmp5)
2023-01-11T21:41:24.0491547Z                         {
2023-01-11T21:41:24.0491715Z                             auto tmp7 = in_ptr0[(-10) + i1 + (80*i0)];
2023-01-11T21:41:24.0491796Z                             tmp6 = tmp7;
2023-01-11T21:41:24.0491850Z                         }
2023-01-11T21:41:24.0491945Z                         auto tmp9 = tmp5 ? tmp6 : tmp8;
2023-01-11T21:41:24.0492051Z                         auto tmp10 = static_cast<int>(i1 % 2);
2023-01-11T21:41:24.0492180Z                         auto tmp11 = static_cast<int>(0);
2023-01-11T21:41:24.0492276Z                         auto tmp12 = tmp10 == tmp11;
2023-01-11T21:41:24.0492366Z                         auto tmp13 = tmp5 & tmp12;
2023-01-11T21:41:24.0492449Z                         float tmp14 = 0.0;
2023-01-11T21:41:24.0492511Z                         if(tmp13)
2023-01-11T21:41:24.0492575Z                         {
2023-01-11T21:41:24.0492752Z                             auto tmp15 = in_ptr0[(-5) + (80*i0) + (i1 / 2)];
2023-01-11T21:41:24.0492831Z                             tmp14 = tmp15;
2023-01-11T21:41:24.0492894Z                         }
2023-01-11T21:41:24.0492992Z                         auto tmp16 = tmp13 ? tmp14 : tmp8;
2023-01-11T21:41:24.0493085Z                         out_ptr0[i1 + (100*i0)] = tmp9;
2023-01-11T21:41:24.0493178Z                         out_ptr1[i1 + (100*i0)] = tmp16;
2023-01-11T21:41:24.0493231Z                     }
2023-01-11T21:41:24.0493292Z                 }
2023-01-11T21:41:24.0493382Z             }
2023-01-11T21:41:24.0493444Z         }
2023-01-11T21:41:24.0493502Z     }
2023-01-11T21:41:24.0493559Z }
2023-01-11T21:41:24.0493625Z ''')
2023-01-11T21:41:24.0493630Z 
2023-01-11T21:41:24.0493644Z 
2023-01-11T21:41:24.0493723Z async_compile.wait(globals())
2023-01-11T21:41:24.0493791Z del async_compile
2023-01-11T21:41:24.0493796Z 
2023-01-11T21:41:24.0493862Z def call(args):
2023-01-11T21:41:24.0493930Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0494000Z     args.clear()
2023-01-11T21:41:24.0494209Z     buf0 = empty_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0494416Z     buf1 = empty_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0494594Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0494657Z     del arg0_1
2023-01-11T21:41:24.0494720Z     del arg1_1
2023-01-11T21:41:24.0494799Z     return (buf0, buf1, )
2023-01-11T21:41:24.0494804Z 
2023-01-11T21:41:24.0494808Z 
2023-01-11T21:41:24.0494882Z if __name__ == "__main__":
2023-01-11T21:41:24.0494993Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0495113Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0495319Z     arg0_1 = rand_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0495512Z     arg1_1 = rand_strided((4, 8, 80), (640, 80, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0495625Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0495630Z 
2023-01-11T21:41:24.0495694Z ok (1.639s)
2023-01-11T21:41:24.0496160Z   test_softmax_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0496284Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0496543Z [2023-01-11 21:38:16,546] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 434
2023-01-11T21:41:24.0496548Z 
2023-01-11T21:41:24.0496640Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0496710Z import torch
2023-01-11T21:41:24.0496780Z import random
2023-01-11T21:41:24.0496882Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0496997Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0497002Z 
2023-01-11T21:41:24.0497076Z aten = torch.ops.aten
2023-01-11T21:41:24.0497205Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0497292Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0497297Z 
2023-01-11T21:41:24.0497301Z 
2023-01-11T21:41:24.0497433Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0497678Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0497792Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0497880Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:24.0497978Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:41:24.0498076Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0498176Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0498270Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0498359Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0498450Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0498530Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:24.0498619Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:24.0498735Z                        float* __restrict__ out_ptr8)
2023-01-11T21:41:24.0498796Z {
2023-01-11T21:41:24.0498879Z     auto out_ptr3 = in_out_ptr0;
2023-01-11T21:41:24.0498961Z     auto out_ptr4 = in_out_ptr1;
2023-01-11T21:41:24.0499041Z     auto out_ptr5 = in_out_ptr2;
2023-01-11T21:41:24.0499125Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0499183Z     {
2023-01-11T21:41:24.0499254Z         #pragma omp for 
2023-01-11T21:41:24.0499333Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0499396Z         {
2023-01-11T21:41:24.0499458Z             {
2023-01-11T21:41:24.0499836Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:24.0500065Z                 float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0500174Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0500379Z                 float tmp4 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0500502Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:24.0500591Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0500654Z                 {
2023-01-11T21:41:24.0500793Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0500925Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0501015Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0501118Z                     tmp3_vec = at::vec::maximum(tmp3_vec, tmp2);
2023-01-11T21:41:24.0501232Z                     tmp4_vec = at::vec::maximum(tmp4_vec, tmp1);
2023-01-11T21:41:24.0501296Z                 }
2023-01-11T21:41:24.0501505Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp3_vec);
2023-01-11T21:41:24.0501709Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp4_vec);
2023-01-11T21:41:24.0501852Z                 #pragma omp simd simdlen(4)  reduction(max:tmp3) reduction(max:tmp4)
2023-01-11T21:41:24.0501937Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0502000Z                 {
2023-01-11T21:41:24.0502098Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0502181Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:24.0502270Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0502532Z                     tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:41:24.0502666Z                     tmp4 = std::max(tmp4, tmp1);
2023-01-11T21:41:24.0502752Z                 }
2023-01-11T21:41:24.0502838Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0502915Z                 out_ptr1[i0] = tmp4;
2023-01-11T21:41:24.0503026Z             }
2023-01-11T21:41:24.0503088Z         }
2023-01-11T21:41:24.0503163Z         #pragma omp for 
2023-01-11T21:41:24.0503244Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0503303Z         {
2023-01-11T21:41:24.0503364Z             {
2023-01-11T21:41:24.0503427Z                 {
2023-01-11T21:41:24.0503639Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0503729Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0503793Z                     {
2023-01-11T21:41:24.0503861Z                         {
2023-01-11T21:41:24.0503963Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:24.0504061Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:24.0504127Z                         }
2023-01-11T21:41:24.0504180Z                     }
2023-01-11T21:41:24.0504264Z                     out_ptr2[i0] = tmp1;
2023-01-11T21:41:24.0504326Z                 }
2023-01-11T21:41:24.0504388Z             }
2023-01-11T21:41:24.0504484Z         }
2023-01-11T21:41:24.0504558Z         #pragma omp for 
2023-01-11T21:41:24.0504635Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0504684Z         {
2023-01-11T21:41:24.0504744Z             {
2023-01-11T21:41:24.0504931Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0505009Z                 float tmp12 = 0;
2023-01-11T21:41:24.0505129Z                 auto tmp12_vec = at::vec::Vectorized<float>(tmp12);
2023-01-11T21:41:24.0505215Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0505278Z                 {
2023-01-11T21:41:24.0505404Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0505537Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0505659Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:24.0505795Z                     auto tmp6 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i1);
2023-01-11T21:41:24.0505916Z                     auto tmp9 = at::vec::Vectorized<float>(out_ptr1[i0]);
2023-01-11T21:41:24.0506005Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0506139Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:24.0506229Z                     auto tmp5 = tmp4.exp();
2023-01-11T21:41:24.0506348Z                     auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:24.0506436Z                     auto tmp8 = tmp7.exp();
2023-01-11T21:41:24.0506569Z                     auto tmp10 = tmp1 - tmp9;
2023-01-11T21:41:24.0506656Z                     auto tmp11 = tmp10.exp();
2023-01-11T21:41:24.0506760Z                     tmp5.store(out_ptr3 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0506859Z                     tmp8.store(out_ptr4 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0506963Z                     tmp11.store(out_ptr5 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0507048Z                     tmp12_vec += tmp5;
2023-01-11T21:41:24.0507099Z                 }
2023-01-11T21:41:24.0507293Z                 tmp12 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp12_vec);
2023-01-11T21:41:24.0507407Z                 #pragma omp simd simdlen(4)  reduction(+:tmp12)
2023-01-11T21:41:24.0507491Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0507555Z                 {
2023-01-11T21:41:24.0507649Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0507741Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:24.0507818Z                     auto tmp3 = out_ptr0[i0];
2023-01-11T21:41:24.0507908Z                     auto tmp6 = out_ptr2[i1];
2023-01-11T21:41:24.0507992Z                     auto tmp9 = out_ptr1[i0];
2023-01-11T21:41:24.0508077Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0508211Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:24.0508336Z                     auto tmp5 = std::exp(tmp4);
2023-01-11T21:41:24.0508467Z                     auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:24.0508561Z                     auto tmp8 = std::exp(tmp7);
2023-01-11T21:41:24.0508683Z                     auto tmp10 = tmp1 - tmp9;
2023-01-11T21:41:24.0508777Z                     auto tmp11 = std::exp(tmp10);
2023-01-11T21:41:24.0508868Z                     out_ptr3[i1 + (8*i0)] = tmp5;
2023-01-11T21:41:24.0508955Z                     out_ptr4[i1 + (8*i0)] = tmp8;
2023-01-11T21:41:24.0509044Z                     out_ptr5[i1 + (8*i0)] = tmp11;
2023-01-11T21:41:24.0509120Z                     tmp12 += tmp5;
2023-01-11T21:41:24.0509179Z                 }
2023-01-11T21:41:24.0509246Z                 out_ptr6[i0] = tmp12;
2023-01-11T21:41:24.0509308Z             }
2023-01-11T21:41:24.0509369Z         }
2023-01-11T21:41:24.0509441Z         #pragma omp for 
2023-01-11T21:41:24.0509519Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0509581Z         {
2023-01-11T21:41:24.0509686Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0509738Z             {
2023-01-11T21:41:24.0509876Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr3 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0509998Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr6[i0]);
2023-01-11T21:41:24.0510081Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0510183Z                 tmp2.store(in_out_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0510243Z             }
2023-01-11T21:41:24.0510331Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0510398Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0510458Z             {
2023-01-11T21:41:24.0510548Z                 auto tmp0 = out_ptr3[i1 + (8*i0)];
2023-01-11T21:41:24.0510634Z                 auto tmp1 = out_ptr6[i0];
2023-01-11T21:41:24.0510715Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0510805Z                 in_out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:24.0510871Z             }
2023-01-11T21:41:24.0510921Z         }
2023-01-11T21:41:24.0510994Z         #pragma omp for 
2023-01-11T21:41:24.0511072Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0511133Z         {
2023-01-11T21:41:24.0511193Z             {
2023-01-11T21:41:24.0511257Z                 {
2023-01-11T21:41:24.0511322Z                     float tmp1 = 0;
2023-01-11T21:41:24.0511409Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0511471Z                     {
2023-01-11T21:41:24.0511535Z                         {
2023-01-11T21:41:24.0511637Z                             auto tmp0 = out_ptr4[i0 + (8*i1)];
2023-01-11T21:41:24.0511715Z                             tmp1 += tmp0;
2023-01-11T21:41:24.0511779Z                         }
2023-01-11T21:41:24.0511832Z                     }
2023-01-11T21:41:24.0511907Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:41:24.0511969Z                 }
2023-01-11T21:41:24.0512028Z             }
2023-01-11T21:41:24.0512091Z         }
2023-01-11T21:41:24.0512164Z         #pragma omp for 
2023-01-11T21:41:24.0512241Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0512289Z         {
2023-01-11T21:41:24.0512364Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0512422Z             {
2023-01-11T21:41:24.0512562Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr4 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0512694Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr7 + 8*i1);
2023-01-11T21:41:24.0512776Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0512878Z                 tmp2.store(in_out_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0512939Z             }
2023-01-11T21:41:24.0513016Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0513093Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0513153Z             {
2023-01-11T21:41:24.0513247Z                 auto tmp0 = out_ptr4[i1 + (8*i0)];
2023-01-11T21:41:24.0513334Z                 auto tmp1 = out_ptr7[i1];
2023-01-11T21:41:24.0513446Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0513526Z                 in_out_ptr1[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:24.0513583Z             }
2023-01-11T21:41:24.0513644Z         }
2023-01-11T21:41:24.0513717Z         #pragma omp for 
2023-01-11T21:41:24.0513854Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0513915Z         {
2023-01-11T21:41:24.0513973Z             {
2023-01-11T21:41:24.0514148Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0514221Z                 float tmp1 = 0;
2023-01-11T21:41:24.0514339Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.0514422Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0514484Z                 {
2023-01-11T21:41:24.0514623Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr5 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0514738Z                     tmp1_vec += tmp0;
2023-01-11T21:41:24.0514800Z                 }
2023-01-11T21:41:24.0514983Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.0515098Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:24.0515182Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0515244Z                 {
2023-01-11T21:41:24.0515342Z                     auto tmp0 = out_ptr5[i1 + (8*i0)];
2023-01-11T21:41:24.0515415Z                     tmp1 += tmp0;
2023-01-11T21:41:24.0515472Z                 }
2023-01-11T21:41:24.0515548Z                 out_ptr8[i0] = tmp1;
2023-01-11T21:41:24.0515597Z             }
2023-01-11T21:41:24.0515658Z         }
2023-01-11T21:41:24.0515728Z         #pragma omp for 
2023-01-11T21:41:24.0515807Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0515867Z         {
2023-01-11T21:41:24.0515944Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0515996Z             {
2023-01-11T21:41:24.0516128Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr5 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0516247Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr8[i0]);
2023-01-11T21:41:24.0516331Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0516432Z                 tmp2.store(in_out_ptr2 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0516491Z             }
2023-01-11T21:41:24.0516578Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0516655Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0516703Z             {
2023-01-11T21:41:24.0516796Z                 auto tmp0 = out_ptr5[i1 + (8*i0)];
2023-01-11T21:41:24.0516879Z                 auto tmp1 = out_ptr8[i0];
2023-01-11T21:41:24.0516962Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0517052Z                 in_out_ptr2[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:24.0517113Z             }
2023-01-11T21:41:24.0517166Z         }
2023-01-11T21:41:24.0517224Z     }
2023-01-11T21:41:24.0517276Z }
2023-01-11T21:41:24.0517358Z ''')
2023-01-11T21:41:24.0517364Z 
2023-01-11T21:41:24.0517368Z 
2023-01-11T21:41:24.0517455Z async_compile.wait(globals())
2023-01-11T21:41:24.0517526Z del async_compile
2023-01-11T21:41:24.0517531Z 
2023-01-11T21:41:24.0517596Z def call(args):
2023-01-11T21:41:24.0517668Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0517728Z     args.clear()
2023-01-11T21:41:24.0517923Z     buf0 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0518116Z     buf8 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0518302Z     buf4 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0518487Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0518671Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0518886Z     buf9 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0519057Z     buf2 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0519140Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0519323Z     buf6 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0519405Z     buf7 = buf5; del buf5  # reuse
2023-01-11T21:41:24.0519597Z     buf10 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0519676Z     buf11 = buf9; del buf9  # reuse
2023-01-11T21:41:24.0520032Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf10.data_ptr()))
2023-01-11T21:41:24.0520099Z     del arg0_1
2023-01-11T21:41:24.0520163Z     del arg1_1
2023-01-11T21:41:24.0520264Z     return (buf3, buf7, buf11, )
2023-01-11T21:41:24.0520270Z 
2023-01-11T21:41:24.0520274Z 
2023-01-11T21:41:24.0520349Z if __name__ == "__main__":
2023-01-11T21:41:24.0520463Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0520584Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0520782Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0520972Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0521082Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0521351Z [2023-01-11 21:38:18,265] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 434
2023-01-11T21:41:24.0521356Z 
2023-01-11T21:41:24.0521410Z ok (1.772s)
2023-01-11T21:41:24.0521884Z   test_softmax_one_kernel_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0522005Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0522259Z [2023-01-11 21:38:18,289] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 435
2023-01-11T21:41:24.0522523Z [2023-01-11 21:38:19,885] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 435
2023-01-11T21:41:24.0522528Z 
2023-01-11T21:41:24.0522618Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0522684Z import torch
2023-01-11T21:41:24.0522752Z import random
2023-01-11T21:41:24.0522861Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0522968Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0522975Z 
2023-01-11T21:41:24.0523049Z aten = torch.ops.aten
2023-01-11T21:41:24.0523178Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0523267Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0523271Z 
2023-01-11T21:41:24.0523276Z 
2023-01-11T21:41:24.0523405Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0523608Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0523722Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0523823Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0523909Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0523999Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0524056Z {
2023-01-11T21:41:24.0524139Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:24.0524233Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0524289Z     {
2023-01-11T21:41:24.0524396Z         #pragma omp for 
2023-01-11T21:41:24.0524464Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:24.0524523Z         {
2023-01-11T21:41:24.0524583Z             {
2023-01-11T21:41:24.0524952Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:24.0525163Z                 float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0525279Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.0525363Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0525422Z                 {
2023-01-11T21:41:24.0525552Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0525664Z                     tmp1_vec = at::vec::maximum(tmp1_vec, tmp0);
2023-01-11T21:41:24.0525725Z                 }
2023-01-11T21:41:24.0525966Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp1_vec);
2023-01-11T21:41:24.0526084Z                 #pragma omp simd simdlen(4)  reduction(max:tmp1)
2023-01-11T21:41:24.0526169Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:24.0526229Z                 {
2023-01-11T21:41:24.0526325Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:24.0526410Z                     tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:24.0526472Z                 }
2023-01-11T21:41:24.0526552Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0526613Z             }
2023-01-11T21:41:24.0526671Z         }
2023-01-11T21:41:24.0526745Z         #pragma omp for 
2023-01-11T21:41:24.0526821Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:24.0526871Z         {
2023-01-11T21:41:24.0526929Z             {
2023-01-11T21:41:24.0527114Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0527190Z                 float tmp4 = 0;
2023-01-11T21:41:24.0527303Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:24.0527387Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0527451Z                 {
2023-01-11T21:41:24.0527577Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0527702Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:24.0527794Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0527882Z                     auto tmp3 = tmp2.exp();
2023-01-11T21:41:24.0527981Z                     tmp3.store(out_ptr1 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0528059Z                     tmp4_vec += tmp3;
2023-01-11T21:41:24.0528121Z                 }
2023-01-11T21:41:24.0528319Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:24.0528426Z                 #pragma omp simd simdlen(4)  reduction(+:tmp4)
2023-01-11T21:41:24.0528513Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:24.0528573Z                 {
2023-01-11T21:41:24.0528669Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:24.0528755Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:24.0528843Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0528939Z                     auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:24.0529033Z                     out_ptr1[i1 + (32*i0)] = tmp3;
2023-01-11T21:41:24.0529097Z                     tmp4 += tmp3;
2023-01-11T21:41:24.0529161Z                 }
2023-01-11T21:41:24.0529241Z                 out_ptr2[i0] = tmp4;
2023-01-11T21:41:24.0529304Z             }
2023-01-11T21:41:24.0529365Z         }
2023-01-11T21:41:24.0529439Z         #pragma omp for 
2023-01-11T21:41:24.0529506Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:24.0529600Z         {
2023-01-11T21:41:24.0529679Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0529738Z             {
2023-01-11T21:41:24.0529875Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0529997Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:24.0530084Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0530187Z                 tmp2.store(in_out_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0530238Z             }
2023-01-11T21:41:24.0530326Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0530408Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:24.0530468Z             {
2023-01-11T21:41:24.0530560Z                 auto tmp0 = out_ptr1[i1 + (32*i0)];
2023-01-11T21:41:24.0530643Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:24.0530727Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0530863Z                 in_out_ptr0[i1 + (32*i0)] = tmp2;
2023-01-11T21:41:24.0530926Z             }
2023-01-11T21:41:24.0530987Z         }
2023-01-11T21:41:24.0531046Z     }
2023-01-11T21:41:24.0531105Z }
2023-01-11T21:41:24.0531181Z ''')
2023-01-11T21:41:24.0531186Z 
2023-01-11T21:41:24.0531190Z 
2023-01-11T21:41:24.0531276Z async_compile.wait(globals())
2023-01-11T21:41:24.0531333Z del async_compile
2023-01-11T21:41:24.0531338Z 
2023-01-11T21:41:24.0531407Z def call(args):
2023-01-11T21:41:24.0531474Z     arg0_1, = args
2023-01-11T21:41:24.0531544Z     args.clear()
2023-01-11T21:41:24.0531742Z     buf0 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0531941Z     buf1 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0532136Z     buf2 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0532209Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0532395Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0532464Z     del arg0_1
2023-01-11T21:41:24.0532534Z     return (buf3, )
2023-01-11T21:41:24.0532539Z 
2023-01-11T21:41:24.0532543Z 
2023-01-11T21:41:24.0532616Z if __name__ == "__main__":
2023-01-11T21:41:24.0532726Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0532846Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0533044Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0533138Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0533142Z 
2023-01-11T21:41:24.0533204Z ok (1.620s)
2023-01-11T21:41:24.0533664Z   test_sort_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0533793Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0534053Z [2023-01-11 21:38:19,901] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 436
2023-01-11T21:41:24.0534267Z [2023-01-11 21:38:19,906] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.sort
2023-01-11T21:41:24.0534528Z [2023-01-11 21:38:19,909] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 436
2023-01-11T21:41:24.0534534Z 
2023-01-11T21:41:24.0534624Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0534689Z import torch
2023-01-11T21:41:24.0534745Z import random
2023-01-11T21:41:24.0534859Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0534976Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0534982Z 
2023-01-11T21:41:24.0535056Z aten = torch.ops.aten
2023-01-11T21:41:24.0535225Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0535311Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0535317Z 
2023-01-11T21:41:24.0535321Z 
2023-01-11T21:41:24.0535406Z async_compile.wait(globals())
2023-01-11T21:41:24.0535475Z del async_compile
2023-01-11T21:41:24.0535479Z 
2023-01-11T21:41:24.0535536Z def call(args):
2023-01-11T21:41:24.0535603Z     arg0_1, = args
2023-01-11T21:41:24.0535673Z     args.clear()
2023-01-11T21:41:24.0535747Z     buf0 = aten.sort(arg0_1)
2023-01-11T21:41:24.0535809Z     del arg0_1
2023-01-11T21:41:24.0535874Z     buf1 = buf0[0]
2023-01-11T21:41:24.0535978Z     assert_size_stride(buf1, (1, 1, 8, 8), (64, 64, 8, 1))
2023-01-11T21:41:24.0536033Z     buf2 = buf0[1]
2023-01-11T21:41:24.0536133Z     assert_size_stride(buf2, (1, 1, 8, 8), (64, 64, 8, 1))
2023-01-11T21:41:24.0536194Z     del buf0
2023-01-11T21:41:24.0536267Z     return (buf1, buf2, )
2023-01-11T21:41:24.0536272Z 
2023-01-11T21:41:24.0536276Z 
2023-01-11T21:41:24.0536376Z if __name__ == "__main__":
2023-01-11T21:41:24.0536486Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0536605Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0536814Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0536908Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0536913Z 
2023-01-11T21:41:24.0536979Z ok (0.023s)
2023-01-11T21:41:24.0537435Z   test_split_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0537558Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0537820Z [2023-01-11 21:38:19,930] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 437
2023-01-11T21:41:24.0538083Z [2023-01-11 21:38:19,933] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 437
2023-01-11T21:41:24.0538510Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0538630Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0538884Z [2023-01-11 21:38:19,955] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 438
2023-01-11T21:41:24.0539143Z [2023-01-11 21:38:21,549] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 438
2023-01-11T21:41:24.0539154Z 
2023-01-11T21:41:24.0539246Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0539303Z import torch
2023-01-11T21:41:24.0539366Z import random
2023-01-11T21:41:24.0539480Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0539598Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0539603Z 
2023-01-11T21:41:24.0539676Z aten = torch.ops.aten
2023-01-11T21:41:24.0539805Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0539894Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0539899Z 
2023-01-11T21:41:24.0539903Z 
2023-01-11T21:41:24.0539985Z async_compile.wait(globals())
2023-01-11T21:41:24.0540044Z del async_compile
2023-01-11T21:41:24.0540049Z 
2023-01-11T21:41:24.0540117Z def call(args):
2023-01-11T21:41:24.0540185Z     arg0_1, = args
2023-01-11T21:41:24.0540254Z     args.clear()
2023-01-11T21:41:24.0540446Z     return (as_strided(arg0_1, (2, 2, 3), (20, 10, 1)), as_strided(arg0_1, (2, 2, 3), (20, 10, 1), 3), as_strided(arg0_1, (2, 2, 3), (20, 10, 1), 6), as_strided(arg0_1, (2, 2, 1), (20, 10, 1), 9), )
2023-01-11T21:41:24.0540480Z 
2023-01-11T21:41:24.0540484Z 
2023-01-11T21:41:24.0540556Z if __name__ == "__main__":
2023-01-11T21:41:24.0540662Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0540777Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0540971Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0541076Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0541081Z 
2023-01-11T21:41:24.0541085Z 
2023-01-11T21:41:24.0541174Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0541242Z import torch
2023-01-11T21:41:24.0541312Z import random
2023-01-11T21:41:24.0541421Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0541535Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0541539Z 
2023-01-11T21:41:24.0541645Z aten = torch.ops.aten
2023-01-11T21:41:24.0541765Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0541852Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0541857Z 
2023-01-11T21:41:24.0541861Z 
2023-01-11T21:41:24.0541992Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0542193Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0542313Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0542579Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0542652Z {
2023-01-11T21:41:24.0542748Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0542797Z     {
2023-01-11T21:41:24.0542871Z         #pragma omp for 
2023-01-11T21:41:24.0542949Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:24.0543008Z         {
2023-01-11T21:41:24.0543141Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0543274Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0543355Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0543431Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0543490Z         }
2023-01-11T21:41:24.0543581Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0543661Z         for(long i0=40; i0<40; i0+=1)
2023-01-11T21:41:24.0543723Z         {
2023-01-11T21:41:24.0543803Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0543897Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0543969Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0544044Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0544106Z         }
2023-01-11T21:41:24.0544165Z     }
2023-01-11T21:41:24.0544222Z }
2023-01-11T21:41:24.0544302Z ''')
2023-01-11T21:41:24.0544308Z 
2023-01-11T21:41:24.0544312Z 
2023-01-11T21:41:24.0544399Z async_compile.wait(globals())
2023-01-11T21:41:24.0544458Z del async_compile
2023-01-11T21:41:24.0544465Z 
2023-01-11T21:41:24.0544532Z def call(args):
2023-01-11T21:41:24.0544597Z     arg0_1, = args
2023-01-11T21:41:24.0544667Z     args.clear()
2023-01-11T21:41:24.0544873Z     buf0 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0545002Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0545068Z     del arg0_1
2023-01-11T21:41:24.0545241Z     return (as_strided(buf0, (2, 2, 3), (20, 10, 1)), as_strided(buf0, (2, 2, 3), (20, 10, 1), 3), as_strided(buf0, (2, 2, 3), (20, 10, 1), 6), as_strided(buf0, (2, 2, 1), (20, 10, 0), 9), )
2023-01-11T21:41:24.0545255Z 
2023-01-11T21:41:24.0545259Z 
2023-01-11T21:41:24.0545322Z if __name__ == "__main__":
2023-01-11T21:41:24.0545432Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0545547Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0545754Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0545923Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0545929Z 
2023-01-11T21:41:24.0545994Z ok (1.642s)
2023-01-11T21:41:24.0546466Z   test_split_with_sizes_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0546587Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0546849Z [2023-01-11 21:38:21,582] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 439
2023-01-11T21:41:24.0547103Z [2023-01-11 21:38:23,208] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 439
2023-01-11T21:41:24.0547566Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0547694Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0547953Z [2023-01-11 21:38:23,238] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 440
2023-01-11T21:41:24.0548215Z [2023-01-11 21:38:24,866] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 440
2023-01-11T21:41:24.0548647Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0548767Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0549018Z [2023-01-11 21:38:24,900] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 441
2023-01-11T21:41:24.0549024Z 
2023-01-11T21:41:24.0549116Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0549183Z import torch
2023-01-11T21:41:24.0549239Z import random
2023-01-11T21:41:24.0549351Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0549467Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0549472Z 
2023-01-11T21:41:24.0549547Z aten = torch.ops.aten
2023-01-11T21:41:24.0549676Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0549765Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0549770Z 
2023-01-11T21:41:24.0549774Z 
2023-01-11T21:41:24.0549905Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0550113Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0550229Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0550315Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0550408Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0550497Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0550555Z {
2023-01-11T21:41:24.0550650Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0550709Z     {
2023-01-11T21:41:24.0550772Z         #pragma omp for 
2023-01-11T21:41:24.0550850Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0550909Z         {
2023-01-11T21:41:24.0550984Z             #pragma GCC ivdep
2023-01-11T21:41:24.0551062Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0551120Z             {
2023-01-11T21:41:24.0551181Z                 {
2023-01-11T21:41:24.0551234Z                     {
2023-01-11T21:41:24.0551370Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.0551474Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0551565Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0551669Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0551761Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0551855Z                         out_ptr0[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:24.0551908Z                     }
2023-01-11T21:41:24.0551969Z                 }
2023-01-11T21:41:24.0552029Z             }
2023-01-11T21:41:24.0552090Z         }
2023-01-11T21:41:24.0552167Z         #pragma omp for 
2023-01-11T21:41:24.0552245Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0552303Z         {
2023-01-11T21:41:24.0552368Z             #pragma GCC ivdep
2023-01-11T21:41:24.0552446Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0552507Z             {
2023-01-11T21:41:24.0552570Z                 {
2023-01-11T21:41:24.0552661Z                     {
2023-01-11T21:41:24.0552759Z                         auto tmp0 = in_ptr0[3 + i1 + (10*i0)];
2023-01-11T21:41:24.0552866Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0552944Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0553046Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0553134Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0553227Z                         out_ptr1[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:24.0553292Z                     }
2023-01-11T21:41:24.0553351Z                 }
2023-01-11T21:41:24.0553416Z             }
2023-01-11T21:41:24.0553465Z         }
2023-01-11T21:41:24.0553539Z         #pragma omp for 
2023-01-11T21:41:24.0553616Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0553675Z         {
2023-01-11T21:41:24.0553812Z             #pragma GCC ivdep
2023-01-11T21:41:24.0553897Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0553950Z             {
2023-01-11T21:41:24.0554010Z                 {
2023-01-11T21:41:24.0554071Z                     {
2023-01-11T21:41:24.0554173Z                         auto tmp0 = in_ptr0[6 + i1 + (10*i0)];
2023-01-11T21:41:24.0554279Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0554366Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0554470Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0554559Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0554639Z                         out_ptr2[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:24.0554704Z                     }
2023-01-11T21:41:24.0554770Z                 }
2023-01-11T21:41:24.0554829Z             }
2023-01-11T21:41:24.0554889Z         }
2023-01-11T21:41:24.0554947Z     }
2023-01-11T21:41:24.0554993Z }
2023-01-11T21:41:24.0555072Z ''')
2023-01-11T21:41:24.0555077Z 
2023-01-11T21:41:24.0555081Z 
2023-01-11T21:41:24.0555171Z async_compile.wait(globals())
2023-01-11T21:41:24.0555239Z del async_compile
2023-01-11T21:41:24.0555245Z 
2023-01-11T21:41:24.0555310Z def call(args):
2023-01-11T21:41:24.0555374Z     arg0_1, = args
2023-01-11T21:41:24.0555436Z     args.clear()
2023-01-11T21:41:24.0555634Z     buf0 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0555822Z     buf1 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0556015Z     buf2 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0556202Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0556266Z     del arg0_1
2023-01-11T21:41:24.0556345Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0556350Z 
2023-01-11T21:41:24.0556354Z 
2023-01-11T21:41:24.0556425Z if __name__ == "__main__":
2023-01-11T21:41:24.0556539Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0556687Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0556879Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0556983Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0556988Z 
2023-01-11T21:41:24.0556992Z 
2023-01-11T21:41:24.0557081Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0557148Z import torch
2023-01-11T21:41:24.0557213Z import random
2023-01-11T21:41:24.0557324Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0557440Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0557444Z 
2023-01-11T21:41:24.0557517Z aten = torch.ops.aten
2023-01-11T21:41:24.0557636Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0557723Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0557728Z 
2023-01-11T21:41:24.0557733Z 
2023-01-11T21:41:24.0557863Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0558097Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0558215Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0558310Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0558403Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0558493Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0558541Z {
2023-01-11T21:41:24.0558633Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0558694Z     {
2023-01-11T21:41:24.0558768Z         #pragma omp for 
2023-01-11T21:41:24.0558847Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0558908Z         {
2023-01-11T21:41:24.0558975Z             #pragma GCC ivdep
2023-01-11T21:41:24.0559056Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0559113Z             {
2023-01-11T21:41:24.0559175Z                 {
2023-01-11T21:41:24.0559238Z                     {
2023-01-11T21:41:24.0559341Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.0559448Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0559528Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0559631Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0559718Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0559808Z                         out_ptr0[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:24.0559873Z                     }
2023-01-11T21:41:24.0559938Z                 }
2023-01-11T21:41:24.0559998Z             }
2023-01-11T21:41:24.0560047Z         }
2023-01-11T21:41:24.0560121Z         #pragma omp for 
2023-01-11T21:41:24.0560201Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0560262Z         {
2023-01-11T21:41:24.0560340Z             #pragma GCC ivdep
2023-01-11T21:41:24.0560420Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0560481Z             {
2023-01-11T21:41:24.0560537Z                 {
2023-01-11T21:41:24.0560599Z                     {
2023-01-11T21:41:24.0560702Z                         auto tmp0 = in_ptr0[4 + i1 + (10*i0)];
2023-01-11T21:41:24.0560807Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0560898Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0561003Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0561094Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0561176Z                         out_ptr1[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:24.0561240Z                     }
2023-01-11T21:41:24.0561303Z                 }
2023-01-11T21:41:24.0561364Z             }
2023-01-11T21:41:24.0561425Z         }
2023-01-11T21:41:24.0561497Z         #pragma omp for 
2023-01-11T21:41:24.0561576Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0561625Z         {
2023-01-11T21:41:24.0561701Z             #pragma GCC ivdep
2023-01-11T21:41:24.0561812Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0561874Z             {
2023-01-11T21:41:24.0561935Z                 {
2023-01-11T21:41:24.0561998Z                     {
2023-01-11T21:41:24.0562087Z                         auto tmp0 = in_ptr0[7 + i1 + (10*i0)];
2023-01-11T21:41:24.0562191Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0562281Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0562387Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0562478Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0562570Z                         out_ptr2[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:24.0562637Z                     }
2023-01-11T21:41:24.0562689Z                 }
2023-01-11T21:41:24.0562750Z             }
2023-01-11T21:41:24.0562811Z         }
2023-01-11T21:41:24.0562872Z     }
2023-01-11T21:41:24.0562931Z }
2023-01-11T21:41:24.0563010Z ''')
2023-01-11T21:41:24.0563015Z 
2023-01-11T21:41:24.0563021Z 
2023-01-11T21:41:24.0563144Z async_compile.wait(globals())
2023-01-11T21:41:24.0563205Z del async_compile
2023-01-11T21:41:24.0563220Z 
2023-01-11T21:41:24.0563276Z def call(args):
2023-01-11T21:41:24.0563341Z     arg0_1, = args
2023-01-11T21:41:24.0563411Z     args.clear()
2023-01-11T21:41:24.0563613Z     buf0 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0563811Z     buf1 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0564009Z     buf2 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0564198Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0564253Z     del arg0_1
2023-01-11T21:41:24.0564332Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0564337Z 
2023-01-11T21:41:24.0564341Z 
2023-01-11T21:41:24.0564413Z if __name__ == "__main__":
2023-01-11T21:41:24.0564533Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0564657Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0564859Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0564966Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0564971Z 
2023-01-11T21:41:24.0565237Z [2023-01-11 21:38:26,542] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 441
2023-01-11T21:41:24.0565242Z 
2023-01-11T21:41:24.0565334Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0565389Z import torch
2023-01-11T21:41:24.0565455Z import random
2023-01-11T21:41:24.0565567Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0565685Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0565689Z 
2023-01-11T21:41:24.0565762Z aten = torch.ops.aten
2023-01-11T21:41:24.0565892Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0565985Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0565991Z 
2023-01-11T21:41:24.0565995Z 
2023-01-11T21:41:24.0566124Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0566316Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0566430Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0566526Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0566622Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0566714Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0566803Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:24.0566861Z {
2023-01-11T21:41:24.0566946Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0567003Z     {
2023-01-11T21:41:24.0567077Z         #pragma omp for 
2023-01-11T21:41:24.0567154Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0567212Z         {
2023-01-11T21:41:24.0567305Z             {
2023-01-11T21:41:24.0567357Z                 {
2023-01-11T21:41:24.0567446Z                     auto tmp0 = in_ptr0[10*i0];
2023-01-11T21:41:24.0567550Z                     auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0567636Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0567737Z                     auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0567824Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0567904Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0567962Z                 }
2023-01-11T21:41:24.0568012Z             }
2023-01-11T21:41:24.0568070Z         }
2023-01-11T21:41:24.0568143Z         #pragma omp for 
2023-01-11T21:41:24.0568221Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0568280Z         {
2023-01-11T21:41:24.0568355Z             #pragma GCC ivdep
2023-01-11T21:41:24.0568423Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:24.0568482Z             {
2023-01-11T21:41:24.0568585Z                 {
2023-01-11T21:41:24.0568650Z                     {
2023-01-11T21:41:24.0568752Z                         auto tmp0 = in_ptr0[1 + i1 + (10*i0)];
2023-01-11T21:41:24.0568856Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0568947Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0569040Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0569128Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0569218Z                         out_ptr1[i1 + (2*i0)] = tmp4;
2023-01-11T21:41:24.0569282Z                     }
2023-01-11T21:41:24.0569343Z                 }
2023-01-11T21:41:24.0569404Z             }
2023-01-11T21:41:24.0569462Z         }
2023-01-11T21:41:24.0569524Z         #pragma omp for 
2023-01-11T21:41:24.0569602Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0569659Z         {
2023-01-11T21:41:24.0569737Z             #pragma GCC ivdep
2023-01-11T21:41:24.0569819Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0569880Z             {
2023-01-11T21:41:24.0569943Z                 {
2023-01-11T21:41:24.0570019Z                     {
2023-01-11T21:41:24.0570164Z                         auto tmp0 = in_ptr0[3 + i1 + (10*i0)];
2023-01-11T21:41:24.0570277Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0570367Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0570471Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0570558Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0570651Z                         out_ptr2[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:24.0570703Z                     }
2023-01-11T21:41:24.0570765Z                 }
2023-01-11T21:41:24.0570823Z             }
2023-01-11T21:41:24.0570881Z         }
2023-01-11T21:41:24.0570956Z         #pragma omp for 
2023-01-11T21:41:24.0571034Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0571086Z         {
2023-01-11T21:41:24.0571183Z             #pragma GCC ivdep
2023-01-11T21:41:24.0571296Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.0571361Z             {
2023-01-11T21:41:24.0571422Z                 {
2023-01-11T21:41:24.0571483Z                     {
2023-01-11T21:41:24.0571581Z                         auto tmp0 = in_ptr0[6 + i1 + (10*i0)];
2023-01-11T21:41:24.0571674Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:24.0571762Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0571870Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:24.0571959Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0572050Z                         out_ptr3[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:24.0572112Z                     }
2023-01-11T21:41:24.0572176Z                 }
2023-01-11T21:41:24.0572225Z             }
2023-01-11T21:41:24.0572283Z         }
2023-01-11T21:41:24.0572340Z     }
2023-01-11T21:41:24.0572443Z }
2023-01-11T21:41:24.0572526Z ''')
2023-01-11T21:41:24.0572532Z 
2023-01-11T21:41:24.0572537Z 
2023-01-11T21:41:24.0572623Z async_compile.wait(globals())
2023-01-11T21:41:24.0572689Z del async_compile
2023-01-11T21:41:24.0572694Z 
2023-01-11T21:41:24.0572764Z def call(args):
2023-01-11T21:41:24.0572819Z     arg0_1, = args
2023-01-11T21:41:24.0572886Z     args.clear()
2023-01-11T21:41:24.0573086Z     buf0 = empty_strided((2, 2, 1), (2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0573286Z     buf1 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0573480Z     buf2 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0573676Z     buf3 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0573884Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:24.0573970Z     del arg0_1
2023-01-11T21:41:24.0574058Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:24.0574064Z 
2023-01-11T21:41:24.0574068Z 
2023-01-11T21:41:24.0574142Z if __name__ == "__main__":
2023-01-11T21:41:24.0574252Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0574369Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0574572Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0574677Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0574681Z 
2023-01-11T21:41:24.0574744Z ok (4.993s)
2023-01-11T21:41:24.0575212Z   test_squeeze1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0575339Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0575588Z [2023-01-11 21:38:26,565] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 442
2023-01-11T21:41:24.0575850Z [2023-01-11 21:38:28,130] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 442
2023-01-11T21:41:24.0575855Z 
2023-01-11T21:41:24.0575943Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0576010Z import torch
2023-01-11T21:41:24.0576078Z import random
2023-01-11T21:41:24.0576189Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0576307Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0576312Z 
2023-01-11T21:41:24.0576385Z aten = torch.ops.aten
2023-01-11T21:41:24.0576506Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0576592Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0576599Z 
2023-01-11T21:41:24.0576605Z 
2023-01-11T21:41:24.0576734Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0576939Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0577055Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0577151Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0577247Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0577296Z {
2023-01-11T21:41:24.0577390Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0577449Z     {
2023-01-11T21:41:24.0577522Z         #pragma omp for 
2023-01-11T21:41:24.0577605Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.0577667Z         {
2023-01-11T21:41:24.0577806Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0577933Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0578005Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0578164Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0578249Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0578332Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0578419Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0578506Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0578564Z         }
2023-01-11T21:41:24.0578643Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0578722Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:24.0578782Z         {
2023-01-11T21:41:24.0578864Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0578960Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0579041Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0579137Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:24.0579208Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0579290Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0579396Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0579476Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:24.0579536Z         }
2023-01-11T21:41:24.0579596Z     }
2023-01-11T21:41:24.0579644Z }
2023-01-11T21:41:24.0579723Z ''')
2023-01-11T21:41:24.0579728Z 
2023-01-11T21:41:24.0579733Z 
2023-01-11T21:41:24.0579815Z async_compile.wait(globals())
2023-01-11T21:41:24.0579883Z del async_compile
2023-01-11T21:41:24.0579887Z 
2023-01-11T21:41:24.0579955Z def call(args):
2023-01-11T21:41:24.0580020Z     arg0_1, = args
2023-01-11T21:41:24.0580088Z     args.clear()
2023-01-11T21:41:24.0580292Z     buf0 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0580481Z     buf1 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0580651Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0580749Z     del arg0_1
2023-01-11T21:41:24.0580862Z     return (buf0, buf1, )
2023-01-11T21:41:24.0580868Z 
2023-01-11T21:41:24.0580873Z 
2023-01-11T21:41:24.0580970Z if __name__ == "__main__":
2023-01-11T21:41:24.0581116Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0581238Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0581469Z     arg0_1 = rand_strided((1, 2, 1, 2, 2, 1, 1), (8, 4, 4, 2, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0581564Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0581569Z 
2023-01-11T21:41:24.0581631Z ok (1.587s)
2023-01-11T21:41:24.0582098Z   test_squeeze2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0582225Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0582659Z [2023-01-11 21:38:28,155] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 443
2023-01-11T21:41:24.0582926Z [2023-01-11 21:38:29,708] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 443
2023-01-11T21:41:24.0582931Z 
2023-01-11T21:41:24.0583024Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0583093Z import torch
2023-01-11T21:41:24.0583162Z import random
2023-01-11T21:41:24.0583262Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0583381Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0583386Z 
2023-01-11T21:41:24.0583463Z aten = torch.ops.aten
2023-01-11T21:41:24.0583589Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0583679Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0583684Z 
2023-01-11T21:41:24.0583688Z 
2023-01-11T21:41:24.0583895Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0584100Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0584217Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0584304Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0584395Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0584453Z {
2023-01-11T21:41:24.0584548Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0584609Z     {
2023-01-11T21:41:24.0584684Z         #pragma omp for 
2023-01-11T21:41:24.0584763Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0584812Z         {
2023-01-11T21:41:24.0584946Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0585077Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0585159Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0585324Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0585408Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0585489Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0585575Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0585652Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0585710Z         }
2023-01-11T21:41:24.0585800Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0585879Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.0585939Z         {
2023-01-11T21:41:24.0586018Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0586104Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0586184Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0586278Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:24.0586358Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0586437Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0586514Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0586595Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:24.0586644Z         }
2023-01-11T21:41:24.0586702Z     }
2023-01-11T21:41:24.0586757Z }
2023-01-11T21:41:24.0586834Z ''')
2023-01-11T21:41:24.0586839Z 
2023-01-11T21:41:24.0586844Z 
2023-01-11T21:41:24.0586929Z async_compile.wait(globals())
2023-01-11T21:41:24.0586998Z del async_compile
2023-01-11T21:41:24.0587003Z 
2023-01-11T21:41:24.0587070Z def call(args):
2023-01-11T21:41:24.0587136Z     arg0_1, = args
2023-01-11T21:41:24.0587193Z     args.clear()
2023-01-11T21:41:24.0587409Z     buf0 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0587629Z     buf1 = empty_strided((2, 1, 2, 2, 2, 1), (8, 8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0587784Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0587851Z     del arg0_1
2023-01-11T21:41:24.0587924Z     return (buf0, buf1, )
2023-01-11T21:41:24.0587934Z 
2023-01-11T21:41:24.0587938Z 
2023-01-11T21:41:24.0588010Z if __name__ == "__main__":
2023-01-11T21:41:24.0588110Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0588229Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0588454Z     arg0_1 = rand_strided((1, 2, 1, 2, 2, 2, 1), (16, 8, 8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0588559Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0588564Z 
2023-01-11T21:41:24.0588629Z ok (1.578s)
2023-01-11T21:41:24.0589089Z   test_stack_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0589244Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0589506Z [2023-01-11 21:38:29,730] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 444
2023-01-11T21:41:24.0589774Z [2023-01-11 21:38:31,812] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 444
2023-01-11T21:41:24.0589779Z 
2023-01-11T21:41:24.0589870Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0589926Z import torch
2023-01-11T21:41:24.0589996Z import random
2023-01-11T21:41:24.0590110Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0590227Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0590232Z 
2023-01-11T21:41:24.0590308Z aten = torch.ops.aten
2023-01-11T21:41:24.0590439Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0590528Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0590533Z 
2023-01-11T21:41:24.0590537Z 
2023-01-11T21:41:24.0590700Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0590893Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0591010Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0591111Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0591206Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0591299Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0591359Z {
2023-01-11T21:41:24.0591455Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0591503Z     {
2023-01-11T21:41:24.0591578Z         #pragma omp for 
2023-01-11T21:41:24.0591656Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.0591716Z         {
2023-01-11T21:41:24.0591794Z             #pragma GCC ivdep
2023-01-11T21:41:24.0591877Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:24.0591939Z             {
2023-01-11T21:41:24.0591990Z                 {
2023-01-11T21:41:24.0592057Z                     {
2023-01-11T21:41:24.0592152Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.0592250Z                         out_ptr0[(2*i1) + (32*i0)] = tmp0;
2023-01-11T21:41:24.0592315Z                     }
2023-01-11T21:41:24.0592377Z                 }
2023-01-11T21:41:24.0592439Z             }
2023-01-11T21:41:24.0592488Z         }
2023-01-11T21:41:24.0592562Z         #pragma omp for 
2023-01-11T21:41:24.0592642Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.0592704Z         {
2023-01-11T21:41:24.0592782Z             #pragma GCC ivdep
2023-01-11T21:41:24.0592866Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:24.0592917Z             {
2023-01-11T21:41:24.0592979Z                 {
2023-01-11T21:41:24.0593047Z                     {
2023-01-11T21:41:24.0593140Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0593239Z                         out_ptr1[(2*i1) + (32*i0)] = tmp0;
2023-01-11T21:41:24.0593304Z                     }
2023-01-11T21:41:24.0593373Z                 }
2023-01-11T21:41:24.0593423Z             }
2023-01-11T21:41:24.0593484Z         }
2023-01-11T21:41:24.0593543Z     }
2023-01-11T21:41:24.0593602Z }
2023-01-11T21:41:24.0593683Z ''')
2023-01-11T21:41:24.0593689Z 
2023-01-11T21:41:24.0593693Z 
2023-01-11T21:41:24.0593839Z async_compile.wait(globals())
2023-01-11T21:41:24.0593910Z del async_compile
2023-01-11T21:41:24.0593915Z 
2023-01-11T21:41:24.0593972Z def call(args):
2023-01-11T21:41:24.0594044Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0594114Z     args.clear()
2023-01-11T21:41:24.0594325Z     buf2 = empty_strided((12, 16, 2), (32, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0594430Z     buf0 = as_strided(buf2, (12, 16, 1), (32, 2, 1))  # alias
2023-01-11T21:41:24.0594538Z     buf1 = as_strided(buf2, (12, 16, 1), (32, 2, 1), 1)  # alias
2023-01-11T21:41:24.0594726Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0594832Z     del arg0_1
2023-01-11T21:41:24.0594885Z     del arg1_1
2023-01-11T21:41:24.0594953Z     return (buf2, )
2023-01-11T21:41:24.0594958Z 
2023-01-11T21:41:24.0594962Z 
2023-01-11T21:41:24.0595035Z if __name__ == "__main__":
2023-01-11T21:41:24.0595147Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0595570Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0595770Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0595964Z     arg1_1 = rand_strided((12, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0596065Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0596079Z 
2023-01-11T21:41:24.0596132Z ok (2.105s)
2023-01-11T21:41:24.0596631Z   test_std_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0596758Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0597018Z [2023-01-11 21:38:31,882] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 445
2023-01-11T21:41:24.0597023Z 
2023-01-11T21:41:24.0597118Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0597184Z import torch
2023-01-11T21:41:24.0597251Z import random
2023-01-11T21:41:24.0597363Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0597470Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0597484Z 
2023-01-11T21:41:24.0597549Z aten = torch.ops.aten
2023-01-11T21:41:24.0597682Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0597769Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0597776Z 
2023-01-11T21:41:24.0597783Z 
2023-01-11T21:41:24.0597911Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0598112Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0598229Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0598328Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:24.0598424Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:41:24.0598509Z                        float* __restrict__ in_out_ptr3,
2023-01-11T21:41:24.0598603Z                        float* __restrict__ in_out_ptr4,
2023-01-11T21:41:24.0598695Z                        float* __restrict__ in_out_ptr5,
2023-01-11T21:41:24.0598792Z                        float* __restrict__ in_out_ptr6,
2023-01-11T21:41:24.0598895Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0598990Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0599086Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0599166Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:24.0599256Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:24.0599349Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:24.0599445Z                        float* __restrict__ out_ptr10,
2023-01-11T21:41:24.0599537Z                        float* __restrict__ out_ptr12,
2023-01-11T21:41:24.0599630Z                        float* __restrict__ out_ptr14)
2023-01-11T21:41:24.0599689Z {
2023-01-11T21:41:24.0599762Z     auto out_ptr6 = in_out_ptr0;
2023-01-11T21:41:24.0599841Z     auto out_ptr8 = in_out_ptr1;
2023-01-11T21:41:24.0599923Z     auto out_ptr11 = in_out_ptr2;
2023-01-11T21:41:24.0600002Z     auto out_ptr13 = in_out_ptr3;
2023-01-11T21:41:24.0600080Z     auto out_ptr1 = in_out_ptr4;
2023-01-11T21:41:24.0600158Z     auto out_ptr3 = in_out_ptr5;
2023-01-11T21:41:24.0600234Z     auto out_ptr9 = in_out_ptr6;
2023-01-11T21:41:24.0600315Z     {
2023-01-11T21:41:24.0600499Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0600572Z         float tmp1 = 0;
2023-01-11T21:41:24.0600686Z         auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.0600785Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0600843Z         {
2023-01-11T21:41:24.0600947Z             #pragma omp for reduction(+:tmp1_vec) 
2023-01-11T21:41:24.0601019Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0601082Z             {
2023-01-11T21:41:24.0601208Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0601287Z                 tmp1_vec += tmp0;
2023-01-11T21:41:24.0601348Z             }
2023-01-11T21:41:24.0601541Z             tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.0601689Z             #pragma omp for simd simdlen(4) reduction(+:tmp1) 
2023-01-11T21:41:24.0601775Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0601825Z             {
2023-01-11T21:41:24.0601908Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0601980Z                 tmp1 += tmp0;
2023-01-11T21:41:24.0602040Z             }
2023-01-11T21:41:24.0602098Z         }
2023-01-11T21:41:24.0602173Z         out_ptr0[0] = tmp1;
2023-01-11T21:41:24.0602234Z     }
2023-01-11T21:41:24.0602281Z     {
2023-01-11T21:41:24.0602461Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0602531Z         float tmp6 = 0;
2023-01-11T21:41:24.0602644Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.0602716Z         float tmp7 = 0;
2023-01-11T21:41:24.0602827Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:24.0602928Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0602981Z         {
2023-01-11T21:41:24.0603109Z             #pragma omp for reduction(+:tmp6_vec) reduction(+:tmp7_vec) 
2023-01-11T21:41:24.0603192Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0603256Z             {
2023-01-11T21:41:24.0603519Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0603642Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr0[0]);
2023-01-11T21:41:24.0603775Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:41:24.0603861Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0603983Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0604069Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.0604148Z                 tmp6_vec += tmp5;
2023-01-11T21:41:24.0604224Z                 tmp7_vec += tmp0;
2023-01-11T21:41:24.0604283Z             }
2023-01-11T21:41:24.0604474Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.0604667Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:24.0604808Z             #pragma omp for simd simdlen(4) reduction(+:tmp6) reduction(+:tmp7) 
2023-01-11T21:41:24.0604883Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0604943Z             {
2023-01-11T21:41:24.0605027Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0605111Z                 auto tmp1 = out_ptr0[0];
2023-01-11T21:41:24.0605212Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:41:24.0605297Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0605421Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0605492Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0605564Z                 tmp6 += tmp5;
2023-01-11T21:41:24.0605713Z                 tmp7 += tmp0;
2023-01-11T21:41:24.0605813Z             }
2023-01-11T21:41:24.0605875Z         }
2023-01-11T21:41:24.0605948Z         out_ptr1[0] = tmp6;
2023-01-11T21:41:24.0606019Z         out_ptr2[0] = tmp7;
2023-01-11T21:41:24.0606067Z     }
2023-01-11T21:41:24.0606126Z     {
2023-01-11T21:41:24.0606308Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0606381Z         float tmp6 = 0;
2023-01-11T21:41:24.0606493Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.0606566Z         float tmp7 = 0;
2023-01-11T21:41:24.0606679Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:24.0606769Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0606830Z         {
2023-01-11T21:41:24.0606960Z             #pragma omp for reduction(+:tmp6_vec) reduction(+:tmp7_vec) 
2023-01-11T21:41:24.0607043Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0607109Z             {
2023-01-11T21:41:24.0607272Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0607393Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[0]);
2023-01-11T21:41:24.0607522Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:41:24.0607596Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0607724Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0607805Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.0607883Z                 tmp6_vec += tmp5;
2023-01-11T21:41:24.0607960Z                 tmp7_vec += tmp0;
2023-01-11T21:41:24.0608020Z             }
2023-01-11T21:41:24.0608209Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.0608396Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:24.0608525Z             #pragma omp for simd simdlen(4) reduction(+:tmp6) reduction(+:tmp7) 
2023-01-11T21:41:24.0608610Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0608671Z             {
2023-01-11T21:41:24.0608753Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0608838Z                 auto tmp1 = out_ptr2[0];
2023-01-11T21:41:24.0608936Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:41:24.0609020Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0609134Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0609215Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0609285Z                 tmp6 += tmp5;
2023-01-11T21:41:24.0609355Z                 tmp7 += tmp0;
2023-01-11T21:41:24.0609414Z             }
2023-01-11T21:41:24.0609473Z         }
2023-01-11T21:41:24.0609544Z         out_ptr3[0] = tmp6;
2023-01-11T21:41:24.0609604Z         out_ptr4[0] = tmp7;
2023-01-11T21:41:24.0609664Z     }
2023-01-11T21:41:24.0609759Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0609821Z     {
2023-01-11T21:41:24.0609893Z         #pragma omp for 
2023-01-11T21:41:24.0609970Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0610020Z         {
2023-01-11T21:41:24.0610078Z             {
2023-01-11T21:41:24.0610263Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0610338Z                 float tmp1 = 0;
2023-01-11T21:41:24.0610455Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.0610541Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0610604Z                 {
2023-01-11T21:41:24.0610744Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0610812Z                     tmp1_vec += tmp0;
2023-01-11T21:41:24.0610872Z                 }
2023-01-11T21:41:24.0611067Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.0611227Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:24.0611310Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0611369Z                 {
2023-01-11T21:41:24.0611467Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0611540Z                     tmp1 += tmp0;
2023-01-11T21:41:24.0611594Z                 }
2023-01-11T21:41:24.0611671Z                 out_ptr5[i0] = tmp1;
2023-01-11T21:41:24.0611732Z             }
2023-01-11T21:41:24.0611792Z         }
2023-01-11T21:41:24.0611867Z         #pragma omp for 
2023-01-11T21:41:24.0612047Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0612097Z         {
2023-01-11T21:41:24.0612159Z             {
2023-01-11T21:41:24.0612347Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0612461Z                 float tmp6 = 0;
2023-01-11T21:41:24.0612580Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.0612653Z                 float tmp7 = 0;
2023-01-11T21:41:24.0612771Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:24.0612859Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0612909Z                 {
2023-01-11T21:41:24.0613049Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0613173Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr5[i0]);
2023-01-11T21:41:24.0613305Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:24.0613394Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0613529Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0613618Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.0613702Z                     tmp6_vec += tmp5;
2023-01-11T21:41:24.0613767Z                     tmp7_vec += tmp0;
2023-01-11T21:41:24.0613828Z                 }
2023-01-11T21:41:24.0614134Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.0614324Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:24.0614460Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:24.0614544Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0614603Z                 {
2023-01-11T21:41:24.0614701Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0614780Z                     auto tmp1 = out_ptr5[i0];
2023-01-11T21:41:24.0614879Z                     auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:24.0614969Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0615105Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0615193Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0615267Z                     tmp6 += tmp5;
2023-01-11T21:41:24.0615341Z                     tmp7 += tmp0;
2023-01-11T21:41:24.0615392Z                 }
2023-01-11T21:41:24.0615539Z                 out_ptr6[i0] = tmp6;
2023-01-11T21:41:24.0615618Z                 out_ptr7[i0] = tmp7;
2023-01-11T21:41:24.0615679Z             }
2023-01-11T21:41:24.0615738Z         }
2023-01-11T21:41:24.0615811Z         #pragma omp for 
2023-01-11T21:41:24.0615887Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0615936Z         {
2023-01-11T21:41:24.0616069Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr6 + 8*i0);
2023-01-11T21:41:24.0616196Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:41:24.0616278Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0616439Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0616500Z         }
2023-01-11T21:41:24.0616590Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0616659Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:24.0616789Z         {
2023-01-11T21:41:24.0616872Z             auto tmp0 = out_ptr6[i0];
2023-01-11T21:41:24.0616969Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:41:24.0617050Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0617130Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0617190Z         }
2023-01-11T21:41:24.0617252Z         #pragma omp for 
2023-01-11T21:41:24.0617333Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0617392Z         {
2023-01-11T21:41:24.0617451Z             {
2023-01-11T21:41:24.0617634Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0617710Z                 float tmp6 = 0;
2023-01-11T21:41:24.0617862Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.0618012Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0618065Z                 {
2023-01-11T21:41:24.0618204Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0618328Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr7[i0]);
2023-01-11T21:41:24.0618460Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:24.0618547Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0618683Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0618770Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.0618838Z                     tmp6_vec += tmp5;
2023-01-11T21:41:24.0618903Z                 }
2023-01-11T21:41:24.0619096Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.0619220Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6)
2023-01-11T21:41:24.0619425Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0619490Z                 {
2023-01-11T21:41:24.0619587Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0619678Z                     auto tmp1 = out_ptr7[i0];
2023-01-11T21:41:24.0619767Z                     auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:24.0619857Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0619993Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0620079Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0620151Z                     tmp6 += tmp5;
2023-01-11T21:41:24.0620215Z                 }
2023-01-11T21:41:24.0620294Z                 out_ptr8[i0] = tmp6;
2023-01-11T21:41:24.0620345Z             }
2023-01-11T21:41:24.0620406Z         }
2023-01-11T21:41:24.0620483Z         #pragma omp for 
2023-01-11T21:41:24.0620566Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0620805Z         {
2023-01-11T21:41:24.0620938Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr8 + 8*i0);
2023-01-11T21:41:24.0621115Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:24.0621186Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0621280Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:24.0621393Z         }
2023-01-11T21:41:24.0621486Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0621566Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:24.0621628Z         {
2023-01-11T21:41:24.0621710Z             auto tmp0 = out_ptr8[i0];
2023-01-11T21:41:24.0621795Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:24.0621874Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0621953Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:24.0622185Z         }
2023-01-11T21:41:24.0622247Z     }
2023-01-11T21:41:24.0622305Z     {
2023-01-11T21:41:24.0622697Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0622760Z         float tmp6 = 0;
2023-01-11T21:41:24.0622876Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.0622972Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0623033Z         {
2023-01-11T21:41:24.0623138Z             #pragma omp for reduction(+:tmp6_vec) 
2023-01-11T21:41:24.0623229Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0623291Z             {
2023-01-11T21:41:24.0623412Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0623535Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr4[0]);
2023-01-11T21:41:24.0623668Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:41:24.0623753Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0623893Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0624038Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.0624115Z                 tmp6_vec += tmp5;
2023-01-11T21:41:24.0624177Z             }
2023-01-11T21:41:24.0624358Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.0624478Z             #pragma omp for simd simdlen(4) reduction(+:tmp6) 
2023-01-11T21:41:24.0624568Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0624631Z             {
2023-01-11T21:41:24.0624714Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0624798Z                 auto tmp1 = out_ptr4[0];
2023-01-11T21:41:24.0624899Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:41:24.0625082Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0625213Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0625296Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0625374Z                 tmp6 += tmp5;
2023-01-11T21:41:24.0625439Z             }
2023-01-11T21:41:24.0625499Z         }
2023-01-11T21:41:24.0625574Z         out_ptr9[0] = tmp6;
2023-01-11T21:41:24.0625623Z     }
2023-01-11T21:41:24.0625717Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0625776Z     {
2023-01-11T21:41:24.0625850Z         #pragma omp for 
2023-01-11T21:41:24.0625929Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0625987Z         {
2023-01-11T21:41:24.0626048Z             {
2023-01-11T21:41:24.0626099Z                 {
2023-01-11T21:41:24.0626176Z                     float tmp1 = 0;
2023-01-11T21:41:24.0626269Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0626333Z                     {
2023-01-11T21:41:24.0626400Z                         {
2023-01-11T21:41:24.0626501Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:24.0626579Z                             tmp1 += tmp0;
2023-01-11T21:41:24.0626633Z                         }
2023-01-11T21:41:24.0626702Z                     }
2023-01-11T21:41:24.0626782Z                     out_ptr10[i0] = tmp1;
2023-01-11T21:41:24.0626845Z                 }
2023-01-11T21:41:24.0626906Z             }
2023-01-11T21:41:24.0626966Z         }
2023-01-11T21:41:24.0627029Z         #pragma omp for 
2023-01-11T21:41:24.0627104Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0627164Z         {
2023-01-11T21:41:24.0627223Z             {
2023-01-11T21:41:24.0627283Z                 {
2023-01-11T21:41:24.0627357Z                     float tmp6 = 0;
2023-01-11T21:41:24.0627433Z                     float tmp7 = 0;
2023-01-11T21:41:24.0627509Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0627574Z                     {
2023-01-11T21:41:24.0627637Z                         {
2023-01-11T21:41:24.0627739Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:24.0627835Z                             auto tmp1 = out_ptr10[i0];
2023-01-11T21:41:24.0627986Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:24.0628083Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0628216Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0628305Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0628386Z                             tmp6 += tmp5;
2023-01-11T21:41:24.0628465Z                             tmp7 += tmp0;
2023-01-11T21:41:24.0628527Z                         }
2023-01-11T21:41:24.0628590Z                     }
2023-01-11T21:41:24.0628672Z                     out_ptr11[i0] = tmp6;
2023-01-11T21:41:24.0628741Z                     out_ptr12[i0] = tmp7;
2023-01-11T21:41:24.0628805Z                 }
2023-01-11T21:41:24.0628866Z             }
2023-01-11T21:41:24.0628926Z         }
2023-01-11T21:41:24.0628999Z         #pragma omp for 
2023-01-11T21:41:24.0629078Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0629139Z         {
2023-01-11T21:41:24.0629296Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr11 + 8*i0);
2023-01-11T21:41:24.0629425Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:41:24.0629507Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0629586Z             auto tmp3 = tmp2.sqrt();
2023-01-11T21:41:24.0629678Z             tmp3.store(in_out_ptr2 + 8*i0);
2023-01-11T21:41:24.0629735Z         }
2023-01-11T21:41:24.0629825Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0629893Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:24.0629957Z         {
2023-01-11T21:41:24.0630041Z             auto tmp0 = out_ptr11[i0];
2023-01-11T21:41:24.0630136Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:41:24.0630218Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0630303Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:24.0630382Z             in_out_ptr2[i0] = tmp3;
2023-01-11T21:41:24.0630430Z         }
2023-01-11T21:41:24.0630499Z         #pragma omp for 
2023-01-11T21:41:24.0630583Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0630721Z         {
2023-01-11T21:41:24.0630783Z             {
2023-01-11T21:41:24.0630843Z                 {
2023-01-11T21:41:24.0630919Z                     float tmp6 = 0;
2023-01-11T21:41:24.0630996Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0631058Z                     {
2023-01-11T21:41:24.0631124Z                         {
2023-01-11T21:41:24.0631226Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:24.0631322Z                             auto tmp1 = out_ptr12[i0];
2023-01-11T21:41:24.0631423Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:24.0631515Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0631646Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0631733Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0631812Z                             tmp6 += tmp5;
2023-01-11T21:41:24.0631883Z                         }
2023-01-11T21:41:24.0631945Z                     }
2023-01-11T21:41:24.0632025Z                     out_ptr13[i0] = tmp6;
2023-01-11T21:41:24.0632087Z                 }
2023-01-11T21:41:24.0632137Z             }
2023-01-11T21:41:24.0632196Z         }
2023-01-11T21:41:24.0632270Z         #pragma omp for 
2023-01-11T21:41:24.0632348Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0632406Z         {
2023-01-11T21:41:24.0632601Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr13 + 8*i0);
2023-01-11T21:41:24.0632730Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:24.0632802Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0632880Z             auto tmp3 = tmp2.sqrt();
2023-01-11T21:41:24.0632973Z             tmp3.store(in_out_ptr3 + 8*i0);
2023-01-11T21:41:24.0633028Z         }
2023-01-11T21:41:24.0633411Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0633531Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:24.0633593Z         {
2023-01-11T21:41:24.0633666Z             auto tmp0 = out_ptr13[i0];
2023-01-11T21:41:24.0633819Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:24.0633903Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0633991Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:24.0634124Z             in_out_ptr3[i0] = tmp3;
2023-01-11T21:41:24.0634184Z         }
2023-01-11T21:41:24.0634258Z         #pragma omp for 
2023-01-11T21:41:24.0634391Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0634449Z         {
2023-01-11T21:41:24.0634528Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0634589Z             {
2023-01-11T21:41:24.0634727Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0634865Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0635043Z                 auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0635249Z                 auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr0 + 24 + (8*i1) + (32*i0));
2023-01-11T21:41:24.0635326Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0635408Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0635490Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0635619Z                 auto tmp7 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:24.0635702Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:24.0635837Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:24.0635923Z                 auto tmp10 = tmp9.pow(2);
2023-01-11T21:41:24.0636039Z                 auto tmp11 = tmp1 - tmp8;
2023-01-11T21:41:24.0636128Z                 auto tmp12 = tmp11.pow(2);
2023-01-11T21:41:24.0636216Z                 auto tmp13 = tmp10 + tmp12;
2023-01-11T21:41:24.0636340Z                 auto tmp14 = tmp3 - tmp8;
2023-01-11T21:41:24.0636433Z                 auto tmp15 = tmp14.pow(2);
2023-01-11T21:41:24.0636520Z                 auto tmp16 = tmp13 + tmp15;
2023-01-11T21:41:24.0636647Z                 auto tmp17 = tmp5 - tmp8;
2023-01-11T21:41:24.0636722Z                 auto tmp18 = tmp17.pow(2);
2023-01-11T21:41:24.0636808Z                 auto tmp19 = tmp16 + tmp18;
2023-01-11T21:41:24.0636940Z                 auto tmp20 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:24.0637027Z                 auto tmp21 = tmp19 / tmp20;
2023-01-11T21:41:24.0637111Z                 auto tmp22 = tmp21.sqrt();
2023-01-11T21:41:24.0637211Z                 tmp22.store(out_ptr14 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0637271Z             }
2023-01-11T21:41:24.0637350Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.0637428Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0637489Z             {
2023-01-11T21:41:24.0637581Z                 auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:24.0637682Z                 auto tmp1 = in_ptr0[8 + i1 + (32*i0)];
2023-01-11T21:41:24.0637778Z                 auto tmp3 = in_ptr0[16 + i1 + (32*i0)];
2023-01-11T21:41:24.0637874Z                 auto tmp5 = in_ptr0[24 + i1 + (32*i0)];
2023-01-11T21:41:24.0637957Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0638089Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0638176Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:24.0638275Z                 auto tmp7 = static_cast<float>(4);
2023-01-11T21:41:24.0638362Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:24.0638487Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:24.0638695Z                 auto tmp10 = tmp9 * tmp9;
2023-01-11T21:41:24.0638823Z                 auto tmp11 = tmp1 - tmp8;
2023-01-11T21:41:24.0638900Z                 auto tmp12 = tmp11 * tmp11;
2023-01-11T21:41:24.0638986Z                 auto tmp13 = tmp10 + tmp12;
2023-01-11T21:41:24.0639115Z                 auto tmp14 = tmp3 - tmp8;
2023-01-11T21:41:24.0639239Z                 auto tmp15 = tmp14 * tmp14;
2023-01-11T21:41:24.0639328Z                 auto tmp16 = tmp13 + tmp15;
2023-01-11T21:41:24.0639454Z                 auto tmp17 = tmp5 - tmp8;
2023-01-11T21:41:24.0639541Z                 auto tmp18 = tmp17 * tmp17;
2023-01-11T21:41:24.0639616Z                 auto tmp19 = tmp16 + tmp18;
2023-01-11T21:41:24.0639716Z                 auto tmp20 = static_cast<float>(3);
2023-01-11T21:41:24.0639800Z                 auto tmp21 = tmp19 / tmp20;
2023-01-11T21:41:24.0639897Z                 auto tmp22 = std::sqrt(tmp21);
2023-01-11T21:41:24.0639987Z                 out_ptr14[i1 + (8*i0)] = tmp22;
2023-01-11T21:41:24.0640049Z             }
2023-01-11T21:41:24.0640113Z         }
2023-01-11T21:41:24.0640178Z         #pragma omp single
2023-01-11T21:41:24.0640237Z         {
2023-01-11T21:41:24.0640299Z             {
2023-01-11T21:41:24.0640361Z                 {
2023-01-11T21:41:24.0640449Z                     auto tmp0 = out_ptr1[0];
2023-01-11T21:41:24.0640582Z                     auto tmp1 = static_cast<float>(255);
2023-01-11T21:41:24.0640661Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0646376Z                     in_out_ptr4[0] = tmp2;
2023-01-11T21:41:24.0646444Z                 }
2023-01-11T21:41:24.0646506Z             }
2023-01-11T21:41:24.0646569Z         }
2023-01-11T21:41:24.0646648Z         #pragma omp single
2023-01-11T21:41:24.0646711Z         {
2023-01-11T21:41:24.0646764Z             {
2023-01-11T21:41:24.0646826Z                 {
2023-01-11T21:41:24.0647040Z                     auto tmp0 = out_ptr3[0];
2023-01-11T21:41:24.0647146Z                     auto tmp1 = static_cast<float>(256);
2023-01-11T21:41:24.0647234Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0647320Z                     in_out_ptr5[0] = tmp2;
2023-01-11T21:41:24.0647384Z                 }
2023-01-11T21:41:24.0647434Z             }
2023-01-11T21:41:24.0647493Z         }
2023-01-11T21:41:24.0647571Z         #pragma omp single
2023-01-11T21:41:24.0647640Z         {
2023-01-11T21:41:24.0647699Z             {
2023-01-11T21:41:24.0647759Z                 {
2023-01-11T21:41:24.0647837Z                     auto tmp0 = out_ptr9[0];
2023-01-11T21:41:24.0647941Z                     auto tmp1 = static_cast<float>(256);
2023-01-11T21:41:24.0648026Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0648127Z                     auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:24.0648213Z                     in_out_ptr6[0] = tmp3;
2023-01-11T21:41:24.0648275Z                 }
2023-01-11T21:41:24.0648335Z             }
2023-01-11T21:41:24.0648383Z         }
2023-01-11T21:41:24.0648440Z     }
2023-01-11T21:41:24.0648499Z }
2023-01-11T21:41:24.0648597Z ''')
2023-01-11T21:41:24.0648603Z 
2023-01-11T21:41:24.0648607Z 
2023-01-11T21:41:24.0648696Z async_compile.wait(globals())
2023-01-11T21:41:24.0649050Z del async_compile
2023-01-11T21:41:24.0649056Z 
2023-01-11T21:41:24.0649125Z def call(args):
2023-01-11T21:41:24.0649246Z     arg0_1, = args
2023-01-11T21:41:24.0649311Z     args.clear()
2023-01-11T21:41:24.0649525Z     buf0 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0649712Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0649920Z     buf2 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0650098Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0650308Z     buf10 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0650513Z     buf4 = empty_strided((2, 4, 4, 1), (16, 4, 1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0650712Z     buf5 = empty_strided((2, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0650905Z     buf7 = empty_strided((2, 4, 4, 1), (16, 4, 1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0650986Z     buf6 = buf5; del buf5  # reuse
2023-01-11T21:41:24.0651262Z     buf8 = empty_strided((2, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0651343Z     buf9 = buf8; del buf8  # reuse
2023-01-11T21:41:24.0651528Z     buf11 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0651736Z     buf12 = empty_strided((1, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0651930Z     buf13 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0652124Z     buf15 = empty_strided((1, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0652206Z     buf14 = buf13; del buf13  # reuse
2023-01-11T21:41:24.0652396Z     buf16 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0652479Z     buf17 = buf16; del buf16  # reuse
2023-01-11T21:41:24.0652684Z     buf18 = empty_strided((2, 4, 1, 8), (32, 8, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0652769Z     buf19 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0652887Z     buf20 = buf3; del buf3  # reuse
2023-01-11T21:41:24.0652971Z     buf21 = buf11; del buf11  # reuse
2023-01-11T21:41:24.0653426Z     kernel_cpp_0(c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf17.data_ptr()), c_void_p(buf19.data_ptr()), c_void_p(buf20.data_ptr()), c_void_p(buf21.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf15.data_ptr()), c_void_p(buf18.data_ptr()))
2023-01-11T21:41:24.0653494Z     del arg0_1
2023-01-11T21:41:24.0653612Z     return (buf19, buf20, buf6, buf9, buf21, buf14, buf17, buf18, )
2023-01-11T21:41:24.0653618Z 
2023-01-11T21:41:24.0653623Z 
2023-01-11T21:41:24.0653697Z if __name__ == "__main__":
2023-01-11T21:41:24.0653811Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0653933Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0654146Z     arg0_1 = rand_strided((2, 4, 4, 8), (128, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0654252Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0654523Z [2023-01-11 21:38:34,169] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 445
2023-01-11T21:41:24.0654528Z 
2023-01-11T21:41:24.0654596Z ok (2.357s)
2023-01-11T21:41:24.0654917Z   test_strided_inputs_cpu (__main__.CpuTests) ... [2023-01-11 21:38:34,186] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 446
2023-01-11T21:41:24.0655185Z [2023-01-11 21:38:36,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 446
2023-01-11T21:41:24.0655190Z 
2023-01-11T21:41:24.0655286Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0655356Z import torch
2023-01-11T21:41:24.0655424Z import random
2023-01-11T21:41:24.0655539Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0655665Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0655670Z 
2023-01-11T21:41:24.0655747Z aten = torch.ops.aten
2023-01-11T21:41:24.0655867Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0655956Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0655962Z 
2023-01-11T21:41:24.0655966Z 
2023-01-11T21:41:24.0656100Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0656304Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0656421Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0656524Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0656623Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0656683Z {
2023-01-11T21:41:24.0656768Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0656828Z     {
2023-01-11T21:41:24.0656903Z         #pragma omp for 
2023-01-11T21:41:24.0657040Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:24.0657104Z         {
2023-01-11T21:41:24.0657169Z             {
2023-01-11T21:41:24.0657220Z                 {
2023-01-11T21:41:24.0657312Z                     auto tmp0 = in_ptr0[2*i0];
2023-01-11T21:41:24.0657399Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.0657489Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0657570Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0657634Z                 }
2023-01-11T21:41:24.0657776Z             }
2023-01-11T21:41:24.0657826Z         }
2023-01-11T21:41:24.0657885Z     }
2023-01-11T21:41:24.0657944Z }
2023-01-11T21:41:24.0658023Z ''')
2023-01-11T21:41:24.0658028Z 
2023-01-11T21:41:24.0658032Z 
2023-01-11T21:41:24.0658124Z async_compile.wait(globals())
2023-01-11T21:41:24.0658194Z del async_compile
2023-01-11T21:41:24.0658198Z 
2023-01-11T21:41:24.0658265Z def call(args):
2023-01-11T21:41:24.0658326Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0658430Z     args.clear()
2023-01-11T21:41:24.0658630Z     buf0 = empty_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0658789Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0658858Z     del arg0_1
2023-01-11T21:41:24.0658922Z     del arg1_1
2023-01-11T21:41:24.0658991Z     return (buf0, )
2023-01-11T21:41:24.0658996Z 
2023-01-11T21:41:24.0659000Z 
2023-01-11T21:41:24.0659074Z if __name__ == "__main__":
2023-01-11T21:41:24.0659175Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0659294Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0659553Z     arg0_1 = rand_strided((8, 16), (32, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0659750Z     arg1_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0659864Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0659871Z 
2023-01-11T21:41:24.0660083Z ok (2.003s)
2023-01-11T21:41:24.0660547Z   test_sum1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0660674Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0660936Z [2023-01-11 21:38:36,203] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 447
2023-01-11T21:41:24.0661263Z [2023-01-11 21:38:38,311] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 447
2023-01-11T21:41:24.0661279Z 
2023-01-11T21:41:24.0661360Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0661426Z import torch
2023-01-11T21:41:24.0661494Z import random
2023-01-11T21:41:24.0661614Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0661732Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0661738Z 
2023-01-11T21:41:24.0661813Z aten = torch.ops.aten
2023-01-11T21:41:24.0662006Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0662086Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0662091Z 
2023-01-11T21:41:24.0662095Z 
2023-01-11T21:41:24.0662228Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0662613Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0662735Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0662837Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0662935Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0662994Z {
2023-01-11T21:41:24.0663079Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0663294Z     {
2023-01-11T21:41:24.0663371Z         #pragma omp for 
2023-01-11T21:41:24.0663451Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0663513Z         {
2023-01-11T21:41:24.0663573Z             {
2023-01-11T21:41:24.0663766Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0663842Z                 float tmp3 = 0;
2023-01-11T21:41:24.0663951Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0664040Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0664102Z                 {
2023-01-11T21:41:24.0664242Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0664377Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0664470Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0664606Z                     tmp3_vec += tmp2;
2023-01-11T21:41:24.0664804Z                 }
2023-01-11T21:41:24.0665003Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:24.0665121Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:24.0665204Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0665268Z                 {
2023-01-11T21:41:24.0665368Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0665461Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:24.0665551Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0665615Z                     tmp3 += tmp2;
2023-01-11T21:41:24.0665675Z                 }
2023-01-11T21:41:24.0665755Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0665815Z             }
2023-01-11T21:41:24.0665876Z         }
2023-01-11T21:41:24.0665936Z     }
2023-01-11T21:41:24.0665994Z }
2023-01-11T21:41:24.0666073Z ''')
2023-01-11T21:41:24.0666081Z 
2023-01-11T21:41:24.0666086Z 
2023-01-11T21:41:24.0666173Z async_compile.wait(globals())
2023-01-11T21:41:24.0666241Z del async_compile
2023-01-11T21:41:24.0666246Z 
2023-01-11T21:41:24.0666312Z def call(args):
2023-01-11T21:41:24.0666385Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0666452Z     args.clear()
2023-01-11T21:41:24.0666643Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0666792Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0666856Z     del arg0_1
2023-01-11T21:41:24.0666920Z     del arg1_1
2023-01-11T21:41:24.0666987Z     return (buf0, )
2023-01-11T21:41:24.0666992Z 
2023-01-11T21:41:24.0666996Z 
2023-01-11T21:41:24.0667072Z if __name__ == "__main__":
2023-01-11T21:41:24.0667183Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0667304Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0667504Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0667683Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0667793Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0667798Z 
2023-01-11T21:41:24.0667865Z ok (2.138s)
2023-01-11T21:41:24.0668323Z   test_sum2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0668507Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0668775Z [2023-01-11 21:38:38,343] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 448
2023-01-11T21:41:24.0669076Z [2023-01-11 21:38:40,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 448
2023-01-11T21:41:24.0669082Z 
2023-01-11T21:41:24.0669172Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0669239Z import torch
2023-01-11T21:41:24.0669296Z import random
2023-01-11T21:41:24.0669404Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0669520Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0669525Z 
2023-01-11T21:41:24.0669603Z aten = torch.ops.aten
2023-01-11T21:41:24.0669733Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0669824Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0669829Z 
2023-01-11T21:41:24.0669833Z 
2023-01-11T21:41:24.0669966Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0670237Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0670375Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0670480Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0670577Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0670672Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0670731Z {
2023-01-11T21:41:24.0670827Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0670887Z     {
2023-01-11T21:41:24.0670950Z         #pragma omp for 
2023-01-11T21:41:24.0671028Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0671088Z         {
2023-01-11T21:41:24.0671166Z             #pragma GCC ivdep
2023-01-11T21:41:24.0671248Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:41:24.0671309Z             {
2023-01-11T21:41:24.0671370Z                 {
2023-01-11T21:41:24.0671423Z                     {
2023-01-11T21:41:24.0671506Z                         float tmp3 = 0;
2023-01-11T21:41:24.0671600Z                         for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:24.0671670Z                         {
2023-01-11T21:41:24.0671745Z                             {
2023-01-11T21:41:24.0672064Z                                 auto tmp0 = in_ptr0[i1 + (21*i2) + (567*i0)];
2023-01-11T21:41:24.0672179Z                                 auto tmp1 = in_ptr1[i1 + (21*i2) + (567*i0)];
2023-01-11T21:41:24.0672265Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0672352Z                                 tmp3 += tmp2;
2023-01-11T21:41:24.0672416Z                             }
2023-01-11T21:41:24.0672483Z                         }
2023-01-11T21:41:24.0672577Z                         out_ptr0[i1 + (21*i0)] = tmp3;
2023-01-11T21:41:24.0672640Z                     }
2023-01-11T21:41:24.0672703Z                 }
2023-01-11T21:41:24.0672754Z             }
2023-01-11T21:41:24.0672816Z         }
2023-01-11T21:41:24.0672889Z         #pragma omp for 
2023-01-11T21:41:24.0672971Z         for(long i0=0; i0<216; i0+=1)
2023-01-11T21:41:24.0673032Z         {
2023-01-11T21:41:24.0673095Z             {
2023-01-11T21:41:24.0673288Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0673469Z                 float tmp3 = 0;
2023-01-11T21:41:24.0673589Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0673673Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:24.0673786Z                 {
2023-01-11T21:41:24.0674145Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (21*i0));
2023-01-11T21:41:24.0674283Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (21*i0));
2023-01-11T21:41:24.0674372Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0674451Z                     tmp3_vec += tmp2;
2023-01-11T21:41:24.0674502Z                 }
2023-01-11T21:41:24.0674701Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:24.0674864Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:24.0674951Z                 for(long i1=16; i1<21; i1+=1)
2023-01-11T21:41:24.0675013Z                 {
2023-01-11T21:41:24.0675109Z                     auto tmp0 = in_ptr0[i1 + (21*i0)];
2023-01-11T21:41:24.0675203Z                     auto tmp1 = in_ptr1[i1 + (21*i0)];
2023-01-11T21:41:24.0675292Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0675356Z                     tmp3 += tmp2;
2023-01-11T21:41:24.0675418Z                 }
2023-01-11T21:41:24.0675495Z                 out_ptr1[i0] = tmp3;
2023-01-11T21:41:24.0675556Z             }
2023-01-11T21:41:24.0675617Z         }
2023-01-11T21:41:24.0675678Z     }
2023-01-11T21:41:24.0675725Z }
2023-01-11T21:41:24.0675818Z ''')
2023-01-11T21:41:24.0675823Z 
2023-01-11T21:41:24.0675828Z 
2023-01-11T21:41:24.0675915Z async_compile.wait(globals())
2023-01-11T21:41:24.0675985Z del async_compile
2023-01-11T21:41:24.0675992Z 
2023-01-11T21:41:24.0676087Z def call(args):
2023-01-11T21:41:24.0676161Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0676228Z     args.clear()
2023-01-11T21:41:24.0676428Z     buf0 = empty_strided((8, 21), (21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0676621Z     buf1 = empty_strided((8, 9, 3), (27, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0676805Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0676869Z     del arg0_1
2023-01-11T21:41:24.0676931Z     del arg1_1
2023-01-11T21:41:24.0677008Z     return (buf0, buf1, )
2023-01-11T21:41:24.0677013Z 
2023-01-11T21:41:24.0677017Z 
2023-01-11T21:41:24.0677091Z if __name__ == "__main__":
2023-01-11T21:41:24.0677202Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0677323Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0677527Z     arg0_1 = rand_strided((8, 9, 3, 21), (567, 63, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0677740Z     arg1_1 = rand_strided((8, 9, 3, 21), (567, 63, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0677854Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0677859Z 
2023-01-11T21:41:24.0677921Z ok (1.969s)
2023-01-11T21:41:24.0678386Z   test_sum3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0678509Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0678770Z [2023-01-11 21:38:40,311] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 449
2023-01-11T21:41:24.0679035Z [2023-01-11 21:38:41,879] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 449
2023-01-11T21:41:24.0679040Z 
2023-01-11T21:41:24.0679132Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0679189Z import torch
2023-01-11T21:41:24.0679254Z import random
2023-01-11T21:41:24.0679366Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0679484Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0679489Z 
2023-01-11T21:41:24.0679564Z aten = torch.ops.aten
2023-01-11T21:41:24.0679694Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0679782Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0679787Z 
2023-01-11T21:41:24.0679791Z 
2023-01-11T21:41:24.0679919Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0680111Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0680226Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0680358Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0680456Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0680549Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0680640Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0680695Z {
2023-01-11T21:41:24.0680781Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0680841Z     {
2023-01-11T21:41:24.0680913Z         #pragma omp for 
2023-01-11T21:41:24.0680991Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.0681050Z         {
2023-01-11T21:41:24.0681110Z             {
2023-01-11T21:41:24.0681295Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0681370Z                 float tmp3 = 0;
2023-01-11T21:41:24.0681478Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0681673Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0681736Z                 {
2023-01-11T21:41:24.0681876Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.0682011Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:24.0682101Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0682203Z                     tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.0682272Z                     tmp3_vec += tmp2;
2023-01-11T21:41:24.0682334Z                 }
2023-01-11T21:41:24.0682525Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:24.0682641Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:24.0682727Z                 for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.0682789Z                 {
2023-01-11T21:41:24.0682884Z                     auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.0682973Z                     auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.0683048Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0683137Z                     out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.0683302Z                     tmp3 += tmp2;
2023-01-11T21:41:24.0683367Z                 }
2023-01-11T21:41:24.0683445Z                 out_ptr1[i0] = tmp3;
2023-01-11T21:41:24.0683504Z             }
2023-01-11T21:41:24.0683919Z         }
2023-01-11T21:41:24.0683986Z         #pragma omp for 
2023-01-11T21:41:24.0684064Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.0684124Z         {
2023-01-11T21:41:24.0684255Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0684388Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:24.0684469Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0684559Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0684612Z         }
2023-01-11T21:41:24.0684702Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0684779Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.0684840Z         {
2023-01-11T21:41:24.0684920Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0685019Z             auto tmp1 = static_cast<float>(10);
2023-01-11T21:41:24.0685097Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0685163Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:24.0685221Z         }
2023-01-11T21:41:24.0685348Z     }
2023-01-11T21:41:24.0685404Z }
2023-01-11T21:41:24.0685484Z ''')
2023-01-11T21:41:24.0685489Z 
2023-01-11T21:41:24.0685605Z 
2023-01-11T21:41:24.0685693Z async_compile.wait(globals())
2023-01-11T21:41:24.0685764Z del async_compile
2023-01-11T21:41:24.0685769Z 
2023-01-11T21:41:24.0685827Z def call(args):
2023-01-11T21:41:24.0685900Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0685969Z     args.clear()
2023-01-11T21:41:24.0686171Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0686398Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0686588Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0686805Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0686871Z     del arg0_1
2023-01-11T21:41:24.0686925Z     del arg1_1
2023-01-11T21:41:24.0687006Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0687011Z 
2023-01-11T21:41:24.0687016Z 
2023-01-11T21:41:24.0687090Z if __name__ == "__main__":
2023-01-11T21:41:24.0687205Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0687326Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0687525Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0687753Z     arg1_1 = rand_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0687858Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0687875Z 
2023-01-11T21:41:24.0687928Z ok (1.604s)
2023-01-11T21:41:24.0688388Z   test_sum4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0688515Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0688775Z [2023-01-11 21:38:41,922] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 450
2023-01-11T21:41:24.0689038Z [2023-01-11 21:38:43,664] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 450
2023-01-11T21:41:24.0689048Z 
2023-01-11T21:41:24.0689143Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0689209Z import torch
2023-01-11T21:41:24.0689277Z import random
2023-01-11T21:41:24.0689387Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0689493Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0689498Z 
2023-01-11T21:41:24.0689573Z aten = torch.ops.aten
2023-01-11T21:41:24.0689703Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0689794Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0689799Z 
2023-01-11T21:41:24.0689804Z 
2023-01-11T21:41:24.0689934Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0690136Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0690251Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0690349Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0690438Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0690530Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0690621Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.0690713Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:24.0690774Z {
2023-01-11T21:41:24.0690870Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0690931Z     {
2023-01-11T21:41:24.0690995Z         #pragma omp for 
2023-01-11T21:41:24.0691076Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:24.0691136Z         {
2023-01-11T21:41:24.0691267Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0691400Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0691485Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0691571Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0691620Z         }
2023-01-11T21:41:24.0691747Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0691829Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:41:24.0691885Z         {
2023-01-11T21:41:24.0691968Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0692066Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0692149Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0692216Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0692274Z         }
2023-01-11T21:41:24.0692348Z         #pragma omp for 
2023-01-11T21:41:24.0692427Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:24.0692560Z         {
2023-01-11T21:41:24.0692622Z             {
2023-01-11T21:41:24.0692809Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0692875Z                 float tmp1 = 0;
2023-01-11T21:41:24.0692994Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.0693079Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0693174Z                 {
2023-01-11T21:41:24.0693317Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0693394Z                     tmp1_vec += tmp0;
2023-01-11T21:41:24.0693456Z                 }
2023-01-11T21:41:24.0693649Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.0693755Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:24.0693841Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0693902Z                 {
2023-01-11T21:41:24.0693999Z                     auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0694076Z                     tmp1 += tmp0;
2023-01-11T21:41:24.0694136Z                 }
2023-01-11T21:41:24.0694217Z                 out_ptr1[i0] = tmp1;
2023-01-11T21:41:24.0694332Z             }
2023-01-11T21:41:24.0694393Z         }
2023-01-11T21:41:24.0694470Z         #pragma omp for 
2023-01-11T21:41:24.0694547Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:24.0694606Z         {
2023-01-11T21:41:24.0694739Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0694990Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:24.0695061Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0695150Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0695210Z         }
2023-01-11T21:41:24.0695414Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0695493Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:24.0695553Z         {
2023-01-11T21:41:24.0695636Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:24.0695804Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:24.0695887Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0695962Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:24.0696027Z         }
2023-01-11T21:41:24.0696103Z         #pragma omp for 
2023-01-11T21:41:24.0696182Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:24.0696243Z         {
2023-01-11T21:41:24.0696294Z             {
2023-01-11T21:41:24.0696477Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0696552Z                 float tmp1 = 0;
2023-01-11T21:41:24.0696673Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.0696758Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0696982Z                 {
2023-01-11T21:41:24.0697291Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0697371Z                     tmp1_vec += tmp0;
2023-01-11T21:41:24.0697423Z                 }
2023-01-11T21:41:24.0697619Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.0697784Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:24.0697874Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0697936Z                 {
2023-01-11T21:41:24.0698035Z                     auto tmp0 = out_ptr2[i1 + (8*i0)];
2023-01-11T21:41:24.0698111Z                     tmp1 += tmp0;
2023-01-11T21:41:24.0698162Z                 }
2023-01-11T21:41:24.0698239Z                 out_ptr3[i0] = tmp1;
2023-01-11T21:41:24.0698299Z             }
2023-01-11T21:41:24.0698357Z         }
2023-01-11T21:41:24.0698431Z         #pragma omp for 
2023-01-11T21:41:24.0698507Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0698567Z         {
2023-01-11T21:41:24.0698684Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr3 + 8*i0);
2023-01-11T21:41:24.0698812Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(5));
2023-01-11T21:41:24.0698894Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0699014Z             tmp2.store(out_ptr4 + 8*i0);
2023-01-11T21:41:24.0699074Z         }
2023-01-11T21:41:24.0699163Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0699242Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.0699290Z         {
2023-01-11T21:41:24.0699370Z             auto tmp0 = out_ptr3[i0];
2023-01-11T21:41:24.0699467Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:41:24.0699551Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0699629Z             out_ptr4[i0] = tmp2;
2023-01-11T21:41:24.0699688Z         }
2023-01-11T21:41:24.0699748Z     }
2023-01-11T21:41:24.0699796Z }
2023-01-11T21:41:24.0699878Z ''')
2023-01-11T21:41:24.0699884Z 
2023-01-11T21:41:24.0699889Z 
2023-01-11T21:41:24.0699975Z async_compile.wait(globals())
2023-01-11T21:41:24.0700042Z del async_compile
2023-01-11T21:41:24.0700047Z 
2023-01-11T21:41:24.0700115Z def call(args):
2023-01-11T21:41:24.0700180Z     arg0_1, = args
2023-01-11T21:41:24.0700248Z     args.clear()
2023-01-11T21:41:24.0700459Z     buf0 = empty_strided((1, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0700664Z     buf1 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0700861Z     buf2 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0701053Z     buf3 = empty_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0701244Z     buf4 = empty_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0701476Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:24.0701542Z     del arg0_1
2023-01-11T21:41:24.0701634Z     return (buf4, buf3, buf2, buf1, buf0, )
2023-01-11T21:41:24.0701640Z 
2023-01-11T21:41:24.0701644Z 
2023-01-11T21:41:24.0701718Z if __name__ == "__main__":
2023-01-11T21:41:24.0701824Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0701945Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0702158Z     arg0_1 = rand_strided((1, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0702265Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0702270Z 
2023-01-11T21:41:24.0702452Z ok (1.779s)
2023-01-11T21:41:24.0702996Z   test_sum5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0703121Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0703394Z [2023-01-11 21:38:43,687] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 451
2023-01-11T21:41:24.0703729Z [2023-01-11 21:38:45,260] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 451
2023-01-11T21:41:24.0703735Z 
2023-01-11T21:41:24.0703817Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0703883Z import torch
2023-01-11T21:41:24.0703952Z import random
2023-01-11T21:41:24.0704064Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0704179Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0704184Z 
2023-01-11T21:41:24.0704260Z aten = torch.ops.aten
2023-01-11T21:41:24.0704392Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0704479Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0704484Z 
2023-01-11T21:41:24.0704489Z 
2023-01-11T21:41:24.0704609Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0704807Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0704965Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0705068Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0705163Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0705316Z {
2023-01-11T21:41:24.0705401Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:24.0705485Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0705544Z     {
2023-01-11T21:41:24.0705619Z         #pragma omp for 
2023-01-11T21:41:24.0705699Z         for(long i0=0; i0<136; i0+=1)
2023-01-11T21:41:24.0705756Z         {
2023-01-11T21:41:24.0705817Z             {
2023-01-11T21:41:24.0706006Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0706073Z                 float tmp3 = 0;
2023-01-11T21:41:24.0706189Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0706276Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0706344Z                 {
2023-01-11T21:41:24.0706483Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (9*i0));
2023-01-11T21:41:24.0706615Z                     auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0706704Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0706840Z                     tmp3_vec += tmp2;
2023-01-11T21:41:24.0706892Z                 }
2023-01-11T21:41:24.0707245Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:24.0707366Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:24.0707452Z                 for(long i1=8; i1<9; i1+=1)
2023-01-11T21:41:24.0707516Z                 {
2023-01-11T21:41:24.0707611Z                     auto tmp0 = in_ptr0[i1 + (9*i0)];
2023-01-11T21:41:24.0707711Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0707807Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0707871Z                     tmp3 += tmp2;
2023-01-11T21:41:24.0707933Z                 }
2023-01-11T21:41:24.0708016Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0708080Z             }
2023-01-11T21:41:24.0708142Z         }
2023-01-11T21:41:24.0708217Z         #pragma omp for 
2023-01-11T21:41:24.0708360Z         for(long i0=0; i0<17; i0+=1)
2023-01-11T21:41:24.0708410Z         {
2023-01-11T21:41:24.0708473Z             {
2023-01-11T21:41:24.0708770Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0708846Z                 float tmp3 = 0;
2023-01-11T21:41:24.0708964Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0709052Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0709116Z                 {
2023-01-11T21:41:24.0709248Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0709415Z                     auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:24.0709505Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0709585Z                     tmp3_vec += tmp2;
2023-01-11T21:41:24.0709762Z                 }
2023-01-11T21:41:24.0709954Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:24.0710073Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:24.0710162Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0710215Z                 {
2023-01-11T21:41:24.0710313Z                     auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0710413Z                     auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:24.0710502Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0710649Z                     tmp3 += tmp2;
2023-01-11T21:41:24.0710711Z                 }
2023-01-11T21:41:24.0710790Z                 out_ptr1[i0] = tmp3;
2023-01-11T21:41:24.0710840Z             }
2023-01-11T21:41:24.0710900Z         }
2023-01-11T21:41:24.0710975Z         #pragma omp for 
2023-01-11T21:41:24.0711053Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0711114Z         {
2023-01-11T21:41:24.0711244Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0711377Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(5));
2023-01-11T21:41:24.0711511Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0711603Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0711664Z         }
2023-01-11T21:41:24.0711756Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0711835Z         for(long i0=16; i0<17; i0+=1)
2023-01-11T21:41:24.0711895Z         {
2023-01-11T21:41:24.0711976Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:24.0712066Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:41:24.0712144Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0712218Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0712278Z         }
2023-01-11T21:41:24.0712336Z     }
2023-01-11T21:41:24.0712391Z }
2023-01-11T21:41:24.0712473Z ''')
2023-01-11T21:41:24.0712479Z 
2023-01-11T21:41:24.0712484Z 
2023-01-11T21:41:24.0712559Z async_compile.wait(globals())
2023-01-11T21:41:24.0712627Z del async_compile
2023-01-11T21:41:24.0712633Z 
2023-01-11T21:41:24.0712699Z def call(args):
2023-01-11T21:41:24.0712768Z     arg0_1, = args
2023-01-11T21:41:24.0712839Z     args.clear()
2023-01-11T21:41:24.0713045Z     buf0 = empty_strided((1, 17, 8), (136, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0713236Z     buf1 = empty_strided((1, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0713317Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0713469Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0713536Z     del arg0_1
2023-01-11T21:41:24.0713600Z     return (buf2, )
2023-01-11T21:41:24.0713605Z 
2023-01-11T21:41:24.0713609Z 
2023-01-11T21:41:24.0713685Z if __name__ == "__main__":
2023-01-11T21:41:24.0713854Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0713978Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0714197Z     arg0_1 = rand_strided((1, 17, 8, 9), (1224, 72, 9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0714291Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0714308Z 
2023-01-11T21:41:24.0714361Z ok (1.595s)
2023-01-11T21:41:24.0714828Z   test_sum_dtype_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0714990Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0715330Z [2023-01-11 21:38:45,282] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 452
2023-01-11T21:41:24.0715596Z [2023-01-11 21:38:45,300] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 452
2023-01-11T21:41:24.0715601Z 
2023-01-11T21:41:24.0715694Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0715760Z import torch
2023-01-11T21:41:24.0715827Z import random
2023-01-11T21:41:24.0715939Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0716046Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0716050Z 
2023-01-11T21:41:24.0716127Z aten = torch.ops.aten
2023-01-11T21:41:24.0716257Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0716381Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0716386Z 
2023-01-11T21:41:24.0716391Z 
2023-01-11T21:41:24.0716524Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0716727Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0716842Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0716940Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:24.0717025Z                        double* __restrict__ out_ptr1,
2023-01-11T21:41:24.0717118Z                        double* __restrict__ out_ptr2)
2023-01-11T21:41:24.0717176Z {
2023-01-11T21:41:24.0717273Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0717329Z     {
2023-01-11T21:41:24.0717402Z         #pragma omp for 
2023-01-11T21:41:24.0717481Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0717529Z         {
2023-01-11T21:41:24.0717588Z             {
2023-01-11T21:41:24.0717649Z                 {
2023-01-11T21:41:24.0717732Z                     double tmp2 = 0;
2023-01-11T21:41:24.0717820Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:24.0717885Z                     {
2023-01-11T21:41:24.0717940Z                         {
2023-01-11T21:41:24.0718043Z                             auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:24.0718153Z                             auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.0718235Z                             tmp2 += tmp1;
2023-01-11T21:41:24.0718302Z                         }
2023-01-11T21:41:24.0718433Z                     }
2023-01-11T21:41:24.0718649Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0718700Z                 }
2023-01-11T21:41:24.0718763Z             }
2023-01-11T21:41:24.0718823Z         }
2023-01-11T21:41:24.0718882Z     }
2023-01-11T21:41:24.0718940Z     {
2023-01-11T21:41:24.0719000Z         {
2023-01-11T21:41:24.0719079Z             double tmp2 = 0;
2023-01-11T21:41:24.0719175Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0719238Z             {
2023-01-11T21:41:24.0719339Z                 #pragma omp for reduction(+:tmp2) 
2023-01-11T21:41:24.0719428Z                 for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:24.0719492Z                 {
2023-01-11T21:41:24.0719556Z                     {
2023-01-11T21:41:24.0719647Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0719744Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.0719822Z                         tmp2 += tmp1;
2023-01-11T21:41:24.0719887Z                     }
2023-01-11T21:41:24.0719951Z                 }
2023-01-11T21:41:24.0720014Z             }
2023-01-11T21:41:24.0720090Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:24.0720152Z         }
2023-01-11T21:41:24.0720201Z     }
2023-01-11T21:41:24.0720296Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0720356Z     {
2023-01-11T21:41:24.0720430Z         #pragma omp for 
2023-01-11T21:41:24.0720650Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0720764Z         {
2023-01-11T21:41:24.0720830Z             #pragma GCC ivdep
2023-01-11T21:41:24.0720911Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:24.0720972Z             {
2023-01-11T21:41:24.0721034Z                 {
2023-01-11T21:41:24.0721096Z                     {
2023-01-11T21:41:24.0721195Z                         auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:24.0721289Z                         auto tmp2 = out_ptr0[i1];
2023-01-11T21:41:24.0721369Z                         auto tmp4 = out_ptr1[0];
2023-01-11T21:41:24.0721476Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.0721568Z                         auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:24.0721658Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:24.0721751Z                         out_ptr2[i1 + (32*i0)] = tmp5;
2023-01-11T21:41:24.0721816Z                     }
2023-01-11T21:41:24.0721879Z                 }
2023-01-11T21:41:24.0721959Z             }
2023-01-11T21:41:24.0722022Z         }
2023-01-11T21:41:24.0722082Z     }
2023-01-11T21:41:24.0722141Z }
2023-01-11T21:41:24.0722225Z ''')
2023-01-11T21:41:24.0722230Z 
2023-01-11T21:41:24.0722235Z 
2023-01-11T21:41:24.0722319Z async_compile.wait(globals())
2023-01-11T21:41:24.0722390Z del async_compile
2023-01-11T21:41:24.0722395Z 
2023-01-11T21:41:24.0722451Z def call(args):
2023-01-11T21:41:24.0722517Z     arg0_1, = args
2023-01-11T21:41:24.0722585Z     args.clear()
2023-01-11T21:41:24.0722777Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.0722958Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.0723150Z     buf2 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.0723336Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0723404Z     del arg0_1
2023-01-11T21:41:24.0723464Z     return (buf2, )
2023-01-11T21:41:24.0723469Z 
2023-01-11T21:41:24.0723473Z 
2023-01-11T21:41:24.0723546Z if __name__ == "__main__":
2023-01-11T21:41:24.0723658Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0723778Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0723973Z     arg0_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0724078Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0724083Z 
2023-01-11T21:41:24.0724336Z ok (0.039s)
2023-01-11T21:41:24.0724797Z   test_sum_int_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0724927Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0725177Z [2023-01-11 21:38:45,320] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 453
2023-01-11T21:41:24.0725446Z [2023-01-11 21:38:45,333] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 453
2023-01-11T21:41:24.0725878Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0725999Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0726252Z [2023-01-11 21:38:45,351] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 454
2023-01-11T21:41:24.0726550Z [2023-01-11 21:38:45,365] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 454
2023-01-11T21:41:24.0726974Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0727095Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0727351Z [2023-01-11 21:38:45,384] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 455
2023-01-11T21:41:24.0727612Z [2023-01-11 21:38:45,396] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 455
2023-01-11T21:41:24.0727617Z 
2023-01-11T21:41:24.0727708Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0727769Z import torch
2023-01-11T21:41:24.0727865Z import random
2023-01-11T21:41:24.0727977Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0728094Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0728099Z 
2023-01-11T21:41:24.0728239Z aten = torch.ops.aten
2023-01-11T21:41:24.0728370Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0728459Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0728464Z 
2023-01-11T21:41:24.0728468Z 
2023-01-11T21:41:24.0728603Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0728794Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0728903Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0729004Z                        const bool* __restrict__ in_ptr0,
2023-01-11T21:41:24.0729099Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:24.0729157Z {
2023-01-11T21:41:24.0729238Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:24.0729302Z     {
2023-01-11T21:41:24.0729351Z         {
2023-01-11T21:41:24.0729422Z             long tmp2 = 0;
2023-01-11T21:41:24.0729493Z             long tmp3 = 0;
2023-01-11T21:41:24.0729593Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0729656Z             {
2023-01-11T21:41:24.0729778Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:24.0729862Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:24.0729914Z                 {
2023-01-11T21:41:24.0729980Z                     {
2023-01-11T21:41:24.0730071Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0730178Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:24.0730253Z                         tmp2 += tmp1;
2023-01-11T21:41:24.0730329Z                         tmp3 += tmp1;
2023-01-11T21:41:24.0730389Z                     }
2023-01-11T21:41:24.0730440Z                 }
2023-01-11T21:41:24.0730501Z             }
2023-01-11T21:41:24.0730582Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:24.0730654Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:24.0730715Z         }
2023-01-11T21:41:24.0730774Z     }
2023-01-11T21:41:24.0730821Z     {
2023-01-11T21:41:24.0730879Z         {
2023-01-11T21:41:24.0731023Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:24.0731103Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:24.0731197Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:24.0731275Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0731352Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0731419Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:24.0731476Z         }
2023-01-11T21:41:24.0731532Z     }
2023-01-11T21:41:24.0731589Z }
2023-01-11T21:41:24.0731665Z ''')
2023-01-11T21:41:24.0731671Z 
2023-01-11T21:41:24.0731675Z 
2023-01-11T21:41:24.0731759Z async_compile.wait(globals())
2023-01-11T21:41:24.0731827Z del async_compile
2023-01-11T21:41:24.0731865Z 
2023-01-11T21:41:24.0731924Z def call(args):
2023-01-11T21:41:24.0732278Z     arg0_1, = args
2023-01-11T21:41:24.0732409Z     args.clear()
2023-01-11T21:41:24.0732593Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0732771Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0732852Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0733013Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0733069Z     del arg0_1
2023-01-11T21:41:24.0733136Z     return (buf2, )
2023-01-11T21:41:24.0733141Z 
2023-01-11T21:41:24.0733146Z 
2023-01-11T21:41:24.0733219Z if __name__ == "__main__":
2023-01-11T21:41:24.0733332Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0733569Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0733759Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.0733898Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0733903Z 
2023-01-11T21:41:24.0733908Z 
2023-01-11T21:41:24.0733999Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0734065Z import torch
2023-01-11T21:41:24.0734121Z import random
2023-01-11T21:41:24.0734231Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0734351Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0734355Z 
2023-01-11T21:41:24.0734431Z aten = torch.ops.aten
2023-01-11T21:41:24.0734562Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0734650Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0734655Z 
2023-01-11T21:41:24.0734659Z 
2023-01-11T21:41:24.0734793Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0735001Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0735104Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0735220Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:41:24.0735313Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:24.0735370Z {
2023-01-11T21:41:24.0735455Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:24.0735512Z     {
2023-01-11T21:41:24.0735560Z         {
2023-01-11T21:41:24.0735631Z             long tmp2 = 0;
2023-01-11T21:41:24.0735700Z             long tmp3 = 0;
2023-01-11T21:41:24.0735802Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0735863Z             {
2023-01-11T21:41:24.0735986Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:24.0736071Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:24.0736123Z                 {
2023-01-11T21:41:24.0736184Z                     {
2023-01-11T21:41:24.0736275Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0736379Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:24.0736454Z                         tmp2 += tmp1;
2023-01-11T21:41:24.0736531Z                         tmp3 += tmp1;
2023-01-11T21:41:24.0736593Z                     }
2023-01-11T21:41:24.0736644Z                 }
2023-01-11T21:41:24.0736703Z             }
2023-01-11T21:41:24.0736777Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:24.0736853Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:24.0736911Z         }
2023-01-11T21:41:24.0736966Z     }
2023-01-11T21:41:24.0737021Z     {
2023-01-11T21:41:24.0737070Z         {
2023-01-11T21:41:24.0737156Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:24.0737234Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:24.0737329Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:24.0737408Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0737486Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0737565Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:24.0737614Z         }
2023-01-11T21:41:24.0737672Z     }
2023-01-11T21:41:24.0737726Z }
2023-01-11T21:41:24.0737850Z ''')
2023-01-11T21:41:24.0737857Z 
2023-01-11T21:41:24.0737861Z 
2023-01-11T21:41:24.0737947Z async_compile.wait(globals())
2023-01-11T21:41:24.0738017Z del async_compile
2023-01-11T21:41:24.0738022Z 
2023-01-11T21:41:24.0738090Z def call(args):
2023-01-11T21:41:24.0738146Z     arg0_1, = args
2023-01-11T21:41:24.0738214Z     args.clear()
2023-01-11T21:41:24.0738392Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0738567Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0738651Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0738880Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0738943Z     del arg0_1
2023-01-11T21:41:24.0739001Z     return (buf2, )
2023-01-11T21:41:24.0739006Z 
2023-01-11T21:41:24.0739020Z 
2023-01-11T21:41:24.0739082Z if __name__ == "__main__":
2023-01-11T21:41:24.0739188Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0739342Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0739535Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.uint8)
2023-01-11T21:41:24.0739640Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0739645Z 
2023-01-11T21:41:24.0739649Z 
2023-01-11T21:41:24.0739740Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0739804Z import torch
2023-01-11T21:41:24.0739860Z import random
2023-01-11T21:41:24.0739971Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0740084Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0740089Z 
2023-01-11T21:41:24.0740165Z aten = torch.ops.aten
2023-01-11T21:41:24.0740296Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0740383Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0740388Z 
2023-01-11T21:41:24.0740392Z 
2023-01-11T21:41:24.0740523Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0740730Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0740832Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0740931Z                        const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.0741023Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:24.0741081Z {
2023-01-11T21:41:24.0741164Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:24.0741222Z     {
2023-01-11T21:41:24.0741281Z         {
2023-01-11T21:41:24.0741342Z             long tmp2 = 0;
2023-01-11T21:41:24.0741498Z             long tmp3 = 0;
2023-01-11T21:41:24.0741603Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0741665Z             {
2023-01-11T21:41:24.0741789Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:24.0741874Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:24.0741935Z                 {
2023-01-11T21:41:24.0741988Z                     {
2023-01-11T21:41:24.0742083Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0742186Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:24.0742261Z                         tmp2 += tmp1;
2023-01-11T21:41:24.0742459Z                         tmp3 += tmp1;
2023-01-11T21:41:24.0742557Z                     }
2023-01-11T21:41:24.0742627Z                 }
2023-01-11T21:41:24.0742677Z             }
2023-01-11T21:41:24.0742754Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:24.0742827Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:24.0742888Z         }
2023-01-11T21:41:24.0742946Z     }
2023-01-11T21:41:24.0743004Z     {
2023-01-11T21:41:24.0743051Z         {
2023-01-11T21:41:24.0743133Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:24.0743215Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:24.0743309Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:24.0743388Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0743467Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0743616Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:24.0743665Z         }
2023-01-11T21:41:24.0743722Z     }
2023-01-11T21:41:24.0743780Z }
2023-01-11T21:41:24.0743864Z ''')
2023-01-11T21:41:24.0743870Z 
2023-01-11T21:41:24.0743875Z 
2023-01-11T21:41:24.0743962Z async_compile.wait(globals())
2023-01-11T21:41:24.0744114Z del async_compile
2023-01-11T21:41:24.0744120Z 
2023-01-11T21:41:24.0744188Z def call(args):
2023-01-11T21:41:24.0744345Z     arg0_1, = args
2023-01-11T21:41:24.0744412Z     args.clear()
2023-01-11T21:41:24.0744597Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0744772Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0744854Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0745015Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0745308Z     del arg0_1
2023-01-11T21:41:24.0745378Z     return (buf2, )
2023-01-11T21:41:24.0745430Z 
2023-01-11T21:41:24.0745436Z 
2023-01-11T21:41:24.0745499Z if __name__ == "__main__":
2023-01-11T21:41:24.0745668Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0745824Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0746027Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.0746161Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0746166Z 
2023-01-11T21:41:24.0746229Z ok (0.096s)
2023-01-11T21:41:24.0746701Z   test_sum_keepdims_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0746826Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0747093Z [2023-01-11 21:38:45,413] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 456
2023-01-11T21:41:24.0747346Z [2023-01-11 21:38:45,423] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 456
2023-01-11T21:41:24.0747361Z 
2023-01-11T21:41:24.0747575Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0747644Z import torch
2023-01-11T21:41:24.0747711Z import random
2023-01-11T21:41:24.0747825Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0747942Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0747947Z 
2023-01-11T21:41:24.0748024Z aten = torch.ops.aten
2023-01-11T21:41:24.0748154Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0748232Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0748237Z 
2023-01-11T21:41:24.0748251Z 
2023-01-11T21:41:24.0748372Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0748578Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0748701Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0748805Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0748902Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0748961Z {
2023-01-11T21:41:24.0749056Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0749104Z     {
2023-01-11T21:41:24.0749178Z         #pragma omp for 
2023-01-11T21:41:24.0749259Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0749319Z         {
2023-01-11T21:41:24.0749382Z             {
2023-01-11T21:41:24.0749575Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0749655Z                 float tmp3 = 0;
2023-01-11T21:41:24.0749763Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:24.0749891Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.0749955Z                 {
2023-01-11T21:41:24.0750098Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0750239Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:24.0750330Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0750410Z                     tmp3_vec += tmp2;
2023-01-11T21:41:24.0750475Z                 }
2023-01-11T21:41:24.0750655Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:24.0750842Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:24.0750926Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.0750989Z                 {
2023-01-11T21:41:24.0751088Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.0751221Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:24.0751315Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0751379Z                     tmp3 += tmp2;
2023-01-11T21:41:24.0751442Z                 }
2023-01-11T21:41:24.0751523Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0751584Z             }
2023-01-11T21:41:24.0751643Z         }
2023-01-11T21:41:24.0751700Z     }
2023-01-11T21:41:24.0751758Z }
2023-01-11T21:41:24.0751827Z ''')
2023-01-11T21:41:24.0751833Z 
2023-01-11T21:41:24.0751837Z 
2023-01-11T21:41:24.0751925Z async_compile.wait(globals())
2023-01-11T21:41:24.0751996Z del async_compile
2023-01-11T21:41:24.0752000Z 
2023-01-11T21:41:24.0752069Z def call(args):
2023-01-11T21:41:24.0752142Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0752210Z     args.clear()
2023-01-11T21:41:24.0752404Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0752555Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0752624Z     del arg0_1
2023-01-11T21:41:24.0752689Z     del arg1_1
2023-01-11T21:41:24.0752758Z     return (buf0, )
2023-01-11T21:41:24.0752763Z 
2023-01-11T21:41:24.0752767Z 
2023-01-11T21:41:24.0752840Z if __name__ == "__main__":
2023-01-11T21:41:24.0752951Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0753071Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0753266Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0753448Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0753562Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0753567Z 
2023-01-11T21:41:24.0753633Z ok (0.027s)
2023-01-11T21:41:24.0754153Z   test_tanh_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0754282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0754544Z [2023-01-11 21:38:45,455] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 457
2023-01-11T21:41:24.0754807Z [2023-01-11 21:38:47,032] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 457
2023-01-11T21:41:24.0754813Z 
2023-01-11T21:41:24.0754901Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0754969Z import torch
2023-01-11T21:41:24.0755025Z import random
2023-01-11T21:41:24.0755140Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0755260Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0755265Z 
2023-01-11T21:41:24.0755375Z aten = torch.ops.aten
2023-01-11T21:41:24.0755507Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0755595Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0755600Z 
2023-01-11T21:41:24.0755605Z 
2023-01-11T21:41:24.0755733Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0755938Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0756055Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0756142Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0756234Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0756292Z {
2023-01-11T21:41:24.0756387Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0756445Z     {
2023-01-11T21:41:24.0756518Z         #pragma omp for 
2023-01-11T21:41:24.0756588Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0756647Z         {
2023-01-11T21:41:24.0756808Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0756895Z             auto tmp1 = tmp0.tanh();
2023-01-11T21:41:24.0757025Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0757106Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0757231Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0757314Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:24.0757382Z             auto tmp6 = tmp5.tanh();
2023-01-11T21:41:24.0757467Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0757553Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0757612Z         }
2023-01-11T21:41:24.0757702Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0757783Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:24.0757842Z         {
2023-01-11T21:41:24.0757911Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0757998Z             auto tmp1 = std::tanh(tmp0);
2023-01-11T21:41:24.0758095Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:24.0758174Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0758270Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:24.0758349Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:24.0758435Z             auto tmp6 = std::tanh(tmp5);
2023-01-11T21:41:24.0758501Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0758575Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.0758635Z         }
2023-01-11T21:41:24.0758696Z     }
2023-01-11T21:41:24.0758749Z }
2023-01-11T21:41:24.0758823Z ''')
2023-01-11T21:41:24.0758829Z 
2023-01-11T21:41:24.0758833Z 
2023-01-11T21:41:24.0758921Z async_compile.wait(globals())
2023-01-11T21:41:24.0758980Z del async_compile
2023-01-11T21:41:24.0758984Z 
2023-01-11T21:41:24.0759051Z def call(args):
2023-01-11T21:41:24.0759115Z     arg0_1, = args
2023-01-11T21:41:24.0759183Z     args.clear()
2023-01-11T21:41:24.0759381Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0759574Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0759737Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0759793Z     del arg0_1
2023-01-11T21:41:24.0759866Z     return (buf0, buf1, )
2023-01-11T21:41:24.0759872Z 
2023-01-11T21:41:24.0759876Z 
2023-01-11T21:41:24.0759947Z if __name__ == "__main__":
2023-01-11T21:41:24.0760056Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0760172Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0760372Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0760478Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0760483Z 
2023-01-11T21:41:24.0760545Z ok (1.610s)
2023-01-11T21:41:24.0761013Z   test_tensor1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0761169Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0761421Z [2023-01-11 21:38:47,052] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 458
2023-01-11T21:41:24.0761685Z [2023-01-11 21:38:48,624] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 458
2023-01-11T21:41:24.0761690Z 
2023-01-11T21:41:24.0761779Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0761847Z import torch
2023-01-11T21:41:24.0761914Z import random
2023-01-11T21:41:24.0762026Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0762170Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0762178Z 
2023-01-11T21:41:24.0762252Z aten = torch.ops.aten
2023-01-11T21:41:24.0762372Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0762459Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0762463Z 
2023-01-11T21:41:24.0762468Z 
2023-01-11T21:41:24.0762600Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0762803Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0762921Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0763019Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0763113Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:24.0763169Z {
2023-01-11T21:41:24.0763253Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0763311Z     {
2023-01-11T21:41:24.0763386Z         #pragma omp for 
2023-01-11T21:41:24.0763463Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.0763525Z         {
2023-01-11T21:41:24.0763586Z             {
2023-01-11T21:41:24.0763637Z                 {
2023-01-11T21:41:24.0763725Z                     auto tmp2 = in_ptr0[i0];
2023-01-11T21:41:24.0763824Z                     auto tmp0 = static_cast<long>(1);
2023-01-11T21:41:24.0763930Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.0764016Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0764096Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0764159Z                 }
2023-01-11T21:41:24.0764210Z             }
2023-01-11T21:41:24.0764267Z         }
2023-01-11T21:41:24.0764342Z         #pragma omp single
2023-01-11T21:41:24.0764400Z         {
2023-01-11T21:41:24.0764462Z             {
2023-01-11T21:41:24.0764523Z                 {
2023-01-11T21:41:24.0764625Z                     auto tmp0 = static_cast<long>(5);
2023-01-11T21:41:24.0764693Z                     out_ptr1[0] = tmp0;
2023-01-11T21:41:24.0764752Z                 }
2023-01-11T21:41:24.0764817Z             }
2023-01-11T21:41:24.0764875Z         }
2023-01-11T21:41:24.0764934Z     }
2023-01-11T21:41:24.0764989Z }
2023-01-11T21:41:24.0765054Z ''')
2023-01-11T21:41:24.0765059Z 
2023-01-11T21:41:24.0765072Z 
2023-01-11T21:41:24.0765148Z async_compile.wait(globals())
2023-01-11T21:41:24.0765221Z del async_compile
2023-01-11T21:41:24.0765225Z 
2023-01-11T21:41:24.0765293Z def call(args):
2023-01-11T21:41:24.0765356Z     arg0_1, = args
2023-01-11T21:41:24.0765426Z     args.clear()
2023-01-11T21:41:24.0765936Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0766114Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0766266Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0766332Z     del arg0_1
2023-01-11T21:41:24.0766406Z     return (buf0, buf1, )
2023-01-11T21:41:24.0766411Z 
2023-01-11T21:41:24.0766415Z 
2023-01-11T21:41:24.0766526Z if __name__ == "__main__":
2023-01-11T21:41:24.0766639Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0766758Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0766953Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0767065Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0767070Z 
2023-01-11T21:41:24.0767124Z ok (1.592s)
2023-01-11T21:41:24.0767967Z   test_tensor2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0768157Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0768453Z [2023-01-11 21:38:48,642] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 459
2023-01-11T21:41:24.0768752Z [2023-01-11 21:38:50,192] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 459
2023-01-11T21:41:24.0768760Z 
2023-01-11T21:41:24.0768866Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0768936Z import torch
2023-01-11T21:41:24.0769004Z import random
2023-01-11T21:41:24.0769118Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0769225Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0769230Z 
2023-01-11T21:41:24.0769307Z aten = torch.ops.aten
2023-01-11T21:41:24.0769437Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0769527Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0769684Z constant0 = None  # 4ebd4ff1c68a89413a036eaaf84436373c4ec2939ac1d7f84e9908772a109281
2023-01-11T21:41:24.0769689Z 
2023-01-11T21:41:24.0769694Z 
2023-01-11T21:41:24.0769833Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0770036Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0770152Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:24.0770243Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0770338Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0770392Z {
2023-01-11T21:41:24.0770490Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0770550Z     {
2023-01-11T21:41:24.0770623Z         #pragma omp for 
2023-01-11T21:41:24.0770701Z         for(long i0=0; i0<19; i0+=1)
2023-01-11T21:41:24.0770752Z         {
2023-01-11T21:41:24.0770810Z             {
2023-01-11T21:41:24.0770871Z                 {
2023-01-11T21:41:24.0770959Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0771046Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:41:24.0771148Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.0771241Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.0771313Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0771375Z                 }
2023-01-11T21:41:24.0771435Z             }
2023-01-11T21:41:24.0771495Z         }
2023-01-11T21:41:24.0771550Z     }
2023-01-11T21:41:24.0771608Z }
2023-01-11T21:41:24.0771674Z ''')
2023-01-11T21:41:24.0771689Z 
2023-01-11T21:41:24.0771693Z 
2023-01-11T21:41:24.0771770Z async_compile.wait(globals())
2023-01-11T21:41:24.0771839Z del async_compile
2023-01-11T21:41:24.0771844Z 
2023-01-11T21:41:24.0771914Z def call(args):
2023-01-11T21:41:24.0771982Z     arg0_1, = args
2023-01-11T21:41:24.0772050Z     args.clear()
2023-01-11T21:41:24.0772242Z     buf0 = empty_strided((19, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0772408Z     kernel_cpp_0(c_void_p(constant0.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0772464Z     del arg0_1
2023-01-11T21:41:24.0772571Z     return (buf0, )
2023-01-11T21:41:24.0772578Z 
2023-01-11T21:41:24.0772582Z 
2023-01-11T21:41:24.0772655Z if __name__ == "__main__":
2023-01-11T21:41:24.0772769Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0772888Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0773084Z     constant0 = rand_strided((19, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0773274Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0773378Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0773383Z 
2023-01-11T21:41:24.0773436Z ok (1.568s)
2023-01-11T21:41:24.0773925Z   test_tensor3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0774053Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0774314Z [2023-01-11 21:38:50,222] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 460
2023-01-11T21:41:24.0774574Z [2023-01-11 21:38:51,763] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 460
2023-01-11T21:41:24.0774579Z 
2023-01-11T21:41:24.0774669Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0774735Z import torch
2023-01-11T21:41:24.0774806Z import random
2023-01-11T21:41:24.0774919Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0775025Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0775029Z 
2023-01-11T21:41:24.0775105Z aten = torch.ops.aten
2023-01-11T21:41:24.0775238Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0775329Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0775338Z 
2023-01-11T21:41:24.0775342Z 
2023-01-11T21:41:24.0775473Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0775678Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0775879Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0775974Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:24.0776055Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:24.0776149Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0776207Z {
2023-01-11T21:41:24.0776282Z     #pragma GCC ivdep
2023-01-11T21:41:24.0776359Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0776417Z     {
2023-01-11T21:41:24.0776477Z         {
2023-01-11T21:41:24.0776527Z             {
2023-01-11T21:41:24.0776624Z                 auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:24.0776719Z                 auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:24.0776808Z                 auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:24.0776901Z                 auto tmp3 = static_cast<long>(2);
2023-01-11T21:41:24.0776994Z                 auto tmp4 = tmp2 ? tmp1 : tmp3;
2023-01-11T21:41:24.0777079Z                 auto tmp5 = tmp4 + tmp1;
2023-01-11T21:41:24.0777147Z                 out_ptr0[i0] = tmp5;
2023-01-11T21:41:24.0777207Z             }
2023-01-11T21:41:24.0777267Z         }
2023-01-11T21:41:24.0777327Z     }
2023-01-11T21:41:24.0777400Z     #pragma GCC ivdep
2023-01-11T21:41:24.0777475Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:24.0777531Z     {
2023-01-11T21:41:24.0777579Z         {
2023-01-11T21:41:24.0777638Z             {
2023-01-11T21:41:24.0777732Z                 auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:24.0777826Z                 auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:24.0777908Z                 auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:24.0777999Z                 auto tmp3 = static_cast<long>(2);
2023-01-11T21:41:24.0778121Z                 auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:24.0778211Z                 auto tmp5 = static_cast<long>(3);
2023-01-11T21:41:24.0778303Z                 auto tmp6 = tmp4 ? tmp3 : tmp5;
2023-01-11T21:41:24.0778391Z                 auto tmp7 = tmp2 ? tmp1 : tmp6;
2023-01-11T21:41:24.0778471Z                 auto tmp8 = tmp7 + tmp3;
2023-01-11T21:41:24.0778553Z                 out_ptr1[i0] = tmp8;
2023-01-11T21:41:24.0778614Z             }
2023-01-11T21:41:24.0778664Z         }
2023-01-11T21:41:24.0778722Z     }
2023-01-11T21:41:24.0778815Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0778874Z     {
2023-01-11T21:41:24.0778947Z         #pragma omp for 
2023-01-11T21:41:24.0779026Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.0779088Z         {
2023-01-11T21:41:24.0779137Z             {
2023-01-11T21:41:24.0779197Z                 {
2023-01-11T21:41:24.0779285Z                     auto tmp12 = in_ptr0[i0];
2023-01-11T21:41:24.0779385Z                     auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:24.0779579Z                     auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:24.0779669Z                     auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:24.0779767Z                     auto tmp3 = static_cast<long>(1);
2023-01-11T21:41:24.0779843Z                     auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:24.0779935Z                     auto tmp5 = tmp4 ? tmp3 : tmp1;
2023-01-11T21:41:24.0780033Z                     auto tmp6 = static_cast<long>(3);
2023-01-11T21:41:24.0780117Z                     auto tmp7 = tmp0 < tmp6;
2023-01-11T21:41:24.0780214Z                     auto tmp8 = static_cast<long>(4);
2023-01-11T21:41:24.0780308Z                     auto tmp9 = tmp7 ? tmp6 : tmp8;
2023-01-11T21:41:24.0780510Z                     auto tmp10 = tmp2 ? tmp5 : tmp9;
2023-01-11T21:41:24.0780606Z                     auto tmp11 = static_cast<float>(tmp10);
2023-01-11T21:41:24.0780696Z                     auto tmp13 = tmp11 + tmp12;
2023-01-11T21:41:24.0780779Z                     out_ptr2[i0] = tmp13;
2023-01-11T21:41:24.0780846Z                 }
2023-01-11T21:41:24.0781080Z             }
2023-01-11T21:41:24.0781143Z         }
2023-01-11T21:41:24.0781200Z     }
2023-01-11T21:41:24.0781247Z }
2023-01-11T21:41:24.0781328Z ''')
2023-01-11T21:41:24.0781333Z 
2023-01-11T21:41:24.0781338Z 
2023-01-11T21:41:24.0781426Z async_compile.wait(globals())
2023-01-11T21:41:24.0781498Z del async_compile
2023-01-11T21:41:24.0781503Z 
2023-01-11T21:41:24.0781570Z def call(args):
2023-01-11T21:41:24.0781637Z     arg0_1, = args
2023-01-11T21:41:24.0781705Z     args.clear()
2023-01-11T21:41:24.0781880Z     buf0 = empty_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0782127Z     buf1 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0782508Z     buf2 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0782741Z     buf3 = empty_strided((0, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0782936Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0783007Z     del arg0_1
2023-01-11T21:41:24.0783093Z     return (buf3, buf0, buf1, buf2, )
2023-01-11T21:41:24.0783099Z 
2023-01-11T21:41:24.0783103Z 
2023-01-11T21:41:24.0783178Z if __name__ == "__main__":
2023-01-11T21:41:24.0783288Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0783397Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0783587Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0783693Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0783697Z 
2023-01-11T21:41:24.0783764Z ok (1.571s)
2023-01-11T21:41:24.0784243Z   test_tmp_not_defined_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0784555Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0784933Z Failed to collect metadata on function, produced code may be suboptimal.  Known situations this can occur are inference mode only compilation involving resize_ or prims (!schema.hasAnyAliasInfo() INTERNAL ASSERT FAILED); if your situation looks different please file a bug to PyTorch.
2023-01-11T21:41:24.0785069Z Traceback (most recent call last):
2023-01-11T21:41:24.0785353Z   File "/opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 1273, in aot_wrapper_dedupe
2023-01-11T21:41:24.0785671Z     fw_metadata, _out, _num_aliasing_metadata_outs = run_functionalized_fw_and_collect_metadata(
2023-01-11T21:41:24.0785972Z   File "/opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 289, in inner
2023-01-11T21:41:24.0786047Z     outs = f(*f_args)
2023-01-11T21:41:24.0786311Z   File "/opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 2327, in functional_call
2023-01-11T21:41:24.0786431Z     out = Interpreter(mod).run(*args[params_len:], **kwargs)
2023-01-11T21:41:24.0786658Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py", line 136, in run
2023-01-11T21:41:24.0786753Z     self.env[node] = self.run_node(node)
2023-01-11T21:41:24.0786986Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py", line 177, in run_node
2023-01-11T21:41:24.0787289Z     return getattr(self, n.op)(n.target, args, kwargs)
2023-01-11T21:41:24.0787537Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py", line 249, in call_function
2023-01-11T21:41:24.0787622Z     return target(*args, **kwargs)
2023-01-11T21:41:24.0787839Z   File "/opt/conda/lib/python3.10/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:41:24.0787942Z     return self._op(*args, **kwargs or {})
2023-01-11T21:41:24.0788203Z   File "/opt/conda/lib/python3.10/site-packages/torch/_inductor/overrides.py", line 36, in __torch_function__
2023-01-11T21:41:24.0788286Z     return func(*args, **kwargs)
2023-01-11T21:41:24.0788503Z   File "/opt/conda/lib/python3.10/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:41:24.0788587Z     return self._op(*args, **kwargs or {})
2023-01-11T21:41:24.0788831Z   File "/opt/conda/lib/python3.10/site-packages/torch/_prims/__init__.py", line 285, in _autograd_impl
2023-01-11T21:41:24.0788950Z     return backwards_not_supported(_prim)(*args, **kwargs)
2023-01-11T21:41:24.0789207Z   File "/opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py", line 309, in _autograd_impl
2023-01-11T21:41:24.0789302Z     return redispatch_prim(args, kwargs)
2023-01-11T21:41:24.0789563Z   File "/opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py", line 279, in redispatch_prim
2023-01-11T21:41:24.0789653Z     return prim(*args, **kwargs)
2023-01-11T21:41:24.0789868Z   File "/opt/conda/lib/python3.10/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:41:24.0789953Z     return self._op(*args, **kwargs or {})
2023-01-11T21:41:24.0790448Z RuntimeError: !schema.hasAnyAliasInfo() INTERNAL ASSERT FAILED at "/var/lib/jenkins/workspace/aten/src/ATen/FunctionalizeFallbackKernel.cpp":32, please report a bug to PyTorch. mutating and aliasing ops should all have codegen'd kernels
2023-01-11T21:41:24.0790454Z 
2023-01-11T21:41:24.0790689Z While executing %broadcast_in_dim_default : [#users=1] = call_function[target=torch.ops.prims.broadcast_in_dim.default](args = (%var_default_1, [1, 512, 1], [0, 1]), kwargs = {})
2023-01-11T21:41:24.0790763Z Original traceback:
2023-01-11T21:41:24.0790834Z Module stack: {}
2023-01-11T21:41:24.0790995Z   File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 4723, in forward
2023-01-11T21:41:24.0791143Z     broadcast_in_dim_default_2 = torch.ops.prims.broadcast_in_dim.default(
2023-01-11T21:41:24.0791336Z  |   File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 318, in run
2023-01-11T21:41:24.0791417Z     return model(*ex, **kwargs)
2023-01-11T21:41:24.0791422Z 
2023-01-11T21:41:24.0791672Z [2023-01-11 21:38:51,997] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 461
2023-01-11T21:41:24.0791687Z 
2023-01-11T21:41:24.0791769Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0791838Z import torch
2023-01-11T21:41:24.0791903Z import random
2023-01-11T21:41:24.0792015Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0792131Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0792136Z 
2023-01-11T21:41:24.0792213Z aten = torch.ops.aten
2023-01-11T21:41:24.0792343Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0792422Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0792429Z 
2023-01-11T21:41:24.0792472Z 
2023-01-11T21:41:24.0792594Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0792793Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0792906Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0793006Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:24.0793106Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0793207Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0793303Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0793390Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:24.0793487Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:24.0793584Z                        const float* __restrict__ in_ptr5,
2023-01-11T21:41:24.0793678Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0793831Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0793925Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.0794014Z                        float* __restrict__ out_ptr5)
2023-01-11T21:41:24.0794062Z {
2023-01-11T21:41:24.0794237Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:24.0794317Z     auto out_ptr4 = in_out_ptr1;
2023-01-11T21:41:24.0794414Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0794474Z     {
2023-01-11T21:41:24.0794546Z         #pragma omp for 
2023-01-11T21:41:24.0794624Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:24.0794674Z         {
2023-01-11T21:41:24.0794734Z             {
2023-01-11T21:41:24.0794929Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0795004Z                 float tmp1 = 0;
2023-01-11T21:41:24.0795123Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.0795216Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:24.0795281Z                 {
2023-01-11T21:41:24.0795427Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (1024*i0));
2023-01-11T21:41:24.0795494Z                     tmp1_vec += tmp0;
2023-01-11T21:41:24.0795556Z                 }
2023-01-11T21:41:24.0795750Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.0795868Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:24.0796148Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:24.0796209Z                 {
2023-01-11T21:41:24.0796309Z                     auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:41:24.0796372Z                     tmp1 += tmp0;
2023-01-11T21:41:24.0796433Z                 }
2023-01-11T21:41:24.0796510Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0796574Z             }
2023-01-11T21:41:24.0796675Z         }
2023-01-11T21:41:24.0796949Z         #pragma omp for 
2023-01-11T21:41:24.0797099Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:24.0797149Z         {
2023-01-11T21:41:24.0797208Z             {
2023-01-11T21:41:24.0797396Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0797473Z                 float tmp6 = 0;
2023-01-11T21:41:24.0797590Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.0797675Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:24.0797737Z                 {
2023-01-11T21:41:24.0797875Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (1024*i0));
2023-01-11T21:41:24.0797987Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:24.0798121Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:24.0798249Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0798395Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0798482Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.0798562Z                     tmp6_vec += tmp5;
2023-01-11T21:41:24.0798632Z                 }
2023-01-11T21:41:24.0798825Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.0798932Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6)
2023-01-11T21:41:24.0799019Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:24.0799081Z                 {
2023-01-11T21:41:24.0799179Z                     auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:41:24.0799268Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:24.0799367Z                     auto tmp2 = static_cast<float>(1024);
2023-01-11T21:41:24.0799452Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0799580Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0799666Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0799742Z                     tmp6 += tmp5;
2023-01-11T21:41:24.0799806Z                 }
2023-01-11T21:41:24.0799883Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.0799943Z             }
2023-01-11T21:41:24.0800004Z         }
2023-01-11T21:41:24.0800067Z         #pragma omp for 
2023-01-11T21:41:24.0800210Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:24.0800271Z         {
2023-01-11T21:41:24.0800403Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0800536Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:24.0800738Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0800831Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0800881Z         }
2023-01-11T21:41:24.0800973Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0801058Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:41:24.0801117Z         {
2023-01-11T21:41:24.0801197Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:24.0801298Z             auto tmp1 = static_cast<float>(1024);
2023-01-11T21:41:24.0801377Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0801447Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0801505Z         }
2023-01-11T21:41:24.0801580Z         #pragma omp for 
2023-01-11T21:41:24.0801661Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:24.0801720Z         {
2023-01-11T21:41:24.0801780Z             {
2023-01-11T21:41:24.0801963Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0802028Z                 float tmp9 = 0;
2023-01-11T21:41:24.0802146Z                 auto tmp9_vec = at::vec::Vectorized<float>(tmp9);
2023-01-11T21:41:24.0802234Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:24.0802362Z                 {
2023-01-11T21:41:24.0802504Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (1024*i0));
2023-01-11T21:41:24.0802629Z                     auto tmp1 = at::vec::Vectorized<float>(in_ptr2[i0]);
2023-01-11T21:41:24.0802749Z                     auto tmp3 = at::vec::Vectorized<float>(in_ptr3[i0]);
2023-01-11T21:41:24.0802882Z                     auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr4 + 8*i1);
2023-01-11T21:41:24.0803010Z                     auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr5 + 8*i1);
2023-01-11T21:41:24.0803137Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:24.0803225Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:24.0803313Z                     auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.0803398Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0803499Z                     tmp8.store(out_ptr2 + (8*i1) + (1024*i0));
2023-01-11T21:41:24.0803611Z                     tmp9_vec += tmp8;
2023-01-11T21:41:24.0803676Z                 }
2023-01-11T21:41:24.0803858Z                 tmp9 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp9_vec);
2023-01-11T21:41:24.0803974Z                 #pragma omp simd simdlen(4)  reduction(+:tmp9)
2023-01-11T21:41:24.0804064Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:24.0804126Z                 {
2023-01-11T21:41:24.0804220Z                     auto tmp0 = in_ptr1[i1 + (1024*i0)];
2023-01-11T21:41:24.0804304Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:24.0804389Z                     auto tmp3 = in_ptr3[i0];
2023-01-11T21:41:24.0804474Z                     auto tmp5 = in_ptr4[i1];
2023-01-11T21:41:24.0804548Z                     auto tmp7 = in_ptr5[i1];
2023-01-11T21:41:24.0804679Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:24.0804762Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:24.0804850Z                     auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.0804932Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:24.0805023Z                     out_ptr2[i1 + (1024*i0)] = tmp8;
2023-01-11T21:41:24.0805094Z                     tmp9 += tmp8;
2023-01-11T21:41:24.0805145Z                 }
2023-01-11T21:41:24.0805222Z                 out_ptr3[i0] = tmp9;
2023-01-11T21:41:24.0805285Z             }
2023-01-11T21:41:24.0805343Z         }
2023-01-11T21:41:24.0805415Z         #pragma omp for 
2023-01-11T21:41:24.0805494Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:24.0805544Z         {
2023-01-11T21:41:24.0805602Z             {
2023-01-11T21:41:24.0805789Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0805864Z                 float tmp6 = 0;
2023-01-11T21:41:24.0805981Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.0806055Z                 float tmp7 = 0;
2023-01-11T21:41:24.0806176Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:24.0806262Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:24.0806313Z                 {
2023-01-11T21:41:24.0806453Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + (8*i1) + (1024*i0));
2023-01-11T21:41:24.0806576Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr3[i0]);
2023-01-11T21:41:24.0806709Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:24.0806797Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0806932Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0807018Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.0807097Z                     tmp6_vec += tmp5;
2023-01-11T21:41:24.0807163Z                     tmp7_vec += tmp0;
2023-01-11T21:41:24.0807291Z                 }
2023-01-11T21:41:24.0807486Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.0807706Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:24.0807843Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:24.0807933Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:24.0807999Z                 {
2023-01-11T21:41:24.0808097Z                     auto tmp0 = out_ptr2[i1 + (1024*i0)];
2023-01-11T21:41:24.0808176Z                     auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:24.0808276Z                     auto tmp2 = static_cast<float>(1024);
2023-01-11T21:41:24.0808364Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.0808496Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.0808581Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.0808685Z                     tmp6 += tmp5;
2023-01-11T21:41:24.0808760Z                     tmp7 += tmp0;
2023-01-11T21:41:24.0808811Z                 }
2023-01-11T21:41:24.0808886Z                 out_ptr4[i0] = tmp6;
2023-01-11T21:41:24.0808963Z                 out_ptr5[i0] = tmp7;
2023-01-11T21:41:24.0809025Z             }
2023-01-11T21:41:24.0809083Z         }
2023-01-11T21:41:24.0809154Z         #pragma omp for 
2023-01-11T21:41:24.0809230Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:24.0809278Z         {
2023-01-11T21:41:24.0809405Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr4 + 8*i0);
2023-01-11T21:41:24.0809538Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:24.0809618Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0809883Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:24.0809965Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0810061Z             tmp4.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:24.0810123Z         }
2023-01-11T21:41:24.0810265Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0810343Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:41:24.0810404Z         {
2023-01-11T21:41:24.0810487Z             auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:24.0810584Z             auto tmp1 = static_cast<float>(1024);
2023-01-11T21:41:24.0810665Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0810805Z             auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:41:24.0810880Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0810960Z             in_out_ptr1[i0] = tmp4;
2023-01-11T21:41:24.0811021Z         }
2023-01-11T21:41:24.0811080Z     }
2023-01-11T21:41:24.0811136Z }
2023-01-11T21:41:24.0811210Z ''')
2023-01-11T21:41:24.0811216Z 
2023-01-11T21:41:24.0811220Z 
2023-01-11T21:41:24.0811306Z async_compile.wait(globals())
2023-01-11T21:41:24.0811365Z del async_compile
2023-01-11T21:41:24.0811370Z 
2023-01-11T21:41:24.0811440Z def call(args):
2023-01-11T21:41:24.0811542Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1 = args
2023-01-11T21:41:24.0811610Z     args.clear()
2023-01-11T21:41:24.0811957Z     buf0 = empty_strided((1, 512, 1), (512, 1, 512), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0812158Z     buf1 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0812238Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:24.0812456Z     buf3 = empty_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0812715Z     buf4 = empty_strided((1, 512, 1), (512, 1, 512), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0812910Z     buf5 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0813103Z     buf6 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0813214Z     buf7 = as_strided(buf5, (1, 512, 1), (512, 1, 1)); del buf5  # reuse
2023-01-11T21:41:24.0813628Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg5_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:41:24.0813764Z     del arg0_1
2023-01-11T21:41:24.0813827Z     del arg1_1
2023-01-11T21:41:24.0813887Z     del arg2_1
2023-01-11T21:41:24.0813940Z     del arg3_1
2023-01-11T21:41:24.0814001Z     del arg4_1
2023-01-11T21:41:24.0814062Z     del arg5_1
2023-01-11T21:41:24.0814301Z     return (buf2, buf6, buf7, )
2023-01-11T21:41:24.0814307Z 
2023-01-11T21:41:24.0814312Z 
2023-01-11T21:41:24.0814388Z if __name__ == "__main__":
2023-01-11T21:41:24.0814500Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0814620Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0814839Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0815036Z     arg1_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0815251Z     arg2_1 = rand_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0815464Z     arg3_1 = rand_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0815670Z     arg4_1 = rand_strided((1, 512, 1), (512, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0815871Z     arg5_1 = rand_strided((1, 512, 1), (512, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0816008Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1]))
2023-01-11T21:41:24.0816337Z [2023-01-11 21:38:53,613] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 461
2023-01-11T21:41:24.0816343Z 
2023-01-11T21:41:24.0816406Z ok (1.863s)
2023-01-11T21:41:24.0816876Z   test_tmp_not_defined_issue2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0817002Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0817263Z [2023-01-11 21:38:53,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 462
2023-01-11T21:41:24.0817520Z [2023-01-11 21:38:55,232] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 462
2023-01-11T21:41:24.0817525Z 
2023-01-11T21:41:24.0817616Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0817683Z import torch
2023-01-11T21:41:24.0817752Z import random
2023-01-11T21:41:24.0817866Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0817989Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0817996Z 
2023-01-11T21:41:24.0818062Z aten = torch.ops.aten
2023-01-11T21:41:24.0818195Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0818287Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0818292Z 
2023-01-11T21:41:24.0818296Z 
2023-01-11T21:41:24.0818430Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0818632Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0818745Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0818846Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0818945Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.0819032Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0819089Z {
2023-01-11T21:41:24.0819150Z     {
2023-01-11T21:41:24.0819388Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.0819464Z         float tmp5 = 0;
2023-01-11T21:41:24.0819580Z         auto tmp5_vec = at::vec::Vectorized<float>(tmp5);
2023-01-11T21:41:24.0819682Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0819731Z         {
2023-01-11T21:41:24.0819834Z             #pragma omp for reduction(+:tmp5_vec) 
2023-01-11T21:41:24.0819921Z             for(long i0=0; i0<17600; i0+=1)
2023-01-11T21:41:24.0819981Z             {
2023-01-11T21:41:24.0820113Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0820233Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:24.0820364Z                 auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:24.0820453Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0820527Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:24.0820633Z                 tmp5_vec += tmp4;
2023-01-11T21:41:24.0820694Z             }
2023-01-11T21:41:24.0820881Z             tmp5 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp5_vec);
2023-01-11T21:41:24.0821002Z             #pragma omp for simd simdlen(4) reduction(+:tmp5) 
2023-01-11T21:41:24.0821094Z             for(long i0=140800; i0<140800; i0+=1)
2023-01-11T21:41:24.0821156Z             {
2023-01-11T21:41:24.0821229Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0821314Z                 auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.0821398Z                 auto tmp3 = in_ptr2[i0];
2023-01-11T21:41:24.0821482Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.0821564Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:24.0821637Z                 tmp5 += tmp4;
2023-01-11T21:41:24.0821699Z             }
2023-01-11T21:41:24.0821748Z         }
2023-01-11T21:41:24.0821820Z         out_ptr0[0] = tmp5;
2023-01-11T21:41:24.0821885Z     }
2023-01-11T21:41:24.0821942Z }
2023-01-11T21:41:24.0822018Z ''')
2023-01-11T21:41:24.0822023Z 
2023-01-11T21:41:24.0822027Z 
2023-01-11T21:41:24.0822113Z async_compile.wait(globals())
2023-01-11T21:41:24.0822185Z del async_compile
2023-01-11T21:41:24.0822190Z 
2023-01-11T21:41:24.0822246Z def call(args):
2023-01-11T21:41:24.0822523Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:24.0822592Z     args.clear()
2023-01-11T21:41:24.0822885Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0823087Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0823200Z     return (buf0, primals_1, primals_2, primals_3, )
2023-01-11T21:41:24.0823205Z 
2023-01-11T21:41:24.0823209Z 
2023-01-11T21:41:24.0823281Z if __name__ == "__main__":
2023-01-11T21:41:24.0823393Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0823506Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0823953Z     primals_1 = rand_strided((1, 88, 40, 40), (140800, 1600, 40, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0824144Z     primals_2 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0824370Z     primals_3 = rand_strided((1, 88, 40, 40), (140800, 1600, 40, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0824515Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:24.0824523Z 
2023-01-11T21:41:24.0824617Z ok (1.608s)
2023-01-11T21:41:24.0824764Z   test_to_device_constant_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:24.0824894Z   test_to_device_cpu (__main__.CpuTests) ... skip: requires cuda (0.000s)
2023-01-11T21:41:24.0825363Z   test_to_dtype_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0825546Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0826027Z [2023-01-11 21:38:55,333] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 463
2023-01-11T21:41:24.0826293Z [2023-01-11 21:38:56,877] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 463
2023-01-11T21:41:24.0826299Z 
2023-01-11T21:41:24.0826390Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0826458Z import torch
2023-01-11T21:41:24.0826525Z import random
2023-01-11T21:41:24.0826635Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0826751Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0826756Z 
2023-01-11T21:41:24.0826829Z aten = torch.ops.aten
2023-01-11T21:41:24.0826994Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0827084Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0827089Z 
2023-01-11T21:41:24.0827093Z 
2023-01-11T21:41:24.0827224Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0827422Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0827537Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.0827634Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0827727Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:24.0827786Z {
2023-01-11T21:41:24.0827871Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0827929Z     {
2023-01-11T21:41:24.0828002Z         #pragma omp for 
2023-01-11T21:41:24.0828082Z         for(long i0=0; i0<40; i0+=1)
2023-01-11T21:41:24.0828141Z         {
2023-01-11T21:41:24.0828201Z             {
2023-01-11T21:41:24.0828255Z                 {
2023-01-11T21:41:24.0828347Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0828449Z                     auto tmp1 = static_cast<double>(1);
2023-01-11T21:41:24.0828537Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0828642Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:24.0828744Z                     auto tmp4 = static_cast<bool>(tmp0);
2023-01-11T21:41:24.0828824Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.0828894Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:24.0828957Z                 }
2023-01-11T21:41:24.0829019Z             }
2023-01-11T21:41:24.0829077Z         }
2023-01-11T21:41:24.0829136Z     }
2023-01-11T21:41:24.0829194Z }
2023-01-11T21:41:24.0829267Z ''')
2023-01-11T21:41:24.0829272Z 
2023-01-11T21:41:24.0829276Z 
2023-01-11T21:41:24.0829352Z async_compile.wait(globals())
2023-01-11T21:41:24.0829421Z del async_compile
2023-01-11T21:41:24.0829427Z 
2023-01-11T21:41:24.0829496Z def call(args):
2023-01-11T21:41:24.0829567Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0829637Z     args.clear()
2023-01-11T21:41:24.0829840Z     buf0 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0830036Z     buf1 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.0830199Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0830277Z     return (arg0_1, buf0, arg1_1, buf1, )
2023-01-11T21:41:24.0830282Z 
2023-01-11T21:41:24.0830296Z 
2023-01-11T21:41:24.0830358Z if __name__ == "__main__":
2023-01-11T21:41:24.0830464Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0830585Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0830790Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0830991Z     arg1_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.0831132Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0831138Z 
2023-01-11T21:41:24.0831200Z ok (1.641s)
2023-01-11T21:41:24.0831660Z   test_topk_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0831772Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0832033Z [2023-01-11 21:38:56,895] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 464
2023-01-11T21:41:24.0832243Z [2023-01-11 21:38:56,900] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.topk
2023-01-11T21:41:24.0832533Z [2023-01-11 21:38:56,903] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 464
2023-01-11T21:41:24.0832541Z 
2023-01-11T21:41:24.0832632Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0832699Z import torch
2023-01-11T21:41:24.0832766Z import random
2023-01-11T21:41:24.0832879Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0832986Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0833000Z 
2023-01-11T21:41:24.0833064Z aten = torch.ops.aten
2023-01-11T21:41:24.0833192Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0833281Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0833286Z 
2023-01-11T21:41:24.0833291Z 
2023-01-11T21:41:24.0833376Z async_compile.wait(globals())
2023-01-11T21:41:24.0833446Z del async_compile
2023-01-11T21:41:24.0833451Z 
2023-01-11T21:41:24.0833516Z def call(args):
2023-01-11T21:41:24.0833580Z     arg0_1, = args
2023-01-11T21:41:24.0833638Z     args.clear()
2023-01-11T21:41:24.0833780Z     buf0 = aten.topk(arg0_1, 2)
2023-01-11T21:41:24.0833849Z     del arg0_1
2023-01-11T21:41:24.0834000Z     buf1 = buf0[0]
2023-01-11T21:41:24.0834104Z     assert_size_stride(buf1, (1, 1, 8, 2), (16, 16, 2, 1))
2023-01-11T21:41:24.0834168Z     buf2 = buf0[1]
2023-01-11T21:41:24.0834271Z     assert_size_stride(buf2, (1, 1, 8, 2), (16, 16, 2, 1))
2023-01-11T21:41:24.0834325Z     del buf0
2023-01-11T21:41:24.0834401Z     return (buf1, buf2, )
2023-01-11T21:41:24.0834406Z 
2023-01-11T21:41:24.0834410Z 
2023-01-11T21:41:24.0834486Z if __name__ == "__main__":
2023-01-11T21:41:24.0834597Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0834718Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0834934Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0835041Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0835045Z 
2023-01-11T21:41:24.0835108Z ok (0.024s)
2023-01-11T21:41:24.0835777Z   test_transpose_add_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0835892Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0836153Z [2023-01-11 21:38:56,917] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 465
2023-01-11T21:41:24.0836422Z [2023-01-11 21:38:58,454] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 465
2023-01-11T21:41:24.0836428Z 
2023-01-11T21:41:24.0836522Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0836591Z import torch
2023-01-11T21:41:24.0836659Z import random
2023-01-11T21:41:24.0836772Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0837132Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0837138Z 
2023-01-11T21:41:24.0837202Z aten = torch.ops.aten
2023-01-11T21:41:24.0837332Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0837421Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0837426Z 
2023-01-11T21:41:24.0837430Z 
2023-01-11T21:41:24.0837613Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0837988Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0838109Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0838212Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0838308Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0838357Z {
2023-01-11T21:41:24.0838457Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0838517Z     {
2023-01-11T21:41:24.0838591Z         #pragma omp for 
2023-01-11T21:41:24.0838712Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:24.0838772Z         {
2023-01-11T21:41:24.0838918Z             #pragma GCC ivdep
2023-01-11T21:41:24.0838991Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:24.0839050Z             {
2023-01-11T21:41:24.0839111Z                 {
2023-01-11T21:41:24.0839173Z                     {
2023-01-11T21:41:24.0839274Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:24.0839375Z                         auto tmp1 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:24.0839527Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0839611Z                         out_ptr0[i0 + (32*i1)] = tmp2;
2023-01-11T21:41:24.0839673Z                     }
2023-01-11T21:41:24.0839736Z                 }
2023-01-11T21:41:24.0839798Z             }
2023-01-11T21:41:24.0839858Z         }
2023-01-11T21:41:24.0839920Z     }
2023-01-11T21:41:24.0839966Z }
2023-01-11T21:41:24.0840045Z ''')
2023-01-11T21:41:24.0840053Z 
2023-01-11T21:41:24.0840060Z 
2023-01-11T21:41:24.0840146Z async_compile.wait(globals())
2023-01-11T21:41:24.0840215Z del async_compile
2023-01-11T21:41:24.0840220Z 
2023-01-11T21:41:24.0840287Z def call(args):
2023-01-11T21:41:24.0840357Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0840423Z     args.clear()
2023-01-11T21:41:24.0840621Z     buf0 = empty_strided((32, 16), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0840771Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0840835Z     del arg0_1
2023-01-11T21:41:24.0840898Z     del arg1_1
2023-01-11T21:41:24.0840966Z     return (buf0, )
2023-01-11T21:41:24.0840970Z 
2023-01-11T21:41:24.0840974Z 
2023-01-11T21:41:24.0841047Z if __name__ == "__main__":
2023-01-11T21:41:24.0841158Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0841279Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0841479Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0841665Z     arg1_1 = rand_strided((32, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0841777Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0841782Z 
2023-01-11T21:41:24.0841845Z ok (1.552s)
2023-01-11T21:41:24.0842312Z   test_transpose_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0842435Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0842690Z [2023-01-11 21:38:58,479] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 466
2023-01-11T21:41:24.0842987Z [2023-01-11 21:38:58,493] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 466
2023-01-11T21:41:24.0842992Z 
2023-01-11T21:41:24.0843080Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0843148Z import torch
2023-01-11T21:41:24.0843205Z import random
2023-01-11T21:41:24.0843314Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0843434Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0843439Z 
2023-01-11T21:41:24.0843516Z aten = torch.ops.aten
2023-01-11T21:41:24.0843644Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0843731Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0843735Z 
2023-01-11T21:41:24.0843739Z 
2023-01-11T21:41:24.0843872Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0844076Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0844181Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0844311Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0844407Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0844499Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0844553Z {
2023-01-11T21:41:24.0844645Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0844706Z     {
2023-01-11T21:41:24.0844769Z         #pragma omp for 
2023-01-11T21:41:24.0844848Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0844906Z         {
2023-01-11T21:41:24.0844981Z             #pragma GCC ivdep
2023-01-11T21:41:24.0845063Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:24.0845122Z             {
2023-01-11T21:41:24.0845269Z                 {
2023-01-11T21:41:24.0845322Z                     {
2023-01-11T21:41:24.0845420Z                         auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:24.0845516Z                         auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:24.0845613Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0845704Z                         out_ptr0[i0 + (8*i1)] = tmp2;
2023-01-11T21:41:24.0845769Z                     }
2023-01-11T21:41:24.0845831Z                 }
2023-01-11T21:41:24.0845880Z             }
2023-01-11T21:41:24.0845941Z         }
2023-01-11T21:41:24.0846015Z         #pragma omp for 
2023-01-11T21:41:24.0846093Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0846150Z         {
2023-01-11T21:41:24.0846286Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0846418Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0846493Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0846622Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:24.0846702Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0846794Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0846851Z         }
2023-01-11T21:41:24.0846945Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0847189Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0847238Z         {
2023-01-11T21:41:24.0847316Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:24.0847411Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:24.0847491Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0847586Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:41:24.0847665Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0847740Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:24.0847789Z         }
2023-01-11T21:41:24.0847846Z     }
2023-01-11T21:41:24.0847904Z }
2023-01-11T21:41:24.0847982Z ''')
2023-01-11T21:41:24.0847988Z 
2023-01-11T21:41:24.0847992Z 
2023-01-11T21:41:24.0848078Z async_compile.wait(globals())
2023-01-11T21:41:24.0848147Z del async_compile
2023-01-11T21:41:24.0848151Z 
2023-01-11T21:41:24.0848218Z def call(args):
2023-01-11T21:41:24.0848279Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0848382Z     args.clear()
2023-01-11T21:41:24.0848573Z     buf0 = empty_strided((8, 8), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0848870Z     buf1 = empty_strided((8, 8), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0849146Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0849211Z     del arg0_1
2023-01-11T21:41:24.0849275Z     del arg1_1
2023-01-11T21:41:24.0849338Z     return (buf0, buf1, )
2023-01-11T21:41:24.0849354Z 
2023-01-11T21:41:24.0849359Z 
2023-01-11T21:41:24.0849420Z if __name__ == "__main__":
2023-01-11T21:41:24.0849534Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0849654Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0849879Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0850215Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0850364Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0850370Z 
2023-01-11T21:41:24.0850432Z ok (0.038s)
2023-01-11T21:41:24.0850780Z   test_transposed_propagates_cpu (__main__.CpuTests) ... [2023-01-11 21:38:58,507] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 467
2023-01-11T21:41:24.0851199Z [2023-01-11 21:39:00,023] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 467
2023-01-11T21:41:24.0851214Z 
2023-01-11T21:41:24.0851294Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0851358Z import torch
2023-01-11T21:41:24.0851427Z import random
2023-01-11T21:41:24.0851602Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0851719Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0851724Z 
2023-01-11T21:41:24.0851798Z aten = torch.ops.aten
2023-01-11T21:41:24.0851930Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0852013Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0852019Z 
2023-01-11T21:41:24.0852023Z 
2023-01-11T21:41:24.0852159Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0852365Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0852480Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0852577Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.0852674Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.0852733Z {
2023-01-11T21:41:24.0852818Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0852878Z     {
2023-01-11T21:41:24.0852954Z         #pragma omp for 
2023-01-11T21:41:24.0853036Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0853096Z         {
2023-01-11T21:41:24.0853229Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0853361Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.0853446Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0853523Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0853583Z         }
2023-01-11T21:41:24.0853673Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0853752Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:24.0853813Z         {
2023-01-11T21:41:24.0853895Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0853962Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.0854041Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0854118Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0854175Z         }
2023-01-11T21:41:24.0854232Z     }
2023-01-11T21:41:24.0854290Z }
2023-01-11T21:41:24.0854370Z ''')
2023-01-11T21:41:24.0854375Z 
2023-01-11T21:41:24.0854379Z 
2023-01-11T21:41:24.0854466Z async_compile.wait(globals())
2023-01-11T21:41:24.0854526Z del async_compile
2023-01-11T21:41:24.0854531Z 
2023-01-11T21:41:24.0854632Z def call(args):
2023-01-11T21:41:24.0854709Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0854782Z     args.clear()
2023-01-11T21:41:24.0854996Z     buf0 = empty_strided((1, 4, 4, 4), (64, 4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0855159Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.0855226Z     del arg0_1
2023-01-11T21:41:24.0855279Z     del arg1_1
2023-01-11T21:41:24.0855349Z     return (buf0, )
2023-01-11T21:41:24.0855354Z 
2023-01-11T21:41:24.0855358Z 
2023-01-11T21:41:24.0855427Z if __name__ == "__main__":
2023-01-11T21:41:24.0855538Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0855660Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0855871Z     arg0_1 = rand_strided((1, 4, 4, 4), (64, 4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0856073Z     arg1_1 = rand_strided((4, 4, 4), (4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0856214Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0856220Z 
2023-01-11T21:41:24.0856274Z ok (1.529s)
2023-01-11T21:41:24.0856408Z   test_triton_conv_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:24.0856735Z   test_triton_mm2_cpu (__main__.CpuTests) ... [2023-01-11 21:39:00,079] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 468
2023-01-11T21:41:24.0857000Z [2023-01-11 21:39:01,619] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 468
2023-01-11T21:41:24.0857005Z 
2023-01-11T21:41:24.0857096Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0857164Z import torch
2023-01-11T21:41:24.0857233Z import random
2023-01-11T21:41:24.0857345Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0857452Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0857456Z 
2023-01-11T21:41:24.0857527Z aten = torch.ops.aten
2023-01-11T21:41:24.0857661Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0857748Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0857753Z 
2023-01-11T21:41:24.0857757Z 
2023-01-11T21:41:24.0857891Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0858088Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0858203Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:24.0858259Z {
2023-01-11T21:41:24.0858342Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0858401Z     {
2023-01-11T21:41:24.0858473Z         #pragma omp for 
2023-01-11T21:41:24.0858559Z         for(long i0=0; i0<131072; i0+=1)
2023-01-11T21:41:24.0858619Z         {
2023-01-11T21:41:24.0858753Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0858877Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:24.0858963Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.0859023Z         }
2023-01-11T21:41:24.0859115Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0859202Z         for(long i0=1048576; i0<1048576; i0+=1)
2023-01-11T21:41:24.0859260Z         {
2023-01-11T21:41:24.0859345Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:24.0859432Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:24.0859504Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0859559Z         }
2023-01-11T21:41:24.0859619Z     }
2023-01-11T21:41:24.0859677Z }
2023-01-11T21:41:24.0859753Z ''')
2023-01-11T21:41:24.0859758Z 
2023-01-11T21:41:24.0859762Z 
2023-01-11T21:41:24.0859847Z async_compile.wait(globals())
2023-01-11T21:41:24.0859915Z del async_compile
2023-01-11T21:41:24.0859920Z 
2023-01-11T21:41:24.0859976Z def call(args):
2023-01-11T21:41:24.0860048Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.0860114Z     args.clear()
2023-01-11T21:41:24.0860322Z     buf0 = empty_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0860458Z     aten.mm.out(arg0_1, arg1_1, out=buf0)
2023-01-11T21:41:24.0860526Z     del arg0_1
2023-01-11T21:41:24.0860589Z     del arg1_1
2023-01-11T21:41:24.0860660Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0860758Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0860824Z     return (buf1, )
2023-01-11T21:41:24.0860829Z 
2023-01-11T21:41:24.0860833Z 
2023-01-11T21:41:24.0860908Z if __name__ == "__main__":
2023-01-11T21:41:24.0861014Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0861134Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0861340Z     arg0_1 = rand_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0861536Z     arg1_1 = rand_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0861637Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.0861642Z 
2023-01-11T21:41:24.0861705Z ok (1.609s)
2023-01-11T21:41:24.0862191Z   test_triu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0862317Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0862728Z [2023-01-11 21:39:01,676] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 469
2023-01-11T21:41:24.0862993Z [2023-01-11 21:39:03,226] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 469
2023-01-11T21:41:24.0862999Z 
2023-01-11T21:41:24.0863090Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0863156Z import torch
2023-01-11T21:41:24.0863225Z import random
2023-01-11T21:41:24.0863331Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0863446Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0863451Z 
2023-01-11T21:41:24.0863526Z aten = torch.ops.aten
2023-01-11T21:41:24.0863656Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0863744Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0863749Z 
2023-01-11T21:41:24.0863753Z 
2023-01-11T21:41:24.0863885Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0864084Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0864200Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0864287Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0864382Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0864471Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:24.0864527Z {
2023-01-11T21:41:24.0864627Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0864685Z     {
2023-01-11T21:41:24.0864771Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0864839Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0864896Z         {
2023-01-11T21:41:24.0864980Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.0865039Z             {
2023-01-11T21:41:24.0865234Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0865321Z                 for(long i2=0; i2<10; i2+=1)
2023-01-11T21:41:24.0865386Z                 {
2023-01-11T21:41:24.0865439Z                     {
2023-01-11T21:41:24.0865505Z                         {
2023-01-11T21:41:24.0865618Z                             auto tmp3 = in_ptr0[i2 + (10*i1) + (100*i0)];
2023-01-11T21:41:24.0865805Z                             auto tmp0 = static_cast<int>((-1) + i2 + ((-1)*i1));
2023-01-11T21:41:24.0865911Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:41:24.0866006Z                             auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:24.0866182Z                             auto tmp4 = static_cast<float>(0);
2023-01-11T21:41:24.0866272Z                             auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:41:24.0866836Z                             auto tmp6 = static_cast<int>(i2 + ((-1)*i1));
2023-01-11T21:41:24.0866995Z                             auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:24.0867150Z                             auto tmp8 = tmp7 ? tmp3 : tmp4;
2023-01-11T21:41:24.0867337Z                             auto tmp9 = static_cast<int>((-2) + i2 + ((-1)*i1));
2023-01-11T21:41:24.0867429Z                             auto tmp10 = tmp9 >= tmp1;
2023-01-11T21:41:24.0867531Z                             auto tmp11 = tmp10 ? tmp3 : tmp4;
2023-01-11T21:41:24.0867628Z                             out_ptr0[i2 + (10*i1) + (100*i0)] = tmp5;
2023-01-11T21:41:24.0867716Z                             out_ptr1[i2 + (10*i1) + (100*i0)] = tmp8;
2023-01-11T21:41:24.0867814Z                             out_ptr2[i2 + (10*i1) + (100*i0)] = tmp11;
2023-01-11T21:41:24.0867923Z                         }
2023-01-11T21:41:24.0867989Z                     }
2023-01-11T21:41:24.0868052Z                 }
2023-01-11T21:41:24.0868113Z             }
2023-01-11T21:41:24.0868175Z         }
2023-01-11T21:41:24.0868223Z     }
2023-01-11T21:41:24.0868279Z }
2023-01-11T21:41:24.0868357Z ''')
2023-01-11T21:41:24.0868363Z 
2023-01-11T21:41:24.0868367Z 
2023-01-11T21:41:24.0868453Z async_compile.wait(globals())
2023-01-11T21:41:24.0868525Z del async_compile
2023-01-11T21:41:24.0868530Z 
2023-01-11T21:41:24.0868598Z def call(args):
2023-01-11T21:41:24.0868664Z     arg0_1, = args
2023-01-11T21:41:24.0868723Z     args.clear()
2023-01-11T21:41:24.0868930Z     buf0 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0869135Z     buf1 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0869336Z     buf2 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0869525Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:24.0869591Z     del arg0_1
2023-01-11T21:41:24.0869668Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:24.0869673Z 
2023-01-11T21:41:24.0869677Z 
2023-01-11T21:41:24.0869751Z if __name__ == "__main__":
2023-01-11T21:41:24.0869852Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0869971Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0870176Z     arg0_1 = rand_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0870288Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0870293Z 
2023-01-11T21:41:24.0870357Z ok (1.595s)
2023-01-11T21:41:24.0870826Z   test_unbind_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0870951Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0871208Z [2023-01-11 21:39:03,250] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 470
2023-01-11T21:41:24.0871475Z [2023-01-11 21:39:03,257] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 470
2023-01-11T21:41:24.0871480Z 
2023-01-11T21:41:24.0871573Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0871629Z import torch
2023-01-11T21:41:24.0871698Z import random
2023-01-11T21:41:24.0871808Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0871924Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0871928Z 
2023-01-11T21:41:24.0872004Z aten = torch.ops.aten
2023-01-11T21:41:24.0872166Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0872254Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0872259Z 
2023-01-11T21:41:24.0872264Z 
2023-01-11T21:41:24.0872347Z async_compile.wait(globals())
2023-01-11T21:41:24.0872405Z del async_compile
2023-01-11T21:41:24.0872410Z 
2023-01-11T21:41:24.0872476Z def call(args):
2023-01-11T21:41:24.0872539Z     arg0_1, = args
2023-01-11T21:41:24.0872606Z     args.clear()
2023-01-11T21:41:24.0872881Z     return (as_strided(arg0_1, (4, 4), (4, 1)), as_strided(arg0_1, (4, 4), (4, 1), 16), as_strided(arg0_1, (4, 4), (4, 1), 32), as_strided(arg0_1, (4, 4), (4, 1), 48), as_strided(arg0_1, (4, 4), (16, 4)), as_strided(arg0_1, (4, 4), (16, 4), 1), as_strided(arg0_1, (4, 4), (16, 4), 2), as_strided(arg0_1, (4, 4), (16, 4), 3), )
2023-01-11T21:41:24.0872887Z 
2023-01-11T21:41:24.0872890Z 
2023-01-11T21:41:24.0872963Z if __name__ == "__main__":
2023-01-11T21:41:24.0873073Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0873224Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0873419Z     arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0873521Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0873525Z 
2023-01-11T21:41:24.0873587Z ok (0.031s)
2023-01-11T21:41:24.0874125Z   test_unroll_small_reduction_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0874251Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0874515Z [2023-01-11 21:39:03,299] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 471
2023-01-11T21:41:24.0874781Z [2023-01-11 21:39:04,873] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 471
2023-01-11T21:41:24.0875209Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0875334Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0875588Z [2023-01-11 21:39:04,913] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 472
2023-01-11T21:41:24.0875594Z 
2023-01-11T21:41:24.0875684Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0875741Z import torch
2023-01-11T21:41:24.0875809Z import random
2023-01-11T21:41:24.0875923Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0876045Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0876050Z 
2023-01-11T21:41:24.0876125Z aten = torch.ops.aten
2023-01-11T21:41:24.0876256Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0876346Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0876352Z 
2023-01-11T21:41:24.0876356Z 
2023-01-11T21:41:24.0876486Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0876678Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0876794Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0876890Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0876985Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:24.0877078Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0877167Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:24.0877293Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:24.0877449Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:41:24.0877538Z                        bool* __restrict__ out_ptr6,
2023-01-11T21:41:24.0877628Z                        long* __restrict__ out_ptr7,
2023-01-11T21:41:24.0877714Z                        long* __restrict__ out_ptr8,
2023-01-11T21:41:24.0877807Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:24.0878176Z                        float* __restrict__ out_ptr10)
2023-01-11T21:41:24.0878237Z {
2023-01-11T21:41:24.0878333Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0878380Z     {
2023-01-11T21:41:24.0878452Z         #pragma omp for 
2023-01-11T21:41:24.0878529Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0878588Z         {
2023-01-11T21:41:24.0878648Z             {
2023-01-11T21:41:24.0878709Z                 {
2023-01-11T21:41:24.0878791Z                     auto tmp0 = in_ptr0[3*i0];
2023-01-11T21:41:24.0878916Z                     auto tmp1 = in_ptr0[1 + (3*i0)];
2023-01-11T21:41:24.0879010Z                     auto tmp3 = in_ptr0[2 + (3*i0)];
2023-01-11T21:41:24.0879140Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:41:24.0879261Z                     auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:41:24.0879519Z                     auto tmp5 = static_cast<long>(0);
2023-01-11T21:41:24.0879617Z                     auto tmp6 = static_cast<long>(1);
2023-01-11T21:41:24.0879708Z                     auto tmp7 = tmp1 < tmp0;
2023-01-11T21:41:24.0880004Z                     auto tmp8 = tmp7 ? tmp6 : tmp5;
2023-01-11T21:41:24.0880126Z                     auto tmp9 = static_cast<long>(2);
2023-01-11T21:41:24.0880216Z                     auto tmp10 = tmp3 < tmp2;
2023-01-11T21:41:24.0880309Z                     auto tmp11 = tmp10 ? tmp9 : tmp8;
2023-01-11T21:41:24.0880438Z                     auto tmp12 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:24.0880567Z                     auto tmp13 = (tmp3 != tmp3) ? tmp3 : std::max(tmp12, tmp3);
2023-01-11T21:41:24.0880654Z                     auto tmp14 = tmp1 > tmp0;
2023-01-11T21:41:24.0880747Z                     auto tmp15 = tmp14 ? tmp6 : tmp5;
2023-01-11T21:41:24.0880826Z                     auto tmp16 = tmp3 > tmp12;
2023-01-11T21:41:24.0880923Z                     auto tmp17 = tmp16 ? tmp9 : tmp15;
2023-01-11T21:41:24.0881011Z                     auto tmp18 = tmp0 + tmp1;
2023-01-11T21:41:24.0881097Z                     auto tmp19 = tmp18 + tmp3;
2023-01-11T21:41:24.0881198Z                     auto tmp20 = static_cast<float>(1);
2023-01-11T21:41:24.0881285Z                     auto tmp21 = tmp0 > tmp20;
2023-01-11T21:41:24.0881556Z                     auto tmp22 = static_cast<long>(tmp21);
2023-01-11T21:41:24.0881651Z                     auto tmp23 = static_cast<bool>(tmp22);
2023-01-11T21:41:24.0881740Z                     auto tmp24 = tmp1 > tmp20;
2023-01-11T21:41:24.0881852Z                     auto tmp25 = static_cast<long>(tmp24);
2023-01-11T21:41:24.0881954Z                     auto tmp26 = static_cast<bool>(tmp25);
2023-01-11T21:41:24.0882044Z                     auto tmp27 = tmp23 || tmp26;
2023-01-11T21:41:24.0882129Z                     auto tmp28 = tmp3 > tmp20;
2023-01-11T21:41:24.0882232Z                     auto tmp29 = static_cast<long>(tmp28);
2023-01-11T21:41:24.0882322Z                     auto tmp30 = static_cast<bool>(tmp29);
2023-01-11T21:41:24.0882452Z                     auto tmp31 = tmp27 || tmp30;
2023-01-11T21:41:24.0882600Z                     auto tmp32 = static_cast<float>(0);
2023-01-11T21:41:24.0882687Z                     auto tmp33 = tmp0 > tmp32;
2023-01-11T21:41:24.0882774Z                     auto tmp34 = tmp33 == 0;
2023-01-11T21:41:24.0882874Z                     auto tmp35 = static_cast<long>(tmp34);
2023-01-11T21:41:24.0882976Z                     auto tmp36 = static_cast<bool>(tmp35);
2023-01-11T21:41:24.0883065Z                     auto tmp37 = tmp1 > tmp32;
2023-01-11T21:41:24.0883187Z                     auto tmp38 = tmp37 == 0;
2023-01-11T21:41:24.0883287Z                     auto tmp39 = static_cast<long>(tmp38);
2023-01-11T21:41:24.0883386Z                     auto tmp40 = static_cast<bool>(tmp39);
2023-01-11T21:41:24.0883476Z                     auto tmp41 = tmp36 || tmp40;
2023-01-11T21:41:24.0883561Z                     auto tmp42 = tmp3 > tmp32;
2023-01-11T21:41:24.0883650Z                     auto tmp43 = tmp42 == 0;
2023-01-11T21:41:24.0883750Z                     auto tmp44 = static_cast<long>(tmp43);
2023-01-11T21:41:24.0883839Z                     auto tmp45 = static_cast<bool>(tmp44);
2023-01-11T21:41:24.0883927Z                     auto tmp46 = tmp41 || tmp45;
2023-01-11T21:41:24.0884011Z                     auto tmp47 = tmp46 == 0;
2023-01-11T21:41:24.0884093Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0884173Z                     out_ptr1[i0] = tmp11;
2023-01-11T21:41:24.0884251Z                     out_ptr2[i0] = tmp13;
2023-01-11T21:41:24.0884362Z                     out_ptr3[i0] = tmp17;
2023-01-11T21:41:24.0884432Z                     out_ptr4[i0] = tmp19;
2023-01-11T21:41:24.0884511Z                     out_ptr5[i0] = tmp31;
2023-01-11T21:41:24.0884588Z                     out_ptr6[i0] = tmp47;
2023-01-11T21:41:24.0884666Z                     out_ptr7[i0] = tmp11;
2023-01-11T21:41:24.0884747Z                     out_ptr8[i0] = tmp17;
2023-01-11T21:41:24.0884823Z                     out_ptr9[i0] = tmp4;
2023-01-11T21:41:24.0884904Z                     out_ptr10[i0] = tmp13;
2023-01-11T21:41:24.0884955Z                 }
2023-01-11T21:41:24.0885016Z             }
2023-01-11T21:41:24.0885076Z         }
2023-01-11T21:41:24.0885137Z     }
2023-01-11T21:41:24.0885198Z }
2023-01-11T21:41:24.0885290Z ''')
2023-01-11T21:41:24.0885296Z 
2023-01-11T21:41:24.0885300Z 
2023-01-11T21:41:24.0885389Z async_compile.wait(globals())
2023-01-11T21:41:24.0885450Z del async_compile
2023-01-11T21:41:24.0885454Z 
2023-01-11T21:41:24.0885527Z def call(args):
2023-01-11T21:41:24.0885591Z     arg0_1, = args
2023-01-11T21:41:24.0885658Z     args.clear()
2023-01-11T21:41:24.0885854Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0886043Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0886231Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0886406Z     buf3 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0886592Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0886776Z     buf5 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.0886955Z     buf6 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.0887134Z     buf7 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0887314Z     buf8 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0887504Z     buf9 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0887693Z     buf10 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0888066Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()))
2023-01-11T21:41:24.0888122Z     del arg0_1
2023-01-11T21:41:24.0888249Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, )
2023-01-11T21:41:24.0888254Z 
2023-01-11T21:41:24.0888259Z 
2023-01-11T21:41:24.0888335Z if __name__ == "__main__":
2023-01-11T21:41:24.0888448Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0888569Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0888796Z     arg0_1 = rand_strided((8, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0888903Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0888908Z 
2023-01-11T21:41:24.0889174Z [2023-01-11 21:39:06,464] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 472
2023-01-11T21:41:24.0889180Z 
2023-01-11T21:41:24.0889272Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0889329Z import torch
2023-01-11T21:41:24.0889398Z import random
2023-01-11T21:41:24.0889510Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0889628Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0889632Z 
2023-01-11T21:41:24.0889709Z aten = torch.ops.aten
2023-01-11T21:41:24.0889842Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0889932Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0889937Z 
2023-01-11T21:41:24.0889941Z 
2023-01-11T21:41:24.0890137Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0890329Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0890439Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0890541Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0890638Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0890731Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:24.0890827Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0890917Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:24.0890998Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:24.0891088Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:41:24.0891175Z                        long* __restrict__ out_ptr7,
2023-01-11T21:41:24.0891261Z                        long* __restrict__ out_ptr8,
2023-01-11T21:41:24.0891359Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:24.0891459Z                        float* __restrict__ out_ptr10)
2023-01-11T21:41:24.0891516Z {
2023-01-11T21:41:24.0891588Z     auto out_ptr6 = in_out_ptr0;
2023-01-11T21:41:24.0891683Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0891744Z     {
2023-01-11T21:41:24.0891816Z         #pragma omp for 
2023-01-11T21:41:24.0891895Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0891955Z         {
2023-01-11T21:41:24.0892019Z             {
2023-01-11T21:41:24.0892069Z                 {
2023-01-11T21:41:24.0892194Z                     float tmp1 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0892316Z                     struct IndexValue_13 {size_t index; float value;};
2023-01-11T21:41:24.0892453Z                     IndexValue_13 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0892588Z                     #pragma omp declare reduction(argmin : struct IndexValue_13 :\
2023-01-11T21:41:24.0892742Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0892885Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0893027Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0893261Z                     float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0893371Z                     struct IndexValue_14 {size_t index; float value;};
2023-01-11T21:41:24.0893605Z                     IndexValue_14 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0893738Z                     #pragma omp declare reduction(argmax : struct IndexValue_14 :\
2023-01-11T21:41:24.0893885Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0894031Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0894294Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0894376Z                     float tmp5 = 0;
2023-01-11T21:41:24.0894453Z                     bool tmp10 = 0;
2023-01-11T21:41:24.0894519Z                     bool tmp16 = 0;
2023-01-11T21:41:24.0894638Z                     struct IndexValue_15 {size_t index; float value;};
2023-01-11T21:41:24.0894774Z                     IndexValue_15 tmp17{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0894910Z                     #pragma omp declare reduction(argmin : struct IndexValue_15 :\
2023-01-11T21:41:24.0895055Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0895200Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0895342Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0895494Z                     struct IndexValue_16 {size_t index; float value;};
2023-01-11T21:41:24.0895727Z                     IndexValue_16 tmp18{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:24.0895853Z                     #pragma omp declare reduction(argmax : struct IndexValue_16 :\
2023-01-11T21:41:24.0896000Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:24.0896138Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:24.0896375Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:24.0896503Z                     float tmp19 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0896713Z                     float tmp20 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:24.0896800Z                     for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.0896869Z                     {
2023-01-11T21:41:24.0896924Z                         {
2023-01-11T21:41:24.0897028Z                             auto tmp0 = in_ptr0[i1 + (3*i0)];
2023-01-11T21:41:24.0897130Z                             auto tmp6 = static_cast<float>(1);
2023-01-11T21:41:24.0897223Z                             auto tmp7 = tmp0 > tmp6;
2023-01-11T21:41:24.0897331Z                             auto tmp8 = static_cast<long>(tmp7);
2023-01-11T21:41:24.0897437Z                             auto tmp9 = static_cast<bool>(tmp8);
2023-01-11T21:41:24.0897544Z                             auto tmp11 = static_cast<float>(0);
2023-01-11T21:41:24.0897629Z                             auto tmp12 = tmp0 > tmp11;
2023-01-11T21:41:24.0897720Z                             auto tmp13 = tmp12 == 0;
2023-01-11T21:41:24.0897824Z                             auto tmp14 = static_cast<long>(tmp13);
2023-01-11T21:41:24.0897924Z                             auto tmp15 = static_cast<bool>(tmp14);
2023-01-11T21:41:24.0898030Z                             tmp1 = std::min(tmp1, tmp0);
2023-01-11T21:41:24.0898122Z                             if (tmp2.value > tmp0) {
2023-01-11T21:41:24.0898229Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:41:24.0898295Z                             }
2023-01-11T21:41:24.0898383Z                             tmp3 = std::max(tmp3, tmp0);
2023-01-11T21:41:24.0898477Z                             if (tmp4.value < tmp0) {
2023-01-11T21:41:24.0898584Z                                 tmp4.index = i1; tmp4.value = tmp0;
2023-01-11T21:41:24.0898653Z                             }
2023-01-11T21:41:24.0898732Z                             tmp5 += tmp0;
2023-01-11T21:41:24.0898821Z                             tmp10 = tmp10 || tmp9;
2023-01-11T21:41:24.0898907Z                             tmp16 = tmp16 || tmp15;
2023-01-11T21:41:24.0898988Z                             if (tmp17.value > tmp0) {
2023-01-11T21:41:24.0899093Z                                 tmp17.index = i1; tmp17.value = tmp0;
2023-01-11T21:41:24.0899210Z                             }
2023-01-11T21:41:24.0899304Z                             if (tmp18.value < tmp0) {
2023-01-11T21:41:24.0899411Z                                 tmp18.index = i1; tmp18.value = tmp0;
2023-01-11T21:41:24.0899478Z                             }
2023-01-11T21:41:24.0899580Z                             tmp19 = std::min(tmp19, tmp0);
2023-01-11T21:41:24.0899683Z                             tmp20 = std::max(tmp20, tmp0);
2023-01-11T21:41:24.0899738Z                         }
2023-01-11T21:41:24.0899800Z                     }
2023-01-11T21:41:24.0899881Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0899976Z                     out_ptr1[i0] = tmp2.index;
2023-01-11T21:41:24.0900054Z                     out_ptr2[i0] = tmp3;
2023-01-11T21:41:24.0900143Z                     out_ptr3[i0] = tmp4.index;
2023-01-11T21:41:24.0900221Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:24.0900292Z                     out_ptr5[i0] = tmp10;
2023-01-11T21:41:24.0900400Z                     out_ptr6[i0] = tmp16;
2023-01-11T21:41:24.0900497Z                     out_ptr7[i0] = tmp17.index;
2023-01-11T21:41:24.0900587Z                     out_ptr8[i0] = tmp18.index;
2023-01-11T21:41:24.0900668Z                     out_ptr9[i0] = tmp19;
2023-01-11T21:41:24.0900751Z                     out_ptr10[i0] = tmp20;
2023-01-11T21:41:24.0900815Z                 }
2023-01-11T21:41:24.0900864Z             }
2023-01-11T21:41:24.0900922Z         }
2023-01-11T21:41:24.0900993Z         #pragma omp for 
2023-01-11T21:41:24.0901074Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0901134Z         {
2023-01-11T21:41:24.0901194Z             {
2023-01-11T21:41:24.0901245Z                 {
2023-01-11T21:41:24.0901332Z                     auto tmp0 = out_ptr6[i0];
2023-01-11T21:41:24.0901416Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:24.0901502Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.0901563Z                 }
2023-01-11T21:41:24.0901627Z             }
2023-01-11T21:41:24.0901687Z         }
2023-01-11T21:41:24.0901736Z     }
2023-01-11T21:41:24.0901798Z }
2023-01-11T21:41:24.0901874Z ''')
2023-01-11T21:41:24.0901879Z 
2023-01-11T21:41:24.0901884Z 
2023-01-11T21:41:24.0901971Z async_compile.wait(globals())
2023-01-11T21:41:24.0902038Z del async_compile
2023-01-11T21:41:24.0902043Z 
2023-01-11T21:41:24.0902110Z def call(args):
2023-01-11T21:41:24.0902176Z     arg0_1, = args
2023-01-11T21:41:24.0902232Z     args.clear()
2023-01-11T21:41:24.0902557Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0902743Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0902932Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0903114Z     buf3 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0903301Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0903488Z     buf5 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.0903665Z     buf6 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.0903835Z     buf8 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0904014Z     buf9 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.0904204Z     buf10 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0904391Z     buf11 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0904474Z     buf7 = buf6; del buf6  # reuse
2023-01-11T21:41:24.0904848Z     kernel_cpp_0(c_void_p(buf7.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()))
2023-01-11T21:41:24.0904969Z     del arg0_1
2023-01-11T21:41:24.0905099Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf8, buf9, buf10, buf11, )
2023-01-11T21:41:24.0905105Z 
2023-01-11T21:41:24.0905109Z 
2023-01-11T21:41:24.0905184Z if __name__ == "__main__":
2023-01-11T21:41:24.0905285Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0905402Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0905596Z     arg0_1 = rand_strided((8, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0905701Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0905706Z 
2023-01-11T21:41:24.0905771Z ok (3.207s)
2023-01-11T21:41:24.0905908Z   test_unspec_inputs_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:24.0906414Z   test_unsqueeze_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0906541Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0906804Z [2023-01-11 21:39:06,499] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 473
2023-01-11T21:41:24.0907060Z [2023-01-11 21:39:08,020] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 473
2023-01-11T21:41:24.0907078Z 
2023-01-11T21:41:24.0907158Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0907224Z import torch
2023-01-11T21:41:24.0907291Z import random
2023-01-11T21:41:24.0907403Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0907523Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0907528Z 
2023-01-11T21:41:24.0907600Z aten = torch.ops.aten
2023-01-11T21:41:24.0907735Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0907813Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0907817Z 
2023-01-11T21:41:24.0907832Z 
2023-01-11T21:41:24.0907952Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0908154Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0908273Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0908369Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0908461Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0908550Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.0908647Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:24.0908694Z {
2023-01-11T21:41:24.0908784Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0908841Z     {
2023-01-11T21:41:24.0908913Z         #pragma omp for 
2023-01-11T21:41:24.0908994Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0909053Z         {
2023-01-11T21:41:24.0909189Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0909309Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0909391Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0909517Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0909598Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0909676Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0909766Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0909851Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0909926Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0910010Z             tmp5.store(out_ptr3 + 8*i0);
2023-01-11T21:41:24.0910069Z         }
2023-01-11T21:41:24.0910157Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0910238Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.0910326Z         {
2023-01-11T21:41:24.0910407Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0910493Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0910574Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0910670Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:24.0910751Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0910828Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:24.0910905Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.0910980Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:24.0911044Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:24.0911117Z             out_ptr3[i0] = tmp5;
2023-01-11T21:41:24.0911173Z         }
2023-01-11T21:41:24.0911234Z     }
2023-01-11T21:41:24.0911287Z }
2023-01-11T21:41:24.0911361Z ''')
2023-01-11T21:41:24.0911367Z 
2023-01-11T21:41:24.0911371Z 
2023-01-11T21:41:24.0911456Z async_compile.wait(globals())
2023-01-11T21:41:24.0911518Z del async_compile
2023-01-11T21:41:24.0911549Z 
2023-01-11T21:41:24.0911620Z def call(args):
2023-01-11T21:41:24.0911684Z     arg0_1, = args
2023-01-11T21:41:24.0911750Z     args.clear()
2023-01-11T21:41:24.0911968Z     buf0 = empty_strided((2, 2, 2, 2, 1), (8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0912175Z     buf1 = empty_strided((2, 2, 1, 2, 2), (8, 4, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0912381Z     buf2 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0912587Z     buf3 = empty_strided((2, 2, 2, 1, 2), (8, 4, 2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0912785Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:24.0912851Z     del arg0_1
2023-01-11T21:41:24.0912938Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:24.0912943Z 
2023-01-11T21:41:24.0912949Z 
2023-01-11T21:41:24.0913023Z if __name__ == "__main__":
2023-01-11T21:41:24.0913133Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0913251Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0913455Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0913559Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0913564Z 
2023-01-11T21:41:24.0913617Z ok (1.555s)
2023-01-11T21:41:24.0914148Z   test_unsqueeze_inplace_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0914278Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0914543Z [2023-01-11 21:39:08,054] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 474
2023-01-11T21:41:24.0914807Z [2023-01-11 21:39:09,568] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 474
2023-01-11T21:41:24.0914813Z 
2023-01-11T21:41:24.0914903Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0914971Z import torch
2023-01-11T21:41:24.0915038Z import random
2023-01-11T21:41:24.0915151Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0915257Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0915276Z 
2023-01-11T21:41:24.0915340Z aten = torch.ops.aten
2023-01-11T21:41:24.0915471Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0915558Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0915563Z 
2023-01-11T21:41:24.0915568Z 
2023-01-11T21:41:24.0915695Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0915934Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0916050Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0916145Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0916228Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0916285Z {
2023-01-11T21:41:24.0916378Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0916435Z     {
2023-01-11T21:41:24.0916505Z         #pragma omp for 
2023-01-11T21:41:24.0916584Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0916643Z         {
2023-01-11T21:41:24.0916764Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.0916892Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.0916973Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0917097Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.0917211Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0917300Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.0917386Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.0917445Z         }
2023-01-11T21:41:24.0917524Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0917603Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.0917661Z         {
2023-01-11T21:41:24.0917740Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.0917838Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.0917918Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0918014Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:24.0918084Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0918160Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.0918235Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:24.0918291Z         }
2023-01-11T21:41:24.0918351Z     }
2023-01-11T21:41:24.0918408Z }
2023-01-11T21:41:24.0918475Z ''')
2023-01-11T21:41:24.0918482Z 
2023-01-11T21:41:24.0918499Z 
2023-01-11T21:41:24.0918576Z async_compile.wait(globals())
2023-01-11T21:41:24.0918647Z del async_compile
2023-01-11T21:41:24.0918652Z 
2023-01-11T21:41:24.0918720Z def call(args):
2023-01-11T21:41:24.0918787Z     arg0_1, = args
2023-01-11T21:41:24.0918855Z     args.clear()
2023-01-11T21:41:24.0919071Z     buf0 = empty_strided((2, 2, 1, 2, 2), (8, 4, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0919283Z     buf1 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0919434Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0919500Z     del arg0_1
2023-01-11T21:41:24.0919577Z     return (buf0, buf1, )
2023-01-11T21:41:24.0919582Z 
2023-01-11T21:41:24.0919587Z 
2023-01-11T21:41:24.0919662Z if __name__ == "__main__":
2023-01-11T21:41:24.0919773Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0919900Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0920108Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0920214Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0920219Z 
2023-01-11T21:41:24.0920272Z ok (1.547s)
2023-01-11T21:41:24.0920743Z   test_upsample_bicubic2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0920869Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0921126Z [2023-01-11 21:39:11,190] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 475
2023-01-11T21:41:24.0921159Z 
2023-01-11T21:41:24.0921253Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0921321Z import torch
2023-01-11T21:41:24.0921391Z import random
2023-01-11T21:41:24.0921507Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0921624Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0921628Z 
2023-01-11T21:41:24.0921693Z aten = torch.ops.aten
2023-01-11T21:41:24.0921820Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0921911Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0921916Z 
2023-01-11T21:41:24.0921920Z 
2023-01-11T21:41:24.0922052Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0922253Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0922368Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0922460Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.0922586Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0922637Z {
2023-01-11T21:41:24.0922737Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0922797Z     {
2023-01-11T21:41:24.0922874Z         #pragma omp for 
2023-01-11T21:41:24.0922954Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.0923014Z         {
2023-01-11T21:41:24.0923090Z             #pragma GCC ivdep
2023-01-11T21:41:24.0923164Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:24.0923226Z             {
2023-01-11T21:41:24.0923308Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0923396Z                 for(long i2=0; i2<128; i2+=1)
2023-01-11T21:41:24.0923458Z                 {
2023-01-11T21:41:24.0923519Z                     {
2023-01-11T21:41:24.0923574Z                         {
2023-01-11T21:41:24.0923681Z                             auto tmp0 = static_cast<float>(i2);
2023-01-11T21:41:24.0923778Z                             auto tmp1 = 0.2440944881889764 * tmp0;
2023-01-11T21:41:24.0923884Z                             auto tmp2 = std::floor(tmp1);
2023-01-11T21:41:24.0924029Z                             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:24.0924134Z                             auto tmp4 = static_cast<float>(i1);
2023-01-11T21:41:24.0924233Z                             auto tmp5 = 0.49606299212598426 * tmp4;
2023-01-11T21:41:24.0924334Z                             auto tmp6 = std::floor(tmp5);
2023-01-11T21:41:24.0924465Z                             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:41:24.0924565Z                             auto tmp8 = static_cast<int>(tmp6);
2023-01-11T21:41:24.0924668Z                             auto tmp9 = static_cast<int>(tmp2);
2023-01-11T21:41:24.0924812Z                             auto tmp10 = tmp8 + -1;
2023-01-11T21:41:24.0924902Z                             auto tmp11 = tmp8 + 0;
2023-01-11T21:41:24.0924987Z                             auto tmp12 = tmp8 + 1;
2023-01-11T21:41:24.0925069Z                             auto tmp13 = tmp8 + 2;
2023-01-11T21:41:24.0925211Z                             auto tmp14 = tmp9 + -1;
2023-01-11T21:41:24.0925285Z                             auto tmp15 = tmp9 + 0;
2023-01-11T21:41:24.0925369Z                             auto tmp16 = tmp9 + 1;
2023-01-11T21:41:24.0925453Z                             auto tmp17 = tmp9 + 2;
2023-01-11T21:41:24.0925582Z                             auto tmp18 = (tmp10 != tmp10) ? tmp10 : std::min(63, tmp10);
2023-01-11T21:41:24.0925705Z                             auto tmp19 = (tmp18 != tmp18) ? tmp18 : std::max(0, tmp18);
2023-01-11T21:41:24.0925828Z                             auto tmp20 = (tmp14 != tmp14) ? tmp14 : std::min(31, tmp14);
2023-01-11T21:41:24.0925952Z                             auto tmp21 = (tmp20 != tmp20) ? tmp20 : std::max(0, tmp20);
2023-01-11T21:41:24.0926067Z                             auto tmp22 = in_ptr0[tmp21 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:24.0926178Z                             auto tmp23 = (tmp15 != tmp15) ? tmp15 : std::min(31, tmp15);
2023-01-11T21:41:24.0926333Z                             auto tmp24 = (tmp23 != tmp23) ? tmp23 : std::max(0, tmp23);
2023-01-11T21:41:24.0926449Z                             auto tmp25 = in_ptr0[tmp24 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:24.0926570Z                             auto tmp26 = (tmp16 != tmp16) ? tmp16 : std::min(31, tmp16);
2023-01-11T21:41:24.0926690Z                             auto tmp27 = (tmp26 != tmp26) ? tmp26 : std::max(0, tmp26);
2023-01-11T21:41:24.0926804Z                             auto tmp28 = in_ptr0[tmp27 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:24.0926926Z                             auto tmp29 = (tmp17 != tmp17) ? tmp17 : std::min(31, tmp17);
2023-01-11T21:41:24.0927045Z                             auto tmp30 = (tmp29 != tmp29) ? tmp29 : std::max(0, tmp29);
2023-01-11T21:41:24.0927157Z                             auto tmp31 = in_ptr0[tmp30 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:24.0927238Z                             auto tmp32 = tmp3 + 1.0;
2023-01-11T21:41:24.0927413Z                             auto tmp33 = -0.75 * tmp32;
2023-01-11T21:41:24.0927561Z                             auto tmp34 = tmp33 - -3.75;
2023-01-11T21:41:24.0927653Z                             auto tmp35 = tmp34 * tmp32;
2023-01-11T21:41:24.0927798Z                             auto tmp36 = tmp35 + -6.0;
2023-01-11T21:41:24.0927889Z                             auto tmp37 = tmp36 * tmp32;
2023-01-11T21:41:24.0928031Z                             auto tmp38 = tmp37 - -3.0;
2023-01-11T21:41:24.0928121Z                             auto tmp39 = 1.25 * tmp3;
2023-01-11T21:41:24.0928253Z                             auto tmp40 = tmp39 - 2.25;
2023-01-11T21:41:24.0928346Z                             auto tmp41 = tmp40 * tmp3;
2023-01-11T21:41:24.0928438Z                             auto tmp42 = tmp41 * tmp3;
2023-01-11T21:41:24.0928527Z                             auto tmp43 = tmp42 + 1.0;
2023-01-11T21:41:24.0928669Z                             auto tmp44 = 1.0 - tmp3;
2023-01-11T21:41:24.0928759Z                             auto tmp45 = 1.25 * tmp44;
2023-01-11T21:41:24.0928903Z                             auto tmp46 = tmp45 - 2.25;
2023-01-11T21:41:24.0928987Z                             auto tmp47 = tmp46 * tmp44;
2023-01-11T21:41:24.0929074Z                             auto tmp48 = tmp47 * tmp44;
2023-01-11T21:41:24.0929166Z                             auto tmp49 = tmp48 + 1.0;
2023-01-11T21:41:24.0929255Z                             auto tmp50 = tmp44 + 1.0;
2023-01-11T21:41:24.0929397Z                             auto tmp51 = -0.75 * tmp50;
2023-01-11T21:41:24.0929540Z                             auto tmp52 = tmp51 - -3.75;
2023-01-11T21:41:24.0929627Z                             auto tmp53 = tmp52 * tmp50;
2023-01-11T21:41:24.0929757Z                             auto tmp54 = tmp53 + -6.0;
2023-01-11T21:41:24.0929849Z                             auto tmp55 = tmp54 * tmp50;
2023-01-11T21:41:24.0929991Z                             auto tmp56 = tmp55 - -3.0;
2023-01-11T21:41:24.0930081Z                             auto tmp57 = tmp22 * tmp38;
2023-01-11T21:41:24.0930176Z                             auto tmp58 = tmp25 * tmp43;
2023-01-11T21:41:24.0930266Z                             auto tmp59 = tmp28 * tmp49;
2023-01-11T21:41:24.0930356Z                             auto tmp60 = tmp31 * tmp56;
2023-01-11T21:41:24.0930443Z                             auto tmp61 = tmp59 + tmp60;
2023-01-11T21:41:24.0930521Z                             auto tmp62 = tmp58 + tmp61;
2023-01-11T21:41:24.0930607Z                             auto tmp63 = tmp57 + tmp62;
2023-01-11T21:41:24.0930731Z                             auto tmp64 = (tmp11 != tmp11) ? tmp11 : std::min(63, tmp11);
2023-01-11T21:41:24.0930853Z                             auto tmp65 = (tmp64 != tmp64) ? tmp64 : std::max(0, tmp64);
2023-01-11T21:41:24.0930968Z                             auto tmp66 = in_ptr0[tmp21 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:24.0931081Z                             auto tmp67 = in_ptr0[tmp24 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:24.0931192Z                             auto tmp68 = in_ptr0[tmp27 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:24.0931334Z                             auto tmp69 = in_ptr0[tmp30 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:24.0931414Z                             auto tmp70 = tmp66 * tmp38;
2023-01-11T21:41:24.0931504Z                             auto tmp71 = tmp67 * tmp43;
2023-01-11T21:41:24.0931593Z                             auto tmp72 = tmp68 * tmp49;
2023-01-11T21:41:24.0931683Z                             auto tmp73 = tmp69 * tmp56;
2023-01-11T21:41:24.0931770Z                             auto tmp74 = tmp72 + tmp73;
2023-01-11T21:41:24.0931857Z                             auto tmp75 = tmp71 + tmp74;
2023-01-11T21:41:24.0931942Z                             auto tmp76 = tmp70 + tmp75;
2023-01-11T21:41:24.0932064Z                             auto tmp77 = (tmp12 != tmp12) ? tmp12 : std::min(63, tmp12);
2023-01-11T21:41:24.0932176Z                             auto tmp78 = (tmp77 != tmp77) ? tmp77 : std::max(0, tmp77);
2023-01-11T21:41:24.0932326Z                             auto tmp79 = in_ptr0[tmp21 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:24.0932441Z                             auto tmp80 = in_ptr0[tmp24 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:24.0932553Z                             auto tmp81 = in_ptr0[tmp27 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:24.0932662Z                             auto tmp82 = in_ptr0[tmp30 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:24.0932754Z                             auto tmp83 = tmp79 * tmp38;
2023-01-11T21:41:24.0932842Z                             auto tmp84 = tmp80 * tmp43;
2023-01-11T21:41:24.0932934Z                             auto tmp85 = tmp81 * tmp49;
2023-01-11T21:41:24.0933015Z                             auto tmp86 = tmp82 * tmp56;
2023-01-11T21:41:24.0933102Z                             auto tmp87 = tmp85 + tmp86;
2023-01-11T21:41:24.0933193Z                             auto tmp88 = tmp84 + tmp87;
2023-01-11T21:41:24.0933280Z                             auto tmp89 = tmp83 + tmp88;
2023-01-11T21:41:24.0933410Z                             auto tmp90 = (tmp13 != tmp13) ? tmp13 : std::min(63, tmp13);
2023-01-11T21:41:24.0933534Z                             auto tmp91 = (tmp90 != tmp90) ? tmp90 : std::max(0, tmp90);
2023-01-11T21:41:24.0933647Z                             auto tmp92 = in_ptr0[tmp21 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:24.0933759Z                             auto tmp93 = in_ptr0[tmp24 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:24.0933858Z                             auto tmp94 = in_ptr0[tmp27 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:24.0933967Z                             auto tmp95 = in_ptr0[tmp30 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:24.0934057Z                             auto tmp96 = tmp92 * tmp38;
2023-01-11T21:41:24.0934147Z                             auto tmp97 = tmp93 * tmp43;
2023-01-11T21:41:24.0934238Z                             auto tmp98 = tmp94 * tmp49;
2023-01-11T21:41:24.0934325Z                             auto tmp99 = tmp95 * tmp56;
2023-01-11T21:41:24.0934425Z                             auto tmp100 = tmp98 + tmp99;
2023-01-11T21:41:24.0934510Z                             auto tmp101 = tmp97 + tmp100;
2023-01-11T21:41:24.0934602Z                             auto tmp102 = tmp96 + tmp101;
2023-01-11T21:41:24.0934698Z                             auto tmp103 = tmp7 + 1.0;
2023-01-11T21:41:24.0934845Z                             auto tmp104 = -0.75 * tmp103;
2023-01-11T21:41:24.0934992Z                             auto tmp105 = tmp104 - -3.75;
2023-01-11T21:41:24.0935088Z                             auto tmp106 = tmp105 * tmp103;
2023-01-11T21:41:24.0935234Z                             auto tmp107 = tmp106 + -6.0;
2023-01-11T21:41:24.0935328Z                             auto tmp108 = tmp107 * tmp103;
2023-01-11T21:41:24.0935465Z                             auto tmp109 = tmp108 - -3.0;
2023-01-11T21:41:24.0935557Z                             auto tmp110 = 1.25 * tmp7;
2023-01-11T21:41:24.0935700Z                             auto tmp111 = tmp110 - 2.25;
2023-01-11T21:41:24.0935843Z                             auto tmp112 = tmp111 * tmp7;
2023-01-11T21:41:24.0935934Z                             auto tmp113 = tmp112 * tmp7;
2023-01-11T21:41:24.0936026Z                             auto tmp114 = tmp113 + 1.0;
2023-01-11T21:41:24.0936170Z                             auto tmp115 = 1.0 - tmp7;
2023-01-11T21:41:24.0936250Z                             auto tmp116 = 1.25 * tmp115;
2023-01-11T21:41:24.0936389Z                             auto tmp117 = tmp116 - 2.25;
2023-01-11T21:41:24.0936484Z                             auto tmp118 = tmp117 * tmp115;
2023-01-11T21:41:24.0936576Z                             auto tmp119 = tmp118 * tmp115;
2023-01-11T21:41:24.0936667Z                             auto tmp120 = tmp119 + 1.0;
2023-01-11T21:41:24.0936755Z                             auto tmp121 = tmp115 + 1.0;
2023-01-11T21:41:24.0936900Z                             auto tmp122 = -0.75 * tmp121;
2023-01-11T21:41:24.0937049Z                             auto tmp123 = tmp122 - -3.75;
2023-01-11T21:41:24.0937172Z                             auto tmp124 = tmp123 * tmp121;
2023-01-11T21:41:24.0937320Z                             auto tmp125 = tmp124 + -6.0;
2023-01-11T21:41:24.0937415Z                             auto tmp126 = tmp125 * tmp121;
2023-01-11T21:41:24.0937562Z                             auto tmp127 = tmp126 - -3.0;
2023-01-11T21:41:24.0937655Z                             auto tmp128 = tmp63 * tmp109;
2023-01-11T21:41:24.0937745Z                             auto tmp129 = tmp76 * tmp114;
2023-01-11T21:41:24.0937837Z                             auto tmp130 = tmp89 * tmp120;
2023-01-11T21:41:24.0937923Z                             auto tmp131 = tmp102 * tmp127;
2023-01-11T21:41:24.0938016Z                             auto tmp132 = tmp130 + tmp131;
2023-01-11T21:41:24.0938111Z                             auto tmp133 = tmp129 + tmp132;
2023-01-11T21:41:24.0938203Z                             auto tmp134 = tmp128 + tmp133;
2023-01-11T21:41:24.0938307Z                             out_ptr0[i2 + (128*i1) + (16384*i0)] = tmp134;
2023-01-11T21:41:24.0938377Z                         }
2023-01-11T21:41:24.0938438Z                     }
2023-01-11T21:41:24.0938489Z                 }
2023-01-11T21:41:24.0938551Z             }
2023-01-11T21:41:24.0938611Z         }
2023-01-11T21:41:24.0938687Z         #pragma omp for 
2023-01-11T21:41:24.0938767Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.0938827Z         {
2023-01-11T21:41:24.0938907Z             #pragma GCC ivdep
2023-01-11T21:41:24.0938981Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:24.0939040Z             {
2023-01-11T21:41:24.0939121Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0939207Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:41:24.0939266Z                 {
2023-01-11T21:41:24.0939327Z                     {
2023-01-11T21:41:24.0939393Z                         {
2023-01-11T21:41:24.0939489Z                             auto tmp0 = static_cast<float>(i2);
2023-01-11T21:41:24.0939582Z                             auto tmp1 = tmp0 + 0.5;
2023-01-11T21:41:24.0939668Z                             auto tmp2 = 0.125 * tmp1;
2023-01-11T21:41:24.0939808Z                             auto tmp3 = tmp2 - 0.5;
2023-01-11T21:41:24.0939909Z                             auto tmp4 = std::floor(tmp3);
2023-01-11T21:41:24.0940051Z                             auto tmp5 = tmp3 - tmp4;
2023-01-11T21:41:24.0940151Z                             auto tmp6 = static_cast<float>(i1);
2023-01-11T21:41:24.0940229Z                             auto tmp7 = tmp6 + 0.5;
2023-01-11T21:41:24.0940313Z                             auto tmp8 = 0.5 * tmp7;
2023-01-11T21:41:24.0940447Z                             auto tmp9 = tmp8 - 0.5;
2023-01-11T21:41:24.0940551Z                             auto tmp10 = std::floor(tmp9);
2023-01-11T21:41:24.0940698Z                             auto tmp11 = tmp9 - tmp10;
2023-01-11T21:41:24.0940801Z                             auto tmp12 = static_cast<int>(tmp10);
2023-01-11T21:41:24.0940906Z                             auto tmp13 = static_cast<int>(tmp4);
2023-01-11T21:41:24.0941074Z                             auto tmp14 = tmp12 + -1;
2023-01-11T21:41:24.0941152Z                             auto tmp15 = tmp12 + 0;
2023-01-11T21:41:24.0941239Z                             auto tmp16 = tmp12 + 1;
2023-01-11T21:41:24.0941327Z                             auto tmp17 = tmp12 + 2;
2023-01-11T21:41:24.0941468Z                             auto tmp18 = tmp13 + -1;
2023-01-11T21:41:24.0941552Z                             auto tmp19 = tmp13 + 0;
2023-01-11T21:41:24.0941638Z                             auto tmp20 = tmp13 + 1;
2023-01-11T21:41:24.0941726Z                             auto tmp21 = tmp13 + 2;
2023-01-11T21:41:24.0941839Z                             auto tmp22 = (tmp14 != tmp14) ? tmp14 : std::min(63, tmp14);
2023-01-11T21:41:24.0941962Z                             auto tmp23 = (tmp22 != tmp22) ? tmp22 : std::max(0, tmp22);
2023-01-11T21:41:24.0942109Z                             auto tmp24 = (tmp18 != tmp18) ? tmp18 : std::min(31, tmp18);
2023-01-11T21:41:24.0942232Z                             auto tmp25 = (tmp24 != tmp24) ? tmp24 : std::max(0, tmp24);
2023-01-11T21:41:24.0942491Z                             auto tmp26 = in_ptr0[tmp25 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:24.0942644Z                             auto tmp27 = (tmp19 != tmp19) ? tmp19 : std::min(31, tmp19);
2023-01-11T21:41:24.0942762Z                             auto tmp28 = (tmp27 != tmp27) ? tmp27 : std::max(0, tmp27);
2023-01-11T21:41:24.0948045Z                             auto tmp29 = in_ptr0[tmp28 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:24.0948198Z                             auto tmp30 = (tmp20 != tmp20) ? tmp20 : std::min(31, tmp20);
2023-01-11T21:41:24.0948326Z                             auto tmp31 = (tmp30 != tmp30) ? tmp30 : std::max(0, tmp30);
2023-01-11T21:41:24.0948431Z                             auto tmp32 = in_ptr0[tmp31 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:24.0948560Z                             auto tmp33 = (tmp21 != tmp21) ? tmp21 : std::min(31, tmp21);
2023-01-11T21:41:24.0948684Z                             auto tmp34 = (tmp33 != tmp33) ? tmp33 : std::max(0, tmp33);
2023-01-11T21:41:24.0948798Z                             auto tmp35 = in_ptr0[tmp34 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:24.0948892Z                             auto tmp36 = tmp5 + 1.0;
2023-01-11T21:41:24.0949063Z                             auto tmp37 = -0.75 * tmp36;
2023-01-11T21:41:24.0949213Z                             auto tmp38 = tmp37 - -3.75;
2023-01-11T21:41:24.0949304Z                             auto tmp39 = tmp38 * tmp36;
2023-01-11T21:41:24.0949439Z                             auto tmp40 = tmp39 + -6.0;
2023-01-11T21:41:24.0949531Z                             auto tmp41 = tmp40 * tmp36;
2023-01-11T21:41:24.0949675Z                             auto tmp42 = tmp41 - -3.0;
2023-01-11T21:41:24.0949767Z                             auto tmp43 = 1.25 * tmp5;
2023-01-11T21:41:24.0949911Z                             auto tmp44 = tmp43 - 2.25;
2023-01-11T21:41:24.0950007Z                             auto tmp45 = tmp44 * tmp5;
2023-01-11T21:41:24.0950097Z                             auto tmp46 = tmp45 * tmp5;
2023-01-11T21:41:24.0950179Z                             auto tmp47 = tmp46 + 1.0;
2023-01-11T21:41:24.0950317Z                             auto tmp48 = 1.0 - tmp5;
2023-01-11T21:41:24.0950406Z                             auto tmp49 = 1.25 * tmp48;
2023-01-11T21:41:24.0950548Z                             auto tmp50 = tmp49 - 2.25;
2023-01-11T21:41:24.0950643Z                             auto tmp51 = tmp50 * tmp48;
2023-01-11T21:41:24.0950734Z                             auto tmp52 = tmp51 * tmp48;
2023-01-11T21:41:24.0950829Z                             auto tmp53 = tmp52 + 1.0;
2023-01-11T21:41:24.0950906Z                             auto tmp54 = tmp48 + 1.0;
2023-01-11T21:41:24.0951047Z                             auto tmp55 = -0.75 * tmp54;
2023-01-11T21:41:24.0951191Z                             auto tmp56 = tmp55 - -3.75;
2023-01-11T21:41:24.0951379Z                             auto tmp57 = tmp56 * tmp54;
2023-01-11T21:41:24.0951524Z                             auto tmp58 = tmp57 + -6.0;
2023-01-11T21:41:24.0951615Z                             auto tmp59 = tmp58 * tmp54;
2023-01-11T21:41:24.0951754Z                             auto tmp60 = tmp59 - -3.0;
2023-01-11T21:41:24.0951847Z                             auto tmp61 = tmp26 * tmp42;
2023-01-11T21:41:24.0951929Z                             auto tmp62 = tmp29 * tmp47;
2023-01-11T21:41:24.0952020Z                             auto tmp63 = tmp32 * tmp53;
2023-01-11T21:41:24.0952112Z                             auto tmp64 = tmp35 * tmp60;
2023-01-11T21:41:24.0952205Z                             auto tmp65 = tmp63 + tmp64;
2023-01-11T21:41:24.0952295Z                             auto tmp66 = tmp62 + tmp65;
2023-01-11T21:41:24.0952384Z                             auto tmp67 = tmp61 + tmp66;
2023-01-11T21:41:24.0952510Z                             auto tmp68 = (tmp15 != tmp15) ? tmp15 : std::min(63, tmp15);
2023-01-11T21:41:24.0952681Z                             auto tmp69 = (tmp68 != tmp68) ? tmp68 : std::max(0, tmp68);
2023-01-11T21:41:24.0952790Z                             auto tmp70 = in_ptr0[tmp25 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:24.0952905Z                             auto tmp71 = in_ptr0[tmp28 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:24.0953020Z                             auto tmp72 = in_ptr0[tmp31 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:24.0953134Z                             auto tmp73 = in_ptr0[tmp34 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:24.0953226Z                             auto tmp74 = tmp70 * tmp42;
2023-01-11T21:41:24.0953318Z                             auto tmp75 = tmp71 * tmp47;
2023-01-11T21:41:24.0953409Z                             auto tmp76 = tmp72 * tmp53;
2023-01-11T21:41:24.0953500Z                             auto tmp77 = tmp73 * tmp60;
2023-01-11T21:41:24.0953581Z                             auto tmp78 = tmp76 + tmp77;
2023-01-11T21:41:24.0953677Z                             auto tmp79 = tmp75 + tmp78;
2023-01-11T21:41:24.0953846Z                             auto tmp80 = tmp74 + tmp79;
2023-01-11T21:41:24.0953978Z                             auto tmp81 = (tmp16 != tmp16) ? tmp16 : std::min(63, tmp16);
2023-01-11T21:41:24.0954104Z                             auto tmp82 = (tmp81 != tmp81) ? tmp81 : std::max(0, tmp81);
2023-01-11T21:41:24.0954220Z                             auto tmp83 = in_ptr0[tmp25 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:24.0954328Z                             auto tmp84 = in_ptr0[tmp28 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:24.0954442Z                             auto tmp85 = in_ptr0[tmp31 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:24.0954543Z                             auto tmp86 = in_ptr0[tmp34 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:24.0954636Z                             auto tmp87 = tmp83 * tmp42;
2023-01-11T21:41:24.0954728Z                             auto tmp88 = tmp84 * tmp47;
2023-01-11T21:41:24.0954825Z                             auto tmp89 = tmp85 * tmp53;
2023-01-11T21:41:24.0954921Z                             auto tmp90 = tmp86 * tmp60;
2023-01-11T21:41:24.0955007Z                             auto tmp91 = tmp89 + tmp90;
2023-01-11T21:41:24.0955098Z                             auto tmp92 = tmp88 + tmp91;
2023-01-11T21:41:24.0955182Z                             auto tmp93 = tmp87 + tmp92;
2023-01-11T21:41:24.0955303Z                             auto tmp94 = (tmp17 != tmp17) ? tmp17 : std::min(63, tmp17);
2023-01-11T21:41:24.0955427Z                             auto tmp95 = (tmp94 != tmp94) ? tmp94 : std::max(0, tmp94);
2023-01-11T21:41:24.0955536Z                             auto tmp96 = in_ptr0[tmp25 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:24.0955649Z                             auto tmp97 = in_ptr0[tmp28 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:24.0955762Z                             auto tmp98 = in_ptr0[tmp31 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:24.0955877Z                             auto tmp99 = in_ptr0[tmp34 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:24.0956009Z                             auto tmp100 = tmp96 * tmp42;
2023-01-11T21:41:24.0956105Z                             auto tmp101 = tmp97 * tmp47;
2023-01-11T21:41:24.0956187Z                             auto tmp102 = tmp98 * tmp53;
2023-01-11T21:41:24.0956281Z                             auto tmp103 = tmp99 * tmp60;
2023-01-11T21:41:24.0956379Z                             auto tmp104 = tmp102 + tmp103;
2023-01-11T21:41:24.0956474Z                             auto tmp105 = tmp101 + tmp104;
2023-01-11T21:41:24.0956568Z                             auto tmp106 = tmp100 + tmp105;
2023-01-11T21:41:24.0956663Z                             auto tmp107 = tmp11 + 1.0;
2023-01-11T21:41:24.0956816Z                             auto tmp108 = -0.75 * tmp107;
2023-01-11T21:41:24.0956955Z                             auto tmp109 = tmp108 - -3.75;
2023-01-11T21:41:24.0957052Z                             auto tmp110 = tmp109 * tmp107;
2023-01-11T21:41:24.0957228Z                             auto tmp111 = tmp110 + -6.0;
2023-01-11T21:41:24.0957323Z                             auto tmp112 = tmp111 * tmp107;
2023-01-11T21:41:24.0957474Z                             auto tmp113 = tmp112 - -3.0;
2023-01-11T21:41:24.0957565Z                             auto tmp114 = 1.25 * tmp11;
2023-01-11T21:41:24.0957711Z                             auto tmp115 = tmp114 - 2.25;
2023-01-11T21:41:24.0957796Z                             auto tmp116 = tmp115 * tmp11;
2023-01-11T21:41:24.0957889Z                             auto tmp117 = tmp116 * tmp11;
2023-01-11T21:41:24.0957981Z                             auto tmp118 = tmp117 + 1.0;
2023-01-11T21:41:24.0958125Z                             auto tmp119 = 1.0 - tmp11;
2023-01-11T21:41:24.0958217Z                             auto tmp120 = 1.25 * tmp119;
2023-01-11T21:41:24.0958365Z                             auto tmp121 = tmp120 - 2.25;
2023-01-11T21:41:24.0958462Z                             auto tmp122 = tmp121 * tmp119;
2023-01-11T21:41:24.0958563Z                             auto tmp123 = tmp122 * tmp119;
2023-01-11T21:41:24.0958643Z                             auto tmp124 = tmp123 + 1.0;
2023-01-11T21:41:24.0958736Z                             auto tmp125 = tmp119 + 1.0;
2023-01-11T21:41:24.0958885Z                             auto tmp126 = -0.75 * tmp125;
2023-01-11T21:41:24.0959032Z                             auto tmp127 = tmp126 - -3.75;
2023-01-11T21:41:24.0959131Z                             auto tmp128 = tmp127 * tmp125;
2023-01-11T21:41:24.0959278Z                             auto tmp129 = tmp128 + -6.0;
2023-01-11T21:41:24.0959372Z                             auto tmp130 = tmp129 * tmp125;
2023-01-11T21:41:24.0959508Z                             auto tmp131 = tmp130 - -3.0;
2023-01-11T21:41:24.0959600Z                             auto tmp132 = tmp67 * tmp113;
2023-01-11T21:41:24.0959693Z                             auto tmp133 = tmp80 * tmp118;
2023-01-11T21:41:24.0959786Z                             auto tmp134 = tmp93 * tmp124;
2023-01-11T21:41:24.0959885Z                             auto tmp135 = tmp106 * tmp131;
2023-01-11T21:41:24.0959980Z                             auto tmp136 = tmp134 + tmp135;
2023-01-11T21:41:24.0960071Z                             auto tmp137 = tmp133 + tmp136;
2023-01-11T21:41:24.0960163Z                             auto tmp138 = tmp132 + tmp137;
2023-01-11T21:41:24.0960258Z                             out_ptr1[i2 + (256*i1) + (32768*i0)] = tmp138;
2023-01-11T21:41:24.0960322Z                         }
2023-01-11T21:41:24.0960386Z                     }
2023-01-11T21:41:24.0960447Z                 }
2023-01-11T21:41:24.0960510Z             }
2023-01-11T21:41:24.0960571Z         }
2023-01-11T21:41:24.0960625Z     }
2023-01-11T21:41:24.0960672Z }
2023-01-11T21:41:24.0960750Z ''')
2023-01-11T21:41:24.0960757Z 
2023-01-11T21:41:24.0960762Z 
2023-01-11T21:41:24.0960849Z async_compile.wait(globals())
2023-01-11T21:41:24.0960919Z del async_compile
2023-01-11T21:41:24.0960924Z 
2023-01-11T21:41:24.0961022Z def call(args):
2023-01-11T21:41:24.0961092Z     arg0_1, = args
2023-01-11T21:41:24.0961161Z     args.clear()
2023-01-11T21:41:24.0961382Z     buf0 = empty_strided((4, 3, 128, 128), (49152, 16384, 128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0961600Z     buf1 = empty_strided((4, 3, 128, 256), (98304, 32768, 256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0961763Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.0961829Z     del arg0_1
2023-01-11T21:41:24.0961903Z     return (buf0, buf1, )
2023-01-11T21:41:24.0961908Z 
2023-01-11T21:41:24.0961913Z 
2023-01-11T21:41:24.0961983Z if __name__ == "__main__":
2023-01-11T21:41:24.0962094Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0962219Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0962429Z     arg0_1 = rand_strided((4, 3, 64, 32), (6144, 2048, 32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0962567Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0962834Z [2023-01-11 21:39:12,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 475
2023-01-11T21:41:24.0962839Z 
2023-01-11T21:41:24.0962905Z ok (3.413s)
2023-01-11T21:41:24.0963383Z   test_upsample_bilinear2d_a_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0963508Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0963770Z [2023-01-11 21:39:13,693] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 476
2023-01-11T21:41:24.0963775Z 
2023-01-11T21:41:24.0963875Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0963941Z import torch
2023-01-11T21:41:24.0963998Z import random
2023-01-11T21:41:24.0964110Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0964224Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0964230Z 
2023-01-11T21:41:24.0964306Z aten = torch.ops.aten
2023-01-11T21:41:24.0964442Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0964532Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0964537Z 
2023-01-11T21:41:24.0964541Z 
2023-01-11T21:41:24.0964670Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0964871Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0964982Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0965070Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:24.0965169Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0965270Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.0965362Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:24.0965425Z {
2023-01-11T21:41:24.0965508Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:24.0965584Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:24.0965670Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0965731Z     {
2023-01-11T21:41:24.0965805Z         #pragma omp for 
2023-01-11T21:41:24.0965888Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0965947Z         {
2023-01-11T21:41:24.0966023Z             #pragma GCC ivdep
2023-01-11T21:41:24.0966096Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:24.0966155Z             {
2023-01-11T21:41:24.0966231Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0966320Z                 for(long i2=0; i2<45; i2+=1)
2023-01-11T21:41:24.0966389Z                 {
2023-01-11T21:41:24.0966451Z                     {
2023-01-11T21:41:24.0966519Z                         {
2023-01-11T21:41:24.0966644Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.0966751Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:24.0966844Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0966958Z                             auto tmp3 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:24.0967054Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:24.0967194Z                             auto tmp5 = tmp4 - tmp1;
2023-01-11T21:41:24.0967304Z                             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:24.0967432Z                             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:41:24.0967524Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:24.0967630Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:24.0967732Z                             auto tmp10 = static_cast<float>(i2);
2023-01-11T21:41:24.0967864Z                             auto tmp11 = tmp10 + tmp1;
2023-01-11T21:41:24.0967979Z                             auto tmp12 = static_cast<float>(0.8444444444444444);
2023-01-11T21:41:24.0968073Z                             auto tmp13 = tmp11 * tmp12;
2023-01-11T21:41:24.0968219Z                             auto tmp14 = tmp13 - tmp1;
2023-01-11T21:41:24.0968348Z                             auto tmp15 = (tmp6 != tmp6) ? tmp6 : std::max(tmp14, tmp6);
2023-01-11T21:41:24.0968442Z                             auto tmp16 = std::floor(tmp15);
2023-01-11T21:41:24.0968550Z                             auto tmp17 = static_cast<long>(tmp16);
2023-01-11T21:41:24.0968662Z                             auto tmp18 = in_ptr0[tmp17 + (38*tmp9) + (1406*i0)];
2023-01-11T21:41:24.0968768Z                             auto tmp19 = static_cast<float>(1.0);
2023-01-11T21:41:24.0968874Z                             auto tmp20 = static_cast<float>(tmp9);
2023-01-11T21:41:24.0969019Z                             auto tmp21 = tmp7 - tmp20;
2023-01-11T21:41:24.0969168Z                             auto tmp22 = tmp19 - tmp21;
2023-01-11T21:41:24.0969250Z                             auto tmp23 = tmp18 * tmp22;
2023-01-11T21:41:24.0969351Z                             auto tmp24 = std::ceil(tmp7);
2023-01-11T21:41:24.0969459Z                             auto tmp25 = static_cast<float>(36.0);
2023-01-11T21:41:24.0969587Z                             auto tmp26 = (tmp25 != tmp25) ? tmp25 : std::min(tmp24, tmp25);
2023-01-11T21:41:24.0969696Z                             auto tmp27 = static_cast<long>(tmp26);
2023-01-11T21:41:24.0969808Z                             auto tmp28 = in_ptr0[tmp17 + (38*tmp27) + (1406*i0)];
2023-01-11T21:41:24.0969902Z                             auto tmp29 = tmp28 * tmp21;
2023-01-11T21:41:24.0969996Z                             auto tmp30 = tmp23 + tmp29;
2023-01-11T21:41:24.0970089Z                             out_ptr0[i2 + (45*i1) + (2025*i0)] = tmp30;
2023-01-11T21:41:24.0970159Z                         }
2023-01-11T21:41:24.0970224Z                     }
2023-01-11T21:41:24.0970287Z                 }
2023-01-11T21:41:24.0970346Z             }
2023-01-11T21:41:24.0970408Z         }
2023-01-11T21:41:24.0970482Z         #pragma omp for 
2023-01-11T21:41:24.0970550Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0970610Z         {
2023-01-11T21:41:24.0970687Z             #pragma GCC ivdep
2023-01-11T21:41:24.0970766Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:24.0970830Z             {
2023-01-11T21:41:24.0970913Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0970998Z                 for(long i2=0; i2<45; i2+=1)
2023-01-11T21:41:24.0971049Z                 {
2023-01-11T21:41:24.0971109Z                     {
2023-01-11T21:41:24.0971175Z                         {
2023-01-11T21:41:24.0971279Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.0971387Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:24.0971512Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0971624Z                             auto tmp3 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:24.0971705Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:24.0971846Z                             auto tmp5 = tmp4 - tmp1;
2023-01-11T21:41:24.0971949Z                             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:24.0972074Z                             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:41:24.0972174Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:24.0972280Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:24.0972385Z                             auto tmp10 = static_cast<float>(i2);
2023-01-11T21:41:24.0972482Z                             auto tmp11 = tmp10 + tmp1;
2023-01-11T21:41:24.0972585Z                             auto tmp12 = static_cast<float>(0.8444444444444444);
2023-01-11T21:41:24.0972707Z                             auto tmp13 = tmp11 * tmp12;
2023-01-11T21:41:24.0972853Z                             auto tmp14 = tmp13 - tmp1;
2023-01-11T21:41:24.0972980Z                             auto tmp15 = (tmp6 != tmp6) ? tmp6 : std::max(tmp14, tmp6);
2023-01-11T21:41:24.0973088Z                             auto tmp16 = std::ceil(tmp15);
2023-01-11T21:41:24.0973191Z                             auto tmp17 = static_cast<float>(37.0);
2023-01-11T21:41:24.0973319Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:41:24.0973428Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:41:24.0973530Z                             auto tmp20 = in_ptr0[tmp19 + (38*tmp9) + (1406*i0)];
2023-01-11T21:41:24.0973635Z                             auto tmp21 = static_cast<float>(1.0);
2023-01-11T21:41:24.0973744Z                             auto tmp22 = static_cast<float>(tmp9);
2023-01-11T21:41:24.0973892Z                             auto tmp23 = tmp7 - tmp22;
2023-01-11T21:41:24.0974039Z                             auto tmp24 = tmp21 - tmp23;
2023-01-11T21:41:24.0974130Z                             auto tmp25 = tmp20 * tmp24;
2023-01-11T21:41:24.0974230Z                             auto tmp26 = std::ceil(tmp7);
2023-01-11T21:41:24.0974334Z                             auto tmp27 = static_cast<float>(36.0);
2023-01-11T21:41:24.0974449Z                             auto tmp28 = (tmp27 != tmp27) ? tmp27 : std::min(tmp26, tmp27);
2023-01-11T21:41:24.0974556Z                             auto tmp29 = static_cast<long>(tmp28);
2023-01-11T21:41:24.0974671Z                             auto tmp30 = in_ptr0[tmp19 + (38*tmp29) + (1406*i0)];
2023-01-11T21:41:24.0974766Z                             auto tmp31 = tmp30 * tmp23;
2023-01-11T21:41:24.0974858Z                             auto tmp32 = tmp25 + tmp31;
2023-01-11T21:41:24.0974958Z                             out_ptr1[i2 + (45*i1) + (2025*i0)] = tmp32;
2023-01-11T21:41:24.0975031Z                         }
2023-01-11T21:41:24.0975085Z                     }
2023-01-11T21:41:24.0975147Z                 }
2023-01-11T21:41:24.0975208Z             }
2023-01-11T21:41:24.0975267Z         }
2023-01-11T21:41:24.0975341Z         #pragma omp for 
2023-01-11T21:41:24.0975423Z         for(long i0=0; i0<360; i0+=1)
2023-01-11T21:41:24.0975481Z         {
2023-01-11T21:41:24.0975546Z             #pragma GCC ivdep
2023-01-11T21:41:24.0975628Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:24.0975690Z             {
2023-01-11T21:41:24.0975749Z                 {
2023-01-11T21:41:24.0975812Z                     {
2023-01-11T21:41:24.0975912Z                         auto tmp0 = out_ptr0[i1 + (45*i0)];
2023-01-11T21:41:24.0976013Z                         auto tmp16 = out_ptr1[i1 + (45*i0)];
2023-01-11T21:41:24.0976106Z                         auto tmp1 = static_cast<float>(1.0);
2023-01-11T21:41:24.0976209Z                         auto tmp2 = static_cast<float>(i1);
2023-01-11T21:41:24.0976387Z                         auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:24.0976479Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.0976591Z                         auto tmp5 = static_cast<float>(0.8444444444444444);
2023-01-11T21:41:24.0976685Z                         auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.0976823Z                         auto tmp7 = tmp6 - tmp3;
2023-01-11T21:41:24.0976919Z                         auto tmp8 = static_cast<float>(0.0);
2023-01-11T21:41:24.0977044Z                         auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::max(tmp7, tmp8);
2023-01-11T21:41:24.0977145Z                         auto tmp10 = std::floor(tmp9);
2023-01-11T21:41:24.0977251Z                         auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:41:24.0977355Z                         auto tmp12 = static_cast<float>(tmp11);
2023-01-11T21:41:24.0977497Z                         auto tmp13 = tmp9 - tmp12;
2023-01-11T21:41:24.0977635Z                         auto tmp14 = tmp1 - tmp13;
2023-01-11T21:41:24.0977751Z                         auto tmp15 = tmp0 * tmp14;
2023-01-11T21:41:24.0977833Z                         auto tmp17 = tmp16 * tmp13;
2023-01-11T21:41:24.0977921Z                         auto tmp18 = tmp15 + tmp17;
2023-01-11T21:41:24.0978017Z                         in_out_ptr0[i1 + (45*i0)] = tmp18;
2023-01-11T21:41:24.0978080Z                     }
2023-01-11T21:41:24.0978142Z                 }
2023-01-11T21:41:24.0978199Z             }
2023-01-11T21:41:24.0978258Z         }
2023-01-11T21:41:24.0978322Z         #pragma omp for 
2023-01-11T21:41:24.0978402Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0978462Z         {
2023-01-11T21:41:24.0978538Z             #pragma GCC ivdep
2023-01-11T21:41:24.0978622Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:24.0978683Z             {
2023-01-11T21:41:24.0978765Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0978842Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:24.0978900Z                 {
2023-01-11T21:41:24.0978969Z                     {
2023-01-11T21:41:24.0979034Z                         {
2023-01-11T21:41:24.0979137Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.0979250Z                             auto tmp1 = static_cast<float>(0.4931506849315068);
2023-01-11T21:41:24.0979341Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0979430Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:24.0979536Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:24.0979640Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:24.0979753Z                             auto tmp6 = static_cast<float>(0.49333333333333335);
2023-01-11T21:41:24.0979846Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:24.0979946Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:24.0980050Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:24.0980158Z                             auto tmp10 = in_ptr0[tmp9 + (38*tmp4) + (1406*i0)];
2023-01-11T21:41:24.0980263Z                             auto tmp11 = static_cast<float>(1.0);
2023-01-11T21:41:24.0980372Z                             auto tmp12 = static_cast<float>(tmp4);
2023-01-11T21:41:24.0980598Z                             auto tmp13 = tmp2 - tmp12;
2023-01-11T21:41:24.0980780Z                             auto tmp14 = tmp11 - tmp13;
2023-01-11T21:41:24.0980871Z                             auto tmp15 = tmp10 * tmp14;
2023-01-11T21:41:24.0980971Z                             auto tmp16 = std::ceil(tmp2);
2023-01-11T21:41:24.0981074Z                             auto tmp17 = static_cast<float>(36.0);
2023-01-11T21:41:24.0981193Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:41:24.0981298Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:41:24.0981416Z                             auto tmp20 = in_ptr0[tmp9 + (38*tmp19) + (1406*i0)];
2023-01-11T21:41:24.0981545Z                             auto tmp21 = tmp20 * tmp13;
2023-01-11T21:41:24.0981638Z                             auto tmp22 = tmp15 + tmp21;
2023-01-11T21:41:24.0981742Z                             auto tmp23 = static_cast<float>(tmp9);
2023-01-11T21:41:24.0981889Z                             auto tmp24 = tmp7 - tmp23;
2023-01-11T21:41:24.0982034Z                             auto tmp25 = tmp11 - tmp24;
2023-01-11T21:41:24.0982116Z                             auto tmp26 = tmp22 * tmp25;
2023-01-11T21:41:24.0982217Z                             out_ptr2[i2 + (76*i1) + (5624*i0)] = tmp26;
2023-01-11T21:41:24.0982283Z                         }
2023-01-11T21:41:24.0982560Z                     }
2023-01-11T21:41:24.0982624Z                 }
2023-01-11T21:41:24.0982686Z             }
2023-01-11T21:41:24.0982747Z         }
2023-01-11T21:41:24.0982812Z         #pragma omp for 
2023-01-11T21:41:24.0982891Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.0983013Z         {
2023-01-11T21:41:24.0983094Z             #pragma GCC ivdep
2023-01-11T21:41:24.0983178Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:24.0983238Z             {
2023-01-11T21:41:24.0983324Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0983454Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:24.0983546Z                 {
2023-01-11T21:41:24.0983612Z                     {
2023-01-11T21:41:24.0983676Z                         {
2023-01-11T21:41:24.0983782Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.0983895Z                             auto tmp1 = static_cast<float>(0.4931506849315068);
2023-01-11T21:41:24.0983989Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0984081Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:24.0984188Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:24.0984295Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:24.0984414Z                             auto tmp6 = static_cast<float>(0.49333333333333335);
2023-01-11T21:41:24.0984507Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:24.0984607Z                             auto tmp8 = std::ceil(tmp7);
2023-01-11T21:41:24.0984711Z                             auto tmp9 = static_cast<float>(37.0);
2023-01-11T21:41:24.0984828Z                             auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp8, tmp9);
2023-01-11T21:41:24.0984938Z                             auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:41:24.0985053Z                             auto tmp12 = in_ptr0[tmp11 + (38*tmp4) + (1406*i0)];
2023-01-11T21:41:24.0985162Z                             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:24.0985271Z                             auto tmp14 = static_cast<float>(tmp4);
2023-01-11T21:41:24.0985427Z                             auto tmp15 = tmp2 - tmp14;
2023-01-11T21:41:24.0985578Z                             auto tmp16 = tmp13 - tmp15;
2023-01-11T21:41:24.0985671Z                             auto tmp17 = tmp12 * tmp16;
2023-01-11T21:41:24.0985761Z                             auto tmp18 = std::ceil(tmp2);
2023-01-11T21:41:24.0985866Z                             auto tmp19 = static_cast<float>(36.0);
2023-01-11T21:41:24.0985997Z                             auto tmp20 = (tmp19 != tmp19) ? tmp19 : std::min(tmp18, tmp19);
2023-01-11T21:41:24.0986102Z                             auto tmp21 = static_cast<long>(tmp20);
2023-01-11T21:41:24.0986219Z                             auto tmp22 = in_ptr0[tmp11 + (38*tmp21) + (1406*i0)];
2023-01-11T21:41:24.0986313Z                             auto tmp23 = tmp22 * tmp15;
2023-01-11T21:41:24.0986406Z                             auto tmp24 = tmp17 + tmp23;
2023-01-11T21:41:24.0986508Z                             auto tmp25 = std::floor(tmp7);
2023-01-11T21:41:24.0986603Z                             auto tmp26 = static_cast<long>(tmp25);
2023-01-11T21:41:24.0986757Z                             auto tmp27 = static_cast<float>(tmp26);
2023-01-11T21:41:24.0986905Z                             auto tmp28 = tmp7 - tmp27;
2023-01-11T21:41:24.0986998Z                             auto tmp29 = tmp24 * tmp28;
2023-01-11T21:41:24.0987100Z                             out_ptr3[i2 + (76*i1) + (5624*i0)] = tmp29;
2023-01-11T21:41:24.0987169Z                         }
2023-01-11T21:41:24.0987232Z                     }
2023-01-11T21:41:24.0987284Z                 }
2023-01-11T21:41:24.0987344Z             }
2023-01-11T21:41:24.0987404Z         }
2023-01-11T21:41:24.0987478Z         #pragma omp for 
2023-01-11T21:41:24.0987560Z         for(long i0=0; i0<5624; i0+=1)
2023-01-11T21:41:24.0987621Z         {
2023-01-11T21:41:24.0987754Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:24.0987872Z             auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr3 + 8*i0);
2023-01-11T21:41:24.0987956Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0988078Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:24.0988141Z         }
2023-01-11T21:41:24.0988233Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.0988318Z         for(long i0=44992; i0<44992; i0+=1)
2023-01-11T21:41:24.0988378Z         {
2023-01-11T21:41:24.0988450Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:24.0988533Z             auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:24.0988612Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.0988693Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:24.0988755Z         }
2023-01-11T21:41:24.0988814Z     }
2023-01-11T21:41:24.0988871Z }
2023-01-11T21:41:24.0988937Z ''')
2023-01-11T21:41:24.0988944Z 
2023-01-11T21:41:24.0988960Z 
2023-01-11T21:41:24.0989037Z async_compile.wait(globals())
2023-01-11T21:41:24.0989105Z del async_compile
2023-01-11T21:41:24.0989110Z 
2023-01-11T21:41:24.0989178Z def call(args):
2023-01-11T21:41:24.0989243Z     arg0_1, = args
2023-01-11T21:41:24.0989318Z     args.clear()
2023-01-11T21:41:24.0989544Z     buf0 = empty_strided((2, 4, 45, 45), (8100, 2025, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0989762Z     buf1 = empty_strided((2, 4, 45, 45), (8100, 2025, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0989834Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:24.0990050Z     buf3 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0990269Z     buf4 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0990350Z     buf5 = buf3; del buf3  # reuse
2023-01-11T21:41:24.0990561Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:24.0990625Z     del arg0_1
2023-01-11T21:41:24.0990698Z     return (buf2, buf5, )
2023-01-11T21:41:24.0990703Z 
2023-01-11T21:41:24.0990707Z 
2023-01-11T21:41:24.0990784Z if __name__ == "__main__":
2023-01-11T21:41:24.0990885Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.0991006Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.0991225Z     arg0_1 = rand_strided((2, 4, 37, 38), (5624, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.0991333Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.0991602Z [2023-01-11 21:39:15,547] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 476
2023-01-11T21:41:24.0991607Z 
2023-01-11T21:41:24.0991670Z ok (2.568s)
2023-01-11T21:41:24.0992148Z   test_upsample_bilinear2d_b_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.0992303Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.0992562Z [2023-01-11 21:39:15,831] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 477
2023-01-11T21:41:24.0992824Z [2023-01-11 21:39:17,456] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 477
2023-01-11T21:41:24.0992829Z 
2023-01-11T21:41:24.0992910Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.0992979Z import torch
2023-01-11T21:41:24.0993045Z import random
2023-01-11T21:41:24.0993156Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.0993277Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.0993282Z 
2023-01-11T21:41:24.0993357Z aten = torch.ops.aten
2023-01-11T21:41:24.0993487Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.0993566Z async_compile = AsyncCompile()
2023-01-11T21:41:24.0993584Z 
2023-01-11T21:41:24.0993616Z 
2023-01-11T21:41:24.0993795Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.0993999Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.0994115Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.0994216Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.0994314Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:24.0994373Z {
2023-01-11T21:41:24.0994459Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:24.0994543Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.0994601Z     {
2023-01-11T21:41:24.0994690Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0994769Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0994829Z         {
2023-01-11T21:41:24.0994911Z             for(long i1=0; i1<80; i1+=1)
2023-01-11T21:41:24.0994975Z             {
2023-01-11T21:41:24.0995043Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0995134Z                 for(long i2=0; i2<118; i2+=1)
2023-01-11T21:41:24.0995196Z                 {
2023-01-11T21:41:24.0995259Z                     {
2023-01-11T21:41:24.0995325Z                         {
2023-01-11T21:41:24.0995432Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.0995546Z                             auto tmp1 = static_cast<float>(0.4936708860759494);
2023-01-11T21:41:24.0995631Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0995733Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:24.0995838Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:24.0995948Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:24.0996062Z                             auto tmp6 = static_cast<float>(0.49572649572649574);
2023-01-11T21:41:24.0996154Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:24.0996256Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:24.0996353Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:24.0996466Z                             auto tmp10 = in_ptr0[tmp9 + (59*tmp4) + (2360*i0)];
2023-01-11T21:41:24.0996568Z                             auto tmp11 = static_cast<float>(1.0);
2023-01-11T21:41:24.0996673Z                             auto tmp12 = static_cast<float>(tmp4);
2023-01-11T21:41:24.0996819Z                             auto tmp13 = tmp2 - tmp12;
2023-01-11T21:41:24.0996961Z                             auto tmp14 = tmp11 - tmp13;
2023-01-11T21:41:24.0997056Z                             auto tmp15 = tmp10 * tmp14;
2023-01-11T21:41:24.0997155Z                             auto tmp16 = std::ceil(tmp2);
2023-01-11T21:41:24.0997248Z                             auto tmp17 = static_cast<float>(39.0);
2023-01-11T21:41:24.0997379Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:41:24.0997518Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:41:24.0997634Z                             auto tmp20 = in_ptr0[tmp9 + (59*tmp19) + (2360*i0)];
2023-01-11T21:41:24.0997727Z                             auto tmp21 = tmp20 * tmp13;
2023-01-11T21:41:24.0997820Z                             auto tmp22 = tmp15 + tmp21;
2023-01-11T21:41:24.0997928Z                             auto tmp23 = static_cast<float>(tmp9);
2023-01-11T21:41:24.0998072Z                             auto tmp24 = tmp7 - tmp23;
2023-01-11T21:41:24.0998206Z                             auto tmp25 = tmp11 - tmp24;
2023-01-11T21:41:24.0998297Z                             auto tmp26 = tmp22 * tmp25;
2023-01-11T21:41:24.0998401Z                             out_ptr0[i2 + (118*i1) + (9440*i0)] = tmp26;
2023-01-11T21:41:24.0998469Z                         }
2023-01-11T21:41:24.0998533Z                     }
2023-01-11T21:41:24.0998594Z                 }
2023-01-11T21:41:24.0998656Z             }
2023-01-11T21:41:24.0998709Z         }
2023-01-11T21:41:24.0998822Z         #pragma omp for  collapse(2)
2023-01-11T21:41:24.0998900Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.0998960Z         {
2023-01-11T21:41:24.0999041Z             for(long i1=0; i1<80; i1+=1)
2023-01-11T21:41:24.0999099Z             {
2023-01-11T21:41:24.0999182Z                 #pragma GCC ivdep
2023-01-11T21:41:24.0999259Z                 for(long i2=0; i2<118; i2+=1)
2023-01-11T21:41:24.0999321Z                 {
2023-01-11T21:41:24.0999384Z                     {
2023-01-11T21:41:24.0999450Z                         {
2023-01-11T21:41:24.0999552Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.0999671Z                             auto tmp1 = static_cast<float>(0.4936708860759494);
2023-01-11T21:41:24.0999765Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.0999853Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:24.0999960Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:24.1000065Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:24.1000178Z                             auto tmp6 = static_cast<float>(0.49572649572649574);
2023-01-11T21:41:24.1000269Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:24.1000363Z                             auto tmp8 = std::ceil(tmp7);
2023-01-11T21:41:24.1000473Z                             auto tmp9 = static_cast<float>(58.0);
2023-01-11T21:41:24.1000600Z                             auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp8, tmp9);
2023-01-11T21:41:24.1000695Z                             auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:41:24.1000809Z                             auto tmp12 = in_ptr0[tmp11 + (59*tmp4) + (2360*i0)];
2023-01-11T21:41:24.1000916Z                             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:24.1001023Z                             auto tmp14 = static_cast<float>(tmp4);
2023-01-11T21:41:24.1001174Z                             auto tmp15 = tmp2 - tmp14;
2023-01-11T21:41:24.1001318Z                             auto tmp16 = tmp13 - tmp15;
2023-01-11T21:41:24.1001410Z                             auto tmp17 = tmp12 * tmp16;
2023-01-11T21:41:24.1001499Z                             auto tmp18 = std::ceil(tmp2);
2023-01-11T21:41:24.1001605Z                             auto tmp19 = static_cast<float>(39.0);
2023-01-11T21:41:24.1001735Z                             auto tmp20 = (tmp19 != tmp19) ? tmp19 : std::min(tmp18, tmp19);
2023-01-11T21:41:24.1001841Z                             auto tmp21 = static_cast<long>(tmp20);
2023-01-11T21:41:24.1001952Z                             auto tmp22 = in_ptr0[tmp11 + (59*tmp21) + (2360*i0)];
2023-01-11T21:41:24.1002042Z                             auto tmp23 = tmp22 * tmp15;
2023-01-11T21:41:24.1002133Z                             auto tmp24 = tmp17 + tmp23;
2023-01-11T21:41:24.1002233Z                             auto tmp25 = std::floor(tmp7);
2023-01-11T21:41:24.1002359Z                             auto tmp26 = static_cast<long>(tmp25);
2023-01-11T21:41:24.1002468Z                             auto tmp27 = static_cast<float>(tmp26);
2023-01-11T21:41:24.1002611Z                             auto tmp28 = tmp7 - tmp27;
2023-01-11T21:41:24.1002701Z                             auto tmp29 = tmp24 * tmp28;
2023-01-11T21:41:24.1002801Z                             out_ptr1[i2 + (118*i1) + (9440*i0)] = tmp29;
2023-01-11T21:41:24.1002869Z                         }
2023-01-11T21:41:24.1002934Z                     }
2023-01-11T21:41:24.1002997Z                 }
2023-01-11T21:41:24.1003047Z             }
2023-01-11T21:41:24.1003109Z         }
2023-01-11T21:41:24.1003183Z         #pragma omp for 
2023-01-11T21:41:24.1003263Z         for(long i0=0; i0<2360; i0+=1)
2023-01-11T21:41:24.1003320Z         {
2023-01-11T21:41:24.1003448Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1003577Z             auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:24.1003679Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1003773Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.1003833Z         }
2023-01-11T21:41:24.1003939Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1004066Z         for(long i0=18880; i0<18880; i0+=1)
2023-01-11T21:41:24.1004156Z         {
2023-01-11T21:41:24.1004233Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:24.1004314Z             auto tmp1 = out_ptr1[i0];
2023-01-11T21:41:24.1004394Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1004479Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1004540Z         }
2023-01-11T21:41:24.1004598Z     }
2023-01-11T21:41:24.1004656Z }
2023-01-11T21:41:24.1004727Z ''')
2023-01-11T21:41:24.1004733Z 
2023-01-11T21:41:24.1004746Z 
2023-01-11T21:41:24.1004825Z async_compile.wait(globals())
2023-01-11T21:41:24.1004895Z del async_compile
2023-01-11T21:41:24.1004899Z 
2023-01-11T21:41:24.1004969Z def call(args):
2023-01-11T21:41:24.1005042Z     arg0_1, = args
2023-01-11T21:41:24.1005112Z     args.clear()
2023-01-11T21:41:24.1005340Z     buf0 = empty_strided((1, 2, 80, 118), (18880, 9440, 118, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1005560Z     buf1 = empty_strided((1, 2, 80, 118), (18880, 9440, 118, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1005634Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:24.1005788Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.1005854Z     del arg0_1
2023-01-11T21:41:24.1005925Z     return (buf2, )
2023-01-11T21:41:24.1005930Z 
2023-01-11T21:41:24.1005934Z 
2023-01-11T21:41:24.1006005Z if __name__ == "__main__":
2023-01-11T21:41:24.1006114Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1006232Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1006452Z     arg0_1 = rand_strided((1, 2, 40, 59), (4720, 2360, 59, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1006551Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1006555Z 
2023-01-11T21:41:24.1006620Z ok (1.908s)
2023-01-11T21:41:24.1007101Z   test_upsample_nearest1d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1007227Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1007486Z [2023-01-11 21:39:17,747] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 478
2023-01-11T21:41:24.1007752Z [2023-01-11 21:39:19,314] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 478
2023-01-11T21:41:24.1007792Z 
2023-01-11T21:41:24.1007888Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1007958Z import torch
2023-01-11T21:41:24.1008026Z import random
2023-01-11T21:41:24.1008128Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1008248Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1008253Z 
2023-01-11T21:41:24.1008329Z aten = torch.ops.aten
2023-01-11T21:41:24.1008461Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1008546Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1008552Z 
2023-01-11T21:41:24.1008556Z 
2023-01-11T21:41:24.1008686Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1008888Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1009004Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1009090Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.1009183Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.1009304Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.1009395Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.1009483Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:24.1009542Z {
2023-01-11T21:41:24.1009639Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1009688Z     {
2023-01-11T21:41:24.1009759Z         #pragma omp for 
2023-01-11T21:41:24.1009837Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1009900Z         {
2023-01-11T21:41:24.1009976Z             #pragma GCC ivdep
2023-01-11T21:41:24.1010058Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:24.1010118Z             {
2023-01-11T21:41:24.1010168Z                 {
2023-01-11T21:41:24.1010230Z                     {
2023-01-11T21:41:24.1010335Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1010439Z                         auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:24.1010534Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1010637Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1010737Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:24.1010820Z                         out_ptr0[i1 + (74*i0)] = tmp4;
2023-01-11T21:41:24.1010911Z                         out_ptr1[i1 + (74*i0)] = tmp4;
2023-01-11T21:41:24.1010975Z                     }
2023-01-11T21:41:24.1011036Z                 }
2023-01-11T21:41:24.1011100Z             }
2023-01-11T21:41:24.1011159Z         }
2023-01-11T21:41:24.1011233Z         #pragma omp for 
2023-01-11T21:41:24.1011301Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1011361Z         {
2023-01-11T21:41:24.1011438Z             #pragma GCC ivdep
2023-01-11T21:41:24.1011520Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:41:24.1011580Z             {
2023-01-11T21:41:24.1011640Z                 {
2023-01-11T21:41:24.1011705Z                     {
2023-01-11T21:41:24.1011802Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1011912Z                         auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:41:24.1012005Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1012109Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1012209Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:24.1012300Z                         out_ptr2[i1 + (70*i0)] = tmp4;
2023-01-11T21:41:24.1012360Z                     }
2023-01-11T21:41:24.1012411Z                 }
2023-01-11T21:41:24.1012470Z             }
2023-01-11T21:41:24.1012529Z         }
2023-01-11T21:41:24.1012603Z         #pragma omp for 
2023-01-11T21:41:24.1012681Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1012741Z         {
2023-01-11T21:41:24.1012807Z             #pragma GCC ivdep
2023-01-11T21:41:24.1012885Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:24.1012993Z             {
2023-01-11T21:41:24.1013054Z                 {
2023-01-11T21:41:24.1013117Z                     {
2023-01-11T21:41:24.1013219Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1013329Z                         auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:24.1013409Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1013510Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1013610Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:24.1013703Z                         out_ptr3[i1 + (45*i0)] = tmp4;
2023-01-11T21:41:24.1013766Z                     }
2023-01-11T21:41:24.1013830Z                 }
2023-01-11T21:41:24.1013891Z             }
2023-01-11T21:41:24.1013939Z         }
2023-01-11T21:41:24.1014014Z         #pragma omp for 
2023-01-11T21:41:24.1014092Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1014153Z         {
2023-01-11T21:41:24.1014230Z             #pragma GCC ivdep
2023-01-11T21:41:24.1014342Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:24.1014410Z             {
2023-01-11T21:41:24.1014461Z                 {
2023-01-11T21:41:24.1014526Z                     {
2023-01-11T21:41:24.1014659Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1014827Z                         auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:41:24.1014961Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1015119Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1015267Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:24.1015348Z                         out_ptr4[i1 + (36*i0)] = tmp4;
2023-01-11T21:41:24.1015415Z                     }
2023-01-11T21:41:24.1015475Z                 }
2023-01-11T21:41:24.1015537Z             }
2023-01-11T21:41:24.1015599Z         }
2023-01-11T21:41:24.1015663Z     }
2023-01-11T21:41:24.1015722Z }
2023-01-11T21:41:24.1015806Z ''')
2023-01-11T21:41:24.1015812Z 
2023-01-11T21:41:24.1015817Z 
2023-01-11T21:41:24.1015907Z async_compile.wait(globals())
2023-01-11T21:41:24.1015977Z del async_compile
2023-01-11T21:41:24.1015982Z 
2023-01-11T21:41:24.1016055Z def call(args):
2023-01-11T21:41:24.1016121Z     arg0_1, = args
2023-01-11T21:41:24.1016190Z     args.clear()
2023-01-11T21:41:24.1016399Z     buf0 = empty_strided((2, 4, 74), (296, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1016589Z     buf4 = empty_strided((2, 4, 74), (296, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1016788Z     buf1 = empty_strided((2, 4, 70), (280, 70, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1016981Z     buf2 = empty_strided((2, 4, 45), (180, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1017175Z     buf3 = empty_strided((2, 4, 36), (144, 36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1017414Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:24.1017483Z     del arg0_1
2023-01-11T21:41:24.1017577Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:24.1017582Z 
2023-01-11T21:41:24.1017587Z 
2023-01-11T21:41:24.1017662Z if __name__ == "__main__":
2023-01-11T21:41:24.1017778Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1017888Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1018096Z     arg0_1 = rand_strided((2, 4, 37), (148, 37, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1018203Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1018208Z 
2023-01-11T21:41:24.1018273Z ok (1.858s)
2023-01-11T21:41:24.1018764Z   test_upsample_nearest2d_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1018930Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1019196Z [2023-01-11 21:39:19,341] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 479
2023-01-11T21:41:24.1019202Z 
2023-01-11T21:41:24.1019295Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1019363Z import torch
2023-01-11T21:41:24.1019420Z import random
2023-01-11T21:41:24.1019533Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1019649Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1019654Z 
2023-01-11T21:41:24.1019730Z aten = torch.ops.aten
2023-01-11T21:41:24.1019859Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1019947Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1019983Z 
2023-01-11T21:41:24.1019988Z 
2023-01-11T21:41:24.1020120Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1020321Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1020426Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1020522Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.1020616Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.1020708Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.1020796Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.1020889Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:24.1020947Z {
2023-01-11T21:41:24.1021031Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1021093Z     {
2023-01-11T21:41:24.1021165Z         #pragma omp for 
2023-01-11T21:41:24.1021247Z         for(long i0=0; i0<27; i0+=1)
2023-01-11T21:41:24.1021309Z         {
2023-01-11T21:41:24.1021384Z             #pragma GCC ivdep
2023-01-11T21:41:24.1021466Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:24.1021516Z             {
2023-01-11T21:41:24.1021577Z                 {
2023-01-11T21:41:24.1021640Z                     {
2023-01-11T21:41:24.1021742Z                         auto tmp0 = in_ptr0[(2*i1) + (24*i0)];
2023-01-11T21:41:24.1021847Z                         auto tmp1 = in_ptr0[1 + (2*i1) + (24*i0)];
2023-01-11T21:41:24.1021948Z                         auto tmp3 = in_ptr0[12 + (2*i1) + (24*i0)];
2023-01-11T21:41:24.1022048Z                         auto tmp5 = in_ptr0[13 + (2*i1) + (24*i0)];
2023-01-11T21:41:24.1022129Z                         auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:24.1022221Z                         auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:24.1022307Z                         auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:24.1022511Z                         auto tmp7 = static_cast<float>(1.0);
2023-01-11T21:41:24.1022604Z                         auto tmp8 = tmp6 * tmp7;
2023-01-11T21:41:24.1022698Z                         out_ptr0[i1 + (6*i0)] = tmp8;
2023-01-11T21:41:24.1022762Z                     }
2023-01-11T21:41:24.1022812Z                 }
2023-01-11T21:41:24.1022872Z             }
2023-01-11T21:41:24.1022935Z         }
2023-01-11T21:41:24.1023009Z         #pragma omp for 
2023-01-11T21:41:24.1023089Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:24.1023149Z         {
2023-01-11T21:41:24.1023225Z             #pragma GCC ivdep
2023-01-11T21:41:24.1023295Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.1023354Z             {
2023-01-11T21:41:24.1023434Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1023520Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:41:24.1023583Z                 {
2023-01-11T21:41:24.1023646Z                     {
2023-01-11T21:41:24.1023711Z                         {
2023-01-11T21:41:24.1023816Z                             auto tmp0 = static_cast<long>(((3 + (6*i1)) / 4));
2023-01-11T21:41:24.1023985Z                             auto tmp1 = static_cast<long>(((9 + (6*i1)) / 4));
2023-01-11T21:41:24.1024080Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:24.1024194Z                             auto tmp3 = static_cast<long>(((4 + (12*i2)) / 5));
2023-01-11T21:41:24.1024307Z                             auto tmp4 = static_cast<long>(((16 + (12*i2)) / 5));
2023-01-11T21:41:24.1024403Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:24.1024493Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:24.1024567Z                             float tmp7 = 0.0;
2023-01-11T21:41:24.1024641Z                             if(tmp6)
2023-01-11T21:41:24.1024708Z                             {
2023-01-11T21:41:24.1024833Z                                 auto tmp8 = in_ptr0[(12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:24.1024915Z                                 tmp7 = tmp8;
2023-01-11T21:41:24.1025022Z                             }
2023-01-11T21:41:24.1025142Z                             auto tmp9 = static_cast<long>(1 + (((4 + (12*i2)) / 5)));
2023-01-11T21:41:24.1025237Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:24.1025320Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:24.1025407Z                             float tmp12 = 0.0;
2023-01-11T21:41:24.1025481Z                             if(tmp11)
2023-01-11T21:41:24.1025549Z                             {
2023-01-11T21:41:24.1025687Z                                 auto tmp13 = in_ptr0[1 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:24.1025817Z                                 tmp12 = tmp13;
2023-01-11T21:41:24.1025916Z                             }
2023-01-11T21:41:24.1026034Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:24.1026186Z                             auto tmp15 = static_cast<long>(2 + (((4 + (12*i2)) / 5)));
2023-01-11T21:41:24.1026281Z                             auto tmp16 = tmp15 < tmp4;
2023-01-11T21:41:24.1026375Z                             auto tmp17 = tmp2 & tmp16;
2023-01-11T21:41:24.1026462Z                             float tmp18 = 0.0;
2023-01-11T21:41:24.1026537Z                             if(tmp17)
2023-01-11T21:41:24.1026604Z                             {
2023-01-11T21:41:24.1026727Z                                 auto tmp19 = in_ptr0[2 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:24.1026800Z                                 tmp18 = tmp19;
2023-01-11T21:41:24.1026865Z                             }
2023-01-11T21:41:24.1026958Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:24.1027073Z                             auto tmp21 = static_cast<long>(1 + (((3 + (6*i1)) / 4)));
2023-01-11T21:41:24.1027168Z                             auto tmp22 = tmp21 < tmp1;
2023-01-11T21:41:24.1027259Z                             auto tmp23 = tmp22 & tmp5;
2023-01-11T21:41:24.1027347Z                             float tmp24 = 0.0;
2023-01-11T21:41:24.1027410Z                             if(tmp23)
2023-01-11T21:41:24.1027475Z                             {
2023-01-11T21:41:24.1027601Z                                 auto tmp25 = in_ptr0[12 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:24.1027686Z                                 tmp24 = tmp25;
2023-01-11T21:41:24.1027752Z                             }
2023-01-11T21:41:24.1027844Z                             auto tmp26 = tmp24 + tmp20;
2023-01-11T21:41:24.1027938Z                             auto tmp27 = tmp22 & tmp10;
2023-01-11T21:41:24.1028019Z                             float tmp28 = 0.0;
2023-01-11T21:41:24.1028082Z                             if(tmp27)
2023-01-11T21:41:24.1028145Z                             {
2023-01-11T21:41:24.1028273Z                                 auto tmp29 = in_ptr0[13 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:24.1028356Z                                 tmp28 = tmp29;
2023-01-11T21:41:24.1028459Z                             }
2023-01-11T21:41:24.1028554Z                             auto tmp30 = tmp28 + tmp26;
2023-01-11T21:41:24.1028647Z                             auto tmp31 = tmp22 & tmp16;
2023-01-11T21:41:24.1028721Z                             float tmp32 = 0.0;
2023-01-11T21:41:24.1028793Z                             if(tmp31)
2023-01-11T21:41:24.1028858Z                             {
2023-01-11T21:41:24.1028986Z                                 auto tmp33 = in_ptr0[14 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:24.1029070Z                                 tmp32 = tmp33;
2023-01-11T21:41:24.1029134Z                             }
2023-01-11T21:41:24.1029225Z                             auto tmp34 = tmp32 + tmp30;
2023-01-11T21:41:24.1029317Z                             out_ptr1[i2 + (5*i1) + (20*i0)] = tmp34;
2023-01-11T21:41:24.1029386Z                         }
2023-01-11T21:41:24.1029451Z                     }
2023-01-11T21:41:24.1029544Z                 }
2023-01-11T21:41:24.1029605Z             }
2023-01-11T21:41:24.1029665Z         }
2023-01-11T21:41:24.1029741Z         #pragma omp for 
2023-01-11T21:41:24.1029809Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:24.1029870Z         {
2023-01-11T21:41:24.1029944Z             #pragma GCC ivdep
2023-01-11T21:41:24.1030024Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:24.1030088Z             {
2023-01-11T21:41:24.1030166Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1030252Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:24.1030302Z                 {
2023-01-11T21:41:24.1030363Z                     {
2023-01-11T21:41:24.1030426Z                         {
2023-01-11T21:41:24.1030532Z                             auto tmp0 = static_cast<long>(3*i1);
2023-01-11T21:41:24.1030644Z                             auto tmp1 = static_cast<long>(3 + (3*i1));
2023-01-11T21:41:24.1030737Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:24.1030857Z                             auto tmp3 = static_cast<long>(((7 + (12*i2)) / 8));
2023-01-11T21:41:24.1030962Z                             auto tmp4 = static_cast<long>(((19 + (12*i2)) / 8));
2023-01-11T21:41:24.1031054Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:24.1031141Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:24.1031225Z                             float tmp7 = 0.0;
2023-01-11T21:41:24.1031298Z                             if(tmp6)
2023-01-11T21:41:24.1031365Z                             {
2023-01-11T21:41:24.1031480Z                                 auto tmp8 = in_ptr0[(36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1031553Z                                 tmp7 = tmp8;
2023-01-11T21:41:24.1031622Z                             }
2023-01-11T21:41:24.1031738Z                             auto tmp9 = static_cast<long>(1 + (((7 + (12*i2)) / 8)));
2023-01-11T21:41:24.1031835Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:24.1031930Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:24.1032015Z                             float tmp12 = 0.0;
2023-01-11T21:41:24.1032090Z                             if(tmp11)
2023-01-11T21:41:24.1032155Z                             {
2023-01-11T21:41:24.1032262Z                                 auto tmp13 = in_ptr0[1 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1032346Z                                 tmp12 = tmp13;
2023-01-11T21:41:24.1032413Z                             }
2023-01-11T21:41:24.1032508Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:24.1032619Z                             auto tmp15 = static_cast<long>(1 + (3*i1));
2023-01-11T21:41:24.1032710Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:41:24.1032803Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:41:24.1032878Z                             float tmp18 = 0.0;
2023-01-11T21:41:24.1032982Z                             if(tmp17)
2023-01-11T21:41:24.1033048Z                             {
2023-01-11T21:41:24.1033165Z                                 auto tmp19 = in_ptr0[12 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1033249Z                                 tmp18 = tmp19;
2023-01-11T21:41:24.1033315Z                             }
2023-01-11T21:41:24.1033410Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:24.1033493Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:41:24.1033578Z                             float tmp22 = 0.0;
2023-01-11T21:41:24.1033651Z                             if(tmp21)
2023-01-11T21:41:24.1033717Z                             {
2023-01-11T21:41:24.1033917Z                                 auto tmp23 = in_ptr0[13 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1033999Z                                 tmp22 = tmp23;
2023-01-11T21:41:24.1034065Z                             }
2023-01-11T21:41:24.1034191Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:41:24.1034292Z                             auto tmp25 = static_cast<long>(2 + (3*i1));
2023-01-11T21:41:24.1034386Z                             auto tmp26 = tmp25 < tmp1;
2023-01-11T21:41:24.1034478Z                             auto tmp27 = tmp26 & tmp5;
2023-01-11T21:41:24.1034563Z                             float tmp28 = 0.0;
2023-01-11T21:41:24.1034639Z                             if(tmp27)
2023-01-11T21:41:24.1034706Z                             {
2023-01-11T21:41:24.1034825Z                                 auto tmp29 = in_ptr0[24 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1034895Z                                 tmp28 = tmp29;
2023-01-11T21:41:24.1034961Z                             }
2023-01-11T21:41:24.1035055Z                             auto tmp30 = tmp28 + tmp24;
2023-01-11T21:41:24.1035149Z                             auto tmp31 = tmp26 & tmp10;
2023-01-11T21:41:24.1035235Z                             float tmp32 = 0.0;
2023-01-11T21:41:24.1035311Z                             if(tmp31)
2023-01-11T21:41:24.1035378Z                             {
2023-01-11T21:41:24.1035483Z                                 auto tmp33 = in_ptr0[25 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1035564Z                                 tmp32 = tmp33;
2023-01-11T21:41:24.1035628Z                             }
2023-01-11T21:41:24.1035721Z                             auto tmp34 = tmp32 + tmp30;
2023-01-11T21:41:24.1035805Z                             float tmp35 = 0.0;
2023-01-11T21:41:24.1035878Z                             if(tmp6)
2023-01-11T21:41:24.1035947Z                             {
2023-01-11T21:41:24.1036053Z                                 auto tmp36 = in_ptr0[(36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1036139Z                                 tmp35 = tmp36;
2023-01-11T21:41:24.1036202Z                             }
2023-01-11T21:41:24.1036286Z                             float tmp37 = 0.0;
2023-01-11T21:41:24.1036365Z                             if(tmp11)
2023-01-11T21:41:24.1036432Z                             {
2023-01-11T21:41:24.1036550Z                                 auto tmp38 = in_ptr0[1 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1036630Z                                 tmp37 = tmp38;
2023-01-11T21:41:24.1036685Z                             }
2023-01-11T21:41:24.1036778Z                             auto tmp39 = tmp37 + tmp35;
2023-01-11T21:41:24.1036862Z                             float tmp40 = 0.0;
2023-01-11T21:41:24.1036933Z                             if(tmp17)
2023-01-11T21:41:24.1036998Z                             {
2023-01-11T21:41:24.1037114Z                                 auto tmp41 = in_ptr0[12 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1037193Z                                 tmp40 = tmp41;
2023-01-11T21:41:24.1037247Z                             }
2023-01-11T21:41:24.1037342Z                             auto tmp42 = tmp40 + tmp39;
2023-01-11T21:41:24.1037459Z                             float tmp43 = 0.0;
2023-01-11T21:41:24.1037534Z                             if(tmp21)
2023-01-11T21:41:24.1037599Z                             {
2023-01-11T21:41:24.1037715Z                                 auto tmp44 = in_ptr0[13 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1037794Z                                 tmp43 = tmp44;
2023-01-11T21:41:24.1037849Z                             }
2023-01-11T21:41:24.1037941Z                             auto tmp45 = tmp43 + tmp42;
2023-01-11T21:41:24.1038025Z                             float tmp46 = 0.0;
2023-01-11T21:41:24.1038099Z                             if(tmp27)
2023-01-11T21:41:24.1038165Z                             {
2023-01-11T21:41:24.1038283Z                                 auto tmp47 = in_ptr0[24 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1038364Z                                 tmp46 = tmp47;
2023-01-11T21:41:24.1038428Z                             }
2023-01-11T21:41:24.1038541Z                             auto tmp48 = tmp46 + tmp45;
2023-01-11T21:41:24.1038626Z                             float tmp49 = 0.0;
2023-01-11T21:41:24.1038700Z                             if(tmp31)
2023-01-11T21:41:24.1038764Z                             {
2023-01-11T21:41:24.1038882Z                                 auto tmp50 = in_ptr0[25 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:24.1038962Z                                 tmp49 = tmp50;
2023-01-11T21:41:24.1039028Z                             }
2023-01-11T21:41:24.1039111Z                             auto tmp51 = tmp49 + tmp48;
2023-01-11T21:41:24.1039210Z                             out_ptr2[i2 + (8*i1) + (16*i0)] = tmp34;
2023-01-11T21:41:24.1039314Z                             out_ptr3[i2 + (8*i1) + (16*i0)] = tmp51;
2023-01-11T21:41:24.1039379Z                         }
2023-01-11T21:41:24.1039445Z                     }
2023-01-11T21:41:24.1039507Z                 }
2023-01-11T21:41:24.1039571Z             }
2023-01-11T21:41:24.1039621Z         }
2023-01-11T21:41:24.1039694Z         #pragma omp for 
2023-01-11T21:41:24.1039772Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:24.1039834Z         {
2023-01-11T21:41:24.1039908Z             #pragma GCC ivdep
2023-01-11T21:41:24.1039988Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:24.1040039Z             {
2023-01-11T21:41:24.1040118Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1040204Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:41:24.1040264Z                 {
2023-01-11T21:41:24.1040327Z                     {
2023-01-11T21:41:24.1040393Z                         {
2023-01-11T21:41:24.1040509Z                             auto tmp0 = static_cast<long>(((3 + (6*i1)) / 4));
2023-01-11T21:41:24.1040613Z                             auto tmp1 = static_cast<long>(((9 + (6*i1)) / 4));
2023-01-11T21:41:24.1040709Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:24.1040826Z                             auto tmp3 = static_cast<long>(((6 + (12*i2)) / 7));
2023-01-11T21:41:24.1040944Z                             auto tmp4 = static_cast<long>(((18 + (12*i2)) / 7));
2023-01-11T21:41:24.1041039Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:24.1041131Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:24.1041218Z                             float tmp7 = 0.0;
2023-01-11T21:41:24.1041297Z                             if(tmp6)
2023-01-11T21:41:24.1041353Z                             {
2023-01-11T21:41:24.1041480Z                                 auto tmp8 = in_ptr0[(12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:24.1041565Z                                 tmp7 = tmp8;
2023-01-11T21:41:24.1041635Z                             }
2023-01-11T21:41:24.1041752Z                             auto tmp9 = static_cast<long>(1 + (((6 + (12*i2)) / 7)));
2023-01-11T21:41:24.1041844Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:24.1041977Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:24.1042052Z                             float tmp12 = 0.0;
2023-01-11T21:41:24.1042126Z                             if(tmp11)
2023-01-11T21:41:24.1042193Z                             {
2023-01-11T21:41:24.1042320Z                                 auto tmp13 = in_ptr0[1 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:24.1042407Z                                 tmp12 = tmp13;
2023-01-11T21:41:24.1042475Z                             }
2023-01-11T21:41:24.1042568Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:24.1042686Z                             auto tmp15 = static_cast<long>(1 + (((3 + (6*i1)) / 4)));
2023-01-11T21:41:24.1042768Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:41:24.1042863Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:41:24.1042949Z                             float tmp18 = 0.0;
2023-01-11T21:41:24.1043056Z                             if(tmp17)
2023-01-11T21:41:24.1043124Z                             {
2023-01-11T21:41:24.1043250Z                                 auto tmp19 = in_ptr0[12 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:24.1043336Z                                 tmp18 = tmp19;
2023-01-11T21:41:24.1043391Z                             }
2023-01-11T21:41:24.1043486Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:24.1043578Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:41:24.1043664Z                             float tmp22 = 0.0;
2023-01-11T21:41:24.1043737Z                             if(tmp21)
2023-01-11T21:41:24.1043806Z                             {
2023-01-11T21:41:24.1043933Z                                 auto tmp23 = in_ptr0[13 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:24.1044017Z                                 tmp22 = tmp23;
2023-01-11T21:41:24.1044074Z                             }
2023-01-11T21:41:24.1044171Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:41:24.1044275Z                             out_ptr4[i2 + (7*i1) + (28*i0)] = tmp24;
2023-01-11T21:41:24.1044343Z                         }
2023-01-11T21:41:24.1044405Z                     }
2023-01-11T21:41:24.1044469Z                 }
2023-01-11T21:41:24.1044531Z             }
2023-01-11T21:41:24.1044580Z         }
2023-01-11T21:41:24.1044641Z     }
2023-01-11T21:41:24.1044699Z }
2023-01-11T21:41:24.1044799Z ''')
2023-01-11T21:41:24.1044805Z 
2023-01-11T21:41:24.1044810Z 
2023-01-11T21:41:24.1044898Z async_compile.wait(globals())
2023-01-11T21:41:24.1044968Z del async_compile
2023-01-11T21:41:24.1044973Z 
2023-01-11T21:41:24.1045038Z def call(args):
2023-01-11T21:41:24.1045093Z     arg0_1, = args
2023-01-11T21:41:24.1045161Z     args.clear()
2023-01-11T21:41:24.1045376Z     buf0 = empty_strided((3, 3, 3, 6), (54, 18, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1045591Z     buf1 = empty_strided((3, 3, 4, 5), (60, 20, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1045797Z     buf2 = empty_strided((3, 3, 2, 8), (48, 16, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1046002Z     buf3 = empty_strided((3, 3, 2, 8), (48, 16, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1046200Z     buf4 = empty_strided((3, 3, 4, 7), (84, 28, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1046436Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:24.1046493Z     del arg0_1
2023-01-11T21:41:24.1046588Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:24.1046593Z 
2023-01-11T21:41:24.1046598Z 
2023-01-11T21:41:24.1046672Z if __name__ == "__main__":
2023-01-11T21:41:24.1046785Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1046906Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1047160Z     arg0_1 = rand_strided((3, 3, 6, 12), (216, 72, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1047262Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1047535Z [2023-01-11 21:39:21,302] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 479
2023-01-11T21:41:24.1047540Z 
2023-01-11T21:41:24.1047594Z ok (1.988s)
2023-01-11T21:41:24.1048067Z   test_upsample_nearest2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1048194Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1048477Z [2023-01-11 21:39:21,735] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 480
2023-01-11T21:41:24.1048745Z [2023-01-11 21:39:23,315] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 480
2023-01-11T21:41:24.1048750Z 
2023-01-11T21:41:24.1048840Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1048907Z import torch
2023-01-11T21:41:24.1048978Z import random
2023-01-11T21:41:24.1049091Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1049198Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1049212Z 
2023-01-11T21:41:24.1049275Z aten = torch.ops.aten
2023-01-11T21:41:24.1049404Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1049493Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1049497Z 
2023-01-11T21:41:24.1049502Z 
2023-01-11T21:41:24.1049632Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1049836Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1049951Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1050052Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.1050144Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.1050225Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.1050317Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.1050409Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:24.1050470Z {
2023-01-11T21:41:24.1050567Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1050628Z     {
2023-01-11T21:41:24.1050692Z         #pragma omp for 
2023-01-11T21:41:24.1050768Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1050828Z         {
2023-01-11T21:41:24.1050907Z             #pragma GCC ivdep
2023-01-11T21:41:24.1050989Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:24.1051048Z             {
2023-01-11T21:41:24.1051132Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1051209Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:24.1051266Z                 {
2023-01-11T21:41:24.1051332Z                     {
2023-01-11T21:41:24.1051399Z                         {
2023-01-11T21:41:24.1051507Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1051612Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:24.1051705Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1051800Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1051903Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1051993Z                             auto tmp5 = tmp4 * tmp1;
2023-01-11T21:41:24.1052096Z                             auto tmp6 = static_cast<int>(tmp5);
2023-01-11T21:41:24.1052210Z                             auto tmp7 = in_ptr0[tmp6 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:24.1052343Z                             out_ptr0[i2 + (76*i1) + (5624*i0)] = tmp7;
2023-01-11T21:41:24.1052445Z                             out_ptr1[i2 + (76*i1) + (5624*i0)] = tmp7;
2023-01-11T21:41:24.1052509Z                         }
2023-01-11T21:41:24.1052562Z                     }
2023-01-11T21:41:24.1052622Z                 }
2023-01-11T21:41:24.1052683Z             }
2023-01-11T21:41:24.1052741Z         }
2023-01-11T21:41:24.1052816Z         #pragma omp for 
2023-01-11T21:41:24.1052890Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1052939Z         {
2023-01-11T21:41:24.1053016Z             #pragma GCC ivdep
2023-01-11T21:41:24.1053099Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:41:24.1053159Z             {
2023-01-11T21:41:24.1053235Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1053324Z                 for(long i2=0; i2<75; i2+=1)
2023-01-11T21:41:24.1053386Z                 {
2023-01-11T21:41:24.1053439Z                     {
2023-01-11T21:41:24.1053506Z                         {
2023-01-11T21:41:24.1053672Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1053787Z                             auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:41:24.1053880Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1053980Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1054082Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1054196Z                             auto tmp5 = static_cast<float>(0.5066666666666667);
2023-01-11T21:41:24.1054278Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.1054379Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:24.1054493Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:24.1054596Z                             out_ptr2[i2 + (75*i1) + (5250*i0)] = tmp8;
2023-01-11T21:41:24.1054661Z                         }
2023-01-11T21:41:24.1054727Z                     }
2023-01-11T21:41:24.1054789Z                 }
2023-01-11T21:41:24.1054839Z             }
2023-01-11T21:41:24.1054898Z         }
2023-01-11T21:41:24.1054973Z         #pragma omp for 
2023-01-11T21:41:24.1055048Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1055109Z         {
2023-01-11T21:41:24.1055187Z             #pragma GCC ivdep
2023-01-11T21:41:24.1055258Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:24.1055319Z             {
2023-01-11T21:41:24.1055399Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1055487Z                 for(long i2=0; i2<74; i2+=1)
2023-01-11T21:41:24.1055550Z                 {
2023-01-11T21:41:24.1055612Z                     {
2023-01-11T21:41:24.1055676Z                         {
2023-01-11T21:41:24.1055768Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1055876Z                             auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:24.1055972Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1056078Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1056179Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1056293Z                             auto tmp5 = static_cast<float>(0.5135135135135135);
2023-01-11T21:41:24.1056386Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.1056487Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:24.1056591Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:24.1056692Z                             out_ptr3[i2 + (74*i1) + (3330*i0)] = tmp8;
2023-01-11T21:41:24.1056757Z                         }
2023-01-11T21:41:24.1056821Z                     }
2023-01-11T21:41:24.1056882Z                 }
2023-01-11T21:41:24.1056942Z             }
2023-01-11T21:41:24.1057002Z         }
2023-01-11T21:41:24.1057066Z         #pragma omp for 
2023-01-11T21:41:24.1057174Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1057230Z         {
2023-01-11T21:41:24.1057304Z             #pragma GCC ivdep
2023-01-11T21:41:24.1057385Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:24.1057445Z             {
2023-01-11T21:41:24.1057513Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1057605Z                 for(long i2=0; i2<39; i2+=1)
2023-01-11T21:41:24.1057665Z                 {
2023-01-11T21:41:24.1057730Z                     {
2023-01-11T21:41:24.1057799Z                         {
2023-01-11T21:41:24.1057902Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1058017Z                             auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:41:24.1058110Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1058202Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1058331Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1058445Z                             auto tmp5 = static_cast<float>(0.9743589743589743);
2023-01-11T21:41:24.1058542Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.1058645Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:24.1058757Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:24.1058862Z                             out_ptr4[i2 + (39*i1) + (1404*i0)] = tmp8;
2023-01-11T21:41:24.1058918Z                         }
2023-01-11T21:41:24.1058983Z                     }
2023-01-11T21:41:24.1059046Z                 }
2023-01-11T21:41:24.1059106Z             }
2023-01-11T21:41:24.1059166Z         }
2023-01-11T21:41:24.1059225Z     }
2023-01-11T21:41:24.1059282Z }
2023-01-11T21:41:24.1059353Z ''')
2023-01-11T21:41:24.1059358Z 
2023-01-11T21:41:24.1059363Z 
2023-01-11T21:41:24.1059450Z async_compile.wait(globals())
2023-01-11T21:41:24.1059522Z del async_compile
2023-01-11T21:41:24.1059531Z 
2023-01-11T21:41:24.1059598Z def call(args):
2023-01-11T21:41:24.1059666Z     arg0_1, = args
2023-01-11T21:41:24.1059736Z     args.clear()
2023-01-11T21:41:24.1059956Z     buf0 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1060163Z     buf4 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1060380Z     buf1 = empty_strided((2, 4, 70, 75), (21000, 5250, 75, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1060592Z     buf2 = empty_strided((2, 4, 45, 74), (13320, 3330, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1060809Z     buf3 = empty_strided((2, 4, 36, 39), (5616, 1404, 39, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1061043Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:24.1061114Z     del arg0_1
2023-01-11T21:41:24.1061211Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:24.1061217Z 
2023-01-11T21:41:24.1061221Z 
2023-01-11T21:41:24.1061295Z if __name__ == "__main__":
2023-01-11T21:41:24.1061406Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1061515Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1061728Z     arg0_1 = rand_strided((2, 4, 37, 38), (5624, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1061834Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1061839Z 
2023-01-11T21:41:24.1061905Z ok (2.017s)
2023-01-11T21:41:24.1062522Z   test_upsample_nearest3d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1062728Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1062998Z [2023-01-11 21:39:23,910] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 481
2023-01-11T21:41:24.1063262Z [2023-01-11 21:39:25,552] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 481
2023-01-11T21:41:24.1063267Z 
2023-01-11T21:41:24.1063357Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1063424Z import torch
2023-01-11T21:41:24.1063481Z import random
2023-01-11T21:41:24.1063595Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1063715Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1063719Z 
2023-01-11T21:41:24.1063793Z aten = torch.ops.aten
2023-01-11T21:41:24.1063922Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1064016Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1064067Z 
2023-01-11T21:41:24.1064072Z 
2023-01-11T21:41:24.1064205Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1064405Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1064511Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1064608Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.1064703Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.1064794Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.1064885Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.1064973Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:24.1065032Z {
2023-01-11T21:41:24.1065116Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1065174Z     {
2023-01-11T21:41:24.1065248Z         #pragma omp for 
2023-01-11T21:41:24.1065327Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1065393Z         {
2023-01-11T21:41:24.1065470Z             #pragma GCC ivdep
2023-01-11T21:41:24.1065542Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:24.1065604Z             {
2023-01-11T21:41:24.1065686Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1065771Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:24.1065833Z                 {
2023-01-11T21:41:24.1065912Z                     #pragma GCC ivdep
2023-01-11T21:41:24.1065999Z                     for(long i3=0; i3<78; i3+=1)
2023-01-11T21:41:24.1066052Z                     {
2023-01-11T21:41:24.1066117Z                         {
2023-01-11T21:41:24.1066184Z                             {
2023-01-11T21:41:24.1066294Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1066404Z                                 auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:24.1066499Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1066612Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1066718Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1066802Z                                 auto tmp5 = tmp4 * tmp1;
2023-01-11T21:41:24.1066909Z                                 auto tmp6 = static_cast<int>(tmp5);
2023-01-11T21:41:24.1067012Z                                 auto tmp7 = static_cast<float>(i3);
2023-01-11T21:41:24.1067105Z                                 auto tmp8 = tmp7 * tmp1;
2023-01-11T21:41:24.1067210Z                                 auto tmp9 = static_cast<int>(tmp8);
2023-01-11T21:41:24.1067336Z                                 auto tmp10 = in_ptr0[tmp9 + (39*tmp6) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:24.1067445Z                                 out_ptr0[i3 + (78*i2) + (5928*i1) + (438672*i0)] = tmp10;
2023-01-11T21:41:24.1067553Z                                 out_ptr1[i3 + (78*i2) + (5928*i1) + (438672*i0)] = tmp10;
2023-01-11T21:41:24.1067611Z                             }
2023-01-11T21:41:24.1067711Z                         }
2023-01-11T21:41:24.1067775Z                     }
2023-01-11T21:41:24.1067835Z                 }
2023-01-11T21:41:24.1067895Z             }
2023-01-11T21:41:24.1067954Z         }
2023-01-11T21:41:24.1068018Z         #pragma omp for 
2023-01-11T21:41:24.1068096Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1068154Z         {
2023-01-11T21:41:24.1068233Z             #pragma GCC ivdep
2023-01-11T21:41:24.1068315Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:41:24.1068375Z             {
2023-01-11T21:41:24.1068456Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1068532Z                 for(long i2=0; i2<75; i2+=1)
2023-01-11T21:41:24.1068594Z                 {
2023-01-11T21:41:24.1068676Z                     #pragma GCC ivdep
2023-01-11T21:41:24.1068765Z                     for(long i3=0; i3<80; i3+=1)
2023-01-11T21:41:24.1068827Z                     {
2023-01-11T21:41:24.1068890Z                         {
2023-01-11T21:41:24.1068993Z                             {
2023-01-11T21:41:24.1069090Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1069205Z                                 auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:41:24.1069300Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1069409Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1069513Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1069631Z                                 auto tmp5 = static_cast<float>(0.5066666666666667);
2023-01-11T21:41:24.1069724Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.1069832Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:24.1069925Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:41:24.1070034Z                                 auto tmp9 = static_cast<float>(0.4875);
2023-01-11T21:41:24.1070137Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:24.1070247Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:41:24.1070371Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:24.1070481Z                                 out_ptr2[i3 + (80*i2) + (6000*i1) + (420000*i0)] = tmp12;
2023-01-11T21:41:24.1070550Z                             }
2023-01-11T21:41:24.1070605Z                         }
2023-01-11T21:41:24.1070671Z                     }
2023-01-11T21:41:24.1070734Z                 }
2023-01-11T21:41:24.1070797Z             }
2023-01-11T21:41:24.1070859Z         }
2023-01-11T21:41:24.1070934Z         #pragma omp for 
2023-01-11T21:41:24.1071012Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1071061Z         {
2023-01-11T21:41:24.1071137Z             #pragma GCC ivdep
2023-01-11T21:41:24.1071217Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:24.1071285Z             {
2023-01-11T21:41:24.1071367Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1071453Z                 for(long i2=0; i2<74; i2+=1)
2023-01-11T21:41:24.1071516Z                 {
2023-01-11T21:41:24.1071585Z                     #pragma GCC ivdep
2023-01-11T21:41:24.1071673Z                     for(long i3=0; i3<103; i3+=1)
2023-01-11T21:41:24.1071738Z                     {
2023-01-11T21:41:24.1071805Z                         {
2023-01-11T21:41:24.1071873Z                             {
2023-01-11T21:41:24.1071983Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1072097Z                                 auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:24.1072182Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1072286Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1072392Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1072536Z                                 auto tmp5 = static_cast<float>(0.5135135135135135);
2023-01-11T21:41:24.1072633Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.1072741Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:24.1072851Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:41:24.1072954Z                                 auto tmp9 = static_cast<float>(0.3786407766990291);
2023-01-11T21:41:24.1073052Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:24.1073163Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:41:24.1073290Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:24.1073404Z                                 out_ptr3[i3 + (103*i2) + (7622*i1) + (342990*i0)] = tmp12;
2023-01-11T21:41:24.1073472Z                             }
2023-01-11T21:41:24.1073566Z                         }
2023-01-11T21:41:24.1073631Z                     }
2023-01-11T21:41:24.1073682Z                 }
2023-01-11T21:41:24.1073805Z             }
2023-01-11T21:41:24.1073866Z         }
2023-01-11T21:41:24.1073942Z         #pragma omp for 
2023-01-11T21:41:24.1074019Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1074078Z         {
2023-01-11T21:41:24.1074158Z             #pragma GCC ivdep
2023-01-11T21:41:24.1074229Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:24.1074292Z             {
2023-01-11T21:41:24.1074371Z                 #pragma GCC ivdep
2023-01-11T21:41:24.1074456Z                 for(long i2=0; i2<39; i2+=1)
2023-01-11T21:41:24.1074521Z                 {
2023-01-11T21:41:24.1074600Z                     #pragma GCC ivdep
2023-01-11T21:41:24.1074678Z                     for(long i3=0; i3<40; i3+=1)
2023-01-11T21:41:24.1074741Z                     {
2023-01-11T21:41:24.1074808Z                         {
2023-01-11T21:41:24.1074873Z                             {
2023-01-11T21:41:24.1074984Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:24.1075099Z                                 auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:41:24.1075196Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1075301Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:24.1075395Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:24.1075508Z                                 auto tmp5 = static_cast<float>(0.9743589743589743);
2023-01-11T21:41:24.1075604Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:24.1075709Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:24.1075812Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:41:24.1075921Z                                 auto tmp9 = static_cast<float>(0.975);
2023-01-11T21:41:24.1076023Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:24.1076121Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:41:24.1076246Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:24.1076357Z                                 out_ptr4[i3 + (40*i2) + (1560*i1) + (56160*i0)] = tmp12;
2023-01-11T21:41:24.1076425Z                             }
2023-01-11T21:41:24.1076487Z                         }
2023-01-11T21:41:24.1076550Z                     }
2023-01-11T21:41:24.1076610Z                 }
2023-01-11T21:41:24.1076671Z             }
2023-01-11T21:41:24.1076719Z         }
2023-01-11T21:41:24.1076773Z     }
2023-01-11T21:41:24.1076832Z }
2023-01-11T21:41:24.1076912Z ''')
2023-01-11T21:41:24.1076917Z 
2023-01-11T21:41:24.1076922Z 
2023-01-11T21:41:24.1077010Z async_compile.wait(globals())
2023-01-11T21:41:24.1077082Z del async_compile
2023-01-11T21:41:24.1077087Z 
2023-01-11T21:41:24.1077154Z def call(args):
2023-01-11T21:41:24.1077246Z     arg0_1, = args
2023-01-11T21:41:24.1077315Z     args.clear()
2023-01-11T21:41:24.1077556Z     buf0 = empty_strided((2, 4, 74, 76, 78), (1754688, 438672, 5928, 78, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1077789Z     buf4 = empty_strided((2, 4, 74, 76, 78), (1754688, 438672, 5928, 78, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1078019Z     buf1 = empty_strided((2, 4, 70, 75, 80), (1680000, 420000, 6000, 80, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1078253Z     buf2 = empty_strided((2, 4, 45, 74, 103), (1371960, 342990, 7622, 103, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1078483Z     buf3 = empty_strided((2, 4, 36, 39, 40), (224640, 56160, 1560, 40, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1078717Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:24.1078804Z     del arg0_1
2023-01-11T21:41:24.1078898Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:24.1078903Z 
2023-01-11T21:41:24.1078907Z 
2023-01-11T21:41:24.1078984Z if __name__ == "__main__":
2023-01-11T21:41:24.1079096Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1079218Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1079448Z     arg0_1 = rand_strided((2, 4, 37, 38, 39), (219336, 54834, 1482, 39, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1079553Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1079557Z 
2023-01-11T21:41:24.1079621Z ok (2.469s)
2023-01-11T21:41:24.1080084Z   test_var_mean_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1080210Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1080460Z [2023-01-11 21:39:25,810] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 482
2023-01-11T21:41:24.1080465Z 
2023-01-11T21:41:24.1080555Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1080621Z import torch
2023-01-11T21:41:24.1080688Z import random
2023-01-11T21:41:24.1080798Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1080914Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1080919Z 
2023-01-11T21:41:24.1080995Z aten = torch.ops.aten
2023-01-11T21:41:24.1081115Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1081206Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1081211Z 
2023-01-11T21:41:24.1081215Z 
2023-01-11T21:41:24.1081345Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1081553Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1081666Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:24.1081768Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:24.1081864Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:41:24.1081961Z                        float* __restrict__ in_out_ptr3,
2023-01-11T21:41:24.1082051Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1082148Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.1082240Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:24.1082295Z {
2023-01-11T21:41:24.1082380Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:24.1082459Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:24.1082535Z     auto out_ptr4 = in_out_ptr2;
2023-01-11T21:41:24.1082603Z     auto out_ptr5 = in_out_ptr3;
2023-01-11T21:41:24.1082749Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1082807Z     {
2023-01-11T21:41:24.1082881Z         #pragma omp for 
2023-01-11T21:41:24.1082960Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1083020Z         {
2023-01-11T21:41:24.1083082Z             {
2023-01-11T21:41:24.1083265Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.1083342Z                 float tmp1 = 0;
2023-01-11T21:41:24.1083461Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.1083548Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1083613Z                 {
2023-01-11T21:41:24.1083754Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.1083836Z                     tmp1_vec += tmp0;
2023-01-11T21:41:24.1083902Z                 }
2023-01-11T21:41:24.1084110Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.1084232Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:24.1084318Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.1084381Z                 {
2023-01-11T21:41:24.1084477Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.1084552Z                     tmp1 += tmp0;
2023-01-11T21:41:24.1084614Z                 }
2023-01-11T21:41:24.1084682Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.1084742Z             }
2023-01-11T21:41:24.1084802Z         }
2023-01-11T21:41:24.1084878Z         #pragma omp for 
2023-01-11T21:41:24.1084957Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:24.1085014Z         {
2023-01-11T21:41:24.1085076Z             {
2023-01-11T21:41:24.1085248Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.1085331Z                 float tmp6 = 0;
2023-01-11T21:41:24.1085450Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.1085526Z                 float tmp7 = 0;
2023-01-11T21:41:24.1085643Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:24.1085729Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1085790Z                 {
2023-01-11T21:41:24.1085929Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:24.1086044Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:24.1086179Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:24.1086268Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.1086406Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.1086492Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.1086573Z                     tmp6_vec += tmp5;
2023-01-11T21:41:24.1086656Z                     tmp7_vec += tmp0;
2023-01-11T21:41:24.1086718Z                 }
2023-01-11T21:41:24.1086900Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.1087093Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:24.1087231Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:24.1087317Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:24.1087378Z                 {
2023-01-11T21:41:24.1087471Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:24.1087559Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:24.1087664Z                     auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:24.1087741Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.1087907Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.1087993Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.1088067Z                     tmp6 += tmp5;
2023-01-11T21:41:24.1088138Z                     tmp7 += tmp0;
2023-01-11T21:41:24.1088199Z                 }
2023-01-11T21:41:24.1088277Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:24.1088343Z                 out_ptr2[i0] = tmp7;
2023-01-11T21:41:24.1088403Z             }
2023-01-11T21:41:24.1088463Z         }
2023-01-11T21:41:24.1088535Z         #pragma omp for 
2023-01-11T21:41:24.1088612Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1088669Z         {
2023-01-11T21:41:24.1088798Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:24.1088918Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:41:24.1089002Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.1089094Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:24.1089184Z         }
2023-01-11T21:41:24.1089273Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1089350Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:24.1089407Z         {
2023-01-11T21:41:24.1089479Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:24.1089575Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:41:24.1089656Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.1089735Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1089794Z         }
2023-01-11T21:41:24.1089865Z         #pragma omp for 
2023-01-11T21:41:24.1089941Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1089990Z         {
2023-01-11T21:41:24.1090116Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:24.1090242Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:24.1090323Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.1090414Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:24.1090479Z         }
2023-01-11T21:41:24.1090570Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1090636Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:24.1090696Z         {
2023-01-11T21:41:24.1090777Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:24.1090872Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:24.1090953Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.1091031Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:24.1091089Z         }
2023-01-11T21:41:24.1091150Z         #pragma omp for 
2023-01-11T21:41:24.1091228Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1091286Z         {
2023-01-11T21:41:24.1091348Z             {
2023-01-11T21:41:24.1091534Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.1091609Z                 float tmp1 = 0;
2023-01-11T21:41:24.1091727Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:24.1091805Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:24.1091868Z                 {
2023-01-11T21:41:24.1091955Z                     for(long i2=0; i2<1; i2+=1)
2023-01-11T21:41:24.1092018Z                     {
2023-01-11T21:41:24.1092165Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i2) + (32*i1));
2023-01-11T21:41:24.1092245Z                         tmp1_vec += tmp0;
2023-01-11T21:41:24.1092311Z                     }
2023-01-11T21:41:24.1092504Z                     tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:24.1092611Z                     #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:24.1092698Z                     for(long i2=8; i2<8; i2+=1)
2023-01-11T21:41:24.1092759Z                     {
2023-01-11T21:41:24.1092864Z                         auto tmp0 = in_ptr0[i2 + (8*i0) + (32*i1)];
2023-01-11T21:41:24.1092983Z                         tmp1 += tmp0;
2023-01-11T21:41:24.1093045Z                     }
2023-01-11T21:41:24.1093105Z                 }
2023-01-11T21:41:24.1093173Z                 out_ptr3[i0] = tmp1;
2023-01-11T21:41:24.1093239Z             }
2023-01-11T21:41:24.1093298Z         }
2023-01-11T21:41:24.1093370Z         #pragma omp for 
2023-01-11T21:41:24.1093449Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1093507Z         {
2023-01-11T21:41:24.1093570Z             {
2023-01-11T21:41:24.1093741Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:24.1093816Z                 float tmp6 = 0;
2023-01-11T21:41:24.1093932Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:24.1094009Z                 float tmp7 = 0;
2023-01-11T21:41:24.1094124Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:24.1094238Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:24.1094300Z                 {
2023-01-11T21:41:24.1094376Z                     for(long i2=0; i2<1; i2+=1)
2023-01-11T21:41:24.1094439Z                     {
2023-01-11T21:41:24.1094584Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i2) + (32*i1));
2023-01-11T21:41:24.1094709Z                         auto tmp1 = at::vec::Vectorized<float>(out_ptr3[i0]);
2023-01-11T21:41:24.1094846Z                         auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(16));
2023-01-11T21:41:24.1094937Z                         auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.1095075Z                         auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.1095167Z                         auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:24.1095238Z                         tmp6_vec += tmp5;
2023-01-11T21:41:24.1095320Z                         tmp7_vec += tmp0;
2023-01-11T21:41:24.1095382Z                     }
2023-01-11T21:41:24.1095578Z                     tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:24.1095762Z                     tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:24.1095899Z                     #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:24.1095985Z                     for(long i2=8; i2<8; i2+=1)
2023-01-11T21:41:24.1096046Z                     {
2023-01-11T21:41:24.1096147Z                         auto tmp0 = in_ptr0[i2 + (8*i0) + (32*i1)];
2023-01-11T21:41:24.1096229Z                         auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:24.1096330Z                         auto tmp2 = static_cast<float>(16);
2023-01-11T21:41:24.1096420Z                         auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:24.1096557Z                         auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:24.1096651Z                         auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:24.1096727Z                         tmp6 += tmp5;
2023-01-11T21:41:24.1096800Z                         tmp7 += tmp0;
2023-01-11T21:41:24.1096852Z                     }
2023-01-11T21:41:24.1096911Z                 }
2023-01-11T21:41:24.1096991Z                 out_ptr4[i0] = tmp6;
2023-01-11T21:41:24.1097066Z                 out_ptr5[i0] = tmp7;
2023-01-11T21:41:24.1097127Z             }
2023-01-11T21:41:24.1097186Z         }
2023-01-11T21:41:24.1097259Z         #pragma omp for 
2023-01-11T21:41:24.1097326Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1097385Z         {
2023-01-11T21:41:24.1097447Z             {
2023-01-11T21:41:24.1097509Z                 {
2023-01-11T21:41:24.1097599Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:24.1097700Z                     auto tmp1 = static_cast<float>(15);
2023-01-11T21:41:24.1097777Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.1097865Z                     in_out_ptr2[i0] = tmp2;
2023-01-11T21:41:24.1097958Z                 }
2023-01-11T21:41:24.1098021Z             }
2023-01-11T21:41:24.1098084Z         }
2023-01-11T21:41:24.1098158Z         #pragma omp for 
2023-01-11T21:41:24.1098242Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1098291Z         {
2023-01-11T21:41:24.1098353Z             {
2023-01-11T21:41:24.1098417Z                 {
2023-01-11T21:41:24.1098507Z                     auto tmp0 = out_ptr5[i0];
2023-01-11T21:41:24.1098611Z                     auto tmp1 = static_cast<float>(16);
2023-01-11T21:41:24.1098699Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:24.1098787Z                     in_out_ptr3[i0] = tmp2;
2023-01-11T21:41:24.1098839Z                 }
2023-01-11T21:41:24.1098902Z             }
2023-01-11T21:41:24.1098965Z         }
2023-01-11T21:41:24.1099027Z     }
2023-01-11T21:41:24.1099087Z }
2023-01-11T21:41:24.1099165Z ''')
2023-01-11T21:41:24.1099171Z 
2023-01-11T21:41:24.1099175Z 
2023-01-11T21:41:24.1099292Z async_compile.wait(globals())
2023-01-11T21:41:24.1099352Z del async_compile
2023-01-11T21:41:24.1099357Z 
2023-01-11T21:41:24.1099422Z def call(args):
2023-01-11T21:41:24.1099489Z     arg0_1, = args
2023-01-11T21:41:24.1099561Z     args.clear()
2023-01-11T21:41:24.1099775Z     buf0 = empty_strided((1, 2, 4, 1), (8, 4, 1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1099978Z     buf1 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1100177Z     buf2 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1100249Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:24.1100328Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:41:24.1100534Z     buf5 = empty_strided((1, 1, 4, 1), (4, 4, 1, 4), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1100725Z     buf6 = empty_strided((1, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1100915Z     buf7 = empty_strided((1, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1101003Z     buf8 = buf6; del buf6  # reuse
2023-01-11T21:41:24.1101083Z     buf9 = buf7; del buf7  # reuse
2023-01-11T21:41:24.1101339Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:24.1101394Z     del arg0_1
2023-01-11T21:41:24.1101480Z     return (buf3, buf4, buf8, buf9, )
2023-01-11T21:41:24.1101486Z 
2023-01-11T21:41:24.1101490Z 
2023-01-11T21:41:24.1101566Z if __name__ == "__main__":
2023-01-11T21:41:24.1101681Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1101807Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1102020Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1102126Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1102529Z [2023-01-11 21:39:27,444] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 482
2023-01-11T21:41:24.1102541Z 
2023-01-11T21:41:24.1102635Z ok (1.656s)
2023-01-11T21:41:24.1103100Z   test_vdd_clamp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1103226Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1103492Z [2023-01-11 21:39:27,481] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 483
2023-01-11T21:41:24.1103755Z [2023-01-11 21:39:28,997] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 483
2023-01-11T21:41:24.1103760Z 
2023-01-11T21:41:24.1103913Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1103983Z import torch
2023-01-11T21:41:24.1104050Z import random
2023-01-11T21:41:24.1104167Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1104287Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1104292Z 
2023-01-11T21:41:24.1104359Z aten = torch.ops.aten
2023-01-11T21:41:24.1104490Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1104579Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1104584Z 
2023-01-11T21:41:24.1104588Z 
2023-01-11T21:41:24.1104719Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1104922Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1105038Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1105136Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.1105230Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:24.1105314Z {
2023-01-11T21:41:24.1105410Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1105466Z     {
2023-01-11T21:41:24.1105541Z         #pragma omp for 
2023-01-11T21:41:24.1105619Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:24.1105681Z         {
2023-01-11T21:41:24.1105732Z             {
2023-01-11T21:41:24.1105793Z                 {
2023-01-11T21:41:24.1105883Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1105984Z                     auto tmp1 = static_cast<float>(3.0);
2023-01-11T21:41:24.1106110Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:24.1106210Z                     auto tmp3 = static_cast<float>(3);
2023-01-11T21:41:24.1106300Z                     auto tmp4 = tmp0 >= tmp3;
2023-01-11T21:41:24.1106379Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1106451Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:24.1106514Z                 }
2023-01-11T21:41:24.1106579Z             }
2023-01-11T21:41:24.1106643Z         }
2023-01-11T21:41:24.1106702Z     }
2023-01-11T21:41:24.1106758Z }
2023-01-11T21:41:24.1106824Z ''')
2023-01-11T21:41:24.1106829Z 
2023-01-11T21:41:24.1106843Z 
2023-01-11T21:41:24.1106918Z async_compile.wait(globals())
2023-01-11T21:41:24.1106987Z del async_compile
2023-01-11T21:41:24.1106991Z 
2023-01-11T21:41:24.1107061Z def call(args):
2023-01-11T21:41:24.1107133Z     primals_1, = args
2023-01-11T21:41:24.1107201Z     args.clear()
2023-01-11T21:41:24.1107394Z     buf0 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1107576Z     buf1 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.1107733Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:24.1107803Z     del primals_1
2023-01-11T21:41:24.1107879Z     return (buf0, buf1, )
2023-01-11T21:41:24.1107884Z 
2023-01-11T21:41:24.1107888Z 
2023-01-11T21:41:24.1107964Z if __name__ == "__main__":
2023-01-11T21:41:24.1108076Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1108197Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1108394Z     primals_1 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1108503Z     print_performance(lambda: call([primals_1]))
2023-01-11T21:41:24.1108508Z 
2023-01-11T21:41:24.1108561Z ok (1.553s)
2023-01-11T21:41:24.1109033Z   test_vertical_fusion1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1109161Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1109422Z [2023-01-11 21:39:29,069] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 484
2023-01-11T21:41:24.1109715Z [2023-01-11 21:39:30,613] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 484
2023-01-11T21:41:24.1109720Z 
2023-01-11T21:41:24.1109813Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1109879Z import torch
2023-01-11T21:41:24.1109947Z import random
2023-01-11T21:41:24.1110058Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1110168Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1110172Z 
2023-01-11T21:41:24.1110247Z aten = torch.ops.aten
2023-01-11T21:41:24.1110379Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1110470Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1110475Z 
2023-01-11T21:41:24.1110479Z 
2023-01-11T21:41:24.1110609Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1110845Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1110968Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1111067Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1111156Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:24.1111251Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1111310Z {
2023-01-11T21:41:24.1111404Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1111466Z     {
2023-01-11T21:41:24.1111536Z         #pragma omp for 
2023-01-11T21:41:24.1111617Z         for(long i0=0; i0<41616; i0+=1)
2023-01-11T21:41:24.1111667Z         {
2023-01-11T21:41:24.1111746Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:24.1111807Z             {
2023-01-11T21:41:24.1111943Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:24.1112087Z                 auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (26*i0));
2023-01-11T21:41:24.1112222Z                 auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i1);
2023-01-11T21:41:24.1112449Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.061519070296458e-11));
2023-01-11T21:41:24.1112534Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1112748Z                 auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(-1.988366587925593e-08));
2023-01-11T21:41:24.1112833Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1112916Z                 auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:24.1113140Z                 auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(-3.087032500374211e-07));
2023-01-11T21:41:24.1113229Z                 auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:24.1113446Z                 auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1.55093272922008e-10));
2023-01-11T21:41:24.1113530Z                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:24.1113624Z                 auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:24.1113714Z                 auto tmp12 = tmp11.reciprocal();
2023-01-11T21:41:24.1113906Z                 auto tmp13 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:41:24.1113996Z                 auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:24.1114083Z                 auto tmp16 = tmp11 * tmp15;
2023-01-11T21:41:24.1114169Z                 auto tmp17 = tmp14 + tmp16;
2023-01-11T21:41:24.1114271Z                 tmp17.store(out_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:24.1114332Z             }
2023-01-11T21:41:24.1114424Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1114496Z             for(long i1=24; i1<26; i1+=1)
2023-01-11T21:41:24.1114557Z             {
2023-01-11T21:41:24.1114651Z                 auto tmp0 = in_ptr0[i1 + (26*i0)];
2023-01-11T21:41:24.1114743Z                 auto tmp8 = in_ptr1[i1 + (26*i0)];
2023-01-11T21:41:24.1114827Z                 auto tmp15 = in_ptr2[i1];
2023-01-11T21:41:24.1115042Z                 auto tmp1 = static_cast<float>(-1.061519070296458e-11);
2023-01-11T21:41:24.1115126Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1115291Z                 auto tmp3 = static_cast<float>(-1.988366587925593e-08);
2023-01-11T21:41:24.1115375Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1115460Z                 auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:24.1115635Z                 auto tmp6 = static_cast<float>(-3.087032500374211e-07);
2023-01-11T21:41:24.1115719Z                 auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:24.1115893Z                 auto tmp9 = static_cast<float>(1.55093272922008e-10);
2023-01-11T21:41:24.1115975Z                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:24.1116051Z                 auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:24.1116129Z                 auto tmp12 = 1 / tmp11;
2023-01-11T21:41:24.1116227Z                 auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:24.1116317Z                 auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:24.1116436Z                 auto tmp16 = tmp11 * tmp15;
2023-01-11T21:41:24.1116522Z                 auto tmp17 = tmp14 + tmp16;
2023-01-11T21:41:24.1116613Z                 out_ptr0[i1 + (26*i0)] = tmp17;
2023-01-11T21:41:24.1116663Z             }
2023-01-11T21:41:24.1116723Z         }
2023-01-11T21:41:24.1116781Z     }
2023-01-11T21:41:24.1116836Z }
2023-01-11T21:41:24.1116918Z ''')
2023-01-11T21:41:24.1116924Z 
2023-01-11T21:41:24.1116928Z 
2023-01-11T21:41:24.1117014Z async_compile.wait(globals())
2023-01-11T21:41:24.1117084Z del async_compile
2023-01-11T21:41:24.1117089Z 
2023-01-11T21:41:24.1117146Z def call(args):
2023-01-11T21:41:24.1117227Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:24.1117294Z     args.clear()
2023-01-11T21:41:24.1117507Z     buf0 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1117693Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1117766Z     del arg0_1
2023-01-11T21:41:24.1117829Z     del arg1_1
2023-01-11T21:41:24.1117880Z     del arg2_1
2023-01-11T21:41:24.1117948Z     return (buf0, )
2023-01-11T21:41:24.1117953Z 
2023-01-11T21:41:24.1117957Z 
2023-01-11T21:41:24.1118027Z if __name__ == "__main__":
2023-01-11T21:41:24.1118139Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1118258Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1118475Z     arg0_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1118685Z     arg1_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1118875Z     arg2_1 = rand_strided((26, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1118991Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:24.1118997Z 
2023-01-11T21:41:24.1119050Z ok (1.651s)
2023-01-11T21:41:24.1119511Z   test_views1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1119642Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1119904Z [2023-01-11 21:39:30,666] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 485
2023-01-11T21:41:24.1120167Z [2023-01-11 21:39:32,178] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 485
2023-01-11T21:41:24.1120595Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1120764Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1121017Z [2023-01-11 21:39:32,198] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 486
2023-01-11T21:41:24.1121279Z [2023-01-11 21:39:33,736] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 486
2023-01-11T21:41:24.1121703Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1121827Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1122109Z [2023-01-11 21:39:33,754] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 487
2023-01-11T21:41:24.1122389Z [2023-01-11 21:39:35,267] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 487
2023-01-11T21:41:24.1123010Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1123235Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1123733Z [2023-01-11 21:39:35,288] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 488
2023-01-11T21:41:24.1124035Z [2023-01-11 21:39:36,911] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 488
2023-01-11T21:41:24.1124459Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1124585Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1124839Z [2023-01-11 21:39:36,934] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 489
2023-01-11T21:41:24.1124846Z 
2023-01-11T21:41:24.1124939Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1125007Z import torch
2023-01-11T21:41:24.1125065Z import random
2023-01-11T21:41:24.1125176Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1125295Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1125301Z 
2023-01-11T21:41:24.1125380Z aten = torch.ops.aten
2023-01-11T21:41:24.1125511Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1125599Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1125604Z 
2023-01-11T21:41:24.1125609Z 
2023-01-11T21:41:24.1125737Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1125945Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1126051Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1126149Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1126246Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1126305Z {
2023-01-11T21:41:24.1126400Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1126458Z     {
2023-01-11T21:41:24.1126531Z         #pragma omp for 
2023-01-11T21:41:24.1126600Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1126659Z         {
2023-01-11T21:41:24.1126796Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1126969Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1127055Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1127144Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1127204Z         }
2023-01-11T21:41:24.1127285Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1127366Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:24.1127426Z         {
2023-01-11T21:41:24.1127507Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1127586Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1127664Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1127740Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1127789Z         }
2023-01-11T21:41:24.1127849Z     }
2023-01-11T21:41:24.1127905Z }
2023-01-11T21:41:24.1127982Z ''')
2023-01-11T21:41:24.1127987Z 
2023-01-11T21:41:24.1127991Z 
2023-01-11T21:41:24.1128078Z async_compile.wait(globals())
2023-01-11T21:41:24.1128180Z del async_compile
2023-01-11T21:41:24.1128185Z 
2023-01-11T21:41:24.1128254Z def call(args):
2023-01-11T21:41:24.1128326Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1128383Z     args.clear()
2023-01-11T21:41:24.1128579Z     buf0 = empty_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1128742Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1128810Z     del arg0_1
2023-01-11T21:41:24.1128874Z     del arg1_1
2023-01-11T21:41:24.1128943Z     return (buf0, )
2023-01-11T21:41:24.1128949Z 
2023-01-11T21:41:24.1128953Z 
2023-01-11T21:41:24.1129027Z if __name__ == "__main__":
2023-01-11T21:41:24.1129127Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1129246Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1129440Z     arg0_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1129637Z     arg1_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1129749Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1129754Z 
2023-01-11T21:41:24.1129758Z 
2023-01-11T21:41:24.1129849Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1129915Z import torch
2023-01-11T21:41:24.1129981Z import random
2023-01-11T21:41:24.1130081Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1130198Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1130203Z 
2023-01-11T21:41:24.1130281Z aten = torch.ops.aten
2023-01-11T21:41:24.1130410Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1130498Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1130503Z 
2023-01-11T21:41:24.1130507Z 
2023-01-11T21:41:24.1130637Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1130840Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1130962Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1131052Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1131148Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1131206Z {
2023-01-11T21:41:24.1131302Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1131361Z     {
2023-01-11T21:41:24.1131435Z         #pragma omp for 
2023-01-11T21:41:24.1131515Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1131564Z         {
2023-01-11T21:41:24.1131694Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1131822Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1131948Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1132030Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1132109Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1132200Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1132282Z         }
2023-01-11T21:41:24.1132376Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1132456Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:24.1132518Z         {
2023-01-11T21:41:24.1132596Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1132673Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1132768Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1132840Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1132918Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1132995Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1133060Z         }
2023-01-11T21:41:24.1133121Z     }
2023-01-11T21:41:24.1133182Z }
2023-01-11T21:41:24.1133261Z ''')
2023-01-11T21:41:24.1133266Z 
2023-01-11T21:41:24.1133270Z 
2023-01-11T21:41:24.1133345Z async_compile.wait(globals())
2023-01-11T21:41:24.1133415Z del async_compile
2023-01-11T21:41:24.1133420Z 
2023-01-11T21:41:24.1133490Z def call(args):
2023-01-11T21:41:24.1133596Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1133669Z     args.clear()
2023-01-11T21:41:24.1133862Z     buf0 = empty_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1134023Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1134089Z     del arg0_1
2023-01-11T21:41:24.1134142Z     del arg1_1
2023-01-11T21:41:24.1134212Z     return (buf0, )
2023-01-11T21:41:24.1134217Z 
2023-01-11T21:41:24.1134221Z 
2023-01-11T21:41:24.1134299Z if __name__ == "__main__":
2023-01-11T21:41:24.1134413Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1134536Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1134730Z     arg0_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1134921Z     arg1_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1135022Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1135045Z 
2023-01-11T21:41:24.1135049Z 
2023-01-11T21:41:24.1135129Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1135199Z import torch
2023-01-11T21:41:24.1135269Z import random
2023-01-11T21:41:24.1135387Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1135505Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1135510Z 
2023-01-11T21:41:24.1135588Z aten = torch.ops.aten
2023-01-11T21:41:24.1135718Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1135797Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1135815Z 
2023-01-11T21:41:24.1135819Z 
2023-01-11T21:41:24.1135938Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1136140Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1136257Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1136361Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1136462Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1136524Z {
2023-01-11T21:41:24.1136622Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1136671Z     {
2023-01-11T21:41:24.1136748Z         #pragma omp for 
2023-01-11T21:41:24.1136830Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1136893Z         {
2023-01-11T21:41:24.1137027Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1137154Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1137237Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1137313Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1137376Z         }
2023-01-11T21:41:24.1137468Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1137553Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1137615Z         {
2023-01-11T21:41:24.1137698Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1137845Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1137913Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1137991Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1138058Z         }
2023-01-11T21:41:24.1138117Z     }
2023-01-11T21:41:24.1138175Z }
2023-01-11T21:41:24.1138253Z ''')
2023-01-11T21:41:24.1138258Z 
2023-01-11T21:41:24.1138263Z 
2023-01-11T21:41:24.1138353Z async_compile.wait(globals())
2023-01-11T21:41:24.1138412Z del async_compile
2023-01-11T21:41:24.1138417Z 
2023-01-11T21:41:24.1138486Z def call(args):
2023-01-11T21:41:24.1138555Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1138624Z     args.clear()
2023-01-11T21:41:24.1138858Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1139018Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1139082Z     del arg0_1
2023-01-11T21:41:24.1139138Z     del arg1_1
2023-01-11T21:41:24.1139236Z     return (buf0, )
2023-01-11T21:41:24.1139241Z 
2023-01-11T21:41:24.1139245Z 
2023-01-11T21:41:24.1139319Z if __name__ == "__main__":
2023-01-11T21:41:24.1139432Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1139551Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1139745Z     arg0_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1139977Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1140089Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1140094Z 
2023-01-11T21:41:24.1140098Z 
2023-01-11T21:41:24.1140185Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1140240Z import torch
2023-01-11T21:41:24.1140306Z import random
2023-01-11T21:41:24.1140418Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1140536Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1140546Z 
2023-01-11T21:41:24.1140622Z aten = torch.ops.aten
2023-01-11T21:41:24.1140751Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1140838Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1140843Z 
2023-01-11T21:41:24.1140847Z 
2023-01-11T21:41:24.1140976Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1141166Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1141286Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1141387Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1141484Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1141544Z {
2023-01-11T21:41:24.1141638Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1141698Z     {
2023-01-11T21:41:24.1141760Z         #pragma omp for 
2023-01-11T21:41:24.1141840Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1141905Z         {
2023-01-11T21:41:24.1142036Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1142163Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1142294Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1142522Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1142591Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1142680Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1142742Z         }
2023-01-11T21:41:24.1142835Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1142919Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1142980Z         {
2023-01-11T21:41:24.1143061Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1143129Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1143225Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1143306Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1143444Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1143524Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1143586Z         }
2023-01-11T21:41:24.1143646Z     }
2023-01-11T21:41:24.1143692Z }
2023-01-11T21:41:24.1143770Z ''')
2023-01-11T21:41:24.1143775Z 
2023-01-11T21:41:24.1143779Z 
2023-01-11T21:41:24.1143867Z async_compile.wait(globals())
2023-01-11T21:41:24.1143938Z del async_compile
2023-01-11T21:41:24.1143944Z 
2023-01-11T21:41:24.1144013Z def call(args):
2023-01-11T21:41:24.1144085Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1144153Z     args.clear()
2023-01-11T21:41:24.1144373Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1144530Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1144596Z     del arg0_1
2023-01-11T21:41:24.1144660Z     del arg1_1
2023-01-11T21:41:24.1144731Z     return (buf0, )
2023-01-11T21:41:24.1144773Z 
2023-01-11T21:41:24.1144778Z 
2023-01-11T21:41:24.1144852Z if __name__ == "__main__":
2023-01-11T21:41:24.1144965Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1145086Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1145271Z     arg0_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1145499Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1145614Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1145619Z 
2023-01-11T21:41:24.1145890Z [2023-01-11 21:39:36,944] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 489
2023-01-11T21:41:24.1146321Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1146448Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1146709Z [2023-01-11 21:39:36,965] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 490
2023-01-11T21:41:24.1146970Z [2023-01-11 21:39:36,977] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 490
2023-01-11T21:41:24.1147394Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1147518Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1147779Z [2023-01-11 21:39:36,994] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 491
2023-01-11T21:41:24.1148030Z [2023-01-11 21:39:38,505] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 491
2023-01-11T21:41:24.1148452Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1148576Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1148827Z [2023-01-11 21:39:38,525] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 492
2023-01-11T21:41:24.1149087Z [2023-01-11 21:39:40,035] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 492
2023-01-11T21:41:24.1149537Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1149662Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1149915Z [2023-01-11 21:39:40,053] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 493
2023-01-11T21:41:24.1149920Z 
2023-01-11T21:41:24.1150010Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1150076Z import torch
2023-01-11T21:41:24.1150142Z import random
2023-01-11T21:41:24.1150243Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1150358Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1150367Z 
2023-01-11T21:41:24.1150473Z aten = torch.ops.aten
2023-01-11T21:41:24.1150608Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1150698Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1150703Z 
2023-01-11T21:41:24.1150707Z 
2023-01-11T21:41:24.1150838Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1151041Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1151161Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1151254Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1151352Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1151412Z {
2023-01-11T21:41:24.1151507Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1151567Z     {
2023-01-11T21:41:24.1151643Z         #pragma omp for 
2023-01-11T21:41:24.1151723Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1151772Z         {
2023-01-11T21:41:24.1151905Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1152032Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1152117Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1152204Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1152264Z         }
2023-01-11T21:41:24.1152356Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1152429Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1152489Z         {
2023-01-11T21:41:24.1152569Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1152647Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1152726Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1152802Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1152865Z         }
2023-01-11T21:41:24.1152912Z     }
2023-01-11T21:41:24.1152969Z }
2023-01-11T21:41:24.1153045Z ''')
2023-01-11T21:41:24.1153050Z 
2023-01-11T21:41:24.1153055Z 
2023-01-11T21:41:24.1153145Z async_compile.wait(globals())
2023-01-11T21:41:24.1153216Z del async_compile
2023-01-11T21:41:24.1153221Z 
2023-01-11T21:41:24.1153291Z def call(args):
2023-01-11T21:41:24.1153362Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1153419Z     args.clear()
2023-01-11T21:41:24.1153653Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1153873Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1153943Z     del arg0_1
2023-01-11T21:41:24.1154008Z     del arg1_1
2023-01-11T21:41:24.1154077Z     return (buf0, )
2023-01-11T21:41:24.1154082Z 
2023-01-11T21:41:24.1154087Z 
2023-01-11T21:41:24.1154162Z if __name__ == "__main__":
2023-01-11T21:41:24.1154276Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1154385Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1154604Z     arg0_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1154874Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1154986Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1154992Z 
2023-01-11T21:41:24.1154996Z 
2023-01-11T21:41:24.1155086Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1155153Z import torch
2023-01-11T21:41:24.1155220Z import random
2023-01-11T21:41:24.1155332Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1155438Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1155443Z 
2023-01-11T21:41:24.1155519Z aten = torch.ops.aten
2023-01-11T21:41:24.1155651Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1155740Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1155745Z 
2023-01-11T21:41:24.1155749Z 
2023-01-11T21:41:24.1155881Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1156113Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1156231Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1156333Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1156419Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1156477Z {
2023-01-11T21:41:24.1156572Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1156630Z     {
2023-01-11T21:41:24.1156705Z         #pragma omp for 
2023-01-11T21:41:24.1156786Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1156852Z         {
2023-01-11T21:41:24.1156973Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1157098Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1157228Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1157312Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1157396Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1157486Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1157545Z         }
2023-01-11T21:41:24.1157628Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1157711Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1157769Z         {
2023-01-11T21:41:24.1157849Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1157928Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1158023Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1158104Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1158172Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1158247Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1158308Z         }
2023-01-11T21:41:24.1158367Z     }
2023-01-11T21:41:24.1158425Z }
2023-01-11T21:41:24.1158501Z ''')
2023-01-11T21:41:24.1158506Z 
2023-01-11T21:41:24.1158510Z 
2023-01-11T21:41:24.1158597Z async_compile.wait(globals())
2023-01-11T21:41:24.1158660Z del async_compile
2023-01-11T21:41:24.1158676Z 
2023-01-11T21:41:24.1158733Z def call(args):
2023-01-11T21:41:24.1158805Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1158870Z     args.clear()
2023-01-11T21:41:24.1159104Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1159265Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1159335Z     del arg0_1
2023-01-11T21:41:24.1159398Z     del arg1_1
2023-01-11T21:41:24.1159456Z     return (buf0, )
2023-01-11T21:41:24.1159462Z 
2023-01-11T21:41:24.1159467Z 
2023-01-11T21:41:24.1159544Z if __name__ == "__main__":
2023-01-11T21:41:24.1159656Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1159777Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1159993Z     arg0_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1160267Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1160380Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1160384Z 
2023-01-11T21:41:24.1160389Z 
2023-01-11T21:41:24.1160479Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1160535Z import torch
2023-01-11T21:41:24.1160604Z import random
2023-01-11T21:41:24.1160715Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1160834Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1160838Z 
2023-01-11T21:41:24.1160915Z aten = torch.ops.aten
2023-01-11T21:41:24.1161046Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1161134Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1161139Z 
2023-01-11T21:41:24.1161144Z 
2023-01-11T21:41:24.1161276Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1161492Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1161610Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1161711Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1161810Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1161870Z {
2023-01-11T21:41:24.1161963Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1162020Z     {
2023-01-11T21:41:24.1162083Z         #pragma omp for 
2023-01-11T21:41:24.1162162Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1162222Z         {
2023-01-11T21:41:24.1162352Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1162480Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1162563Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1162651Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1162700Z         }
2023-01-11T21:41:24.1162795Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1162881Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1162944Z         {
2023-01-11T21:41:24.1163025Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1163104Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1163181Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1163247Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1163307Z         }
2023-01-11T21:41:24.1163366Z     }
2023-01-11T21:41:24.1163424Z }
2023-01-11T21:41:24.1163499Z ''')
2023-01-11T21:41:24.1163504Z 
2023-01-11T21:41:24.1163509Z 
2023-01-11T21:41:24.1163596Z async_compile.wait(globals())
2023-01-11T21:41:24.1163667Z del async_compile
2023-01-11T21:41:24.1163672Z 
2023-01-11T21:41:24.1163729Z def call(args):
2023-01-11T21:41:24.1163800Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1163868Z     args.clear()
2023-01-11T21:41:24.1164074Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1164238Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1164304Z     del arg0_1
2023-01-11T21:41:24.1164368Z     del arg1_1
2023-01-11T21:41:24.1164426Z     return (buf0, )
2023-01-11T21:41:24.1164440Z 
2023-01-11T21:41:24.1164444Z 
2023-01-11T21:41:24.1164505Z if __name__ == "__main__":
2023-01-11T21:41:24.1164619Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1164741Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1164941Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1165153Z     arg1_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1165268Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1165272Z 
2023-01-11T21:41:24.1165276Z 
2023-01-11T21:41:24.1165369Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1165443Z import torch
2023-01-11T21:41:24.1165535Z import random
2023-01-11T21:41:24.1165652Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1165771Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1165775Z 
2023-01-11T21:41:24.1165851Z aten = torch.ops.aten
2023-01-11T21:41:24.1165986Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1166076Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1166080Z 
2023-01-11T21:41:24.1166084Z 
2023-01-11T21:41:24.1166217Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1166419Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1166525Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1166628Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1166728Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1166789Z {
2023-01-11T21:41:24.1166886Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1166978Z     {
2023-01-11T21:41:24.1167057Z         #pragma omp for 
2023-01-11T21:41:24.1167127Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1167188Z         {
2023-01-11T21:41:24.1167320Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1167448Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1167578Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1167661Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1167742Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1167818Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1167878Z         }
2023-01-11T21:41:24.1167972Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1168058Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1168121Z         {
2023-01-11T21:41:24.1168204Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1168295Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1168381Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1168465Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1168546Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1168626Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1168686Z         }
2023-01-11T21:41:24.1168746Z     }
2023-01-11T21:41:24.1168805Z }
2023-01-11T21:41:24.1168873Z ''')
2023-01-11T21:41:24.1168878Z 
2023-01-11T21:41:24.1168882Z 
2023-01-11T21:41:24.1168970Z async_compile.wait(globals())
2023-01-11T21:41:24.1169043Z del async_compile
2023-01-11T21:41:24.1169048Z 
2023-01-11T21:41:24.1169119Z def call(args):
2023-01-11T21:41:24.1169190Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1169259Z     args.clear()
2023-01-11T21:41:24.1169470Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1169629Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1169688Z     del arg0_1
2023-01-11T21:41:24.1169754Z     del arg1_1
2023-01-11T21:41:24.1169826Z     return (buf0, )
2023-01-11T21:41:24.1169831Z 
2023-01-11T21:41:24.1169835Z 
2023-01-11T21:41:24.1169906Z if __name__ == "__main__":
2023-01-11T21:41:24.1170020Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1170139Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1170341Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1170537Z     arg1_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1170651Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1170656Z 
2023-01-11T21:41:24.1170925Z [2023-01-11 21:39:41,557] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 493
2023-01-11T21:41:24.1171359Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1171511Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1171768Z [2023-01-11 21:39:41,578] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 494
2023-01-11T21:41:24.1172033Z [2023-01-11 21:39:43,112] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 494
2023-01-11T21:41:24.1172456Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1172609Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1172863Z [2023-01-11 21:39:43,129] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 495
2023-01-11T21:41:24.1173123Z [2023-01-11 21:39:43,138] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 495
2023-01-11T21:41:24.1173549Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1173661Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1173914Z [2023-01-11 21:39:43,156] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 496
2023-01-11T21:41:24.1174178Z [2023-01-11 21:39:43,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 496
2023-01-11T21:41:24.1174602Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1174724Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1174975Z [2023-01-11 21:39:43,194] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 497
2023-01-11T21:41:24.1174980Z 
2023-01-11T21:41:24.1175072Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1175139Z import torch
2023-01-11T21:41:24.1175207Z import random
2023-01-11T21:41:24.1175323Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1175435Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1175440Z 
2023-01-11T21:41:24.1175522Z aten = torch.ops.aten
2023-01-11T21:41:24.1175655Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1175745Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1175751Z 
2023-01-11T21:41:24.1175755Z 
2023-01-11T21:41:24.1175883Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1176088Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1176204Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1176308Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1176395Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1176455Z {
2023-01-11T21:41:24.1176551Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1176612Z     {
2023-01-11T21:41:24.1176691Z         #pragma omp for 
2023-01-11T21:41:24.1176801Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1176861Z         {
2023-01-11T21:41:24.1176981Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1177112Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1177193Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1177285Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1177345Z         }
2023-01-11T21:41:24.1177437Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1177515Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1177563Z         {
2023-01-11T21:41:24.1177642Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1177719Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1177797Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1177875Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1177935Z         }
2023-01-11T21:41:24.1177992Z     }
2023-01-11T21:41:24.1178042Z }
2023-01-11T21:41:24.1178142Z ''')
2023-01-11T21:41:24.1178148Z 
2023-01-11T21:41:24.1178152Z 
2023-01-11T21:41:24.1178243Z async_compile.wait(globals())
2023-01-11T21:41:24.1178314Z del async_compile
2023-01-11T21:41:24.1178319Z 
2023-01-11T21:41:24.1178388Z def call(args):
2023-01-11T21:41:24.1178461Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1178531Z     args.clear()
2023-01-11T21:41:24.1178716Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1178876Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1178943Z     del arg0_1
2023-01-11T21:41:24.1179010Z     del arg1_1
2023-01-11T21:41:24.1179079Z     return (buf0, )
2023-01-11T21:41:24.1179084Z 
2023-01-11T21:41:24.1179088Z 
2023-01-11T21:41:24.1179162Z if __name__ == "__main__":
2023-01-11T21:41:24.1179274Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1179396Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1179595Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1179786Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1179898Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1179903Z 
2023-01-11T21:41:24.1179907Z 
2023-01-11T21:41:24.1179998Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1180064Z import torch
2023-01-11T21:41:24.1180131Z import random
2023-01-11T21:41:24.1180251Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1180357Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1180373Z 
2023-01-11T21:41:24.1180437Z aten = torch.ops.aten
2023-01-11T21:41:24.1180567Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1180656Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1180661Z 
2023-01-11T21:41:24.1180666Z 
2023-01-11T21:41:24.1180797Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1181008Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1181124Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1181225Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1181322Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1181369Z {
2023-01-11T21:41:24.1181463Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1181523Z     {
2023-01-11T21:41:24.1181598Z         #pragma omp for 
2023-01-11T21:41:24.1181674Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1181735Z         {
2023-01-11T21:41:24.1181853Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1181983Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1182114Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1182231Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1182316Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1182561Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1182651Z         }
2023-01-11T21:41:24.1182745Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1182812Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1182872Z         {
2023-01-11T21:41:24.1182952Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1183031Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1183127Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1183205Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1183286Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1183352Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1183411Z         }
2023-01-11T21:41:24.1183470Z     }
2023-01-11T21:41:24.1183527Z }
2023-01-11T21:41:24.1183610Z ''')
2023-01-11T21:41:24.1183615Z 
2023-01-11T21:41:24.1183619Z 
2023-01-11T21:41:24.1183762Z async_compile.wait(globals())
2023-01-11T21:41:24.1183832Z del async_compile
2023-01-11T21:41:24.1183837Z 
2023-01-11T21:41:24.1183894Z def call(args):
2023-01-11T21:41:24.1183965Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1184036Z     args.clear()
2023-01-11T21:41:24.1184232Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1184392Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1184458Z     del arg0_1
2023-01-11T21:41:24.1184527Z     del arg1_1
2023-01-11T21:41:24.1184585Z     return (buf0, )
2023-01-11T21:41:24.1184590Z 
2023-01-11T21:41:24.1184607Z 
2023-01-11T21:41:24.1184670Z if __name__ == "__main__":
2023-01-11T21:41:24.1184783Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1184902Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1185113Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1185311Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1185424Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1185430Z 
2023-01-11T21:41:24.1185434Z 
2023-01-11T21:41:24.1185527Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1185583Z import torch
2023-01-11T21:41:24.1185651Z import random
2023-01-11T21:41:24.1185764Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1185882Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1185887Z 
2023-01-11T21:41:24.1185961Z aten = torch.ops.aten
2023-01-11T21:41:24.1186091Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1186184Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1186188Z 
2023-01-11T21:41:24.1186193Z 
2023-01-11T21:41:24.1186324Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1186513Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1186632Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1186733Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1186831Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1186889Z {
2023-01-11T21:41:24.1186985Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1187045Z     {
2023-01-11T21:41:24.1187108Z         #pragma omp for 
2023-01-11T21:41:24.1187188Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1187250Z         {
2023-01-11T21:41:24.1187382Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1187509Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1187591Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1187678Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1187739Z         }
2023-01-11T21:41:24.1187820Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1187951Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1188013Z         {
2023-01-11T21:41:24.1188095Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1188175Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1188255Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1188321Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1188380Z         }
2023-01-11T21:41:24.1188440Z     }
2023-01-11T21:41:24.1188498Z }
2023-01-11T21:41:24.1188577Z ''')
2023-01-11T21:41:24.1188583Z 
2023-01-11T21:41:24.1188587Z 
2023-01-11T21:41:24.1188674Z async_compile.wait(globals())
2023-01-11T21:41:24.1188744Z del async_compile
2023-01-11T21:41:24.1188749Z 
2023-01-11T21:41:24.1188815Z def call(args):
2023-01-11T21:41:24.1188878Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1188947Z     args.clear()
2023-01-11T21:41:24.1189148Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1189336Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1189406Z     del arg0_1
2023-01-11T21:41:24.1189471Z     del arg1_1
2023-01-11T21:41:24.1189541Z     return (buf0, )
2023-01-11T21:41:24.1189546Z 
2023-01-11T21:41:24.1189550Z 
2023-01-11T21:41:24.1189611Z if __name__ == "__main__":
2023-01-11T21:41:24.1189722Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1189842Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1190068Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1190268Z     arg1_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1190382Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1190387Z 
2023-01-11T21:41:24.1190391Z 
2023-01-11T21:41:24.1190483Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1190549Z import torch
2023-01-11T21:41:24.1190605Z import random
2023-01-11T21:41:24.1190724Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1190841Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1190846Z 
2023-01-11T21:41:24.1190922Z aten = torch.ops.aten
2023-01-11T21:41:24.1191051Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1191139Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1191144Z 
2023-01-11T21:41:24.1191149Z 
2023-01-11T21:41:24.1191277Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1191481Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1191586Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1191687Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1191782Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1191842Z {
2023-01-11T21:41:24.1191937Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1192000Z     {
2023-01-11T21:41:24.1192075Z         #pragma omp for 
2023-01-11T21:41:24.1192143Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1192205Z         {
2023-01-11T21:41:24.1192334Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1192460Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1192587Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1192670Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1192749Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1192839Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1192889Z         }
2023-01-11T21:41:24.1192980Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1193063Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1193129Z         {
2023-01-11T21:41:24.1193210Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1193289Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1193418Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1193489Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1193567Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1193645Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1193704Z         }
2023-01-11T21:41:24.1193825Z     }
2023-01-11T21:41:24.1193889Z }
2023-01-11T21:41:24.1193957Z ''')
2023-01-11T21:41:24.1193962Z 
2023-01-11T21:41:24.1193978Z 
2023-01-11T21:41:24.1194055Z async_compile.wait(globals())
2023-01-11T21:41:24.1194125Z del async_compile
2023-01-11T21:41:24.1194132Z 
2023-01-11T21:41:24.1194199Z def call(args):
2023-01-11T21:41:24.1194271Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1194340Z     args.clear()
2023-01-11T21:41:24.1194541Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1194702Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1194762Z     del arg0_1
2023-01-11T21:41:24.1194859Z     del arg1_1
2023-01-11T21:41:24.1194933Z     return (buf0, )
2023-01-11T21:41:24.1194938Z 
2023-01-11T21:41:24.1194941Z 
2023-01-11T21:41:24.1195016Z if __name__ == "__main__":
2023-01-11T21:41:24.1195128Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1195249Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1195480Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1195680Z     arg1_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1195781Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1195786Z 
2023-01-11T21:41:24.1196055Z [2023-01-11 21:39:44,718] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 497
2023-01-11T21:41:24.1196483Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1196610Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1196868Z [2023-01-11 21:39:44,738] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 498
2023-01-11T21:41:24.1197129Z [2023-01-11 21:39:46,284] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 498
2023-01-11T21:41:24.1197556Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1197685Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1197943Z [2023-01-11 21:39:46,301] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 499
2023-01-11T21:41:24.1198205Z [2023-01-11 21:39:46,309] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 499
2023-01-11T21:41:24.1198632Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1198756Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1198998Z [2023-01-11 21:39:46,326] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 500
2023-01-11T21:41:24.1199306Z [2023-01-11 21:39:46,339] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 500
2023-01-11T21:41:24.1199734Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1199858Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1200113Z [2023-01-11 21:39:46,355] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 501
2023-01-11T21:41:24.1200118Z 
2023-01-11T21:41:24.1200215Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1200284Z import torch
2023-01-11T21:41:24.1200355Z import random
2023-01-11T21:41:24.1200470Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1200610Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1200617Z 
2023-01-11T21:41:24.1200698Z aten = torch.ops.aten
2023-01-11T21:41:24.1200832Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1200927Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1200933Z 
2023-01-11T21:41:24.1200937Z 
2023-01-11T21:41:24.1201071Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1201273Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1201394Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1201497Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1201582Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1201643Z {
2023-01-11T21:41:24.1201743Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1201806Z     {
2023-01-11T21:41:24.1201884Z         #pragma omp for 
2023-01-11T21:41:24.1201973Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1202036Z         {
2023-01-11T21:41:24.1202157Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1202288Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1202373Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1202462Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1202524Z         }
2023-01-11T21:41:24.1202617Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1202697Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.1202748Z         {
2023-01-11T21:41:24.1202831Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1202914Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1202996Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1203076Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1203138Z         }
2023-01-11T21:41:24.1203198Z     }
2023-01-11T21:41:24.1203246Z }
2023-01-11T21:41:24.1203326Z ''')
2023-01-11T21:41:24.1203334Z 
2023-01-11T21:41:24.1203339Z 
2023-01-11T21:41:24.1203428Z async_compile.wait(globals())
2023-01-11T21:41:24.1203500Z del async_compile
2023-01-11T21:41:24.1203505Z 
2023-01-11T21:41:24.1203575Z def call(args):
2023-01-11T21:41:24.1203647Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1203719Z     args.clear()
2023-01-11T21:41:24.1203902Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1204066Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1204135Z     del arg0_1
2023-01-11T21:41:24.1204200Z     del arg1_1
2023-01-11T21:41:24.1204271Z     return (buf0, )
2023-01-11T21:41:24.1204276Z 
2023-01-11T21:41:24.1204280Z 
2023-01-11T21:41:24.1204357Z if __name__ == "__main__":
2023-01-11T21:41:24.1204467Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1204589Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1204819Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1205011Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1205125Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1205130Z 
2023-01-11T21:41:24.1205134Z 
2023-01-11T21:41:24.1205230Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1205299Z import torch
2023-01-11T21:41:24.1205366Z import random
2023-01-11T21:41:24.1205481Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1205598Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1205603Z 
2023-01-11T21:41:24.1205667Z aten = torch.ops.aten
2023-01-11T21:41:24.1205798Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1205886Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1205890Z 
2023-01-11T21:41:24.1205895Z 
2023-01-11T21:41:24.1206025Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1206257Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1206377Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1206477Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1206576Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1206624Z {
2023-01-11T21:41:24.1206720Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1206780Z     {
2023-01-11T21:41:24.1206856Z         #pragma omp for 
2023-01-11T21:41:24.1206938Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1206999Z         {
2023-01-11T21:41:24.1207125Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1207242Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1207366Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1207457Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1207541Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1207630Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1207689Z         }
2023-01-11T21:41:24.1207782Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1207852Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.1207911Z         {
2023-01-11T21:41:24.1207990Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1208069Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1208166Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1208247Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1208331Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1208398Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1208458Z         }
2023-01-11T21:41:24.1208516Z     }
2023-01-11T21:41:24.1208576Z }
2023-01-11T21:41:24.1208652Z ''')
2023-01-11T21:41:24.1208657Z 
2023-01-11T21:41:24.1208662Z 
2023-01-11T21:41:24.1208747Z async_compile.wait(globals())
2023-01-11T21:41:24.1208821Z del async_compile
2023-01-11T21:41:24.1208827Z 
2023-01-11T21:41:24.1208884Z def call(args):
2023-01-11T21:41:24.1208956Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1209022Z     args.clear()
2023-01-11T21:41:24.1209215Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1209375Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1209442Z     del arg0_1
2023-01-11T21:41:24.1209506Z     del arg1_1
2023-01-11T21:41:24.1209564Z     return (buf0, )
2023-01-11T21:41:24.1209581Z 
2023-01-11T21:41:24.1209585Z 
2023-01-11T21:41:24.1209648Z if __name__ == "__main__":
2023-01-11T21:41:24.1209760Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1209881Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1210087Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1210316Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1210430Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1210435Z 
2023-01-11T21:41:24.1210439Z 
2023-01-11T21:41:24.1210531Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1210598Z import torch
2023-01-11T21:41:24.1210654Z import random
2023-01-11T21:41:24.1210766Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1210882Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1210887Z 
2023-01-11T21:41:24.1210963Z aten = torch.ops.aten
2023-01-11T21:41:24.1211092Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1211180Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1211185Z 
2023-01-11T21:41:24.1211189Z 
2023-01-11T21:41:24.1211317Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1211519Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1211652Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1211752Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1211847Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1211910Z {
2023-01-11T21:41:24.1212006Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1212065Z     {
2023-01-11T21:41:24.1212140Z         #pragma omp for 
2023-01-11T21:41:24.1212208Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1212271Z         {
2023-01-11T21:41:24.1212397Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1212529Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1212611Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1212701Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1212761Z         }
2023-01-11T21:41:24.1212842Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1212926Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:24.1212989Z         {
2023-01-11T21:41:24.1213072Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1213151Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1213230Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1213309Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1213357Z         }
2023-01-11T21:41:24.1213415Z     }
2023-01-11T21:41:24.1213474Z }
2023-01-11T21:41:24.1213551Z ''')
2023-01-11T21:41:24.1213556Z 
2023-01-11T21:41:24.1213560Z 
2023-01-11T21:41:24.1213648Z async_compile.wait(globals())
2023-01-11T21:41:24.1213717Z del async_compile
2023-01-11T21:41:24.1213722Z 
2023-01-11T21:41:24.1213790Z def call(args):
2023-01-11T21:41:24.1213850Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1213919Z     args.clear()
2023-01-11T21:41:24.1214111Z     buf0 = empty_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1214269Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1214339Z     del arg0_1
2023-01-11T21:41:24.1214404Z     del arg1_1
2023-01-11T21:41:24.1214473Z     return (buf0, )
2023-01-11T21:41:24.1214478Z 
2023-01-11T21:41:24.1214483Z 
2023-01-11T21:41:24.1214556Z if __name__ == "__main__":
2023-01-11T21:41:24.1214656Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1214778Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1214972Z     arg0_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1215163Z     arg1_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1215275Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1215280Z 
2023-01-11T21:41:24.1215284Z 
2023-01-11T21:41:24.1215374Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1215440Z import torch
2023-01-11T21:41:24.1215497Z import random
2023-01-11T21:41:24.1215607Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1215760Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1215765Z 
2023-01-11T21:41:24.1215839Z aten = torch.ops.aten
2023-01-11T21:41:24.1215968Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1216056Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1216061Z 
2023-01-11T21:41:24.1216065Z 
2023-01-11T21:41:24.1216194Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1216394Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1216511Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1216601Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1216697Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1216756Z {
2023-01-11T21:41:24.1216851Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1216910Z     {
2023-01-11T21:41:24.1216984Z         #pragma omp for 
2023-01-11T21:41:24.1217087Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:24.1217151Z         {
2023-01-11T21:41:24.1217278Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1217408Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1217536Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1217620Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1217701Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1217789Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1217839Z         }
2023-01-11T21:41:24.1217931Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1218013Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:24.1218070Z         {
2023-01-11T21:41:24.1218151Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1218230Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1218329Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1218403Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1218483Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1218559Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1218619Z         }
2023-01-11T21:41:24.1218678Z     }
2023-01-11T21:41:24.1218736Z }
2023-01-11T21:41:24.1218802Z ''')
2023-01-11T21:41:24.1218817Z 
2023-01-11T21:41:24.1218821Z 
2023-01-11T21:41:24.1218897Z async_compile.wait(globals())
2023-01-11T21:41:24.1218967Z del async_compile
2023-01-11T21:41:24.1218972Z 
2023-01-11T21:41:24.1219042Z def call(args):
2023-01-11T21:41:24.1219112Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1219180Z     args.clear()
2023-01-11T21:41:24.1219373Z     buf0 = empty_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1219530Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1219584Z     del arg0_1
2023-01-11T21:41:24.1219646Z     del arg1_1
2023-01-11T21:41:24.1219720Z     return (buf0, )
2023-01-11T21:41:24.1219725Z 
2023-01-11T21:41:24.1219730Z 
2023-01-11T21:41:24.1219805Z if __name__ == "__main__":
2023-01-11T21:41:24.1219915Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1220037Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1220230Z     arg0_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1220420Z     arg1_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1220521Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1220526Z 
2023-01-11T21:41:24.1220792Z [2023-01-11 21:39:46,362] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 501
2023-01-11T21:41:24.1221219Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1221373Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1221631Z [2023-01-11 21:39:46,380] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 502
2023-01-11T21:41:24.1221894Z [2023-01-11 21:39:46,406] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 502
2023-01-11T21:41:24.1222427Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1222550Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1222893Z [2023-01-11 21:39:46,423] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 503
2023-01-11T21:41:24.1223157Z [2023-01-11 21:39:46,433] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 503
2023-01-11T21:41:24.1228857Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1229013Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1229287Z [2023-01-11 21:39:46,452] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 504
2023-01-11T21:41:24.1229564Z [2023-01-11 21:39:46,498] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 504
2023-01-11T21:41:24.1230004Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1230130Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1230388Z [2023-01-11 21:39:46,516] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 505
2023-01-11T21:41:24.1230394Z 
2023-01-11T21:41:24.1230487Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1230556Z import torch
2023-01-11T21:41:24.1230624Z import random
2023-01-11T21:41:24.1230737Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1230846Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1230854Z 
2023-01-11T21:41:24.1230936Z aten = torch.ops.aten
2023-01-11T21:41:24.1231069Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1231158Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1231164Z 
2023-01-11T21:41:24.1231168Z 
2023-01-11T21:41:24.1231302Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1231504Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1231621Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1231723Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1231810Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1231869Z {
2023-01-11T21:41:24.1231966Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1232027Z     {
2023-01-11T21:41:24.1232101Z         #pragma omp for 
2023-01-11T21:41:24.1232181Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1232240Z         {
2023-01-11T21:41:24.1232454Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1232583Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1232668Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1232756Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1232817Z         }
2023-01-11T21:41:24.1232908Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1232993Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1233042Z         {
2023-01-11T21:41:24.1233125Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1233206Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1233285Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1233363Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1233422Z         }
2023-01-11T21:41:24.1233479Z     }
2023-01-11T21:41:24.1233525Z }
2023-01-11T21:41:24.1233601Z ''')
2023-01-11T21:41:24.1233606Z 
2023-01-11T21:41:24.1233610Z 
2023-01-11T21:41:24.1233806Z async_compile.wait(globals())
2023-01-11T21:41:24.1233884Z del async_compile
2023-01-11T21:41:24.1233889Z 
2023-01-11T21:41:24.1233957Z def call(args):
2023-01-11T21:41:24.1234031Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1234100Z     args.clear()
2023-01-11T21:41:24.1234289Z     buf0 = empty_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1234451Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1234515Z     del arg0_1
2023-01-11T21:41:24.1234579Z     del arg1_1
2023-01-11T21:41:24.1234648Z     return (buf0, )
2023-01-11T21:41:24.1234654Z 
2023-01-11T21:41:24.1234658Z 
2023-01-11T21:41:24.1234731Z if __name__ == "__main__":
2023-01-11T21:41:24.1234845Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1234965Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1235190Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1235387Z     arg1_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1235499Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1235504Z 
2023-01-11T21:41:24.1235508Z 
2023-01-11T21:41:24.1235601Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1235668Z import torch
2023-01-11T21:41:24.1235736Z import random
2023-01-11T21:41:24.1235846Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1235961Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1235966Z 
2023-01-11T21:41:24.1236030Z aten = torch.ops.aten
2023-01-11T21:41:24.1236160Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1236248Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1236253Z 
2023-01-11T21:41:24.1236257Z 
2023-01-11T21:41:24.1236389Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1236593Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1236711Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1236812Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1236906Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1236954Z {
2023-01-11T21:41:24.1237047Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1237105Z     {
2023-01-11T21:41:24.1237179Z         #pragma omp for 
2023-01-11T21:41:24.1237259Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1237319Z         {
2023-01-11T21:41:24.1237449Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1237566Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1237690Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1237771Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1237853Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1237970Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1238030Z         }
2023-01-11T21:41:24.1238122Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1238194Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1238255Z         {
2023-01-11T21:41:24.1238336Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1238416Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1238512Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1238591Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1238669Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1238735Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1238793Z         }
2023-01-11T21:41:24.1238852Z     }
2023-01-11T21:41:24.1238909Z }
2023-01-11T21:41:24.1238985Z ''')
2023-01-11T21:41:24.1238990Z 
2023-01-11T21:41:24.1238994Z 
2023-01-11T21:41:24.1239082Z async_compile.wait(globals())
2023-01-11T21:41:24.1239150Z del async_compile
2023-01-11T21:41:24.1239185Z 
2023-01-11T21:41:24.1239254Z def call(args):
2023-01-11T21:41:24.1239315Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1239382Z     args.clear()
2023-01-11T21:41:24.1239579Z     buf0 = empty_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1239736Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1239800Z     del arg0_1
2023-01-11T21:41:24.1239864Z     del arg1_1
2023-01-11T21:41:24.1239921Z     return (buf0, )
2023-01-11T21:41:24.1239937Z 
2023-01-11T21:41:24.1239941Z 
2023-01-11T21:41:24.1240004Z if __name__ == "__main__":
2023-01-11T21:41:24.1240116Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1240237Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1240469Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1240729Z     arg1_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1240865Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1240870Z 
2023-01-11T21:41:24.1240874Z 
2023-01-11T21:41:24.1240966Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1241033Z import torch
2023-01-11T21:41:24.1241089Z import random
2023-01-11T21:41:24.1241200Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1241317Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1241321Z 
2023-01-11T21:41:24.1241395Z aten = torch.ops.aten
2023-01-11T21:41:24.1241522Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1241610Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1241614Z 
2023-01-11T21:41:24.1241618Z 
2023-01-11T21:41:24.1241750Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1241952Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1242060Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1242163Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1242260Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1242319Z {
2023-01-11T21:41:24.1242415Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1242473Z     {
2023-01-11T21:41:24.1242547Z         #pragma omp for 
2023-01-11T21:41:24.1242616Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1242675Z         {
2023-01-11T21:41:24.1242809Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1242937Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1243021Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1243108Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1243167Z         }
2023-01-11T21:41:24.1243249Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1243332Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1243455Z         {
2023-01-11T21:41:24.1243542Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1243620Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1243700Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1243778Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1243825Z         }
2023-01-11T21:41:24.1243884Z     }
2023-01-11T21:41:24.1243943Z }
2023-01-11T21:41:24.1244019Z ''')
2023-01-11T21:41:24.1244025Z 
2023-01-11T21:41:24.1244029Z 
2023-01-11T21:41:24.1244113Z async_compile.wait(globals())
2023-01-11T21:41:24.1244187Z del async_compile
2023-01-11T21:41:24.1244192Z 
2023-01-11T21:41:24.1244258Z def call(args):
2023-01-11T21:41:24.1244320Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1244388Z     args.clear()
2023-01-11T21:41:24.1244605Z     buf0 = empty_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1244798Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1244867Z     del arg0_1
2023-01-11T21:41:24.1244929Z     del arg1_1
2023-01-11T21:41:24.1244997Z     return (buf0, )
2023-01-11T21:41:24.1245001Z 
2023-01-11T21:41:24.1245005Z 
2023-01-11T21:41:24.1245078Z if __name__ == "__main__":
2023-01-11T21:41:24.1245179Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1245299Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1245530Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1245745Z     arg1_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1245856Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1245862Z 
2023-01-11T21:41:24.1245866Z 
2023-01-11T21:41:24.1245957Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1246023Z import torch
2023-01-11T21:41:24.1246091Z import random
2023-01-11T21:41:24.1246198Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1246316Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1246321Z 
2023-01-11T21:41:24.1246397Z aten = torch.ops.aten
2023-01-11T21:41:24.1246525Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1246617Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1246621Z 
2023-01-11T21:41:24.1246625Z 
2023-01-11T21:41:24.1246757Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1246960Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1247076Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1247166Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1247263Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1247323Z {
2023-01-11T21:41:24.1247421Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1247479Z     {
2023-01-11T21:41:24.1247557Z         #pragma omp for 
2023-01-11T21:41:24.1247636Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:24.1247685Z         {
2023-01-11T21:41:24.1247815Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1247942Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1248066Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1248148Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1248228Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1248317Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1248367Z         }
2023-01-11T21:41:24.1248458Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1248539Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:24.1248600Z         {
2023-01-11T21:41:24.1248680Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1248759Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1248890Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1248961Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1249040Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1249117Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1249176Z         }
2023-01-11T21:41:24.1249237Z     }
2023-01-11T21:41:24.1249296Z }
2023-01-11T21:41:24.1249372Z ''')
2023-01-11T21:41:24.1249377Z 
2023-01-11T21:41:24.1249381Z 
2023-01-11T21:41:24.1249458Z async_compile.wait(globals())
2023-01-11T21:41:24.1249530Z del async_compile
2023-01-11T21:41:24.1249535Z 
2023-01-11T21:41:24.1249603Z def call(args):
2023-01-11T21:41:24.1249674Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1249746Z     args.clear()
2023-01-11T21:41:24.1249960Z     buf0 = empty_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1250120Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1250187Z     del arg0_1
2023-01-11T21:41:24.1250266Z     del arg1_1
2023-01-11T21:41:24.1250337Z     return (buf0, )
2023-01-11T21:41:24.1250342Z 
2023-01-11T21:41:24.1250346Z 
2023-01-11T21:41:24.1250420Z if __name__ == "__main__":
2023-01-11T21:41:24.1250537Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1250655Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1250890Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1251101Z     arg1_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1251214Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1251219Z 
2023-01-11T21:41:24.1251475Z [2023-01-11 21:39:46,524] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 505
2023-01-11T21:41:24.1251906Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1252033Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1252287Z [2023-01-11 21:39:46,542] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 506
2023-01-11T21:41:24.1252551Z [2023-01-11 21:39:46,564] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 506
2023-01-11T21:41:24.1252978Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1253106Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1253356Z [2023-01-11 21:39:46,580] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 507
2023-01-11T21:41:24.1253612Z [2023-01-11 21:39:46,588] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 507
2023-01-11T21:41:24.1254034Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1254157Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1254411Z [2023-01-11 21:39:46,606] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 508
2023-01-11T21:41:24.1254695Z [2023-01-11 21:39:46,615] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 508
2023-01-11T21:41:24.1255119Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1255245Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1255501Z [2023-01-11 21:39:46,631] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 509
2023-01-11T21:41:24.1255506Z 
2023-01-11T21:41:24.1255599Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1255668Z import torch
2023-01-11T21:41:24.1255737Z import random
2023-01-11T21:41:24.1255851Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1256000Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1256007Z 
2023-01-11T21:41:24.1256073Z aten = torch.ops.aten
2023-01-11T21:41:24.1256205Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1256298Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1256303Z 
2023-01-11T21:41:24.1256307Z 
2023-01-11T21:41:24.1256440Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1256644Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1256762Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1256865Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1256964Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1257014Z {
2023-01-11T21:41:24.1257110Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1257169Z     {
2023-01-11T21:41:24.1257247Z         #pragma omp for 
2023-01-11T21:41:24.1257332Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1257395Z         {
2023-01-11T21:41:24.1257526Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1257644Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1257728Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1257819Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1257882Z         }
2023-01-11T21:41:24.1257973Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1258058Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1258120Z         {
2023-01-11T21:41:24.1258190Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1258271Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1258352Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1258429Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1258489Z         }
2023-01-11T21:41:24.1258548Z     }
2023-01-11T21:41:24.1258608Z }
2023-01-11T21:41:24.1258676Z ''')
2023-01-11T21:41:24.1258681Z 
2023-01-11T21:41:24.1258685Z 
2023-01-11T21:41:24.1258772Z async_compile.wait(globals())
2023-01-11T21:41:24.1258843Z del async_compile
2023-01-11T21:41:24.1258848Z 
2023-01-11T21:41:24.1258918Z def call(args):
2023-01-11T21:41:24.1258990Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1259058Z     args.clear()
2023-01-11T21:41:24.1259258Z     buf0 = empty_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1259408Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1259475Z     del arg0_1
2023-01-11T21:41:24.1259538Z     del arg1_1
2023-01-11T21:41:24.1259607Z     return (buf0, )
2023-01-11T21:41:24.1259611Z 
2023-01-11T21:41:24.1259615Z 
2023-01-11T21:41:24.1259688Z if __name__ == "__main__":
2023-01-11T21:41:24.1259798Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1259918Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1260155Z     arg0_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1260340Z     arg1_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1260453Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1260458Z 
2023-01-11T21:41:24.1260462Z 
2023-01-11T21:41:24.1260553Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1260619Z import torch
2023-01-11T21:41:24.1260687Z import random
2023-01-11T21:41:24.1260798Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1260914Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1260919Z 
2023-01-11T21:41:24.1260992Z aten = torch.ops.aten
2023-01-11T21:41:24.1261111Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1261200Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1261205Z 
2023-01-11T21:41:24.1261209Z 
2023-01-11T21:41:24.1261366Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1261573Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1261688Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1261788Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1261884Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1261946Z {
2023-01-11T21:41:24.1262030Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1262089Z     {
2023-01-11T21:41:24.1262165Z         #pragma omp for 
2023-01-11T21:41:24.1262247Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1262307Z         {
2023-01-11T21:41:24.1262671Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1262804Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1262919Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1263006Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1263085Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1263173Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1263237Z         }
2023-01-11T21:41:24.1263327Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1263412Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1263460Z         {
2023-01-11T21:41:24.1263542Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1263621Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1263722Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1263802Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1263881Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1263959Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1264007Z         }
2023-01-11T21:41:24.1264066Z     }
2023-01-11T21:41:24.1264122Z }
2023-01-11T21:41:24.1264205Z ''')
2023-01-11T21:41:24.1264210Z 
2023-01-11T21:41:24.1264214Z 
2023-01-11T21:41:24.1264305Z async_compile.wait(globals())
2023-01-11T21:41:24.1264374Z del async_compile
2023-01-11T21:41:24.1264380Z 
2023-01-11T21:41:24.1264447Z def call(args):
2023-01-11T21:41:24.1264510Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1264578Z     args.clear()
2023-01-11T21:41:24.1264778Z     buf0 = empty_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1264937Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1265004Z     del arg0_1
2023-01-11T21:41:24.1265068Z     del arg1_1
2023-01-11T21:41:24.1265137Z     return (buf0, )
2023-01-11T21:41:24.1265141Z 
2023-01-11T21:41:24.1265147Z 
2023-01-11T21:41:24.1265220Z if __name__ == "__main__":
2023-01-11T21:41:24.1265321Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1265442Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1265651Z     arg0_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1265910Z     arg1_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1266023Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1266028Z 
2023-01-11T21:41:24.1266032Z 
2023-01-11T21:41:24.1266124Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1266191Z import torch
2023-01-11T21:41:24.1266259Z import random
2023-01-11T21:41:24.1266359Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1266476Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1266480Z 
2023-01-11T21:41:24.1266556Z aten = torch.ops.aten
2023-01-11T21:41:24.1266687Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1266777Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1266782Z 
2023-01-11T21:41:24.1266786Z 
2023-01-11T21:41:24.1266918Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1267154Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1267273Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1267363Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1267459Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1267519Z {
2023-01-11T21:41:24.1267613Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1267672Z     {
2023-01-11T21:41:24.1267747Z         #pragma omp for 
2023-01-11T21:41:24.1267825Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1267875Z         {
2023-01-11T21:41:24.1268003Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1268126Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1268209Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1268297Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1268355Z         }
2023-01-11T21:41:24.1268446Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1268519Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1268577Z         {
2023-01-11T21:41:24.1268656Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1268735Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1268812Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1268890Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1268949Z         }
2023-01-11T21:41:24.1268996Z     }
2023-01-11T21:41:24.1269052Z }
2023-01-11T21:41:24.1269127Z ''')
2023-01-11T21:41:24.1269132Z 
2023-01-11T21:41:24.1269136Z 
2023-01-11T21:41:24.1269221Z async_compile.wait(globals())
2023-01-11T21:41:24.1269290Z del async_compile
2023-01-11T21:41:24.1269295Z 
2023-01-11T21:41:24.1269363Z def call(args):
2023-01-11T21:41:24.1269435Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1269492Z     args.clear()
2023-01-11T21:41:24.1269697Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1269861Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1269928Z     del arg0_1
2023-01-11T21:41:24.1269991Z     del arg1_1
2023-01-11T21:41:24.1270058Z     return (buf0, )
2023-01-11T21:41:24.1270063Z 
2023-01-11T21:41:24.1270067Z 
2023-01-11T21:41:24.1270139Z if __name__ == "__main__":
2023-01-11T21:41:24.1270249Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1270359Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1270550Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1270753Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1270865Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1270870Z 
2023-01-11T21:41:24.1270874Z 
2023-01-11T21:41:24.1270964Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1271032Z import torch
2023-01-11T21:41:24.1271099Z import random
2023-01-11T21:41:24.1271232Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1271350Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1271355Z 
2023-01-11T21:41:24.1271430Z aten = torch.ops.aten
2023-01-11T21:41:24.1271560Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1271649Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1271654Z 
2023-01-11T21:41:24.1271658Z 
2023-01-11T21:41:24.1271788Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1271990Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1272102Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1272203Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1272288Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1272344Z {
2023-01-11T21:41:24.1272439Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1272501Z     {
2023-01-11T21:41:24.1272601Z         #pragma omp for 
2023-01-11T21:41:24.1272680Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1272729Z         {
2023-01-11T21:41:24.1272857Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1272982Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1273106Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1273187Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1273270Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1273358Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1273419Z         }
2023-01-11T21:41:24.1273499Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1273576Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1273634Z         {
2023-01-11T21:41:24.1273713Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1273852Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1273955Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1274036Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1274106Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1274182Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1274242Z         }
2023-01-11T21:41:24.1274301Z     }
2023-01-11T21:41:24.1274359Z }
2023-01-11T21:41:24.1274435Z ''')
2023-01-11T21:41:24.1274440Z 
2023-01-11T21:41:24.1274444Z 
2023-01-11T21:41:24.1274533Z async_compile.wait(globals())
2023-01-11T21:41:24.1274592Z del async_compile
2023-01-11T21:41:24.1274597Z 
2023-01-11T21:41:24.1274665Z def call(args):
2023-01-11T21:41:24.1274739Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1274808Z     args.clear()
2023-01-11T21:41:24.1275014Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1275173Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1275239Z     del arg0_1
2023-01-11T21:41:24.1275295Z     del arg1_1
2023-01-11T21:41:24.1275363Z     return (buf0, )
2023-01-11T21:41:24.1275368Z 
2023-01-11T21:41:24.1275371Z 
2023-01-11T21:41:24.1275445Z if __name__ == "__main__":
2023-01-11T21:41:24.1275557Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1275677Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1275869Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1276072Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1276184Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1276189Z 
2023-01-11T21:41:24.1276447Z [2023-01-11 21:39:46,641] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 509
2023-01-11T21:41:24.1276877Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1277033Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1277293Z [2023-01-11 21:39:46,660] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 510
2023-01-11T21:41:24.1277556Z [2023-01-11 21:39:46,671] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 510
2023-01-11T21:41:24.1277983Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1278138Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1278393Z [2023-01-11 21:39:46,687] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 511
2023-01-11T21:41:24.1278659Z [2023-01-11 21:39:46,697] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 511
2023-01-11T21:41:24.1279083Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1279209Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1279462Z [2023-01-11 21:39:46,717] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 512
2023-01-11T21:41:24.1279712Z [2023-01-11 21:39:46,728] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 512
2023-01-11T21:41:24.1279730Z 
2023-01-11T21:41:24.1279809Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1279875Z import torch
2023-01-11T21:41:24.1279942Z import random
2023-01-11T21:41:24.1280056Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1280177Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1280181Z 
2023-01-11T21:41:24.1280256Z aten = torch.ops.aten
2023-01-11T21:41:24.1280390Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1280468Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1280474Z 
2023-01-11T21:41:24.1280488Z 
2023-01-11T21:41:24.1280608Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1280809Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1280925Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1281031Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1281127Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1281184Z {
2023-01-11T21:41:24.1281282Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1281331Z     {
2023-01-11T21:41:24.1281405Z         #pragma omp for 
2023-01-11T21:41:24.1281487Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1281547Z         {
2023-01-11T21:41:24.1281676Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1281805Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1281888Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1281965Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1282025Z         }
2023-01-11T21:41:24.1282115Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1282199Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1282258Z         {
2023-01-11T21:41:24.1282384Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1282463Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1282531Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1282610Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1282671Z         }
2023-01-11T21:41:24.1282729Z     }
2023-01-11T21:41:24.1282785Z }
2023-01-11T21:41:24.1282865Z ''')
2023-01-11T21:41:24.1282870Z 
2023-01-11T21:41:24.1282875Z 
2023-01-11T21:41:24.1282961Z async_compile.wait(globals())
2023-01-11T21:41:24.1283020Z del async_compile
2023-01-11T21:41:24.1283025Z 
2023-01-11T21:41:24.1283093Z def call(args):
2023-01-11T21:41:24.1283165Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1283233Z     args.clear()
2023-01-11T21:41:24.1283460Z     buf0 = empty_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1283621Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1283689Z     del arg0_1
2023-01-11T21:41:24.1283773Z     del arg1_1
2023-01-11T21:41:24.1283844Z     return (buf0, )
2023-01-11T21:41:24.1283849Z 
2023-01-11T21:41:24.1283853Z 
2023-01-11T21:41:24.1283929Z if __name__ == "__main__":
2023-01-11T21:41:24.1284041Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1284164Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1284368Z     arg0_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1284593Z     arg1_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1284704Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1284708Z 
2023-01-11T21:41:24.1284712Z 
2023-01-11T21:41:24.1284801Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1284857Z import torch
2023-01-11T21:41:24.1284925Z import random
2023-01-11T21:41:24.1285037Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1285159Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1285164Z 
2023-01-11T21:41:24.1285238Z aten = torch.ops.aten
2023-01-11T21:41:24.1285368Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1285457Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1285461Z 
2023-01-11T21:41:24.1285465Z 
2023-01-11T21:41:24.1285596Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1285787Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1285902Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1286004Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1286100Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1286159Z {
2023-01-11T21:41:24.1286254Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1286313Z     {
2023-01-11T21:41:24.1286375Z         #pragma omp for 
2023-01-11T21:41:24.1286462Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1286525Z         {
2023-01-11T21:41:24.1286655Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1286782Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1286906Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1286988Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1287057Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1287144Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1287205Z         }
2023-01-11T21:41:24.1287296Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1287380Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1287439Z         {
2023-01-11T21:41:24.1287521Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1287589Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1287686Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1287802Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1287881Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1287959Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1288019Z         }
2023-01-11T21:41:24.1288077Z     }
2023-01-11T21:41:24.1288124Z }
2023-01-11T21:41:24.1288200Z ''')
2023-01-11T21:41:24.1288205Z 
2023-01-11T21:41:24.1288209Z 
2023-01-11T21:41:24.1288298Z async_compile.wait(globals())
2023-01-11T21:41:24.1288368Z del async_compile
2023-01-11T21:41:24.1288373Z 
2023-01-11T21:41:24.1288442Z def call(args):
2023-01-11T21:41:24.1288512Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1288578Z     args.clear()
2023-01-11T21:41:24.1288793Z     buf0 = empty_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1288952Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1289018Z     del arg0_1
2023-01-11T21:41:24.1289081Z     del arg1_1
2023-01-11T21:41:24.1289192Z     return (buf0, )
2023-01-11T21:41:24.1289199Z 
2023-01-11T21:41:24.1289203Z 
2023-01-11T21:41:24.1289274Z if __name__ == "__main__":
2023-01-11T21:41:24.1289383Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1289504Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1289693Z     arg0_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1289917Z     arg1_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1290030Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1290035Z 
2023-01-11T21:41:24.1290039Z 
2023-01-11T21:41:24.1290129Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1290196Z import torch
2023-01-11T21:41:24.1290265Z import random
2023-01-11T21:41:24.1290381Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1290500Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1290509Z 
2023-01-11T21:41:24.1290574Z aten = torch.ops.aten
2023-01-11T21:41:24.1290705Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1290795Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1290800Z 
2023-01-11T21:41:24.1290804Z 
2023-01-11T21:41:24.1290935Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1291138Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1291256Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1291357Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1291457Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1291505Z {
2023-01-11T21:41:24.1291600Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1291663Z     {
2023-01-11T21:41:24.1291739Z         #pragma omp for 
2023-01-11T21:41:24.1291818Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1291880Z         {
2023-01-11T21:41:24.1292012Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1292130Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1292212Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1292298Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1292361Z         }
2023-01-11T21:41:24.1292453Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1292533Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.1292592Z         {
2023-01-11T21:41:24.1292662Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1292742Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1292821Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1292898Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1292957Z         }
2023-01-11T21:41:24.1293018Z     }
2023-01-11T21:41:24.1293074Z }
2023-01-11T21:41:24.1293138Z ''')
2023-01-11T21:41:24.1293143Z 
2023-01-11T21:41:24.1293177Z 
2023-01-11T21:41:24.1293268Z async_compile.wait(globals())
2023-01-11T21:41:24.1293337Z del async_compile
2023-01-11T21:41:24.1293342Z 
2023-01-11T21:41:24.1293411Z def call(args):
2023-01-11T21:41:24.1293483Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1293552Z     args.clear()
2023-01-11T21:41:24.1293757Z     buf0 = empty_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1293907Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1293973Z     del arg0_1
2023-01-11T21:41:24.1294037Z     del arg1_1
2023-01-11T21:41:24.1294106Z     return (buf0, )
2023-01-11T21:41:24.1294111Z 
2023-01-11T21:41:24.1294115Z 
2023-01-11T21:41:24.1294187Z if __name__ == "__main__":
2023-01-11T21:41:24.1294300Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1294421Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1294640Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1294838Z     arg1_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1294949Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1294954Z 
2023-01-11T21:41:24.1294958Z 
2023-01-11T21:41:24.1295051Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1295119Z import torch
2023-01-11T21:41:24.1295190Z import random
2023-01-11T21:41:24.1295304Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1295420Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1295425Z 
2023-01-11T21:41:24.1295499Z aten = torch.ops.aten
2023-01-11T21:41:24.1295619Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1295708Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1295713Z 
2023-01-11T21:41:24.1295718Z 
2023-01-11T21:41:24.1295849Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1296053Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1296170Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1296271Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1296366Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1296425Z {
2023-01-11T21:41:24.1296507Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1296567Z     {
2023-01-11T21:41:24.1296640Z         #pragma omp for 
2023-01-11T21:41:24.1296719Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1296778Z         {
2023-01-11T21:41:24.1296908Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1297034Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1297149Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1297232Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1297315Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1297403Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1297463Z         }
2023-01-11T21:41:24.1297555Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1297635Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.1297684Z         {
2023-01-11T21:41:24.1297762Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1297839Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:24.1297935Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1298014Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1298092Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1298167Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1298215Z         }
2023-01-11T21:41:24.1298272Z     }
2023-01-11T21:41:24.1298331Z }
2023-01-11T21:41:24.1298408Z ''')
2023-01-11T21:41:24.1298413Z 
2023-01-11T21:41:24.1298417Z 
2023-01-11T21:41:24.1298501Z async_compile.wait(globals())
2023-01-11T21:41:24.1298569Z del async_compile
2023-01-11T21:41:24.1298604Z 
2023-01-11T21:41:24.1298670Z def call(args):
2023-01-11T21:41:24.1298731Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1298798Z     args.clear()
2023-01-11T21:41:24.1299007Z     buf0 = empty_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1299167Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1299233Z     del arg0_1
2023-01-11T21:41:24.1299298Z     del arg1_1
2023-01-11T21:41:24.1299366Z     return (buf0, )
2023-01-11T21:41:24.1299371Z 
2023-01-11T21:41:24.1299375Z 
2023-01-11T21:41:24.1299447Z if __name__ == "__main__":
2023-01-11T21:41:24.1299546Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1299664Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1299858Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1300089Z     arg1_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1300202Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1300207Z 
2023-01-11T21:41:24.1300271Z ok (16.080s)
2023-01-11T21:41:24.1300734Z   test_views2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1300861Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1301120Z [2023-01-11 21:39:46,745] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 513
2023-01-11T21:41:24.1301374Z [2023-01-11 21:39:48,253] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 513
2023-01-11T21:41:24.1301802Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1301928Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1302179Z [2023-01-11 21:39:48,271] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 514
2023-01-11T21:41:24.1302555Z [2023-01-11 21:39:49,791] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 514
2023-01-11T21:41:24.1302981Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1303105Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1303359Z [2023-01-11 21:39:49,807] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 515
2023-01-11T21:41:24.1303621Z [2023-01-11 21:39:51,343] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 515
2023-01-11T21:41:24.1304046Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1304168Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1304422Z [2023-01-11 21:39:51,362] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 516
2023-01-11T21:41:24.1304721Z [2023-01-11 21:39:52,871] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 516
2023-01-11T21:41:24.1305147Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1305271Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1305523Z [2023-01-11 21:39:52,888] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 517
2023-01-11T21:41:24.1305529Z 
2023-01-11T21:41:24.1305620Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1305692Z import torch
2023-01-11T21:41:24.1305799Z import random
2023-01-11T21:41:24.1305912Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1306031Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1306036Z 
2023-01-11T21:41:24.1306100Z aten = torch.ops.aten
2023-01-11T21:41:24.1306232Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1306322Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1306327Z 
2023-01-11T21:41:24.1306331Z 
2023-01-11T21:41:24.1306466Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1306670Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1306785Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1306883Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1306941Z {
2023-01-11T21:41:24.1307026Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1307087Z     {
2023-01-11T21:41:24.1307162Z         #pragma omp for 
2023-01-11T21:41:24.1307245Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1307308Z         {
2023-01-11T21:41:24.1307440Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1307568Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1307640Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1307729Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1307788Z         }
2023-01-11T21:41:24.1307883Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1307965Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.1308024Z         {
2023-01-11T21:41:24.1308105Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1308191Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1308272Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1308350Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1308408Z         }
2023-01-11T21:41:24.1308466Z     }
2023-01-11T21:41:24.1308525Z }
2023-01-11T21:41:24.1308603Z ''')
2023-01-11T21:41:24.1308608Z 
2023-01-11T21:41:24.1308612Z 
2023-01-11T21:41:24.1308689Z async_compile.wait(globals())
2023-01-11T21:41:24.1308758Z del async_compile
2023-01-11T21:41:24.1308763Z 
2023-01-11T21:41:24.1308831Z def call(args):
2023-01-11T21:41:24.1308898Z     arg0_1, = args
2023-01-11T21:41:24.1308964Z     args.clear()
2023-01-11T21:41:24.1309160Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1309292Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1309346Z     del arg0_1
2023-01-11T21:41:24.1309415Z     return (buf0, )
2023-01-11T21:41:24.1309420Z 
2023-01-11T21:41:24.1309424Z 
2023-01-11T21:41:24.1309498Z if __name__ == "__main__":
2023-01-11T21:41:24.1309611Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1309730Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1309937Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1310077Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1310082Z 
2023-01-11T21:41:24.1310086Z 
2023-01-11T21:41:24.1310178Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1310233Z import torch
2023-01-11T21:41:24.1310299Z import random
2023-01-11T21:41:24.1310410Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1310527Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1310532Z 
2023-01-11T21:41:24.1310608Z aten = torch.ops.aten
2023-01-11T21:41:24.1310738Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1310828Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1310833Z 
2023-01-11T21:41:24.1310837Z 
2023-01-11T21:41:24.1310967Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1311159Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1311304Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1311403Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1311463Z {
2023-01-11T21:41:24.1311558Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1311617Z     {
2023-01-11T21:41:24.1311690Z         #pragma omp for 
2023-01-11T21:41:24.1311759Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1311818Z         {
2023-01-11T21:41:24.1311948Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1312077Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.1312161Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1312287Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1312367Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1312455Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1312503Z         }
2023-01-11T21:41:24.1312598Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1312680Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:24.1312739Z         {
2023-01-11T21:41:24.1312819Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1312915Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:24.1312997Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1313083Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:24.1313162Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1313238Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1313296Z         }
2023-01-11T21:41:24.1313355Z     }
2023-01-11T21:41:24.1313413Z }
2023-01-11T21:41:24.1313477Z ''')
2023-01-11T21:41:24.1313494Z 
2023-01-11T21:41:24.1313498Z 
2023-01-11T21:41:24.1313572Z async_compile.wait(globals())
2023-01-11T21:41:24.1313642Z del async_compile
2023-01-11T21:41:24.1313647Z 
2023-01-11T21:41:24.1313718Z def call(args):
2023-01-11T21:41:24.1313849Z     arg0_1, = args
2023-01-11T21:41:24.1313920Z     args.clear()
2023-01-11T21:41:24.1314124Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1314254Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1314309Z     del arg0_1
2023-01-11T21:41:24.1314378Z     return (buf0, )
2023-01-11T21:41:24.1314383Z 
2023-01-11T21:41:24.1314387Z 
2023-01-11T21:41:24.1314461Z if __name__ == "__main__":
2023-01-11T21:41:24.1314575Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1314696Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1314906Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1315012Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1315017Z 
2023-01-11T21:41:24.1315021Z 
2023-01-11T21:41:24.1315115Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1315170Z import torch
2023-01-11T21:41:24.1315236Z import random
2023-01-11T21:41:24.1315351Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1315541Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1315546Z 
2023-01-11T21:41:24.1315622Z aten = torch.ops.aten
2023-01-11T21:41:24.1315753Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1315842Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1315847Z 
2023-01-11T21:41:24.1315851Z 
2023-01-11T21:41:24.1315982Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1316171Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1316291Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1316389Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1316452Z {
2023-01-11T21:41:24.1316546Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1316606Z     {
2023-01-11T21:41:24.1316680Z         #pragma omp for 
2023-01-11T21:41:24.1316749Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1316842Z         {
2023-01-11T21:41:24.1316973Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1317102Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1317186Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1317275Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1317337Z         }
2023-01-11T21:41:24.1317417Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1317502Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1317563Z         {
2023-01-11T21:41:24.1317643Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1317740Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1317821Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1317900Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1317949Z         }
2023-01-11T21:41:24.1318008Z     }
2023-01-11T21:41:24.1318065Z }
2023-01-11T21:41:24.1318141Z ''')
2023-01-11T21:41:24.1318152Z 
2023-01-11T21:41:24.1318156Z 
2023-01-11T21:41:24.1318242Z async_compile.wait(globals())
2023-01-11T21:41:24.1318314Z del async_compile
2023-01-11T21:41:24.1318319Z 
2023-01-11T21:41:24.1318386Z def call(args):
2023-01-11T21:41:24.1318442Z     arg0_1, = args
2023-01-11T21:41:24.1318509Z     args.clear()
2023-01-11T21:41:24.1318709Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1318840Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1318906Z     del arg0_1
2023-01-11T21:41:24.1318975Z     return (buf0, )
2023-01-11T21:41:24.1318980Z 
2023-01-11T21:41:24.1318984Z 
2023-01-11T21:41:24.1319059Z if __name__ == "__main__":
2023-01-11T21:41:24.1319168Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1319278Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1319507Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1319616Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1319621Z 
2023-01-11T21:41:24.1319625Z 
2023-01-11T21:41:24.1319716Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1319782Z import torch
2023-01-11T21:41:24.1319850Z import random
2023-01-11T21:41:24.1319962Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1320083Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1320088Z 
2023-01-11T21:41:24.1320152Z aten = torch.ops.aten
2023-01-11T21:41:24.1320282Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1320371Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1320375Z 
2023-01-11T21:41:24.1320379Z 
2023-01-11T21:41:24.1320510Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1320712Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1320826Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1320966Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1321023Z {
2023-01-11T21:41:24.1321108Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1321169Z     {
2023-01-11T21:41:24.1321245Z         #pragma omp for 
2023-01-11T21:41:24.1321324Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1321386Z         {
2023-01-11T21:41:24.1321513Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1321643Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.1321714Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1321841Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1321926Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1322015Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1322073Z         }
2023-01-11T21:41:24.1322165Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1322279Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1322329Z         {
2023-01-11T21:41:24.1322410Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1322506Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:24.1322588Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1322683Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:24.1322764Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1322845Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1322893Z         }
2023-01-11T21:41:24.1322953Z     }
2023-01-11T21:41:24.1323009Z }
2023-01-11T21:41:24.1323087Z ''')
2023-01-11T21:41:24.1323093Z 
2023-01-11T21:41:24.1323097Z 
2023-01-11T21:41:24.1323182Z async_compile.wait(globals())
2023-01-11T21:41:24.1323253Z del async_compile
2023-01-11T21:41:24.1323257Z 
2023-01-11T21:41:24.1323323Z def call(args):
2023-01-11T21:41:24.1323379Z     arg0_1, = args
2023-01-11T21:41:24.1323445Z     args.clear()
2023-01-11T21:41:24.1323654Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1323786Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1323850Z     del arg0_1
2023-01-11T21:41:24.1323918Z     return (buf0, )
2023-01-11T21:41:24.1323923Z 
2023-01-11T21:41:24.1323927Z 
2023-01-11T21:41:24.1324000Z if __name__ == "__main__":
2023-01-11T21:41:24.1324109Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1324218Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1324443Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1324550Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1324554Z 
2023-01-11T21:41:24.1324822Z [2023-01-11 21:39:52,896] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 517
2023-01-11T21:41:24.1325258Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1325387Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1325648Z [2023-01-11 21:39:52,913] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 518
2023-01-11T21:41:24.1325909Z [2023-01-11 21:39:52,921] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 518
2023-01-11T21:41:24.1325914Z 
2023-01-11T21:41:24.1326006Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1326073Z import torch
2023-01-11T21:41:24.1326130Z import random
2023-01-11T21:41:24.1326242Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1326362Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1326396Z 
2023-01-11T21:41:24.1326473Z aten = torch.ops.aten
2023-01-11T21:41:24.1326604Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1326692Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1326697Z 
2023-01-11T21:41:24.1326701Z 
2023-01-11T21:41:24.1326832Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1327022Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1327140Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1327237Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1327296Z {
2023-01-11T21:41:24.1327389Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1327447Z     {
2023-01-11T21:41:24.1327521Z         #pragma omp for 
2023-01-11T21:41:24.1327591Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1327650Z         {
2023-01-11T21:41:24.1327808Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1327940Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1328022Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1328111Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1328172Z         }
2023-01-11T21:41:24.1328264Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1328336Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1328399Z         {
2023-01-11T21:41:24.1328481Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1328580Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1328666Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1328743Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1328804Z         }
2023-01-11T21:41:24.1328853Z     }
2023-01-11T21:41:24.1328915Z }
2023-01-11T21:41:24.1328992Z ''')
2023-01-11T21:41:24.1328997Z 
2023-01-11T21:41:24.1329001Z 
2023-01-11T21:41:24.1329086Z async_compile.wait(globals())
2023-01-11T21:41:24.1329159Z del async_compile
2023-01-11T21:41:24.1329164Z 
2023-01-11T21:41:24.1329232Z def call(args):
2023-01-11T21:41:24.1329298Z     arg0_1, = args
2023-01-11T21:41:24.1329355Z     args.clear()
2023-01-11T21:41:24.1329565Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1329695Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1329762Z     del arg0_1
2023-01-11T21:41:24.1329830Z     return (buf0, )
2023-01-11T21:41:24.1329835Z 
2023-01-11T21:41:24.1329839Z 
2023-01-11T21:41:24.1329912Z if __name__ == "__main__":
2023-01-11T21:41:24.1330022Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1330131Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1330330Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1330434Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1330439Z 
2023-01-11T21:41:24.1330445Z 
2023-01-11T21:41:24.1330538Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1330604Z import torch
2023-01-11T21:41:24.1330672Z import random
2023-01-11T21:41:24.1330784Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1330901Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1330905Z 
2023-01-11T21:41:24.1330969Z aten = torch.ops.aten
2023-01-11T21:41:24.1331098Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1331187Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1331192Z 
2023-01-11T21:41:24.1331196Z 
2023-01-11T21:41:24.1331327Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1331527Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1331641Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1331739Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1331797Z {
2023-01-11T21:41:24.1331915Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1331974Z     {
2023-01-11T21:41:24.1332047Z         #pragma omp for 
2023-01-11T21:41:24.1332126Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:24.1332187Z         {
2023-01-11T21:41:24.1332316Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1332445Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:24.1332517Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1332642Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1332722Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1332809Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1332868Z         }
2023-01-11T21:41:24.1332958Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1333042Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:24.1333093Z         {
2023-01-11T21:41:24.1333205Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1333304Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:24.1333384Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:24.1333479Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:24.1333558Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:24.1333635Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:24.1333684Z         }
2023-01-11T21:41:24.1333743Z     }
2023-01-11T21:41:24.1333801Z }
2023-01-11T21:41:24.1333876Z ''')
2023-01-11T21:41:24.1333882Z 
2023-01-11T21:41:24.1333886Z 
2023-01-11T21:41:24.1333972Z async_compile.wait(globals())
2023-01-11T21:41:24.1334043Z del async_compile
2023-01-11T21:41:24.1334047Z 
2023-01-11T21:41:24.1334115Z def call(args):
2023-01-11T21:41:24.1334180Z     arg0_1, = args
2023-01-11T21:41:24.1334236Z     args.clear()
2023-01-11T21:41:24.1334446Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1334580Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1334647Z     del arg0_1
2023-01-11T21:41:24.1334717Z     return (buf0, )
2023-01-11T21:41:24.1334721Z 
2023-01-11T21:41:24.1334725Z 
2023-01-11T21:41:24.1334798Z if __name__ == "__main__":
2023-01-11T21:41:24.1334909Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1335018Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1335215Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1335318Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1335323Z 
2023-01-11T21:41:24.1335386Z ok (6.193s)
2023-01-11T21:41:24.1335851Z   test_views3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1335977Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1336239Z [2023-01-11 21:39:52,983] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 519
2023-01-11T21:41:24.1336502Z [2023-01-11 21:39:54,529] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 519
2023-01-11T21:41:24.1336507Z 
2023-01-11T21:41:24.1336598Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1336665Z import torch
2023-01-11T21:41:24.1336722Z import random
2023-01-11T21:41:24.1336832Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1336951Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1336956Z 
2023-01-11T21:41:24.1337031Z aten = torch.ops.aten
2023-01-11T21:41:24.1337160Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1337250Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1337286Z 
2023-01-11T21:41:24.1337290Z 
2023-01-11T21:41:24.1337422Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1337624Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1337730Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:24.1337832Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1337929Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1337988Z {
2023-01-11T21:41:24.1338085Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1338145Z     {
2023-01-11T21:41:24.1338218Z         #pragma omp for 
2023-01-11T21:41:24.1338288Z         for(long i0=0; i0<744; i0+=1)
2023-01-11T21:41:24.1338348Z         {
2023-01-11T21:41:24.1338424Z             #pragma GCC ivdep
2023-01-11T21:41:24.1338511Z             for(long i1=0; i1<192; i1+=1)
2023-01-11T21:41:24.1338575Z             {
2023-01-11T21:41:24.1338638Z                 {
2023-01-11T21:41:24.1338723Z                     {
2023-01-11T21:41:24.1338830Z                         auto tmp0 = in_ptr0[(3*i0) + (i1 / 64)];
2023-01-11T21:41:24.1338937Z                         auto tmp1 = in_ptr1[(64*tmp0) + (i1 % 64)];
2023-01-11T21:41:24.1339030Z                         out_ptr0[i1 + (192*i0)] = tmp1;
2023-01-11T21:41:24.1339094Z                     }
2023-01-11T21:41:24.1339156Z                 }
2023-01-11T21:41:24.1339221Z             }
2023-01-11T21:41:24.1339270Z         }
2023-01-11T21:41:24.1339330Z     }
2023-01-11T21:41:24.1339389Z }
2023-01-11T21:41:24.1339465Z ''')
2023-01-11T21:41:24.1339470Z 
2023-01-11T21:41:24.1339474Z 
2023-01-11T21:41:24.1339560Z async_compile.wait(globals())
2023-01-11T21:41:24.1339629Z del async_compile
2023-01-11T21:41:24.1339634Z 
2023-01-11T21:41:24.1339702Z def call(args):
2023-01-11T21:41:24.1339763Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1339830Z     args.clear()
2023-01-11T21:41:24.1340060Z     buf0 = empty_strided((1, 12, 62, 192), (142848, 11904, 192, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1340224Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1340289Z     del arg0_1
2023-01-11T21:41:24.1340352Z     del arg1_1
2023-01-11T21:41:24.1340421Z     return (buf0, )
2023-01-11T21:41:24.1340426Z 
2023-01-11T21:41:24.1340430Z 
2023-01-11T21:41:24.1340503Z if __name__ == "__main__":
2023-01-11T21:41:24.1340603Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1340725Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1340923Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1341117Z     arg1_1 = rand_strided((2232, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:24.1341228Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1341233Z 
2023-01-11T21:41:24.1341298Z ok (1.610s)
2023-01-11T21:41:24.1341643Z   test_zero_dim_reductions_cpu (__main__.CpuTests) ... [2023-01-11 21:39:54,593] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 520
2023-01-11T21:41:24.1341910Z [2023-01-11 21:39:56,089] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 520
2023-01-11T21:41:24.1342164Z [2023-01-11 21:39:56,150] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 521
2023-01-11T21:41:24.1342572Z [2023-01-11 21:39:56,157] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 521
2023-01-11T21:41:24.1342583Z 
2023-01-11T21:41:24.1342686Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1342755Z import torch
2023-01-11T21:41:24.1342825Z import random
2023-01-11T21:41:24.1342938Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1343058Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1343063Z 
2023-01-11T21:41:24.1343137Z aten = torch.ops.aten
2023-01-11T21:41:24.1343325Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1343417Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1343422Z 
2023-01-11T21:41:24.1343426Z 
2023-01-11T21:41:24.1343565Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1343768Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1343878Z extern "C" void kernel(bool* __restrict__ out_ptr0)
2023-01-11T21:41:24.1343939Z {
2023-01-11T21:41:24.1344013Z     #pragma GCC ivdep
2023-01-11T21:41:24.1344092Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1344139Z     {
2023-01-11T21:41:24.1344200Z         {
2023-01-11T21:41:24.1344263Z             {
2023-01-11T21:41:24.1344366Z                 auto tmp0 = static_cast<bool>(false);
2023-01-11T21:41:24.1344451Z                 auto tmp1 = tmp0 == 0;
2023-01-11T21:41:24.1344532Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.1344593Z             }
2023-01-11T21:41:24.1344644Z         }
2023-01-11T21:41:24.1344739Z     }
2023-01-11T21:41:24.1344798Z }
2023-01-11T21:41:24.1344877Z ''')
2023-01-11T21:41:24.1344883Z 
2023-01-11T21:41:24.1344887Z 
2023-01-11T21:41:24.1344975Z async_compile.wait(globals())
2023-01-11T21:41:24.1345045Z del async_compile
2023-01-11T21:41:24.1345050Z 
2023-01-11T21:41:24.1345119Z def call(args):
2023-01-11T21:41:24.1345175Z     arg0_1, = args
2023-01-11T21:41:24.1345240Z     args.clear()
2023-01-11T21:41:24.1345429Z     buf0 = empty_strided((2, 1), (1, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.1345530Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1345600Z     return (buf0, )
2023-01-11T21:41:24.1345604Z 
2023-01-11T21:41:24.1345609Z 
2023-01-11T21:41:24.1345682Z if __name__ == "__main__":
2023-01-11T21:41:24.1345791Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1345901Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1346098Z     arg0_1 = rand_strided((2, 0), (1, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:41:24.1346205Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1346210Z 
2023-01-11T21:41:24.1346214Z 
2023-01-11T21:41:24.1346305Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1346372Z import torch
2023-01-11T21:41:24.1346439Z import random
2023-01-11T21:41:24.1346550Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1346666Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1346670Z 
2023-01-11T21:41:24.1346736Z aten = torch.ops.aten
2023-01-11T21:41:24.1346869Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1346958Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1346963Z 
2023-01-11T21:41:24.1346968Z 
2023-01-11T21:41:24.1347102Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1347305Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1347411Z extern "C" void kernel(bool* __restrict__ out_ptr0)
2023-01-11T21:41:24.1347475Z {
2023-01-11T21:41:24.1347550Z     #pragma GCC ivdep
2023-01-11T21:41:24.1347617Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:24.1347678Z     {
2023-01-11T21:41:24.1347738Z         {
2023-01-11T21:41:24.1347798Z             {
2023-01-11T21:41:24.1347901Z                 auto tmp0 = static_cast<bool>(false);
2023-01-11T21:41:24.1347984Z                 auto tmp1 = tmp0 == 0;
2023-01-11T21:41:24.1348064Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:24.1348114Z             }
2023-01-11T21:41:24.1348174Z         }
2023-01-11T21:41:24.1348233Z     }
2023-01-11T21:41:24.1348289Z }
2023-01-11T21:41:24.1348363Z ''')
2023-01-11T21:41:24.1348368Z 
2023-01-11T21:41:24.1348372Z 
2023-01-11T21:41:24.1348458Z async_compile.wait(globals())
2023-01-11T21:41:24.1348528Z del async_compile
2023-01-11T21:41:24.1348533Z 
2023-01-11T21:41:24.1348590Z def call(args):
2023-01-11T21:41:24.1348656Z     arg0_1, = args
2023-01-11T21:41:24.1348725Z     args.clear()
2023-01-11T21:41:24.1348940Z     buf0 = empty_strided((2, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:24.1349044Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1349111Z     return (buf0, )
2023-01-11T21:41:24.1349117Z 
2023-01-11T21:41:24.1349121Z 
2023-01-11T21:41:24.1349195Z if __name__ == "__main__":
2023-01-11T21:41:24.1349295Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1349413Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1349609Z     arg0_1 = rand_strided((2, 0), (1, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:41:24.1349714Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1349719Z 
2023-01-11T21:41:24.1349783Z ok (1.627s)
2023-01-11T21:41:24.1350276Z   test_zeros_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1350403Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1350662Z [2023-01-11 21:39:56,267] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 522
2023-01-11T21:41:24.1350927Z [2023-01-11 21:39:57,826] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 522
2023-01-11T21:41:24.1350932Z 
2023-01-11T21:41:24.1351028Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1351085Z import torch
2023-01-11T21:41:24.1351154Z import random
2023-01-11T21:41:24.1351269Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1351387Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1351392Z 
2023-01-11T21:41:24.1351467Z aten = torch.ops.aten
2023-01-11T21:41:24.1351597Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1351693Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1351698Z 
2023-01-11T21:41:24.1351702Z 
2023-01-11T21:41:24.1351833Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1352024Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1352140Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1352237Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:24.1352333Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:24.1352425Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:24.1352515Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:24.1352606Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:24.1352684Z                        float* __restrict__ out_ptr5)
2023-01-11T21:41:24.1352744Z {
2023-01-11T21:41:24.1352840Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1352903Z     {
2023-01-11T21:41:24.1352981Z         #pragma omp for 
2023-01-11T21:41:24.1353059Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1353118Z         {
2023-01-11T21:41:24.1353240Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1353370Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:24.1353453Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1353541Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1353629Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:24.1353689Z         }
2023-01-11T21:41:24.1353841Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1353909Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:24.1353969Z         {
2023-01-11T21:41:24.1354051Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1354149Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:24.1354230Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1354346Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1354424Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:24.1354473Z         }
2023-01-11T21:41:24.1354549Z         #pragma omp for 
2023-01-11T21:41:24.1354631Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:24.1354691Z         {
2023-01-11T21:41:24.1354823Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:24.1354910Z             tmp0.store(out_ptr2 + 8*i0);
2023-01-11T21:41:24.1354971Z         }
2023-01-11T21:41:24.1355050Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1355136Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:41:24.1355196Z         {
2023-01-11T21:41:24.1355294Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:24.1355371Z             out_ptr2[i0] = tmp0;
2023-01-11T21:41:24.1355431Z         }
2023-01-11T21:41:24.1355504Z         #pragma omp for 
2023-01-11T21:41:24.1355573Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:24.1355635Z         {
2023-01-11T21:41:24.1355791Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:24.1355879Z             tmp0.store(out_ptr3 + 8*i0);
2023-01-11T21:41:24.1355940Z         }
2023-01-11T21:41:24.1356030Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1356114Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:41:24.1356164Z         {
2023-01-11T21:41:24.1356256Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:24.1356333Z             out_ptr3[i0] = tmp0;
2023-01-11T21:41:24.1356393Z         }
2023-01-11T21:41:24.1356468Z         #pragma omp for 
2023-01-11T21:41:24.1356546Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:24.1356595Z         {
2023-01-11T21:41:24.1356656Z             {
2023-01-11T21:41:24.1356719Z                 {
2023-01-11T21:41:24.1356821Z                     auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:24.1356904Z                     out_ptr4[i0] = tmp0;
2023-01-11T21:41:24.1356968Z                 }
2023-01-11T21:41:24.1357031Z             }
2023-01-11T21:41:24.1357079Z         }
2023-01-11T21:41:24.1357152Z         #pragma omp for 
2023-01-11T21:41:24.1357232Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:24.1357292Z         {
2023-01-11T21:41:24.1357356Z             {
2023-01-11T21:41:24.1357420Z                 {
2023-01-11T21:41:24.1357528Z                     auto tmp0 = static_cast<float>(3.1416);
2023-01-11T21:41:24.1357598Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:41:24.1357660Z                 }
2023-01-11T21:41:24.1357721Z             }
2023-01-11T21:41:24.1357780Z         }
2023-01-11T21:41:24.1357842Z     }
2023-01-11T21:41:24.1357900Z }
2023-01-11T21:41:24.1357968Z ''')
2023-01-11T21:41:24.1357973Z 
2023-01-11T21:41:24.1357988Z 
2023-01-11T21:41:24.1358064Z async_compile.wait(globals())
2023-01-11T21:41:24.1358133Z del async_compile
2023-01-11T21:41:24.1358138Z 
2023-01-11T21:41:24.1358205Z def call(args):
2023-01-11T21:41:24.1358271Z     arg0_1, = args
2023-01-11T21:41:24.1358344Z     args.clear()
2023-01-11T21:41:24.1358536Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1358723Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1358932Z     buf1 = empty_strided((1, 8, 64, 64), (32768, 4096, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1359146Z     buf2 = empty_strided((1, 8, 64, 64), (32768, 4096, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1359335Z     buf3 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1359523Z     buf5 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1359779Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:24.1359844Z     del arg0_1
2023-01-11T21:41:24.1359993Z     return (buf0, buf1, buf2, buf3, buf4, buf5, )
2023-01-11T21:41:24.1359999Z 
2023-01-11T21:41:24.1360003Z 
2023-01-11T21:41:24.1360077Z if __name__ == "__main__":
2023-01-11T21:41:24.1360190Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1360300Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1360489Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1360594Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:24.1360599Z 
2023-01-11T21:41:24.1360664Z ok (1.670s)
2023-01-11T21:41:24.1360784Z   test_print_pow (__main__.ExprPrinterTests) ... ok (0.003s)
2023-01-11T21:41:24.1361311Z   test_cpu_broadcast1_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1361440Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1361701Z [2023-01-11 21:39:57,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 523
2023-01-11T21:41:24.1361968Z [2023-01-11 21:39:57,853] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 523
2023-01-11T21:41:24.1361974Z 
2023-01-11T21:41:24.1362055Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1362122Z import torch
2023-01-11T21:41:24.1362191Z import random
2023-01-11T21:41:24.1362301Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1362420Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1362426Z 
2023-01-11T21:41:24.1362500Z aten = torch.ops.aten
2023-01-11T21:41:24.1362629Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1362723Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1362728Z 
2023-01-11T21:41:24.1362733Z 
2023-01-11T21:41:24.1362853Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1363053Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1363172Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1363272Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1363369Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1363427Z {
2023-01-11T21:41:24.1363523Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1363571Z     {
2023-01-11T21:41:24.1363644Z         #pragma omp for 
2023-01-11T21:41:24.1363724Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1363783Z         {
2023-01-11T21:41:24.1363911Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1364038Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1364125Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1364213Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1364262Z         }
2023-01-11T21:41:24.1364354Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1364433Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1364493Z         {
2023-01-11T21:41:24.1364573Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1364651Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1364718Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1364795Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1364854Z         }
2023-01-11T21:41:24.1364912Z     }
2023-01-11T21:41:24.1364970Z }
2023-01-11T21:41:24.1365046Z ''')
2023-01-11T21:41:24.1365051Z 
2023-01-11T21:41:24.1365055Z 
2023-01-11T21:41:24.1365141Z async_compile.wait(globals())
2023-01-11T21:41:24.1365200Z del async_compile
2023-01-11T21:41:24.1365216Z 
2023-01-11T21:41:24.1365272Z def call(args):
2023-01-11T21:41:24.1365379Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1365449Z     args.clear()
2023-01-11T21:41:24.1365643Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1365804Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1365870Z     del arg0_1
2023-01-11T21:41:24.1365934Z     del arg1_1
2023-01-11T21:41:24.1365993Z     return (buf0, )
2023-01-11T21:41:24.1365998Z 
2023-01-11T21:41:24.1366002Z 
2023-01-11T21:41:24.1366077Z if __name__ == "__main__":
2023-01-11T21:41:24.1366188Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1366308Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1366499Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1366688Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1366804Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1366839Z 
2023-01-11T21:41:24.1366904Z ok (0.021s)
2023-01-11T21:41:24.1367390Z   test_cpu_broadcast1_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1367515Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1367773Z [2023-01-11 21:39:57,866] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 524
2023-01-11T21:41:24.1368037Z [2023-01-11 21:39:59,385] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 524
2023-01-11T21:41:24.1368042Z 
2023-01-11T21:41:24.1368134Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1368205Z import torch
2023-01-11T21:41:24.1368273Z import random
2023-01-11T21:41:24.1368383Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1368499Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1368504Z 
2023-01-11T21:41:24.1368568Z aten = torch.ops.aten
2023-01-11T21:41:24.1368699Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1368788Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1368793Z 
2023-01-11T21:41:24.1368798Z 
2023-01-11T21:41:24.1368931Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1369132Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1369246Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1369347Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1369442Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1369490Z {
2023-01-11T21:41:24.1369592Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1369651Z     {
2023-01-11T21:41:24.1369726Z         #pragma omp for 
2023-01-11T21:41:24.1369805Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1369868Z         {
2023-01-11T21:41:24.1369948Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1369998Z             {
2023-01-11T21:41:24.1370128Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:24.1370253Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[i0]);
2023-01-11T21:41:24.1370339Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1370439Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1370499Z             }
2023-01-11T21:41:24.1370588Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1370658Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1370718Z             {
2023-01-11T21:41:24.1370800Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1370913Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1370996Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1371085Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1371146Z             }
2023-01-11T21:41:24.1371197Z         }
2023-01-11T21:41:24.1371258Z     }
2023-01-11T21:41:24.1371316Z }
2023-01-11T21:41:24.1371393Z ''')
2023-01-11T21:41:24.1371398Z 
2023-01-11T21:41:24.1371402Z 
2023-01-11T21:41:24.1371489Z async_compile.wait(globals())
2023-01-11T21:41:24.1371560Z del async_compile
2023-01-11T21:41:24.1371565Z 
2023-01-11T21:41:24.1371632Z def call(args):
2023-01-11T21:41:24.1371693Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1371764Z     args.clear()
2023-01-11T21:41:24.1371974Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1372131Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1372195Z     del arg0_1
2023-01-11T21:41:24.1372288Z     del arg1_1
2023-01-11T21:41:24.1372358Z     return (buf0, )
2023-01-11T21:41:24.1372362Z 
2023-01-11T21:41:24.1372366Z 
2023-01-11T21:41:24.1372443Z if __name__ == "__main__":
2023-01-11T21:41:24.1372544Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1372665Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1372860Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1373061Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1373173Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1373178Z 
2023-01-11T21:41:24.1373242Z ok (1.533s)
2023-01-11T21:41:24.1373738Z   test_cpu_broadcast1_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1373865Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1374123Z [2023-01-11 21:39:59,400] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 525
2023-01-11T21:41:24.1374376Z [2023-01-11 21:40:00,925] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 525
2023-01-11T21:41:24.1374381Z 
2023-01-11T21:41:24.1374471Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1374542Z import torch
2023-01-11T21:41:24.1374612Z import random
2023-01-11T21:41:24.1374724Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1374842Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1374847Z 
2023-01-11T21:41:24.1374922Z aten = torch.ops.aten
2023-01-11T21:41:24.1375055Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1375135Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1375141Z 
2023-01-11T21:41:24.1375145Z 
2023-01-11T21:41:24.1375275Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1375477Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1375594Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1375697Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1375793Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1375854Z {
2023-01-11T21:41:24.1375937Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1375995Z     {
2023-01-11T21:41:24.1376070Z         #pragma omp for 
2023-01-11T21:41:24.1376149Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1376211Z         {
2023-01-11T21:41:24.1376344Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1376518Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:24.1376636Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1376746Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1376830Z         }
2023-01-11T21:41:24.1376927Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1377008Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1377070Z         {
2023-01-11T21:41:24.1377153Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1377223Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.1377305Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1377383Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1377445Z         }
2023-01-11T21:41:24.1377506Z     }
2023-01-11T21:41:24.1377565Z }
2023-01-11T21:41:24.1377645Z ''')
2023-01-11T21:41:24.1377651Z 
2023-01-11T21:41:24.1377657Z 
2023-01-11T21:41:24.1377733Z async_compile.wait(globals())
2023-01-11T21:41:24.1377807Z del async_compile
2023-01-11T21:41:24.1377815Z 
2023-01-11T21:41:24.1377920Z def call(args):
2023-01-11T21:41:24.1377995Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1378064Z     args.clear()
2023-01-11T21:41:24.1378257Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1378417Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1378484Z     del arg0_1
2023-01-11T21:41:24.1378539Z     del arg1_1
2023-01-11T21:41:24.1378609Z     return (buf0, )
2023-01-11T21:41:24.1378615Z 
2023-01-11T21:41:24.1378619Z 
2023-01-11T21:41:24.1378694Z if __name__ == "__main__":
2023-01-11T21:41:24.1378804Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1378925Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1379117Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1379307Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1379413Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1379429Z 
2023-01-11T21:41:24.1379484Z ok (1.540s)
2023-01-11T21:41:24.1379977Z   test_cpu_broadcast1_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1380104Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1380364Z [2023-01-11 21:40:00,941] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 526
2023-01-11T21:41:24.1380628Z [2023-01-11 21:40:02,454] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 526
2023-01-11T21:41:24.1380633Z 
2023-01-11T21:41:24.1380733Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1380802Z import torch
2023-01-11T21:41:24.1380870Z import random
2023-01-11T21:41:24.1380983Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1381088Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1381093Z 
2023-01-11T21:41:24.1381169Z aten = torch.ops.aten
2023-01-11T21:41:24.1381299Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1381387Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1381392Z 
2023-01-11T21:41:24.1381396Z 
2023-01-11T21:41:24.1381526Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1381728Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1381847Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1381951Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1382037Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1382127Z {
2023-01-11T21:41:24.1382224Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1382284Z     {
2023-01-11T21:41:24.1382462Z         #pragma omp for 
2023-01-11T21:41:24.1382545Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1382606Z         {
2023-01-11T21:41:24.1382676Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1382736Z             {
2023-01-11T21:41:24.1382870Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:24.1383013Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1383100Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1383204Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1383266Z             }
2023-01-11T21:41:24.1383343Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1383426Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1383489Z             {
2023-01-11T21:41:24.1383625Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1383720Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1383802Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1383897Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1383947Z             }
2023-01-11T21:41:24.1384008Z         }
2023-01-11T21:41:24.1384069Z     }
2023-01-11T21:41:24.1384129Z }
2023-01-11T21:41:24.1384208Z ''')
2023-01-11T21:41:24.1384213Z 
2023-01-11T21:41:24.1384217Z 
2023-01-11T21:41:24.1384305Z async_compile.wait(globals())
2023-01-11T21:41:24.1384375Z del async_compile
2023-01-11T21:41:24.1384380Z 
2023-01-11T21:41:24.1384436Z def call(args):
2023-01-11T21:41:24.1384509Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1384577Z     args.clear()
2023-01-11T21:41:24.1384775Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1384943Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1385013Z     del arg0_1
2023-01-11T21:41:24.1385080Z     del arg1_1
2023-01-11T21:41:24.1385137Z     return (buf0, )
2023-01-11T21:41:24.1385154Z 
2023-01-11T21:41:24.1385158Z 
2023-01-11T21:41:24.1385219Z if __name__ == "__main__":
2023-01-11T21:41:24.1385329Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1385448Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1385641Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1385840Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1385951Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1385956Z 
2023-01-11T21:41:24.1386020Z ok (1.528s)
2023-01-11T21:41:24.1386512Z   test_cpu_broadcast1_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1386638Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1386887Z [2023-01-11 21:40:02,468] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 527
2023-01-11T21:41:24.1387152Z [2023-01-11 21:40:04,013] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 527
2023-01-11T21:41:24.1387157Z 
2023-01-11T21:41:24.1387271Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1387369Z import torch
2023-01-11T21:41:24.1387467Z import random
2023-01-11T21:41:24.1387620Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1387766Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1387772Z 
2023-01-11T21:41:24.1387902Z aten = torch.ops.aten
2023-01-11T21:41:24.1388026Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1388115Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1388120Z 
2023-01-11T21:41:24.1388125Z 
2023-01-11T21:41:24.1388264Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1388469Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1388585Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1388690Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1388788Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1388848Z {
2023-01-11T21:41:24.1388932Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1388993Z     {
2023-01-11T21:41:24.1389069Z         #pragma omp for 
2023-01-11T21:41:24.1389150Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1389211Z         {
2023-01-11T21:41:24.1389290Z             #pragma GCC ivdep
2023-01-11T21:41:24.1389396Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1389458Z             {
2023-01-11T21:41:24.1389521Z                 {
2023-01-11T21:41:24.1389587Z                     {
2023-01-11T21:41:24.1389681Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1389781Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1389891Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.1389970Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1390063Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1390128Z                     }
2023-01-11T21:41:24.1390194Z                 }
2023-01-11T21:41:24.1390255Z             }
2023-01-11T21:41:24.1390316Z         }
2023-01-11T21:41:24.1390382Z     }
2023-01-11T21:41:24.1390429Z }
2023-01-11T21:41:24.1390506Z ''')
2023-01-11T21:41:24.1390511Z 
2023-01-11T21:41:24.1390515Z 
2023-01-11T21:41:24.1390605Z async_compile.wait(globals())
2023-01-11T21:41:24.1390680Z del async_compile
2023-01-11T21:41:24.1390685Z 
2023-01-11T21:41:24.1390754Z def call(args):
2023-01-11T21:41:24.1390828Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1390900Z     args.clear()
2023-01-11T21:41:24.1391086Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1391248Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1391314Z     del arg0_1
2023-01-11T21:41:24.1391379Z     del arg1_1
2023-01-11T21:41:24.1391448Z     return (buf0, )
2023-01-11T21:41:24.1391452Z 
2023-01-11T21:41:24.1391456Z 
2023-01-11T21:41:24.1391531Z if __name__ == "__main__":
2023-01-11T21:41:24.1391642Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1391762Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1391942Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1392145Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1392257Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1392261Z 
2023-01-11T21:41:24.1392327Z ok (1.560s)
2023-01-11T21:41:24.1392814Z   test_cpu_broadcast1_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1392937Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1393195Z [2023-01-11 21:40:04,028] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 528
2023-01-11T21:41:24.1393464Z [2023-01-11 21:40:05,670] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 528
2023-01-11T21:41:24.1393500Z 
2023-01-11T21:41:24.1393592Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1393659Z import torch
2023-01-11T21:41:24.1393715Z import random
2023-01-11T21:41:24.1393885Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1394004Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1394009Z 
2023-01-11T21:41:24.1394084Z aten = torch.ops.aten
2023-01-11T21:41:24.1394216Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1394305Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1394310Z 
2023-01-11T21:41:24.1394314Z 
2023-01-11T21:41:24.1394452Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1394642Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1394758Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1394857Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1394988Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1395048Z {
2023-01-11T21:41:24.1395144Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1395205Z     {
2023-01-11T21:41:24.1395268Z         #pragma omp for 
2023-01-11T21:41:24.1395348Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1395407Z         {
2023-01-11T21:41:24.1395468Z             {
2023-01-11T21:41:24.1395529Z                 {
2023-01-11T21:41:24.1395619Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1395706Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1395800Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:24.1395888Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1395971Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1396033Z                 }
2023-01-11T21:41:24.1396094Z             }
2023-01-11T21:41:24.1396153Z         }
2023-01-11T21:41:24.1396214Z     }
2023-01-11T21:41:24.1396263Z }
2023-01-11T21:41:24.1396340Z ''')
2023-01-11T21:41:24.1396345Z 
2023-01-11T21:41:24.1396349Z 
2023-01-11T21:41:24.1396434Z async_compile.wait(globals())
2023-01-11T21:41:24.1396504Z del async_compile
2023-01-11T21:41:24.1396509Z 
2023-01-11T21:41:24.1396577Z def call(args):
2023-01-11T21:41:24.1396649Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1396716Z     args.clear()
2023-01-11T21:41:24.1396896Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1397054Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1397121Z     del arg0_1
2023-01-11T21:41:24.1397185Z     del arg1_1
2023-01-11T21:41:24.1397254Z     return (buf0, )
2023-01-11T21:41:24.1397259Z 
2023-01-11T21:41:24.1397263Z 
2023-01-11T21:41:24.1397337Z if __name__ == "__main__":
2023-01-11T21:41:24.1397448Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1397570Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1397752Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1397940Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1398098Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1398107Z 
2023-01-11T21:41:24.1398190Z ok (1.657s)
2023-01-11T21:41:24.1398685Z   test_cpu_broadcast1_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1398813Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1399079Z [2023-01-11 21:40:05,687] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 529
2023-01-11T21:41:24.1399396Z [2023-01-11 21:40:07,278] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 529
2023-01-11T21:41:24.1399401Z 
2023-01-11T21:41:24.1399495Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1399562Z import torch
2023-01-11T21:41:24.1399620Z import random
2023-01-11T21:41:24.1399730Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1399848Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1399853Z 
2023-01-11T21:41:24.1399930Z aten = torch.ops.aten
2023-01-11T21:41:24.1400063Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1400153Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1400158Z 
2023-01-11T21:41:24.1400162Z 
2023-01-11T21:41:24.1400294Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1400498Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1400670Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1400776Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1400873Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1400932Z {
2023-01-11T21:41:24.1401027Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1401088Z     {
2023-01-11T21:41:24.1401163Z         #pragma omp for 
2023-01-11T21:41:24.1401233Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1401293Z         {
2023-01-11T21:41:24.1401371Z             #pragma GCC ivdep
2023-01-11T21:41:24.1401455Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1401520Z             {
2023-01-11T21:41:24.1401585Z                 {
2023-01-11T21:41:24.1401639Z                     {
2023-01-11T21:41:24.1401733Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1401838Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1401936Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1402032Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1402095Z                     }
2023-01-11T21:41:24.1402160Z                 }
2023-01-11T21:41:24.1402208Z             }
2023-01-11T21:41:24.1402271Z         }
2023-01-11T21:41:24.1402332Z     }
2023-01-11T21:41:24.1402392Z }
2023-01-11T21:41:24.1402470Z ''')
2023-01-11T21:41:24.1402475Z 
2023-01-11T21:41:24.1402479Z 
2023-01-11T21:41:24.1402566Z async_compile.wait(globals())
2023-01-11T21:41:24.1402635Z del async_compile
2023-01-11T21:41:24.1402640Z 
2023-01-11T21:41:24.1402696Z def call(args):
2023-01-11T21:41:24.1402769Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1402838Z     args.clear()
2023-01-11T21:41:24.1403036Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1403193Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1403262Z     del arg0_1
2023-01-11T21:41:24.1403328Z     del arg1_1
2023-01-11T21:41:24.1403398Z     return (buf0, )
2023-01-11T21:41:24.1403418Z 
2023-01-11T21:41:24.1403424Z 
2023-01-11T21:41:24.1403514Z if __name__ == "__main__":
2023-01-11T21:41:24.1403664Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1403831Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1404053Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1404376Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1404649Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1404659Z 
2023-01-11T21:41:24.1404790Z ok (1.608s)
2023-01-11T21:41:24.1405370Z   test_cpu_broadcast1_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1405548Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1405829Z [2023-01-11 21:40:07,293] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 530
2023-01-11T21:41:24.1406119Z [2023-01-11 21:40:08,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 530
2023-01-11T21:41:24.1406125Z 
2023-01-11T21:41:24.1406227Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1406295Z import torch
2023-01-11T21:41:24.1406352Z import random
2023-01-11T21:41:24.1406462Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1406580Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1406585Z 
2023-01-11T21:41:24.1406660Z aten = torch.ops.aten
2023-01-11T21:41:24.1406890Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1406985Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1406990Z 
2023-01-11T21:41:24.1406994Z 
2023-01-11T21:41:24.1407129Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1407333Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1407439Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1407579Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1407695Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1407756Z {
2023-01-11T21:41:24.1407853Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1407913Z     {
2023-01-11T21:41:24.1407988Z         #pragma omp for 
2023-01-11T21:41:24.1408057Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1408118Z         {
2023-01-11T21:41:24.1408198Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1408270Z             {
2023-01-11T21:41:24.1408460Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:24.1408644Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1408731Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1408835Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1408886Z             }
2023-01-11T21:41:24.1408975Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1409058Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1409149Z             {
2023-01-11T21:41:24.1409241Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1409336Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1409407Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1409499Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1409558Z             }
2023-01-11T21:41:24.1409620Z         }
2023-01-11T21:41:24.1409687Z     }
2023-01-11T21:41:24.1409748Z }
2023-01-11T21:41:24.1409828Z ''')
2023-01-11T21:41:24.1409834Z 
2023-01-11T21:41:24.1409838Z 
2023-01-11T21:41:24.1409923Z async_compile.wait(globals())
2023-01-11T21:41:24.1409982Z del async_compile
2023-01-11T21:41:24.1409987Z 
2023-01-11T21:41:24.1410056Z def call(args):
2023-01-11T21:41:24.1410130Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1410198Z     args.clear()
2023-01-11T21:41:24.1410400Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1410559Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1410625Z     del arg0_1
2023-01-11T21:41:24.1410677Z     del arg1_1
2023-01-11T21:41:24.1410746Z     return (buf0, )
2023-01-11T21:41:24.1410751Z 
2023-01-11T21:41:24.1410755Z 
2023-01-11T21:41:24.1410828Z if __name__ == "__main__":
2023-01-11T21:41:24.1410939Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1411062Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1411290Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1411486Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1411598Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1411603Z 
2023-01-11T21:41:24.1411656Z ok (1.539s)
2023-01-11T21:41:24.1412154Z   test_cpu_broadcast2_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1412281Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1412569Z [2023-01-11 21:40:08,833] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 531
2023-01-11T21:41:24.1412836Z [2023-01-11 21:40:10,346] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 531
2023-01-11T21:41:24.1412842Z 
2023-01-11T21:41:24.1412934Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1413001Z import torch
2023-01-11T21:41:24.1413070Z import random
2023-01-11T21:41:24.1413183Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1413289Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1413295Z 
2023-01-11T21:41:24.1413373Z aten = torch.ops.aten
2023-01-11T21:41:24.1413503Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1413593Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1413598Z 
2023-01-11T21:41:24.1413602Z 
2023-01-11T21:41:24.1413736Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1413940Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1414060Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1414160Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1414246Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1414306Z {
2023-01-11T21:41:24.1414404Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1414466Z     {
2023-01-11T21:41:24.1414543Z         #pragma omp for 
2023-01-11T21:41:24.1414623Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1414687Z         {
2023-01-11T21:41:24.1414757Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1414823Z             {
2023-01-11T21:41:24.1414949Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:24.1415083Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:24.1415170Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1415274Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1415339Z             }
2023-01-11T21:41:24.1415418Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1415499Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1415561Z             {
2023-01-11T21:41:24.1415645Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1415727Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1415809Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1415897Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1415947Z             }
2023-01-11T21:41:24.1416008Z         }
2023-01-11T21:41:24.1416068Z     }
2023-01-11T21:41:24.1416129Z }
2023-01-11T21:41:24.1416204Z ''')
2023-01-11T21:41:24.1416209Z 
2023-01-11T21:41:24.1416214Z 
2023-01-11T21:41:24.1416301Z async_compile.wait(globals())
2023-01-11T21:41:24.1416372Z del async_compile
2023-01-11T21:41:24.1416377Z 
2023-01-11T21:41:24.1416433Z def call(args):
2023-01-11T21:41:24.1416505Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1416605Z     args.clear()
2023-01-11T21:41:24.1416813Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1416972Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1417037Z     del arg0_1
2023-01-11T21:41:24.1417101Z     del arg1_1
2023-01-11T21:41:24.1417160Z     return (buf0, )
2023-01-11T21:41:24.1417177Z 
2023-01-11T21:41:24.1417181Z 
2023-01-11T21:41:24.1417242Z if __name__ == "__main__":
2023-01-11T21:41:24.1417354Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1417477Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1417681Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1417871Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1417984Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1417991Z 
2023-01-11T21:41:24.1418093Z ok (1.530s)
2023-01-11T21:41:24.1418589Z   test_cpu_broadcast2_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1418715Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1418966Z [2023-01-11 21:40:10,367] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 532
2023-01-11T21:41:24.1419232Z [2023-01-11 21:40:10,379] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 532
2023-01-11T21:41:24.1419237Z 
2023-01-11T21:41:24.1419329Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1419398Z import torch
2023-01-11T21:41:24.1419478Z import random
2023-01-11T21:41:24.1419590Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1419708Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1419713Z 
2023-01-11T21:41:24.1419788Z aten = torch.ops.aten
2023-01-11T21:41:24.1419910Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1420001Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1420006Z 
2023-01-11T21:41:24.1420011Z 
2023-01-11T21:41:24.1420143Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1420345Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1420463Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1420565Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1420662Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1420722Z {
2023-01-11T21:41:24.1420811Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1420873Z     {
2023-01-11T21:41:24.1420948Z         #pragma omp for 
2023-01-11T21:41:24.1421027Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1421089Z         {
2023-01-11T21:41:24.1421218Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1421346Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1421419Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1421505Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1421566Z         }
2023-01-11T21:41:24.1421659Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1421739Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1421802Z         {
2023-01-11T21:41:24.1421884Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1421952Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1422030Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1422109Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1422198Z         }
2023-01-11T21:41:24.1422257Z     }
2023-01-11T21:41:24.1422315Z }
2023-01-11T21:41:24.1422555Z ''')
2023-01-11T21:41:24.1422562Z 
2023-01-11T21:41:24.1422583Z 
2023-01-11T21:41:24.1422682Z async_compile.wait(globals())
2023-01-11T21:41:24.1422780Z del async_compile
2023-01-11T21:41:24.1422786Z 
2023-01-11T21:41:24.1422877Z def call(args):
2023-01-11T21:41:24.1422970Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1423059Z     args.clear()
2023-01-11T21:41:24.1423272Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1423433Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1423489Z     del arg0_1
2023-01-11T21:41:24.1423554Z     del arg1_1
2023-01-11T21:41:24.1423627Z     return (buf0, )
2023-01-11T21:41:24.1423632Z 
2023-01-11T21:41:24.1423636Z 
2023-01-11T21:41:24.1423709Z if __name__ == "__main__":
2023-01-11T21:41:24.1423896Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1424020Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1424227Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1424424Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1424524Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1424529Z 
2023-01-11T21:41:24.1424594Z ok (0.032s)
2023-01-11T21:41:24.1425088Z   test_cpu_broadcast2_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1425215Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1425477Z [2023-01-11 21:40:10,397] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 533
2023-01-11T21:41:24.1425741Z [2023-01-11 21:40:10,405] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 533
2023-01-11T21:41:24.1425746Z 
2023-01-11T21:41:24.1425838Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1425908Z import torch
2023-01-11T21:41:24.1425977Z import random
2023-01-11T21:41:24.1426077Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1426194Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1426198Z 
2023-01-11T21:41:24.1426276Z aten = torch.ops.aten
2023-01-11T21:41:24.1426407Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1426497Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1426502Z 
2023-01-11T21:41:24.1426506Z 
2023-01-11T21:41:24.1426638Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1426842Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1426959Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1427049Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1427146Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1427206Z {
2023-01-11T21:41:24.1427301Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1427361Z     {
2023-01-11T21:41:24.1427435Z         #pragma omp for 
2023-01-11T21:41:24.1427515Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1427563Z         {
2023-01-11T21:41:24.1427697Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1427819Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:24.1427902Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1427991Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1428094Z         }
2023-01-11T21:41:24.1428187Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1428254Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1428314Z         {
2023-01-11T21:41:24.1428393Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1428473Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.1428552Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1428629Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1428689Z         }
2023-01-11T21:41:24.1428735Z     }
2023-01-11T21:41:24.1428793Z }
2023-01-11T21:41:24.1428870Z ''')
2023-01-11T21:41:24.1428875Z 
2023-01-11T21:41:24.1428879Z 
2023-01-11T21:41:24.1428966Z async_compile.wait(globals())
2023-01-11T21:41:24.1429034Z del async_compile
2023-01-11T21:41:24.1429038Z 
2023-01-11T21:41:24.1429108Z def call(args):
2023-01-11T21:41:24.1429182Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1429239Z     args.clear()
2023-01-11T21:41:24.1429476Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1429641Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1429708Z     del arg0_1
2023-01-11T21:41:24.1429773Z     del arg1_1
2023-01-11T21:41:24.1429843Z     return (buf0, )
2023-01-11T21:41:24.1429848Z 
2023-01-11T21:41:24.1429852Z 
2023-01-11T21:41:24.1429923Z if __name__ == "__main__":
2023-01-11T21:41:24.1430036Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1430145Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1430348Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1430539Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1430651Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1430656Z 
2023-01-11T21:41:24.1430719Z ok (0.025s)
2023-01-11T21:41:24.1431217Z   test_cpu_broadcast2_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1431345Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1431605Z [2023-01-11 21:40:10,419] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 534
2023-01-11T21:41:24.1431871Z [2023-01-11 21:40:10,427] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 534
2023-01-11T21:41:24.1431876Z 
2023-01-11T21:41:24.1431968Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1432025Z import torch
2023-01-11T21:41:24.1432092Z import random
2023-01-11T21:41:24.1432208Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1432332Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1432337Z 
2023-01-11T21:41:24.1432413Z aten = torch.ops.aten
2023-01-11T21:41:24.1432545Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1432635Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1432639Z 
2023-01-11T21:41:24.1432643Z 
2023-01-11T21:41:24.1432763Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1432966Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1433082Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1433185Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1433284Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1433345Z {
2023-01-11T21:41:24.1433441Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1433500Z     {
2023-01-11T21:41:24.1433564Z         #pragma omp for 
2023-01-11T21:41:24.1433679Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1433805Z         {
2023-01-11T21:41:24.1433894Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1433956Z             {
2023-01-11T21:41:24.1434083Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:24.1434222Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1434296Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1434396Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1434458Z             }
2023-01-11T21:41:24.1434548Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1434629Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1434692Z             {
2023-01-11T21:41:24.1434776Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1434859Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1434946Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1435085Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1435147Z             }
2023-01-11T21:41:24.1435207Z         }
2023-01-11T21:41:24.1435267Z     }
2023-01-11T21:41:24.1435622Z }
2023-01-11T21:41:24.1435750Z ''')
2023-01-11T21:41:24.1435756Z 
2023-01-11T21:41:24.1435761Z 
2023-01-11T21:41:24.1435854Z async_compile.wait(globals())
2023-01-11T21:41:24.1435936Z del async_compile
2023-01-11T21:41:24.1435940Z 
2023-01-11T21:41:24.1436012Z def call(args):
2023-01-11T21:41:24.1436094Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1436166Z     args.clear()
2023-01-11T21:41:24.1436378Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1436534Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1436603Z     del arg0_1
2023-01-11T21:41:24.1436672Z     del arg1_1
2023-01-11T21:41:24.1436742Z     return (buf0, )
2023-01-11T21:41:24.1436747Z 
2023-01-11T21:41:24.1436754Z 
2023-01-11T21:41:24.1436831Z if __name__ == "__main__":
2023-01-11T21:41:24.1436940Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1437067Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1437273Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1437459Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1437572Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1437577Z 
2023-01-11T21:41:24.1437643Z ok (0.022s)
2023-01-11T21:41:24.1438138Z   test_cpu_broadcast2_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1438267Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1438528Z [2023-01-11 21:40:10,441] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 535
2023-01-11T21:41:24.1438796Z [2023-01-11 21:40:11,954] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 535
2023-01-11T21:41:24.1438801Z 
2023-01-11T21:41:24.1438896Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1438966Z import torch
2023-01-11T21:41:24.1439024Z import random
2023-01-11T21:41:24.1439139Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1439259Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1439264Z 
2023-01-11T21:41:24.1439341Z aten = torch.ops.aten
2023-01-11T21:41:24.1439476Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1439567Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1439642Z 
2023-01-11T21:41:24.1439649Z 
2023-01-11T21:41:24.1439783Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1439991Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1440099Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1440207Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1440305Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1440370Z {
2023-01-11T21:41:24.1440467Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1440533Z     {
2023-01-11T21:41:24.1440611Z         #pragma omp for 
2023-01-11T21:41:24.1440680Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1440742Z         {
2023-01-11T21:41:24.1440824Z             #pragma GCC ivdep
2023-01-11T21:41:24.1440911Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1440974Z             {
2023-01-11T21:41:24.1441039Z                 {
2023-01-11T21:41:24.1441106Z                     {
2023-01-11T21:41:24.1441230Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1441333Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1441444Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.1441536Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1441632Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1441700Z                     }
2023-01-11T21:41:24.1441765Z                 }
2023-01-11T21:41:24.1441817Z             }
2023-01-11T21:41:24.1441878Z         }
2023-01-11T21:41:24.1441939Z     }
2023-01-11T21:41:24.1442000Z }
2023-01-11T21:41:24.1442082Z ''')
2023-01-11T21:41:24.1442086Z 
2023-01-11T21:41:24.1442090Z 
2023-01-11T21:41:24.1442179Z async_compile.wait(globals())
2023-01-11T21:41:24.1442248Z del async_compile
2023-01-11T21:41:24.1442253Z 
2023-01-11T21:41:24.1442309Z def call(args):
2023-01-11T21:41:24.1442382Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1442456Z     args.clear()
2023-01-11T21:41:24.1442667Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1442830Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1442897Z     del arg0_1
2023-01-11T21:41:24.1442961Z     del arg1_1
2023-01-11T21:41:24.1443018Z     return (buf0, )
2023-01-11T21:41:24.1443023Z 
2023-01-11T21:41:24.1443039Z 
2023-01-11T21:41:24.1443101Z if __name__ == "__main__":
2023-01-11T21:41:24.1443212Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1443337Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1443544Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1443741Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1443853Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1443861Z 
2023-01-11T21:41:24.1443927Z ok (1.527s)
2023-01-11T21:41:24.1444413Z   test_cpu_broadcast2_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1444537Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1444786Z [2023-01-11 21:40:11,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 536
2023-01-11T21:41:24.1445051Z [2023-01-11 21:40:13,493] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 536
2023-01-11T21:41:24.1445056Z 
2023-01-11T21:41:24.1445149Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1445219Z import torch
2023-01-11T21:41:24.1445326Z import random
2023-01-11T21:41:24.1445439Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1445558Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1445563Z 
2023-01-11T21:41:24.1445638Z aten = torch.ops.aten
2023-01-11T21:41:24.1445759Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1445848Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1445852Z 
2023-01-11T21:41:24.1445857Z 
2023-01-11T21:41:24.1445990Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1446192Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1446310Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1446410Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1446512Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1446573Z {
2023-01-11T21:41:24.1446659Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1446749Z     {
2023-01-11T21:41:24.1446828Z         #pragma omp for 
2023-01-11T21:41:24.1446907Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1446970Z         {
2023-01-11T21:41:24.1447051Z             #pragma GCC ivdep
2023-01-11T21:41:24.1447123Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1447186Z             {
2023-01-11T21:41:24.1447248Z                 {
2023-01-11T21:41:24.1447315Z                     {
2023-01-11T21:41:24.1447411Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1447506Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1447617Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:24.1447696Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1447790Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1447854Z                     }
2023-01-11T21:41:24.1447918Z                 }
2023-01-11T21:41:24.1447979Z             }
2023-01-11T21:41:24.1448048Z         }
2023-01-11T21:41:24.1448108Z     }
2023-01-11T21:41:24.1448159Z }
2023-01-11T21:41:24.1448237Z ''')
2023-01-11T21:41:24.1448242Z 
2023-01-11T21:41:24.1448246Z 
2023-01-11T21:41:24.1448336Z async_compile.wait(globals())
2023-01-11T21:41:24.1448407Z del async_compile
2023-01-11T21:41:24.1448411Z 
2023-01-11T21:41:24.1448479Z def call(args):
2023-01-11T21:41:24.1448552Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1448623Z     args.clear()
2023-01-11T21:41:24.1448819Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1448982Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1449049Z     del arg0_1
2023-01-11T21:41:24.1449113Z     del arg1_1
2023-01-11T21:41:24.1449186Z     return (buf0, )
2023-01-11T21:41:24.1449190Z 
2023-01-11T21:41:24.1449194Z 
2023-01-11T21:41:24.1449268Z if __name__ == "__main__":
2023-01-11T21:41:24.1449382Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1449508Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1449700Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1449891Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1450005Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1450009Z 
2023-01-11T21:41:24.1450074Z ok (1.539s)
2023-01-11T21:41:24.1450571Z   test_cpu_broadcast2_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1450696Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1450989Z [2023-01-11 21:40:13,509] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 537
2023-01-11T21:41:24.1451254Z [2023-01-11 21:40:15,036] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 537
2023-01-11T21:41:24.1451259Z 
2023-01-11T21:41:24.1451352Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1451419Z import torch
2023-01-11T21:41:24.1451476Z import random
2023-01-11T21:41:24.1451589Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1451708Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1451713Z 
2023-01-11T21:41:24.1451792Z aten = torch.ops.aten
2023-01-11T21:41:24.1451923Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1452013Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1452018Z 
2023-01-11T21:41:24.1452022Z 
2023-01-11T21:41:24.1452152Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1452384Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1452503Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1452606Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1452706Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1452768Z {
2023-01-11T21:41:24.1452864Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1452924Z     {
2023-01-11T21:41:24.1452986Z         #pragma omp for 
2023-01-11T21:41:24.1453066Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1453127Z         {
2023-01-11T21:41:24.1453205Z             #pragma GCC ivdep
2023-01-11T21:41:24.1453287Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1453349Z             {
2023-01-11T21:41:24.1453412Z                 {
2023-01-11T21:41:24.1453465Z                     {
2023-01-11T21:41:24.1453560Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1453668Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1453765Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1453859Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1453924Z                     }
2023-01-11T21:41:24.1453987Z                 }
2023-01-11T21:41:24.1454040Z             }
2023-01-11T21:41:24.1454103Z         }
2023-01-11T21:41:24.1454162Z     }
2023-01-11T21:41:24.1454222Z }
2023-01-11T21:41:24.1454301Z ''')
2023-01-11T21:41:24.1454306Z 
2023-01-11T21:41:24.1454311Z 
2023-01-11T21:41:24.1454399Z async_compile.wait(globals())
2023-01-11T21:41:24.1454468Z del async_compile
2023-01-11T21:41:24.1454472Z 
2023-01-11T21:41:24.1454528Z def call(args):
2023-01-11T21:41:24.1454601Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1454671Z     args.clear()
2023-01-11T21:41:24.1454877Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1455043Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1455112Z     del arg0_1
2023-01-11T21:41:24.1455177Z     del arg1_1
2023-01-11T21:41:24.1455235Z     return (buf0, )
2023-01-11T21:41:24.1455251Z 
2023-01-11T21:41:24.1455255Z 
2023-01-11T21:41:24.1455319Z if __name__ == "__main__":
2023-01-11T21:41:24.1455432Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1455557Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1455767Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1455970Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1456085Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1456091Z 
2023-01-11T21:41:24.1456156Z ok (1.543s)
2023-01-11T21:41:24.1456659Z   test_cpu_broadcast2_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1456833Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1457081Z [2023-01-11 21:40:15,052] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 538
2023-01-11T21:41:24.1457343Z [2023-01-11 21:40:15,061] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 538
2023-01-11T21:41:24.1457348Z 
2023-01-11T21:41:24.1457440Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1457508Z import torch
2023-01-11T21:41:24.1457578Z import random
2023-01-11T21:41:24.1457691Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1457810Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1457849Z 
2023-01-11T21:41:24.1457927Z aten = torch.ops.aten
2023-01-11T21:41:24.1458047Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1458136Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1458141Z 
2023-01-11T21:41:24.1458145Z 
2023-01-11T21:41:24.1458276Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1458479Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1458595Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1458701Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1458799Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1458859Z {
2023-01-11T21:41:24.1458944Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1459003Z     {
2023-01-11T21:41:24.1459078Z         #pragma omp for 
2023-01-11T21:41:24.1459160Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1459224Z         {
2023-01-11T21:41:24.1459308Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1459359Z             {
2023-01-11T21:41:24.1459498Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:24.1459633Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1459719Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1459822Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1459883Z             }
2023-01-11T21:41:24.1459971Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1460052Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1460101Z             {
2023-01-11T21:41:24.1460184Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1460278Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1460359Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1460449Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1460516Z             }
2023-01-11T21:41:24.1460576Z         }
2023-01-11T21:41:24.1460624Z     }
2023-01-11T21:41:24.1460682Z }
2023-01-11T21:41:24.1460758Z ''')
2023-01-11T21:41:24.1460763Z 
2023-01-11T21:41:24.1460768Z 
2023-01-11T21:41:24.1460853Z async_compile.wait(globals())
2023-01-11T21:41:24.1460925Z del async_compile
2023-01-11T21:41:24.1460930Z 
2023-01-11T21:41:24.1461002Z def call(args):
2023-01-11T21:41:24.1461073Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1461130Z     args.clear()
2023-01-11T21:41:24.1461340Z     buf0 = empty_strided((1, 10, 10), (100, 1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1461507Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1461575Z     del arg0_1
2023-01-11T21:41:24.1461637Z     del arg1_1
2023-01-11T21:41:24.1461708Z     return (buf0, )
2023-01-11T21:41:24.1461712Z 
2023-01-11T21:41:24.1461721Z 
2023-01-11T21:41:24.1461793Z if __name__ == "__main__":
2023-01-11T21:41:24.1461943Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1462055Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1462260Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1462586Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1462702Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1462707Z 
2023-01-11T21:41:24.1462773Z ok (0.024s)
2023-01-11T21:41:24.1463268Z   test_cpu_broadcast3_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1463463Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1463728Z [2023-01-11 21:40:15,075] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 539
2023-01-11T21:41:24.1463991Z [2023-01-11 21:40:16,614] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 539
2023-01-11T21:41:24.1463996Z 
2023-01-11T21:41:24.1464077Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1464143Z import torch
2023-01-11T21:41:24.1464211Z import random
2023-01-11T21:41:24.1464325Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1464444Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1464449Z 
2023-01-11T21:41:24.1464528Z aten = torch.ops.aten
2023-01-11T21:41:24.1464660Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1464749Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1464754Z 
2023-01-11T21:41:24.1464758Z 
2023-01-11T21:41:24.1464885Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1465087Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1465204Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1465308Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1465409Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1465471Z {
2023-01-11T21:41:24.1465566Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1465614Z     {
2023-01-11T21:41:24.1465690Z         #pragma omp for 
2023-01-11T21:41:24.1465770Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1465835Z         {
2023-01-11T21:41:24.1465958Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:24.1466086Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1466168Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1466261Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1466314Z         }
2023-01-11T21:41:24.1466408Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1466488Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1466550Z         {
2023-01-11T21:41:24.1466630Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1466712Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1466781Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1466862Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1466926Z         }
2023-01-11T21:41:24.1466988Z     }
2023-01-11T21:41:24.1467047Z }
2023-01-11T21:41:24.1467123Z ''')
2023-01-11T21:41:24.1467127Z 
2023-01-11T21:41:24.1467132Z 
2023-01-11T21:41:24.1467220Z async_compile.wait(globals())
2023-01-11T21:41:24.1467280Z del async_compile
2023-01-11T21:41:24.1467296Z 
2023-01-11T21:41:24.1467354Z def call(args):
2023-01-11T21:41:24.1467427Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1467502Z     args.clear()
2023-01-11T21:41:24.1467699Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1467908Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1467974Z     del arg0_1
2023-01-11T21:41:24.1468041Z     del arg1_1
2023-01-11T21:41:24.1468102Z     return (buf0, )
2023-01-11T21:41:24.1468107Z 
2023-01-11T21:41:24.1468111Z 
2023-01-11T21:41:24.1468185Z if __name__ == "__main__":
2023-01-11T21:41:24.1468297Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1468419Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1468612Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1468804Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1468918Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1468923Z 
2023-01-11T21:41:24.1468993Z ok (1.554s)
2023-01-11T21:41:24.1469507Z   test_cpu_broadcast3_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1469636Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1469895Z [2023-01-11 21:40:16,629] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 540
2023-01-11T21:41:24.1470161Z [2023-01-11 21:40:16,638] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 540
2023-01-11T21:41:24.1470167Z 
2023-01-11T21:41:24.1470259Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1470326Z import torch
2023-01-11T21:41:24.1470394Z import random
2023-01-11T21:41:24.1470510Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1470635Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1470640Z 
2023-01-11T21:41:24.1470704Z aten = torch.ops.aten
2023-01-11T21:41:24.1470835Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1470926Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1470931Z 
2023-01-11T21:41:24.1470935Z 
2023-01-11T21:41:24.1471068Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1471269Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1471385Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1471490Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1471591Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1471638Z {
2023-01-11T21:41:24.1471732Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1471794Z     {
2023-01-11T21:41:24.1471869Z         #pragma omp for 
2023-01-11T21:41:24.1471952Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:24.1472014Z         {
2023-01-11T21:41:24.1472135Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:24.1472253Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1472336Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1472425Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1472490Z         }
2023-01-11T21:41:24.1472585Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1472667Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:24.1472727Z         {
2023-01-11T21:41:24.1472797Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1472876Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1472956Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1473034Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1473094Z         }
2023-01-11T21:41:24.1473152Z     }
2023-01-11T21:41:24.1473237Z }
2023-01-11T21:41:24.1473315Z ''')
2023-01-11T21:41:24.1473320Z 
2023-01-11T21:41:24.1473325Z 
2023-01-11T21:41:24.1473413Z async_compile.wait(globals())
2023-01-11T21:41:24.1473488Z del async_compile
2023-01-11T21:41:24.1473493Z 
2023-01-11T21:41:24.1473563Z def call(args):
2023-01-11T21:41:24.1473634Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1473706Z     args.clear()
2023-01-11T21:41:24.1473988Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1474138Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1474205Z     del arg0_1
2023-01-11T21:41:24.1474271Z     del arg1_1
2023-01-11T21:41:24.1474343Z     return (buf0, )
2023-01-11T21:41:24.1474348Z 
2023-01-11T21:41:24.1474353Z 
2023-01-11T21:41:24.1474426Z if __name__ == "__main__":
2023-01-11T21:41:24.1474541Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1474664Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1474892Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1475088Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1475202Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1475207Z 
2023-01-11T21:41:24.1475272Z ok (0.023s)
2023-01-11T21:41:24.1475770Z   test_cpu_broadcast3_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1475892Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1476153Z [2023-01-11 21:40:16,652] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 541
2023-01-11T21:41:24.1476420Z [2023-01-11 21:40:18,156] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 541
2023-01-11T21:41:24.1476425Z 
2023-01-11T21:41:24.1476520Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1476590Z import torch
2023-01-11T21:41:24.1476646Z import random
2023-01-11T21:41:24.1476758Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1476879Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1476884Z 
2023-01-11T21:41:24.1476962Z aten = torch.ops.aten
2023-01-11T21:41:24.1477094Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1477188Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1477193Z 
2023-01-11T21:41:24.1477197Z 
2023-01-11T21:41:24.1477332Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1477539Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1477648Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1477748Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1477847Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1477911Z {
2023-01-11T21:41:24.1477970Z     {
2023-01-11T21:41:24.1478040Z         {
2023-01-11T21:41:24.1478123Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1478193Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.1478275Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1478353Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:24.1478412Z         }
2023-01-11T21:41:24.1478475Z     }
2023-01-11T21:41:24.1478532Z }
2023-01-11T21:41:24.1478608Z ''')
2023-01-11T21:41:24.1478612Z 
2023-01-11T21:41:24.1478616Z 
2023-01-11T21:41:24.1478691Z async_compile.wait(globals())
2023-01-11T21:41:24.1478760Z del async_compile
2023-01-11T21:41:24.1478765Z 
2023-01-11T21:41:24.1478834Z def call(args):
2023-01-11T21:41:24.1478961Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1479029Z     args.clear()
2023-01-11T21:41:24.1479223Z     buf0 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1479382Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1479449Z     del arg0_1
2023-01-11T21:41:24.1479504Z     del arg1_1
2023-01-11T21:41:24.1479572Z     return (buf0, )
2023-01-11T21:41:24.1479577Z 
2023-01-11T21:41:24.1479581Z 
2023-01-11T21:41:24.1479655Z if __name__ == "__main__":
2023-01-11T21:41:24.1479767Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1479886Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1480079Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1480268Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1480370Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1480388Z 
2023-01-11T21:41:24.1480469Z ok (1.518s)
2023-01-11T21:41:24.1480962Z   test_cpu_broadcast3_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1481089Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1481350Z [2023-01-11 21:40:18,170] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 542
2023-01-11T21:41:24.1481616Z [2023-01-11 21:40:19,830] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 542
2023-01-11T21:41:24.1481621Z 
2023-01-11T21:41:24.1481712Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1481780Z import torch
2023-01-11T21:41:24.1481852Z import random
2023-01-11T21:41:24.1481955Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1482075Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1482079Z 
2023-01-11T21:41:24.1482155Z aten = torch.ops.aten
2023-01-11T21:41:24.1482290Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1482380Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1482385Z 
2023-01-11T21:41:24.1482389Z 
2023-01-11T21:41:24.1482521Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1482725Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1482844Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1482946Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1483032Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1483090Z {
2023-01-11T21:41:24.1483188Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1483251Z     {
2023-01-11T21:41:24.1483324Z         #pragma omp for 
2023-01-11T21:41:24.1483406Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.1483455Z         {
2023-01-11T21:41:24.1483576Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:24.1483704Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1483788Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1483882Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1483943Z         }
2023-01-11T21:41:24.1484037Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1484117Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:24.1484166Z         {
2023-01-11T21:41:24.1484247Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1484328Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1484408Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1484486Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1484579Z         }
2023-01-11T21:41:24.1484627Z     }
2023-01-11T21:41:24.1484685Z }
2023-01-11T21:41:24.1484763Z ''')
2023-01-11T21:41:24.1484768Z 
2023-01-11T21:41:24.1484773Z 
2023-01-11T21:41:24.1484861Z async_compile.wait(globals())
2023-01-11T21:41:24.1484936Z del async_compile
2023-01-11T21:41:24.1484940Z 
2023-01-11T21:41:24.1485011Z def call(args):
2023-01-11T21:41:24.1485084Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1485153Z     args.clear()
2023-01-11T21:41:24.1485339Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1485500Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1485567Z     del arg0_1
2023-01-11T21:41:24.1485631Z     del arg1_1
2023-01-11T21:41:24.1485701Z     return (buf0, )
2023-01-11T21:41:24.1485705Z 
2023-01-11T21:41:24.1485709Z 
2023-01-11T21:41:24.1485784Z if __name__ == "__main__":
2023-01-11T21:41:24.1485927Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1486040Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1486232Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1486428Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1486542Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1486547Z 
2023-01-11T21:41:24.1486610Z ok (1.674s)
2023-01-11T21:41:24.1487103Z   test_cpu_broadcast3_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1487227Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1487489Z [2023-01-11 21:40:19,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 543
2023-01-11T21:41:24.1487752Z [2023-01-11 21:40:21,370] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 543
2023-01-11T21:41:24.1487757Z 
2023-01-11T21:41:24.1487848Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1487905Z import torch
2023-01-11T21:41:24.1487975Z import random
2023-01-11T21:41:24.1488093Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1488210Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1488214Z 
2023-01-11T21:41:24.1488290Z aten = torch.ops.aten
2023-01-11T21:41:24.1488422Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1488512Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1488516Z 
2023-01-11T21:41:24.1488521Z 
2023-01-11T21:41:24.1488651Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1488849Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1488966Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1489072Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1489172Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1489231Z {
2023-01-11T21:41:24.1489327Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1489387Z     {
2023-01-11T21:41:24.1489450Z         #pragma omp for 
2023-01-11T21:41:24.1489531Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:24.1489592Z         {
2023-01-11T21:41:24.1489654Z             {
2023-01-11T21:41:24.1489716Z                 {
2023-01-11T21:41:24.1489806Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1489896Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:24.1489991Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.1490083Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1490243Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1490305Z                 }
2023-01-11T21:41:24.1490367Z             }
2023-01-11T21:41:24.1490427Z         }
2023-01-11T21:41:24.1490487Z     }
2023-01-11T21:41:24.1490533Z }
2023-01-11T21:41:24.1490614Z ''')
2023-01-11T21:41:24.1490619Z 
2023-01-11T21:41:24.1490623Z 
2023-01-11T21:41:24.1490710Z async_compile.wait(globals())
2023-01-11T21:41:24.1490782Z del async_compile
2023-01-11T21:41:24.1490787Z 
2023-01-11T21:41:24.1490855Z def call(args):
2023-01-11T21:41:24.1490930Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1490998Z     args.clear()
2023-01-11T21:41:24.1491187Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1491350Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1491416Z     del arg0_1
2023-01-11T21:41:24.1491482Z     del arg1_1
2023-01-11T21:41:24.1491556Z     return (buf0, )
2023-01-11T21:41:24.1491588Z 
2023-01-11T21:41:24.1491593Z 
2023-01-11T21:41:24.1491666Z if __name__ == "__main__":
2023-01-11T21:41:24.1491779Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1491900Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1492081Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1492276Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1492387Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1492392Z 
2023-01-11T21:41:24.1492457Z ok (1.541s)
2023-01-11T21:41:24.1492951Z   test_cpu_broadcast3_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1493078Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1493337Z [2023-01-11 21:40:21,385] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 544
2023-01-11T21:41:24.1493601Z [2023-01-11 21:40:22,902] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 544
2023-01-11T21:41:24.1493606Z 
2023-01-11T21:41:24.1493697Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1493753Z import torch
2023-01-11T21:41:24.1493819Z import random
2023-01-11T21:41:24.1493935Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1494054Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1494059Z 
2023-01-11T21:41:24.1494135Z aten = torch.ops.aten
2023-01-11T21:41:24.1494266Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1494355Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1494366Z 
2023-01-11T21:41:24.1494370Z 
2023-01-11T21:41:24.1494501Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1494691Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1494808Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1494909Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1495005Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1495065Z {
2023-01-11T21:41:24.1495160Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1495218Z     {
2023-01-11T21:41:24.1495281Z         #pragma omp for 
2023-01-11T21:41:24.1495360Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1495419Z         {
2023-01-11T21:41:24.1495479Z             {
2023-01-11T21:41:24.1495542Z                 {
2023-01-11T21:41:24.1495630Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1495722Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1495847Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:24.1495937Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1496019Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1496083Z                 }
2023-01-11T21:41:24.1496145Z             }
2023-01-11T21:41:24.1496207Z         }
2023-01-11T21:41:24.1496267Z     }
2023-01-11T21:41:24.1496314Z }
2023-01-11T21:41:24.1496391Z ''')
2023-01-11T21:41:24.1496396Z 
2023-01-11T21:41:24.1496400Z 
2023-01-11T21:41:24.1496487Z async_compile.wait(globals())
2023-01-11T21:41:24.1496558Z del async_compile
2023-01-11T21:41:24.1496562Z 
2023-01-11T21:41:24.1496630Z def call(args):
2023-01-11T21:41:24.1496703Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1496771Z     args.clear()
2023-01-11T21:41:24.1496956Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1497150Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1497220Z     del arg0_1
2023-01-11T21:41:24.1497285Z     del arg1_1
2023-01-11T21:41:24.1497355Z     return (buf0, )
2023-01-11T21:41:24.1497360Z 
2023-01-11T21:41:24.1497365Z 
2023-01-11T21:41:24.1497439Z if __name__ == "__main__":
2023-01-11T21:41:24.1497551Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1497671Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1497851Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1498042Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1498154Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1498159Z 
2023-01-11T21:41:24.1498225Z ok (1.532s)
2023-01-11T21:41:24.1498721Z   test_cpu_broadcast3_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1498848Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1499106Z [2023-01-11 21:40:22,917] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 545
2023-01-11T21:41:24.1499368Z [2023-01-11 21:40:24,448] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 545
2023-01-11T21:41:24.1499373Z 
2023-01-11T21:41:24.1499470Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1499526Z import torch
2023-01-11T21:41:24.1499592Z import random
2023-01-11T21:41:24.1499704Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1499824Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1499829Z 
2023-01-11T21:41:24.1499909Z aten = torch.ops.aten
2023-01-11T21:41:24.1500040Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1500129Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1500134Z 
2023-01-11T21:41:24.1500138Z 
2023-01-11T21:41:24.1500269Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1500460Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1500576Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1500678Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1500778Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1500841Z {
2023-01-11T21:41:24.1500935Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1500994Z     {
2023-01-11T21:41:24.1501056Z         #pragma omp for 
2023-01-11T21:41:24.1501136Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1501198Z         {
2023-01-11T21:41:24.1501274Z             #pragma GCC ivdep
2023-01-11T21:41:24.1501393Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1501454Z             {
2023-01-11T21:41:24.1501519Z                 {
2023-01-11T21:41:24.1501573Z                     {
2023-01-11T21:41:24.1501664Z                         auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1501769Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1501865Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1501959Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1502023Z                     }
2023-01-11T21:41:24.1502086Z                 }
2023-01-11T21:41:24.1502135Z             }
2023-01-11T21:41:24.1502194Z         }
2023-01-11T21:41:24.1502255Z     }
2023-01-11T21:41:24.1502313Z }
2023-01-11T21:41:24.1502552Z ''')
2023-01-11T21:41:24.1502561Z 
2023-01-11T21:41:24.1502568Z 
2023-01-11T21:41:24.1502670Z async_compile.wait(globals())
2023-01-11T21:41:24.1502741Z del async_compile
2023-01-11T21:41:24.1502748Z 
2023-01-11T21:41:24.1502864Z def call(args):
2023-01-11T21:41:24.1502942Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1503010Z     args.clear()
2023-01-11T21:41:24.1503213Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1503379Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1503445Z     del arg0_1
2023-01-11T21:41:24.1503510Z     del arg1_1
2023-01-11T21:41:24.1503567Z     return (buf0, )
2023-01-11T21:41:24.1503572Z 
2023-01-11T21:41:24.1503587Z 
2023-01-11T21:41:24.1503649Z if __name__ == "__main__":
2023-01-11T21:41:24.1503760Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1503884Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1504076Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1504275Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1504394Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1504399Z 
2023-01-11T21:41:24.1504468Z ok (1.546s)
2023-01-11T21:41:24.1504970Z   test_cpu_broadcast3_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1505094Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1505344Z [2023-01-11 21:40:24,463] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 546
2023-01-11T21:41:24.1505606Z [2023-01-11 21:40:24,471] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 546
2023-01-11T21:41:24.1505614Z 
2023-01-11T21:41:24.1505712Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1505780Z import torch
2023-01-11T21:41:24.1505848Z import random
2023-01-11T21:41:24.1505964Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1506083Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1506089Z 
2023-01-11T21:41:24.1506165Z aten = torch.ops.aten
2023-01-11T21:41:24.1506284Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1506374Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1506378Z 
2023-01-11T21:41:24.1506382Z 
2023-01-11T21:41:24.1506515Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1506718Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1506839Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1506939Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1507040Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1507129Z {
2023-01-11T21:41:24.1507224Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1507283Z     {
2023-01-11T21:41:24.1507358Z         #pragma omp for 
2023-01-11T21:41:24.1507438Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.1507499Z         {
2023-01-11T21:41:24.1507622Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:24.1507740Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1507822Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1507911Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1507972Z         }
2023-01-11T21:41:24.1508070Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1508151Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:24.1508211Z         {
2023-01-11T21:41:24.1508280Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:24.1508360Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1508472Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1508552Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1508612Z         }
2023-01-11T21:41:24.1508677Z     }
2023-01-11T21:41:24.1508734Z }
2023-01-11T21:41:24.1508800Z ''')
2023-01-11T21:41:24.1508805Z 
2023-01-11T21:41:24.1508809Z 
2023-01-11T21:41:24.1508895Z async_compile.wait(globals())
2023-01-11T21:41:24.1508965Z del async_compile
2023-01-11T21:41:24.1508970Z 
2023-01-11T21:41:24.1509038Z def call(args):
2023-01-11T21:41:24.1509112Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1509179Z     args.clear()
2023-01-11T21:41:24.1509378Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1509539Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1509594Z     del arg0_1
2023-01-11T21:41:24.1509658Z     del arg1_1
2023-01-11T21:41:24.1509727Z     return (buf0, )
2023-01-11T21:41:24.1509732Z 
2023-01-11T21:41:24.1509738Z 
2023-01-11T21:41:24.1509815Z if __name__ == "__main__":
2023-01-11T21:41:24.1509926Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1510046Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1510238Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1510423Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1510536Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1510541Z 
2023-01-11T21:41:24.1510606Z ok (0.023s)
2023-01-11T21:41:24.1511098Z   test_cpu_dense_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1511227Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1511486Z [2023-01-11 21:40:24,485] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 547
2023-01-11T21:41:24.1511749Z [2023-01-11 21:40:26,063] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 547
2023-01-11T21:41:24.1511754Z 
2023-01-11T21:41:24.1511845Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1511912Z import torch
2023-01-11T21:41:24.1511982Z import random
2023-01-11T21:41:24.1512083Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1512202Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1512207Z 
2023-01-11T21:41:24.1512285Z aten = torch.ops.aten
2023-01-11T21:41:24.1512415Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1512504Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1512509Z 
2023-01-11T21:41:24.1512548Z 
2023-01-11T21:41:24.1512684Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1512886Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1513002Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1513091Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1513189Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1513249Z {
2023-01-11T21:41:24.1513345Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1513405Z     {
2023-01-11T21:41:24.1513485Z         #pragma omp for 
2023-01-11T21:41:24.1513564Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1513614Z         {
2023-01-11T21:41:24.1513694Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1513817Z             {
2023-01-11T21:41:24.1513960Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1514128Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:24.1514219Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1514319Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1514370Z             }
2023-01-11T21:41:24.1514459Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1514541Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1514602Z             {
2023-01-11T21:41:24.1514696Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1514781Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1514865Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1514944Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1515006Z             }
2023-01-11T21:41:24.1515066Z         }
2023-01-11T21:41:24.1515125Z     }
2023-01-11T21:41:24.1515184Z }
2023-01-11T21:41:24.1520923Z ''')
2023-01-11T21:41:24.1520935Z 
2023-01-11T21:41:24.1520940Z 
2023-01-11T21:41:24.1521062Z async_compile.wait(globals())
2023-01-11T21:41:24.1521131Z del async_compile
2023-01-11T21:41:24.1521136Z 
2023-01-11T21:41:24.1521207Z def call(args):
2023-01-11T21:41:24.1521284Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1521360Z     args.clear()
2023-01-11T21:41:24.1521581Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1521746Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1521812Z     del arg0_1
2023-01-11T21:41:24.1521865Z     del arg1_1
2023-01-11T21:41:24.1521933Z     return (buf0, )
2023-01-11T21:41:24.1521938Z 
2023-01-11T21:41:24.1521942Z 
2023-01-11T21:41:24.1522017Z if __name__ == "__main__":
2023-01-11T21:41:24.1522132Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1522256Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1522460Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1522663Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1522777Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1522782Z 
2023-01-11T21:41:24.1522835Z ok (1.592s)
2023-01-11T21:41:24.1523333Z   test_cpu_dense_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1523463Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1523730Z [2023-01-11 21:40:26,078] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 548
2023-01-11T21:41:24.1524001Z [2023-01-11 21:40:27,632] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 548
2023-01-11T21:41:24.1524101Z 
2023-01-11T21:41:24.1524197Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1524268Z import torch
2023-01-11T21:41:24.1524338Z import random
2023-01-11T21:41:24.1524456Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1524564Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1524582Z 
2023-01-11T21:41:24.1524648Z aten = torch.ops.aten
2023-01-11T21:41:24.1524783Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1524876Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1524881Z 
2023-01-11T21:41:24.1524886Z 
2023-01-11T21:41:24.1525021Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1525224Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1525345Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1525489Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1525592Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1525641Z {
2023-01-11T21:41:24.1525740Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1525802Z     {
2023-01-11T21:41:24.1525877Z         #pragma omp for 
2023-01-11T21:41:24.1525958Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1526020Z         {
2023-01-11T21:41:24.1526091Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1526157Z             {
2023-01-11T21:41:24.1526300Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1526424Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[i0]);
2023-01-11T21:41:24.1526513Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1526617Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1526681Z             }
2023-01-11T21:41:24.1526774Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1526851Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1526917Z             {
2023-01-11T21:41:24.1527016Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1527104Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1527189Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1527282Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1527347Z             }
2023-01-11T21:41:24.1527396Z         }
2023-01-11T21:41:24.1527459Z     }
2023-01-11T21:41:24.1527523Z }
2023-01-11T21:41:24.1527604Z ''')
2023-01-11T21:41:24.1527609Z 
2023-01-11T21:41:24.1527613Z 
2023-01-11T21:41:24.1527704Z async_compile.wait(globals())
2023-01-11T21:41:24.1527778Z del async_compile
2023-01-11T21:41:24.1527782Z 
2023-01-11T21:41:24.1527852Z def call(args):
2023-01-11T21:41:24.1527915Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1527985Z     args.clear()
2023-01-11T21:41:24.1528198Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1528366Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1528436Z     del arg0_1
2023-01-11T21:41:24.1528502Z     del arg1_1
2023-01-11T21:41:24.1528574Z     return (buf0, )
2023-01-11T21:41:24.1528579Z 
2023-01-11T21:41:24.1528583Z 
2023-01-11T21:41:24.1528645Z if __name__ == "__main__":
2023-01-11T21:41:24.1528758Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1528882Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1529086Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1529292Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1529407Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1529412Z 
2023-01-11T21:41:24.1529481Z ok (1.570s)
2023-01-11T21:41:24.1529976Z   test_cpu_dense_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1530136Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1530383Z [2023-01-11 21:40:27,648] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 549
2023-01-11T21:41:24.1530649Z [2023-01-11 21:40:29,362] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 549
2023-01-11T21:41:24.1530654Z 
2023-01-11T21:41:24.1530749Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1530824Z import torch
2023-01-11T21:41:24.1530895Z import random
2023-01-11T21:41:24.1531012Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1531166Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1531171Z 
2023-01-11T21:41:24.1531252Z aten = torch.ops.aten
2023-01-11T21:41:24.1531373Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1531466Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1531471Z 
2023-01-11T21:41:24.1531475Z 
2023-01-11T21:41:24.1531612Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1531819Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1531938Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1532047Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1532148Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1532209Z {
2023-01-11T21:41:24.1532294Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1532355Z     {
2023-01-11T21:41:24.1532433Z         #pragma omp for 
2023-01-11T21:41:24.1532520Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.1532584Z         {
2023-01-11T21:41:24.1532717Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1532837Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:24.1532908Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1532997Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1533062Z         }
2023-01-11T21:41:24.1533154Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1533233Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:24.1533296Z         {
2023-01-11T21:41:24.1533378Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1533447Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.1533527Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1533608Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1533670Z         }
2023-01-11T21:41:24.1533729Z     }
2023-01-11T21:41:24.1533786Z }
2023-01-11T21:41:24.1533861Z ''')
2023-01-11T21:41:24.1533869Z 
2023-01-11T21:41:24.1533875Z 
2023-01-11T21:41:24.1533952Z async_compile.wait(globals())
2023-01-11T21:41:24.1534025Z del async_compile
2023-01-11T21:41:24.1534030Z 
2023-01-11T21:41:24.1534099Z def call(args):
2023-01-11T21:41:24.1534171Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1534239Z     args.clear()
2023-01-11T21:41:24.1534440Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1534600Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1534655Z     del arg0_1
2023-01-11T21:41:24.1534719Z     del arg1_1
2023-01-11T21:41:24.1534788Z     return (buf0, )
2023-01-11T21:41:24.1534793Z 
2023-01-11T21:41:24.1534797Z 
2023-01-11T21:41:24.1534870Z if __name__ == "__main__":
2023-01-11T21:41:24.1534981Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1535101Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1535301Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1535524Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1535627Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1535643Z 
2023-01-11T21:41:24.1535696Z ok (1.729s)
2023-01-11T21:41:24.1536179Z   test_cpu_dense_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1536303Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1536563Z [2023-01-11 21:40:29,380] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 550
2023-01-11T21:41:24.1536857Z [2023-01-11 21:40:29,389] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 550
2023-01-11T21:41:24.1536864Z 
2023-01-11T21:41:24.1536956Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1537024Z import torch
2023-01-11T21:41:24.1537093Z import random
2023-01-11T21:41:24.1537194Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1537311Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1537316Z 
2023-01-11T21:41:24.1537397Z aten = torch.ops.aten
2023-01-11T21:41:24.1537529Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1537618Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1537623Z 
2023-01-11T21:41:24.1537627Z 
2023-01-11T21:41:24.1537760Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1537961Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1538079Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1538184Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1538270Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1538331Z {
2023-01-11T21:41:24.1538427Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1538488Z     {
2023-01-11T21:41:24.1538562Z         #pragma omp for 
2023-01-11T21:41:24.1538646Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.1538695Z         {
2023-01-11T21:41:24.1538825Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1538952Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1539034Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1539122Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1539183Z         }
2023-01-11T21:41:24.1539276Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1539357Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:24.1539408Z         {
2023-01-11T21:41:24.1539494Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1539575Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1539653Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1539733Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1539792Z         }
2023-01-11T21:41:24.1539840Z     }
2023-01-11T21:41:24.1539901Z }
2023-01-11T21:41:24.1539979Z ''')
2023-01-11T21:41:24.1539984Z 
2023-01-11T21:41:24.1539988Z 
2023-01-11T21:41:24.1540075Z async_compile.wait(globals())
2023-01-11T21:41:24.1540146Z del async_compile
2023-01-11T21:41:24.1540150Z 
2023-01-11T21:41:24.1540218Z def call(args):
2023-01-11T21:41:24.1540291Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1540359Z     args.clear()
2023-01-11T21:41:24.1540545Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1540705Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1540808Z     del arg0_1
2023-01-11T21:41:24.1540875Z     del arg1_1
2023-01-11T21:41:24.1540945Z     return (buf0, )
2023-01-11T21:41:24.1540950Z 
2023-01-11T21:41:24.1540954Z 
2023-01-11T21:41:24.1541026Z if __name__ == "__main__":
2023-01-11T21:41:24.1541137Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1541247Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1541447Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1541644Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1541757Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1541762Z 
2023-01-11T21:41:24.1541827Z ok (0.027s)
2023-01-11T21:41:24.1542482Z   test_cpu_dense_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1542613Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1542879Z [2023-01-11 21:40:29,406] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 551
2023-01-11T21:41:24.1543146Z [2023-01-11 21:40:30,972] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 551
2023-01-11T21:41:24.1543152Z 
2023-01-11T21:41:24.1543246Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1543302Z import torch
2023-01-11T21:41:24.1543370Z import random
2023-01-11T21:41:24.1543486Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1543605Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1543610Z 
2023-01-11T21:41:24.1543687Z aten = torch.ops.aten
2023-01-11T21:41:24.1543825Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1543918Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1543923Z 
2023-01-11T21:41:24.1543927Z 
2023-01-11T21:41:24.1544060Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1544252Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1544370Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1544475Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1544574Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1544633Z {
2023-01-11T21:41:24.1544728Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1544788Z     {
2023-01-11T21:41:24.1544851Z         #pragma omp for 
2023-01-11T21:41:24.1544932Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:24.1544999Z         {
2023-01-11T21:41:24.1545061Z             {
2023-01-11T21:41:24.1545124Z                 {
2023-01-11T21:41:24.1545219Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1545308Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:24.1545404Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.1545492Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1545575Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1545639Z                 }
2023-01-11T21:41:24.1545699Z             }
2023-01-11T21:41:24.1545758Z         }
2023-01-11T21:41:24.1545806Z     }
2023-01-11T21:41:24.1545864Z }
2023-01-11T21:41:24.1545940Z ''')
2023-01-11T21:41:24.1545945Z 
2023-01-11T21:41:24.1545949Z 
2023-01-11T21:41:24.1546037Z async_compile.wait(globals())
2023-01-11T21:41:24.1546108Z del async_compile
2023-01-11T21:41:24.1546113Z 
2023-01-11T21:41:24.1546181Z def call(args):
2023-01-11T21:41:24.1546255Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1546325Z     args.clear()
2023-01-11T21:41:24.1546517Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1546735Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1546803Z     del arg0_1
2023-01-11T21:41:24.1546869Z     del arg1_1
2023-01-11T21:41:24.1546941Z     return (buf0, )
2023-01-11T21:41:24.1546946Z 
2023-01-11T21:41:24.1546950Z 
2023-01-11T21:41:24.1547026Z if __name__ == "__main__":
2023-01-11T21:41:24.1547137Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1547245Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1547446Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1547645Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1547758Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1547763Z 
2023-01-11T21:41:24.1547830Z ok (1.583s)
2023-01-11T21:41:24.1548351Z   test_cpu_dense_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1548480Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1548744Z [2023-01-11 21:40:30,987] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 552
2023-01-11T21:41:24.1549010Z [2023-01-11 21:40:32,571] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 552
2023-01-11T21:41:24.1549015Z 
2023-01-11T21:41:24.1549108Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1549165Z import torch
2023-01-11T21:41:24.1549232Z import random
2023-01-11T21:41:24.1549345Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1549470Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1549476Z 
2023-01-11T21:41:24.1549552Z aten = torch.ops.aten
2023-01-11T21:41:24.1549684Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1549773Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1549778Z 
2023-01-11T21:41:24.1549782Z 
2023-01-11T21:41:24.1549913Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1550104Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1550222Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1550323Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1550420Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1550479Z {
2023-01-11T21:41:24.1550575Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1550635Z     {
2023-01-11T21:41:24.1550698Z         #pragma omp for 
2023-01-11T21:41:24.1550783Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1550844Z         {
2023-01-11T21:41:24.1550923Z             #pragma GCC ivdep
2023-01-11T21:41:24.1551006Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1551068Z             {
2023-01-11T21:41:24.1551131Z                 {
2023-01-11T21:41:24.1551183Z                     {
2023-01-11T21:41:24.1551286Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1551378Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1551485Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:24.1551577Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1551670Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1551737Z                     }
2023-01-11T21:41:24.1551788Z                 }
2023-01-11T21:41:24.1551850Z             }
2023-01-11T21:41:24.1551910Z         }
2023-01-11T21:41:24.1551971Z     }
2023-01-11T21:41:24.1552028Z }
2023-01-11T21:41:24.1552138Z ''')
2023-01-11T21:41:24.1552146Z 
2023-01-11T21:41:24.1552151Z 
2023-01-11T21:41:24.1552240Z async_compile.wait(globals())
2023-01-11T21:41:24.1552299Z del async_compile
2023-01-11T21:41:24.1552304Z 
2023-01-11T21:41:24.1552371Z def call(args):
2023-01-11T21:41:24.1552443Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1552513Z     args.clear()
2023-01-11T21:41:24.1552713Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1552878Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1552943Z     del arg0_1
2023-01-11T21:41:24.1552997Z     del arg1_1
2023-01-11T21:41:24.1553065Z     return (buf0, )
2023-01-11T21:41:24.1553071Z 
2023-01-11T21:41:24.1553075Z 
2023-01-11T21:41:24.1553148Z if __name__ == "__main__":
2023-01-11T21:41:24.1553260Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1553380Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1553610Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1553873Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1553991Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1553996Z 
2023-01-11T21:41:24.1554048Z ok (1.599s)
2023-01-11T21:41:24.1554536Z   test_cpu_dense_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1554664Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1554928Z [2023-01-11 21:40:32,587] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 553
2023-01-11T21:41:24.1555200Z [2023-01-11 21:40:34,145] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 553
2023-01-11T21:41:24.1555205Z 
2023-01-11T21:41:24.1555297Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1555367Z import torch
2023-01-11T21:41:24.1555439Z import random
2023-01-11T21:41:24.1555555Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1555662Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1555679Z 
2023-01-11T21:41:24.1555743Z aten = torch.ops.aten
2023-01-11T21:41:24.1555874Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1555965Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1555970Z 
2023-01-11T21:41:24.1555974Z 
2023-01-11T21:41:24.1556108Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1556313Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1556433Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1556538Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1556625Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1556683Z {
2023-01-11T21:41:24.1556778Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1556841Z     {
2023-01-11T21:41:24.1556916Z         #pragma omp for 
2023-01-11T21:41:24.1556995Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1557056Z         {
2023-01-11T21:41:24.1557121Z             #pragma GCC ivdep
2023-01-11T21:41:24.1557204Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1557266Z             {
2023-01-11T21:41:24.1557328Z                 {
2023-01-11T21:41:24.1557394Z                     {
2023-01-11T21:41:24.1557494Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1557598Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1557679Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1557811Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1557876Z                     }
2023-01-11T21:41:24.1557939Z                 }
2023-01-11T21:41:24.1558000Z             }
2023-01-11T21:41:24.1558060Z         }
2023-01-11T21:41:24.1558119Z     }
2023-01-11T21:41:24.1558165Z }
2023-01-11T21:41:24.1558243Z ''')
2023-01-11T21:41:24.1558248Z 
2023-01-11T21:41:24.1558252Z 
2023-01-11T21:41:24.1558340Z async_compile.wait(globals())
2023-01-11T21:41:24.1558410Z del async_compile
2023-01-11T21:41:24.1558415Z 
2023-01-11T21:41:24.1558486Z def call(args):
2023-01-11T21:41:24.1558561Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1558633Z     args.clear()
2023-01-11T21:41:24.1558822Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1558986Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1559055Z     del arg0_1
2023-01-11T21:41:24.1559153Z     del arg1_1
2023-01-11T21:41:24.1559226Z     return (buf0, )
2023-01-11T21:41:24.1559231Z 
2023-01-11T21:41:24.1559235Z 
2023-01-11T21:41:24.1559310Z if __name__ == "__main__":
2023-01-11T21:41:24.1559424Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1559547Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1559735Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1559936Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1560051Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1560055Z 
2023-01-11T21:41:24.1560127Z ok (1.574s)
2023-01-11T21:41:24.1560626Z   test_cpu_dense_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1560755Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1561016Z [2023-01-11 21:40:34,160] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 554
2023-01-11T21:41:24.1561283Z [2023-01-11 21:40:35,714] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 554
2023-01-11T21:41:24.1561289Z 
2023-01-11T21:41:24.1561383Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1561440Z import torch
2023-01-11T21:41:24.1561510Z import random
2023-01-11T21:41:24.1561624Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1561745Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1561750Z 
2023-01-11T21:41:24.1561829Z aten = torch.ops.aten
2023-01-11T21:41:24.1561963Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1562058Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1562063Z 
2023-01-11T21:41:24.1562067Z 
2023-01-11T21:41:24.1562202Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1562392Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1562513Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1562618Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1562719Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1562780Z {
2023-01-11T21:41:24.1562878Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1562937Z     {
2023-01-11T21:41:24.1563001Z         #pragma omp for 
2023-01-11T21:41:24.1563083Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1563149Z         {
2023-01-11T21:41:24.1563230Z             #pragma GCC ivdep
2023-01-11T21:41:24.1563318Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1563432Z             {
2023-01-11T21:41:24.1563498Z                 {
2023-01-11T21:41:24.1563550Z                     {
2023-01-11T21:41:24.1563654Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1563755Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:41:24.1563849Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1563945Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1564012Z                     }
2023-01-11T21:41:24.1564075Z                 }
2023-01-11T21:41:24.1564125Z             }
2023-01-11T21:41:24.1564188Z         }
2023-01-11T21:41:24.1564253Z     }
2023-01-11T21:41:24.1564315Z }
2023-01-11T21:41:24.1564394Z ''')
2023-01-11T21:41:24.1564399Z 
2023-01-11T21:41:24.1564403Z 
2023-01-11T21:41:24.1564492Z async_compile.wait(globals())
2023-01-11T21:41:24.1564567Z del async_compile
2023-01-11T21:41:24.1564572Z 
2023-01-11T21:41:24.1564628Z def call(args):
2023-01-11T21:41:24.1564702Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1564804Z     args.clear()
2023-01-11T21:41:24.1565003Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1565168Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1565234Z     del arg0_1
2023-01-11T21:41:24.1565300Z     del arg1_1
2023-01-11T21:41:24.1565357Z     return (buf0, )
2023-01-11T21:41:24.1565363Z 
2023-01-11T21:41:24.1565378Z 
2023-01-11T21:41:24.1565440Z if __name__ == "__main__":
2023-01-11T21:41:24.1565554Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1565674Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1565872Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1566068Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1566180Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1566190Z 
2023-01-11T21:41:24.1566255Z ok (1.569s)
2023-01-11T21:41:24.1566742Z   test_cpu_double_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1566867Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1567112Z [2023-01-11 21:40:35,731] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 555
2023-01-11T21:41:24.1567378Z [2023-01-11 21:40:37,295] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 555
2023-01-11T21:41:24.1567383Z 
2023-01-11T21:41:24.1567477Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1567549Z import torch
2023-01-11T21:41:24.1567620Z import random
2023-01-11T21:41:24.1567737Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1567857Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1567863Z 
2023-01-11T21:41:24.1567938Z aten = torch.ops.aten
2023-01-11T21:41:24.1568060Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1568151Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1568155Z 
2023-01-11T21:41:24.1568159Z 
2023-01-11T21:41:24.1568292Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1568496Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1568617Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1568720Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1568822Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1568883Z {
2023-01-11T21:41:24.1569001Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1569062Z     {
2023-01-11T21:41:24.1569136Z         #pragma omp for 
2023-01-11T21:41:24.1569218Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1569280Z         {
2023-01-11T21:41:24.1569364Z             #pragma GCC ivdep
2023-01-11T21:41:24.1569439Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1569503Z             {
2023-01-11T21:41:24.1569569Z                 {
2023-01-11T21:41:24.1569634Z                     {
2023-01-11T21:41:24.1569737Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1569830Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1569939Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:24.1570018Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1570111Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1570174Z                     }
2023-01-11T21:41:24.1570239Z                 }
2023-01-11T21:41:24.1570328Z             }
2023-01-11T21:41:24.1570389Z         }
2023-01-11T21:41:24.1570450Z     }
2023-01-11T21:41:24.1570497Z }
2023-01-11T21:41:24.1570576Z ''')
2023-01-11T21:41:24.1570583Z 
2023-01-11T21:41:24.1570587Z 
2023-01-11T21:41:24.1570675Z async_compile.wait(globals())
2023-01-11T21:41:24.1570746Z del async_compile
2023-01-11T21:41:24.1570751Z 
2023-01-11T21:41:24.1570820Z def call(args):
2023-01-11T21:41:24.1570892Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1570960Z     args.clear()
2023-01-11T21:41:24.1571146Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1571304Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1571369Z     del arg0_1
2023-01-11T21:41:24.1571433Z     del arg1_1
2023-01-11T21:41:24.1571501Z     return (buf0, )
2023-01-11T21:41:24.1571506Z 
2023-01-11T21:41:24.1571510Z 
2023-01-11T21:41:24.1571582Z if __name__ == "__main__":
2023-01-11T21:41:24.1571695Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1571816Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1572003Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1572195Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1572309Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1572314Z 
2023-01-11T21:41:24.1572382Z ok (1.581s)
2023-01-11T21:41:24.1572869Z   test_cpu_double_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1572998Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1573260Z [2023-01-11 21:40:37,311] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 556
2023-01-11T21:41:24.1573525Z [2023-01-11 21:40:38,818] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 556
2023-01-11T21:41:24.1573530Z 
2023-01-11T21:41:24.1573622Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1573689Z import torch
2023-01-11T21:41:24.1573747Z import random
2023-01-11T21:41:24.1573861Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1573981Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1573986Z 
2023-01-11T21:41:24.1574064Z aten = torch.ops.aten
2023-01-11T21:41:24.1574197Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1574287Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1574292Z 
2023-01-11T21:41:24.1574296Z 
2023-01-11T21:41:24.1574430Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1574662Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1574782Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1574884Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1574985Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1575048Z {
2023-01-11T21:41:24.1575143Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1575202Z     {
2023-01-11T21:41:24.1575267Z         #pragma omp for 
2023-01-11T21:41:24.1575350Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1575410Z         {
2023-01-11T21:41:24.1575491Z             #pragma GCC ivdep
2023-01-11T21:41:24.1575575Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1575641Z             {
2023-01-11T21:41:24.1575705Z                 {
2023-01-11T21:41:24.1575760Z                     {
2023-01-11T21:41:24.1575860Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1575989Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1576101Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:24.1576195Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1576288Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1576352Z                     }
2023-01-11T21:41:24.1576405Z                 }
2023-01-11T21:41:24.1576468Z             }
2023-01-11T21:41:24.1576528Z         }
2023-01-11T21:41:24.1576591Z     }
2023-01-11T21:41:24.1576650Z }
2023-01-11T21:41:24.1576732Z ''')
2023-01-11T21:41:24.1576737Z 
2023-01-11T21:41:24.1576741Z 
2023-01-11T21:41:24.1576831Z async_compile.wait(globals())
2023-01-11T21:41:24.1576891Z del async_compile
2023-01-11T21:41:24.1576907Z 
2023-01-11T21:41:24.1576963Z def call(args):
2023-01-11T21:41:24.1577037Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1577106Z     args.clear()
2023-01-11T21:41:24.1577319Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1577484Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1577551Z     del arg0_1
2023-01-11T21:41:24.1577619Z     del arg1_1
2023-01-11T21:41:24.1577678Z     return (buf0, )
2023-01-11T21:41:24.1577683Z 
2023-01-11T21:41:24.1577687Z 
2023-01-11T21:41:24.1577764Z if __name__ == "__main__":
2023-01-11T21:41:24.1577877Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1578005Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1578203Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1578410Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1578522Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1578527Z 
2023-01-11T21:41:24.1578591Z ok (1.522s)
2023-01-11T21:41:24.1579077Z   test_cpu_double_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1579201Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1579458Z [2023-01-11 21:40:38,833] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 557
2023-01-11T21:41:24.1579722Z [2023-01-11 21:40:40,409] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 557
2023-01-11T21:41:24.1579727Z 
2023-01-11T21:41:24.1579821Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1579889Z import torch
2023-01-11T21:41:24.1579957Z import random
2023-01-11T21:41:24.1580073Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1580221Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1580227Z 
2023-01-11T21:41:24.1580291Z aten = torch.ops.aten
2023-01-11T21:41:24.1580421Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1580511Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1580517Z 
2023-01-11T21:41:24.1580521Z 
2023-01-11T21:41:24.1580653Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1580855Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1580975Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1581078Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1581176Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1581223Z {
2023-01-11T21:41:24.1581318Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1581378Z     {
2023-01-11T21:41:24.1581514Z         #pragma omp for 
2023-01-11T21:41:24.1581596Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:24.1581656Z         {
2023-01-11T21:41:24.1581705Z             {
2023-01-11T21:41:24.1581767Z                 {
2023-01-11T21:41:24.1581859Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1581951Z                     auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.1582058Z                     auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:24.1582148Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1582230Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1582281Z                 }
2023-01-11T21:41:24.1582474Z             }
2023-01-11T21:41:24.1582567Z         }
2023-01-11T21:41:24.1582647Z     }
2023-01-11T21:41:24.1582707Z }
2023-01-11T21:41:24.1582790Z ''')
2023-01-11T21:41:24.1582795Z 
2023-01-11T21:41:24.1582799Z 
2023-01-11T21:41:24.1582890Z async_compile.wait(globals())
2023-01-11T21:41:24.1582948Z del async_compile
2023-01-11T21:41:24.1582968Z 
2023-01-11T21:41:24.1583027Z def call(args):
2023-01-11T21:41:24.1583100Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1583170Z     args.clear()
2023-01-11T21:41:24.1583371Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1583532Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1583598Z     del arg0_1
2023-01-11T21:41:24.1583663Z     del arg1_1
2023-01-11T21:41:24.1583720Z     return (buf0, )
2023-01-11T21:41:24.1583724Z 
2023-01-11T21:41:24.1583730Z 
2023-01-11T21:41:24.1583803Z if __name__ == "__main__":
2023-01-11T21:41:24.1583917Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1584039Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1584238Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1584429Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1584543Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1584548Z 
2023-01-11T21:41:24.1584614Z ok (1.591s)
2023-01-11T21:41:24.1585086Z   test_cpu_double_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1585209Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1585470Z [2023-01-11 21:40:40,425] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 558
2023-01-11T21:41:24.1585732Z [2023-01-11 21:40:41,995] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 558
2023-01-11T21:41:24.1585737Z 
2023-01-11T21:41:24.1585896Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1585965Z import torch
2023-01-11T21:41:24.1586034Z import random
2023-01-11T21:41:24.1586148Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1586267Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1586272Z 
2023-01-11T21:41:24.1586336Z aten = torch.ops.aten
2023-01-11T21:41:24.1586469Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1586566Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1586571Z 
2023-01-11T21:41:24.1586575Z 
2023-01-11T21:41:24.1586713Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1586916Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1587035Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1587138Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1587237Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1587322Z {
2023-01-11T21:41:24.1587419Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1587483Z     {
2023-01-11T21:41:24.1587557Z         #pragma omp for 
2023-01-11T21:41:24.1587638Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:24.1587698Z         {
2023-01-11T21:41:24.1587758Z             {
2023-01-11T21:41:24.1587809Z                 {
2023-01-11T21:41:24.1587899Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1587989Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1588094Z                     auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:24.1588184Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1588271Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1588334Z                 }
2023-01-11T21:41:24.1588383Z             }
2023-01-11T21:41:24.1588444Z         }
2023-01-11T21:41:24.1588506Z     }
2023-01-11T21:41:24.1588566Z }
2023-01-11T21:41:24.1588648Z ''')
2023-01-11T21:41:24.1588655Z 
2023-01-11T21:41:24.1588662Z 
2023-01-11T21:41:24.1588747Z async_compile.wait(globals())
2023-01-11T21:41:24.1588818Z del async_compile
2023-01-11T21:41:24.1588823Z 
2023-01-11T21:41:24.1588879Z def call(args):
2023-01-11T21:41:24.1588955Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1589025Z     args.clear()
2023-01-11T21:41:24.1589225Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1589382Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1589449Z     del arg0_1
2023-01-11T21:41:24.1589515Z     del arg1_1
2023-01-11T21:41:24.1589573Z     return (buf0, )
2023-01-11T21:41:24.1589578Z 
2023-01-11T21:41:24.1589582Z 
2023-01-11T21:41:24.1589655Z if __name__ == "__main__":
2023-01-11T21:41:24.1589766Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1589887Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1590087Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1590286Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1590401Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1590405Z 
2023-01-11T21:41:24.1590471Z ok (1.585s)
2023-01-11T21:41:24.1590950Z   test_cpu_double_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1591075Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1591336Z [2023-01-11 21:40:42,010] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 559
2023-01-11T21:41:24.1591633Z [2023-01-11 21:40:43,542] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 559
2023-01-11T21:41:24.1591638Z 
2023-01-11T21:41:24.1591733Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1591803Z import torch
2023-01-11T21:41:24.1591873Z import random
2023-01-11T21:41:24.1591988Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1592110Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1592115Z 
2023-01-11T21:41:24.1592180Z aten = torch.ops.aten
2023-01-11T21:41:24.1592314Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1592406Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1592411Z 
2023-01-11T21:41:24.1592415Z 
2023-01-11T21:41:24.1592550Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1592756Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1592877Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1593017Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1593121Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1593169Z {
2023-01-11T21:41:24.1593268Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1593331Z     {
2023-01-11T21:41:24.1593409Z         #pragma omp for 
2023-01-11T21:41:24.1593495Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:24.1593560Z         {
2023-01-11T21:41:24.1593624Z             {
2023-01-11T21:41:24.1593675Z                 {
2023-01-11T21:41:24.1593824Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1593917Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1594007Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1594092Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1594157Z                 }
2023-01-11T21:41:24.1594219Z             }
2023-01-11T21:41:24.1594268Z         }
2023-01-11T21:41:24.1594333Z     }
2023-01-11T21:41:24.1594395Z }
2023-01-11T21:41:24.1594477Z ''')
2023-01-11T21:41:24.1594482Z 
2023-01-11T21:41:24.1594487Z 
2023-01-11T21:41:24.1594575Z async_compile.wait(globals())
2023-01-11T21:41:24.1594650Z del async_compile
2023-01-11T21:41:24.1594654Z 
2023-01-11T21:41:24.1594724Z def call(args):
2023-01-11T21:41:24.1594785Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1594856Z     args.clear()
2023-01-11T21:41:24.1595057Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1595218Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1595290Z     del arg0_1
2023-01-11T21:41:24.1595356Z     del arg1_1
2023-01-11T21:41:24.1595428Z     return (buf0, )
2023-01-11T21:41:24.1595433Z 
2023-01-11T21:41:24.1595437Z 
2023-01-11T21:41:24.1595499Z if __name__ == "__main__":
2023-01-11T21:41:24.1595613Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1595738Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1595939Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1596136Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1596251Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1596256Z 
2023-01-11T21:41:24.1596322Z ok (1.547s)
2023-01-11T21:41:24.1596810Z   test_cpu_double_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1596938Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1597190Z [2023-01-11 21:40:43,559] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 560
2023-01-11T21:41:24.1597487Z [2023-01-11 21:40:45,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 560
2023-01-11T21:41:24.1597493Z 
2023-01-11T21:41:24.1597585Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1597657Z import torch
2023-01-11T21:41:24.1597724Z import random
2023-01-11T21:41:24.1597837Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1597956Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1597962Z 
2023-01-11T21:41:24.1598039Z aten = torch.ops.aten
2023-01-11T21:41:24.1598159Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1598253Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1598257Z 
2023-01-11T21:41:24.1598261Z 
2023-01-11T21:41:24.1598393Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1598624Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1598751Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1598850Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1598948Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1599007Z {
2023-01-11T21:41:24.1599091Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1599152Z     {
2023-01-11T21:41:24.1599227Z         #pragma omp for 
2023-01-11T21:41:24.1599307Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1599367Z         {
2023-01-11T21:41:24.1599443Z             #pragma GCC ivdep
2023-01-11T21:41:24.1599526Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1599575Z             {
2023-01-11T21:41:24.1599638Z                 {
2023-01-11T21:41:24.1599701Z                     {
2023-01-11T21:41:24.1599802Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1599894Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1600010Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:24.1600104Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1600187Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1600252Z                     }
2023-01-11T21:41:24.1600314Z                 }
2023-01-11T21:41:24.1600375Z             }
2023-01-11T21:41:24.1600437Z         }
2023-01-11T21:41:24.1600497Z     }
2023-01-11T21:41:24.1600544Z }
2023-01-11T21:41:24.1600619Z ''')
2023-01-11T21:41:24.1600625Z 
2023-01-11T21:41:24.1600629Z 
2023-01-11T21:41:24.1600718Z async_compile.wait(globals())
2023-01-11T21:41:24.1600789Z del async_compile
2023-01-11T21:41:24.1600794Z 
2023-01-11T21:41:24.1600861Z def call(args):
2023-01-11T21:41:24.1600933Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1601001Z     args.clear()
2023-01-11T21:41:24.1601204Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1601356Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1601425Z     del arg0_1
2023-01-11T21:41:24.1601488Z     del arg1_1
2023-01-11T21:41:24.1601556Z     return (buf0, )
2023-01-11T21:41:24.1601560Z 
2023-01-11T21:41:24.1601565Z 
2023-01-11T21:41:24.1601637Z if __name__ == "__main__":
2023-01-11T21:41:24.1601749Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1601872Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1602068Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1602245Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1602359Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1602364Z 
2023-01-11T21:41:24.1602429Z ok (1.584s)
2023-01-11T21:41:24.1602922Z   test_cpu_double_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1603094Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1603353Z [2023-01-11 21:40:45,141] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 561
2023-01-11T21:41:24.1603617Z [2023-01-11 21:40:46,691] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 561
2023-01-11T21:41:24.1603623Z 
2023-01-11T21:41:24.1603716Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1603785Z import torch
2023-01-11T21:41:24.1603842Z import random
2023-01-11T21:41:24.1603955Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1604074Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1604081Z 
2023-01-11T21:41:24.1604187Z aten = torch.ops.aten
2023-01-11T21:41:24.1604324Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1604420Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1604425Z 
2023-01-11T21:41:24.1604429Z 
2023-01-11T21:41:24.1604563Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1604767Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1604873Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1604976Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1605076Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1605137Z {
2023-01-11T21:41:24.1605232Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1605293Z     {
2023-01-11T21:41:24.1605370Z         #pragma omp for 
2023-01-11T21:41:24.1605439Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1605499Z         {
2023-01-11T21:41:24.1605582Z             #pragma GCC ivdep
2023-01-11T21:41:24.1605665Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1605726Z             {
2023-01-11T21:41:24.1605788Z                 {
2023-01-11T21:41:24.1605852Z                     {
2023-01-11T21:41:24.1605941Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1606043Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1606152Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:24.1606244Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1606338Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1606402Z                     }
2023-01-11T21:41:24.1606465Z                 }
2023-01-11T21:41:24.1606514Z             }
2023-01-11T21:41:24.1606574Z         }
2023-01-11T21:41:24.1606633Z     }
2023-01-11T21:41:24.1606691Z }
2023-01-11T21:41:24.1606768Z ''')
2023-01-11T21:41:24.1606773Z 
2023-01-11T21:41:24.1606779Z 
2023-01-11T21:41:24.1606868Z async_compile.wait(globals())
2023-01-11T21:41:24.1606938Z del async_compile
2023-01-11T21:41:24.1606943Z 
2023-01-11T21:41:24.1607005Z def call(args):
2023-01-11T21:41:24.1607080Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1607151Z     args.clear()
2023-01-11T21:41:24.1607351Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1607513Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1607579Z     del arg0_1
2023-01-11T21:41:24.1607645Z     del arg1_1
2023-01-11T21:41:24.1607704Z     return (buf0, )
2023-01-11T21:41:24.1607709Z 
2023-01-11T21:41:24.1607724Z 
2023-01-11T21:41:24.1607787Z if __name__ == "__main__":
2023-01-11T21:41:24.1607900Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1608023Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1608224Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1608453Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1608567Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1608572Z 
2023-01-11T21:41:24.1608637Z ok (1.566s)
2023-01-11T21:41:24.1609132Z   test_cpu_double_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1609256Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1609503Z [2023-01-11 21:40:46,707] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 562
2023-01-11T21:41:24.1609801Z [2023-01-11 21:40:48,262] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 562
2023-01-11T21:41:24.1609809Z 
2023-01-11T21:41:24.1609905Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1609972Z import torch
2023-01-11T21:41:24.1610041Z import random
2023-01-11T21:41:24.1610152Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1610270Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1610274Z 
2023-01-11T21:41:24.1610351Z aten = torch.ops.aten
2023-01-11T21:41:24.1610470Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1610561Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1610566Z 
2023-01-11T21:41:24.1610571Z 
2023-01-11T21:41:24.1610702Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1610903Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1611021Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:24.1611129Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1611227Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1611277Z {
2023-01-11T21:41:24.1611372Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1611432Z     {
2023-01-11T21:41:24.1611511Z         #pragma omp for 
2023-01-11T21:41:24.1611592Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1611654Z         {
2023-01-11T21:41:24.1611732Z             #pragma GCC ivdep
2023-01-11T21:41:24.1611803Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1611869Z             {
2023-01-11T21:41:24.1611933Z                 {
2023-01-11T21:41:24.1611996Z                     {
2023-01-11T21:41:24.1612098Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1612197Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:41:24.1612308Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:24.1612394Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1612486Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1612550Z                     }
2023-01-11T21:41:24.1612612Z                 }
2023-01-11T21:41:24.1612673Z             }
2023-01-11T21:41:24.1612732Z         }
2023-01-11T21:41:24.1612792Z     }
2023-01-11T21:41:24.1612839Z }
2023-01-11T21:41:24.1612919Z ''')
2023-01-11T21:41:24.1612923Z 
2023-01-11T21:41:24.1612928Z 
2023-01-11T21:41:24.1613014Z async_compile.wait(globals())
2023-01-11T21:41:24.1613084Z del async_compile
2023-01-11T21:41:24.1613088Z 
2023-01-11T21:41:24.1613158Z def call(args):
2023-01-11T21:41:24.1613231Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1613303Z     args.clear()
2023-01-11T21:41:24.1613489Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1613648Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1613752Z     del arg0_1
2023-01-11T21:41:24.1613820Z     del arg1_1
2023-01-11T21:41:24.1613889Z     return (buf0, )
2023-01-11T21:41:24.1613894Z 
2023-01-11T21:41:24.1613898Z 
2023-01-11T21:41:24.1613971Z if __name__ == "__main__":
2023-01-11T21:41:24.1614082Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1614204Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1614390Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1614586Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1614699Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1614704Z 
2023-01-11T21:41:24.1614768Z ok (1.570s)
2023-01-11T21:41:24.1615284Z   test_cpu_int_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1615414Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1615672Z [2023-01-11 21:40:48,277] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 563
2023-01-11T21:41:24.1615933Z [2023-01-11 21:40:49,806] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 563
2023-01-11T21:41:24.1615938Z 
2023-01-11T21:41:24.1616032Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1616088Z import torch
2023-01-11T21:41:24.1616155Z import random
2023-01-11T21:41:24.1616268Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1616385Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1616390Z 
2023-01-11T21:41:24.1616465Z aten = torch.ops.aten
2023-01-11T21:41:24.1616602Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1616692Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1616698Z 
2023-01-11T21:41:24.1616702Z 
2023-01-11T21:41:24.1616835Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1617025Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1617139Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1617242Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1617342Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1617402Z {
2023-01-11T21:41:24.1617496Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1617556Z     {
2023-01-11T21:41:24.1617619Z         #pragma omp for 
2023-01-11T21:41:24.1617699Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1617762Z         {
2023-01-11T21:41:24.1617827Z             {
2023-01-11T21:41:24.1617889Z                 {
2023-01-11T21:41:24.1617986Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1618078Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:24.1618172Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.1618262Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1618344Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1618408Z                 }
2023-01-11T21:41:24.1618468Z             }
2023-01-11T21:41:24.1618528Z         }
2023-01-11T21:41:24.1618589Z     }
2023-01-11T21:41:24.1618635Z }
2023-01-11T21:41:24.1618713Z ''')
2023-01-11T21:41:24.1618718Z 
2023-01-11T21:41:24.1618721Z 
2023-01-11T21:41:24.1618808Z async_compile.wait(globals())
2023-01-11T21:41:24.1618879Z del async_compile
2023-01-11T21:41:24.1618884Z 
2023-01-11T21:41:24.1618952Z def call(args):
2023-01-11T21:41:24.1619024Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1619092Z     args.clear()
2023-01-11T21:41:24.1619272Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1619469Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1619536Z     del arg0_1
2023-01-11T21:41:24.1619599Z     del arg1_1
2023-01-11T21:41:24.1619667Z     return (buf0, )
2023-01-11T21:41:24.1619671Z 
2023-01-11T21:41:24.1619676Z 
2023-01-11T21:41:24.1619750Z if __name__ == "__main__":
2023-01-11T21:41:24.1619863Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1619984Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1620161Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1620354Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1620465Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1620470Z 
2023-01-11T21:41:24.1620535Z ok (1.544s)
2023-01-11T21:41:24.1621051Z   test_cpu_int_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1621182Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1621443Z [2023-01-11 21:40:49,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 564
2023-01-11T21:41:24.1621709Z [2023-01-11 21:40:51,386] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 564
2023-01-11T21:41:24.1621714Z 
2023-01-11T21:41:24.1621806Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1621874Z import torch
2023-01-11T21:41:24.1621931Z import random
2023-01-11T21:41:24.1622044Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1622170Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1622176Z 
2023-01-11T21:41:24.1622251Z aten = torch.ops.aten
2023-01-11T21:41:24.1622495Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1622586Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1622591Z 
2023-01-11T21:41:24.1622595Z 
2023-01-11T21:41:24.1622730Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1622921Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1623038Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1623143Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1623242Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1623303Z {
2023-01-11T21:41:24.1623399Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1623460Z     {
2023-01-11T21:41:24.1623523Z         #pragma omp for 
2023-01-11T21:41:24.1623603Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1623671Z         {
2023-01-11T21:41:24.1623752Z             #pragma GCC ivdep
2023-01-11T21:41:24.1623837Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1623901Z             {
2023-01-11T21:41:24.1623965Z                 {
2023-01-11T21:41:24.1624019Z                     {
2023-01-11T21:41:24.1624115Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1624210Z                         auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:24.1624321Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.1624414Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1624511Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1624579Z                     }
2023-01-11T21:41:24.1624631Z                 }
2023-01-11T21:41:24.1624698Z             }
2023-01-11T21:41:24.1624761Z         }
2023-01-11T21:41:24.1624823Z     }
2023-01-11T21:41:24.1624884Z }
2023-01-11T21:41:24.1624963Z ''')
2023-01-11T21:41:24.1625026Z 
2023-01-11T21:41:24.1625030Z 
2023-01-11T21:41:24.1625120Z async_compile.wait(globals())
2023-01-11T21:41:24.1625179Z del async_compile
2023-01-11T21:41:24.1625184Z 
2023-01-11T21:41:24.1625256Z def call(args):
2023-01-11T21:41:24.1625331Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1625404Z     args.clear()
2023-01-11T21:41:24.1625615Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1625779Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1625846Z     del arg0_1
2023-01-11T21:41:24.1625898Z     del arg1_1
2023-01-11T21:41:24.1625969Z     return (buf0, )
2023-01-11T21:41:24.1625974Z 
2023-01-11T21:41:24.1625979Z 
2023-01-11T21:41:24.1626055Z if __name__ == "__main__":
2023-01-11T21:41:24.1626167Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1626290Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1626522Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1626731Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1626845Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1626849Z 
2023-01-11T21:41:24.1626916Z ok (1.580s)
2023-01-11T21:41:24.1627393Z   test_cpu_int_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1627520Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1627783Z [2023-01-11 21:40:51,403] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 565
2023-01-11T21:41:24.1628052Z [2023-01-11 21:40:53,005] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 565
2023-01-11T21:41:24.1628058Z 
2023-01-11T21:41:24.1628151Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1628224Z import torch
2023-01-11T21:41:24.1628294Z import random
2023-01-11T21:41:24.1628409Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1628531Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1628536Z 
2023-01-11T21:41:24.1628601Z aten = torch.ops.aten
2023-01-11T21:41:24.1628735Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1628826Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1628831Z 
2023-01-11T21:41:24.1628835Z 
2023-01-11T21:41:24.1628968Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1629174Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1629292Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1629399Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1629496Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1629544Z {
2023-01-11T21:41:24.1629641Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1629706Z     {
2023-01-11T21:41:24.1629783Z         #pragma omp for 
2023-01-11T21:41:24.1629861Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1629925Z         {
2023-01-11T21:41:24.1629976Z             {
2023-01-11T21:41:24.1630037Z                 {
2023-01-11T21:41:24.1630126Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1630217Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:41:24.1630320Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.1630411Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1630496Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:24.1630550Z                 }
2023-01-11T21:41:24.1630646Z             }
2023-01-11T21:41:24.1630706Z         }
2023-01-11T21:41:24.1630765Z     }
2023-01-11T21:41:24.1630825Z }
2023-01-11T21:41:24.1630902Z ''')
2023-01-11T21:41:24.1630907Z 
2023-01-11T21:41:24.1630911Z 
2023-01-11T21:41:24.1631000Z async_compile.wait(globals())
2023-01-11T21:41:24.1631059Z del async_compile
2023-01-11T21:41:24.1631075Z 
2023-01-11T21:41:24.1631132Z def call(args):
2023-01-11T21:41:24.1631204Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1631273Z     args.clear()
2023-01-11T21:41:24.1631469Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1631631Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1631699Z     del arg0_1
2023-01-11T21:41:24.1631762Z     del arg1_1
2023-01-11T21:41:24.1631820Z     return (buf0, )
2023-01-11T21:41:24.1631825Z 
2023-01-11T21:41:24.1631829Z 
2023-01-11T21:41:24.1631902Z if __name__ == "__main__":
2023-01-11T21:41:24.1632046Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1632168Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1632359Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1632548Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1632660Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1632665Z 
2023-01-11T21:41:24.1632730Z ok (1.619s)
2023-01-11T21:41:24.1633199Z   test_cpu_int_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1633326Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1633586Z [2023-01-11 21:40:53,021] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 566
2023-01-11T21:41:24.1633942Z [2023-01-11 21:40:54,577] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 566
2023-01-11T21:41:24.1633949Z 
2023-01-11T21:41:24.1634044Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1634113Z import torch
2023-01-11T21:41:24.1634183Z import random
2023-01-11T21:41:24.1634297Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1634417Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1634421Z 
2023-01-11T21:41:24.1634485Z aten = torch.ops.aten
2023-01-11T21:41:24.1634617Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1634709Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1634714Z 
2023-01-11T21:41:24.1634718Z 
2023-01-11T21:41:24.1634857Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1635064Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1635178Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1635282Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1635380Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1635426Z {
2023-01-11T21:41:24.1635522Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1635583Z     {
2023-01-11T21:41:24.1635658Z         #pragma omp for 
2023-01-11T21:41:24.1635740Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1635801Z         {
2023-01-11T21:41:24.1635867Z             #pragma GCC ivdep
2023-01-11T21:41:24.1635951Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1636014Z             {
2023-01-11T21:41:24.1636077Z                 {
2023-01-11T21:41:24.1636141Z                     {
2023-01-11T21:41:24.1636236Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1636379Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1636475Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.1636566Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1636660Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1636725Z                     }
2023-01-11T21:41:24.1636787Z                 }
2023-01-11T21:41:24.1636849Z             }
2023-01-11T21:41:24.1636910Z         }
2023-01-11T21:41:24.1636959Z     }
2023-01-11T21:41:24.1637016Z }
2023-01-11T21:41:24.1637094Z ''')
2023-01-11T21:41:24.1637100Z 
2023-01-11T21:41:24.1637104Z 
2023-01-11T21:41:24.1637192Z async_compile.wait(globals())
2023-01-11T21:41:24.1637261Z del async_compile
2023-01-11T21:41:24.1637266Z 
2023-01-11T21:41:24.1637332Z def call(args):
2023-01-11T21:41:24.1637405Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1637462Z     args.clear()
2023-01-11T21:41:24.1637695Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1637856Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1637921Z     del arg0_1
2023-01-11T21:41:24.1637984Z     del arg1_1
2023-01-11T21:41:24.1638054Z     return (buf0, )
2023-01-11T21:41:24.1638059Z 
2023-01-11T21:41:24.1638063Z 
2023-01-11T21:41:24.1638135Z if __name__ == "__main__":
2023-01-11T21:41:24.1638251Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1638361Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1638555Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1638752Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1638863Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1638868Z 
2023-01-11T21:41:24.1638933Z ok (1.572s)
2023-01-11T21:41:24.1639417Z   test_cpu_int_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1639544Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1639805Z [2023-01-11 21:40:54,593] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 567
2023-01-11T21:41:24.1640069Z [2023-01-11 21:40:56,260] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 567
2023-01-11T21:41:24.1640074Z 
2023-01-11T21:41:24.1640169Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1640226Z import torch
2023-01-11T21:41:24.1640294Z import random
2023-01-11T21:41:24.1640408Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1640533Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1640538Z 
2023-01-11T21:41:24.1640617Z aten = torch.ops.aten
2023-01-11T21:41:24.1640749Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1640840Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1640845Z 
2023-01-11T21:41:24.1640849Z 
2023-01-11T21:41:24.1640983Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1641172Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1641288Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1641397Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1641497Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1641556Z {
2023-01-11T21:41:24.1641650Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1641711Z     {
2023-01-11T21:41:24.1641773Z         #pragma omp for 
2023-01-11T21:41:24.1641904Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1641965Z         {
2023-01-11T21:41:24.1642042Z             #pragma GCC ivdep
2023-01-11T21:41:24.1642126Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1642190Z             {
2023-01-11T21:41:24.1642243Z                 {
2023-01-11T21:41:24.1642306Z                     {
2023-01-11T21:41:24.1642399Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1642500Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1642609Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.1642700Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1642793Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1642847Z                     }
2023-01-11T21:41:24.1642911Z                 }
2023-01-11T21:41:24.1642973Z             }
2023-01-11T21:41:24.1643035Z         }
2023-01-11T21:41:24.1643096Z     }
2023-01-11T21:41:24.1643155Z }
2023-01-11T21:41:24.1643234Z ''')
2023-01-11T21:41:24.1643268Z 
2023-01-11T21:41:24.1643273Z 
2023-01-11T21:41:24.1643349Z async_compile.wait(globals())
2023-01-11T21:41:24.1643419Z del async_compile
2023-01-11T21:41:24.1643424Z 
2023-01-11T21:41:24.1643492Z def call(args):
2023-01-11T21:41:24.1643565Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1643636Z     args.clear()
2023-01-11T21:41:24.1643834Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1643993Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1644061Z     del arg0_1
2023-01-11T21:41:24.1644112Z     del arg1_1
2023-01-11T21:41:24.1644180Z     return (buf0, )
2023-01-11T21:41:24.1644184Z 
2023-01-11T21:41:24.1644188Z 
2023-01-11T21:41:24.1644261Z if __name__ == "__main__":
2023-01-11T21:41:24.1644372Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1644492Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1644689Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1644890Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1644993Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1645009Z 
2023-01-11T21:41:24.1645063Z ok (1.682s)
2023-01-11T21:41:24.1645541Z   test_cpu_int_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1645666Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1645923Z [2023-01-11 21:40:56,275] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 568
2023-01-11T21:41:24.1646191Z [2023-01-11 21:40:57,921] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 568
2023-01-11T21:41:24.1646196Z 
2023-01-11T21:41:24.1646286Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1646355Z import torch
2023-01-11T21:41:24.1646423Z import random
2023-01-11T21:41:24.1646537Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1646645Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1646650Z 
2023-01-11T21:41:24.1646726Z aten = torch.ops.aten
2023-01-11T21:41:24.1646856Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1646946Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1646950Z 
2023-01-11T21:41:24.1646955Z 
2023-01-11T21:41:24.1647086Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1647289Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1647405Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1647537Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1647619Z                        int* __restrict__ out_ptr0)
2023-01-11T21:41:24.1647676Z {
2023-01-11T21:41:24.1647772Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1647831Z     {
2023-01-11T21:41:24.1647905Z         #pragma omp for 
2023-01-11T21:41:24.1647985Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1648046Z         {
2023-01-11T21:41:24.1648095Z             {
2023-01-11T21:41:24.1648159Z                 {
2023-01-11T21:41:24.1648249Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1648338Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1648424Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1648508Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1648561Z                 }
2023-01-11T21:41:24.1648621Z             }
2023-01-11T21:41:24.1648681Z         }
2023-01-11T21:41:24.1648774Z     }
2023-01-11T21:41:24.1648833Z }
2023-01-11T21:41:24.1648911Z ''')
2023-01-11T21:41:24.1648916Z 
2023-01-11T21:41:24.1648920Z 
2023-01-11T21:41:24.1649007Z async_compile.wait(globals())
2023-01-11T21:41:24.1649066Z del async_compile
2023-01-11T21:41:24.1649081Z 
2023-01-11T21:41:24.1649138Z def call(args):
2023-01-11T21:41:24.1649210Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1649279Z     args.clear()
2023-01-11T21:41:24.1649471Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1649629Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1649694Z     del arg0_1
2023-01-11T21:41:24.1649758Z     del arg1_1
2023-01-11T21:41:24.1649816Z     return (buf0, )
2023-01-11T21:41:24.1649821Z 
2023-01-11T21:41:24.1649825Z 
2023-01-11T21:41:24.1649901Z if __name__ == "__main__":
2023-01-11T21:41:24.1650012Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1650139Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1650331Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1650519Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1650631Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1650636Z 
2023-01-11T21:41:24.1650701Z ok (1.661s)
2023-01-11T21:41:24.1651173Z   test_cpu_int_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1651298Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1651558Z [2023-01-11 21:40:57,938] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 569
2023-01-11T21:41:24.1651823Z [2023-01-11 21:40:59,536] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 569
2023-01-11T21:41:24.1651828Z 
2023-01-11T21:41:24.1651920Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1651990Z import torch
2023-01-11T21:41:24.1652059Z import random
2023-01-11T21:41:24.1652171Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1652289Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1652294Z 
2023-01-11T21:41:24.1652358Z aten = torch.ops.aten
2023-01-11T21:41:24.1652488Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1652577Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1652582Z 
2023-01-11T21:41:24.1652586Z 
2023-01-11T21:41:24.1652718Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1652923Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1653070Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1653174Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1653271Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1653319Z {
2023-01-11T21:41:24.1653413Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1653473Z     {
2023-01-11T21:41:24.1653548Z         #pragma omp for 
2023-01-11T21:41:24.1653628Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1653688Z         {
2023-01-11T21:41:24.1653766Z             #pragma GCC ivdep
2023-01-11T21:41:24.1653838Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1653900Z             {
2023-01-11T21:41:24.1653961Z                 {
2023-01-11T21:41:24.1654028Z                     {
2023-01-11T21:41:24.1654120Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:24.1654222Z                         auto tmp2 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1654357Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.1654452Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1654544Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1654609Z                     }
2023-01-11T21:41:24.1654672Z                 }
2023-01-11T21:41:24.1654735Z             }
2023-01-11T21:41:24.1654796Z         }
2023-01-11T21:41:24.1654844Z     }
2023-01-11T21:41:24.1654902Z }
2023-01-11T21:41:24.1654979Z ''')
2023-01-11T21:41:24.1654984Z 
2023-01-11T21:41:24.1654988Z 
2023-01-11T21:41:24.1655076Z async_compile.wait(globals())
2023-01-11T21:41:24.1655145Z del async_compile
2023-01-11T21:41:24.1655150Z 
2023-01-11T21:41:24.1655220Z def call(args):
2023-01-11T21:41:24.1655296Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1655366Z     args.clear()
2023-01-11T21:41:24.1655554Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1655725Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1655796Z     del arg0_1
2023-01-11T21:41:24.1655864Z     del arg1_1
2023-01-11T21:41:24.1655935Z     return (buf0, )
2023-01-11T21:41:24.1655939Z 
2023-01-11T21:41:24.1655943Z 
2023-01-11T21:41:24.1656020Z if __name__ == "__main__":
2023-01-11T21:41:24.1656135Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1656244Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1656435Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1656636Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1656750Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1656754Z 
2023-01-11T21:41:24.1656825Z ok (1.615s)
2023-01-11T21:41:24.1657316Z   test_cpu_int_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1657446Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1657709Z [2023-01-11 21:40:59,553] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 570
2023-01-11T21:41:24.1657977Z [2023-01-11 21:41:01,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 570
2023-01-11T21:41:24.1657982Z 
2023-01-11T21:41:24.1658079Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1658137Z import torch
2023-01-11T21:41:24.1658209Z import random
2023-01-11T21:41:24.1658324Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1658444Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1658483Z 
2023-01-11T21:41:24.1658565Z aten = torch.ops.aten
2023-01-11T21:41:24.1658700Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1658791Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1658796Z 
2023-01-11T21:41:24.1658800Z 
2023-01-11T21:41:24.1658935Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1659125Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1659242Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:24.1659347Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1659447Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1659509Z {
2023-01-11T21:41:24.1659607Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1659669Z     {
2023-01-11T21:41:24.1659733Z         #pragma omp for 
2023-01-11T21:41:24.1659815Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1659880Z         {
2023-01-11T21:41:24.1659993Z             #pragma GCC ivdep
2023-01-11T21:41:24.1660080Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1660143Z             {
2023-01-11T21:41:24.1660194Z                 {
2023-01-11T21:41:24.1660259Z                     {
2023-01-11T21:41:24.1660355Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1660458Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1660567Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:24.1660664Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1660763Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1660827Z                     }
2023-01-11T21:41:24.1660878Z                 }
2023-01-11T21:41:24.1660942Z             }
2023-01-11T21:41:24.1661001Z         }
2023-01-11T21:41:24.1661063Z     }
2023-01-11T21:41:24.1661123Z }
2023-01-11T21:41:24.1661205Z ''')
2023-01-11T21:41:24.1661210Z 
2023-01-11T21:41:24.1661217Z 
2023-01-11T21:41:24.1661305Z async_compile.wait(globals())
2023-01-11T21:41:24.1661364Z del async_compile
2023-01-11T21:41:24.1661368Z 
2023-01-11T21:41:24.1661436Z def call(args):
2023-01-11T21:41:24.1661507Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1661582Z     args.clear()
2023-01-11T21:41:24.1661781Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1661942Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1662007Z     del arg0_1
2023-01-11T21:41:24.1662060Z     del arg1_1
2023-01-11T21:41:24.1662128Z     return (buf0, )
2023-01-11T21:41:24.1662133Z 
2023-01-11T21:41:24.1662137Z 
2023-01-11T21:41:24.1662211Z if __name__ == "__main__":
2023-01-11T21:41:24.1662436Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1662622Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1662823Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1663026Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1663139Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1663144Z 
2023-01-11T21:41:24.1663197Z ok (1.588s)
2023-01-11T21:41:24.1663692Z   test_cpu_strided_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1663817Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1664078Z [2023-01-11 21:41:01,140] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 571
2023-01-11T21:41:24.1664342Z [2023-01-11 21:41:02,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 571
2023-01-11T21:41:24.1664410Z 
2023-01-11T21:41:24.1664504Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1664572Z import torch
2023-01-11T21:41:24.1664641Z import random
2023-01-11T21:41:24.1664753Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1664857Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1664862Z 
2023-01-11T21:41:24.1664937Z aten = torch.ops.aten
2023-01-11T21:41:24.1665067Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1665157Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1665162Z 
2023-01-11T21:41:24.1665166Z 
2023-01-11T21:41:24.1665299Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1665501Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1665620Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1665760Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1665847Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1665908Z {
2023-01-11T21:41:24.1666004Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1666066Z     {
2023-01-11T21:41:24.1666139Z         #pragma omp for 
2023-01-11T21:41:24.1666221Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1666282Z         {
2023-01-11T21:41:24.1666348Z             #pragma GCC ivdep
2023-01-11T21:41:24.1666430Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1666491Z             {
2023-01-11T21:41:24.1666554Z                 {
2023-01-11T21:41:24.1666617Z                     {
2023-01-11T21:41:24.1666723Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1666815Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1666895Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1666987Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1667057Z                     }
2023-01-11T21:41:24.1667120Z                 }
2023-01-11T21:41:24.1667181Z             }
2023-01-11T21:41:24.1667241Z         }
2023-01-11T21:41:24.1667289Z     }
2023-01-11T21:41:24.1667347Z }
2023-01-11T21:41:24.1667424Z ''')
2023-01-11T21:41:24.1667429Z 
2023-01-11T21:41:24.1667433Z 
2023-01-11T21:41:24.1667520Z async_compile.wait(globals())
2023-01-11T21:41:24.1667590Z del async_compile
2023-01-11T21:41:24.1667594Z 
2023-01-11T21:41:24.1667662Z def call(args):
2023-01-11T21:41:24.1667734Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1667801Z     args.clear()
2023-01-11T21:41:24.1667986Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1668148Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1668213Z     del arg0_1
2023-01-11T21:41:24.1668277Z     del arg1_1
2023-01-11T21:41:24.1668345Z     return (buf0, )
2023-01-11T21:41:24.1668354Z 
2023-01-11T21:41:24.1668358Z 
2023-01-11T21:41:24.1668432Z if __name__ == "__main__":
2023-01-11T21:41:24.1668543Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1668654Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1668853Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1669045Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1669157Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1669162Z 
2023-01-11T21:41:24.1669226Z ok (1.614s)
2023-01-11T21:41:24.1669721Z   test_cpu_strided_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1669916Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1670174Z [2023-01-11 21:41:02,754] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 572
2023-01-11T21:41:24.1670436Z [2023-01-11 21:41:04,314] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 572
2023-01-11T21:41:24.1670441Z 
2023-01-11T21:41:24.1670534Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1670590Z import torch
2023-01-11T21:41:24.1670660Z import random
2023-01-11T21:41:24.1670773Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1670892Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1670896Z 
2023-01-11T21:41:24.1670974Z aten = torch.ops.aten
2023-01-11T21:41:24.1671106Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1671198Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1671234Z 
2023-01-11T21:41:24.1671238Z 
2023-01-11T21:41:24.1671371Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1671559Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1671676Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1671780Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1671878Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1671937Z {
2023-01-11T21:41:24.1672031Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1672091Z     {
2023-01-11T21:41:24.1672154Z         #pragma omp for 
2023-01-11T21:41:24.1672234Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1672296Z         {
2023-01-11T21:41:24.1672372Z             #pragma GCC ivdep
2023-01-11T21:41:24.1672457Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1672518Z             {
2023-01-11T21:41:24.1672581Z                 {
2023-01-11T21:41:24.1672638Z                     {
2023-01-11T21:41:24.1672742Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1672833Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1672926Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1673020Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1673084Z                     }
2023-01-11T21:41:24.1673146Z                 }
2023-01-11T21:41:24.1673196Z             }
2023-01-11T21:41:24.1673256Z         }
2023-01-11T21:41:24.1673315Z     }
2023-01-11T21:41:24.1673374Z }
2023-01-11T21:41:24.1673452Z ''')
2023-01-11T21:41:24.1673457Z 
2023-01-11T21:41:24.1673461Z 
2023-01-11T21:41:24.1673550Z async_compile.wait(globals())
2023-01-11T21:41:24.1673619Z del async_compile
2023-01-11T21:41:24.1673624Z 
2023-01-11T21:41:24.1673681Z def call(args):
2023-01-11T21:41:24.1673810Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1673883Z     args.clear()
2023-01-11T21:41:24.1674097Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1674259Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1674326Z     del arg0_1
2023-01-11T21:41:24.1674392Z     del arg1_1
2023-01-11T21:41:24.1674449Z     return (buf0, )
2023-01-11T21:41:24.1674454Z 
2023-01-11T21:41:24.1674458Z 
2023-01-11T21:41:24.1674533Z if __name__ == "__main__":
2023-01-11T21:41:24.1674644Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1674770Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1674970Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1675177Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1675292Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1675297Z 
2023-01-11T21:41:24.1675365Z ok (1.576s)
2023-01-11T21:41:24.1675891Z   test_cpu_strided_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1676007Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1676267Z [2023-01-11 21:41:04,330] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 573
2023-01-11T21:41:24.1676530Z [2023-01-11 21:41:05,874] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 573
2023-01-11T21:41:24.1676535Z 
2023-01-11T21:41:24.1676629Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1676696Z import torch
2023-01-11T21:41:24.1676765Z import random
2023-01-11T21:41:24.1676908Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1677033Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1677038Z 
2023-01-11T21:41:24.1677104Z aten = torch.ops.aten
2023-01-11T21:41:24.1677235Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1677326Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1677331Z 
2023-01-11T21:41:24.1677335Z 
2023-01-11T21:41:24.1677469Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1677671Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1677790Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1677895Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1677992Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1678041Z {
2023-01-11T21:41:24.1678136Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1678198Z     {
2023-01-11T21:41:24.1678273Z         #pragma omp for 
2023-01-11T21:41:24.1678355Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1678417Z         {
2023-01-11T21:41:24.1678495Z             #pragma GCC ivdep
2023-01-11T21:41:24.1678567Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1678629Z             {
2023-01-11T21:41:24.1678692Z                 {
2023-01-11T21:41:24.1678756Z                     {
2023-01-11T21:41:24.1678860Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1678950Z                         auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.1679042Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1679125Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1679188Z                     }
2023-01-11T21:41:24.1679250Z                 }
2023-01-11T21:41:24.1679311Z             }
2023-01-11T21:41:24.1679370Z         }
2023-01-11T21:41:24.1679430Z     }
2023-01-11T21:41:24.1679477Z }
2023-01-11T21:41:24.1679555Z ''')
2023-01-11T21:41:24.1679562Z 
2023-01-11T21:41:24.1679566Z 
2023-01-11T21:41:24.1679653Z async_compile.wait(globals())
2023-01-11T21:41:24.1679725Z del async_compile
2023-01-11T21:41:24.1679730Z 
2023-01-11T21:41:24.1679800Z def call(args):
2023-01-11T21:41:24.1679873Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1679942Z     args.clear()
2023-01-11T21:41:24.1680138Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1680286Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1680351Z     del arg0_1
2023-01-11T21:41:24.1680414Z     del arg1_1
2023-01-11T21:41:24.1680483Z     return (buf0, )
2023-01-11T21:41:24.1680488Z 
2023-01-11T21:41:24.1680492Z 
2023-01-11T21:41:24.1680565Z if __name__ == "__main__":
2023-01-11T21:41:24.1680675Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1680794Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1681040Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1681217Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1681329Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1681334Z 
2023-01-11T21:41:24.1681401Z ok (1.560s)
2023-01-11T21:41:24.1681889Z   test_cpu_strided_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1682015Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1682275Z [2023-01-11 21:41:05,894] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 574
2023-01-11T21:41:24.1682572Z [2023-01-11 21:41:07,492] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 574
2023-01-11T21:41:24.1682578Z 
2023-01-11T21:41:24.1682671Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1682742Z import torch
2023-01-11T21:41:24.1682800Z import random
2023-01-11T21:41:24.1682910Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1683028Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1683033Z 
2023-01-11T21:41:24.1683107Z aten = torch.ops.aten
2023-01-11T21:41:24.1683238Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1683326Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1683331Z 
2023-01-11T21:41:24.1683335Z 
2023-01-11T21:41:24.1683467Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1683670Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1683781Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1683883Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1683979Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1684038Z {
2023-01-11T21:41:24.1684133Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1684194Z     {
2023-01-11T21:41:24.1684269Z         #pragma omp for 
2023-01-11T21:41:24.1684336Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1684396Z         {
2023-01-11T21:41:24.1684474Z             #pragma GCC ivdep
2023-01-11T21:41:24.1684556Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1684618Z             {
2023-01-11T21:41:24.1684680Z                 {
2023-01-11T21:41:24.1684744Z                     {
2023-01-11T21:41:24.1684836Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1684937Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1685030Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1685128Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1685192Z                     }
2023-01-11T21:41:24.1685252Z                 }
2023-01-11T21:41:24.1685314Z             }
2023-01-11T21:41:24.1685363Z         }
2023-01-11T21:41:24.1685422Z     }
2023-01-11T21:41:24.1685480Z }
2023-01-11T21:41:24.1685556Z ''')
2023-01-11T21:41:24.1685561Z 
2023-01-11T21:41:24.1685565Z 
2023-01-11T21:41:24.1685652Z async_compile.wait(globals())
2023-01-11T21:41:24.1685721Z del async_compile
2023-01-11T21:41:24.1685725Z 
2023-01-11T21:41:24.1685792Z def call(args):
2023-01-11T21:41:24.1685854Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1685922Z     args.clear()
2023-01-11T21:41:24.1686118Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1686279Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1686344Z     del arg0_1
2023-01-11T21:41:24.1686439Z     del arg1_1
2023-01-11T21:41:24.1686509Z     return (buf0, )
2023-01-11T21:41:24.1686513Z 
2023-01-11T21:41:24.1686517Z 
2023-01-11T21:41:24.1686589Z if __name__ == "__main__":
2023-01-11T21:41:24.1686691Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1686812Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1687009Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1687204Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1687316Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1687321Z 
2023-01-11T21:41:24.1687388Z ok (1.618s)
2023-01-11T21:41:24.1687907Z   test_cpu_strided_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1688037Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1688296Z [2023-01-11 21:41:07,509] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 575
2023-01-11T21:41:24.1688549Z [2023-01-11 21:41:09,077] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 575
2023-01-11T21:41:24.1688553Z 
2023-01-11T21:41:24.1688649Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1688719Z import torch
2023-01-11T21:41:24.1688790Z import random
2023-01-11T21:41:24.1688905Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1689025Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1689030Z 
2023-01-11T21:41:24.1689108Z aten = torch.ops.aten
2023-01-11T21:41:24.1689230Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1689323Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1689328Z 
2023-01-11T21:41:24.1689332Z 
2023-01-11T21:41:24.1689466Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1689668Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1689787Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1689895Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1689997Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1690058Z {
2023-01-11T21:41:24.1690143Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1690203Z     {
2023-01-11T21:41:24.1690279Z         #pragma omp for 
2023-01-11T21:41:24.1690361Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1690423Z         {
2023-01-11T21:41:24.1690504Z             #pragma GCC ivdep
2023-01-11T21:41:24.1690589Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1690645Z             {
2023-01-11T21:41:24.1690710Z                 {
2023-01-11T21:41:24.1690776Z                     {
2023-01-11T21:41:24.1690881Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1690984Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1691095Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.1691190Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1691272Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1691339Z                     }
2023-01-11T21:41:24.1691407Z                 }
2023-01-11T21:41:24.1691471Z             }
2023-01-11T21:41:24.1691535Z         }
2023-01-11T21:41:24.1691597Z     }
2023-01-11T21:41:24.1691658Z }
2023-01-11T21:41:24.1691723Z ''')
2023-01-11T21:41:24.1691728Z 
2023-01-11T21:41:24.1691732Z 
2023-01-11T21:41:24.1691822Z async_compile.wait(globals())
2023-01-11T21:41:24.1691894Z del async_compile
2023-01-11T21:41:24.1691927Z 
2023-01-11T21:41:24.1692000Z def call(args):
2023-01-11T21:41:24.1692076Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1692146Z     args.clear()
2023-01-11T21:41:24.1692348Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1692498Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1692566Z     del arg0_1
2023-01-11T21:41:24.1692633Z     del arg1_1
2023-01-11T21:41:24.1692701Z     return (buf0, )
2023-01-11T21:41:24.1692706Z 
2023-01-11T21:41:24.1692710Z 
2023-01-11T21:41:24.1692786Z if __name__ == "__main__":
2023-01-11T21:41:24.1692899Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1693022Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1693220Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1693403Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1693562Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1693567Z 
2023-01-11T21:41:24.1693634Z ok (1.585s)
2023-01-11T21:41:24.1694118Z   test_cpu_strided_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1694241Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1694502Z [2023-01-11 21:41:09,092] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 576
2023-01-11T21:41:24.1694771Z [2023-01-11 21:41:10,658] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 576
2023-01-11T21:41:24.1694779Z 
2023-01-11T21:41:24.1694878Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1694951Z import torch
2023-01-11T21:41:24.1695007Z import random
2023-01-11T21:41:24.1695118Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1695236Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1695240Z 
2023-01-11T21:41:24.1695315Z aten = torch.ops.aten
2023-01-11T21:41:24.1695447Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1695536Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1695541Z 
2023-01-11T21:41:24.1695545Z 
2023-01-11T21:41:24.1695678Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1695881Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1695987Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1696088Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1696189Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1696251Z {
2023-01-11T21:41:24.1696348Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1696409Z     {
2023-01-11T21:41:24.1696485Z         #pragma omp for 
2023-01-11T21:41:24.1696554Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1696614Z         {
2023-01-11T21:41:24.1696693Z             #pragma GCC ivdep
2023-01-11T21:41:24.1696775Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1696836Z             {
2023-01-11T21:41:24.1696902Z                 {
2023-01-11T21:41:24.1696966Z                     {
2023-01-11T21:41:24.1697058Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1697149Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1697258Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:24.1697350Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1697444Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1697540Z                     }
2023-01-11T21:41:24.1697604Z                 }
2023-01-11T21:41:24.1697654Z             }
2023-01-11T21:41:24.1697713Z         }
2023-01-11T21:41:24.1697773Z     }
2023-01-11T21:41:24.1697833Z }
2023-01-11T21:41:24.1697915Z ''')
2023-01-11T21:41:24.1697920Z 
2023-01-11T21:41:24.1697924Z 
2023-01-11T21:41:24.1698012Z async_compile.wait(globals())
2023-01-11T21:41:24.1698081Z del async_compile
2023-01-11T21:41:24.1698085Z 
2023-01-11T21:41:24.1698142Z def call(args):
2023-01-11T21:41:24.1698216Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1698287Z     args.clear()
2023-01-11T21:41:24.1698484Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1698643Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1698709Z     del arg0_1
2023-01-11T21:41:24.1698774Z     del arg1_1
2023-01-11T21:41:24.1698831Z     return (buf0, )
2023-01-11T21:41:24.1698850Z 
2023-01-11T21:41:24.1698885Z 
2023-01-11T21:41:24.1698947Z if __name__ == "__main__":
2023-01-11T21:41:24.1699058Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1699178Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1699374Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1699567Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1699677Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1699682Z 
2023-01-11T21:41:24.1699748Z ok (1.581s)
2023-01-11T21:41:24.1700233Z   test_cpu_strided_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1700359Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1700608Z [2023-01-11 21:41:10,674] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 577
2023-01-11T21:41:24.1700866Z [2023-01-11 21:41:12,725] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 577
2023-01-11T21:41:24.1700871Z 
2023-01-11T21:41:24.1700962Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1701030Z import torch
2023-01-11T21:41:24.1701098Z import random
2023-01-11T21:41:24.1701207Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1701323Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1701328Z 
2023-01-11T21:41:24.1701402Z aten = torch.ops.aten
2023-01-11T21:41:24.1701520Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1701609Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1701616Z 
2023-01-11T21:41:24.1701622Z 
2023-01-11T21:41:24.1701754Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1701956Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1702074Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1702172Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1702274Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1702466Z {
2023-01-11T21:41:24.1702553Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1702613Z     {
2023-01-11T21:41:24.1702689Z         #pragma omp for 
2023-01-11T21:41:24.1702770Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1702834Z         {
2023-01-11T21:41:24.1702912Z             #pragma GCC ivdep
2023-01-11T21:41:24.1702985Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1703049Z             {
2023-01-11T21:41:24.1703112Z                 {
2023-01-11T21:41:24.1703231Z                     {
2023-01-11T21:41:24.1703340Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1703442Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1703536Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1703619Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1703686Z                     }
2023-01-11T21:41:24.1703748Z                 }
2023-01-11T21:41:24.1703812Z             }
2023-01-11T21:41:24.1703874Z         }
2023-01-11T21:41:24.1703938Z     }
2023-01-11T21:41:24.1703998Z }
2023-01-11T21:41:24.1704066Z ''')
2023-01-11T21:41:24.1704070Z 
2023-01-11T21:41:24.1704075Z 
2023-01-11T21:41:24.1704164Z async_compile.wait(globals())
2023-01-11T21:41:24.1704234Z del async_compile
2023-01-11T21:41:24.1704239Z 
2023-01-11T21:41:24.1704307Z def call(args):
2023-01-11T21:41:24.1704381Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1704450Z     args.clear()
2023-01-11T21:41:24.1704684Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1704837Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1704906Z     del arg0_1
2023-01-11T21:41:24.1704969Z     del arg1_1
2023-01-11T21:41:24.1705038Z     return (buf0, )
2023-01-11T21:41:24.1705044Z 
2023-01-11T21:41:24.1705048Z 
2023-01-11T21:41:24.1705124Z if __name__ == "__main__":
2023-01-11T21:41:24.1705235Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1705356Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1705554Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1705738Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1705851Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1705855Z 
2023-01-11T21:41:24.1705920Z ok (2.067s)
2023-01-11T21:41:24.1706418Z   test_cpu_strided_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1706544Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1706801Z [2023-01-11 21:41:12,742] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 578
2023-01-11T21:41:24.1707066Z [2023-01-11 21:41:14,658] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 578
2023-01-11T21:41:24.1707071Z 
2023-01-11T21:41:24.1707162Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1707229Z import torch
2023-01-11T21:41:24.1707297Z import random
2023-01-11T21:41:24.1707401Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1707522Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1707527Z 
2023-01-11T21:41:24.1707601Z aten = torch.ops.aten
2023-01-11T21:41:24.1707733Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1707821Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1707826Z 
2023-01-11T21:41:24.1707830Z 
2023-01-11T21:41:24.1707962Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1708165Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1708281Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1708372Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1708469Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1708528Z {
2023-01-11T21:41:24.1708626Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1708688Z     {
2023-01-11T21:41:24.1708794Z         #pragma omp for 
2023-01-11T21:41:24.1708875Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1708925Z         {
2023-01-11T21:41:24.1709004Z             #pragma GCC ivdep
2023-01-11T21:41:24.1709086Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1709147Z             {
2023-01-11T21:41:24.1709208Z                 {
2023-01-11T21:41:24.1709271Z                     {
2023-01-11T21:41:24.1709361Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1709461Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:41:24.1709553Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1709648Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1709712Z                     }
2023-01-11T21:41:24.1709774Z                 }
2023-01-11T21:41:24.1709835Z             }
2023-01-11T21:41:24.1709884Z         }
2023-01-11T21:41:24.1709945Z     }
2023-01-11T21:41:24.1710004Z }
2023-01-11T21:41:24.1710082Z ''')
2023-01-11T21:41:24.1710115Z 
2023-01-11T21:41:24.1710120Z 
2023-01-11T21:41:24.1710208Z async_compile.wait(globals())
2023-01-11T21:41:24.1710278Z del async_compile
2023-01-11T21:41:24.1710283Z 
2023-01-11T21:41:24.1710351Z def call(args):
2023-01-11T21:41:24.1710413Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1710482Z     args.clear()
2023-01-11T21:41:24.1710680Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1710839Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1710905Z     del arg0_1
2023-01-11T21:41:24.1710969Z     del arg1_1
2023-01-11T21:41:24.1711039Z     return (buf0, )
2023-01-11T21:41:24.1711044Z 
2023-01-11T21:41:24.1711048Z 
2023-01-11T21:41:24.1711120Z if __name__ == "__main__":
2023-01-11T21:41:24.1711221Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1711341Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1711543Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1711742Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1711853Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1711858Z 
2023-01-11T21:41:24.1711922Z ok (1.933s)
2023-01-11T21:41:24.1712416Z   test_cpu_transposed_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1712541Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1712799Z [2023-01-11 21:41:14,681] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 579
2023-01-11T21:41:24.1713055Z [2023-01-11 21:41:14,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 579
2023-01-11T21:41:24.1713072Z 
2023-01-11T21:41:24.1713153Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1713220Z import torch
2023-01-11T21:41:24.1713288Z import random
2023-01-11T21:41:24.1713400Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1713519Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1713524Z 
2023-01-11T21:41:24.1713602Z aten = torch.ops.aten
2023-01-11T21:41:24.1713793Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1713877Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1713882Z 
2023-01-11T21:41:24.1713901Z 
2023-01-11T21:41:24.1714025Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1714228Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1714347Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1714491Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1714590Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1714651Z {
2023-01-11T21:41:24.1714747Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1714795Z     {
2023-01-11T21:41:24.1714872Z         #pragma omp for 
2023-01-11T21:41:24.1714957Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1715020Z         {
2023-01-11T21:41:24.1715103Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1715164Z             {
2023-01-11T21:41:24.1715318Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1715431Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[i0]);
2023-01-11T21:41:24.1715516Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1715618Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1715683Z             }
2023-01-11T21:41:24.1715806Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1715889Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1715951Z             {
2023-01-11T21:41:24.1716034Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1716116Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1716200Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1716289Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1716352Z             }
2023-01-11T21:41:24.1716411Z         }
2023-01-11T21:41:24.1716475Z     }
2023-01-11T21:41:24.1716521Z }
2023-01-11T21:41:24.1716598Z ''')
2023-01-11T21:41:24.1716603Z 
2023-01-11T21:41:24.1716607Z 
2023-01-11T21:41:24.1716695Z async_compile.wait(globals())
2023-01-11T21:41:24.1716765Z del async_compile
2023-01-11T21:41:24.1716770Z 
2023-01-11T21:41:24.1716837Z def call(args):
2023-01-11T21:41:24.1716911Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1716980Z     args.clear()
2023-01-11T21:41:24.1717173Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1717333Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1717401Z     del arg0_1
2023-01-11T21:41:24.1717465Z     del arg1_1
2023-01-11T21:41:24.1717536Z     return (buf0, )
2023-01-11T21:41:24.1717541Z 
2023-01-11T21:41:24.1717545Z 
2023-01-11T21:41:24.1717620Z if __name__ == "__main__":
2023-01-11T21:41:24.1717731Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1717851Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1718037Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1718229Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1718340Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1718345Z 
2023-01-11T21:41:24.1718409Z ok (0.037s)
2023-01-11T21:41:24.1718909Z   test_cpu_transposed_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1719033Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1719293Z [2023-01-11 21:41:14,713] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 580
2023-01-11T21:41:24.1719556Z [2023-01-11 21:41:14,722] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 580
2023-01-11T21:41:24.1719561Z 
2023-01-11T21:41:24.1719654Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1719709Z import torch
2023-01-11T21:41:24.1719776Z import random
2023-01-11T21:41:24.1719934Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1720053Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1720058Z 
2023-01-11T21:41:24.1720136Z aten = torch.ops.aten
2023-01-11T21:41:24.1720269Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1720362Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1720367Z 
2023-01-11T21:41:24.1720371Z 
2023-01-11T21:41:24.1720506Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1720696Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1720816Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1720920Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1721020Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1721085Z {
2023-01-11T21:41:24.1721185Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1721246Z     {
2023-01-11T21:41:24.1721339Z         #pragma omp for 
2023-01-11T21:41:24.1721423Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1721487Z         {
2023-01-11T21:41:24.1721571Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:24.1721635Z             {
2023-01-11T21:41:24.1721775Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1721910Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:24.1721998Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1722089Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:24.1722156Z             }
2023-01-11T21:41:24.1722248Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:24.1722331Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:24.1722394Z             {
2023-01-11T21:41:24.1722493Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1722578Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:24.1722655Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1722747Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:24.1722815Z             }
2023-01-11T21:41:24.1722878Z         }
2023-01-11T21:41:24.1722941Z     }
2023-01-11T21:41:24.1723003Z }
2023-01-11T21:41:24.1723068Z ''')
2023-01-11T21:41:24.1723074Z 
2023-01-11T21:41:24.1723091Z 
2023-01-11T21:41:24.1723167Z async_compile.wait(globals())
2023-01-11T21:41:24.1723238Z del async_compile
2023-01-11T21:41:24.1723243Z 
2023-01-11T21:41:24.1723314Z def call(args):
2023-01-11T21:41:24.1723389Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1723460Z     args.clear()
2023-01-11T21:41:24.1723673Z     buf0 = empty_strided((1, 10, 10), (100, 1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1723836Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1723891Z     del arg0_1
2023-01-11T21:41:24.1723957Z     del arg1_1
2023-01-11T21:41:24.1724033Z     return (buf0, )
2023-01-11T21:41:24.1724039Z 
2023-01-11T21:41:24.1724042Z 
2023-01-11T21:41:24.1724117Z if __name__ == "__main__":
2023-01-11T21:41:24.1724232Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1724356Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1724556Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1724762Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1724865Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1724870Z 
2023-01-11T21:41:24.1724937Z ok (0.026s)
2023-01-11T21:41:24.1725441Z   test_cpu_transposed_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1725598Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1725861Z [2023-01-11 21:41:14,736] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 581
2023-01-11T21:41:24.1726126Z [2023-01-11 21:41:14,746] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 581
2023-01-11T21:41:24.1726132Z 
2023-01-11T21:41:24.1726226Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1726293Z import torch
2023-01-11T21:41:24.1726360Z import random
2023-01-11T21:41:24.1726460Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1726576Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1726581Z 
2023-01-11T21:41:24.1726659Z aten = torch.ops.aten
2023-01-11T21:41:24.1726792Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1726911Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1726917Z 
2023-01-11T21:41:24.1726921Z 
2023-01-11T21:41:24.1727054Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1727253Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1727371Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1727461Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1727558Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1727620Z {
2023-01-11T21:41:24.1727718Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1727778Z     {
2023-01-11T21:41:24.1727851Z         #pragma omp for 
2023-01-11T21:41:24.1727931Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.1727981Z         {
2023-01-11T21:41:24.1728113Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1728235Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:24.1728320Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1728409Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1728468Z         }
2023-01-11T21:41:24.1728562Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1728633Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:24.1728694Z         {
2023-01-11T21:41:24.1728778Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1728858Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:24.1728940Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1729021Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1729082Z         }
2023-01-11T21:41:24.1729129Z     }
2023-01-11T21:41:24.1729186Z }
2023-01-11T21:41:24.1729265Z ''')
2023-01-11T21:41:24.1729270Z 
2023-01-11T21:41:24.1729274Z 
2023-01-11T21:41:24.1729361Z async_compile.wait(globals())
2023-01-11T21:41:24.1729431Z del async_compile
2023-01-11T21:41:24.1729436Z 
2023-01-11T21:41:24.1729504Z def call(args):
2023-01-11T21:41:24.1729581Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1729638Z     args.clear()
2023-01-11T21:41:24.1729838Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1729998Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1730065Z     del arg0_1
2023-01-11T21:41:24.1730129Z     del arg1_1
2023-01-11T21:41:24.1730199Z     return (buf0, )
2023-01-11T21:41:24.1730204Z 
2023-01-11T21:41:24.1730208Z 
2023-01-11T21:41:24.1730280Z if __name__ == "__main__":
2023-01-11T21:41:24.1730390Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1730499Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1730703Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1730893Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1731007Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1731041Z 
2023-01-11T21:41:24.1731107Z ok (0.026s)
2023-01-11T21:41:24.1731604Z   test_cpu_transposed_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1731731Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1731988Z [2023-01-11 21:41:14,772] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 582
2023-01-11T21:41:24.1732257Z [2023-01-11 21:41:16,690] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 582
2023-01-11T21:41:24.1732262Z 
2023-01-11T21:41:24.1732353Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1732439Z import torch
2023-01-11T21:41:24.1732509Z import random
2023-01-11T21:41:24.1732622Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1732739Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1732744Z 
2023-01-11T21:41:24.1732819Z aten = torch.ops.aten
2023-01-11T21:41:24.1732950Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1733041Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1733046Z 
2023-01-11T21:41:24.1733050Z 
2023-01-11T21:41:24.1733170Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1733373Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1733491Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1733594Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1733695Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1733754Z {
2023-01-11T21:41:24.1733854Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1733912Z     {
2023-01-11T21:41:24.1733975Z         #pragma omp for 
2023-01-11T21:41:24.1734055Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1734117Z         {
2023-01-11T21:41:24.1734195Z             #pragma GCC ivdep
2023-01-11T21:41:24.1734278Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1734341Z             {
2023-01-11T21:41:24.1734391Z                 {
2023-01-11T21:41:24.1734455Z                     {
2023-01-11T21:41:24.1734560Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:41:24.1734657Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1734748Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1734842Z                         out_ptr0[i0 + (10*i1)] = tmp2;
2023-01-11T21:41:24.1734906Z                     }
2023-01-11T21:41:24.1734958Z                 }
2023-01-11T21:41:24.1735018Z             }
2023-01-11T21:41:24.1735081Z         }
2023-01-11T21:41:24.1735144Z     }
2023-01-11T21:41:24.1735204Z }
2023-01-11T21:41:24.1735282Z ''')
2023-01-11T21:41:24.1735287Z 
2023-01-11T21:41:24.1735291Z 
2023-01-11T21:41:24.1735382Z async_compile.wait(globals())
2023-01-11T21:41:24.1735440Z del async_compile
2023-01-11T21:41:24.1735457Z 
2023-01-11T21:41:24.1735514Z def call(args):
2023-01-11T21:41:24.1735586Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1735655Z     args.clear()
2023-01-11T21:41:24.1735855Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1736013Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1736078Z     del arg0_1
2023-01-11T21:41:24.1736131Z     del arg1_1
2023-01-11T21:41:24.1736199Z     return (buf0, )
2023-01-11T21:41:24.1736204Z 
2023-01-11T21:41:24.1736208Z 
2023-01-11T21:41:24.1736282Z if __name__ == "__main__":
2023-01-11T21:41:24.1736393Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1736548Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1736746Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1736942Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1737054Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1737059Z 
2023-01-11T21:41:24.1737122Z ok (1.944s)
2023-01-11T21:41:24.1737604Z   test_cpu_transposed_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1737729Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1738019Z [2023-01-11 21:41:16,710] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 583
2023-01-11T21:41:24.1738284Z [2023-01-11 21:41:18,662] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 583
2023-01-11T21:41:24.1738289Z 
2023-01-11T21:41:24.1738381Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1738447Z import torch
2023-01-11T21:41:24.1738516Z import random
2023-01-11T21:41:24.1738629Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1738751Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1738755Z 
2023-01-11T21:41:24.1738820Z aten = torch.ops.aten
2023-01-11T21:41:24.1738951Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1739040Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1739045Z 
2023-01-11T21:41:24.1739049Z 
2023-01-11T21:41:24.1739181Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1739387Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1739506Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1739610Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:24.1739713Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:24.1739760Z {
2023-01-11T21:41:24.1739857Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1739917Z     {
2023-01-11T21:41:24.1739994Z         #pragma omp for 
2023-01-11T21:41:24.1740076Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1740136Z         {
2023-01-11T21:41:24.1740202Z             #pragma GCC ivdep
2023-01-11T21:41:24.1740284Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1740347Z             {
2023-01-11T21:41:24.1740408Z                 {
2023-01-11T21:41:24.1740473Z                     {
2023-01-11T21:41:24.1740576Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:41:24.1740677Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:24.1740777Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:24.1740869Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:24.1740961Z                         out_ptr0[i0 + (10*i1)] = tmp3;
2023-01-11T21:41:24.1741025Z                     }
2023-01-11T21:41:24.1741089Z                 }
2023-01-11T21:41:24.1741150Z             }
2023-01-11T21:41:24.1741210Z         }
2023-01-11T21:41:24.1741258Z     }
2023-01-11T21:41:24.1741316Z }
2023-01-11T21:41:24.1741392Z ''')
2023-01-11T21:41:24.1741398Z 
2023-01-11T21:41:24.1741401Z 
2023-01-11T21:41:24.1741490Z async_compile.wait(globals())
2023-01-11T21:41:24.1741560Z del async_compile
2023-01-11T21:41:24.1741565Z 
2023-01-11T21:41:24.1741633Z def call(args):
2023-01-11T21:41:24.1741705Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1741762Z     args.clear()
2023-01-11T21:41:24.1741959Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1742163Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1742232Z     del arg0_1
2023-01-11T21:41:24.1742298Z     del arg1_1
2023-01-11T21:41:24.1742508Z     return (buf0, )
2023-01-11T21:41:24.1742516Z 
2023-01-11T21:41:24.1742521Z 
2023-01-11T21:41:24.1742616Z if __name__ == "__main__":
2023-01-11T21:41:24.1742733Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1742843Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1743048Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1743243Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:24.1743358Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1743363Z 
2023-01-11T21:41:24.1743430Z ok (1.970s)
2023-01-11T21:41:24.1743980Z   test_cpu_transposed_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1744112Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1744377Z [2023-01-11 21:41:18,677] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 584
2023-01-11T21:41:24.1744644Z [2023-01-11 21:41:20,452] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 584
2023-01-11T21:41:24.1744650Z 
2023-01-11T21:41:24.1744743Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1744803Z import torch
2023-01-11T21:41:24.1744873Z import random
2023-01-11T21:41:24.1744989Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1745116Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1745121Z 
2023-01-11T21:41:24.1745203Z aten = torch.ops.aten
2023-01-11T21:41:24.1745338Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1745428Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1745434Z 
2023-01-11T21:41:24.1745438Z 
2023-01-11T21:41:24.1745575Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1745769Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1745886Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1745992Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:24.1746090Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1746149Z {
2023-01-11T21:41:24.1746245Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1746303Z     {
2023-01-11T21:41:24.1746366Z         #pragma omp for 
2023-01-11T21:41:24.1746447Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1746511Z         {
2023-01-11T21:41:24.1746589Z             #pragma GCC ivdep
2023-01-11T21:41:24.1746674Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1746735Z             {
2023-01-11T21:41:24.1746786Z                 {
2023-01-11T21:41:24.1746851Z                     {
2023-01-11T21:41:24.1746952Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:24.1747046Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1747155Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:24.1747246Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:24.1747341Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:24.1747405Z                     }
2023-01-11T21:41:24.1747457Z                 }
2023-01-11T21:41:24.1747518Z             }
2023-01-11T21:41:24.1747579Z         }
2023-01-11T21:41:24.1747641Z     }
2023-01-11T21:41:24.1747701Z }
2023-01-11T21:41:24.1747823Z ''')
2023-01-11T21:41:24.1747831Z 
2023-01-11T21:41:24.1747835Z 
2023-01-11T21:41:24.1747927Z async_compile.wait(globals())
2023-01-11T21:41:24.1747988Z del async_compile
2023-01-11T21:41:24.1747993Z 
2023-01-11T21:41:24.1748062Z def call(args):
2023-01-11T21:41:24.1748136Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1748205Z     args.clear()
2023-01-11T21:41:24.1748403Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1748566Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1748633Z     del arg0_1
2023-01-11T21:41:24.1748687Z     del arg1_1
2023-01-11T21:41:24.1748761Z     return (buf0, )
2023-01-11T21:41:24.1748765Z 
2023-01-11T21:41:24.1748769Z 
2023-01-11T21:41:24.1748843Z if __name__ == "__main__":
2023-01-11T21:41:24.1748959Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1749082Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1749311Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1749505Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:24.1749619Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1749624Z 
2023-01-11T21:41:24.1749676Z ok (1.791s)
2023-01-11T21:41:24.1750170Z   test_cpu_transposed_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1750295Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1750556Z [2023-01-11 21:41:20,468] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 585
2023-01-11T21:41:24.1750823Z [2023-01-11 21:41:22,295] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 585
2023-01-11T21:41:24.1750828Z 
2023-01-11T21:41:24.1750923Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1750992Z import torch
2023-01-11T21:41:24.1751059Z import random
2023-01-11T21:41:24.1751171Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1751279Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1751284Z 
2023-01-11T21:41:24.1751360Z aten = torch.ops.aten
2023-01-11T21:41:24.1751491Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1751581Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1751586Z 
2023-01-11T21:41:24.1751590Z 
2023-01-11T21:41:24.1751721Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1751925Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1752045Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1752150Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1752237Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1752296Z {
2023-01-11T21:41:24.1752391Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1752453Z     {
2023-01-11T21:41:24.1752533Z         #pragma omp for 
2023-01-11T21:41:24.1752617Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:24.1752681Z         {
2023-01-11T21:41:24.1752746Z             #pragma GCC ivdep
2023-01-11T21:41:24.1752832Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:24.1752896Z             {
2023-01-11T21:41:24.1752960Z                 {
2023-01-11T21:41:24.1753028Z                     {
2023-01-11T21:41:24.1753132Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:41:24.1753237Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:24.1753321Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1753450Z                         out_ptr0[i0 + (10*i1)] = tmp2;
2023-01-11T21:41:24.1753517Z                     }
2023-01-11T21:41:24.1753582Z                 }
2023-01-11T21:41:24.1753647Z             }
2023-01-11T21:41:24.1753709Z         }
2023-01-11T21:41:24.1753817Z     }
2023-01-11T21:41:24.1753878Z }
2023-01-11T21:41:24.1753959Z ''')
2023-01-11T21:41:24.1753965Z 
2023-01-11T21:41:24.1753969Z 
2023-01-11T21:41:24.1754061Z async_compile.wait(globals())
2023-01-11T21:41:24.1754133Z del async_compile
2023-01-11T21:41:24.1754137Z 
2023-01-11T21:41:24.1754207Z def call(args):
2023-01-11T21:41:24.1754282Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1754353Z     args.clear()
2023-01-11T21:41:24.1754541Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1754704Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1754773Z     del arg0_1
2023-01-11T21:41:24.1754841Z     del arg1_1
2023-01-11T21:41:24.1754980Z     return (buf0, )
2023-01-11T21:41:24.1754987Z 
2023-01-11T21:41:24.1754991Z 
2023-01-11T21:41:24.1755065Z if __name__ == "__main__":
2023-01-11T21:41:24.1755179Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1755288Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1755492Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1755689Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1755802Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1755806Z 
2023-01-11T21:41:24.1755874Z ok (1.842s)
2023-01-11T21:41:24.1756373Z   test_cpu_transposed_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:24.1756504Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:24.1756766Z [2023-01-11 21:41:22,311] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 586
2023-01-11T21:41:24.1757035Z [2023-01-11 21:41:22,320] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 586
2023-01-11T21:41:24.1757040Z 
2023-01-11T21:41:24.1757135Z from ctypes import c_void_p, c_long
2023-01-11T21:41:24.1757190Z import torch
2023-01-11T21:41:24.1757261Z import random
2023-01-11T21:41:24.1757376Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:24.1757496Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:24.1757501Z 
2023-01-11T21:41:24.1757579Z aten = torch.ops.aten
2023-01-11T21:41:24.1757714Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:24.1757808Z async_compile = AsyncCompile()
2023-01-11T21:41:24.1757814Z 
2023-01-11T21:41:24.1757818Z 
2023-01-11T21:41:24.1757950Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:24.1758140Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:24.1758260Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:24.1758364Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:24.1758463Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:24.1758522Z {
2023-01-11T21:41:24.1758617Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:24.1758676Z     {
2023-01-11T21:41:24.1758739Z         #pragma omp for 
2023-01-11T21:41:24.1758817Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:24.1758881Z         {
2023-01-11T21:41:24.1759021Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:24.1759196Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:24.1759279Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1759369Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:24.1759420Z         }
2023-01-11T21:41:24.1759511Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:24.1759591Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:24.1759651Z         {
2023-01-11T21:41:24.1759732Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:24.1759814Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:24.1759894Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:24.1759960Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:24.1760020Z         }
2023-01-11T21:41:24.1760079Z     }
2023-01-11T21:41:24.1760139Z }
2023-01-11T21:41:24.1760218Z ''')
2023-01-11T21:41:24.1760224Z 
2023-01-11T21:41:24.1760228Z 
2023-01-11T21:41:24.1760315Z async_compile.wait(globals())
2023-01-11T21:41:24.1760386Z del async_compile
2023-01-11T21:41:24.1760393Z 
2023-01-11T21:41:24.1760475Z def call(args):
2023-01-11T21:41:24.1760550Z     arg0_1, arg1_1 = args
2023-01-11T21:41:24.1760618Z     args.clear()
2023-01-11T21:41:24.1760819Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1760976Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:24.1761045Z     del arg0_1
2023-01-11T21:41:24.1761109Z     del arg1_1
2023-01-11T21:41:24.1761167Z     return (buf0, )
2023-01-11T21:41:24.1761182Z 
2023-01-11T21:41:24.1761186Z 
2023-01-11T21:41:24.1761248Z if __name__ == "__main__":
2023-01-11T21:41:24.1761359Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:24.1761479Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:24.1761677Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1761872Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:24.1761989Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:24.1761994Z 
2023-01-11T21:41:24.1762059Z ok (0.025s)
2023-01-11T21:41:24.1762206Z   test_indexing_join (__main__.TestIndexingSimplification) ... ok (0.064s)
2023-01-11T21:41:24.1762357Z   test_indexing_simplification (__main__.TestIndexingSimplification) ... ok (0.070s)
2023-01-11T21:41:24.1762362Z 
2023-01-11T21:41:24.1762560Z ----------------------------------------------------------------------
2023-01-11T21:41:24.1762639Z Ran 362 tests in 1063.565s
2023-01-11T21:41:24.1762644Z 
2023-01-11T21:41:24.1762712Z OK (skipped=17)
2023-01-11T21:41:24.1762717Z 
2023-01-11T21:41:24.1762798Z Generating XML reports...
2023-01-11T21:41:24.1763116Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-CPUReproTests-20230111212338.xml
2023-01-11T21:41:24.1763405Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-CpuTests-20230111212338.xml
2023-01-11T21:41:24.1763722Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-ExprPrinterTests-20230111212338.xml
2023-01-11T21:41:24.1764029Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-SweepInputsCpuTest-20230111212338.xml
2023-01-11T21:41:24.1764373Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-TestIndexingSimplification-20230111212338.xml
2023-01-11T21:41:24.1764379Z 
2023-01-11T21:41:24.1764734Z ##[endgroup]
2023-01-11T21:41:24.1765063Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_yykynwpa)
2023-01-11T21:41:24.1765070Z 
2023-01-11T21:41:26.1278136Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:26.2319261Z Ignoring disabled issues:  []
2023-01-11T21:41:26.2549416Z Running test_mkldnn_fusion ... [2023-01-11 21:41:26.254472]
2023-01-11T21:41:26.2551139Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn_fusion.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:26.254757]
2023-01-11T21:41:58.6381578Z 
2023-01-11T21:41:58.6382236Z Expand the folded group to see the log file of test_mkldnn
2023-01-11T21:41:58.6383457Z ##[group]PRINTING LOG FILE of test_mkldnn (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_1zhyjxun)
2023-01-11T21:41:58.6383817Z 
2023-01-11T21:41:58.6424225Z Running tests...
2023-01-11T21:41:58.6424913Z ----------------------------------------------------------------------
2023-01-11T21:41:58.6425593Z Test results will be stored in test-reports/python-unittest/test_mkldnn
2023-01-11T21:41:58.6426163Z   test_0_dimension_tensor (__main__.TestMkldnn) ... ok (0.289s)
2023-01-11T21:41:58.6426635Z   test_adaptive_avg_pool2d (__main__.TestMkldnn) ... ok (0.144s)
2023-01-11T21:41:58.6427099Z   test_adaptive_avg_pool2d_bf16 (__main__.TestMkldnn) ... ok (0.061s)
2023-01-11T21:41:58.6427536Z   test_add (__main__.TestMkldnn) ... ok (0.078s)
2023-01-11T21:41:58.6428216Z   test_autograd_from_mkldnn (__main__.TestMkldnn) ... ok (0.006s)
2023-01-11T21:41:58.6428688Z   test_autograd_to_mkldnn (__main__.TestMkldnn) ... ok (0.009s)
2023-01-11T21:41:58.6429112Z   test_avg_pool2d (__main__.TestMkldnn) ... ok (0.022s)
2023-01-11T21:41:58.6429546Z   test_avg_pool2d_bf16 (__main__.TestMkldnn) ... ok (0.009s)
2023-01-11T21:41:58.6430000Z   test_avg_pool2d_stride_none (__main__.TestMkldnn) ... ok (0.008s)
2023-01-11T21:41:58.6430431Z   test_avg_pool3d (__main__.TestMkldnn) ... ok (1.471s)
2023-01-11T21:41:58.6430860Z   test_avg_pool3d_bf16 (__main__.TestMkldnn) ... ok (0.438s)
2023-01-11T21:41:58.6431939Z   test_batch_norm_2d (__main__.TestMkldnn) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py:780: UserWarning: The input to trace is already a ScriptModule, tracing it is a no-op. Returning the object as is.
2023-01-11T21:41:58.6432580Z   warnings.warn(
2023-01-11T21:41:58.6432854Z ok (0.080s)
2023-01-11T21:41:58.6433219Z   test_batch_norm_2d_bf16 (__main__.TestMkldnn) ... ok (0.022s)
2023-01-11T21:41:58.6433670Z   test_batch_norm_3d (__main__.TestMkldnn) ... ok (0.949s)
2023-01-11T21:41:58.6434088Z   test_batch_norm_3d_bf16 (__main__.TestMkldnn) ... ok (0.439s)
2023-01-11T21:41:58.6434398Z   test_clone (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6434750Z   test_conv1d (__main__.TestMkldnn) ... ok (0.118s)
2023-01-11T21:41:58.6435119Z   test_conv1d_bf16 (__main__.TestMkldnn) ... ok (0.049s)
2023-01-11T21:41:58.6435554Z   test_conv1d_functional (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6435975Z   test_conv2d (__main__.TestMkldnn) ... ok (0.846s)
2023-01-11T21:41:58.6436352Z   test_conv2d_bf16 (__main__.TestMkldnn) ... ok (0.296s)
2023-01-11T21:41:58.6436766Z   test_conv2d_legacy_jit_model (__main__.TestMkldnn)
2023-01-11T21:41:58.6437263Z MKLDNN integration used to serialize models with 5d weight for grouped ... ok (0.011s)
2023-01-11T21:41:58.6437740Z   test_conv2d_nhwc (__main__.TestMkldnn) ... ok (0.953s)
2023-01-11T21:41:58.6438144Z   test_conv2d_nhwc_bf16 (__main__.TestMkldnn) ... ok (1.584s)
2023-01-11T21:41:58.6438561Z   test_conv3d (__main__.TestMkldnn) ... ok (2.332s)
2023-01-11T21:41:58.6438967Z   test_conv3d_bf16 (__main__.TestMkldnn) ... ok (0.493s)
2023-01-11T21:41:58.6439352Z   test_conversion (__main__.TestMkldnn) ... ok (0.020s)
2023-01-11T21:41:58.6439761Z   test_copy (__main__.TestMkldnn) ... ok (0.013s)
2023-01-11T21:41:58.6440146Z   test_detach (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6440526Z   test_empty (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6440915Z   test_gelu (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6441304Z   test_gelu_bf16 (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6441698Z   test_is_mkldnn (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6442118Z   test_is_mkldnn_jit (__main__.TestMkldnn) ... ok (0.005s)
2023-01-11T21:41:58.6443131Z   test_legacy_new_failure (__main__.TestMkldnn) ... /var/lib/jenkins/workspace/test/test_mkldnn.py:1222: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:58.6444444Z   self.assertRaises(RuntimeError, lambda: x_mkldnn.new(x.storage()))
2023-01-11T21:41:58.6444826Z ok (0.007s)
2023-01-11T21:41:58.6445149Z   test_linear (__main__.TestMkldnn) ... ok (0.022s)
2023-01-11T21:41:58.6445580Z   test_linear_backward (__main__.TestMkldnn) ... ok (0.006s)
2023-01-11T21:41:58.6445999Z   test_linear_bf16 (__main__.TestMkldnn) ... ok (0.012s)
2023-01-11T21:41:58.6446444Z   test_linear_non_contiguous_weight (__main__.TestMkldnn) ... ok (0.004s)
2023-01-11T21:41:58.6446879Z   test_max_pool2d (__main__.TestMkldnn) ... ok (0.114s)
2023-01-11T21:41:58.6447297Z   test_max_pool2d_bf16 (__main__.TestMkldnn) ... ok (0.052s)
2023-01-11T21:41:58.6447848Z   test_max_pool2d_stride_none (__main__.TestMkldnn) ... ok (0.014s)
2023-01-11T21:41:58.6448290Z   test_max_pool3d (__main__.TestMkldnn) ... ok (14.397s)
2023-01-11T21:41:58.6448721Z   test_max_pool3d_bf16 (__main__.TestMkldnn) ... ok (10.001s)
2023-01-11T21:41:58.6449172Z   test_max_pool_unsupported (__main__.TestMkldnn) ... ok (0.023s)
2023-01-11T21:41:58.6449620Z   test_mkldnn_conv_shapecheck (__main__.TestMkldnn) ... ok (0.039s)
2023-01-11T21:41:58.6450034Z   test_mul (__main__.TestMkldnn) ... ok (0.091s)
2023-01-11T21:41:58.6450427Z   test_prelu (__main__.TestMkldnn) ... ok (8.069s)
2023-01-11T21:41:58.6450814Z   test_prelu_bf16 (__main__.TestMkldnn) ... ok (3.983s)
2023-01-11T21:41:58.6451218Z   test_relu (__main__.TestMkldnn) ... ok (0.003s)
2023-01-11T21:41:58.6451609Z   test_relu_ (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6452184Z   test_relu_bf16 (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6452582Z   test_relu_inplace_bf16 (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6453016Z   test_repr (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6453428Z   test_reshape (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6453831Z   test_reshape_backward (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6454303Z   test_reshape_blocked_format (__main__.TestMkldnn) ... ok (0.007s)
2023-01-11T21:41:58.6455447Z   test_resnet18 (__main__.TestMkldnn) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
2023-01-11T21:41:58.6456139Z   warnings.warn(
2023-01-11T21:41:58.6457190Z /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
2023-01-11T21:41:58.6457899Z   warnings.warn(msg)
2023-01-11T21:41:58.6458208Z ok (0.412s)
2023-01-11T21:41:58.6458535Z   test_resnext50_32x4d (__main__.TestMkldnn) ... ok (0.922s)
2023-01-11T21:41:58.6458981Z   test_set_data_tensorimpl_type (__main__.TestMkldnn) ... ok (0.003s)
2023-01-11T21:41:58.6459407Z   test_sigmoid (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6459813Z   test_softmax (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6460192Z   test_tanh (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:41:58.6460594Z   test_transpose (__main__.TestMkldnn) ... ok (0.003s)
2023-01-11T21:41:58.6461040Z   test_transpose_invalid_dime (__main__.TestMkldnn) ... ok (0.005s)
2023-01-11T21:41:58.6461448Z   test_unsupported (__main__.TestMkldnn) ... ok (0.055s)
2023-01-11T21:41:58.6461858Z   test_view (__main__.TestMkldnn) ... ok (0.007s)
2023-01-11T21:41:58.6462241Z   test_zero_ (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:41:58.6462608Z 
2023-01-11T21:41:58.6462978Z ----------------------------------------------------------------------
2023-01-11T21:41:58.6463527Z Ran 68 tests in 49.028s
2023-01-11T21:41:58.6463722Z 
2023-01-11T21:41:58.6463829Z OK
2023-01-11T21:41:58.6463982Z 
2023-01-11T21:41:58.6464122Z Generating XML reports...
2023-01-11T21:41:58.6464754Z Generated XML report: test-reports/python-unittest/test_mkldnn/TEST-TestMkldnn-20230111214109.xml
2023-01-11T21:41:58.6465117Z 
2023-01-11T21:41:58.6465618Z ##[endgroup]
2023-01-11T21:41:58.6466248Z FINISHED PRINTING LOG FILE of test_mkldnn (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_1zhyjxun)
2023-01-11T21:41:58.6466567Z 
2023-01-11T21:42:01.0995484Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:01.2339694Z Ignoring disabled issues:  []
2023-01-11T21:42:01.2567700Z Running test_mkldnn_verbose ... [2023-01-11 21:42:01.256370]
2023-01-11T21:42:01.2569450Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn_verbose.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:01.256684]
2023-01-11T21:42:07.0464295Z 
2023-01-11T21:42:07.0465137Z Expand the folded group to see the log file of test_mkldnn_verbose
2023-01-11T21:42:07.0466185Z ##[group]PRINTING LOG FILE of test_mkldnn_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_verbose_759gktct)
2023-01-11T21:42:07.0466556Z 
2023-01-11T21:42:07.0466663Z Running tests...
2023-01-11T21:42:07.0467816Z ----------------------------------------------------------------------
2023-01-11T21:42:07.0515357Z Test results will be stored in test-reports/python-unittest/test_mkldnn_verbose
2023-01-11T21:42:07.0515811Z   test_verbose_off (__main__.TestMKLDNNVerbose) ... ok (1.739s)
2023-01-11T21:42:07.0516180Z   test_verbose_on (__main__.TestMKLDNNVerbose) ... ok (1.818s)
2023-01-11T21:42:07.0516400Z 
2023-01-11T21:42:07.0516675Z ----------------------------------------------------------------------
2023-01-11T21:42:07.0516987Z Ran 2 tests in 3.557s
2023-01-11T21:42:07.0517140Z 
2023-01-11T21:42:07.0517220Z OK
2023-01-11T21:42:07.0517342Z 
2023-01-11T21:42:07.0517456Z Generating XML reports...
2023-01-11T21:42:07.0518082Z Generated XML report: test-reports/python-unittest/test_mkldnn_verbose/TEST-TestMKLDNNVerbose-20230111214202.xml
2023-01-11T21:42:07.0518495Z 
2023-01-11T21:42:07.0519049Z ##[endgroup]
2023-01-11T21:42:07.0519767Z FINISHED PRINTING LOG FILE of test_mkldnn_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_verbose_759gktct)
2023-01-11T21:42:07.0520166Z 
2023-01-11T21:42:10.0070631Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:10.1339328Z Ignoring disabled issues:  []
2023-01-11T21:42:10.1572714Z Running test_model_dump ... [2023-01-11 21:42:10.156900]
2023-01-11T21:42:10.1573343Z Executing ['/opt/conda/bin/python', '-bb', 'test_model_dump.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:10.157149]
2023-01-11T21:42:13.1909614Z 
2023-01-11T21:42:13.1910201Z Expand the folded group to see the log file of test_model_dump
2023-01-11T21:42:13.1911661Z ##[group]PRINTING LOG FILE of test_model_dump (/var/lib/jenkins/workspace/test/test-reports/test_model_dump_0gdwguc_)
2023-01-11T21:42:13.1912189Z 
2023-01-11T21:42:13.1912350Z Running tests...
2023-01-11T21:42:13.1913069Z ----------------------------------------------------------------------
2023-01-11T21:42:13.1913753Z Test results will be stored in test-reports/python-unittest/test_model_dump
2023-01-11T21:42:13.1914345Z   test_inline_skeleton (__main__.TestModelDump) ... ok (0.240s)
2023-01-11T21:42:13.1914848Z   test_invalid_json (__main__.TestModelDump) ... ok (0.019s)
2023-01-11T21:42:13.1915187Z   test_main (__main__.TestModelDump) ... ok (0.015s)
2023-01-11T21:42:13.1915580Z   test_memory_computation (__main__.TestModelDump) ... skip: Webdriver not requested (0.001s)
2023-01-11T21:42:13.1915960Z   test_model_with_lists (__main__.TestModelDump) ... ok (0.004s)
2023-01-11T21:42:13.1916379Z   test_optimized_quantized_model (__main__.TestModelDump) ... ok (0.322s)
2023-01-11T21:42:13.1916831Z   test_quantized_model (__main__.TestModelDump) ... ok (0.214s)
2023-01-11T21:42:13.1917484Z   test_scripted_model (__main__.TestModelDump) ... ok (0.010s)
2023-01-11T21:42:13.1918140Z   test_traced_model (__main__.TestModelDump) ... ok (0.017s)
2023-01-11T21:42:13.1918586Z 
2023-01-11T21:42:13.1919022Z ----------------------------------------------------------------------
2023-01-11T21:42:13.1919447Z Ran 9 tests in 0.843s
2023-01-11T21:42:13.1919628Z 
2023-01-11T21:42:13.1919748Z OK (skipped=1)
2023-01-11T21:42:13.1919932Z 
2023-01-11T21:42:13.1920074Z Generating XML reports...
2023-01-11T21:42:13.1962622Z Generated XML report: test-reports/python-unittest/test_model_dump/TEST-TestModelDump-20230111214211.xml
2023-01-11T21:42:13.1963012Z 
2023-01-11T21:42:13.1963420Z ##[endgroup]
2023-01-11T21:42:13.1964269Z FINISHED PRINTING LOG FILE of test_model_dump (/var/lib/jenkins/workspace/test/test-reports/test_model_dump_0gdwguc_)
2023-01-11T21:42:13.1964949Z 
2023-01-11T21:42:15.8138035Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:15.9008260Z Ignoring disabled issues:  []
2023-01-11T21:42:15.9172240Z Running test_module_init ... [2023-01-11 21:42:15.916773]
2023-01-11T21:42:15.9173265Z Executing ['/opt/conda/bin/python', '-bb', 'test_module_init.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:15.917083]
2023-01-11T21:42:18.1776884Z 
2023-01-11T21:42:18.1784593Z Expand the folded group to see the log file of test_module_init
2023-01-11T21:42:18.1785585Z ##[group]PRINTING LOG FILE of test_module_init (/var/lib/jenkins/workspace/test/test-reports/test_module_init_rxooqqb7)
2023-01-11T21:42:18.1785966Z 
2023-01-11T21:42:18.1786077Z Running tests...
2023-01-11T21:42:18.1786709Z ----------------------------------------------------------------------
2023-01-11T21:42:18.1787251Z 
2023-01-11T21:42:18.1787834Z ----------------------------------------------------------------------
2023-01-11T21:42:18.1788261Z Ran 0 tests in 0.000s
2023-01-11T21:42:18.1788505Z 
2023-01-11T21:42:18.1788637Z OK
2023-01-11T21:42:18.1788834Z 
2023-01-11T21:42:18.1789052Z Generating XML reports...
2023-01-11T21:42:18.1789773Z Test results will be stored in test-reports/python-unittest/test_module_init
2023-01-11T21:42:18.1790175Z 
2023-01-11T21:42:18.1790662Z ##[endgroup]
2023-01-11T21:42:18.1791532Z FINISHED PRINTING LOG FILE of test_module_init (/var/lib/jenkins/workspace/test/test-reports/test_module_init_rxooqqb7)
2023-01-11T21:42:18.1792030Z 
2023-01-11T21:42:20.1393573Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:20.2226773Z Ignoring disabled issues:  []
2023-01-11T21:42:20.2386060Z Running test_monitor ... [2023-01-11 21:42:20.238076]
2023-01-11T21:42:20.2387179Z Executing ['/opt/conda/bin/python', '-bb', 'test_monitor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:20.238370]
2023-01-11T21:42:22.6406453Z 
2023-01-11T21:42:22.6407069Z Expand the folded group to see the log file of test_monitor
2023-01-11T21:42:22.6408531Z ##[group]PRINTING LOG FILE of test_monitor (/var/lib/jenkins/workspace/test/test-reports/test_monitor_hf6cj7zk)
2023-01-11T21:42:22.6409116Z 
2023-01-11T21:42:22.6409291Z Running tests...
2023-01-11T21:42:22.6410125Z ----------------------------------------------------------------------
2023-01-11T21:42:22.6411016Z Test results will be stored in test-reports/python-unittest/test_monitor
2023-01-11T21:42:22.6411704Z   test_event_handler (__main__.TestMonitor) ... ok (0.250s)
2023-01-11T21:42:22.6412334Z   test_fixed_count_stat (__main__.TestMonitor) ... ok (0.001s)
2023-01-11T21:42:22.6412947Z   test_interval_stat (__main__.TestMonitor) ... ok (0.002s)
2023-01-11T21:42:22.6413539Z   test_log_event (__main__.TestMonitor) ... ok (0.001s)
2023-01-11T21:42:22.6414193Z   test_event_handler (__main__.TestMonitorTensorboard) ... ok (0.168s)
2023-01-11T21:42:22.6414618Z 
2023-01-11T21:42:22.6415098Z ----------------------------------------------------------------------
2023-01-11T21:42:22.6415642Z Ran 5 tests in 0.423s
2023-01-11T21:42:22.6415899Z 
2023-01-11T21:42:22.6416312Z OK
2023-01-11T21:42:22.6416533Z 
2023-01-11T21:42:22.6416731Z Generating XML reports...
2023-01-11T21:42:22.6417674Z Generated XML report: test-reports/python-unittest/test_monitor/TEST-TestMonitor-20230111214221.xml
2023-01-11T21:42:22.6418925Z Generated XML report: test-reports/python-unittest/test_monitor/TEST-TestMonitorTensorboard-20230111214221.xml
2023-01-11T21:42:22.6419525Z 
2023-01-11T21:42:22.6420045Z ##[endgroup]
2023-01-11T21:42:22.6420907Z FINISHED PRINTING LOG FILE of test_monitor (/var/lib/jenkins/workspace/test/test-reports/test_monitor_hf6cj7zk)
2023-01-11T21:42:22.6421416Z 
2023-01-11T21:42:25.1911570Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:25.2761965Z Ignoring disabled issues:  []
2023-01-11T21:42:25.2924180Z Running test_namedtensor ... [2023-01-11 21:42:25.292049]
2023-01-11T21:42:25.2925680Z Executing ['/opt/conda/bin/python', '-bb', 'test_namedtensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:25.292346]
2023-01-11T21:42:28.2385492Z 
2023-01-11T21:42:28.2386003Z Expand the folded group to see the log file of test_namedtensor
2023-01-11T21:42:28.2387009Z ##[group]PRINTING LOG FILE of test_namedtensor (/var/lib/jenkins/workspace/test/test-reports/test_namedtensor_giocblkf)
2023-01-11T21:42:28.2387385Z 
2023-01-11T21:42:28.2387585Z Running tests...
2023-01-11T21:42:28.2388187Z ----------------------------------------------------------------------
2023-01-11T21:42:28.2388830Z Test results will be stored in test-reports/python-unittest/test_namedtensor
2023-01-11T21:42:28.2389401Z   test_aaa_must_run_first_check_experimental_warning (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:42:28.2389927Z   test_addcmul_addcdiv (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:42:28.2390362Z   test_addmm (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:42:28.2390785Z   test_addmv (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:42:28.2391211Z   test_align_as (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2391690Z   test_align_tensors (__main__.TestNamedTensor) ... skip: Not implemented yet (0.001s)
2023-01-11T21:42:28.2392256Z   test_align_tensors_two_inputs (__main__.TestNamedTensor) ... skip: Not implemented yet (0.002s)
2023-01-11T21:42:28.2392765Z   test_align_to (__main__.TestNamedTensor) ... ok (0.009s)
2023-01-11T21:42:28.2393209Z   test_align_to_ellipsis (__main__.TestNamedTensor) ... ok (0.010s)
2023-01-11T21:42:28.2393639Z   test_any_all (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2394124Z   test_as_strided (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2394593Z   test_as_strided_cuda (__main__.TestNamedTensor) ... skip: no CUDA (0.000s)
2023-01-11T21:42:28.2395077Z   test_autograd_ignores_names (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2395549Z   test_autograd_smoke (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2396035Z   test_autograd_warns_named_grad (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2396506Z   test_bernoulli (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2396967Z   test_big_tensor_repr_has_names (__main__.TestNamedTensor) ... ok (0.032s)
2023-01-11T21:42:28.2397427Z   test_binary_ops (__main__.TestNamedTensor) ... ok (0.341s)
2023-01-11T21:42:28.2397867Z   test_bitwise_not (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2398271Z   test_bmm (__main__.TestNamedTensor) ... ok (0.019s)
2023-01-11T21:42:28.2398677Z   test_cat (__main__.TestNamedTensor) ... ok (0.015s)
2023-01-11T21:42:28.2399085Z   test_cdist (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2399509Z   test_comparison_ops (__main__.TestNamedTensor) ... ok (0.005s)
2023-01-11T21:42:28.2399966Z   test_copy_transpose (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2400418Z   test_cummax_cummin (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2400841Z   test_detach (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2401269Z   test_diagonal (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2401831Z   test_dot (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2402249Z   test_equal (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2402650Z   test_expand (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2403090Z   test_factory_coverage (__main__.TestNamedTensor) ... ok (0.004s)
2023-01-11T21:42:28.2403565Z   test_factory_edge_cases (__main__.TestNamedTensor) ... ok (0.020s)
2023-01-11T21:42:28.2403997Z   test_flatten (__main__.TestNamedTensor) ... ok (0.010s)
2023-01-11T21:42:28.2404429Z   test_flatten_nodims (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:42:28.2404879Z   test_has_names (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2405296Z   test_index_fill (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2406390Z   test_info_smoke (__main__.TestNamedTensor) ... /var/lib/jenkins/workspace/test/test_namedtensor.py:617: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:28.2407327Z   tensor.storage()
2023-01-11T21:42:28.2408238Z /var/lib/jenkins/workspace/test/test_namedtensor.py:619: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:28.2409096Z   tensor.storage_type()
2023-01-11T21:42:28.2410578Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:28.2411577Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:42:28.2412923Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:28.2413957Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:42:28.2414294Z ok (0.001s)
2023-01-11T21:42:28.2414658Z   test_logcumsumexp (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2415107Z   test_logical_not (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2415548Z   test_logical_ops (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:42:28.2415976Z   test_masked_fill (__main__.TestNamedTensor) ... ok (0.014s)
2023-01-11T21:42:28.2416430Z   test_masked_select (__main__.TestNamedTensor) ... ok (0.006s)
2023-01-11T21:42:28.2416860Z   test_matmul (__main__.TestNamedTensor) ... ok (0.021s)
2023-01-11T21:42:28.2417282Z   test_max_pooling (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:42:28.2417776Z   test_max_pooling_without_names_does_not_warn (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2418249Z   test_mm (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:42:28.2418639Z   test_mv (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2419083Z   test_no_jit_script_support (__main__.TestNamedTensor) ... ok (0.047s)
2023-01-11T21:42:28.2419567Z   test_no_jit_tracer_support (__main__.TestNamedTensor) ... ok (0.009s)
2023-01-11T21:42:28.2420075Z   test_no_multiprocessing_support (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2420548Z   test_no_pickle_support (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2421021Z   test_no_save_support (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2421572Z   test_noncontig_contiguous (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2422036Z   test_none_names_refcount (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2422718Z   test_nyi_dimname_overload_msg (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:42:28.2423202Z   test_out_fn_semantics (__main__.TestNamedTensor) ... ok (0.021s)
2023-01-11T21:42:28.2423651Z   test_pow_special (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2424083Z   test_py3_ellipsis (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2424536Z   test_reduction_fns (__main__.TestNamedTensor) ... ok (0.106s)
2023-01-11T21:42:28.2424978Z   test_refine_names (__main__.TestNamedTensor) ... ok (0.009s)
2023-01-11T21:42:28.2425399Z   test_rename (__main__.TestNamedTensor) ... ok (0.008s)
2023-01-11T21:42:28.2425820Z   test_rename_ (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:42:28.2426345Z   test_rename_globber (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:42:28.2426807Z   test_rename_rename_map (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:42:28.2427251Z   test_repr (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2427673Z   test_resize (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:42:28.2428090Z   test_select (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:42:28.2428530Z   test_select_cuda (__main__.TestNamedTensor) ... skip: no CUDA (0.000s)
2023-01-11T21:42:28.2429011Z   test_set_names_property (__main__.TestNamedTensor) ... ok (0.005s)
2023-01-11T21:42:28.2429445Z   test_size (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:42:28.2429897Z   test_split_fns_propagates_names (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2430365Z   test_squeeze (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2430790Z   test_stride (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:42:28.2431220Z   test_tensor_from_lists (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:42:28.2432155Z   test_tensor_from_named_tensor (__main__.TestNamedTensor) ... /var/lib/jenkins/workspace/test/test_namedtensor.py:516: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:42:28.2432973Z   tensor = torch.tensor(x)
2023-01-11T21:42:28.2433729Z /var/lib/jenkins/workspace/test/test_namedtensor.py:522: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:42:28.2434552Z   tensor = torch.tensor(x, names=None)
2023-01-11T21:42:28.2435315Z /var/lib/jenkins/workspace/test/test_namedtensor.py:527: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:42:28.2436183Z   tensor = torch.tensor(x, names=('N', 'C'))
2023-01-11T21:42:28.2436513Z ok (0.003s)
2023-01-11T21:42:28.2436869Z   test_tensor_from_numpy (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2437780Z   test_tensor_from_tensor (__main__.TestNamedTensor) ... /var/lib/jenkins/workspace/test/test_namedtensor.py:511: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:42:28.2438600Z   tensor = torch.tensor(x, names=names)
2023-01-11T21:42:28.2438921Z ok (0.001s)
2023-01-11T21:42:28.2439282Z   test_tensor_grad_is_unnamed (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2439765Z   test_transpose_variants (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2440219Z   test_trivial (__main__.TestNamedTensor) ... ok (0.000s)
2023-01-11T21:42:28.2440668Z   test_unary_propagate_names_fns (__main__.TestNamedTensor) ... ok (0.018s)
2023-01-11T21:42:28.2441251Z   test_unflatten (__main__.TestNamedTensor) ... ok (0.016s)
2023-01-11T21:42:28.2441724Z   test_unsupported_op_error_msg (__main__.TestNamedTensor) ... ok (0.010s)
2023-01-11T21:42:28.2442270Z   test_using_seen_interned_string_doesnt_bump_refcount (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2442861Z   test_using_unseen_interned_string_bumps_refcount_permanently (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2443450Z   test_using_unseen_uninterned_string_refcounts (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:42:28.2443762Z 
2023-01-11T21:42:28.2444108Z ----------------------------------------------------------------------
2023-01-11T21:42:28.2444492Z Ran 86 tests in 0.869s
2023-01-11T21:42:28.2444677Z 
2023-01-11T21:42:28.2444790Z OK (skipped=4)
2023-01-11T21:42:28.2444964Z 
2023-01-11T21:42:28.2445106Z Generating XML reports...
2023-01-11T21:42:28.2445862Z Generated XML report: test-reports/python-unittest/test_namedtensor/TEST-TestNamedTensor-20230111214226.xml
2023-01-11T21:42:28.2446267Z 
2023-01-11T21:42:28.2446674Z ##[endgroup]
2023-01-11T21:42:28.2447337Z FINISHED PRINTING LOG FILE of test_namedtensor (/var/lib/jenkins/workspace/test/test-reports/test_namedtensor_giocblkf)
2023-01-11T21:42:28.2447716Z 
2023-01-11T21:42:30.5099894Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:30.6042204Z Ignoring disabled issues:  []
2023-01-11T21:42:30.6208265Z Running test_native_functions ... [2023-01-11 21:42:30.620301]
2023-01-11T21:42:30.6209232Z Executing ['/opt/conda/bin/python', '-bb', 'test_native_functions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:30.620581]
2023-01-11T21:42:32.9319107Z 
2023-01-11T21:42:32.9319953Z Expand the folded group to see the log file of test_native_functions
2023-01-11T21:42:32.9321118Z ##[group]PRINTING LOG FILE of test_native_functions (/var/lib/jenkins/workspace/test/test-reports/test_native_functions_qz4w1sz5)
2023-01-11T21:42:32.9321512Z 
2023-01-11T21:42:32.9321643Z Running tests...
2023-01-11T21:42:32.9322270Z ----------------------------------------------------------------------
2023-01-11T21:42:32.9322920Z Test results will be stored in test-reports/python-unittest/test_native_functions
2023-01-11T21:42:32.9323453Z   test_intlist_error_with_overload (__main__.TestNativeFunctions) ... ok (0.265s)
2023-01-11T21:42:32.9324332Z   test_optional_filled_intlist (__main__.TestNativeFunctions) ... pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9325085Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9325765Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9326458Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9327152Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9327814Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9328427Z pad(): argument 'pad' (position 2) must be tuple of ints, not str
2023-01-11T21:42:32.9328801Z ok (0.050s)
2023-01-11T21:42:32.9329186Z   test_optional_floatlist (__main__.TestNativeFunctions) ... ok (0.010s)
2023-01-11T21:42:32.9329700Z   test_optional_floatlist_invalid (__main__.TestNativeFunctions) ... ok (0.010s)
2023-01-11T21:42:32.9330204Z   test_optional_intlist (__main__.TestNativeFunctions) ... ok (0.008s)
2023-01-11T21:42:32.9330692Z   test_optional_intlist_invalid (__main__.TestNativeFunctions) ... ok (0.009s)
2023-01-11T21:42:32.9331186Z   test_string_defaults (__main__.TestNativeFunctions) ... ok (0.006s)
2023-01-11T21:42:32.9332006Z   test_symintlist_error (__main__.TestNativeFunctions) ... pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9333076Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9333735Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9334579Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9335328Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9336116Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9336795Z pad(): argument 'pad' (position 2) must be tuple of ints, not str
2023-01-11T21:42:32.9337222Z ok (0.016s)
2023-01-11T21:42:32.9338083Z   test_symintlist_error_with_overload (__main__.TestNativeFunctions) ... view() received an invalid combination of arguments - got (tuple), but expected one of:
2023-01-11T21:42:32.9338694Z  * (torch.dtype dtype)
2023-01-11T21:42:32.9339536Z       didn't match because some of the arguments have invalid types: (!tuple of (str,)!)
2023-01-11T21:42:32.9340025Z  * (tuple of ints size)
2023-01-11T21:42:32.9340721Z       didn't match because some of the arguments have invalid types: (!tuple of (str,)!)
2023-01-11T21:42:32.9341032Z 
2023-01-11T21:42:32.9341395Z view(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9342066Z view() received an invalid combination of arguments - got (tuple), but expected one of:
2023-01-11T21:42:32.9342709Z  * (torch.dtype dtype)
2023-01-11T21:42:32.9343257Z       didn't match because some of the arguments have invalid types: (!tuple of (str, int)!)
2023-01-11T21:42:32.9343665Z  * (tuple of ints size)
2023-01-11T21:42:32.9344220Z       didn't match because some of the arguments have invalid types: (!tuple of (str, int)!)
2023-01-11T21:42:32.9344519Z 
2023-01-11T21:42:32.9344879Z view() received an invalid combination of arguments - got (list), but expected one of:
2023-01-11T21:42:32.9345324Z  * (torch.dtype dtype)
2023-01-11T21:42:32.9345869Z       didn't match because some of the arguments have invalid types: (!list of [str]!)
2023-01-11T21:42:32.9346285Z  * (tuple of ints size)
2023-01-11T21:42:32.9346804Z       didn't match because some of the arguments have invalid types: (!list of [str]!)
2023-01-11T21:42:32.9347099Z 
2023-01-11T21:42:32.9347459Z view(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9348109Z view() received an invalid combination of arguments - got (list), but expected one of:
2023-01-11T21:42:32.9348509Z  * (torch.dtype dtype)
2023-01-11T21:42:32.9349061Z       didn't match because some of the arguments have invalid types: (!list of [str, int]!)
2023-01-11T21:42:32.9349476Z  * (tuple of ints size)
2023-01-11T21:42:32.9350027Z       didn't match because some of the arguments have invalid types: (!list of [str, int]!)
2023-01-11T21:42:32.9350313Z 
2023-01-11T21:42:32.9350693Z view() received an invalid combination of arguments - got (str), but expected one of:
2023-01-11T21:42:32.9351116Z  * (torch.dtype dtype)
2023-01-11T21:42:32.9351764Z       didn't match because some of the arguments have invalid types: (!str!)
2023-01-11T21:42:32.9352206Z  * (tuple of ints size)
2023-01-11T21:42:32.9352796Z       didn't match because some of the arguments have invalid types: (!str!)
2023-01-11T21:42:32.9353119Z 
2023-01-11T21:42:32.9353247Z ok (0.016s)
2023-01-11T21:42:32.9354289Z   test_symintlist_error_with_overload_but_is_unique (__main__.TestNativeFunctions) ... set_() received an invalid combination of arguments - got (Tensor, int, tuple), but expected one of:
2023-01-11T21:42:32.9354914Z  * ()
2023-01-11T21:42:32.9355244Z  * (torch.Storage source)
2023-01-11T21:42:32.9355754Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9356214Z  * (Tensor source)
2023-01-11T21:42:32.9356683Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9357270Z 
2023-01-11T21:42:32.9357709Z set_(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9358596Z set_() received an invalid combination of arguments - got (Tensor, int, tuple), but expected one of:
2023-01-11T21:42:32.9359010Z  * ()
2023-01-11T21:42:32.9359308Z  * (torch.Storage source)
2023-01-11T21:42:32.9359755Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9360261Z  * (Tensor source)
2023-01-11T21:42:32.9360645Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9360933Z 
2023-01-11T21:42:32.9361347Z set_() received an invalid combination of arguments - got (Tensor, int, list), but expected one of:
2023-01-11T21:42:32.9361761Z  * ()
2023-01-11T21:42:32.9362021Z  * (torch.Storage source)
2023-01-11T21:42:32.9362451Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9363040Z  * (Tensor source)
2023-01-11T21:42:32.9363427Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9363716Z 
2023-01-11T21:42:32.9364092Z set_(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9364780Z set_() received an invalid combination of arguments - got (Tensor, int, list), but expected one of:
2023-01-11T21:42:32.9365190Z  * ()
2023-01-11T21:42:32.9365463Z  * (torch.Storage source)
2023-01-11T21:42:32.9365901Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9366310Z  * (Tensor source)
2023-01-11T21:42:32.9366706Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9366992Z 
2023-01-11T21:42:32.9367401Z set_() received an invalid combination of arguments - got (Tensor, int, str), but expected one of:
2023-01-11T21:42:32.9367816Z  * ()
2023-01-11T21:42:32.9368079Z  * (torch.Storage source)
2023-01-11T21:42:32.9368549Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9369092Z  * (Tensor source)
2023-01-11T21:42:32.9369569Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:32.9369873Z 
2023-01-11T21:42:32.9370003Z ok (0.016s)
2023-01-11T21:42:32.9370867Z   test_vararg_symintlist_error (__main__.TestNativeFunctions) ... rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9371745Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9372509Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9373304Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9374074Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9374872Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9375659Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9376467Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9377227Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9377993Z rand() received an invalid combination of arguments - got (str, int), but expected one of:
2023-01-11T21:42:32.9378805Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9379764Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9380769Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9381500Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9381901Z 
2023-01-11T21:42:32.9382303Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:32.9383121Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:32.9383797Z rand() received an invalid combination of arguments - got (str, int), but expected one of:
2023-01-11T21:42:32.9384503Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9385456Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9386220Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9386940Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9387327Z 
2023-01-11T21:42:32.9387749Z rand() received an invalid combination of arguments - got (str, str, str), but expected one of:
2023-01-11T21:42:32.9388441Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9389262Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9390021Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9390727Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:32.9391111Z 
2023-01-11T21:42:32.9391208Z ok (0.032s)
2023-01-11T21:42:32.9391367Z 
2023-01-11T21:42:32.9391715Z ----------------------------------------------------------------------
2023-01-11T21:42:32.9392125Z Ran 11 tests in 0.438s
2023-01-11T21:42:32.9392307Z 
2023-01-11T21:42:32.9392417Z OK
2023-01-11T21:42:32.9392562Z 
2023-01-11T21:42:32.9392702Z Generating XML reports...
2023-01-11T21:42:32.9393431Z Generated XML report: test-reports/python-unittest/test_native_functions/TEST-TestNativeFunctions-20230111214232.xml
2023-01-11T21:42:32.9393837Z 
2023-01-11T21:42:32.9394384Z ##[endgroup]
2023-01-11T21:42:32.9395051Z FINISHED PRINTING LOG FILE of test_native_functions (/var/lib/jenkins/workspace/test/test-reports/test_native_functions_qz4w1sz5)
2023-01-11T21:42:32.9395440Z 
2023-01-11T21:42:35.3325124Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:35.4198505Z Ignoring disabled issues:  []
2023-01-11T21:42:35.4362858Z Running test_native_mha ... [2023-01-11 21:42:35.435887]
2023-01-11T21:42:35.4364443Z Executing ['/opt/conda/bin/python', '-bb', 'test_native_mha.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:35.436215]
2023-01-11T21:42:37.4097322Z 
2023-01-11T21:42:37.4097820Z Expand the folded group to see the log file of test_native_mha
2023-01-11T21:42:37.4098762Z ##[group]PRINTING LOG FILE of test_native_mha (/var/lib/jenkins/workspace/test/test-reports/test_native_mha_57c20dz5)
2023-01-11T21:42:37.4099166Z 
2023-01-11T21:42:37.4099300Z Running tests...
2023-01-11T21:42:37.4099959Z ----------------------------------------------------------------------
2023-01-11T21:42:37.4100270Z 
2023-01-11T21:42:37.4100918Z ----------------------------------------------------------------------
2023-01-11T21:42:37.4102004Z Ran 0 tests in 0.000s
2023-01-11T21:42:37.4102199Z 
2023-01-11T21:42:37.4102309Z OK
2023-01-11T21:42:37.4102626Z 
2023-01-11T21:42:37.4102764Z Generating XML reports...
2023-01-11T21:42:37.4103140Z Test results will be stored in test-reports/python-unittest/test_native_mha
2023-01-11T21:42:37.4103416Z 
2023-01-11T21:42:37.4103697Z ##[endgroup]
2023-01-11T21:42:37.4104092Z FINISHED PRINTING LOG FILE of test_native_mha (/var/lib/jenkins/workspace/test/test-reports/test_native_mha_57c20dz5)
2023-01-11T21:42:37.4104312Z 
2023-01-11T21:42:39.5231449Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:39.6110103Z Ignoring disabled issues:  []
2023-01-11T21:42:39.6271592Z Running test_nestedtensor ... [2023-01-11 21:42:39.626788]
2023-01-11T21:42:39.6273405Z Executing ['/opt/conda/bin/python', '-bb', 'test_nestedtensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:39.627068]
2023-01-11T21:42:41.7582868Z 
2023-01-11T21:42:41.7583436Z Expand the folded group to see the log file of test_nestedtensor
2023-01-11T21:42:41.7584418Z ##[group]PRINTING LOG FILE of test_nestedtensor (/var/lib/jenkins/workspace/test/test-reports/test_nestedtensor_g9fx0ytb)
2023-01-11T21:42:41.7584662Z 
2023-01-11T21:42:41.7584738Z Running tests...
2023-01-11T21:42:41.7585130Z ----------------------------------------------------------------------
2023-01-11T21:42:41.7585664Z Test results will be stored in test-reports/python-unittest/test_nestedtensor
2023-01-11T21:42:41.7586725Z   test_2d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... /var/lib/jenkins/workspace/test/test_nestedtensor.py:113: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:42:41.7587572Z   nested_tensor = torch.nested.nested_tensor(data, dtype=torch.int64)
2023-01-11T21:42:41.7587909Z ok (0.003s)
2023-01-11T21:42:41.7588387Z   test_2d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7588944Z   test_2d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7589300Z   test_2d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7589657Z   test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7590276Z   test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7590856Z   test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7591204Z   test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7591662Z   test_3d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7592246Z   test_3d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7592821Z   test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7593200Z   test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7593534Z   test_3d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7593853Z   test_3d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7594286Z   test_3d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7594860Z   test_3d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7595201Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7595564Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7595930Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7596289Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7596631Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7596992Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7597412Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7597775Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7598066Z   test_copy_ (__main__.TestNestedTensor) ... ok (0.006s)
2023-01-11T21:42:41.7598343Z   test_default_nested_tensor (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:41.7598612Z   test_dim (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7598852Z   test_fill_ (__main__.TestNestedTensor) ... ok (0.006s)
2023-01-11T21:42:41.7599117Z   test_is_contiguous (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7599395Z   test_nested_namespace (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7599671Z   test_nested_tensor (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:41.7599944Z   test_nested_tensor_matching_dim (__main__.TestNestedTensor) ... ok (0.006s)
2023-01-11T21:42:41.7600219Z   test_numel (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7600476Z   test_ones_like (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7600727Z   test_repr_string (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7600982Z   test_size (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7601235Z   test_size_dim (__main__.TestNestedTensor) ... ok (0.005s)
2023-01-11T21:42:41.7601477Z   test_stride (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:41.7601723Z   test_to (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:41.7602001Z   test_to_padded_tensor_on_empty_tensor (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7602298Z   test_unbind_0 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7602540Z   test_unbind_1 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7602793Z   test_unbind_3 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7603040Z   test_unbind_4 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:41.7603286Z   test_unbind_dim (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:41.7603436Z 
2023-01-11T21:42:41.7603690Z ----------------------------------------------------------------------
2023-01-11T21:42:41.7603930Z Ran 45 tests in 0.089s
2023-01-11T21:42:41.7604043Z 
2023-01-11T21:42:41.7604103Z OK
2023-01-11T21:42:41.7604181Z 
2023-01-11T21:42:41.7604265Z Generating XML reports...
2023-01-11T21:42:41.7604697Z Generated XML report: test-reports/python-unittest/test_nestedtensor/TEST-TestNestedTensor-20230111214241.xml
2023-01-11T21:42:41.7604937Z 
2023-01-11T21:42:41.7605221Z ##[endgroup]
2023-01-11T21:42:41.7605606Z FINISHED PRINTING LOG FILE of test_nestedtensor (/var/lib/jenkins/workspace/test/test-reports/test_nestedtensor_g9fx0ytb)
2023-01-11T21:42:41.7605833Z 
2023-01-11T21:42:43.8807538Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:43.9683522Z Ignoring disabled issues:  []
2023-01-11T21:42:43.9842401Z Running test_numba_integration ... [2023-01-11 21:42:43.983953]
2023-01-11T21:42:43.9844364Z Executing ['/opt/conda/bin/python', '-bb', 'test_numba_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:43.984209]
2023-01-11T21:42:46.4311981Z 
2023-01-11T21:42:46.4312983Z Expand the folded group to see the log file of test_numba_integration
2023-01-11T21:42:46.4314684Z ##[group]PRINTING LOG FILE of test_numba_integration (/var/lib/jenkins/workspace/test/test-reports/test_numba_integration_4aqtnrb2)
2023-01-11T21:42:46.4315366Z 
2023-01-11T21:42:46.4315542Z Running tests...
2023-01-11T21:42:46.4317335Z ----------------------------------------------------------------------
2023-01-11T21:42:46.4318556Z Test results will be stored in test-reports/python-unittest/test_numba_integration
2023-01-11T21:42:46.4319244Z   test_active_device (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4320348Z 'as_cuda_array' tensor device must match active numba context. ... skip: No cuda (0.002s)
2023-01-11T21:42:46.4326966Z   test_array_adaptor (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4327732Z Torch __cuda_array_adaptor__ exposes tensor data to numba.cuda. ... skip: No cuda (0.003s)
2023-01-11T21:42:46.4328364Z   test_conversion_errors (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4329088Z Numba properly detects array interface for tensor.Tensor variants. ... skip: No cuda (0.002s)
2023-01-11T21:42:46.4329559Z   test_cuda_array_interface (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4329990Z torch.Tensor exposes __cuda_array_interface__ for cuda tensors. ... skip: No cuda (0.003s)
2023-01-11T21:42:46.4330701Z   test_from_cuda_array_interface (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4331775Z torch.as_tensor() and torch.tensor() supports the __cuda_array_interface__ protocol. ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.003s)
2023-01-11T21:42:46.4332586Z   test_from_cuda_array_interface_active_device (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4333343Z torch.as_tensor() tensor device must match active numba context. ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.002s)
2023-01-11T21:42:46.4334196Z   test_from_cuda_array_interface_inferred_strides (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4334932Z torch.as_tensor(numba_ary) should have correct inferred (contiguous) strides ... skip: No cuda (0.001s)
2023-01-11T21:42:46.4335620Z   test_from_cuda_array_interface_lifetime (__main__.TestNumbaIntegration)
2023-01-11T21:42:46.4336534Z torch.as_tensor(obj) tensor grabs a reference to obj so that the lifetime of obj exceeds the tensor ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.001s)
2023-01-11T21:42:46.4337139Z 
2023-01-11T21:42:46.4337628Z ----------------------------------------------------------------------
2023-01-11T21:42:46.4338123Z Ran 8 tests in 0.016s
2023-01-11T21:42:46.4338332Z 
2023-01-11T21:42:46.4338470Z OK (skipped=8)
2023-01-11T21:42:46.4338698Z 
2023-01-11T21:42:46.4338865Z Generating XML reports...
2023-01-11T21:42:46.4339767Z Generated XML report: test-reports/python-unittest/test_numba_integration/TEST-TestNumbaIntegration-20230111214245.xml
2023-01-11T21:42:46.4340167Z 
2023-01-11T21:42:46.4340846Z ##[endgroup]
2023-01-11T21:42:46.4341941Z FINISHED PRINTING LOG FILE of test_numba_integration (/var/lib/jenkins/workspace/test/test-reports/test_numba_integration_4aqtnrb2)
2023-01-11T21:42:46.4342785Z 
2023-01-11T21:42:48.7659810Z 
2023-01-11T21:42:48.7660571Z Expand the folded group to see the log file of test_mkldnn_fusion
2023-01-11T21:42:48.7661826Z ##[group]PRINTING LOG FILE of test_mkldnn_fusion (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_fusion_8ljahok8)
2023-01-11T21:42:48.7662188Z 
2023-01-11T21:42:48.7662297Z Running tests...
2023-01-11T21:42:48.7663101Z ----------------------------------------------------------------------
2023-01-11T21:42:48.7663791Z Test results will be stored in test-reports/python-unittest/test_mkldnn_fusion
2023-01-11T21:42:48.7664705Z   test_conv_binary_fusion_ops (__main__.TestMkldnnFusion) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:42:48.7665336Z   test_conv_unary_fusion_nnc (__main__.TestMkldnnFusion) ... ok (1.036s)
2023-01-11T21:42:48.7665860Z   test_conv_unary_fusion_ops (__main__.TestMkldnnFusion) ... ok (65.061s)
2023-01-11T21:42:48.7666378Z   test_linear_binary_fusion_ops (__main__.TestMkldnnFusion) ... ok (0.016s)
2023-01-11T21:42:48.7666910Z   test_linear_unary_fusion_ops (__main__.TestMkldnnFusion) ... ok (0.022s)
2023-01-11T21:42:48.7667421Z   test_single_conv (__main__.TestMkldnnFusion) ... ok (1.465s)
2023-01-11T21:42:48.7667899Z   test_unsupported_conv (__main__.TestMkldnnFusion) ... ok (12.641s)
2023-01-11T21:42:48.7668194Z 
2023-01-11T21:42:48.7668563Z ----------------------------------------------------------------------
2023-01-11T21:42:48.7669000Z Ran 7 tests in 80.243s
2023-01-11T21:42:48.7669206Z 
2023-01-11T21:42:48.7669429Z OK (skipped=1)
2023-01-11T21:42:48.7669618Z 
2023-01-11T21:42:48.7669747Z Generating XML reports...
2023-01-11T21:42:48.7670528Z Generated XML report: test-reports/python-unittest/test_mkldnn_fusion/TEST-TestMkldnnFusion-20230111214128.xml
2023-01-11T21:42:48.7670959Z 
2023-01-11T21:42:48.7671391Z ##[endgroup]
2023-01-11T21:42:48.7672078Z FINISHED PRINTING LOG FILE of test_mkldnn_fusion (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_fusion_8ljahok8)
2023-01-11T21:42:48.7672478Z 
2023-01-11T21:42:48.8545110Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:48.9402271Z Ignoring disabled issues:  []
2023-01-11T21:42:48.9565479Z Running test_numpy_interop ... [2023-01-11 21:42:48.956122]
2023-01-11T21:42:48.9566114Z Executing ['/opt/conda/bin/python', '-bb', 'test_numpy_interop.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:48.956387]
2023-01-11T21:42:50.6887099Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:50.7555387Z Ignoring disabled issues:  []
2023-01-11T21:42:50.7718370Z Running test_nvfuser_dynamo ... [2023-01-11 21:42:50.771536]
2023-01-11T21:42:50.7720689Z Executing ['/opt/conda/bin/python', '-bb', 'test_nvfuser_dynamo.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:50.771790]
2023-01-11T21:42:50.9576440Z 
2023-01-11T21:42:50.9577104Z Expand the folded group to see the log file of test_numpy_interop
2023-01-11T21:42:50.9578141Z ##[group]PRINTING LOG FILE of test_numpy_interop (/var/lib/jenkins/workspace/test/test-reports/test_numpy_interop_dh069_5_)
2023-01-11T21:42:50.9578381Z 
2023-01-11T21:42:50.9578458Z Running tests...
2023-01-11T21:42:50.9578849Z ----------------------------------------------------------------------
2023-01-11T21:42:50.9579024Z 
2023-01-11T21:42:50.9579222Z ----------------------------------------------------------------------
2023-01-11T21:42:50.9579582Z Ran 0 tests in 0.000s
2023-01-11T21:42:50.9579757Z 
2023-01-11T21:42:50.9579878Z OK
2023-01-11T21:42:50.9580057Z 
2023-01-11T21:42:50.9580197Z Generating XML reports...
2023-01-11T21:42:50.9580816Z Test results will be stored in test-reports/python-unittest/test_numpy_interop
2023-01-11T21:42:50.9581151Z 
2023-01-11T21:42:50.9581535Z ##[endgroup]
2023-01-11T21:42:50.9582083Z FINISHED PRINTING LOG FILE of test_numpy_interop (/var/lib/jenkins/workspace/test/test-reports/test_numpy_interop_dh069_5_)
2023-01-11T21:42:50.9582308Z 
2023-01-11T21:42:52.6597935Z 
2023-01-11T21:42:52.6598547Z Expand the folded group to see the log file of test_nvfuser_dynamo
2023-01-11T21:42:52.6601085Z ##[group]PRINTING LOG FILE of test_nvfuser_dynamo (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_dynamo_ult0d520)
2023-01-11T21:42:52.6601660Z 
2023-01-11T21:42:52.6601825Z Running tests...
2023-01-11T21:42:52.6602640Z ----------------------------------------------------------------------
2023-01-11T21:42:52.6603777Z   test_basic (__main__.TestNvFuserDynamo) ... Test results will be stored in test-reports/python-unittest/test_nvfuser_dynamo
2023-01-11T21:42:52.6604769Z skip: requires CUDA (0.001s)
2023-01-11T21:42:52.6605463Z   test_batch_norm_implicit_dtype_promotion (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:52.6606312Z   test_dtype_correctness (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.000s)
2023-01-11T21:42:52.6607053Z   test_min_cut (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:52.6607459Z 
2023-01-11T21:42:52.6607935Z ----------------------------------------------------------------------
2023-01-11T21:42:52.6608472Z Ran 4 tests in 0.003s
2023-01-11T21:42:52.6608731Z 
2023-01-11T21:42:52.6608894Z OK (skipped=4)
2023-01-11T21:42:52.6609148Z 
2023-01-11T21:42:52.6609350Z Generating XML reports...
2023-01-11T21:42:52.6610403Z Generated XML report: test-reports/python-unittest/test_nvfuser_dynamo/TEST-TestNvFuserDynamo-20230111214252.xml
2023-01-11T21:42:52.6610958Z 
2023-01-11T21:42:52.6611510Z ##[endgroup]
2023-01-11T21:42:52.6612651Z FINISHED PRINTING LOG FILE of test_nvfuser_dynamo (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_dynamo_ult0d520)
2023-01-11T21:42:52.6613199Z 
2023-01-11T21:42:52.8705240Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:52.9374382Z Ignoring disabled issues:  []
2023-01-11T21:42:52.9536506Z Running test_nvfuser_frontend ... [2023-01-11 21:42:52.953373]
2023-01-11T21:42:52.9538915Z Executing ['/opt/conda/bin/python', '-bb', 'test_nvfuser_frontend.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:52.953618]
2023-01-11T21:42:54.5930632Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:54.6421342Z 
2023-01-11T21:42:54.6421891Z Expand the folded group to see the log file of test_nvfuser_frontend
2023-01-11T21:42:54.6423170Z ##[group]PRINTING LOG FILE of test_nvfuser_frontend (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_frontend_wsi6t0nt)
2023-01-11T21:42:54.6423623Z 
2023-01-11T21:42:54.6423758Z Running tests...
2023-01-11T21:42:54.6424200Z ----------------------------------------------------------------------
2023-01-11T21:42:54.6424680Z   test_basic (__main__.TestNvFuserFrontend) ... Test results will be stored in test-reports/python-unittest/test_nvfuser_frontend
2023-01-11T21:42:54.6424979Z skip: requires CUDA (0.001s)
2023-01-11T21:42:54.6425244Z   test_basic_fp16 (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:54.6425573Z   test_broadcast_mixing (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:54.6425900Z   test_cast_double_to_half (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:54.6426242Z   test_explicit_broadcast_input (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:54.6426591Z   test_implicit_broadcast_input (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:54.6426931Z   test_ops_broadcast (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.000s)
2023-01-11T21:42:54.6427249Z   test_prim_layer_norm_fwd (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.003s)
2023-01-11T21:42:54.6427581Z   test_prim_rms_norm_fwd (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:54.6427910Z   test_promote_to_double (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.000s)
2023-01-11T21:42:54.6428091Z 
2023-01-11T21:42:54.6428280Z ----------------------------------------------------------------------
2023-01-11T21:42:54.6428522Z Ran 10 tests in 0.009s
2023-01-11T21:42:54.6428635Z 
2023-01-11T21:42:54.6428708Z OK (skipped=10)
2023-01-11T21:42:54.6428816Z 
2023-01-11T21:42:54.6428899Z Generating XML reports...
2023-01-11T21:42:54.6429332Z Generated XML report: test-reports/python-unittest/test_nvfuser_frontend/TEST-TestNvFuserFrontend-20230111214254.xml
2023-01-11T21:42:54.6429584Z 
2023-01-11T21:42:54.6429808Z ##[endgroup]
2023-01-11T21:42:54.6430211Z FINISHED PRINTING LOG FILE of test_nvfuser_frontend (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_frontend_wsi6t0nt)
2023-01-11T21:42:54.6430635Z 
2023-01-11T21:42:54.6584145Z Ignoring disabled issues:  []
2023-01-11T21:42:54.6744557Z Running test_openmp ... [2023-01-11 21:42:54.674175]
2023-01-11T21:42:54.6746520Z Executing ['/opt/conda/bin/python', '-bb', 'test_openmp.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:54.674434]
2023-01-11T21:42:56.7108689Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:56.8033717Z Ignoring disabled issues:  []
2023-01-11T21:42:56.8285796Z Running test_optim ... [2023-01-11 21:42:56.828193]
2023-01-11T21:42:56.8287492Z Executing ['/opt/conda/bin/python', '-bb', 'test_optim.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:56.828506]
2023-01-11T21:42:59.5996548Z 
2023-01-11T21:42:59.5997043Z Expand the folded group to see the log file of test_openmp
2023-01-11T21:42:59.5998217Z ##[group]PRINTING LOG FILE of test_openmp (/var/lib/jenkins/workspace/test/test-reports/test_openmp_m_mtlrq1)
2023-01-11T21:42:59.5998445Z 
2023-01-11T21:42:59.5998520Z Running tests...
2023-01-11T21:42:59.5998937Z ----------------------------------------------------------------------
2023-01-11T21:42:59.5999314Z Test results will be stored in test-reports/python-unittest/test_openmp
2023-01-11T21:42:59.5999591Z   test_n_threads (__main__.TestOpenMP_ParallelFor)
2023-01-11T21:42:59.5999862Z Make sure there is no memory leak with many threads ... ok (1.165s)
2023-01-11T21:42:59.6000127Z   test_one_thread (__main__.TestOpenMP_ParallelFor)
2023-01-11T21:42:59.6000487Z Make sure there is no memory leak with one thread: issue gh-32284 ... ok (2.007s)
2023-01-11T21:42:59.6000656Z 
2023-01-11T21:42:59.6000856Z ----------------------------------------------------------------------
2023-01-11T21:42:59.6001097Z Ran 2 tests in 3.172s
2023-01-11T21:42:59.6001211Z 
2023-01-11T21:42:59.6001259Z OK
2023-01-11T21:42:59.6001350Z 
2023-01-11T21:42:59.6001439Z Generating XML reports...
2023-01-11T21:42:59.6001851Z Generated XML report: test-reports/python-unittest/test_openmp/TEST-TestOpenMP_ParallelFor-20230111214256.xml
2023-01-11T21:42:59.6002082Z 
2023-01-11T21:42:59.6002301Z ##[endgroup]
2023-01-11T21:42:59.6002667Z FINISHED PRINTING LOG FILE of test_openmp (/var/lib/jenkins/workspace/test/test-reports/test_openmp_m_mtlrq1)
2023-01-11T21:42:59.6002878Z 
2023-01-11T21:43:01.5127146Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:43:01.5784861Z Ignoring disabled issues:  []
2023-01-11T21:43:01.5945313Z Running test_package ... [2023-01-11 21:43:01.594201]
2023-01-11T21:43:01.5947037Z Executing ['/opt/conda/bin/python', '-bb', 'test_package.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:43:01.594465]
2023-01-11T21:43:05.7991357Z 
2023-01-11T21:43:05.7992242Z Expand the folded group to see the log file of test_package
2023-01-11T21:43:05.7993244Z ##[group]PRINTING LOG FILE of test_package (/var/lib/jenkins/workspace/test/test-reports/test_package_8fwzjqhn)
2023-01-11T21:43:05.7993567Z 
2023-01-11T21:43:05.7993670Z Running tests...
2023-01-11T21:43:05.7994306Z ----------------------------------------------------------------------
2023-01-11T21:43:05.7994844Z Test results will be stored in test-reports/python-unittest/test_package
2023-01-11T21:43:05.7996160Z   test_trace_dependencies (test_analyze.TestAnalyze) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/81213 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.213s)
2023-01-11T21:43:05.7997104Z   test_allow_empty_with_error (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.7997619Z If an error occurs during packaging, it should not be shadowed by the allow_empty error. ... ok (0.002s)
2023-01-11T21:43:05.7998119Z   test_broken_dependency (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.7998823Z A unpackageable dependency should raise a PackagingError. ... ok (0.002s)
2023-01-11T21:43:05.7999258Z   test_deny (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.7999686Z Test marking packages as "deny" during export. ... ok (0.002s)
2023-01-11T21:43:05.8000114Z   test_deny_glob (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8000572Z Test marking packages as "deny" using globs instead of package names. ... ok (0.003s)
2023-01-11T21:43:05.8001099Z   test_extern (test_dependency_api.TestDependencyAPI) ... ok (0.001s)
2023-01-11T21:43:05.8001592Z   test_extern_glob (test_dependency_api.TestDependencyAPI) ... ok (0.002s)
2023-01-11T21:43:05.8002129Z   test_extern_glob_allow_empty (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8002668Z Test that an error is thrown when a extern glob is specified with allow_empty=True ... ok (0.001s)
2023-01-11T21:43:05.8003196Z   test_externing_c_extension (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8004892Z Externing c extensions modules should allow us to still access them especially those found in torch._C. ... /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:900: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8041150Z   storage_type_str = obj.pickle_storage_type()
2023-01-11T21:43:05.8042762Z /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:903: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8043763Z   storage_numel = obj.size()
2023-01-11T21:43:05.8045042Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8045901Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:43:05.8046198Z ok (0.007s)
2023-01-11T21:43:05.8046592Z   test_implicit_intern (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8047133Z The save_module APIs should implicitly intern the module being saved. ... ok (0.001s)
2023-01-11T21:43:05.8047670Z   test_intern_error (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8048180Z Failure to handle all dependencies should lead to an error. ... ok (0.002s)
2023-01-11T21:43:05.8048694Z   test_invalid_import (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8049353Z An incorrectly-formed import should raise a PackagingError. ... ok (0.001s)
2023-01-11T21:43:05.8049900Z   test_mock (test_dependency_api.TestDependencyAPI) ... ok (0.002s)
2023-01-11T21:43:05.8050404Z   test_mock_glob (test_dependency_api.TestDependencyAPI) ... ok (0.002s)
2023-01-11T21:43:05.8050941Z   test_mock_glob_allow_empty (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8051512Z Test that an error is thrown when a mock glob is specified with allow_empty=True ... ok (0.001s)
2023-01-11T21:43:05.8052074Z   test_pickle_mocked (test_dependency_api.TestDependencyAPI) ... ok (0.001s)
2023-01-11T21:43:05.8052630Z   test_pickle_mocked_all (test_dependency_api.TestDependencyAPI) ... ok (0.001s)
2023-01-11T21:43:05.8053203Z   test_repackage_mocked_module (test_dependency_api.TestDependencyAPI)
2023-01-11T21:43:05.8053925Z Re-packaging a package that contains a mocked module should work correctly. ... ok (0.003s)
2023-01-11T21:43:05.8054513Z   test_extern_and_mock_hook (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:43:05.8055283Z   test_multiple_extern_hooks (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:43:05.8055881Z   test_multiple_mock_hooks (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:43:05.8056466Z   test_remove_hooks (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:43:05.8057007Z   test_single_hook (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:43:05.8057521Z   test_all_paths (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8058002Z   test_contains (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8058474Z   test_contains_non_hashable (test_digraph.TestDiGraph) ... ok (0.000s)
2023-01-11T21:43:05.8058961Z   test_edges (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8059440Z   test_forward_closure (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8059887Z   test_iter (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8060436Z   test_node_attr_update (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8060925Z   test_node_attrs (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8061424Z   test_predecessor_not_in_graph (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8061921Z   test_predecessors (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8062620Z   test_successor_not_in_graph (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8063061Z   test_successors (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:43:05.8063396Z   test_importer_access (test_directory_reader.DirectoryReaderTest) ... ok (0.003s)
2023-01-11T21:43:05.8063727Z   test_loading_has_record (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:43:05.8064182Z Test DirectoryReader's has_record(). ... ok (0.003s)
2023-01-11T21:43:05.8064714Z   test_loading_module (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:43:05.8065191Z Test basic saving and loading of a packages from a DirectoryReader. ... ok (0.003s)
2023-01-11T21:43:05.8065767Z   test_loading_pickle (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:43:05.8066462Z Test basic saving and loading of modules and pickles from a DirectoryReader. ... skip: Does not work with latest TorchVision, see https://github.com/pytorch/pytorch/issues/81115 (0.001s)
2023-01-11T21:43:05.8067040Z   test_package_resource_access (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:43:05.8067530Z Packaged modules should be able to use the importlib.resources API to access ... ok (0.003s)
2023-01-11T21:43:05.8068110Z   test_resource_access_by_path (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:43:05.8068606Z Tests that packaged code can used importlib.resources.path. ... ok (0.004s)
2023-01-11T21:43:05.8069141Z   test_resource_reader (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:43:05.8069682Z Tests DirectoryReader as the base for get_resource_reader. ... ok (0.007s)
2023-01-11T21:43:05.8070293Z   test_scriptobject_failure_message (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:43:05.8070885Z Test basic saving and loading of a ScriptModule in a directory. ... ok (0.011s)
2023-01-11T21:43:05.8071262Z   test_exclude (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8071548Z   test_exclude_from_all (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8071970Z   test_invalid_raw (test_glob_group.TestGlobGroup) ... ok (0.000s)
2023-01-11T21:43:05.8072373Z   test_list_include_exclude (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8072879Z   test_one_star (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8073346Z   test_one_star_middle (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8073903Z   test_one_star_multiple_in_component (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8074552Z   test_one_star_partial (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8075283Z   test_one_star_partial_extension (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8075831Z   test_raw_two_star (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8076304Z   test_two_star (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8076812Z   test_two_star_end (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8077200Z   test_two_star_middle (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8077497Z   test_two_star_multiple (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:43:05.8077798Z   test_ordered_importer_basic (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:43:05.8078157Z   test_ordered_importer_whichmodule (test_importer.TestImporter)
2023-01-11T21:43:05.8078574Z OrderedImporter's implementation of whichmodule should try each ... ok (0.001s)
2023-01-11T21:43:05.8078896Z   test_package_importer_whichmodule_no_dunder_module (test_importer.TestImporter)
2023-01-11T21:43:05.8079489Z Exercise corner case where we try to pickle an object whose ... ok (0.001s)
2023-01-11T21:43:05.8080013Z   test_single_ordered_importer (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:43:05.8080310Z   test_sys_importer (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:43:05.8080594Z   test_sys_importer_roundtrip (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:43:05.8080916Z   test_load_bc_packages_fx_module (test_load_bc_packages.TestLoadBCPackages)
2023-01-11T21:43:05.8081217Z Tests for backwards compatible fx module ... ok (0.011s)
2023-01-11T21:43:05.8081504Z   test_load_bc_packages_nn_module (test_load_bc_packages.TestLoadBCPackages)
2023-01-11T21:43:05.8081795Z Tests for backwards compatible nn module ... ok (0.007s)
2023-01-11T21:43:05.8082108Z   test_load_bc_packages_torchscript_module (test_load_bc_packages.TestLoadBCPackages)
2023-01-11T21:43:05.8082429Z Tests for backwards compatible torchscript module ... ok (0.036s)
2023-01-11T21:43:05.8082692Z   test_demangle_base (test_mangling.TestMangling)
2023-01-11T21:43:05.8083000Z Demangling a mangle parent directly should currently return an empty string. ... ok (0.001s)
2023-01-11T21:43:05.8083320Z   test_demangler_multiple_manglers (test_mangling.TestMangling)
2023-01-11T21:43:05.8083645Z PackageDemangler should be able to demangle name generated by any PackageMangler. ... ok (0.001s)
2023-01-11T21:43:05.8083967Z   test_is_mangled (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:43:05.8084256Z   test_mangle_empty_errors (test_mangling.TestMangling) ... ok (0.000s)
2023-01-11T21:43:05.8084535Z   test_mangle_prefix (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:43:05.8084813Z   test_mangler_is_consistent (test_mangling.TestMangling)
2023-01-11T21:43:05.8085104Z Mangling the same name twice should produce the same result. ... ok (0.001s)
2023-01-11T21:43:05.8085401Z   test_package_mangler (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:43:05.8085685Z   test_roundtrip_mangling (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:43:05.8085965Z   test_unique_manglers (test_mangling.TestMangling)
2023-01-11T21:43:05.8086270Z Each mangler instance should generate a unique mangled name for a given input. ... ok (0.001s)
2023-01-11T21:43:05.8086577Z   test_unique_module_names (test_mangling.TestMangling) ... ok (0.002s)
2023-01-11T21:43:05.8086851Z   test_dunder_package_present (test_misc.TestMisc)
2023-01-11T21:43:05.8087235Z The attribute '__torch_package__' should be populated on imported modules. ... ok (0.001s)
2023-01-11T21:43:05.8087542Z   test_dunder_package_works_from_package (test_misc.TestMisc)
2023-01-11T21:43:05.8087892Z The attribute '__torch_package__' should be accessible from within ... ok (0.002s)
2023-01-11T21:43:05.8088171Z   test_exporter_content_lists (test_misc.TestMisc)
2023-01-11T21:43:05.8088520Z Test content list API for PackageExporter's contained modules. ... ok (0.003s)
2023-01-11T21:43:05.8088778Z   test_file_structure (test_misc.TestMisc)
2023-01-11T21:43:05.8089187Z Tests package's Directory structure representation of a zip file. Ensures ... ok (0.002s)
2023-01-11T21:43:05.8089480Z   test_file_structure_has_file (test_misc.TestMisc)
2023-01-11T21:43:05.8089761Z Test Directory's has_file() method. ... ok (0.001s)
2023-01-11T21:43:05.8089995Z   test_inspect_class (test_misc.TestMisc)
2023-01-11T21:43:05.8090256Z Should be able to retrieve source for a packaged class. ... ok (0.002s)
2023-01-11T21:43:05.8090515Z   test_is_from_package (test_misc.TestMisc)
2023-01-11T21:43:05.8090760Z is_from_package should work for objects and modules ... ok (0.001s)
2023-01-11T21:43:05.8091036Z   test_load_python_version_from_package (test_misc.TestMisc)
2023-01-11T21:43:05.8091315Z Tests loading a package with a python version embdded ... ok (0.002s)
2023-01-11T21:43:05.8091598Z   test_loaders_that_remap_files_work_ok (test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:43:05.8091860Z   test_python_version (test_misc.TestMisc)
2023-01-11T21:43:05.8092181Z Tests that the current python version is stored in the package and is available ... ok (0.002s)
2023-01-11T21:43:05.8092471Z   test_std_lib_sys_hackery_checks (test_misc.TestMisc)
2023-01-11T21:43:05.8092759Z The standard library performs sys.module assignment hackery which ... ok (0.003s)
2023-01-11T21:43:05.8093166Z   test_model_save (test_model.ModelTest) ... skip: Does not work with recent torchvision, see https://github.com/pytorch/pytorch/issues/81115 (0.001s)
2023-01-11T21:43:05.8093622Z   test_resnet (test_model.ModelTest) ... skip: Does not work with recent torchvision, see https://github.com/pytorch/pytorch/issues/81115 (0.001s)
2023-01-11T21:43:05.8094069Z   test_script_resnet (test_model.ModelTest) ... skip: Does not work with recent torchvision, see https://github.com/pytorch/pytorch/issues/81115 (0.000s)
2023-01-11T21:43:05.8094456Z   test_package_fx_custom_tracer (test_package_fx.TestPackageFX) ... ok (0.009s)
2023-01-11T21:43:05.8094762Z   test_package_fx_package (test_package_fx.TestPackageFX) ... ok (0.017s)
2023-01-11T21:43:05.8095066Z   test_package_fx_simple (test_package_fx.TestPackageFX) ... ok (0.005s)
2023-01-11T21:43:05.8095359Z   test_package_fx_with_imports (test_package_fx.TestPackageFX) ... ok (0.005s)
2023-01-11T21:43:05.8095658Z   test_package_then_fx (test_package_fx.TestPackageFX) ... ok (0.004s)
2023-01-11T21:43:05.8095972Z   test_different_package_interface (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8096341Z Test a case where the interface defined in the package is ... ok (0.012s)
2023-01-11T21:43:05.8096646Z   test_different_package_script_class (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8096964Z Test a case where the script class defined in the package is ... ok (0.006s)
2023-01-11T21:43:05.8097282Z   test_load_shared_scriptmodules (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8097602Z Test loading of single ScriptModule shared by multiple eager ... ok (0.008s)
2023-01-11T21:43:05.8097897Z   test_load_shared_tensors (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8098563Z Test tensors shared across eager and ScriptModules on load ... /var/lib/jenkins/workspace/test/package/test_package_script.py:547: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8099157Z   shared_tensor.storage()._cdata,
2023-01-11T21:43:05.8099703Z /var/lib/jenkins/workspace/test/package/test_package_script.py:548: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8100235Z   scripted_mod_0.tensor.storage()._cdata,
2023-01-11T21:43:05.8100775Z /var/lib/jenkins/workspace/test/package/test_package_script.py:551: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8101323Z   shared_tensor.storage()._cdata,
2023-01-11T21:43:05.8101858Z /var/lib/jenkins/workspace/test/package/test_package_script.py:552: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8102502Z   scripted_mod_1.tensor.storage()._cdata,
2023-01-11T21:43:05.8103165Z /var/lib/jenkins/workspace/test/package/test_package_script.py:565: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8103686Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:43:05.8104229Z /var/lib/jenkins/workspace/test/package/test_package_script.py:566: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8104766Z   loaded_mod_1.sub_mod_0.tensor.storage()._cdata,
2023-01-11T21:43:05.8105317Z /var/lib/jenkins/workspace/test/package/test_package_script.py:569: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8105839Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:43:05.8106371Z /var/lib/jenkins/workspace/test/package/test_package_script.py:570: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8106899Z   loaded_mod_1.sub_mod_1.tensor.storage()._cdata,
2023-01-11T21:43:05.8107102Z ok (0.008s)
2023-01-11T21:43:05.8107344Z   test_load_shared_tensors_repackaged (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8108015Z Test tensors shared across eager and ScriptModules on load ... /var/lib/jenkins/workspace/test/package/test_package_script.py:619: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8108605Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:43:05.8109160Z /var/lib/jenkins/workspace/test/package/test_package_script.py:620: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8109700Z   loaded_mod_1.sub_mod_0.tensor.storage()._cdata,
2023-01-11T21:43:05.8110258Z /var/lib/jenkins/workspace/test/package/test_package_script.py:623: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8110804Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:43:05.8111349Z /var/lib/jenkins/workspace/test/package/test_package_script.py:624: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:43:05.8111882Z   loaded_mod_1.sub_mod_1.tensor.storage()._cdata,
2023-01-11T21:43:05.8112083Z ok (0.012s)
2023-01-11T21:43:05.8112329Z   test_mixing_packaged_and_inline_modules (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8112662Z Test saving inline and imported modules in same package with ... ok (0.047s)
2023-01-11T21:43:05.8113006Z   test_mixing_packaged_and_inline_modules_shared_code (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8113360Z Test saving inline and imported modules in same package that ... ok (1.047s)
2023-01-11T21:43:05.8113669Z   test_package_interface (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8113970Z Packaging an interface class should work correctly. ... ok (0.040s)
2023-01-11T21:43:05.8114351Z   test_package_script_class (test_package_script.TestPackageScript) ... ok (0.005s)
2023-01-11T21:43:05.8114702Z   test_package_script_class_referencing_self (test_package_script.TestPackageScript) ... ok (0.013s)
2023-01-11T21:43:05.8115067Z   test_save_eager_mods_sharing_scriptmodule (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8115389Z Test saving of single ScriptModule shared by multiple ... ok (0.005s)
2023-01-11T21:43:05.8115695Z   test_save_independent_scriptmodules (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8116022Z Test to verify saving multiple ScriptModules with completely ... ok (0.008s)
2023-01-11T21:43:05.8116339Z   test_save_repeat_scriptmodules (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8116654Z Test to verify saving multiple different modules and ... ok (0.042s)
2023-01-11T21:43:05.8116941Z   test_save_scriptmodule (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8117216Z Test basic saving of ScriptModule. ... ok (0.004s)
2023-01-11T21:43:05.8117498Z   test_save_scriptmodule_file (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8117774Z Test basic saving of ScriptModule in file. ... ok (0.004s)
2023-01-11T21:43:05.8118079Z   test_save_scriptmodule_only_necessary_code (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8118402Z Test to verify when saving multiple packages with same CU ... ok (0.431s)
2023-01-11T21:43:05.8118704Z   test_save_scriptmodule_with_submods (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8119014Z Test basic saving of ScriptModule with submodule. ... ok (0.007s)
2023-01-11T21:43:05.8119322Z   test_save_scriptmodules_in_container (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8119660Z Test saving of ScriptModules inside of container. Checks that relations ... ok (0.035s)
2023-01-11T21:43:05.8119995Z   test_save_scriptmodules_submod_redefinition (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8120330Z Test to verify saving multiple ScriptModules with same top module ... ok (0.012s)
2023-01-11T21:43:05.8120638Z   test_save_shared_tensors (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8120946Z Test tensors shared across eager and ScriptModules are serialized once. ... ok (0.007s)
2023-01-11T21:43:05.8121280Z   test_saving_and_scripting_packaged_mod (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8121589Z Test scripting a module loaded from a package ... ok (0.008s)
2023-01-11T21:43:05.8121886Z   test_scriptmodules_repeat_save (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8122191Z Test to verify saving and loading same ScriptModule object works ... ok (0.010s)
2023-01-11T21:43:05.8122500Z   test_tensor_sharing_pickle (test_package_script.TestPackageScript)
2023-01-11T21:43:05.8122877Z Test that saving a ScriptModule and a separately saving a tensor ... ok (0.004s)
2023-01-11T21:43:05.8123216Z   test_repackage_import_indirectly_via_parent_module (test_repackage.TestRepackage) ... ok (0.005s)
2023-01-11T21:43:05.8123552Z   test_importer_access (test_resources.TestResources) ... ok (0.002s)
2023-01-11T21:43:05.8123852Z   test_package_resource_access (test_resources.TestResources)
2023-01-11T21:43:05.8124176Z Packaged modules should be able to use the importlib.resources API to access ... ok (0.001s)
2023-01-11T21:43:05.8124484Z   test_resource_access_by_path (test_resources.TestResources)
2023-01-11T21:43:05.8124792Z Tests that packaged code can used importlib.resources.path. ... ok (0.002s)
2023-01-11T21:43:05.8125091Z   test_resource_reader (test_resources.TestResources)
2023-01-11T21:43:05.8125365Z Test compliance with the get_resource_reader importlib API. ... ok (0.003s)
2023-01-11T21:43:05.8125654Z   test_bad_dunder_imports (test_save_load.TestSaveLoad)
2023-01-11T21:43:05.8126097Z Test to ensure bad __imports__ don't cause PackageExporter to fail. ... ok (0.001s)
2023-01-11T21:43:05.8126403Z   test_dunder_imports (test_save_load.TestSaveLoad) ... ok (0.003s)
2023-01-11T21:43:05.8126678Z   test_exporting_mismatched_code (test_save_load.TestSaveLoad)
2023-01-11T21:43:05.8126979Z If an object with the same qualified name is loaded from different ... ok (0.004s)
2023-01-11T21:43:05.8127270Z   test_pickle (test_save_load.TestSaveLoad) ... ok (0.002s)
2023-01-11T21:43:05.8127527Z   test_save_imported_module (test_save_load.TestSaveLoad)
2023-01-11T21:43:05.8127826Z Saving a module that came from another PackageImporter should work. ... ok (0.002s)
2023-01-11T21:43:05.8128152Z   test_save_imported_module_using_package_importer (test_save_load.TestSaveLoad)
2023-01-11T21:43:05.8128563Z Exercise a corner case: re-packaging a module that uses `torch_package_importer` ... ok (0.002s)
2023-01-11T21:43:05.8128879Z   test_save_module (test_save_load.TestSaveLoad) ... ok (0.002s)
2023-01-11T21:43:05.8129173Z   test_save_module_binary (test_save_load.TestSaveLoad) ... ok (0.001s)
2023-01-11T21:43:05.8129458Z   test_saving_source (test_save_load.TestSaveLoad) ... ok (0.004s)
2023-01-11T21:43:05.8129722Z   test_saving_string (test_save_load.TestSaveLoad) ... ok (0.001s)
2023-01-11T21:43:05.8129883Z 
2023-01-11T21:43:05.8130086Z ----------------------------------------------------------------------
2023-01-11T21:43:05.8130331Z Ran 133 tests in 2.249s
2023-01-11T21:43:05.8130447Z 
2023-01-11T21:43:05.8130505Z OK (skipped=5)
2023-01-11T21:43:05.8130612Z 
2023-01-11T21:43:05.8130696Z Generating XML reports...
2023-01-11T21:43:05.8131145Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_dependency_api.TestDependencyAPI-20230111214303.xml
2023-01-11T21:43:05.8131732Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_dependency_hooks.TestDependencyHooks-20230111214303.xml
2023-01-11T21:43:05.8132276Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_digraph.TestDiGraph-20230111214303.xml
2023-01-11T21:43:05.8132839Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_directory_reader.DirectoryReaderTest-20230111214303.xml
2023-01-11T21:43:05.8133406Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_glob_group.TestGlobGroup-20230111214303.xml
2023-01-11T21:43:05.8133937Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_importer.TestImporter-20230111214303.xml
2023-01-11T21:43:05.8134497Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_load_bc_packages.TestLoadBCPackages-20230111214303.xml
2023-01-11T21:43:05.8135055Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_mangling.TestMangling-20230111214303.xml
2023-01-11T21:43:05.8135557Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_misc.TestMisc-20230111214303.xml
2023-01-11T21:43:05.8136070Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_package_fx.TestPackageFX-20230111214303.xml
2023-01-11T21:43:05.8136654Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_package_script.TestPackageScript-20230111214303.xml
2023-01-11T21:43:05.8137212Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_repackage.TestRepackage-20230111214303.xml
2023-01-11T21:43:05.8137752Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_resources.TestResources-20230111214303.xml
2023-01-11T21:43:05.8138283Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_save_load.TestSaveLoad-20230111214303.xml
2023-01-11T21:43:05.8138787Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_analyze.TestAnalyze-20230111214303.xml
2023-01-11T21:43:05.8139291Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_model.ModelTest-20230111214303.xml
2023-01-11T21:43:05.8139513Z 
2023-01-11T21:43:05.8139870Z ##[endgroup]
2023-01-11T21:43:05.8140281Z FINISHED PRINTING LOG FILE of test_package (/var/lib/jenkins/workspace/test/test-reports/test_package_8fwzjqhn)
2023-01-11T21:43:05.8140493Z 
2023-01-11T21:43:08.0118223Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:43:08.1063455Z Ignoring disabled issues:  []
2023-01-11T21:43:08.1262753Z Running test_per_overload_api ... [2023-01-11 21:43:08.125890]
2023-01-11T21:43:08.1265278Z Executing ['/opt/conda/bin/python', '-bb', 'test_per_overload_api.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:43:08.126166]
2023-01-11T21:43:10.3788557Z 
2023-01-11T21:43:10.3789125Z Expand the folded group to see the log file of test_per_overload_api
2023-01-11T21:43:10.3790302Z ##[group]PRINTING LOG FILE of test_per_overload_api (/var/lib/jenkins/workspace/test/test-reports/test_per_overload_api_e6flvlj6)
2023-01-11T21:43:10.3790803Z 
2023-01-11T21:43:10.3791323Z Running tests...
2023-01-11T21:43:10.3791883Z ----------------------------------------------------------------------
2023-01-11T21:43:10.3792475Z Test results will be stored in test-reports/python-unittest/test_per_overload_api
2023-01-11T21:43:10.3792894Z   test_basics_opoverload (__main__.TestPerOverloadAPI) ... ok (0.270s)
2023-01-11T21:43:10.3793303Z   test_basics_opoverloadpacket (__main__.TestPerOverloadAPI) ... ok (0.002s)
2023-01-11T21:43:10.3793685Z   test_decompose (__main__.TestPerOverloadAPI) ... ok (0.001s)
2023-01-11T21:43:10.3793887Z 
2023-01-11T21:43:10.3794200Z ----------------------------------------------------------------------
2023-01-11T21:43:10.3794515Z Ran 3 tests in 0.274s
2023-01-11T21:43:10.3794660Z 
2023-01-11T21:43:10.3794739Z OK
2023-01-11T21:43:10.3798865Z 
2023-01-11T21:43:10.3799251Z Generating XML reports...
2023-01-11T21:43:10.3799923Z Generated XML report: test-reports/python-unittest/test_per_overload_api/TEST-TestPerOverloadAPI-20230111214309.xml
2023-01-11T21:43:10.3800248Z 
2023-01-11T21:43:10.3800601Z ##[endgroup]
2023-01-11T21:43:10.3801796Z FINISHED PRINTING LOG FILE of test_per_overload_api (/var/lib/jenkins/workspace/test/test-reports/test_per_overload_api_e6flvlj6)
2023-01-11T21:43:10.3802090Z 
2023-01-11T21:43:12.4821536Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:43:12.5659726Z Ignoring disabled issues:  []
2023-01-11T21:43:12.5825243Z Running test_pruning_op ... [2023-01-11 21:43:12.582174]
2023-01-11T21:43:12.5827848Z Executing ['/opt/conda/bin/python', '-bb', 'test_pruning_op.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:43:12.582488]
2023-01-11T21:43:14.2196904Z 
2023-01-11T21:43:14.2197542Z Expand the folded group to see the log file of test_pruning_op
2023-01-11T21:43:14.2199745Z ##[group]PRINTING LOG FILE of test_pruning_op (/var/lib/jenkins/workspace/test/test-reports/test_pruning_op_uran59ue)
2023-01-11T21:43:14.2200283Z 
2023-01-11T21:43:14.2200928Z ##[endgroup]
2023-01-11T21:43:14.2201978Z FINISHED PRINTING LOG FILE of test_pruning_op (/var/lib/jenkins/workspace/test/test-reports/test_pruning_op_uran59ue)
2023-01-11T21:43:14.2202791Z 
2023-01-11T21:43:16.2016409Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:43:16.2861420Z Ignoring disabled issues:  []
2023-01-11T21:43:16.3027888Z Running test_pytree ... [2023-01-11 21:43:16.302495]
2023-01-11T21:43:16.3030570Z Executing ['/opt/conda/bin/python', '-bb', 'test_pytree.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:43:16.302781]
2023-01-11T21:43:18.9189651Z 
2023-01-11T21:43:18.9190123Z Expand the folded group to see the log file of test_pytree
2023-01-11T21:43:18.9191156Z ##[group]PRINTING LOG FILE of test_pytree (/var/lib/jenkins/workspace/test/test-reports/test_pytree_7sx5t_qy)
2023-01-11T21:43:18.9191562Z 
2023-01-11T21:43:18.9191693Z Running tests...
2023-01-11T21:43:18.9192365Z ----------------------------------------------------------------------
2023-01-11T21:43:18.9193037Z Test results will be stored in test-reports/python-unittest/test_pytree
2023-01-11T21:43:18.9193805Z   test_broadcast_to_and_flatten (__main__.TestPytree) ... ok (0.230s)
2023-01-11T21:43:18.9194292Z   test_flatten_unflatten_dict (__main__.TestPytree) ... ok (0.003s)
2023-01-11T21:43:18.9194692Z   test_flatten_unflatten_leaf (__main__.TestPytree) ... ok (0.002s)
2023-01-11T21:43:18.9195113Z   test_flatten_unflatten_list (__main__.TestPytree) ... ok (0.002s)
2023-01-11T21:43:18.9195436Z   test_flatten_unflatten_namedtuple (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9195924Z   test_flatten_unflatten_nested (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9196417Z   test_flatten_unflatten_odict (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9196939Z   test_flatten_unflatten_return_type_max (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9197482Z   test_flatten_unflatten_return_type_min (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9197896Z   test_flatten_unflatten_tuple (__main__.TestPytree) ... ok (0.002s)
2023-01-11T21:43:18.9198172Z   test_tree_all_any (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9198417Z   test_tree_only (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9198649Z   test_treemap (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9198903Z   test_treespec_equality (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9199161Z   test_treespec_repr (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:43:18.9199304Z 
2023-01-11T21:43:18.9199525Z ----------------------------------------------------------------------
2023-01-11T21:43:18.9199755Z Ran 15 tests in 0.249s
2023-01-11T21:43:18.9199867Z 
2023-01-11T21:43:18.9199928Z OK
2023-01-11T21:43:18.9200019Z 
2023-01-11T21:43:18.9200101Z Generating XML reports...
2023-01-11T21:43:18.9200494Z Generated XML report: test-reports/python-unittest/test_pytree/TEST-TestPytree-20230111214318.xml
2023-01-11T21:43:18.9200712Z 
2023-01-11T21:43:18.9200975Z ##[endgroup]
2023-01-11T21:43:18.9201347Z FINISHED PRINTING LOG FILE of test_pytree (/var/lib/jenkins/workspace/test/test-reports/test_pytree_7sx5t_qy)
2023-01-11T21:43:18.9205690Z 
2023-01-11T21:43:20.7820121Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:43:20.8484882Z Ignoring disabled issues:  []
2023-01-11T21:43:20.8651163Z Running test_quantization ... [2023-01-11 21:43:20.864812]
2023-01-11T21:43:20.8653663Z Executing ['/opt/conda/bin/python', '-bb', 'test_quantization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:43:20.865089]
2023-01-11T21:44:23.5730183Z 
2023-01-11T21:44:23.5730627Z Expand the folded group to see the log file of test_optim
2023-01-11T21:44:23.5731371Z ##[group]PRINTING LOG FILE of test_optim (/var/lib/jenkins/workspace/test/test-reports/test_optim_a93x3xeo)
2023-01-11T21:44:23.5800963Z 
2023-01-11T21:44:23.5943512Z Running tests...
2023-01-11T21:44:23.5944153Z ----------------------------------------------------------------------
2023-01-11T21:44:23.5944772Z Test results will be stored in test-reports/python-unittest/test_optim
2023-01-11T21:44:23.5945772Z   test_adadelta (__main__.TestDifferentiableOptimizer) ... ok (0.239s)
2023-01-11T21:44:23.5946336Z   test_adagrad (__main__.TestDifferentiableOptimizer) ... ok (0.006s)
2023-01-11T21:44:23.5946849Z   test_adam (__main__.TestDifferentiableOptimizer) ... ok (0.014s)
2023-01-11T21:44:23.5947192Z   test_adamax (__main__.TestDifferentiableOptimizer) ... ok (0.010s)
2023-01-11T21:44:23.5947613Z   test_adamw (__main__.TestDifferentiableOptimizer) ... ok (0.014s)
2023-01-11T21:44:23.5947991Z   test_asgd (__main__.TestDifferentiableOptimizer) ... ok (0.005s)
2023-01-11T21:44:23.5948360Z   test_nadam (__main__.TestDifferentiableOptimizer) ... ok (0.014s)
2023-01-11T21:44:23.5948739Z   test_radam (__main__.TestDifferentiableOptimizer) ... ok (0.009s)
2023-01-11T21:44:23.5949123Z   test_rmsprop (__main__.TestDifferentiableOptimizer) ... ok (0.013s)
2023-01-11T21:44:23.5949508Z   test_rprop (__main__.TestDifferentiableOptimizer) ... ok (0.010s)
2023-01-11T21:44:23.5949953Z   test_sgd (__main__.TestDifferentiableOptimizer) ... ok (0.004s)
2023-01-11T21:44:23.5950367Z   test_CosineAnnealingWarmRestarts_lr1_T_mult_1 (__main__.TestLRScheduler) ... ok (0.007s)
2023-01-11T21:44:23.5950813Z   test_CosineAnnealingWarmRestarts_lr1_T_mult_2 (__main__.TestLRScheduler) ... ok (0.007s)
2023-01-11T21:44:23.5951243Z   test_CosineAnnealingWarmRestarts_lr1_T_mult_4 (__main__.TestLRScheduler) ... ok (0.007s)
2023-01-11T21:44:23.5951676Z   test_CosineAnnealingWarmRestarts_lr2 (__main__.TestLRScheduler) ... ok (0.057s)
2023-01-11T21:44:23.5952096Z   test_CosineAnnealingWarmRestarts_lr3 (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:44:23.5952539Z   test_CosineAnnealingWarmRestarts_lr_state_dict (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5954035Z   test_chained_lr1 (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.5955119Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.5955441Z ok (0.002s)
2023-01-11T21:44:23.5955722Z   test_chained_lr2 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5956087Z   test_chained_lr2_get_last_lr_before_step (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5956462Z   test_chained_lr3 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5956801Z   test_chained_lr4 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5957136Z   test_chained_lr5 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5957477Z   test_closed_form_constantlr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:44:23.5957862Z   test_closed_form_cos_anneal_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:44:23.5958234Z   test_closed_form_exp_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:44:23.5958586Z   test_closed_form_linearlr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:44:23.5958960Z   test_closed_form_multi_step_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:44:23.5959329Z   test_closed_form_poly_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:44:23.5959676Z   test_closed_form_step_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:44:23.5961132Z   test_compound_cosanneal_and_exp_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.5962163Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.5962479Z ok (0.002s)
2023-01-11T21:44:23.5962793Z   test_compound_cosanneal_and_linearlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5963194Z   test_compound_cosanneal_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5963604Z   test_compound_cosanneal_and_step_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5963995Z   test_compound_exp_and_linearlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5964373Z   test_compound_exp_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5964777Z   test_compound_linearlr_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5965221Z   test_compound_reduce_lr_on_plateau1 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5965619Z   test_compound_reduce_lr_on_plateau2 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5965997Z   test_compound_reduce_lr_on_plateau3 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5966382Z   test_compound_reduce_lr_on_plateau4 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5966771Z   test_compound_reduce_lr_on_plateau5 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5967154Z   test_compound_step_and_constantlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5967540Z   test_compound_step_and_exp_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5967931Z   test_compound_step_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5968296Z   test_constantlr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5968670Z   test_constantlr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5969072Z   test_constantlr_with_epoch (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5970503Z   test_cos_anneal_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.5971460Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.5971765Z ok (0.002s)
2023-01-11T21:44:23.5972055Z   test_cos_anneal_lr_continue (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5972424Z   test_cosine_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5972790Z   test_cosine_then_cyclic (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5973198Z   test_cycle_lr_cycle_momentum_fail_with_momentumless_optimizer (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5973617Z   test_cycle_lr_exp_range_mode (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5974000Z   test_cycle_lr_exp_range_mode_one_lr (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5974397Z   test_cycle_lr_exp_range_mode_step_size_up_down (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5974789Z   test_cycle_lr_invalid_mode (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5975177Z   test_cycle_lr_removed_after_out_of_scope (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5975592Z   test_cycle_lr_scale_fn_restored_from_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5975986Z   test_cycle_lr_state_dict_picklable (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5976373Z   test_cycle_lr_triangular2_mode (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:44:23.5976802Z   test_cycle_lr_triangular2_mode_one_lr (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:44:23.5977204Z   test_cycle_lr_triangular2_mode_step_size_up_down (__main__.TestLRScheduler) ... ok (0.005s)
2023-01-11T21:44:23.5977601Z   test_cycle_lr_triangular_mode (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5977994Z   test_cycle_lr_triangular_mode_one_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5978409Z   test_cycle_lr_triangular_mode_one_lr_no_momentum (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5978824Z   test_cycle_lr_triangular_mode_step_size_up_down (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5979212Z   test_cycle_lr_with_adam (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5979601Z   test_cycle_lr_with_momentumless_optimizer (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5980021Z   test_error_when_getlr_has_epoch (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5980377Z   test_exp_lr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5980725Z   test_exp_step_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5981134Z   test_exponential_lr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5981521Z   test_get_last_lr_constantlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5981887Z   test_get_last_lr_linearlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5982261Z   test_get_last_lr_multi_step_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5983954Z   test_get_last_lr_sequentiallr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:152: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
2023-01-11T21:44:23.5984909Z   warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
2023-01-11T21:44:23.5985191Z ok (0.002s)
2023-01-11T21:44:23.5985482Z   test_get_last_lr_step_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5986890Z   test_lambda_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.5987848Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.5988154Z ok (0.002s)
2023-01-11T21:44:23.5988449Z   test_lambda_lr_state_dict_fn (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5988951Z   test_lambda_lr_state_dict_obj (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5989415Z   test_linear_linearlr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.5991100Z   test_linearlr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.5992297Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.5992660Z ok (0.002s)
2023-01-11T21:44:23.5992997Z   test_linearlr_start_factor_limits1 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5993452Z   test_linearlr_start_factor_limits2 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5993890Z   test_linearlr_with_epoch (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5995594Z   test_multi_step_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.5996751Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.5997201Z ok (0.001s)
2023-01-11T21:44:23.5997496Z   test_multi_step_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.5997877Z   test_multi_step_lr_with_epoch (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.5999303Z   test_multiplicative_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.6000244Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.6000561Z ok (0.002s)
2023-01-11T21:44:23.6000859Z   test_new_pattern_no_warning (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6001250Z   test_new_pattern_no_warning_with_arg (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6001671Z   test_new_pattern_no_warning_with_overridden_optim_step (__main__.TestLRScheduler) ... ok (0.005s)
2023-01-11T21:44:23.6002079Z   test_no_cyclic_references (__main__.TestLRScheduler) ... ok (0.134s)
2023-01-11T21:44:23.6002460Z   test_no_cyclic_references_in_step (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6002824Z   test_old_pattern_warning (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:44:23.6003203Z   test_old_pattern_warning_resuming (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:44:23.6003609Z   test_old_pattern_warning_resuming_with_arg (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.6004013Z   test_old_pattern_warning_with_arg (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.6004420Z   test_old_pattern_warning_with_overridden_optim_step (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.6004863Z   test_onecycle_lr_cannot_calculate_total_steps (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6005271Z   test_onecycle_lr_cosine_annealing (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:44:23.6005657Z   test_onecycle_lr_invalid_anneal_strategy (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6006064Z   test_onecycle_lr_invalid_pct_start (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6006461Z   test_onecycle_lr_linear_annealing (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6006875Z   test_onecycle_lr_linear_annealing_three_phases (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6008296Z   test_poly_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.6009287Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.6009601Z ok (0.001s)
2023-01-11T21:44:23.6009931Z   test_polynomial_lr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6010320Z   test_reduce_lr_on_plateau1 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6010723Z   test_reduce_lr_on_plateau2 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6011091Z   test_reduce_lr_on_plateau3 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6011460Z   test_reduce_lr_on_plateau4 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6011848Z   test_reduce_lr_on_plateau5 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6012216Z   test_reduce_lr_on_plateau6 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6012607Z   test_reduce_lr_on_plateau7 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6012958Z   test_reduce_lr_on_plateau8 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6013342Z   test_reduce_lr_on_plateau_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6014799Z   test_sequentiallr1 (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.6015764Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.6017192Z /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:152: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
2023-01-11T21:44:23.6018072Z   warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
2023-01-11T21:44:23.6018337Z ok (0.002s)
2023-01-11T21:44:23.6018621Z   test_sequentiallr2 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6018970Z   test_sequentiallr3 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6019310Z   test_sequentiallr4 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6019649Z   test_step_lr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6020025Z   test_step_lr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6020402Z   test_step_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6020761Z   test_swa_lr_state_dict (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6021157Z   test_swalr_cosine_anneal_after_multiplicative (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6021549Z   test_swalr_hypers (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:44:23.6021922Z   test_swalr_linear_anneal_after_multiplicative (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6022308Z   test_swalr_no_anneal (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:44:23.6023787Z   test_adadelta (__main__.TestOptim) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.6024800Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.6025103Z ok (2.912s)
2023-01-11T21:44:23.6025380Z   test_adadelta_complex (__main__.TestOptim) ... ok (0.008s)
2023-01-11T21:44:23.6025702Z   test_adagrad (__main__.TestOptim) ... ok (3.915s)
2023-01-11T21:44:23.6026030Z   test_adagrad_complex (__main__.TestOptim) ... ok (0.007s)
2023-01-11T21:44:23.6026352Z   test_adagrad_sparse (__main__.TestOptim) ... ok (11.989s)
2023-01-11T21:44:23.6026669Z   test_adam (__main__.TestOptim) ... ok (10.080s)
2023-01-11T21:44:23.6026972Z   test_adamax (__main__.TestOptim) ... ok (3.287s)
2023-01-11T21:44:23.6027329Z   test_adamw (__main__.TestOptim) ... ok (3.020s)
2023-01-11T21:44:23.6027631Z   test_asgd (__main__.TestOptim) ... ok (4.117s)
2023-01-11T21:44:23.6027976Z   test_duplicate_params_in_param_group (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:44:23.6028309Z   test_empty_grad (__main__.TestOptim) ... ok (0.010s)
2023-01-11T21:44:23.6028696Z   test_functional_fused_adam_with_foundinf (__main__.TestOptim) ... skip: CUDA is required. (0.002s)
2023-01-11T21:44:23.6029088Z   test_fused_optimizers (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:44:23.6029416Z   test_invalid_param_type (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:44:23.6029736Z   test_lbfgs (__main__.TestOptim) ... ok (0.407s)
2023-01-11T21:44:23.6030057Z   test_lbfgs_return_type (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:44:23.6030402Z   test_multi_tensor_optimizers (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:44:23.6031805Z   test_nadam (__main__.TestOptim) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:44:23.6032743Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:44:23.6033061Z ok (1.545s)
2023-01-11T21:44:23.6033349Z   test_no_grad_for_all_params (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:44:23.6033666Z   test_post_hook (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:44:23.6033994Z   test_pre_and_post_hook (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:44:23.6034309Z   test_pre_hook (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:44:23.6034764Z   test_radam (__main__.TestOptim) ... ok (1.734s)
2023-01-11T21:44:23.6035115Z   test_rmsprop (__main__.TestOptim) ... ok (7.732s)
2023-01-11T21:44:23.6035468Z   test_rprop (__main__.TestOptim) ... ok (3.392s)
2023-01-11T21:44:23.6035809Z   test_sgd (__main__.TestOptim) ... ok (7.871s)
2023-01-11T21:44:23.6036145Z   test_sgd_complex (__main__.TestOptim) ... ok (0.010s)
2023-01-11T21:44:23.6036507Z   test_sgd_sparse (__main__.TestOptim) ... ok (17.984s)
2023-01-11T21:44:23.6036871Z   test_sparse_adam (__main__.TestOptim) ... ok (2.975s)
2023-01-11T21:44:23.6037259Z   test_averaged_model_all_devices (__main__.TestSWAUtils) ... ok (0.012s)
2023-01-11T21:44:23.6037693Z   test_averaged_model_exponential (__main__.TestSWAUtils) ... ok (0.009s)
2023-01-11T21:44:23.6038143Z   test_averaged_model_exponential_buffers (__main__.TestSWAUtils) ... ok (0.006s)
2023-01-11T21:44:23.6038577Z   test_averaged_model_mixed_device (__main__.TestSWAUtils) ... ok (0.001s)
2023-01-11T21:44:23.6039008Z   test_averaged_model_state_dict (__main__.TestSWAUtils) ... ok (0.005s)
2023-01-11T21:44:23.6039432Z   test_bn_update_eval_momentum (__main__.TestSWAUtils) ... ok (0.112s)
2023-01-11T21:44:23.6039886Z   test_update_bn_cnn (__main__.TestSWAUtils) ... ok (0.728s)
2023-01-11T21:44:23.6040251Z   test_update_bn_dnn (__main__.TestSWAUtils) ... ok (0.060s)
2023-01-11T21:44:23.6040468Z 
2023-01-11T21:44:23.6040786Z ----------------------------------------------------------------------
2023-01-11T21:44:23.6041149Z Ran 166 tests in 84.751s
2023-01-11T21:44:23.6041317Z 
2023-01-11T21:44:23.6041403Z OK (skipped=1)
2023-01-11T21:44:23.6041652Z 
2023-01-11T21:44:23.6041761Z Generating XML reports...
2023-01-11T21:44:23.6042345Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestDifferentiableOptimizer-20230111214258.xml
2023-01-11T21:44:23.6043038Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestLRScheduler-20230111214258.xml
2023-01-11T21:44:23.6043652Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestOptim-20230111214258.xml
2023-01-11T21:44:23.6044309Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestSWAUtils-20230111214258.xml
2023-01-11T21:44:23.6044590Z 
2023-01-11T21:44:23.6044994Z ##[endgroup]
2023-01-11T21:44:23.6045464Z FINISHED PRINTING LOG FILE of test_optim (/var/lib/jenkins/workspace/test/test-reports/test_optim_a93x3xeo)
2023-01-11T21:44:23.6045729Z 
2023-01-11T21:44:25.6855803Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:44:25.7706801Z Ignoring disabled issues:  []
2023-01-11T21:44:25.7874151Z Running test_schema_check ... [2023-01-11 21:44:25.787129]
2023-01-11T21:44:25.7875873Z Executing ['/opt/conda/bin/python', '-bb', 'test_schema_check.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:44:25.787369]
2023-01-11T21:44:28.6984340Z 
2023-01-11T21:44:28.6985026Z Expand the folded group to see the log file of test_schema_check
2023-01-11T21:44:28.6985848Z ##[group]PRINTING LOG FILE of test_schema_check (/var/lib/jenkins/workspace/test/test-reports/test_schema_check_xb88u8nc)
2023-01-11T21:44:28.6986109Z 
2023-01-11T21:44:28.6986511Z Running tests...
2023-01-11T21:44:28.6987235Z ----------------------------------------------------------------------
2023-01-11T21:44:28.6987954Z Test results will be stored in test-reports/python-unittest/test_schema_check
2023-01-11T21:44:28.6988541Z   test_alias_check_fail_multiple_operators (__main__.TestSchemaCheck) ... ok (0.004s)
2023-01-11T21:44:28.6989163Z   test_alias_check_fail_multiple_operators_centered (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.6989801Z   test_alias_check_fail_outputs_unexpectedly_aliasing (__main__.TestSchemaCheck) ... ok (0.004s)
2023-01-11T21:44:28.6990386Z   test_alias_check_fail_simple (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.6990849Z   test_is_alias_of_basic (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.6991369Z   test_is_alias_of_empty_container (__main__.TestSchemaCheck) ... ok (0.004s)
2023-01-11T21:44:28.6991896Z   test_mutation_check_fail (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.6992481Z   test_mutation_check_fail_multiple_operators (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.6992994Z   test_overlaps_basic (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.6993476Z   test_overlaps_empty_container (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.6994031Z   test_schema_check_mode_empty_list_input (__main__.TestSchemaCheck) ... ok (0.010s)
2023-01-11T21:44:28.6994656Z   test_schema_check_mode_functionality (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.6995256Z   test_schema_check_mode_functionality_aliasing_inputs (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.6995894Z   test_schema_check_mode_functionality_default_replaced (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.6996500Z   test_schema_check_mode_functionality_device_input (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:44:28.6997066Z   test_schema_check_mode_functionality_kwarg_tensor (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:44:28.6997907Z   test_schema_check_mode_functionality_list_input (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.6998492Z   test_schema_check_mode_functionality_mutable_inputs (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.6999022Z   test_schema_check_mode_functionality_nested_training_op (__main__.TestSchemaCheck) ... ok (0.012s)
2023-01-11T21:44:28.6999499Z   test_schema_check_mode_functionality_training_op (__main__.TestSchemaCheck) ... ok (0.011s)
2023-01-11T21:44:28.6999965Z   test_schema_check_mode_functionality_wildcard_after (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.7000480Z   test_schema_check_mode_functionality_with_multiple_outputs (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.7002215Z   test_schema_check_mode_functionality_with_multiple_outputs_aliasing (__main__.TestSchemaCheck) ... /var/lib/jenkins/workspace/test/test_schema_check.py:278: UserWarning: An output with one or more elements was resized since it had shape [1, 1, 3], which does not match the required output shape [1, 3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:44:28.7003598Z   torch.aminmax(x, dim=0, out=[actual, actual])
2023-01-11T21:44:28.7003934Z ok (0.002s)
2023-01-11T21:44:28.7053167Z   test_schema_check_mode_mutated_aliasing_aliasing_inputs (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.7054277Z   test_schema_check_mode_mutated_aliasing_aliasing_outputs (__main__.TestSchemaCheck) ... /var/lib/jenkins/workspace/test/test_schema_check.py:180: UserWarning: An output with one or more elements was resized since it had shape [1, 1, 3], which does not match the required output shape [1, 3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:44:28.7055349Z   torch.aminmax(x, dim=0, out=[actual, actual])
2023-01-11T21:44:28.7055593Z ok (0.002s)
2023-01-11T21:44:28.7055933Z   test_schema_check_mode_mutated_aliasing_as_strided (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.7056383Z   test_schema_check_mode_mutated_aliasing_multiple_outputs (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.7056833Z   test_schema_check_mode_mutated_aliasing_mutation (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.7057247Z   test_schema_check_mode_mutated_aliasing_none (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.7057676Z   test_schema_check_mode_mutated_aliasing_resize_ (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:44:28.7058099Z   test_schema_check_mode_operator_order (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:44:28.7058513Z   test_schema_check_mode_operator_order_without_grad (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:44:28.7058911Z   test_schema_info_bind_basic (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:44:28.7059124Z 
2023-01-11T21:44:28.7059493Z ----------------------------------------------------------------------
2023-01-11T21:44:28.7059804Z Ran 33 tests in 0.104s
2023-01-11T21:44:28.7059948Z 
2023-01-11T21:44:28.7060014Z OK
2023-01-11T21:44:28.7060127Z 
2023-01-11T21:44:28.7060235Z Generating XML reports...
2023-01-11T21:44:28.7060788Z Generated XML report: test-reports/python-unittest/test_schema_check/TEST-TestSchemaCheck-20230111214428.xml
2023-01-11T21:44:28.7061090Z 
2023-01-11T21:44:28.7061455Z ##[endgroup]
2023-01-11T21:44:28.7061969Z FINISHED PRINTING LOG FILE of test_schema_check (/var/lib/jenkins/workspace/test/test-reports/test_schema_check_xb88u8nc)
2023-01-11T21:44:28.7062528Z 
2023-01-11T21:44:30.5964973Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:44:30.6615602Z Ignoring disabled issues:  []
2023-01-11T21:44:30.6785961Z Running test_serialization ... [2023-01-11 21:44:30.678243]
2023-01-11T21:44:30.6787018Z Executing ['/opt/conda/bin/python', '-bb', 'test_serialization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:44:30.678520]
2023-01-11T21:45:18.2121444Z 
2023-01-11T21:45:18.2122357Z Expand the folded group to see the log file of test_serialization
2023-01-11T21:45:18.2123919Z ##[group]PRINTING LOG FILE of test_serialization (/var/lib/jenkins/workspace/test/test-reports/test_serialization_vschjqal)
2023-01-11T21:45:18.2124359Z 
2023-01-11T21:45:18.2124455Z Running tests...
2023-01-11T21:45:18.2124873Z ----------------------------------------------------------------------
2023-01-11T21:45:18.2125479Z Test results will be stored in test-reports/python-unittest/test_serialization
2023-01-11T21:45:18.2126188Z   test_load_error_msg (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2128729Z   test_load_nonexistent_device (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2129375Z   test_load_python2_unicode_module (__main__.TestOldSerialization) ... ok (0.036s)
2023-01-11T21:45:18.2129772Z   test_load_unicode_error_msg (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2130648Z   test_save_different_dtype_error (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:719: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2131590Z   torch.save([a.storage(), a.imag], f)
2023-01-11T21:45:18.2132623Z /var/lib/jenkins/workspace/test/test_serialization.py:722: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2133602Z   torch.save([a, a.imag.storage()], f)
2023-01-11T21:45:18.2134633Z /var/lib/jenkins/workspace/test/test_serialization.py:725: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2135642Z   torch.save([a.storage(), a.imag.storage()], f)
2023-01-11T21:45:18.2136696Z /var/lib/jenkins/workspace/test/test_serialization.py:729: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2137690Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:45:18.2138720Z /var/lib/jenkins/workspace/test/test_serialization.py:728: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2139679Z   s_bytes = torch.TypedStorage(
2023-01-11T21:45:18.2140773Z /var/lib/jenkins/workspace/test/test_serialization.py:736: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2141995Z   torch.save([a.storage(), s_bytes], f)
2023-01-11T21:45:18.2142602Z ok (0.002s)
2023-01-11T21:45:18.2143744Z   test_save_different_dtype_unallocated (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:697: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2144879Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:45:18.2145905Z /var/lib/jenkins/workspace/test/test_serialization.py:696: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2146867Z   s = torch.TypedStorage(
2023-01-11T21:45:18.2148620Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2149658Z   device=typed_storage.device,
2023-01-11T21:45:18.2150669Z /var/lib/jenkins/workspace/test/test_serialization.py:700: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2151634Z   save_load_check(a.storage(), s)
2023-01-11T21:45:18.2151968Z ok (0.178s)
2023-01-11T21:45:18.2153056Z   test_serialization (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2154092Z   b += [a[0].storage()]  # 4
2023-01-11T21:45:18.2155142Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2156240Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:45:18.2157281Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2158485Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2159578Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2160767Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2161859Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2162979Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:45:18.2164014Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2165079Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:45:18.2166152Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2167215Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:45:18.2168320Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2169297Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:45:18.2170309Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2171312Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:45:18.2171704Z ok (0.007s)
2023-01-11T21:45:18.2172829Z   test_serialization_backwards_compat (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:403: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2173898Z   b += [a[0].storage()]
2023-01-11T21:45:18.2174871Z /var/lib/jenkins/workspace/test/test_serialization.py:404: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2175946Z   b += [a[0].reshape(-1)[1:4].clone().storage()]
2023-01-11T21:45:18.2176976Z /var/lib/jenkins/workspace/test/test_serialization.py:416: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2178027Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:45:18.2179115Z /var/lib/jenkins/workspace/test/test_serialization.py:426: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2180097Z   return (self.new_tensor.storage(),
2023-01-11T21:45:18.2181116Z /var/lib/jenkins/workspace/test/test_serialization.py:446: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2182210Z   self.assertEqual(x.storage(), load_x.storage())
2023-01-11T21:45:18.2182708Z ok (0.018s)
2023-01-11T21:45:18.2184397Z   test_serialization_backwards_compat_safe (__main__.TestOldSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2185522Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:45:18.2185877Z ok (0.005s)
2023-01-11T21:45:18.2186437Z   test_serialization_container (__main__.TestOldSerialization) ... ok (0.009s)
2023-01-11T21:45:18.2187056Z   test_serialization_container_filelike (__main__.TestOldSerialization) ... ok (0.008s)
2023-01-11T21:45:18.2187722Z   test_serialization_dill (__main__.TestOldSerialization) ... skip: "dill" not found or not correct version (0.000s)
2023-01-11T21:45:18.2188486Z   test_serialization_dill_version_not_supported (__main__.TestOldSerialization) ... skip: "dill" not found or is correct version (0.000s)
2023-01-11T21:45:18.2189175Z   test_serialization_fake_zip (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2190418Z   test_serialization_filelike (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2191466Z   b += [a[0].storage()]  # 4
2023-01-11T21:45:18.2192465Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2193554Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:45:18.2194576Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2195852Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2196935Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2198141Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2199240Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2200222Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:45:18.2201254Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2202444Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:45:18.2203958Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2204953Z   device=typed_storage.device,
2023-01-11T21:45:18.2206034Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2207101Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:45:18.2208174Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2209143Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:45:18.2210162Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2211189Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:45:18.2211571Z ok (0.003s)
2023-01-11T21:45:18.2212037Z   test_serialization_filelike_api_requirements (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2213343Z   test_serialization_filelike_exceptions (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:616: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2214324Z   s = torch.CharStorage(s_data)
2023-01-11T21:45:18.2214629Z ok (0.001s)
2023-01-11T21:45:18.2215018Z   test_serialization_filelike_missing_attrs (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2215595Z   test_serialization_filelike_stress (__main__.TestOldSerialization) ... ok (0.467s)
2023-01-11T21:45:18.2216224Z   test_serialization_filelike_uses_readinto (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2216624Z   test_serialization_gzip (__main__.TestOldSerialization) ... ok (0.004s)
2023-01-11T21:45:18.2216941Z   test_serialization_map_location (__main__.TestOldSerialization) ... ok (0.078s)
2023-01-11T21:45:18.2217257Z   test_serialization_offset (__main__.TestOldSerialization) ... ok (12.293s)
2023-01-11T21:45:18.2217606Z   test_serialization_offset_filelike_weights_only_False (__main__.TestOldSerialization) ... ok (11.714s)
2023-01-11T21:45:18.2218607Z   test_serialization_offset_filelike_weights_only_True (__main__.TestOldSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2219307Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:45:18.2219510Z ok (11.793s)
2023-01-11T21:45:18.2219758Z   test_serialization_offset_gzip (__main__.TestOldSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2220057Z   test_serialization_safe (__main__.TestOldSerialization) ... ok (0.008s)
2023-01-11T21:45:18.2220373Z   test_serialization_save_warnings (__main__.TestOldSerialization) ... ok (0.003s)
2023-01-11T21:45:18.2221026Z   test_serialization_sparse (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2221578Z   b += [a[0].storage()]  # 4
2023-01-11T21:45:18.2222131Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2223082Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:45:18.2224030Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2224886Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2225601Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2226244Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2226805Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2227340Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:45:18.2227885Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2228450Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:45:18.2229240Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2229761Z   device=typed_storage.device,
2023-01-11T21:45:18.2230298Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2231074Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:45:18.2231634Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2232150Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:45:18.2232682Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2233273Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:45:18.2233476Z ok (0.007s)
2023-01-11T21:45:18.2234102Z   test_serialization_sparse_bsc_invalid (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:391: UserWarning: Sparse BSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:45:18.2234837Z   lambda x: x.to_sparse_bsc(1, 1), torch.Tensor.ccol_indices, torch.Tensor.row_indices)
2023-01-11T21:45:18.2235187Z /opt/conda/lib/python3.10/unittest/case.py:172: FutureWarning: Possible nested set at position 14
2023-01-11T21:45:18.2235486Z   expected_regex = re.compile(expected_regex)
2023-01-11T21:45:18.2235674Z ok (0.007s)
2023-01-11T21:45:18.2235926Z   test_serialization_sparse_bsr_invalid (__main__.TestOldSerialization) ... ok (0.006s)
2023-01-11T21:45:18.2236273Z   test_serialization_sparse_csc_invalid (__main__.TestOldSerialization) ... ok (0.006s)
2023-01-11T21:45:18.2236594Z   test_serialization_sparse_csr_invalid (__main__.TestOldSerialization) ... ok (0.006s)
2023-01-11T21:45:18.2236925Z   test_serialization_sparse_invalid (__main__.TestOldSerialization) ... ok (0.004s)
2023-01-11T21:45:18.2237848Z   test_serialization_sparse_safe (__main__.TestOldSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2238441Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:45:18.2238624Z ok (0.008s)
2023-01-11T21:45:18.2239207Z   test_serialization_storage_slice (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:651: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2239790Z   self.assertEqual(s1[0], 0)
2023-01-11T21:45:18.2240323Z /var/lib/jenkins/workspace/test/test_serialization.py:652: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2240829Z   self.assertEqual(s2[0], 0)
2023-01-11T21:45:18.2241346Z /var/lib/jenkins/workspace/test/test_serialization.py:653: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2241946Z   self.assertEqual(s1.data_ptr() + 4, s2.data_ptr())
2023-01-11T21:45:18.2242150Z ok (0.001s)
2023-01-11T21:45:18.2242399Z   test_serialization_zipfile_utils (__main__.TestOldSerialization) ... ok (0.004s)
2023-01-11T21:45:18.2242697Z   test_serialize_device (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2242986Z   test_load_error_msg (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2243274Z   test_load_nonexistent_device (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2243568Z   test_load_python2_unicode_module (__main__.TestSerialization) ... ok (0.008s)
2023-01-11T21:45:18.2243868Z   test_load_unicode_error_msg (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2244186Z   test_meta_serialization_weights_only_False (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2244558Z   test_meta_serialization_weights_only_True (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2244886Z   test_pathlike_serialization_weights_only_False (__main__.TestSerialization) ... ok (0.008s)
2023-01-11T21:45:18.2245831Z   test_pathlike_serialization_weights_only_True (__main__.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2246434Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:45:18.2246630Z ok (0.007s)
2023-01-11T21:45:18.2247200Z   test_save_different_dtype_error (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:719: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2247773Z   torch.save([a.storage(), a.imag], f)
2023-01-11T21:45:18.2248313Z /var/lib/jenkins/workspace/test/test_serialization.py:722: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2248825Z   torch.save([a, a.imag.storage()], f)
2023-01-11T21:45:18.2249364Z /var/lib/jenkins/workspace/test/test_serialization.py:725: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2249883Z   torch.save([a.storage(), a.imag.storage()], f)
2023-01-11T21:45:18.2250432Z /var/lib/jenkins/workspace/test/test_serialization.py:729: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2250945Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:45:18.2251485Z /var/lib/jenkins/workspace/test/test_serialization.py:728: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2251994Z   s_bytes = torch.TypedStorage(
2023-01-11T21:45:18.2252571Z /var/lib/jenkins/workspace/test/test_serialization.py:736: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2253070Z   torch.save([a.storage(), s_bytes], f)
2023-01-11T21:45:18.2253262Z ok (0.002s)
2023-01-11T21:45:18.2253844Z   test_save_different_dtype_unallocated (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:697: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2254414Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:45:18.2255001Z /var/lib/jenkins/workspace/test/test_serialization.py:696: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2255501Z   s = torch.TypedStorage(
2023-01-11T21:45:18.2256298Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2256832Z   device=typed_storage.device,
2023-01-11T21:45:18.2257357Z /var/lib/jenkins/workspace/test/test_serialization.py:700: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2257867Z   save_load_check(a.storage(), s)
2023-01-11T21:45:18.2258051Z ok (0.194s)
2023-01-11T21:45:18.2258612Z   test_serialization (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2259164Z   b += [a[0].storage()]  # 4
2023-01-11T21:45:18.2259679Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2260245Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:45:18.2260790Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2261422Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2261999Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2263011Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2263736Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2264681Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:45:18.2265462Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2266593Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:45:18.2267176Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2267740Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:45:18.2268317Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2268829Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:45:18.2269367Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2269894Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:45:18.2270105Z ok (0.007s)
2023-01-11T21:45:18.2270339Z   test_serialization_2gb_file (__main__.TestSerialization) ... ok (7.838s)
2023-01-11T21:45:18.2270986Z   test_serialization_backwards_compat (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:403: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2271530Z   b += [a[0].storage()]
2023-01-11T21:45:18.2272042Z /var/lib/jenkins/workspace/test/test_serialization.py:404: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2272613Z   b += [a[0].reshape(-1)[1:4].clone().storage()]
2023-01-11T21:45:18.2273153Z /var/lib/jenkins/workspace/test/test_serialization.py:416: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2273702Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:45:18.2274265Z /var/lib/jenkins/workspace/test/test_serialization.py:426: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2274915Z   return (self.new_tensor.storage(),
2023-01-11T21:45:18.2275450Z /var/lib/jenkins/workspace/test/test_serialization.py:446: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2275978Z   self.assertEqual(x.storage(), load_x.storage())
2023-01-11T21:45:18.2276168Z ok (0.006s)
2023-01-11T21:45:18.2276419Z   test_serialization_backwards_compat_safe (__main__.TestSerialization) ... ok (0.005s)
2023-01-11T21:45:18.2276834Z   test_serialization_dill (__main__.TestSerialization) ... skip: "dill" not found or not correct version (0.001s)
2023-01-11T21:45:18.2277232Z   test_serialization_dill_version_not_supported (__main__.TestSerialization) ... skip: "dill" not found or is correct version (0.000s)
2023-01-11T21:45:18.2277624Z   test_serialization_efficient_zerotensor_weights_only_False (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2277993Z   test_serialization_efficient_zerotensor_weights_only_True (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2278322Z   test_serialization_fake_zip (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2278621Z   test_serialization_filelike (__main__.TestSerialization) ... ok (0.003s)
2023-01-11T21:45:18.2278924Z   test_serialization_filelike_api_requirements (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2279607Z   test_serialization_filelike_exceptions (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:616: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2280189Z   s = torch.CharStorage(s_data)
2023-01-11T21:45:18.2280376Z ok (0.001s)
2023-01-11T21:45:18.2280614Z   test_serialization_filelike_missing_attrs (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2280936Z   test_serialization_filelike_stress (__main__.TestSerialization) ... ok (0.595s)
2023-01-11T21:45:18.2281258Z   test_serialization_filelike_uses_readinto (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2281555Z   test_serialization_gzip (__main__.TestSerialization) ... ok (0.004s)
2023-01-11T21:45:18.2281859Z   test_serialization_map_location (__main__.TestSerialization) ... ok (0.004s)
2023-01-11T21:45:18.2282188Z   test_serialization_math_bits_weights_only_False (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2282530Z   test_serialization_math_bits_weights_only_True (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2282838Z   test_serialization_offset_gzip (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2283146Z   test_serialization_python_attr (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2283440Z   test_serialization_safe (__main__.TestSerialization) ... ok (0.008s)
2023-01-11T21:45:18.2283728Z   test_serialization_save_warnings (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2284376Z   test_serialization_sparse (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2284982Z   b += [a[0].storage()]  # 4
2023-01-11T21:45:18.2285508Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2286080Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:45:18.2286608Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2287243Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2287860Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2288482Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:45:18.2289050Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2289576Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:45:18.2290108Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2290670Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:45:18.2291463Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2291994Z   device=typed_storage.device,
2023-01-11T21:45:18.2292517Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2293232Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:45:18.2293810Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2294327Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:45:18.2294864Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2295451Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:45:18.2295649Z ok (0.007s)
2023-01-11T21:45:18.2295896Z   test_serialization_sparse_bsc_invalid (__main__.TestSerialization) ... ok (0.007s)
2023-01-11T21:45:18.2296220Z   test_serialization_sparse_bsr_invalid (__main__.TestSerialization) ... ok (0.007s)
2023-01-11T21:45:18.2296532Z   test_serialization_sparse_csc_invalid (__main__.TestSerialization) ... ok (0.007s)
2023-01-11T21:45:18.2296857Z   test_serialization_sparse_csr_invalid (__main__.TestSerialization) ... ok (0.007s)
2023-01-11T21:45:18.2297173Z   test_serialization_sparse_invalid (__main__.TestSerialization) ... ok (0.004s)
2023-01-11T21:45:18.2298126Z   test_serialization_sparse_safe (__main__.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2298706Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:45:18.2298903Z ok (0.008s)
2023-01-11T21:45:18.2299478Z   test_serialization_storage_slice (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:651: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2300042Z   self.assertEqual(s1[0], 0)
2023-01-11T21:45:18.2300561Z /var/lib/jenkins/workspace/test/test_serialization.py:652: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2301060Z   self.assertEqual(s2[0], 0)
2023-01-11T21:45:18.2301592Z /var/lib/jenkins/workspace/test/test_serialization.py:653: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:45:18.2302128Z   self.assertEqual(s1.data_ptr() + 4, s2.data_ptr())
2023-01-11T21:45:18.2302460Z ok (0.001s)
2023-01-11T21:45:18.2303147Z   test_serialization_zipfile_actually_jit (__main__.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/serialization.py:801: UserWarning: 'torch.load' received a zip file that looks like a TorchScript archive dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to silence this warning)
2023-01-11T21:45:18.2303779Z   warnings.warn("'torch.load' received a zip file that looks like a TorchScript archive"
2023-01-11T21:45:18.2304026Z ok (0.012s)
2023-01-11T21:45:18.2304266Z   test_serialization_zipfile_utils (__main__.TestSerialization) ... ok (0.004s)
2023-01-11T21:45:18.2304581Z   test_serialization_zipfile_weights_only_False (__main__.TestSerialization) ... ok (0.006s)
2023-01-11T21:45:18.2304921Z   test_serialization_zipfile_weights_only_True (__main__.TestSerialization) ... ok (0.010s)
2023-01-11T21:45:18.2305233Z   test_serialize_device (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2305513Z   test_weights_only_assert (__main__.TestSerialization) ... Hello World!
2023-01-11T21:45:18.2305740Z ok (0.001s)
2023-01-11T21:45:18.2306010Z   test_cloned_deepcopy_requires_grad_False (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2306356Z   test_cloned_deepcopy_requires_grad_True (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2306793Z   test_empty_class_serialization (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2307130Z   test_tensor_subclass_deepcopy (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2307476Z   test_tensor_subclass_getstate_overwrite (__main__.TestSubclassSerialization) ... ok (0.002s)
2023-01-11T21:45:18.2307823Z   test_tensor_subclass_wrapper_serialization (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:45:18.2308024Z 
2023-01-11T21:45:18.2308227Z ----------------------------------------------------------------------
2023-01-11T21:45:18.2308469Z Ran 91 tests in 45.498s
2023-01-11T21:45:18.2308583Z 
2023-01-11T21:45:18.2308654Z OK (skipped=4)
2023-01-11T21:45:18.2308748Z 
2023-01-11T21:45:18.2308833Z Generating XML reports...
2023-01-11T21:45:18.2309263Z Generated XML report: test-reports/python-unittest/test_serialization/TEST-TestOldSerialization-20230111214432.xml
2023-01-11T21:45:18.2309853Z Generated XML report: test-reports/python-unittest/test_serialization/TEST-TestSerialization-20230111214432.xml
2023-01-11T21:45:18.2310397Z Generated XML report: test-reports/python-unittest/test_serialization/TEST-TestSubclassSerialization-20230111214432.xml
2023-01-11T21:45:18.2310657Z 
2023-01-11T21:45:18.2311054Z ##[endgroup]
2023-01-11T21:45:18.2311452Z FINISHED PRINTING LOG FILE of test_serialization (/var/lib/jenkins/workspace/test/test-reports/test_serialization_vschjqal)
2023-01-11T21:45:18.2311677Z 
2023-01-11T21:45:20.2080474Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:20.3056028Z Ignoring disabled issues:  []
2023-01-11T21:45:20.4684953Z Running test_set_default_mobile_cpu_allocator ... [2023-01-11 21:45:20.468133]
2023-01-11T21:45:20.4686041Z Executing ['/opt/conda/bin/python', '-bb', 'test_set_default_mobile_cpu_allocator.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:20.468399]
2023-01-11T21:45:22.4960114Z 
2023-01-11T21:45:22.4960755Z Expand the folded group to see the log file of test_set_default_mobile_cpu_allocator
2023-01-11T21:45:22.4961882Z ##[group]PRINTING LOG FILE of test_set_default_mobile_cpu_allocator (/var/lib/jenkins/workspace/test/test-reports/test_set_default_mobile_cpu_allocator_1n51_p9l)
2023-01-11T21:45:22.4962300Z 
2023-01-11T21:45:22.4962409Z Running tests...
2023-01-11T21:45:22.4963014Z ----------------------------------------------------------------------
2023-01-11T21:45:22.4963737Z Test results will be stored in test-reports/python-unittest/test_set_default_mobile_cpu_allocator
2023-01-11T21:45:22.4964364Z   test_exception (__main__.TestSetDefaultMobileCPUAllocator) ... ok (0.240s)
2023-01-11T21:45:22.4964993Z   test_no_exception (__main__.TestSetDefaultMobileCPUAllocator) ... ok (0.001s)
2023-01-11T21:45:22.4965342Z 
2023-01-11T21:45:22.4965624Z ----------------------------------------------------------------------
2023-01-11T21:45:22.4965876Z Ran 2 tests in 0.241s
2023-01-11T21:45:22.4965977Z 
2023-01-11T21:45:22.4966039Z OK
2023-01-11T21:45:22.4966136Z 
2023-01-11T21:45:22.4966224Z Generating XML reports...
2023-01-11T21:45:22.4966738Z Generated XML report: test-reports/python-unittest/test_set_default_mobile_cpu_allocator/TEST-TestSetDefaultMobileCPUAllocator-20230111214521.xml
2023-01-11T21:45:22.4967039Z 
2023-01-11T21:45:22.4967257Z ##[endgroup]
2023-01-11T21:45:22.4967715Z FINISHED PRINTING LOG FILE of test_set_default_mobile_cpu_allocator (/var/lib/jenkins/workspace/test/test-reports/test_set_default_mobile_cpu_allocator_1n51_p9l)
2023-01-11T21:45:22.4967971Z 
2023-01-11T21:45:24.3382012Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:24.4034748Z Ignoring disabled issues:  []
2023-01-11T21:45:24.4202543Z Running test_shape_ops ... [2023-01-11 21:45:24.419956]
2023-01-11T21:45:24.4203859Z Executing ['/opt/conda/bin/python', '-bb', 'test_shape_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:24.420214]
2023-01-11T21:45:26.2855443Z 
2023-01-11T21:45:26.2855966Z Expand the folded group to see the log file of test_shape_ops
2023-01-11T21:45:26.2857308Z ##[group]PRINTING LOG FILE of test_shape_ops (/var/lib/jenkins/workspace/test/test-reports/test_shape_ops_2z6w1ps7)
2023-01-11T21:45:26.2857726Z 
2023-01-11T21:45:26.2857837Z Running tests...
2023-01-11T21:45:26.2858491Z ----------------------------------------------------------------------
2023-01-11T21:45:26.2858801Z 
2023-01-11T21:45:26.2859150Z ----------------------------------------------------------------------
2023-01-11T21:45:26.2859547Z Ran 0 tests in 0.000s
2023-01-11T21:45:26.2859754Z 
2023-01-11T21:45:26.2859876Z OK
2023-01-11T21:45:26.2860043Z 
2023-01-11T21:45:26.2860189Z Generating XML reports...
2023-01-11T21:45:26.2860536Z Test results will be stored in test-reports/python-unittest/test_shape_ops
2023-01-11T21:45:26.2860717Z 
2023-01-11T21:45:26.2860930Z ##[endgroup]
2023-01-11T21:45:26.2861326Z FINISHED PRINTING LOG FILE of test_shape_ops (/var/lib/jenkins/workspace/test/test-reports/test_shape_ops_2z6w1ps7)
2023-01-11T21:45:26.2861628Z 
2023-01-11T21:45:28.1667521Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:28.2504696Z Ignoring disabled issues:  []
2023-01-11T21:45:28.2670981Z Running test_subclass ... [2023-01-11 21:45:28.266773]
2023-01-11T21:45:28.2672519Z Executing ['/opt/conda/bin/python', '-bb', 'test_subclass.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:28.267025]
2023-01-11T21:45:30.4314334Z 
2023-01-11T21:45:30.4315409Z Expand the folded group to see the log file of test_subclass
2023-01-11T21:45:30.4316614Z ##[group]PRINTING LOG FILE of test_subclass (/var/lib/jenkins/workspace/test/test-reports/test_subclass_k6dkwalo)
2023-01-11T21:45:30.4316979Z 
2023-01-11T21:45:30.4317135Z Running tests...
2023-01-11T21:45:30.4317781Z ----------------------------------------------------------------------
2023-01-11T21:45:30.4318472Z Test results will be stored in test-reports/python-unittest/test_subclass
2023-01-11T21:45:30.4319015Z   test_deepcopy_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.236s)
2023-01-11T21:45:30.4319525Z   test_deepcopy_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4320108Z   test_deepcopy_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4320698Z   test_deepcopy_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4321284Z   test_deepcopy_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4321846Z   test_deepcopy_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4322273Z   test_deepcopy_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4322600Z   test_deepcopy_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4322905Z   test_deepcopy_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4323222Z   test_deepcopy_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4323537Z   test_lazy_module_base_tensor (__main__.TestSubclass) ... expected failure (0.001s)
2023-01-11T21:45:30.4324236Z   test_lazy_module_diag_tensor_below (__main__.TestSubclass) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:45:30.4324784Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:45:30.4325021Z expected failure (0.002s)
2023-01-11T21:45:30.4325282Z   test_lazy_module_logging_tensor (__main__.TestSubclass) ... expected failure (0.001s)
2023-01-11T21:45:30.4325609Z   test_lazy_module_non_wrapper_tensor (__main__.TestSubclass) ... expected failure (0.001s)
2023-01-11T21:45:30.4325919Z   test_lazy_module_sparse_tensor (__main__.TestSubclass) ... expected failure (0.002s)
2023-01-11T21:45:30.4326437Z   test_module_optimization_base_tensor (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4326747Z   test_module_optimization_diag_tensor_below (__main__.TestSubclass) ... ok (0.006s)
2023-01-11T21:45:30.4327059Z   test_module_optimization_logging_tensor (__main__.TestSubclass) ... ok (0.004s)
2023-01-11T21:45:30.4327359Z   test_module_optimization_non_wrapper_tensor (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4327669Z   test_module_optimization_sparse_tensor (__main__.TestSubclass) ... ok (0.007s)
2023-01-11T21:45:30.4328019Z   test_non_rewrapping_torch_dispatch_subclass_as_parameter_throws_for_detach (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4328374Z   test_param_invariants_base_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4328721Z   test_param_invariants_base_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4329136Z   test_param_invariants_diag_tensor_below_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4329530Z   test_param_invariants_diag_tensor_below_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4329882Z   test_param_invariants_logging_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4330220Z   test_param_invariants_logging_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4330578Z   test_param_invariants_non_wrapper_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4330944Z   test_param_invariants_non_wrapper_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4331285Z   test_param_invariants_sparse_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4331640Z   test_param_invariants_sparse_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4332000Z   test_parametrization_base_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4332354Z   test_parametrization_base_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4332699Z   test_parametrization_diag_tensor_below_leave_parametrized_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4333062Z   test_parametrization_diag_tensor_below_leave_parametrized_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4333423Z   test_parametrization_logging_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4333783Z   test_parametrization_logging_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4334137Z   test_parametrization_non_wrapper_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4334506Z   test_parametrization_non_wrapper_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4334870Z   test_parametrization_sparse_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4335225Z   test_parametrization_sparse_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4335536Z   test_repr_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4335829Z   test_repr_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4336132Z   test_repr_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4336426Z   test_repr_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4336731Z   test_repr_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4337038Z   test_repr_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4337408Z   test_repr_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4337853Z   test_repr_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4338250Z   test_repr_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4338638Z   test_repr_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4339024Z   test_serialization_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4339433Z   test_serialization_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4339855Z   test_serialization_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4340284Z   test_serialization_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4340692Z   test_serialization_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4341149Z   test_serialization_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4341578Z   test_serialization_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4341994Z   test_serialization_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:45:30.4342564Z   test_serialization_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4342985Z   test_serialization_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:45:30.4343402Z   test_type_propagation_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4343811Z   test_type_propagation_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4344239Z   test_type_propagation_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4344675Z   test_type_propagation_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4345118Z   test_type_propagation_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4345535Z   test_type_propagation_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4345967Z   test_type_propagation_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4346405Z   test_type_propagation_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4346828Z   test_type_propagation_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4347247Z   test_type_propagation_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:45:30.4347480Z 
2023-01-11T21:45:30.4347776Z ----------------------------------------------------------------------
2023-01-11T21:45:30.4348083Z Ran 71 tests in 0.357s
2023-01-11T21:45:30.4348215Z 
2023-01-11T21:45:30.4348318Z OK (expected failures=5)
2023-01-11T21:45:30.4348474Z 
2023-01-11T21:45:30.4348578Z Generating XML reports...
2023-01-11T21:45:30.4349099Z Generated XML report: test-reports/python-unittest/test_subclass/TEST-TestSubclass-20230111214529.xml
2023-01-11T21:45:30.4349385Z 
2023-01-11T21:45:30.4349690Z ##[endgroup]
2023-01-11T21:45:30.4350189Z FINISHED PRINTING LOG FILE of test_subclass (/var/lib/jenkins/workspace/test/test-reports/test_subclass_k6dkwalo)
2023-01-11T21:45:30.4350463Z 
2023-01-11T21:45:32.3921135Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:32.4564751Z Ignoring disabled issues:  []
2023-01-11T21:45:32.4730507Z Running test_tensorboard ... [2023-01-11 21:45:32.472754]
2023-01-11T21:45:32.4732325Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorboard.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:32.473008]
2023-01-11T21:46:03.5770678Z 
2023-01-11T21:46:03.5771204Z Expand the folded group to see the log file of test_tensorboard
2023-01-11T21:46:03.5775675Z ##[group]PRINTING LOG FILE of test_tensorboard (/var/lib/jenkins/workspace/test/test-reports/test_tensorboard_42urr5_2)
2023-01-11T21:46:03.5776192Z 
2023-01-11T21:46:03.5776331Z Running tests...
2023-01-11T21:46:03.5776800Z ----------------------------------------------------------------------
2023-01-11T21:46:03.5777194Z Test results will be stored in test-reports/python-unittest/test_tensorboard
2023-01-11T21:46:03.5777592Z   test_embedding (__main__.TestTensorBoardEmbedding) ... warning: Embedding dir exists, did you set global_step for add_embedding()?
2023-01-11T21:46:03.5777872Z ok (0.038s)
2023-01-11T21:46:03.5778184Z   test_embedding_64 (__main__.TestTensorBoardEmbedding) ... warning: Embedding dir exists, did you set global_step for add_embedding()?
2023-01-11T21:46:03.5778477Z ok (0.023s)
2023-01-11T21:46:03.5778723Z   test_figure (__main__.TestTensorBoardFigure) ... skip: no matplotlib (0.001s)
2023-01-11T21:46:03.5779039Z   test_figure_list (__main__.TestTensorBoardFigure) ... skip: no matplotlib (0.001s)
2023-01-11T21:46:03.5779492Z   test_caffe2_np (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.000s)
2023-01-11T21:46:03.5779817Z   test_caffe2_np_expect_fail (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.000s)
2023-01-11T21:46:03.5780138Z   test_caffe2_simple_cnnmodel (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.001s)
2023-01-11T21:46:03.5780471Z   test_caffe2_simple_model (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.001s)
2023-01-11T21:46:03.5780793Z   test_pytorch_np_expect_fail (__main__.TestTensorBoardNumpy) ... ok (0.000s)
2023-01-11T21:46:03.5781088Z   test_scalar (__main__.TestTensorBoardNumpy) ... ok (0.001s)
2023-01-11T21:46:03.5781387Z   test_pytorch_autograd_np (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.000s)
2023-01-11T21:46:03.5781724Z   test_pytorch_histogram (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.004s)
2023-01-11T21:46:03.5782064Z   test_pytorch_histogram_raw (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.006s)
2023-01-11T21:46:03.5782595Z   test_pytorch_np (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.001s)
2023-01-11T21:46:03.5782922Z   test_pytorch_write (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.003s)
2023-01-11T21:46:03.5783242Z   test_mlp_graph (__main__.TestTensorBoardPytorchGraph) ... ok (0.135s)
2023-01-11T21:46:03.5783568Z   test_nested_nn_squential (__main__.TestTensorBoardPytorchGraph) ... ok (0.068s)
2023-01-11T21:46:03.5783884Z   test_pytorch_graph (__main__.TestTensorBoardPytorchGraph) ... ok (0.020s)
2023-01-11T21:46:03.5784924Z   test_pytorch_graph_dict_input (__main__.TestTensorBoardPytorchGraph) ... Encountering a dict at the output of the tracer might cause the trace to be incorrect, this is only valid if the container structure does not change based on the module's inputs. Consider using a constant container instead (e.g. for `list`, use a `tuple` instead. for `dict`, use a `NamedTuple` instead). If you absolutely need this and know the side effects, pass strict=False to trace() to allow this behavior.
2023-01-11T21:46:03.5785565Z Error occurs, No graph saved
2023-01-11T21:46:03.5785755Z ok (0.030s)
2023-01-11T21:46:03.5786010Z   test_torchvision_smoke (__main__.TestTensorBoardPytorchGraph) ... ok (28.234s)
2023-01-11T21:46:03.5786380Z   test_wrong_input_size (__main__.TestTensorBoardPytorchGraph) ... mat1 and mat2 shapes cannot be multiplied (1x9 and 3x5)
2023-01-11T21:46:03.5786685Z Error occurs, No graph saved
2023-01-11T21:46:03.5786872Z ok (0.012s)
2023-01-11T21:46:03.5787134Z   test_audio (__main__.TestTensorBoardSummary) ... warning: audio amplitude out of range, auto clipped.
2023-01-11T21:46:03.5787398Z ok (0.013s)
2023-01-11T21:46:03.5787635Z   test_custom_scalars (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5787921Z   test_empty_input (__main__.TestTensorBoardSummary) ... ok (0.000s)
2023-01-11T21:46:03.5788206Z   test_float32_image (__main__.TestTensorBoardSummary)
2023-01-11T21:46:03.5788508Z Tests that float32 image (pixel values in [0, 1]) are scaled correctly ... ok (0.001s)
2023-01-11T21:46:03.5788907Z   test_histogram_auto (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:46:03.5789195Z   test_histogram_doane (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:46:03.5789499Z   test_histogram_fd (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5789797Z   test_hparams_bool (__main__.TestTensorBoardSummary) ... ok (0.004s)
2023-01-11T21:46:03.5790090Z   test_hparams_domain_discrete (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5790404Z   test_hparams_number (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5790704Z   test_hparams_smoke (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5790999Z   test_hparams_string (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5791359Z   test_hparams_wrong_parameter (__main__.TestTensorBoardSummary) ... parameter: hparam_dict should be a dictionary, nothing logged.
2023-01-11T21:46:03.5791769Z parameter: metric_dict should be a dictionary, nothing logged.
2023-01-11T21:46:03.5791987Z ok (0.017s)
2023-01-11T21:46:03.5792221Z   test_image_with_3_channel_batched (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:46:03.5792531Z   test_image_with_boxes (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:46:03.5792840Z   test_image_with_one_channel (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:46:03.5793163Z   test_image_with_one_channel_batched (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5793477Z   test_image_without_channel (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5793778Z   test_list_input (__main__.TestTensorBoardSummary) ... ok (0.000s)
2023-01-11T21:46:03.5794064Z   test_mesh (__main__.TestTensorBoardSummary) ... ok (0.003s)
2023-01-11T21:46:03.5794343Z   test_scalar_new_style (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5794630Z   test_text (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5794963Z   test_uint8_image (__main__.TestTensorBoardSummary)
2023-01-11T21:46:03.5795237Z Tests that uint8 image (pixel values in [0, 255]) is not changed ... ok (0.000s)
2023-01-11T21:46:03.5795525Z   test_video (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:46:03.5795823Z   test_pathlib (__main__.TestTensorBoardSummaryWriter) ... ok (0.003s)
2023-01-11T21:46:03.5796149Z   test_summary_writer_close (__main__.TestTensorBoardSummaryWriter) ... ok (0.003s)
2023-01-11T21:46:03.5796470Z   test_summary_writer_ctx (__main__.TestTensorBoardSummaryWriter) ... ok (0.004s)
2023-01-11T21:46:03.5796800Z   test_convert_to_HWC_dtype_remains_same (__main__.TestTensorBoardUtils) ... ok (0.001s)
2023-01-11T21:46:03.5797111Z   test_numpy_vid_uint8 (__main__.TestTensorBoardUtils) ... ok (0.015s)
2023-01-11T21:46:03.5797391Z   test_prepare_video (__main__.TestTensorBoardUtils) ... ok (0.086s)
2023-01-11T21:46:03.5797672Z   test_to_HWC (__main__.TestTensorBoardUtils) ... ok (0.001s)
2023-01-11T21:46:03.5797972Z   test_writer (__main__.TestTensorBoardWriter) ... add_video needs package moviepy
2023-01-11T21:46:03.5798210Z ok (0.057s)
2023-01-11T21:46:03.5798299Z 
2023-01-11T21:46:03.5798516Z ----------------------------------------------------------------------
2023-01-11T21:46:03.5798754Z Ran 53 tests in 28.810s
2023-01-11T21:46:03.5798869Z 
2023-01-11T21:46:03.5798939Z OK (skipped=6)
2023-01-11T21:46:03.5799044Z 
2023-01-11T21:46:03.5799115Z Generating XML reports...
2023-01-11T21:46:03.5799569Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardEmbedding-20230111214534.xml
2023-01-11T21:46:03.5800119Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardNumpy-20230111214534.xml
2023-01-11T21:46:03.5800677Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPyTorchNumpy-20230111214534.xml
2023-01-11T21:46:03.5801251Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPytorchGraph-20230111214534.xml
2023-01-11T21:46:03.5801861Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummary-20230111214534.xml
2023-01-11T21:46:03.5802433Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummaryWriter-20230111214534.xml
2023-01-11T21:46:03.5802988Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardUtils-20230111214534.xml
2023-01-11T21:46:03.5803509Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardWriter-20230111214534.xml
2023-01-11T21:46:03.5804038Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardFigure-20230111214534.xml
2023-01-11T21:46:03.5804284Z 
2023-01-11T21:46:03.5804562Z ##[endgroup]
2023-01-11T21:46:03.5804934Z FINISHED PRINTING LOG FILE of test_tensorboard (/var/lib/jenkins/workspace/test/test-reports/test_tensorboard_42urr5_2)
2023-01-11T21:46:03.5805159Z 
2023-01-11T21:46:05.5337732Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:05.6184925Z Ignoring disabled issues:  []
2023-01-11T21:46:05.6352504Z Running test_tensorexpr_pybind ... [2023-01-11 21:46:05.634948]
2023-01-11T21:46:05.6354692Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorexpr_pybind.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:05.635235]
2023-01-11T21:46:08.0947804Z 
2023-01-11T21:46:08.0948892Z Expand the folded group to see the log file of test_tensorexpr_pybind
2023-01-11T21:46:08.0950060Z ##[group]PRINTING LOG FILE of test_tensorexpr_pybind (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_pybind_fknumtnl)
2023-01-11T21:46:08.0984310Z 
2023-01-11T21:46:08.0986786Z Running tests...
2023-01-11T21:46:08.0987505Z ----------------------------------------------------------------------
2023-01-11T21:46:08.0988423Z Test results will be stored in test-reports/python-unittest/test_tensorexpr_pybind
2023-01-11T21:46:08.0989184Z   test_unary_ops (__main__.TestExprHandlePyBind) ... ok (0.239s)
2023-01-11T21:46:08.0989795Z   test_alloc_in_loop (__main__.TestTensorExprPyBind) ... ok (0.026s)
2023-01-11T21:46:08.0990378Z   test_call_raw (__main__.TestTensorExprPyBind) ... ok (0.002s)
2023-01-11T21:46:08.0990985Z   test_dtype_error (__main__.TestTensorExprPyBind) ... ok (0.001s)
2023-01-11T21:46:08.0991615Z   test_dynamic_shape (__main__.TestTensorExprPyBind) ... ok (0.002s)
2023-01-11T21:46:08.0992238Z   test_dynamic_shape_2d (__main__.TestTensorExprPyBind) ... ok (0.002s)
2023-01-11T21:46:08.0992883Z   test_external_calls (__main__.TestTensorExprPyBind) ... ok (0.001s)
2023-01-11T21:46:08.0993528Z   test_kernel_shape_prop (__main__.TestTensorExprPyBind) ... ok (0.194s)
2023-01-11T21:46:08.0994187Z   test_kernel_shape_prop_module (__main__.TestTensorExprPyBind) ... ok (0.040s)
2023-01-11T21:46:08.0994931Z   test_kernel_with_custom_lowering (__main__.TestTensorExprPyBind) ... ok (0.026s)
2023-01-11T21:46:08.0995627Z   test_kernel_with_expand (__main__.TestTensorExprPyBind) ... ok (0.026s)
2023-01-11T21:46:08.1184177Z   test_kernel_with_permute (__main__.TestTensorExprPyBind) ... ok (0.062s)
2023-01-11T21:46:08.1184745Z   test_kernel_with_scalar_inputs (__main__.TestTensorExprPyBind) ... ok (0.022s)
2023-01-11T21:46:08.1185326Z   test_kernel_with_t (__main__.TestTensorExprPyBind) ... ok (0.029s)
2023-01-11T21:46:08.1185896Z   test_kernel_with_tensor_inputs (__main__.TestTensorExprPyBind) ... ok (0.026s)
2023-01-11T21:46:08.1186452Z   test_kernel_with_transpose (__main__.TestTensorExprPyBind) ... ok (0.029s)
2023-01-11T21:46:08.1187006Z   test_simple_sum (__main__.TestTensorExprPyBind) ... ok (0.001s)
2023-01-11T21:46:08.1187350Z 
2023-01-11T21:46:08.1282212Z ----------------------------------------------------------------------
2023-01-11T21:46:08.1282577Z Ran 17 tests in 0.729s
2023-01-11T21:46:08.1282763Z 
2023-01-11T21:46:08.1282852Z OK
2023-01-11T21:46:08.1282987Z 
2023-01-11T21:46:08.1283115Z Generating XML reports...
2023-01-11T21:46:08.1284011Z Generated XML report: test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestExprHandlePyBind-20230111214607.xml
2023-01-11T21:46:08.1284863Z Generated XML report: test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestTensorExprPyBind-20230111214607.xml
2023-01-11T21:46:08.1285235Z 
2023-01-11T21:46:08.1285805Z ##[endgroup]
2023-01-11T21:46:08.1286349Z FINISHED PRINTING LOG FILE of test_tensorexpr_pybind (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_pybind_fknumtnl)
2023-01-11T21:46:08.1286640Z 
2023-01-11T21:46:09.9788396Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:10.0466727Z Ignoring disabled issues:  []
2023-01-11T21:46:10.0635375Z Running test_type_hints ... [2023-01-11 21:46:10.063210]
2023-01-11T21:46:10.0636568Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_hints.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:10.063450]
2023-01-11T21:46:11.9521132Z 
2023-01-11T21:46:11.9521886Z Expand the folded group to see the log file of test_type_hints
2023-01-11T21:46:11.9522694Z ##[group]PRINTING LOG FILE of test_type_hints (/var/lib/jenkins/workspace/test/test-reports/test_type_hints_n0jk_6i6)
2023-01-11T21:46:11.9522994Z 
2023-01-11T21:46:11.9524486Z Running tests...
2023-01-11T21:46:11.9525531Z ----------------------------------------------------------------------
2023-01-11T21:46:11.9526233Z Test results will be stored in test-reports/python-unittest/test_type_hints
2023-01-11T21:46:11.9526729Z   test_doc_examples (__main__.TestTypeHints)
2023-01-11T21:46:11.9527310Z Run documentation examples through mypy. ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:46:11.9527685Z 
2023-01-11T21:46:11.9528042Z ----------------------------------------------------------------------
2023-01-11T21:46:11.9528450Z Ran 1 test in 0.001s
2023-01-11T21:46:11.9528655Z 
2023-01-11T21:46:11.9528779Z OK (skipped=1)
2023-01-11T21:46:11.9528967Z 
2023-01-11T21:46:11.9529141Z Generating XML reports...
2023-01-11T21:46:11.9529840Z Generated XML report: test-reports/python-unittest/test_type_hints/TEST-TestTypeHints-20230111214611.xml
2023-01-11T21:46:11.9530228Z 
2023-01-11T21:46:11.9530699Z ##[endgroup]
2023-01-11T21:46:11.9531374Z FINISHED PRINTING LOG FILE of test_type_hints (/var/lib/jenkins/workspace/test/test-reports/test_type_hints_n0jk_6i6)
2023-01-11T21:46:11.9531741Z 
2023-01-11T21:46:14.4767086Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:14.5584879Z Ignoring disabled issues:  []
2023-01-11T21:46:14.5751790Z Running test_type_info ... [2023-01-11 21:46:14.574855]
2023-01-11T21:46:14.5753121Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_info.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:14.575108]
2023-01-11T21:46:16.8700787Z 
2023-01-11T21:46:16.8701530Z Expand the folded group to see the log file of test_type_info
2023-01-11T21:46:16.8702713Z ##[group]PRINTING LOG FILE of test_type_info (/var/lib/jenkins/workspace/test/test-reports/test_type_info_w18u_1wg)
2023-01-11T21:46:16.8703128Z 
2023-01-11T21:46:16.8703261Z Running tests...
2023-01-11T21:46:16.8703916Z ----------------------------------------------------------------------
2023-01-11T21:46:16.8704590Z Test results will be stored in test-reports/python-unittest/test_type_info
2023-01-11T21:46:16.8705122Z   test_finfo (__main__.TestDTypeInfo) ... ok (0.240s)
2023-01-11T21:46:16.8705556Z   test_iinfo (__main__.TestDTypeInfo) ... ok (0.002s)
2023-01-11T21:46:16.8709566Z   test_invalid_input (__main__.TestDTypeInfo) ... ok (0.001s)
2023-01-11T21:46:16.8709815Z 
2023-01-11T21:46:16.8710188Z ----------------------------------------------------------------------
2023-01-11T21:46:16.8710631Z Ran 3 tests in 0.243s
2023-01-11T21:46:16.8713092Z 
2023-01-11T21:46:16.8713460Z OK
2023-01-11T21:46:16.8713808Z 
2023-01-11T21:46:16.8714371Z Generating XML reports...
2023-01-11T21:46:16.8715342Z Generated XML report: test-reports/python-unittest/test_type_info/TEST-TestDTypeInfo-20230111214616.xml
2023-01-11T21:46:16.8716166Z 
2023-01-11T21:46:16.8716894Z ##[endgroup]
2023-01-11T21:46:16.8717716Z FINISHED PRINTING LOG FILE of test_type_info (/var/lib/jenkins/workspace/test/test-reports/test_type_info_w18u_1wg)
2023-01-11T21:46:16.8718157Z 
2023-01-11T21:46:19.2649538Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:19.3518294Z Ignoring disabled issues:  []
2023-01-11T21:46:19.3689098Z Running test_type_promotion ... [2023-01-11 21:46:19.368533]
2023-01-11T21:46:19.3690389Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_promotion.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:19.368796]
2023-01-11T21:46:21.7242547Z 
2023-01-11T21:46:21.7243139Z Expand the folded group to see the log file of test_type_promotion
2023-01-11T21:46:21.7244326Z ##[group]PRINTING LOG FILE of test_type_promotion (/var/lib/jenkins/workspace/test/test-reports/test_type_promotion_c5ufw1fu)
2023-01-11T21:46:21.7244754Z 
2023-01-11T21:46:21.7245137Z Running tests...
2023-01-11T21:46:21.7246135Z ----------------------------------------------------------------------
2023-01-11T21:46:21.7246438Z 
2023-01-11T21:46:21.7246790Z ----------------------------------------------------------------------
2023-01-11T21:46:21.7247207Z Ran 0 tests in 0.000s
2023-01-11T21:46:21.7247407Z 
2023-01-11T21:46:21.7247504Z OK
2023-01-11T21:46:21.7250107Z 
2023-01-11T21:46:21.7250599Z Generating XML reports...
2023-01-11T21:46:21.7251453Z Test results will be stored in test-reports/python-unittest/test_type_promotion
2023-01-11T21:46:21.7251818Z 
2023-01-11T21:46:21.7252511Z ##[endgroup]
2023-01-11T21:46:21.7253298Z FINISHED PRINTING LOG FILE of test_type_promotion (/var/lib/jenkins/workspace/test/test-reports/test_type_promotion_c5ufw1fu)
2023-01-11T21:46:21.7253710Z 
2023-01-11T21:46:23.9360416Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:24.0180986Z Ignoring disabled issues:  []
2023-01-11T21:46:24.0351066Z Running test_unary_ufuncs ... [2023-01-11 21:46:24.034616]
2023-01-11T21:46:24.0351929Z Executing ['/opt/conda/bin/python', '-bb', 'test_unary_ufuncs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:24.034890]
2023-01-11T21:46:26.7213802Z 
2023-01-11T21:46:26.7214334Z Expand the folded group to see the log file of test_unary_ufuncs
2023-01-11T21:46:26.7215369Z ##[group]PRINTING LOG FILE of test_unary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_unary_ufuncs_xlrccb_s)
2023-01-11T21:46:26.7215792Z 
2023-01-11T21:46:26.7215923Z Running tests...
2023-01-11T21:46:26.7216602Z ----------------------------------------------------------------------
2023-01-11T21:46:26.7216898Z 
2023-01-11T21:46:26.7217261Z ----------------------------------------------------------------------
2023-01-11T21:46:26.7217701Z Ran 0 tests in 0.000s
2023-01-11T21:46:26.7217916Z 
2023-01-11T21:46:26.7218029Z OK
2023-01-11T21:46:26.7218179Z 
2023-01-11T21:46:26.7295835Z Generating XML reports...
2023-01-11T21:46:26.7296542Z Test results will be stored in test-reports/python-unittest/test_unary_ufuncs
2023-01-11T21:46:26.7296810Z 
2023-01-11T21:46:26.7297147Z ##[endgroup]
2023-01-11T21:46:26.7297538Z FINISHED PRINTING LOG FILE of test_unary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_unary_ufuncs_xlrccb_s)
2023-01-11T21:46:26.7297757Z 
2023-01-11T21:46:28.7455986Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:28.8318909Z Ignoring disabled issues:  []
2023-01-11T21:46:28.8492651Z Running test_view_ops ... [2023-01-11 21:46:28.848756]
2023-01-11T21:46:28.8493894Z Executing ['/opt/conda/bin/python', '-bb', 'test_view_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:28.849036]
2023-01-11T21:46:31.0117047Z 
2023-01-11T21:46:31.0117665Z Expand the folded group to see the log file of test_view_ops
2023-01-11T21:46:31.0118716Z ##[group]PRINTING LOG FILE of test_view_ops (/var/lib/jenkins/workspace/test/test-reports/test_view_ops_yvbkbf9w)
2023-01-11T21:46:31.0119403Z 
2023-01-11T21:46:31.0119535Z Running tests...
2023-01-11T21:46:31.0120163Z ----------------------------------------------------------------------
2023-01-11T21:46:31.0120450Z 
2023-01-11T21:46:31.0120776Z ----------------------------------------------------------------------
2023-01-11T21:46:31.0121186Z Ran 0 tests in 0.000s
2023-01-11T21:46:31.0121376Z 
2023-01-11T21:46:31.0121466Z OK
2023-01-11T21:46:31.0121623Z 
2023-01-11T21:46:31.0121760Z Generating XML reports...
2023-01-11T21:46:31.0122300Z Test results will be stored in test-reports/python-unittest/test_view_ops
2023-01-11T21:46:31.0122593Z 
2023-01-11T21:46:31.0122972Z ##[endgroup]
2023-01-11T21:46:31.0123616Z FINISHED PRINTING LOG FILE of test_view_ops (/var/lib/jenkins/workspace/test/test-reports/test_view_ops_yvbkbf9w)
2023-01-11T21:46:31.0123962Z 
2023-01-11T21:46:33.0006235Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:33.0833780Z Ignoring disabled issues:  []
2023-01-11T21:46:33.1005963Z Running test_vulkan ... [2023-01-11 21:46:33.100122]
2023-01-11T21:46:33.1007295Z Executing ['/opt/conda/bin/python', '-bb', 'test_vulkan.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:33.100407]
2023-01-11T21:46:34.9922887Z 
2023-01-11T21:46:34.9923418Z Expand the folded group to see the log file of test_vulkan
2023-01-11T21:46:34.9924436Z ##[group]PRINTING LOG FILE of test_vulkan (/var/lib/jenkins/workspace/test/test-reports/test_vulkan_enndtzqm)
2023-01-11T21:46:34.9924773Z 
2023-01-11T21:46:34.9924904Z Running tests...
2023-01-11T21:46:34.9925523Z ----------------------------------------------------------------------
2023-01-11T21:46:34.9926296Z   test_conv (__main__.TestVulkanRewritePass) ... Test results will be stored in test-reports/python-unittest/test_vulkan
2023-01-11T21:46:34.9926852Z skip: Vulkan backend must be available for these tests. (0.002s)
2023-01-11T21:46:34.9927115Z 
2023-01-11T21:46:34.9927473Z ----------------------------------------------------------------------
2023-01-11T21:46:34.9927878Z Ran 1 test in 0.002s
2023-01-11T21:46:34.9928072Z 
2023-01-11T21:46:34.9928195Z OK (skipped=1)
2023-01-11T21:46:34.9928471Z 
2023-01-11T21:46:34.9928634Z Generating XML reports...
2023-01-11T21:46:34.9929468Z Generated XML report: test-reports/python-unittest/test_vulkan/TEST-TestVulkanRewritePass-20230111214634.xml
2023-01-11T21:46:34.9929929Z 
2023-01-11T21:46:34.9930385Z ##[endgroup]
2023-01-11T21:46:34.9931090Z FINISHED PRINTING LOG FILE of test_vulkan (/var/lib/jenkins/workspace/test/test-reports/test_vulkan_enndtzqm)
2023-01-11T21:46:34.9931494Z 
2023-01-11T21:46:37.0603855Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:37.1482729Z Ignoring disabled issues:  []
2023-01-11T21:46:37.1657165Z Running test_weak ... [2023-01-11 21:46:37.165329]
2023-01-11T21:46:37.1659231Z Executing ['/opt/conda/bin/python', '-bb', 'test_weak.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:37.165604]
2023-01-11T21:46:42.9549324Z 
2023-01-11T21:46:42.9551177Z Expand the folded group to see the log file of test_weak
2023-01-11T21:46:42.9552219Z ##[group]PRINTING LOG FILE of test_weak (/var/lib/jenkins/workspace/test/test-reports/test_weak_n_pno514)
2023-01-11T21:46:42.9552594Z 
2023-01-11T21:46:42.9552721Z Running tests...
2023-01-11T21:46:42.9553382Z ----------------------------------------------------------------------
2023-01-11T21:46:42.9554062Z Test results will be stored in test-reports/python-unittest/test_weak
2023-01-11T21:46:42.9554592Z   test_bool (__main__.WeakKeyDictionaryTestCase) ... ok (0.260s)
2023-01-11T21:46:42.9555161Z   test_constructor (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9555642Z   test_get (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9556100Z   test_getitem (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9556580Z   test_items (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9557344Z   test_keys (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9557839Z   test_len (__main__.WeakKeyDictionaryTestCase) ... ok (0.000s)
2023-01-11T21:46:42.9558327Z   test_pop (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9558807Z   test_popitem (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9559328Z   test_read (__main__.WeakKeyDictionaryTestCase) ... ok (0.002s)
2023-01-11T21:46:42.9559832Z   test_setdefault (__main__.WeakKeyDictionaryTestCase) ... ok (0.000s)
2023-01-11T21:46:42.9560367Z   test_update (__main__.WeakKeyDictionaryTestCase) ... ok (0.004s)
2023-01-11T21:46:42.9560893Z   test_values (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:46:42.9561383Z   test_write (__main__.WeakKeyDictionaryTestCase) ... ok (0.006s)
2023-01-11T21:46:42.9561855Z   test_make_weak_keyed_dict_from_dict (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9562550Z   test_make_weak_keyed_dict_from_weak_keyed_dict (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9563068Z   test_make_weak_keyed_dict_repr (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9563544Z   test_threaded_weak_key_dict_copy (__main__.WeakTest) ... ok (1.588s)
2023-01-11T21:46:42.9563989Z   test_threaded_weak_key_dict_deepcopy (__main__.WeakTest) ... ok (1.953s)
2023-01-11T21:46:42.9564434Z   test_weak_keyed_bad_delitem (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9564825Z   test_weak_keyed_delitem (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9565233Z   test_weak_keyed_dict_popitem (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9565657Z   test_weak_keyed_dict_setdefault (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9566063Z   test_weak_keyed_dict_update (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:46:42.9568578Z   test_weak_keyed_union_operators (__main__.WeakTest) ... ok (0.003s)
2023-01-11T21:46:42.9568855Z 
2023-01-11T21:46:42.9569266Z ----------------------------------------------------------------------
2023-01-11T21:46:42.9569607Z Ran 25 tests in 3.828s
2023-01-11T21:46:42.9569761Z 
2023-01-11T21:46:42.9569841Z OK
2023-01-11T21:46:42.9569978Z 
2023-01-11T21:46:42.9570093Z Generating XML reports...
2023-01-11T21:46:42.9570795Z Generated XML report: test-reports/python-unittest/test_weak/TEST-WeakKeyDictionaryTestCase-20230111214638.xml
2023-01-11T21:46:42.9571623Z Generated XML report: test-reports/python-unittest/test_weak/TEST-WeakTest-20230111214638.xml
2023-01-11T21:46:42.9571972Z 
2023-01-11T21:46:42.9572494Z ##[endgroup]
2023-01-11T21:46:42.9573067Z FINISHED PRINTING LOG FILE of test_weak (/var/lib/jenkins/workspace/test/test-reports/test_weak_n_pno514)
2023-01-11T21:46:42.9573375Z 
2023-01-11T21:46:44.8891551Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:44.9767258Z Ignoring disabled issues:  []
2023-01-11T21:46:44.9947649Z Running test_xnnpack_integration ... [2023-01-11 21:46:44.994469]
2023-01-11T21:46:44.9949385Z Executing ['/opt/conda/bin/python', '-bb', 'test_xnnpack_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:46:44.994725]
2023-01-11T21:46:55.3833116Z 
2023-01-11T21:46:55.3833591Z Expand the folded group to see the log file of test_xnnpack_integration
2023-01-11T21:46:55.3834438Z ##[group]PRINTING LOG FILE of test_xnnpack_integration (/var/lib/jenkins/workspace/test/test-reports/test_xnnpack_integration_zff63w3_)
2023-01-11T21:46:55.3834777Z 
2023-01-11T21:46:55.3834872Z Running tests...
2023-01-11T21:46:55.3835420Z ----------------------------------------------------------------------
2023-01-11T21:46:55.3835941Z Test results will be stored in test-reports/python-unittest/test_xnnpack_integration
2023-01-11T21:46:55.3836487Z   test_conv1d_basic (__main__.TestXNNPACKConv1dTransformPass) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:46:55.3837106Z   test_conv1d_with_relu_fc (__main__.TestXNNPACKConv1dTransformPass) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:46:55.3837767Z   test_conv2d (__main__.TestXNNPACKOps) ... ok (0.997s)
2023-01-11T21:46:55.3838102Z   test_conv2d_transpose (__main__.TestXNNPACKOps) ... ok (1.586s)
2023-01-11T21:46:55.3838594Z   test_linear (__main__.TestXNNPACKOps) ... skip: Fails on some platforms, see https://github.com/pytorch/pytorch/issues/73488 (0.001s)
2023-01-11T21:46:55.3839052Z   test_linear_1d_input (__main__.TestXNNPACKOps) ... ok (0.249s)
2023-01-11T21:46:55.3839434Z   test_decomposed_linear (__main__.TestXNNPACKRewritePass) ... ok (0.092s)
2023-01-11T21:46:55.3839805Z   test_linear (__main__.TestXNNPACKRewritePass) ... ok (1.052s)
2023-01-11T21:46:55.3840295Z   test_combined_model (__main__.TestXNNPACKSerDes) ... skip: Fails on some platforms, see https://github.com/pytorch/pytorch/issues/73488 (0.003s)
2023-01-11T21:46:55.3840758Z   test_conv2d (__main__.TestXNNPACKSerDes) ... ok (2.120s)
2023-01-11T21:46:55.3841102Z   test_conv2d_transpose (__main__.TestXNNPACKSerDes) ... ok (2.406s)
2023-01-11T21:46:55.3841658Z   test_linear (__main__.TestXNNPACKSerDes) ... skip: Fails on some platforms, see https://github.com/pytorch/pytorch/issues/73488 (0.001s)
2023-01-11T21:46:55.3841963Z 
2023-01-11T21:46:55.3842231Z ----------------------------------------------------------------------
2023-01-11T21:46:55.3842542Z Ran 12 tests in 8.512s
2023-01-11T21:46:55.3842688Z 
2023-01-11T21:46:55.3842766Z OK (skipped=5)
2023-01-11T21:46:55.3842906Z 
2023-01-11T21:46:55.3843013Z Generating XML reports...
2023-01-11T21:46:55.3843577Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKOps-20230111214646.xml
2023-01-11T21:46:55.3844305Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKRewritePass-20230111214646.xml
2023-01-11T21:46:55.3845035Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKSerDes-20230111214646.xml
2023-01-11T21:46:55.3845807Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKConv1dTransformPass-20230111214646.xml
2023-01-11T21:46:55.3846159Z 
2023-01-11T21:46:55.3846444Z ##[endgroup]
2023-01-11T21:46:55.3846985Z FINISHED PRINTING LOG FILE of test_xnnpack_integration (/var/lib/jenkins/workspace/test/test-reports/test_xnnpack_integration_zff63w3_)
2023-01-11T21:46:55.3847288Z 
2023-01-11T21:46:57.3261773Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:46:57.3908115Z Ignoring disabled issues:  []
2023-01-11T21:56:30.4894754Z 
2023-01-11T21:56:30.4895309Z Expand the folded group to see the log file of test_quantization
2023-01-11T21:56:30.4896197Z ##[group]PRINTING LOG FILE of test_quantization (/var/lib/jenkins/workspace/test/test-reports/test_quantization_9fc5vzw9)
2023-01-11T21:56:30.4901163Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:56:30.4901958Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:56:30.4902255Z 
2023-01-11T21:56:30.4902532Z Running tests...
2023-01-11T21:56:30.4903093Z ----------------------------------------------------------------------
2023-01-11T21:56:30.4903756Z Test results will be stored in test-reports/python-unittest/test_quantization
2023-01-11T21:56:30.4904917Z   test_modules_import_nn_intrinsic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.009s)
2023-01-11T21:56:30.4905892Z   test_modules_import_nn_intrinsic_qat (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:30.4906926Z   test_modules_import_nn_intrinsic_quantized (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:30.4907957Z   test_modules_intrinsic_qat_conv_fused (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:30.4909301Z   test_modules_intrinsic_qat_linear_fused (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:30.4910317Z   test_modules_intrinsic_qat_linear_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:30.4911347Z   test_modules_intrinsic_quantized_bn_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:30.4912395Z   test_modules_intrinsic_quantized_conv_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:30.4929597Z   test_modules_intrinsic_quantized_linear_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:30.4930207Z   test_modules_nn_intrinsic_fused (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:30.4930813Z   test_package_import_nn_intrinsic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:30.4931274Z   test_package_import_nn_intrinsic_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic)
2023-01-11T21:56:30.4931648Z Tests the migration of the torch.nn.intrinsic.modules ... ok (0.001s)
2023-01-11T21:56:30.4933639Z   test_package_import_nn_intrinsic_qat (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic)
2023-01-11T21:56:30.4934326Z Tests the migration of the torch.nn.intrinsic.modules ... ok (0.001s)
2023-01-11T21:56:30.4935044Z   test_package_import_nn_intrinsic_quantized (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic)
2023-01-11T21:56:30.4935483Z Tests the migration of the torch.nn.intrinsic.quantized ... ok (0.001s)
2023-01-11T21:56:30.4935849Z   test_functional_import (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:30.4936208Z Tests the migration of the torch.nn.quantized.functional ... ok (0.001s)
2023-01-11T21:56:30.4936634Z   test_import_nn_qat_conv (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4937075Z   test_import_nn_qat_dynamic_linear (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:30.4937512Z   test_import_nn_qat_embedding_ops (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:30.4938108Z   test_import_nn_qat_linear (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:30.4938878Z   test_import_nn_quantizable_activation (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4939690Z   test_import_nn_quantizable_rnn (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4940438Z   test_import_nn_quantized_dynamic_import (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4941200Z   test_modules_activation (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4941856Z   test_modules_batchnorm (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4942304Z   test_modules_conv (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4942908Z   test_modules_dropout (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4943388Z   test_modules_embedding_ops (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4943829Z   test_modules_functional_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4944314Z   test_modules_import (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4944894Z   test_modules_linear (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4945322Z   test_modules_normalization (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4945890Z   test_modules_utils (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:30.4946560Z   test_package_import_nn_qat (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:30.4947323Z   test_package_import_nn_qat_dynamic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:30.4947687Z Tests the migration of the torch.nn.qat.modules ... ok (0.001s)
2023-01-11T21:56:30.4948049Z   test_package_import_nn_qat_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:30.4948466Z Tests the migration of the torch.nn.qat.modules ... ok (0.001s)
2023-01-11T21:56:30.4948834Z   test_package_import_nn_quantizable (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:30.4949357Z   test_package_import_nn_quantizable_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:30.4949722Z Tests the migration of the torch.nn.quantizable.modules ... ok (0.001s)
2023-01-11T21:56:30.4950108Z   test_package_import_nn_quantized (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:30.4950571Z   test_package_import_nn_quantized_dynamic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:30.4951147Z   test_package_import_nn_quantized_dynamic_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:30.4951584Z Tests the migration of the torch.nn.quantized.modules ... ok (0.001s)
2023-01-11T21:56:30.4952186Z   test_package_import_nn_quantized_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:30.4952591Z Tests the migration of the torch.nn.quantized.modules ... ok (0.001s)
2023-01-11T21:56:30.4953209Z   test_function_import_fake_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4953968Z   test_function_import_fuse_modules (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4954698Z   test_function_import_fuser_method_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4955455Z   test_function_import_observer (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4956141Z   test_function_import_qconfig (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4956853Z   test_function_import_quant_type (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4957786Z   test_function_import_quantization_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4958534Z   test_function_import_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4959265Z   test_function_import_quantize_jit (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4960017Z   test_function_import_stubs (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4960731Z   test_function_import_utils (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:30.4961205Z   test_package_import_fake_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4961748Z   test_package_import_fuse_modules (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4962209Z   test_package_import_fuser_method_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4962678Z   test_package_import_observer (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4963137Z   test_package_import_qconfig (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4963596Z   test_package_import_quant_type (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4964062Z   test_package_import_quantization_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4964564Z   test_package_import_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4965026Z   test_package_import_quantize_jit (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4965469Z   test_package_import_stubs (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4965913Z   test_package_import_utils (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:30.4966354Z   test_function_import_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4966797Z   test_function_import_fx_convert (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4967234Z   test_function_import_fx_equalize (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4967679Z   test_function_import_fx_fuse (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4968131Z   test_function_import_fx_fusion_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4968643Z   test_function_import_fx_graph_module (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4969083Z   test_function_import_fx_match_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4969536Z   test_function_import_fx_pattern_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4969989Z   test_function_import_fx_prepare (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4970452Z   test_function_import_fx_quantization_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4970903Z   test_function_import_fx_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4971346Z   test_function_import_quantize_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:30.4971788Z   test_package_import_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4972227Z   test_package_import_fx_convert (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4972663Z   test_package_import_fx_equalize (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4973110Z   test_package_import_fx_fuse (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4973594Z   test_package_import_fx_fusion_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4974055Z   test_package_import_fx_graph_module (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4974499Z   test_package_import_fx_match_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4974956Z   test_package_import_fx_pattern_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4975482Z   test_package_import_fx_prepare (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4975946Z   test_package_import_fx_quantization_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4976427Z   test_package_import_fx_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4976875Z   test_package_import_quantize_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:30.4977296Z   test_backend_config_from_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.004s)
2023-01-11T21:56:30.4977708Z   test_backend_config_set_backend_pattern_config (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4978103Z   test_backend_config_set_name (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4978490Z   test_backend_config_to_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.002s)
2023-01-11T21:56:30.4978888Z   test_backend_op_config_add_dtype_config (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4979299Z   test_backend_op_config_from_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.002s)
2023-01-11T21:56:30.4979698Z   test_backend_op_config_set_extra_inputs_getter (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4980112Z   test_backend_op_config_set_fused_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4980519Z   test_backend_op_config_set_fuser_method (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4980937Z   test_backend_op_config_set_input_output_observed (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4981350Z   test_backend_op_config_set_input_type_to_index (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4981837Z   test_backend_op_config_set_num_tensor_args_to_observation_type (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4982279Z   test_backend_op_config_set_observation_type (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4982899Z   test_backend_op_config_set_qat_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4983400Z   test_backend_op_config_set_reference_quantized_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4983882Z   test_backend_op_config_set_root_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4984423Z   test_backend_op_config_set_root_node_getter (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.4985017Z   test_backend_op_config_to_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.002s)
2023-01-11T21:56:30.5024369Z   test_dtype_config_from_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.5025584Z   test_dtype_config_to_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:30.5026543Z   test_conv_chain (quantization.eager.test_bias_correction_eager.TestBiasCorrectionEager) ... ok (1.321s)
2023-01-11T21:56:30.5027531Z   test_linear_chain (quantization.eager.test_bias_correction_eager.TestBiasCorrectionEager) ... ok (0.735s)
2023-01-11T21:56:30.5028542Z   test_compare_tensor_scalar (quantization.core.test_quantized_op.TestComparatorOps) ... ok (0.508s)
2023-01-11T21:56:30.5029504Z   test_compare_tensor_tensor (quantization.core.test_quantized_op.TestComparatorOps) ... ok (0.217s)
2023-01-11T21:56:30.5030529Z   test_erase_class_tensor_shapes (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... ok (0.017s)
2023-01-11T21:56:30.5031606Z   test_quantization_modules (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... ok (0.092s)
2023-01-11T21:56:30.5034013Z   test_rnn_cell_quantized (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:510: UserWarning: quantize_rnn_cell_modules function has been deprecated. Please use torch.ao.quantization.quantize_dynamic API instead.
2023-01-11T21:56:30.5035525Z   warnings.warn("quantize_rnn_cell_modules function has been deprecated. "
2023-01-11T21:56:30.5037283Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:100: UserWarning: torch.jit.QuantizedRNNCellBase is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.RNNCell instead.
2023-01-11T21:56:30.5038384Z   warnings.warn(
2023-01-11T21:56:30.5040037Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:213: UserWarning: torch.jit.QuantizedLSTMCell is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.LSTMCell instead.
2023-01-11T21:56:30.5041123Z   warnings.warn(
2023-01-11T21:56:30.5042667Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:236: UserWarning: torch.jit.QuantizedGRUCell is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.GRUCell instead.
2023-01-11T21:56:30.5043750Z   warnings.warn(
2023-01-11T21:56:30.5045238Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:178: UserWarning: torch.jit.QuantizedRNNCell is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.RNNCell instead.
2023-01-11T21:56:30.5046306Z   warnings.warn(
2023-01-11T21:56:30.5046696Z ok (0.107s)
2023-01-11T21:56:30.5048429Z   test_rnn_quantized (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:556: UserWarning: quantize_rnn_modules function has been deprecated. Please use torch.ao.quantization.quantize_dynamic API instead.
2023-01-11T21:56:30.5049809Z   warnings.warn("quantize_rnn_modules function has been deprecated. "
2023-01-11T21:56:30.5051422Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:264: UserWarning: torch.jit.QuantizedRNNBase is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic instead.
2023-01-11T21:56:30.5052498Z   warnings.warn(
2023-01-11T21:56:30.5053958Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:369: UserWarning: torch.jit.QuantizedLSTM is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.LSTM instead.
2023-01-11T21:56:30.5055025Z   warnings.warn(
2023-01-11T21:56:30.5056483Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:449: UserWarning: torch.jit.QuantizedGRU is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.GRU instead.
2023-01-11T21:56:30.5057532Z   warnings.warn(
2023-01-11T21:56:30.5057921Z ok (0.089s)
2023-01-11T21:56:30.5058860Z   test_device_affinity (quantization.core.test_workflow_module.TestDistributed) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:56:30.5059972Z   test_fake_quant_preserves_buffers (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:30.5060858Z Tests that fake quant only modifies buffers in place. Note: this is important ... ok (0.003s)
2023-01-11T21:56:30.5061693Z   test_observers_preserve_buffers (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:30.5062650Z Tests that observers only modify buffers in place. Note: this is important ... ok (0.009s)
2023-01-11T21:56:30.5063560Z   test_qat_convbn_fused_syncbn_replacement (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:30.5065661Z Tests that SyncBatchNorm replacement works for fused ConvBN. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:56:30.5066811Z   warnings.warn(
2023-01-11T21:56:30.5067165Z ok (0.005s)
2023-01-11T21:56:30.5067733Z   test_qat_data_parallel (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:30.5068714Z Tests that doing QAT in nn.DataParallel does not crash. ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:56:30.5069523Z   test_syncbn_preserves_qconfig (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:30.5070300Z Makes sure that if a BatchNorm is not fused and a qconfig exists, ... ok (0.001s)
2023-01-11T21:56:30.5071595Z   test_cell_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... [W qlinear_dynamic.cpp:247] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator())
2023-01-11T21:56:30.5074221Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:309: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5075521Z   device=storage.device,
2023-01-11T21:56:30.5075934Z ok (0.977s)
2023-01-11T21:56:30.5077896Z   test_dynamic_conv1d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:59: UserWarning: The current implementation of the DynamicQuantizedConv1d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:30.5078717Z   warnings.warn(
2023-01-11T21:56:30.5079203Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5080471Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/quantized/cpu/qconv_dynamic.cpp:82.)
2023-01-11T21:56:30.5081215Z   return callable(*args, **kwargs)
2023-01-11T21:56:30.5081748Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5082451Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5083147Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5083849Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5084683Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5085403Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5086094Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5086796Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5087600Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5088315Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5088983Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5089652Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5090385Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5091099Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5091793Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5093272Z ok (1.348s)
2023-01-11T21:56:30.5094388Z   test_dynamic_conv2d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:124: UserWarning: The current implementation of the DynamicQuantizedConv2d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:30.5095126Z   warnings.warn(
2023-01-11T21:56:30.5095637Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5096336Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5097027Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5097739Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5098438Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5099140Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5099818Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5100565Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5101219Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5101942Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5102802Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5103635Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5104345Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5105002Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5105700Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5106374Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5106825Z ok (1.383s)
2023-01-11T21:56:30.5107946Z   test_dynamic_conv3d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:189: UserWarning: The current implementation of the DynamicQuantizedConv3d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:30.5108707Z   warnings.warn(
2023-01-11T21:56:30.5108935Z ok (0.449s)
2023-01-11T21:56:30.5109978Z   test_dynamic_convtranspose1d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:259: UserWarning: The current implementation of the DynamicQuantizedConvTranpose1d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:30.5110750Z   warnings.warn(
2023-01-11T21:56:30.5111261Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5111990Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5112740Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5113472Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5114188Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5114882Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5115590Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5116409Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5117180Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5117874Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5118582Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5119401Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5120111Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5120795Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5121477Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5122138Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5122574Z ok (1.456s)
2023-01-11T21:56:30.5123729Z   test_dynamic_convtranspose2d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:320: UserWarning: The current implementation of the DynamicQuantizedConvTranpose2d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:30.5124556Z   warnings.warn(
2023-01-11T21:56:30.5125037Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5125719Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5126414Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5127115Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5127812Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5128508Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5129203Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5129910Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5130599Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5131435Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5132259Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5132960Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5133608Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5134332Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5135160Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5135907Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:30.5136370Z ok (1.238s)
2023-01-11T21:56:30.5137552Z   test_dynamic_convtranspose3d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:381: UserWarning: The current implementation of the DynamicQuantizedConvTranpose3d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:30.5138336Z   warnings.warn(
2023-01-11T21:56:30.5138597Z ok (0.496s)
2023-01-11T21:56:30.5139006Z   test_gru_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... ok (0.331s)
2023-01-11T21:56:30.5139624Z   test_linear_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... ok (23.480s)
2023-01-11T21:56:30.5140211Z   test_lstm_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... ok (3.901s)
2023-01-11T21:56:30.5140793Z   test_dynamic_conv1d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.049s)
2023-01-11T21:56:30.5141360Z   test_dynamic_conv2d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.069s)
2023-01-11T21:56:30.5141904Z   test_dynamic_conv3d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.050s)
2023-01-11T21:56:30.5142649Z   test_dynamic_convtranspose1d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.068s)
2023-01-11T21:56:30.5143281Z   test_dynamic_convtranspose2d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.048s)
2023-01-11T21:56:30.5143808Z   test_dynamic_convtranspose3d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.012s)
2023-01-11T21:56:30.5144517Z   test_linear_prepack_fp16_numerics (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5145215Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5145738Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5146223Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5146705Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5147180Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5147692Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5148318Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5148805Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5149322Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5149814Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5150282Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5150771Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5151275Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5151775Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5152410Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5152943Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5153445Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5153916Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5154280Z ok (0.106s)
2023-01-11T21:56:30.5154653Z   test_qlinear (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (2.240s)
2023-01-11T21:56:30.5155207Z   test_qlinear_dynamic_fp16 (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.166s)
2023-01-11T21:56:30.5155777Z   test_qlinear_legacy (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.241s)
2023-01-11T21:56:30.5156340Z   test_qlstmGRU (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (9.458s)
2023-01-11T21:56:30.5156969Z   test_qrnncell (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (11.447s)
2023-01-11T21:56:30.5157494Z   test_converged (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:30.5157950Z Sanity checks on _equalize.converged working ... ok (0.002s)
2023-01-11T21:56:30.5158413Z   test_cross_layer_equalization (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:30.5158934Z applies _equalize.cross_layer_equalization on two modules and checks ... ok (0.002s)
2023-01-11T21:56:30.5159403Z   test_equalize (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:30.5159887Z First checks to see if _equalize.equalize can handle multiple ... ok (0.014s)
2023-01-11T21:56:30.5160370Z   test_equalize_fused_convrelu (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:30.5160837Z Checks to see if eager mode equalization supports fused ... ok (0.028s)
2023-01-11T21:56:30.5161342Z   test_equalize_fused_linearrelu (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:30.5161842Z Checks to see if eager mode equalization supports fused ... ok (0.022s)
2023-01-11T21:56:30.5162321Z   test_input_weight_eq_observer (quantization.fx.test_equalize_fx.TestEqualizeFx) ... ok (0.232s)
2023-01-11T21:56:30.5162855Z   test_input_weight_equalization_activation_values (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5164114Z After applying the equalization functions check if the input ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:1435: UserWarning: Passing a QConfig dictionary to prepare is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:30.5164817Z   warnings.warn(
2023-01-11T21:56:30.5165775Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:1441: UserWarning: Passing a QConfig dictionary to prepare for equalization is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:30.5166494Z   warnings.warn(
2023-01-11T21:56:30.5166741Z ok (0.074s)
2023-01-11T21:56:30.5167145Z   test_input_weight_equalization_branching (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5168074Z Tests that graphs containing branches are prepared correctly. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:712: UserWarning: Cannot equalize linear1 because it is part of a branch.
2023-01-11T21:56:30.5168625Z   warnings.warn(
2023-01-11T21:56:30.5169286Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:712: UserWarning: Cannot equalize linear2 because it is part of a branch.
2023-01-11T21:56:30.5169746Z   warnings.warn(
2023-01-11T21:56:30.5169986Z ok (0.023s)
2023-01-11T21:56:30.5170384Z   test_input_weight_equalization_convert (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5171721Z Tests that the modified model for equalization (before quantization) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node linear1_packed_weight_0 target linear1_packed_weight_0 linear1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5172688Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5173703Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node linear2_packed_weight_0 target linear2_packed_weight_0 linear2_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5174543Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5175559Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node linear_packed_weight_0 target linear_packed_weight_0 linear_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5176393Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5177398Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node conv1_packed_weight_0 target conv1_packed_weight_0 conv1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5178216Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5179223Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node conv2_packed_weight_0 target conv2_packed_weight_0 conv2_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5180022Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5181007Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node conv_packed_weight_0 target conv_packed_weight_0 conv_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5181801Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5182160Z ok (2.550s)
2023-01-11T21:56:30.5182708Z   test_input_weight_equalization_equalization_scales (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5183243Z After applying the equalization functions, check if the equalization ... ok (0.071s)
2023-01-11T21:56:30.5183724Z   test_input_weight_equalization_graphs (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5184686Z Tests that the modified model for equalization has the same graph ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:302: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:56:30.5185373Z   warnings.warn(
2023-01-11T21:56:30.5186178Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/_equalize.py:190: UserWarning: Must run observer before calling calculate_equalization_scale. Returning default equalization scale torch.tensor(1).
2023-01-11T21:56:30.5186703Z   warnings.warn(
2023-01-11T21:56:30.5187514Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/_equalize.py:102: UserWarning: Must call calculate_equalization_scale before calling calculate_scaled_minmax. Will not scale the next quantization observer.
2023-01-11T21:56:30.5188065Z   warnings.warn(
2023-01-11T21:56:30.5188717Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:56:30.5189192Z   warnings.warn(
2023-01-11T21:56:30.5189438Z ok (0.506s)
2023-01-11T21:56:30.5189844Z   test_input_weight_equalization_prepare (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5190409Z Tests that graphs created after prepare_fx is as expected ... ok (0.176s)
2023-01-11T21:56:30.5190893Z   test_input_weight_equalization_results (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5191392Z Tests that for small models, the results of quantized models that ... ok (0.233s)
2023-01-11T21:56:30.5191847Z   test_input_weight_equalization_weights_bias (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5192306Z After applying the equalization functions check if the weights and ... ok (0.065s)
2023-01-11T21:56:30.5192762Z   test_selective_equalization (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:30.5193216Z Tests that we are able to run numeric suite on the equalized model ... ok (0.231s)
2023-01-11T21:56:30.5193675Z   test_dict_return_type (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.078s)
2023-01-11T21:56:30.5194216Z   test_matching_failure_node_count (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.044s)
2023-01-11T21:56:30.5194785Z   test_matching_failure_node_type (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.033s)
2023-01-11T21:56:30.5195285Z   test_methods (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher)
2023-01-11T21:56:30.5195709Z Verify that graph matching works on methods ... ok (0.048s)
2023-01-11T21:56:30.5196141Z   test_nodes_before_cat (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.121s)
2023-01-11T21:56:30.5197365Z   test_nodes_with_equal_types_get_matched (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:1204: UserWarning: must run observer before calling calculate_qparams.                                    Returning default scale and zero point 
2023-01-11T21:56:30.5198056Z   warnings.warn(
2023-01-11T21:56:30.5198287Z ok (0.113s)
2023-01-11T21:56:30.5198681Z   test_op_relationship_mapping (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher)
2023-01-11T21:56:30.5199168Z Tests that the mapping of op relationships is complete. ... ok (0.007s)
2023-01-11T21:56:30.5199630Z   test_results_order (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.177s)
2023-01-11T21:56:30.5200831Z   test_simple_fun (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_0 target _packed_weight_0 _packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5201744Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5202098Z ok (0.081s)
2023-01-11T21:56:30.5202460Z   test_simple_fusion (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.080s)
2023-01-11T21:56:30.5202974Z   test_simple_mod (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.060s)
2023-01-11T21:56:30.5203556Z   test_simple_mod_multi (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.069s)
2023-01-11T21:56:30.5204063Z   test_simple_tensor_ops (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.190s)
2023-01-11T21:56:30.5204576Z   test_user_defined_function (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher)
2023-01-11T21:56:30.5205033Z Verify that graph matching works on user defined functions ... ok (0.044s)
2023-01-11T21:56:30.5206149Z   test_mobilenet_v2 (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcherModels) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
2023-01-11T21:56:30.5206798Z   warnings.warn(
2023-01-11T21:56:30.5207812Z /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
2023-01-11T21:56:30.5208440Z   warnings.warn(msg)
2023-01-11T21:56:30.5208683Z ok (3.141s)
2023-01-11T21:56:30.5209084Z   test_mobilenet_v2_qat (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcherModels) ... ok (3.803s)
2023-01-11T21:56:30.5209692Z   test_add_loggers_cuda (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:56:30.5211135Z   test_add_mul_inputs_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:56:30.5212137Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:56:30.5212436Z ok (2.689s)
2023-01-11T21:56:30.5212896Z   test_add_shadow_loggers_cuda (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:56:30.5214228Z   test_add_shadow_loggers_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_1 target _packed_weight_1 _packed_weight_1 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5215178Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5215515Z ok (0.774s)
2023-01-11T21:56:30.5215931Z   test_add_shadow_loggers_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.325s)
2023-01-11T21:56:30.5216546Z   test_add_shadow_loggers_meth_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5217152Z Verify that add_loggers works on methods ... skipping shadow loggers for node_b: str, start_node_a: str, unknown dtype cast
2023-01-11T21:56:30.5217771Z skipping shadow loggers for node_b: torch.ao.quantization.observer.FixedQParamsObserver, start_node_a: str, unknown dtype cast
2023-01-11T21:56:30.5218368Z skipping shadow loggers for node_b: str, start_node_a: str, unknown dtype cast
2023-01-11T21:56:30.5218706Z ok (0.121s)
2023-01-11T21:56:30.5219142Z   test_add_shadow_loggers_mod_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.771s)
2023-01-11T21:56:30.5219763Z   test_add_shadow_loggers_mod_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.458s)
2023-01-11T21:56:30.5220404Z   test_extend_logger_results_with_comparison (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.072s)
2023-01-11T21:56:30.5221762Z   test_extract_weights_conv_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_2 target _packed_weight_2 _packed_weight_2 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5223022Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5224027Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_3 target _packed_weight_3 _packed_weight_3 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5224838Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5225941Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_4 target _packed_weight_4 _packed_weight_4 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5226774Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5227775Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_5 target _packed_weight_5 _packed_weight_5 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5228593Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5228959Z ok (0.700s)
2023-01-11T21:56:30.5229410Z   test_extract_weights_conv_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.854s)
2023-01-11T21:56:30.5230067Z   test_extract_weights_cuda (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:56:30.5230743Z   test_extract_weights_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.171s)
2023-01-11T21:56:30.5231342Z   test_extract_weights_fqn (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.076s)
2023-01-11T21:56:30.5231970Z   test_extract_weights_linear_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.380s)
2023-01-11T21:56:30.5232593Z   test_extract_weights_linear_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.412s)
2023-01-11T21:56:30.5233234Z   test_extract_weights_mod_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.816s)
2023-01-11T21:56:30.5233814Z   test_extract_weights_mod_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.987s)
2023-01-11T21:56:30.5234415Z   test_fp16_shadows_fp32 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.054s)
2023-01-11T21:56:30.5235363Z   test_int8_shadows_fp32_coverage (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skipping shadow loggers for node_b: torch.nn.modules.pooling.AdaptiveAvgPool2d, start_node_a: torch.nn.modules.pooling.AdaptiveAvgPool2d, unknown dtype cast
2023-01-11T21:56:30.5236391Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch.nn.modules.pooling.AdaptiveAvgPool2d, unknown dtype cast
2023-01-11T21:56:30.5237227Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.mul, start_node_a: torch._ops.quantized.PyCapsule.mul, unsupported
2023-01-11T21:56:30.5237978Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._ops.quantized.PyCapsule.mul, unknown dtype cast
2023-01-11T21:56:30.5238711Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._ops.quantized.PyCapsule.add_relu, unsupported
2023-01-11T21:56:30.5239440Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._ops.quantized.PyCapsule.add_relu, unknown dtype cast
2023-01-11T21:56:30.5240088Z ok (0.157s)
2023-01-11T21:56:30.5240506Z   test_int8_shadows_fp32_simple (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.197s)
2023-01-11T21:56:30.5241087Z   test_int8_shadows_int8_fun (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.103s)
2023-01-11T21:56:30.5241662Z   test_int8_shadows_int8_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.080s)
2023-01-11T21:56:30.5242219Z   test_layer_names (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.352s)
2023-01-11T21:56:30.5242796Z   test_linear_fp16_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.306s)
2023-01-11T21:56:30.5243389Z   test_linear_fp16_shadow_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.406s)
2023-01-11T21:56:30.5244109Z   test_linear_fp16_vs_linear_fp16_shadow_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.051s)
2023-01-11T21:56:30.5244749Z   test_linear_fp16_weights (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.162s)
2023-01-11T21:56:30.5245323Z   test_linear_kwargs_shadow (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.047s)
2023-01-11T21:56:30.5245909Z   test_loggers_preserve_qat_numerics (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.193s)
2023-01-11T21:56:30.5246467Z   test_logging_inputs (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5247147Z Verifies that logging inputs works correctly ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5247886Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5248318Z ok (0.587s)
2023-01-11T21:56:30.5248760Z   test_match_activations_fqn (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.078s)
2023-01-11T21:56:30.5249350Z   test_match_activations_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.608s)
2023-01-11T21:56:30.5249951Z   test_match_activations_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.385s)
2023-01-11T21:56:30.5250534Z   test_match_activations_meth_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5251024Z Verify that add_loggers works on methods ... ok (0.144s)
2023-01-11T21:56:30.5251514Z   test_match_activations_mod_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (1.018s)
2023-01-11T21:56:30.5252107Z   test_match_activations_mod_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.283s)
2023-01-11T21:56:30.5252865Z   test_mul_add_cat_stack_skips_shadowing (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skipping shadow loggers for node_b: _operator.mul, start_node_a: _operator.mul, unsupported
2023-01-11T21:56:30.5253660Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: _operator.mul, unsupported
2023-01-11T21:56:30.5254349Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.mul, start_node_a: torch._VariableFunctionsClass.mul, unsupported
2023-01-11T21:56:30.5255029Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch._VariableFunctionsClass.mul, unsupported
2023-01-11T21:56:30.5255671Z skipping shadow loggers for node_b: _operator.add, start_node_a: _operator.add, unsupported
2023-01-11T21:56:30.5256265Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: _operator.add, unsupported
2023-01-11T21:56:30.5256981Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5257794Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5258553Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5259240Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:30.5259914Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.mul, start_node_a: _operator.mul, unsupported
2023-01-11T21:56:30.5260573Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.mul, start_node_a: torch._VariableFunctionsClass.mul, unsupported
2023-01-11T21:56:30.5261238Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: _operator.add, unsupported
2023-01-11T21:56:30.5261990Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5262848Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5263511Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:30.5263971Z ok (1.183s)
2023-01-11T21:56:30.5264381Z   test_op_io_dtype_coverage (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5264898Z Tests that all the ops quantization cares about have input and output ... ok (0.009s)
2023-01-11T21:56:30.5265420Z   test_op_with_either_fp32_or_int8_input (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5266092Z Verify that shadowing works with ops which accept either fp32 or ... skipping shadow loggers for node_b: torch.nn.modules.activation.ReLU, start_node_a: torch.nn.modules.activation.ReLU, unknown dtype cast
2023-01-11T21:56:30.5266956Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.modules.activation.ReLU, unknown dtype cast
2023-01-11T21:56:30.5267654Z skipping shadow loggers for node_b: torch.nn.functional.relu, start_node_a: torch.nn.functional.relu, unknown dtype cast
2023-01-11T21:56:30.5268333Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.functional.relu, unknown dtype cast
2023-01-11T21:56:30.5269099Z skipping shadow loggers for node_b: torch.nn.modules.activation.ReLU, start_node_a: torch.nn.modules.activation.ReLU, unknown dtype cast
2023-01-11T21:56:30.5269763Z skipping shadow loggers for node_b: torch.nn.functional.relu, start_node_a: torch.nn.functional.relu, unknown dtype cast
2023-01-11T21:56:30.5270180Z ok (0.509s)
2023-01-11T21:56:30.5270816Z   test_op_with_only_kwargs_skips_shadowing (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5271714Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:30.5272385Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5273066Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:30.5273491Z ok (0.259s)
2023-01-11T21:56:30.5273927Z   test_ops_with_same_fp32_and_int8_signature (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5274437Z Verifies that we can match pairs of ops which have the same aten ... ok (2.809s)
2023-01-11T21:56:30.5274957Z   test_shadow_activations_fqn (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.194s)
2023-01-11T21:56:30.5275697Z   test_shadow_loggers_preserve_qat_numerics (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.096s)
2023-01-11T21:56:30.5276306Z   test_unsupported_op_copy_skips_shadowing (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5277120Z Copying a `call_function` node is not implemented, test that this ... skipping shadow loggers for node_b: torch.nn.functional.layer_norm, start_node_a: torch.nn.functional.layer_norm, unhandled logic in subgraph copy
2023-01-11T21:56:30.5277958Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.functional.layer_norm, unhandled logic in subgraph copy
2023-01-11T21:56:30.5278695Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.layer_norm, start_node_a: torch.nn.functional.layer_norm, unhandled logic in subgraph copy
2023-01-11T21:56:30.5279293Z ok (0.413s)
2023-01-11T21:56:30.5279701Z   test_user_defined_function (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5280367Z Verify that NS APIs work on user defined functions ... skipping shadow loggers for node_b: torch._C._nn.linear, start_node_a: quantization.fx.test_numeric_suite_fx._wrapped_linear, unknown dtype cast
2023-01-11T21:56:30.5281173Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: quantization.fx.test_numeric_suite_fx._wrapped_linear, unknown dtype cast
2023-01-11T21:56:30.5281688Z ok (0.330s)
2023-01-11T21:56:30.5282061Z   test_user_module (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:30.5283142Z For user defined modules, ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py:148: UserWarning: Passing a prepare_custom_config_dict to prepare is deprecated and will not be supported in a future version. Please pass in a PrepareCustomConfig instead.
2023-01-11T21:56:30.5283785Z   warnings.warn(
2023-01-11T21:56:30.5284028Z ok (0.175s)
2023-01-11T21:56:30.5284444Z   test_user_module_scriptable (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.214s)
2023-01-11T21:56:30.5285032Z   test_compare_activations_conv (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (1.142s)
2023-01-11T21:56:30.5285665Z   test_compare_activations_linear (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.501s)
2023-01-11T21:56:30.5287126Z   test_compare_activations_lstm_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py:320: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:30.5288065Z   torch.tensor(weight_qparams["scale"], dtype=torch.float, device=device))
2023-01-11T21:56:30.5289121Z /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py:323: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:30.5289844Z   torch.tensor(weight_qparams["zero_point"], dtype=torch.int, device=device))
2023-01-11T21:56:30.5290172Z ok (0.251s)
2023-01-11T21:56:30.5290630Z   test_compare_shadow_activations_conv (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (1.204s)
2023-01-11T21:56:30.5291286Z   test_compare_shadow_activations_linear (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.640s)
2023-01-11T21:56:30.5291940Z   test_compare_shadow_activations_lstm_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.134s)
2023-01-11T21:56:30.5292677Z   test_compare_weights_conv (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.713s)
2023-01-11T21:56:30.5293270Z   test_compare_weights_linear (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.420s)
2023-01-11T21:56:30.5293951Z   test_compare_weights_lstm_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.227s)
2023-01-11T21:56:30.5294837Z   test_mobilenet_v2 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5295745Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5296477Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5297254Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5297930Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5298646Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5299351Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5300057Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5300767Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5301490Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5302218Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5303062Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5303717Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5304421Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5305168Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5305932Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5306666Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5307414Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5308132Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5308869Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5309609Z skipping shadow loggers for node_b: torch.nn.functional.adaptive_avg_pool2d, start_node_a: torch.nn.functional.adaptive_avg_pool2d, unhandled logic in subgraph copy
2023-01-11T21:56:30.5310519Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch.nn.functional.adaptive_avg_pool2d, unhandled logic in subgraph copy
2023-01-11T21:56:30.5311297Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5311971Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5312640Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5313311Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5314055Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5314881Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5315558Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5316206Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5316874Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5317612Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5318300Z skipping shadow loggers for node_b: torch.nn.functional.adaptive_avg_pool2d, start_node_a: torch.nn.functional.adaptive_avg_pool2d, unhandled logic in subgraph copy
2023-01-11T21:56:30.5318762Z ok (10.281s)
2023-01-11T21:56:30.5319392Z   test_resnet18 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5320249Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5320924Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5321668Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5322386Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5323072Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5323765Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5324464Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5325172Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5325887Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5326617Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5327307Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5328136Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5328851Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5329567Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5330381Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5331275Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5331963Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5332732Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5333425Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5334196Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5334995Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5335724Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5336404Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:30.5337201Z skipping shadow loggers for node_b: torch.nn.modules.pooling.AdaptiveAvgPool2d, start_node_a: torch.nn.modules.pooling.AdaptiveAvgPool2d, unknown dtype cast
2023-01-11T21:56:30.5337994Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.flatten, start_node_a: torch._VariableFunctionsClass.flatten, unknown dtype cast
2023-01-11T21:56:30.5338457Z ok (4.487s)
2023-01-11T21:56:30.5338905Z   test_sparsenn_compare_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (4.209s)
2023-01-11T21:56:30.5339815Z   test_sparsenn_shadow (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:30.5340794Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:30.5341577Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5342287Z skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:30.5343133Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5343856Z skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:30.5344648Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:30.5345349Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5346174Z skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:30.5346895Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:30.5347342Z ok (4.419s)
2023-01-11T21:56:30.5347907Z   test_conv_bn_relu_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5348479Z ok (0.095s)
2023-01-11T21:56:30.5348895Z   test_custom_functions_and_tracer (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... working
2023-01-11T21:56:30.5349258Z working
2023-01-11T21:56:30.5349472Z GraphModule(
2023-01-11T21:56:30.5349775Z   (fc1): Linear(in_features=2, out_features=2, bias=True)
2023-01-11T21:56:30.5350210Z   (fc2): Linear(in_features=2, out_features=2, bias=True)
2023-01-11T21:56:30.5350585Z   (shadow_0_0): OutputLogger(ref_name=model, model_name=subgraph_0_0,
2023-01-11T21:56:30.5350934Z   prev_node_name=fc1, ref_node_name=fc1,
2023-01-11T21:56:30.5351285Z   ref_node_target_type=torch.nn.modules.linear.Linear
2023-01-11T21:56:30.5351643Z   results_type=node_output, index_within_arg=0,
2023-01-11T21:56:30.5351931Z   index_of_arg=0, fqn=fc1)
2023-01-11T21:56:30.5352205Z   (shadow_wrapper_0_1): GraphModule(
2023-01-11T21:56:30.5352627Z     (mod_0): QuantizedLinear(in_features=2, out_features=2, scale=0.019699346274137497, zero_point=58, qscheme=torch.per_tensor_affine)
2023-01-11T21:56:30.5353048Z     (shadow_0_1): OutputComparisonLogger
2023-01-11T21:56:30.5353306Z   )
2023-01-11T21:56:30.5353609Z   (shadow_1_0): OutputLogger(ref_name=model, model_name=subgraph_1_0,
2023-01-11T21:56:30.5353942Z   prev_node_name=fc2, ref_node_name=fc2,
2023-01-11T21:56:30.5354288Z   ref_node_target_type=torch.nn.modules.linear.Linear
2023-01-11T21:56:30.5354680Z   results_type=node_output, index_within_arg=0,
2023-01-11T21:56:30.5354961Z   index_of_arg=0, fqn=fc2)
2023-01-11T21:56:30.5355247Z   (shadow_wrapper_1_1): GraphModule(
2023-01-11T21:56:30.5355714Z     (mod_0): QuantizedLinear(in_features=2, out_features=2, scale=0.01274071168154478, zero_point=96, qscheme=torch.per_tensor_affine)
2023-01-11T21:56:30.5356142Z     (shadow_1_1): OutputComparisonLogger
2023-01-11T21:56:30.5356419Z   )
2023-01-11T21:56:30.5356621Z )
2023-01-11T21:56:30.5356751Z 
2023-01-11T21:56:30.5356757Z 
2023-01-11T21:56:30.5356763Z 
2023-01-11T21:56:30.5356873Z def forward(self, x):
2023-01-11T21:56:30.5357174Z     fc1 = self.fc1(x)
2023-01-11T21:56:30.5357498Z     shadow_wrapper_0_1 = self.shadow_wrapper_0_1(fc1, x);  x = None
2023-01-11T21:56:30.5357834Z     shadow_0_0 = self.shadow_0_0(fc1)
2023-01-11T21:56:30.5358086Z     fc2 = self.fc2(fc1)
2023-01-11T21:56:30.5358408Z     shadow_wrapper_1_1 = self.shadow_wrapper_1_1(fc2, fc1);  fc1 = None
2023-01-11T21:56:30.5358758Z     shadow_1_0 = self.shadow_1_0(fc2)
2023-01-11T21:56:30.5359006Z     return fc2
2023-01-11T21:56:30.5359231Z     
2023-01-11T21:56:30.5359556Z # To see more debug info, please use `graph_module.print_readable()`
2023-01-11T21:56:30.5360047Z `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5360477Z ok (0.078s)
2023-01-11T21:56:30.5362329Z   test_functions (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}).
2023-01-11T21:56:30.5363812Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:30.5364338Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:30.5364735Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:30.5365186Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:30.5365774Z `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5366182Z ok (0.227s)
2023-01-11T21:56:30.5366751Z   test_linear_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5367291Z ok (0.030s)
2023-01-11T21:56:30.5367933Z   test_linear_relu_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5368489Z ok (0.086s)
2023-01-11T21:56:30.5368934Z   test_logger_enabled_and_save_activations_flags (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.040s)
2023-01-11T21:56:30.5369739Z   test_mobilenet_v2 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5370294Z ok (3.527s)
2023-01-11T21:56:30.5370864Z   test_partial_qconfig_mapping (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... unable to find at least one qconfig for node %fc : [#users=1] = call_module[target=fc](args = (%x,), kwargs = {}), skipping
2023-01-11T21:56:30.5371605Z unable to find at least one qconfig for node %add : [#users=1] = call_function[target=operator.add](args = (%relu, %relu), kwargs = {}), skipping
2023-01-11T21:56:30.5372239Z `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5372651Z ok (0.170s)
2023-01-11T21:56:30.5373098Z   test_qconfig_multi_mapping_deduplication (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:30.5373902Z   test_qconfig_multi_mapping_end_to_end (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5374481Z ok (0.091s)
2023-01-11T21:56:30.5375091Z   test_qconfig_multi_mapping_from_list (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5375675Z ok (0.085s)
2023-01-11T21:56:30.5376097Z   test_qconfig_multi_mapping_insert_padding (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:30.5376928Z   test_qconfig_multi_mapping_ordering (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:30.5377528Z ok (0.086s)
2023-01-11T21:56:30.5377950Z   test_qconfig_multi_mapping_repr (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:30.5378571Z   test_qconfig_multi_mapping_retroactive_padding (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:30.5379185Z   test_fq_module_per_channel (quantization.core.test_workflow_module.TestFakeQuantize) ... ok (0.167s)
2023-01-11T21:56:30.5379835Z   test_fq_serializable_per_channel (quantization.core.test_workflow_module.TestFakeQuantize) ... ok (0.003s)
2023-01-11T21:56:30.5380365Z   test_quant_min_max_override (quantization.core.test_workflow_module.TestFakeQuantize) ... ok (0.001s)
2023-01-11T21:56:30.5380871Z   test_backward_per_channel (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:30.5381431Z Tests the backward method. ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.100s)
2023-01-11T21:56:30.5382030Z   test_backward_per_channel_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.014s)
2023-01-11T21:56:30.5383057Z   test_backward_per_channel_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:30.5383522Z   test_backward_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:30.5383976Z Tests the backward method. ... skip: temporarily disable the test (0.004s)
2023-01-11T21:56:30.5384347Z   test_backward_per_tensor_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.010s)
2023-01-11T21:56:30.5384783Z   test_backward_per_tensor_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:30.5385205Z   test_fake_quant_control (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.008s)
2023-01-11T21:56:30.5385606Z   test_fake_quant_per_channel_qparam_range (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.360s)
2023-01-11T21:56:30.5386035Z   test_fake_quant_preserves_qparam_shapes_for_activations (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.004s)
2023-01-11T21:56:30.5386439Z   test_fixed_qparams_fq_module (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.102s)
2023-01-11T21:56:30.5387215Z   test_forward_backward_per_tensor_with_amp (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... /opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
2023-01-11T21:56:30.5387828Z   warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
2023-01-11T21:56:30.5388087Z ok (0.006s)
2023-01-11T21:56:30.5388342Z   test_forward_per_channel (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:30.5388698Z Tests the forward path of the FakeQuantizePerTensorAffine op. ... ok (0.161s)
2023-01-11T21:56:30.5389082Z   test_forward_per_channel_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.011s)
2023-01-11T21:56:30.5389530Z   test_forward_per_channel_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:30.5389969Z   test_forward_per_channel_half_precision_numerics (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.011s)
2023-01-11T21:56:30.5390361Z   test_forward_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:30.5390706Z Tests the forward path of the FakeQuantizePerTensorAffine op. ... ok (0.093s)
2023-01-11T21:56:30.5391068Z   test_forward_per_tensor_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.011s)
2023-01-11T21:56:30.5391509Z   test_forward_per_tensor_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:30.5391952Z   test_forward_per_tensor_half_precision_numerics (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.005s)
2023-01-11T21:56:30.5392353Z   test_fq_module_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.134s)
2023-01-11T21:56:30.5392731Z   test_fq_serializable_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.007s)
2023-01-11T21:56:30.5393298Z   test_learnable_backward_per_channel_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.003s)
2023-01-11T21:56:30.5393837Z   test_learnable_backward_per_channel_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:30.5394281Z   test_learnable_backward_per_tensor_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.277s)
2023-01-11T21:56:30.5394726Z   test_learnable_backward_per_tensor_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:30.5395152Z   test_learnable_forward_per_channel_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.135s)
2023-01-11T21:56:30.5395591Z   test_learnable_forward_per_channel_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:30.5396158Z   test_learnable_forward_per_tensor_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.003s)
2023-01-11T21:56:30.5396687Z   test_learnable_forward_per_tensor_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:30.5397162Z   test_numerical_consistency_per_channel (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.078s)
2023-01-11T21:56:30.5397575Z   test_numerical_consistency_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.029s)
2023-01-11T21:56:30.5397951Z   test_forward_hooks_preserved (quantization.eager.test_fuse_eager.TestFuseEager)
2023-01-11T21:56:30.5398290Z Test case that checks whether forward pre hooks of the first module and ... ok (0.015s)
2023-01-11T21:56:30.5398637Z   test_fuse_function_customization (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.001s)
2023-01-11T21:56:30.5399008Z   test_fuse_module_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.065s)
2023-01-11T21:56:30.5399356Z   test_fuse_module_train (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.387s)
2023-01-11T21:56:30.5399694Z   test_fusion_conv_with_bias (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.057s)
2023-01-11T21:56:30.5400061Z   test_fusion_convtranspose_bn_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.005s)
2023-01-11T21:56:30.5400429Z   test_fusion_linear_bn_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.002s)
2023-01-11T21:56:30.5400803Z   test_fusion_sequential_model_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (1.115s)
2023-01-11T21:56:30.5401165Z   test_fusion_sequential_model_train (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.252s)
2023-01-11T21:56:30.5401638Z   test_fuse_addtional_fuser_method (quantization.fx.test_quantize_fx.TestFuseFx) ... skip: Temporarily skipping the test case, will enable after the simplepattern format is supported (0.002s)
2023-01-11T21:56:30.5402073Z   test_fuse_conv_bn_relu (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.025s)
2023-01-11T21:56:30.5402414Z   test_fuse_convtranspose_bn_eval (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.006s)
2023-01-11T21:56:30.5402743Z   test_fuse_custom_pattern (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.008s)
2023-01-11T21:56:30.5403074Z   test_fuse_linear_bn_eval (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.005s)
2023-01-11T21:56:30.5403418Z   test_fuse_linear_bn_leaky_relu_onednn (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.009s)
2023-01-11T21:56:30.5403771Z   test_fuse_linear_tanh_for_onednn_backend (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.004s)
2023-01-11T21:56:30.5404113Z   test_fuse_module_relu (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.007s)
2023-01-11T21:56:30.5404450Z   test_fusion_pattern_with_matchallnode (quantization.fx.test_quantize_fx.TestFuseFx)
2023-01-11T21:56:30.5404851Z This test tests that the node matched by MatchAllNode will be regared as an input ... ok (0.005s)
2023-01-11T21:56:30.5405187Z   test_fusion_pattern_with_multiple_inputs (quantization.fx.test_quantize_fx.TestFuseFx)
2023-01-11T21:56:30.5405511Z This test tests two keys in backend_config: root_node_getter and ... ok (0.005s)
2023-01-11T21:56:30.5405853Z   test_linear_bn_leaky_relu_not_fused_by_default (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.009s)
2023-01-11T21:56:30.5406223Z   test_linear_tanh_not_fused_by_default (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.004s)
2023-01-11T21:56:30.5406567Z   test_problematic_fuse_example (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.013s)
2023-01-11T21:56:30.5406893Z   test_qconfig_fused_module (quantization.fx.test_quantize_fx.TestFuseFx)
2023-01-11T21:56:30.5407172Z TODO: add test for all fused modules ... ok (0.035s)
2023-01-11T21:56:30.5407538Z   test_fused_backward_op_fake_quant_off (quantization.core.test_workflow_ops.TestFusedObsFakeQuant) ... ok (0.005s)
2023-01-11T21:56:30.5407954Z   test_fused_obs_fake_quant_backward_op (quantization.core.test_workflow_ops.TestFusedObsFakeQuant) ... ok (0.005s)
2023-01-11T21:56:30.5408352Z   test_fused_obs_fake_quant_moving_avg (quantization.core.test_workflow_ops.TestFusedObsFakeQuant)
2023-01-11T21:56:30.5408709Z Tests the case where we call the fused_obs_fake_quant op multiple times ... ok (0.011s)
2023-01-11T21:56:30.5409064Z   test_fused_obs_fake_quant_moving_avg_per_channel (quantization.core.test_workflow_ops.TestFusedObsFakeQuant)
2023-01-11T21:56:30.5409428Z Tests the case where we call the fused_obs_fake_quant op multiple times ... ok (0.036s)
2023-01-11T21:56:30.5409816Z   test_compare_fused_obs_fq_oss_module (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.009s)
2023-01-11T21:56:30.5410265Z   test_default_fused_qat_config (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.015s)
2023-01-11T21:56:30.5410694Z   test_embedding_bag_qat_config (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.017s)
2023-01-11T21:56:30.5411127Z   test_embedding_qat_config (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.047s)
2023-01-11T21:56:30.5411553Z   test_fused_mod_per_channel (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.025s)
2023-01-11T21:56:30.5411971Z   test_fused_mod_reduce_range (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.001s)
2023-01-11T21:56:30.5412398Z   test_fused_obs_fq_module (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.005s)
2023-01-11T21:56:30.5412834Z   test_fused_obs_fq_moving_avg_module (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.007s)
2023-01-11T21:56:30.5413250Z   test_quantized_add_relu_fusion (quantization.jit.test_fusion_passes.TestFusionPasses) ... ok (0.018s)
2023-01-11T21:56:30.5413685Z   test_input_weight_equalization_determine_points (quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization) ... ok (0.052s)
2023-01-11T21:56:30.5414179Z   test_input_weight_equalization_report_gen (quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization) ... ok (0.064s)
2023-01-11T21:56:30.5414663Z   test_input_weight_equalization_report_gen_empty (quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization) ... ok (0.021s)
2023-01-11T21:56:30.5415099Z   test_all_outlier_report_gen (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.034s)
2023-01-11T21:56:30.5415500Z   test_multiple_run_consistent_spike_outlier_report_gen (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.097s)
2023-01-11T21:56:30.5415903Z   test_no_outlier_report_gen (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.023s)
2023-01-11T21:56:30.5416303Z   test_outlier_detection_determine_points (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.196s)
2023-01-11T21:56:30.5416720Z   test_constructor (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:30.5417025Z Tests the constructor of the ModelReport class. ... ok (0.025s)
2023-01-11T21:56:30.5417364Z   test_equalization_mapping_generation (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:30.5417709Z Tests for generation of qconfigs by ModelReport API ... ok (0.101s)
2023-01-11T21:56:30.5418022Z   test_generate_report (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:30.5418636Z Tests model_report.generate_model_report to ensure report generation ... /var/lib/jenkins/workspace/test/quantization/fx/test_model_report_fx.py:1061: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:30.5419238Z   example_input = torch.tensor(torch.randint(100, (1, 3, 3, 3)), dtype=torch.float)
2023-01-11T21:56:30.5419472Z ok (0.156s)
2023-01-11T21:56:30.5419732Z   test_generate_visualizer (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:30.5420111Z Tests that the ModelReport class can properly create the ModelReportVisualizer instance ... ok (0.031s)
2023-01-11T21:56:30.5420502Z   test_prepare_model_callibration (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:30.5420883Z Tests model_report.prepare_detailed_calibration that prepares the model for callibration ... ok (0.023s)
2023-01-11T21:56:30.5421255Z   test_qconfig_mapping_generation (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:30.5421587Z Tests for generation of qconfigs by ModelReport API ... ok (0.053s)
2023-01-11T21:56:30.5421967Z   test_nested_detection_case (quantization.fx.test_model_report_fx.TestFxModelReportDetectDynamicStatic) ... ok (0.042s)
2023-01-11T21:56:30.5422589Z   test_conv_sub_class_considered (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.021s)
2023-01-11T21:56:30.5422999Z   test_fusion_layer_in_sequential (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.018s)
2023-01-11T21:56:30.5423423Z   test_multi_linear_model_without_per_channel (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.014s)
2023-01-11T21:56:30.5423842Z   test_multiple_q_config_options (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.018s)
2023-01-11T21:56:30.5424254Z   test_qat_aware_model_example (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.010s)
2023-01-11T21:56:30.5424645Z   test_sequential_model_format (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.020s)
2023-01-11T21:56:30.5425038Z   test_simple_conv (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.011s)
2023-01-11T21:56:30.5425437Z   test_observer_after_relu (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.018s)
2023-01-11T21:56:30.5425837Z   test_random_epochs_and_batches (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.103s)
2023-01-11T21:56:30.5426244Z   test_single_batch_of_ones (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.003s)
2023-01-11T21:56:30.5426644Z   test_zero_tensor_errors (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.013s)
2023-01-11T21:56:30.5427051Z   test_generate_tables_match_with_report (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:30.5427367Z Tests the generate_table_view() ... ok (0.031s)
2023-01-11T21:56:30.5427690Z   test_generate_tables_no_match (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:30.5428000Z Tests the generate_table_view() ... ok (0.029s)
2023-01-11T21:56:30.5428331Z   test_generate_tables_single_feat_match (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:30.5428693Z Tests the generate_table_view() ... ok (0.028s)
2023-01-11T21:56:30.5429008Z   test_get_modules_and_features (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:30.5429373Z Tests the get_all_unique_module_fqns and get_all_unique_feature_names methods of ... ok (0.029s)
2023-01-11T21:56:30.5429735Z   test_histogram_observer (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (21.203s)
2023-01-11T21:56:30.5430155Z   test_histogram_observer_against_reference (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (4.883s)
2023-01-11T21:56:30.5430594Z   test_histogram_observer_correct_numel (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.003s)
2023-01-11T21:56:30.5431014Z   test_histogram_observer_extreme_inputs (quantization.core.test_workflow_module.TestHistogramObserver)
2023-01-11T21:56:30.5431402Z Ensures that the HistogramObserver is able to work correctly in ... ok (0.001s)
2023-01-11T21:56:30.5431775Z   test_histogram_observer_one_sided (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.210s)
2023-01-11T21:56:30.5432194Z   test_histogram_observer_same_inputs (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.641s)
2023-01-11T21:56:30.5432607Z   test_observer_scriptable (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.454s)
2023-01-11T21:56:30.5433010Z   test_fake_quant_true_quant_compare (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.193s)
2023-01-11T21:56:30.5433445Z   test_float_quant_compare_per_channel (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.051s)
2023-01-11T21:56:30.5433874Z   test_float_quant_compare_per_tensor (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.148s)
2023-01-11T21:56:30.5434303Z   test_weight_only_activation_only_fakequant (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.232s)
2023-01-11T21:56:30.5434748Z   test_compare_model_outputs_conv_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.179s)
2023-01-11T21:56:30.5435193Z   test_compare_model_outputs_functional_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.178s)
2023-01-11T21:56:30.5435638Z   test_compare_model_outputs_linear_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.029s)
2023-01-11T21:56:30.5436065Z   test_compare_model_outputs_linear_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.727s)
2023-01-11T21:56:30.5436505Z   test_compare_model_outputs_lstm_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.128s)
2023-01-11T21:56:30.5436987Z   test_compare_model_stub_conv_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.223s)
2023-01-11T21:56:30.5437425Z   test_compare_model_stub_functional_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.169s)
2023-01-11T21:56:30.5437849Z   test_compare_model_stub_linear_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.029s)
2023-01-11T21:56:30.5438280Z   test_compare_model_stub_linear_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.718s)
2023-01-11T21:56:30.5438708Z   test_compare_model_stub_lstm_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.079s)
2023-01-11T21:56:30.5439134Z   test_compare_model_stub_partial (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.669s)
2023-01-11T21:56:30.5439552Z   test_compare_model_stub_submodule_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.046s)
2023-01-11T21:56:30.5439991Z   test_compare_weights_conv_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.119s)
2023-01-11T21:56:30.5440461Z   test_compare_weights_linear_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.012s)
2023-01-11T21:56:30.5440894Z   test_compare_weights_linear_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.710s)
2023-01-11T21:56:30.5441308Z   test_compare_weights_lstm_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.031s)
2023-01-11T21:56:30.5442458Z   test_mobilenet_v2 (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
2023-01-11T21:56:30.5443104Z   warnings.warn(msg)
2023-01-11T21:56:30.5443617Z Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
2023-01-11T21:56:30.5443877Z 
2023-01-11T21:56:30.5443962Z   0%|          | 0.00/13.6M [00:00<?, ?B/s]
2023-01-11T21:56:30.5444153Z 100%|##########| 13.6M/13.6M [00:00<00:00, 397MB/s]
2023-01-11T21:56:30.5444331Z ok (0.885s)
2023-01-11T21:56:30.5445332Z   test_mobilenet_v3 (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.
2023-01-11T21:56:30.5445986Z   warnings.warn(msg)
2023-01-11T21:56:30.5446466Z Downloading: "https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/mobilenet_v3_large-8738ca79.pth
2023-01-11T21:56:30.5446740Z 
2023-01-11T21:56:30.5446822Z   0%|          | 0.00/21.1M [00:00<?, ?B/s]
2023-01-11T21:56:30.5447022Z 100%|##########| 21.1M/21.1M [00:00<00:00, 389MB/s]
2023-01-11T21:56:30.5447203Z ok (1.346s)
2023-01-11T21:56:30.5447479Z   test_output_logger (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.002s)
2023-01-11T21:56:30.5447879Z   test_shadow_logger (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.001s)
2023-01-11T21:56:30.5448277Z   test_histogram_observer_consistent_buffer_shape (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:30.5448622Z Ensures that the buffer shapes do not change from uninitialized to ... ok (0.003s)
2023-01-11T21:56:30.5448976Z   test_histogram_observer_save_load_state_dict (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:30.5449296Z Smoke test on saving/loading state_dict ... ok (0.001s)
2023-01-11T21:56:30.5449621Z   test_memoryless_minmaxobserver (quantization.core.test_workflow_module.TestObserver) ... ok (0.020s)
2023-01-11T21:56:30.5450016Z   test_memoryless_perchannelminmaxobserver (quantization.core.test_workflow_module.TestObserver) ... ok (0.023s)
2023-01-11T21:56:30.5450422Z   test_observer_qparams_respects_device_affinity (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:30.5450892Z Ensure that the scale and zero_point returned by the observer ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:56:30.5451259Z   test_observer_scriptable (quantization.core.test_workflow_module.TestObserver) ... ok (0.070s)
2023-01-11T21:56:30.5451617Z   test_per_channel_observers (quantization.core.test_workflow_module.TestObserver) ... ok (0.287s)
2023-01-11T21:56:30.5451984Z   test_per_tensor_observers (quantization.core.test_workflow_module.TestObserver) ... ok (0.069s)
2023-01-11T21:56:30.5452382Z   test_save_load_state_dict_script (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:30.5452713Z Tests that we can save and load state_dict for observers that are scripted ... ok (0.174s)
2023-01-11T21:56:30.5453218Z   test_state_dict_respects_device_affinity (quantization.core.test_workflow_module.TestObserver) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:56:30.5453618Z   test_zero_numel (quantization.core.test_workflow_module.TestObserver) ... ok (0.002s)
2023-01-11T21:56:30.5454197Z   test_constant_padNd (quantization.core.test_quantized_op.TestPadding) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:767: UserWarning: non-inplace resize is deprecated
2023-01-11T21:56:30.5454631Z   warnings.warn("non-inplace resize is deprecated")
2023-01-11T21:56:30.5454842Z ok (7.058s)
2023-01-11T21:56:30.5455104Z   test_reflection_pad1d (quantization.core.test_quantized_op.TestPadding) ... ok (0.197s)
2023-01-11T21:56:30.5455471Z   test_reflection_pad2d (quantization.core.test_quantized_op.TestPadding) ... ok (20.680s)
2023-01-11T21:56:30.5455836Z   test_adaptive_avg_pool2d (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.187s)
2023-01-11T21:56:30.5456190Z   test_avg_pool2d (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.718s)
2023-01-11T21:56:30.5456535Z   test_hardtanh (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.059s)
2023-01-11T21:56:30.5456861Z   test_mean (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.380s)
2023-01-11T21:56:30.5457200Z   test_qnnpack_add (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.349s)
2023-01-11T21:56:30.5457947Z   test_qnnpack_add_broadcast (quantization.core.test_quantized_op.TestQNNPackOps) ... [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:30.5458957Z [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:30.5459875Z [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:30.5460784Z [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:30.5461305Z ok (0.004s)
2023-01-11T21:56:30.5461562Z   test_qnnpack_maxpool2d (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.896s)
2023-01-11T21:56:30.5461916Z   test_qnnpack_mul (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.152s)
2023-01-11T21:56:30.5462266Z   test_qnnpack_relu (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.104s)
2023-01-11T21:56:30.5462749Z   test_qnnpack_sigmoid (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.003s)
2023-01-11T21:56:30.5463161Z   test_qnnpack_sigmoid_sweep (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.001s)
2023-01-11T21:56:30.5463521Z   test_qnnpack_tanh (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.003s)
2023-01-11T21:56:30.5464352Z   test_quantization_doc_custom (quantization.core.test_docs.TestQuantizationDocs) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py:540: UserWarning: Passing a convert_custom_config_dict to convert is deprecated and will not be supported in a future version. Please pass in a ConvertCustomConfig instead.
2023-01-11T21:56:30.5464860Z   warnings.warn(
2023-01-11T21:56:30.5465017Z ok (0.024s)
2023-01-11T21:56:30.5465288Z   test_quantization_doc_fx (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.050s)
2023-01-11T21:56:30.5465662Z   test_quantization_doc_ptdq (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.004s)
2023-01-11T21:56:30.5466059Z   test_quantization_doc_ptsq (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.082s)
2023-01-11T21:56:30.5466432Z   test_quantization_doc_qat (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.016s)
2023-01-11T21:56:30.5467800Z   test_embedding_bag (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:818: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:56:30.5468631Z   if param.grad is not None:
2023-01-11T21:56:30.5468813Z ok (0.065s)
2023-01-11T21:56:30.5469101Z   test_embedding_bag_padding_idx_error (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps) ... ok (0.072s)
2023-01-11T21:56:30.5469501Z   test_linear (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps) ... ok (1.234s)
2023-01-11T21:56:30.5469896Z   test_convert_dynamic_fp16 (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.013s)
2023-01-11T21:56:30.5470658Z   test_dynamic_multi_op (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... [W insert_quant_dequant.cpp:1359] Warning: debug option for add_scalar and mul_scalar is not supported, please don't use debug option for models that uses these ops. (function operator())
2023-01-11T21:56:30.5471081Z ok (0.452s)
2023-01-11T21:56:30.5471380Z   test_dynamic_quant_multi_uses (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.473s)
2023-01-11T21:56:30.5471799Z   test_dynamic_shared_weights (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.839s)
2023-01-11T21:56:30.5472229Z   test_dynamic_weight_observer (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.127s)
2023-01-11T21:56:30.5472632Z   test_dynamic_with_if (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (1.677s)
2023-01-11T21:56:30.5473057Z   test_insert_quant_dequant_linear_dynamic (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.177s)
2023-01-11T21:56:30.5473485Z   test_prepare_dynamic (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.039s)
2023-01-11T21:56:30.5473895Z   test_prepare_dynamic_child_qconfig (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.042s)
2023-01-11T21:56:30.5474322Z   test_quantize_dynamic_fp16 (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.023s)
2023-01-11T21:56:30.5474720Z   test_conv_1d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.043s)
2023-01-11T21:56:30.5475093Z   test_conv_2d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.086s)
2023-01-11T21:56:30.5475479Z   test_conv_3d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.041s)
2023-01-11T21:56:30.5475972Z   test_conv_transpose_1d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.137s)
2023-01-11T21:56:30.5476368Z   test_conv_transpose_2d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.056s)
2023-01-11T21:56:30.5476759Z   test_conv_transpose_3d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.068s)
2023-01-11T21:56:30.5477188Z   test_functional_module (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.974s)
2023-01-11T21:56:30.5477600Z   test_int16_reference_module (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.021s)
2023-01-11T21:56:30.5477992Z   test_leaky_relu (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.004s)
2023-01-11T21:56:30.5478401Z   test_linear (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.084s)
2023-01-11T21:56:30.5478761Z   test_relu (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.004s)
2023-01-11T21:56:30.5479167Z   test_embedding_ops_dynamic (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic) ... ok (0.035s)
2023-01-11T21:56:30.5479599Z   test_forward_hooks_preserved (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5480017Z Test post-training dynamic quantization on preserving ... ok (0.004s)
2023-01-11T21:56:30.5480387Z   test_linear_relu_fusion (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic) ... ok (0.228s)
2023-01-11T21:56:30.5480789Z   test_nested1 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5481189Z Test quantization for nested model, top level 'fc3' and ... ok (1.407s)
2023-01-11T21:56:30.5481517Z   test_nested2 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5481862Z Another test case for quantized, we will quantize all submodules ... ok (2.023s)
2023-01-11T21:56:30.5482208Z   test_nested3 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5482539Z More complicated nested test case with child qconfig overrides ... ok (1.588s)
2023-01-11T21:56:30.5482909Z   test_per_channel_linear_quantize (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5483269Z Test quantization for per_channel dynamic quantization ... ok (0.541s)
2023-01-11T21:56:30.5483616Z   test_quantized_rnn (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5484008Z Test dynamic quantization, scriptability and serialization for dynamic quantized lstm modules on int8 and fp16 ... ok (1.267s)
2023-01-11T21:56:30.5484412Z   test_quantized_rnn_cell (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5484827Z Test dynamic quantization, scriptability and serialization for dynamic quantized rnn cell modules on int8 and fp16 ... ok (0.859s)
2023-01-11T21:56:30.5485226Z   test_single_layer (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5485592Z Dynamic Quantize SingleLayerLinearDynamicModel which has one Linear module, ... ok (0.430s)
2023-01-11T21:56:30.5485967Z   test_two_layers (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5486332Z TwoLayerLinearModel has two Linear modules but we only quantize the second one ... ok (0.379s)
2023-01-11T21:56:30.5486690Z   test_type_match_rule (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:30.5487096Z Test quantization for nested model, top level 'fc3' and ... ok (0.652s)
2023-01-11T21:56:30.5487437Z   test_activations (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5487807Z Test quantization of activations ... ok (0.596s)
2023-01-11T21:56:30.5488139Z   test_activations_in_non_leaf_module_list (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5488518Z Ensure activations like `nn.Sigmoid` and `nn.Tanh` are properly handled in ... ok (0.003s)
2023-01-11T21:56:30.5488909Z   test_convtranspose_per_channel_fails_early (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5489378Z Verifies that attempting to quantize a ConvTranspose module with per-Channel ... ok (0.002s)
2023-01-11T21:56:30.5489765Z   test_convtranspose_per_channel_qconfig_none (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5490144Z Verifies that having qconfig==None for conv transpose does not crash ... ok (0.001s)
2023-01-11T21:56:30.5490522Z   test_custom_module_class (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.019s)
2023-01-11T21:56:30.5490963Z   test_dequant_stub (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.005s)
2023-01-11T21:56:30.5491391Z   test_forward_hooks_preserved (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.690s)
2023-01-11T21:56:30.5491797Z   test_manual (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5492132Z User inserts QuantStub and DeQuantStub in model code ... ok (0.470s)
2023-01-11T21:56:30.5492456Z   test_nested1 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5492850Z Test quantization for nested model, top level 'fc3' and ... ok (2.014s)
2023-01-11T21:56:30.5493201Z   test_nested2 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.347s)
2023-01-11T21:56:30.5493578Z   test_nested3 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5493920Z More complicated nested test case with child qconfig overrides ... ok (2.780s)
2023-01-11T21:56:30.5494277Z   test_normalization (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5495067Z Test quantization of normalization layers ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize.py:310: UserWarning: None of the submodule got qconfig applied. Make sure you passed correct configuration through `qconfig_dict` or by assigning the `.qconfig` attribute directly on submodules
2023-01-11T21:56:30.5495597Z   warnings.warn("None of the submodule got qconfig applied. Make sure you "
2023-01-11T21:56:30.5495818Z ok (0.573s)
2023-01-11T21:56:30.5496112Z   test_quantized_embedding (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5496559Z Test the post-training quantization flow, serialization and scripting ... ok (0.110s)
2023-01-11T21:56:30.5496927Z   test_quantized_embedding_bag (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5497376Z Test the post-training quantization flow, serialization and scripting ... ok (0.097s)
2023-01-11T21:56:30.5497791Z   test_quantwrapper_attaches_qconfig_to_dequant (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.004s)
2023-01-11T21:56:30.5498218Z   test_resnet_base (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5498554Z Test quantization for bottleneck topology used in resnet/resnext ... ok (0.327s)
2023-01-11T21:56:30.5498912Z   test_save_load_state_dict (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5499299Z Test PTQ flow of creating a model and quantizing it and saving the quantized state_dict ... ok (1.067s)
2023-01-11T21:56:30.5499660Z   test_single_layer (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5500042Z Quantize SingleLayerLinearModel which has one Linear module, make sure it is swapped ... ok (3.344s)
2023-01-11T21:56:30.5500456Z   test_skip_quant (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5500789Z The case when we want to skip quantizing some layers ... ok (3.308s)
2023-01-11T21:56:30.5501110Z   test_two_layers (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:30.5501475Z TwoLayerLinearModel has two Linear modules but we only quantize the second one ... ok (0.439s)
2023-01-11T21:56:30.5501862Z   test_add_scalar_uses_input_qparams (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.005s)
2023-01-11T21:56:30.5502263Z   test_conv_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (1.920s)
2023-01-11T21:56:30.5502729Z   test_conv_linear_symm (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT)
2023-01-11T21:56:30.5503059Z Same as test_conv_linear but with Symmetric quantization. ... ok (0.711s)
2023-01-11T21:56:30.5503476Z   test_defused_embedding_bag_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.600s)
2023-01-11T21:56:30.5503868Z   test_dropout (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.639s)
2023-01-11T21:56:30.5504252Z   test_dynamic_qat_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.640s)
2023-01-11T21:56:30.5504650Z   test_embedding_bag_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.603s)
2023-01-11T21:56:30.5505059Z   test_embedding_qat_qconfig_equal (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.006s)
2023-01-11T21:56:30.5505437Z   test_eval_only_fake_quant (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT)
2023-01-11T21:56:30.5505754Z Using FakeQuant in evaluation only mode, ... ok (0.029s)
2023-01-11T21:56:30.5506094Z   test_forward_hooks_preserved (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.024s)
2023-01-11T21:56:30.5506470Z   test_manual (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (1.374s)
2023-01-11T21:56:30.5506867Z   test_mul_scalar_uses_input_qparams (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.004s)
2023-01-11T21:56:30.5507277Z   test_qat_embedding_bag_errors (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.004s)
2023-01-11T21:56:30.5507667Z   test_train_save_load_eval (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT)
2023-01-11T21:56:30.5508015Z Test QAT flow of creating a model, doing QAT and saving the quantized state_dict ... ok (0.317s)
2023-01-11T21:56:30.5508413Z   test_conv_bn_folded_vs_unfolded (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.893s)
2023-01-11T21:56:30.5508850Z   test_conv_bn_relu (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.346s)
2023-01-11T21:56:30.5509274Z   test_fixed_qparam_ops (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.012s)
2023-01-11T21:56:30.5509686Z   test_leaky_relu (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.006s)
2023-01-11T21:56:30.5510111Z   test_linear_bn_numerics (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.008s)
2023-01-11T21:56:30.5510541Z   test_linear_bn_symm_numerics (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.002s)
2023-01-11T21:56:30.5510970Z   test_linear_bn_workflow (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.039s)
2023-01-11T21:56:30.5511370Z   test_relu (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.002s)
2023-01-11T21:56:30.5511763Z   test__convert_to_reference_decomposed_fx (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.025s)
2023-01-11T21:56:30.5512165Z   test__convert_to_reference_decomposed_fx_dynamic_quant (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:30.5512628Z   test__convert_to_reference_decomposed_fx_per_channel_quant (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:30.5513001Z   test_assert_on_size_after_quant_layer (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5513327Z Verifies that calculating a size of a quantized tensor works ... ok (0.021s)
2023-01-11T21:56:30.5513644Z   test_attention (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.134s)
2023-01-11T21:56:30.5513987Z   test_backend_config_check_for_weight_and_bias (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5514319Z Test to make sure the backend_config check for weight and bias ... ok (0.008s)
2023-01-11T21:56:30.5514648Z   test_backend_config_quantization_range (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5515376Z Check that quantization ranges specified through the BackendConfig are reflected in ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:783: UserWarning: QConfig activation quantization range must fall within the backend's:
2023-01-11T21:56:30.5516800Z QConfig range = (0, 63), BackendConfig range = (0, 31), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=63, dtype=torch.quint8){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f6135b8b370>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f6135b8b370>})
2023-01-11T21:56:30.5517774Z   warnings.warn(("QConfig %s quantization range must fall within the backend's:\n"
2023-01-11T21:56:30.5518290Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:783: UserWarning: QConfig activation quantization range must fall within the backend's:
2023-01-11T21:56:30.5519618Z QConfig range = (0, 255), BackendConfig range = (0, 31), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.quint8){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f6135b88700>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f6135b88700>})
2023-01-11T21:56:30.5520531Z   warnings.warn(("QConfig %s quantization range must fall within the backend's:\n"
2023-01-11T21:56:30.5521367Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:779: UserWarning: QConfig activation must specify 'quant_min' and 'quant_max', ignoring QConfig(activation=<class 'torch.ao.quantization.observer.ReuseInputObserver'>, weight=<class 'torch.ao.quantization.observer.ReuseInputObserver'>)
2023-01-11T21:56:30.5522053Z   warnings.warn("QConfig %s must specify 'quant_min' and 'quant_max', ignoring %s" %
2023-01-11T21:56:30.5522281Z ok (0.034s)
2023-01-11T21:56:30.5522536Z   test_backend_config_scale_min (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5524136Z Test QConfig eps validation against the BackendConfig's min scale value. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:794: UserWarning: QConfig activation eps (tensor([6.1035e-05])) must be greater than or equal to the backend's min scale value (0.000244140625), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.quint8, eps=6.103515625e-05){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f6135499870>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f6135499870>})
2023-01-11T21:56:30.5525212Z   warnings.warn(("QConfig %s eps (%s) must be greater than or equal to "
2023-01-11T21:56:30.5526703Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:794: UserWarning: QConfig activation eps (tensor([1.1921e-07])) must be greater than or equal to the backend's min scale value (0.000244140625), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.quint8){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613549a7a0>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, eps=6.103515625e-05){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613549a7a0>})
2023-01-11T21:56:30.5527671Z   warnings.warn(("QConfig %s eps (%s) must be greater than or equal to "
2023-01-11T21:56:30.5528611Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:791: UserWarning: QConfig activation must specify 'eps', ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=1.0, zero_point=0){}, weight=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=1.0, zero_point=0){})
2023-01-11T21:56:30.5529377Z   warnings.warn("QConfig %s must specify 'eps', ignoring %s" % (debug_string, qconfig))
2023-01-11T21:56:30.5529607Z ok (0.046s)
2023-01-11T21:56:30.5529883Z   test_change_backend_config_for_fixed_qparam_ops (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5530238Z Making sure we can skip validation of qconfigs for fixedqparam ops based ... ok (0.008s)
2023-01-11T21:56:30.5530578Z   test_channel_shuffle_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.076s)
2023-01-11T21:56:30.5530917Z   test_conv_bn_relu (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5531298Z Tests fusion and quantization for "Conv - Bn" and "Conv - Bn - ReLU" ... ok (1.289s)
2023-01-11T21:56:30.5531625Z   test_conv_linear_not_reference (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5531898Z Test quantizing conv and linear ... ok (1.865s)
2023-01-11T21:56:30.5532185Z   test_conv_linear_reference (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5532513Z Test quantizing functional conv and linear with reference option ... ok (1.667s)
2023-01-11T21:56:30.5532831Z   test_conv_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.073s)
2023-01-11T21:56:30.5533188Z   test_convert_custom_config_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5533590Z   test_convert_custom_config_set_observed_to_quantized_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5534008Z   test_convert_custom_config_set_preserved_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5534383Z   test_convert_custom_config_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5535186Z   test_convert_qconfig_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/convert.py:886: UserWarning: Passing a QConfig dictionary to convert is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:30.5535670Z   warnings.warn(
2023-01-11T21:56:30.5536290Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods1_1_packed_weight_0 target mods1_1_packed_weight_0 mods1_1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5536889Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5537561Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods1_0_packed_weight_0 target mods1_0_packed_weight_0 mods1_0_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5538110Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5538349Z ok (0.102s)
2023-01-11T21:56:30.5538611Z   test_convtranspose_per_channel_fails_early (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5539048Z Verifies that attempting to quantize a ConvTranspose module with per-Channel ... ok (0.013s)
2023-01-11T21:56:30.5539409Z   test_copy_node_has_shared_actpp_instance (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5539718Z Test the output of CopyNode to have the same ... ok (0.042s)
2023-01-11T21:56:30.5540047Z   test_custom_module_class (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.093s)
2023-01-11T21:56:30.5540411Z   test_custom_module_class_input_has_multiple_users (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5540752Z Tests that the flow still works when the input of custom module ... ok (0.026s)
2023-01-11T21:56:30.5541097Z   test_deepcopy_preserve_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.014s)
2023-01-11T21:56:30.5541474Z   test_default_qconfig_mapping_override_global (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.012s)
2023-01-11T21:56:30.5541846Z   test_default_quant_after_none_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5542154Z Make sure default quant is inserted properly ... ok (0.020s)
2023-01-11T21:56:30.5542652Z   test_dequantize (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5542958Z Test to make sure dequantize node are placed before ... ok (0.230s)
2023-01-11T21:56:30.5543259Z   test_dict_output (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5543569Z Make sure quantization runs for models with dictionary output ... ok (0.020s)
2023-01-11T21:56:30.5543886Z   test_dynamic_linear_input_multiple_use (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5544214Z Tests input for dynamic linear being used by multiple ops ... ok (0.043s)
2023-01-11T21:56:30.5544535Z   test_dynamic_quant_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.057s)
2023-01-11T21:56:30.5544867Z   test_dynamic_quant_weight_observer (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5545177Z Test that weight observer is run in convert step ... ok (0.015s)
2023-01-11T21:56:30.5545476Z   test_dynamic_with_fusion (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5546279Z Tests that dynamic quantization APIs work with Linear + Relu fusion ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods2_packed_weight_0 target mods2_packed_weight_0 mods2_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5546892Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5547129Z ok (0.057s)
2023-01-11T21:56:30.5547392Z   test_dynamic_with_fusion_multiple_uses (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5547727Z Tests that dynamic quantization APIs work with Linear + Relu fusion ... ok (0.045s)
2023-01-11T21:56:30.5547864Z   test_fold_quant_dequant (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5548057Z Test that the sequence of quant-dequant nodes in the ... ok (0.024s)
2023-01-11T21:56:30.5548227Z   test_fp32_input_fp32_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5548402Z   test_fp32_input_quantized_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:30.5548602Z   test_fp32_sum (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5548814Z Verifies that fp32 sum works correctly if it's before or after ... ok (0.051s)
2023-01-11T21:56:30.5548989Z   test_fuse_custom_config_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5549180Z   test_fuse_custom_config_set_preserved_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5549340Z   test_fuse_custom_config_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5549508Z   test_fused_module_qat_swap (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:30.5549666Z   test_fusion_pattern_unquantized (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5549814Z Ensure that leaving a possible fusion pattern of multiple nodes ... ok (0.160s)
2023-01-11T21:56:30.5550022Z   test_get_default_qconfig_valid_backend (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5550194Z Checks that AssertionError is raised when non expected backend input is specified ... ok (0.001s)
2023-01-11T21:56:30.5550376Z   test_get_executorch_backend_config (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5550536Z   test_getattr_with_nontensor_result (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5550661Z Verifies that binary ops get quantized correctly if some ... ok (0.054s)
2023-01-11T21:56:30.5550811Z   test_linear_bn (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.756s)
2023-01-11T21:56:30.5550968Z   test_linear_leaky_relu_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5551160Z Test fusion and lowering of Linear - (bn -) LeakyReLU ... ok (0.049s)
2023-01-11T21:56:30.5551314Z   test_linear_qint8_activation (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5551454Z Test support for qint8 activation in reference pattern ... ok (0.017s)
2023-01-11T21:56:30.5551608Z   test_linear_tanh_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5551777Z Test fusion and lowering of Linear - Tanh ... ok (0.025s)
2023-01-11T21:56:30.5551963Z   test_masked_fill_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.024s)
2023-01-11T21:56:30.5552118Z   test_match_pattern_with_multiple_args (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5552256Z Test that we can match a pattern that has multiple arguments ... ok (0.010s)
2023-01-11T21:56:30.5552421Z   test_mul_add_fp16_config (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.029s)
2023-01-11T21:56:30.5552598Z   test_no_obs_between_unmatched_node_and_copy_node (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5552740Z Verifies that an observer is not inserted between an unmatched ... ok (0.019s)
2023-01-11T21:56:30.5552912Z   test_non_traceable_module (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.014s)
2023-01-11T21:56:30.5553047Z   test_not_used (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5553152Z Test quantizing a not used value ... ok (0.015s)
2023-01-11T21:56:30.5553281Z   test_observer_fqn (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5553454Z Test to make sure the observer FQN is based on the quantizable op/module that it is observing ... ok (0.024s)
2023-01-11T21:56:30.5553607Z   test_output_lists_and_dicts (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5553751Z Verify that specifying complicated output types does not crash. ... ok (0.018s)
2023-01-11T21:56:30.5553921Z   test_packed_weight_fused_op (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.033s)
2023-01-11T21:56:30.5554065Z   test_pattern_match (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5554158Z test MatchAllNode with ... ok (0.007s)
2023-01-11T21:56:30.5554365Z   test_pattern_match_constant (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.005s)
2023-01-11T21:56:30.5554536Z   test_permute_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5554717Z   test_prepare_custom_config_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5554916Z   test_prepare_custom_config_set_float_to_observed_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5555115Z   test_prepare_custom_config_set_input_quantized_indexes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5555323Z   test_prepare_custom_config_set_non_traceable_module_classes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5555521Z   test_prepare_custom_config_set_non_traceable_module_names (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5555752Z   test_prepare_custom_config_set_output_quantized_indexes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5555950Z   test_prepare_custom_config_set_preserved_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5556150Z   test_prepare_custom_config_set_standalone_module_class (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5556332Z   test_prepare_custom_config_set_standalone_module_name (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5556506Z   test_prepare_custom_config_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5556664Z   test_prepare_mode (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.054s)
2023-01-11T21:56:30.5556825Z   test_prepared_model_deepcopy (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5557050Z Ensures that copy.deepcopy works correctly on a prepared model. ... ok (0.021s)
2023-01-11T21:56:30.5557222Z   test_preserve_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.017s)
2023-01-11T21:56:30.5557370Z   test_preserve_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5557528Z Test to make sure the temporary config option to preserve qconfig attributes ... ok (0.021s)
2023-01-11T21:56:30.5557674Z   test_preserve_tuple (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5557772Z Test tuple input type is preserved ... ok (0.013s)
2023-01-11T21:56:30.5557966Z   test_propagate_dtypes_for_known_nodes_dict_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:30.5558168Z   test_propagate_dtypes_for_known_nodes_dict_split_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5558364Z   test_propagate_dtypes_for_known_nodes_dict_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:30.5558562Z   test_propagate_dtypes_for_known_nodes_list_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5558756Z   test_propagate_dtypes_for_known_nodes_split_list_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:30.5558952Z   test_propagate_dtypes_for_known_nodes_split_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:30.5559143Z   test_propagate_dtypes_for_known_nodes_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:30.5559300Z   test_qat_and_script (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.207s)
2023-01-11T21:56:30.5559593Z   test_qat_prepare_device_affinity (quantization.fx.test_quantize_fx.TestQuantizeFx) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:56:30.5559755Z   test_qat_skip_untraced (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.148s)
2023-01-11T21:56:30.5559981Z   test_qconfig_dict_setup (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.158s)
2023-01-11T21:56:30.5560159Z   test_qconfig_dict_with_fused_modules (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.116s)
2023-01-11T21:56:30.5560323Z   test_qconfig_for_call_func (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.031s)
2023-01-11T21:56:30.5560492Z   test_qconfig_for_call_method (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.057s)
2023-01-11T21:56:30.5560649Z   test_qconfig_function (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:30.5560821Z   test_qconfig_mapping_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5560988Z   test_qconfig_mapping_repr (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5561148Z   test_qconfig_mapping_set_global (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:30.5561352Z   test_qconfig_mapping_set_module_name (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5561551Z   test_qconfig_mapping_set_module_name_object_type_order (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.003s)
2023-01-11T21:56:30.5561736Z   test_qconfig_mapping_set_module_name_regex (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5561914Z   test_qconfig_mapping_set_object_type (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5562083Z   test_qconfig_mapping_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5562264Z   test_qconfig_module_name_object_type_order (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.075s)
2023-01-11T21:56:30.5562433Z   test_qconfig_module_name_regex (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:30.5562597Z   test_qconfig_module_type (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.025s)
2023-01-11T21:56:30.5562744Z   test_qconfig_none (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:30.5562908Z   test_qconfig_precedence (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:30.5563075Z   test_qconfig_qat_module_type (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.032s)
2023-01-11T21:56:30.5563231Z   test_qnnpack_backend_config (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5563412Z Test whether default QNNPACK QConfigs are compatible with the QNNPACK BackendConfig. ... ok (1.045s)
2023-01-11T21:56:30.5563572Z   test_qparams_buffers (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.051s)
2023-01-11T21:56:30.5563715Z   test_qparams_fqn (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5563841Z Test that the FQN of input_scale/zero_point is set ... ok (0.032s)
2023-01-11T21:56:30.5563990Z   test_quant_output_always_observed (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5564129Z If the output is hardcoded to be quantized, ensure that ... ok (0.097s)
2023-01-11T21:56:30.5564304Z   test_quantized_input_fp32_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:30.5564483Z   test_quantized_input_quantized_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:30.5564637Z   test_quantized_model_type (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5564787Z Test state_dict and deepcopy works properly in the quantized model ... ok (0.045s)
2023-01-11T21:56:30.5564931Z   test_ref_conv_module (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5565059Z Make sure the numerics for models with ref conv module ... ok (0.141s)
2023-01-11T21:56:30.5565194Z   test_ref_linear_module (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5565325Z Make sure the numerics for models with ref linear module ... ok (0.046s)
2023-01-11T21:56:30.5565519Z   test_register_patterns (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:30.5565677Z   test_relu_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.020s)
2023-01-11T21:56:30.5565833Z   test_remove_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.016s)
2023-01-11T21:56:30.5566015Z   test_repeat_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5566180Z   test_reroute_tuple_getitem_patterns (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5566344Z The following graph should redirect the output to `b`. After the transformation, ... ok (0.002s)
2023-01-11T21:56:30.5566528Z   test_reshape_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:30.5566669Z   test_return_none (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.013s)
2023-01-11T21:56:30.5566858Z   test_reuse_input_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.246s)
2023-01-11T21:56:30.5567031Z   test_save_observer_state_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.588s)
2023-01-11T21:56:30.5567184Z   test_sequential (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.331s)
2023-01-11T21:56:30.5567348Z   test_shape_followed_by_quantized_op (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5567465Z Make sure that shape does not dequantize ... ok (0.028s)
2023-01-11T21:56:30.5567643Z   test_size_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.018s)
2023-01-11T21:56:30.5567821Z   test_stack_trace_preserved_linear (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:30.5567989Z   test_standalone_module_float_interface (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.118s)
2023-01-11T21:56:30.5568175Z   test_standalone_module_quantized_interface (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.115s)
2023-01-11T21:56:30.5568319Z   test_state_dict (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5568444Z Make sure packed params appear in state_dict ... ok (0.085s)
2023-01-11T21:56:30.5568584Z   test_static_lstm (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5568754Z Test statically quantized custom module LSTM followed by ops that consume individual ... ok (0.266s)
2023-01-11T21:56:30.5568911Z   test_static_lstm_consume_tuple (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5569078Z Test statically quantized custom module LSTM followed by a module that consumes the ... ok (0.241s)
2023-01-11T21:56:30.5569248Z   test_static_lstm_with_custom_fixed_qparams (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5569398Z Test statically quantized LSTM with custom fixed qparams assigned to each of the ... ok (0.071s)
2023-01-11T21:56:30.5569550Z   test_sub_scalar (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.020s)
2023-01-11T21:56:30.5569724Z   test_symmetric_qnnpack_qconfig_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:30.5569917Z Test whether `torch.ao.quantization.qconfig_mapping._get_symmetric_qnnpack_qconfig_mapping` ... ok (0.501s)
2023-01-11T21:56:30.5570109Z   test_torch_transpose_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.024s)
2023-01-11T21:56:30.5570298Z   test_torch_unsqueeze_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5570471Z   test_trace_quantize_per_tensor (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.017s)
2023-01-11T21:56:30.5570652Z   test_transpose_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5570837Z   test_unsqueeze__nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:30.5571007Z   test_unsqueeze_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.150s)
2023-01-11T21:56:30.5571216Z   test_view_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.024s)
2023-01-11T21:56:30.5571385Z   test_model_dropout (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (1.621s)
2023-01-11T21:56:30.5571617Z   test_prepare_serialize_switch_device_convert (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: gpu is not available. (0.001s)
2023-01-11T21:56:30.5571793Z   test_qat_embedding_linear (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (0.612s)
2023-01-11T21:56:30.5571975Z   test_qat_embeddingbag_linear (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (0.662s)
2023-01-11T21:56:30.5572152Z   test_qat_functional_linear (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (0.292s)
2023-01-11T21:56:30.5572595Z   test_resnet18_ddp (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: TODO: Test is always failing - https://github.com/pytorch/pytorch/issues/54979 (0.001s)
2023-01-11T21:56:30.5572765Z   test_resnet_base (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (1.152s)
2023-01-11T21:56:30.5572962Z   test_static_gpu_convert_basic (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: gpu is not available. (0.001s)
2023-01-11T21:56:30.5573179Z   test_switch_device_prepare_convert (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: gpu is not available. (0.001s)
2023-01-11T21:56:30.5573382Z   test_torchvision (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: skip for now since tbb failed (0.001s)
2023-01-11T21:56:30.5573536Z   test_add (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (5.957s)
2023-01-11T21:56:30.5573697Z   test_add_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (6.143s)
2023-01-11T21:56:30.5573878Z   test_add_relu_multiple_uses_of_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.036s)
2023-01-11T21:56:30.5574045Z   test_ave_pool_with_custom_cfg (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5574180Z A test that checks correct patterns are produced for ... ok (0.016s)
2023-01-11T21:56:30.5574406Z   test_bmm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:30.5574552Z   test_bmm_int_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5574756Z int8 is not supported for bmm so we won't produce reference ... ok (0.013s)
2023-01-11T21:56:30.5574910Z   test_boolean_tensor (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5575102Z Make sure we don't insert observer for boolean Tensors ... ok (0.016s)
2023-01-11T21:56:30.5575240Z   test_cat (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5575371Z quantization of the output of cat will depend on the ... ok (0.880s)
2023-01-11T21:56:30.5575530Z   test_chunk (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.020s)
2023-01-11T21:56:30.5575683Z   test_clamp (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (1.246s)
2023-01-11T21:56:30.5575831Z   test_conv_module (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.451s)
2023-01-11T21:56:30.5575996Z   test_conv_transpose_1d (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.712s)
2023-01-11T21:56:30.5576162Z   test_conv_transpose_2d (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.243s)
2023-01-11T21:56:30.5576322Z   test_copy_node_fp32_input (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5576466Z CopyNode works for both fp32 and int8 inputs, this is a test to make ... ok (0.017s)
2023-01-11T21:56:30.5576690Z   test_div (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.000s)
2023-01-11T21:56:30.5576843Z   test_elu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.776s)
2023-01-11T21:56:30.5577041Z   test_embedding (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.085s)
2023-01-11T21:56:30.5577203Z   test_embedding_bag (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.076s)
2023-01-11T21:56:30.5577361Z   test_fixed_qparams_ops (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.344s)
2023-01-11T21:56:30.5577532Z   test_fixed_qparams_ops_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.035s)
2023-01-11T21:56:30.5577709Z   test_fixed_qparams_ops_qint8 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.044s)
2023-01-11T21:56:30.5577884Z   test_fixed_qparams_ops_wrong_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5579328Z Test that wrong qconfigs for fixed qparams ops results in the ops not being quantized. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543d2d0>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543d2d0>}).
2023-01-11T21:56:30.5579566Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:30.5579695Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:30.5579824Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:30.5580011Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:30.5581290Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543e7a0>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543e7a0>}).
2023-01-11T21:56:30.5581516Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:30.5581644Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:30.5581779Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:30.5581962Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:30.5583361Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543dbd0>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543dbd0>}).
2023-01-11T21:56:30.5583589Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:30.5583754Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:30.5583881Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:30.5584063Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:30.5585380Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543cc10>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7f613543cc10>}).
2023-01-11T21:56:30.5585610Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:30.5585734Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:30.5585862Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:30.5586047Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:30.5586113Z ok (0.027s)
2023-01-11T21:56:30.5586284Z   test_float_functional (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.086s)
2023-01-11T21:56:30.5586452Z   test_functional_conv (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (10.054s)
2023-01-11T21:56:30.5586610Z   test_functional_linear (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (12.238s)
2023-01-11T21:56:30.5586824Z   test_gelu_normal (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: TODO: reenable with backend_config api (0.000s)
2023-01-11T21:56:30.5587062Z   test_gelu_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:30.5587219Z   test_general_shape_ops (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5587347Z A test that checks dequantize will be swapped for ... ok (0.203s)
2023-01-11T21:56:30.5587501Z   test_general_value_ops (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5587634Z A test that checks correct patterns are produced for ... ok (0.062s)
2023-01-11T21:56:30.5587776Z   test_getitem (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5587920Z Make sure we only insert observer for getitem if the following node is matched ... ok (0.047s)
2023-01-11T21:56:30.5588084Z   test_hardswish (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.732s)
2023-01-11T21:56:30.5588247Z   test_instance_norm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (2.418s)
2023-01-11T21:56:30.5588413Z   test_int8_input_no_unnecessary_fq (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5588546Z If the inputs to the graph are quantized and the only node ... ok (0.014s)
2023-01-11T21:56:30.5588706Z   test_layer_norm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.539s)
2023-01-11T21:56:30.5588862Z   test_leaky_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.655s)
2023-01-11T21:56:30.5589035Z   test_linear_dynamic_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.406s)
2023-01-11T21:56:30.5589196Z   test_linear_module (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (3.713s)
2023-01-11T21:56:30.5589350Z   test_linear_static_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.554s)
2023-01-11T21:56:30.5589621Z   test_mish_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:30.5589773Z   test_mul (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (6.016s)
2023-01-11T21:56:30.5589929Z   test_mul_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (6.177s)
2023-01-11T21:56:30.5590102Z   test_multiple_qconfigs_for_single_value (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5590219Z Test multiple qconfigs for a single value ... ok (0.021s)
2023-01-11T21:56:30.5590867Z   test_norm_weight_bias (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods1_packed_weight_0 target mods1_packed_weight_0 mods1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5591112Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5591184Z ok (1.017s)
2023-01-11T21:56:30.5591327Z   test_prelu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.374s)
2023-01-11T21:56:30.5591486Z   test_qbatch_norm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.371s)
2023-01-11T21:56:30.5591651Z   test_qbatch_norm_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (1.698s)
2023-01-11T21:56:30.5591809Z   test_qmatmul (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (1.306s)
2023-01-11T21:56:30.5591976Z   test_quantized_add_qat (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.025s)
2023-01-11T21:56:30.5592132Z   test_quantized_conv_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:30.5592250Z tests for conv1d_relu/conv2d_relu/conv3d_relu ... ok (1.826s)
2023-01-11T21:56:30.5592416Z   test_quantized_mul_qat (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.025s)
2023-01-11T21:56:30.5592578Z   test_ref_pattern_multi_use (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.024s)
2023-01-11T21:56:30.5592737Z   test_reshape_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.027s)
2023-01-11T21:56:30.5592887Z   test_rnn (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (5.093s)
2023-01-11T21:56:30.5593041Z   test_rnn_cell (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (3.086s)
2023-01-11T21:56:30.5593276Z   test_silu_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:30.5593488Z   test_softmax_normal (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: TODO: reenable with backend_config api (0.000s)
2023-01-11T21:56:30.5593728Z   test_softmax_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:30.5593955Z   test_sub (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.000s)
2023-01-11T21:56:30.5594175Z   test_sum (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.000s)
2023-01-11T21:56:30.5594329Z   test_conv (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (2.220s)
2023-01-11T21:56:30.5594473Z   test_conv_bn (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (0.389s)
2023-01-11T21:56:30.5594825Z   test_conv_transpose (quantization.jit.test_quantize_jit.TestQuantizeJit) ... [W insert_observers.cpp:1580] Warning: prim::Loop is not yet supported in quantization, please make sure nothing needs to be quantized in the loop (function operator())
2023-01-11T21:56:30.5594891Z ok (0.593s)
2023-01-11T21:56:30.5595159Z   test_linear_dynamic_fp16 (quantization.jit.test_quantize_jit.TestQuantizeJit) ... [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5595362Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:30.5595428Z ok (0.035s)
2023-01-11T21:56:30.5596554Z   test_nested (quantization.jit.test_quantize_jit.TestQuantizeJit) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:818: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:56:30.5596640Z   if param.grad is not None:
2023-01-11T21:56:30.5596704Z ok (1.187s)
2023-01-11T21:56:30.5596967Z   test_observer_with_ignored_function (quantization.jit.test_quantize_jit.TestQuantizeJit)
2023-01-11T21:56:30.5597115Z Test observers with ignored function and make sure it works in ... ok (1.368s)
2023-01-11T21:56:30.5597714Z   test_single_linear (quantization.jit.test_quantize_jit.TestQuantizeJit) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:56:30.5597776Z   warnings.warn(
2023-01-11T21:56:30.5597841Z ok (3.961s)
2023-01-11T21:56:30.5598014Z   test_single_linear_dynamic (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (0.132s)
2023-01-11T21:56:30.5598173Z   test_skip_quant (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (5.457s)
2023-01-11T21:56:30.5598338Z   test_cat_linear (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (0.081s)
2023-01-11T21:56:30.5598504Z   test_clamp (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (3.960s)
2023-01-11T21:56:30.5598693Z   test_conv_with_benchmark_flag (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (0.295s)
2023-01-11T21:56:30.5598855Z   test_dequantize_tuple (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:30.5598971Z Make sure dequantize can support Tuple of tensor ... ok (1.310s)
2023-01-11T21:56:30.5599129Z   test_elu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (15.467s)
2023-01-11T21:56:30.5599293Z   test_general_shape_ops (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:30.5599417Z A test that checks dequantize will be swapped for ... ok (0.116s)
2023-01-11T21:56:30.5599576Z   test_general_value_ops (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:30.5599708Z A test that checks correct patterns are produced for ... ok (0.319s)
2023-01-11T21:56:30.5599877Z   test_group_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (11.925s)
2023-01-11T21:56:30.5600045Z   test_hardswish (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (11.774s)
2023-01-11T21:56:30.5600337Z   test_instance_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5600553Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5600768Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5600981Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5601240Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5601448Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5601652Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5601857Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5602061Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5602298Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5602502Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5602708Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5602913Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5603105Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5603307Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5603513Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5603716Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5603919Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5604120Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5604323Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5604528Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5604732Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5604933Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5605139Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:30.5605194Z ok (11.437s)
2023-01-11T21:56:30.5605362Z   test_layer_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (12.064s)
2023-01-11T21:56:30.5605564Z   test_linear (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (14.986s)
2023-01-11T21:56:30.5605730Z   test_qbatch_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (8.536s)
2023-01-11T21:56:30.5605928Z   test_qbatch_norm_relu_BNFuncInplaceRelu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (5.638s)
2023-01-11T21:56:30.5606115Z   test_qbatch_norm_relu_BNFuncRelu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (5.681s)
2023-01-11T21:56:30.5606302Z   test_qbatch_norm_relu_BNRelu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (11.356s)
2023-01-11T21:56:30.5606472Z   test_quantized_add (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (4.294s)
2023-01-11T21:56:30.5606642Z   test_quantized_add_alpha (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:30.5606767Z Test quant fusion for multiple aten::add using same ... ok (2.344s)
2023-01-11T21:56:30.5606983Z   test_quantized_add_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (8.516s)
2023-01-11T21:56:30.5607158Z   test_quantized_add_relu_alpha (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:30.5607292Z Test quant fusion for multiple aten::add using same ... ok (17.343s)
2023-01-11T21:56:30.5607471Z   test_quantized_add_scalar (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (2.309s)
2023-01-11T21:56:30.5607656Z   test_quantized_add_scalar_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (5.665s)
2023-01-11T21:56:30.5607813Z   test_quantized_cat (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:30.5607946Z quantization of the output of cat will be depend on the ... ok (1.949s)
2023-01-11T21:56:30.5608102Z   test_quantized_conv (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (3.389s)
2023-01-11T21:56:30.5608265Z   test_quantized_conv_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:30.5608387Z tests for conv1d_relu/conv2d_relu/conv3d_relu ... ok (13.610s)
2023-01-11T21:56:30.5608557Z   test_quantized_mul (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (1.767s)
2023-01-11T21:56:30.5608731Z   test_quantized_mul_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (8.500s)
2023-01-11T21:56:30.5608908Z   test_quantized_mul_scalar (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (1.649s)
2023-01-11T21:56:30.5609093Z   test_quantized_mul_scalar_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (5.681s)
2023-01-11T21:56:30.5609266Z   test_conv_trace (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.049s)
2023-01-11T21:56:30.5609451Z   test_convtranspose_trace (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.049s)
2023-01-11T21:56:30.5609622Z   test_dedup_module_uses (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.006s)
2023-01-11T21:56:30.5609803Z   test_finalize_debug (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.074s)
2023-01-11T21:56:30.5609989Z   test_finalize_for_linear (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.072s)
2023-01-11T21:56:30.5610173Z   test_foldbn_complex_cases (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.782s)
2023-01-11T21:56:30.5610353Z   test_foldbn_in_submodule (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.063s)
2023-01-11T21:56:30.5610520Z   test_foldbn_no_fusion (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:30.5610736Z Test that we don't fuse the cases when module type does not match ... ok (0.007s)
2023-01-11T21:56:30.5610924Z   test_foldbn_shared_classtype (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.512s)
2023-01-11T21:56:30.5611087Z   test_foldbn_trivial (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.051s)
2023-01-11T21:56:30.5611273Z   test_foldbn_trivial_nobias (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.051s)
2023-01-11T21:56:30.5611483Z   test_fuse_linear (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.036s)
2023-01-11T21:56:30.5611662Z   test_inplace_option (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.164s)
2023-01-11T21:56:30.5611842Z   test_insert_observers (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.028s)
2023-01-11T21:56:30.5612037Z   test_insert_observers_child_qconfig (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.034s)
2023-01-11T21:56:30.5612223Z   test_insert_observers_for_general_ops (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:30.5612420Z Make sure we skip observers for ops that doesn't require ... ok (0.028s)
2023-01-11T21:56:30.5612606Z   test_insert_observers_for_if (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.098s)
2023-01-11T21:56:30.5612830Z   test_insert_observers_for_if_consistent_observation (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:30.5612952Z check quantization for if works as long as ... ok (0.252s)
2023-01-11T21:56:30.5613143Z   test_insert_observers_for_nested_if (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.097s)
2023-01-11T21:56:30.5613341Z   test_insert_observers_for_reused_weight (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.023s)
2023-01-11T21:56:30.5613529Z   test_insert_observers_interface (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.069s)
2023-01-11T21:56:30.5613735Z   test_insert_observers_interface_unshare_type (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.257s)
2023-01-11T21:56:30.5613928Z   test_insert_observers_propagate_observed (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:30.5614073Z Make sure we propagate observed property through general ops ... ok (0.033s)
2023-01-11T21:56:30.5614293Z   test_insert_observers_propagate_observed_for_function (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.041s)
2023-01-11T21:56:30.5614489Z   test_insert_observers_propagate_observed_in_submodule (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:30.5614638Z Make sure we propagate observed property through general ops ... ok (0.038s)
2023-01-11T21:56:30.5614834Z   test_insert_observers_shared_class_type (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.032s)
2023-01-11T21:56:30.5615024Z   test_insert_observers_skip_values (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.115s)
2023-01-11T21:56:30.5615215Z   test_insert_observers_weight_dtype (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.028s)
2023-01-11T21:56:30.5615400Z   test_insert_quant_dequant (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.148s)
2023-01-11T21:56:30.5615604Z   test_insert_quant_dequant_shared_class_type (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.167s)
2023-01-11T21:56:30.5615792Z   test_interface_with_fork (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.080s)
2023-01-11T21:56:30.5615965Z   test_module_list (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.088s)
2023-01-11T21:56:30.5616123Z   test_quantize_fork_wait (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:30.5616378Z Tests the case where fork and wait calls are in different subgraphs ... [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:30.5616446Z ok (0.073s)
2023-01-11T21:56:30.5616641Z   test_replicate_dequant_same_value (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.076s)
2023-01-11T21:56:30.5616826Z   test_replicate_dequantize (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.007s)
2023-01-11T21:56:30.5617020Z   test_replicate_dequantize_in_block (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.008s)
2023-01-11T21:56:30.5617241Z   test_replicate_quantize_for_if (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:30.5617478Z We want to move quantize nodes for output of prim::If ... [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:30.5617661Z [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:30.5617829Z [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:30.5618006Z [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:30.5618071Z ok (0.085s)
2023-01-11T21:56:30.5618266Z   test_skip_dequant_constant_prop (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.083s)
2023-01-11T21:56:30.5618493Z   test_swap_functional_linear (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.007s)
2023-01-11T21:56:30.5619324Z   test_resnet18 (quantization.fx.test_quantize_pt2e.TestQuantizePT2EModels) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/_pt2e/utils.py:101: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
2023-01-11T21:56:30.5619436Z   get_bias_node = m.graph.get_attr(bias_attr_name)
2023-01-11T21:56:30.5619502Z ok (3.834s)
2023-01-11T21:56:30.5619734Z   test_benchmark (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: used for local benchmarking, comment when we want to run it (0.002s)
2023-01-11T21:56:30.5619904Z   test_conv_reorder_issue_onednn (quantization.core.test_quantized_op.TestQuantizedConv)
2023-01-11T21:56:30.5620041Z Ensure reorder failure issue in conv is fixed for onednn backend. ... ok (0.008s)
2023-01-11T21:56:30.5620205Z   test_qconv1d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.777s)
2023-01-11T21:56:30.5620401Z   test_qconv1d_cudnn (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: cudnn is not enabled. (0.005s)
2023-01-11T21:56:30.5620572Z   test_qconv1d_unpack (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (0.937s)
2023-01-11T21:56:30.5620733Z   test_qconv2d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (5.493s)
2023-01-11T21:56:30.5620930Z   test_qconv2d_cudnn (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: cudnn is not enabled. (0.005s)
2023-01-11T21:56:30.5621101Z   test_qconv2d_unpack (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.035s)
2023-01-11T21:56:30.5621263Z   test_qconv3d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.210s)
2023-01-11T21:56:30.5621434Z   test_qconv3d_unpack (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (0.648s)
2023-01-11T21:56:30.5621602Z   test_qconv_transpose1d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.516s)
2023-01-11T21:56:30.5621876Z   test_qconv_transpose2d (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.004s)
2023-01-11T21:56:30.5622149Z   test_qconv_transpose3d (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.003s)
2023-01-11T21:56:30.5622486Z   test_embedding (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.258s)
2023-01-11T21:56:30.5622676Z   test_embedding_2d_indices (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps)
2023-01-11T21:56:30.5622820Z Tests the case where 2D indices are passed into the operator ... ok (0.003s)
2023-01-11T21:56:30.5623017Z   test_embedding_bag_2bit (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.117s)
2023-01-11T21:56:30.5623301Z   test_embedding_bag_2bit_unpack (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... skip: Test needs Caffe2 (0.000s)
2023-01-11T21:56:30.5623490Z   test_embedding_bag_2d_indices (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps)
2023-01-11T21:56:30.5623618Z Tests the case where 2D indices are passed into the operator ... ok (0.004s)
2023-01-11T21:56:30.5623810Z   test_embedding_bag_4bit (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.186s)
2023-01-11T21:56:30.5624033Z   test_embedding_bag_4bit_unpack (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... skip: Test needs Caffe2 (0.000s)
2023-01-11T21:56:30.5624225Z   test_embedding_bag_byte (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.188s)
2023-01-11T21:56:30.5624485Z   test_embedding_bag_byte_unpack (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... skip: Test needs Caffe2 (0.000s)
2023-01-11T21:56:30.5624697Z   test_conv1d_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.304s)
2023-01-11T21:56:30.5624906Z   test_conv2d_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.254s)
2023-01-11T21:56:30.5625112Z   test_conv3d_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.200s)
2023-01-11T21:56:30.5625856Z   test_grid_sample (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4235: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
2023-01-11T21:56:30.5625932Z   warnings.warn(
2023-01-11T21:56:30.5625986Z ok (0.056s)
2023-01-11T21:56:30.5626194Z   test_relu_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.001s)
2023-01-11T21:56:30.5626364Z   test_qlinear (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (1.723s)
2023-01-11T21:56:30.5626569Z   test_qlinear_cudnn (quantization.core.test_quantized_op.TestQuantizedLinear) ... skip: cudnn is not enabled. (0.006s)
2023-01-11T21:56:30.5626748Z   test_qlinear_leaky_relu (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (1.030s)
2023-01-11T21:56:30.5626920Z   test_qlinear_relu (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (1.710s)
2023-01-11T21:56:30.5627091Z   test_qlinear_tanh (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (0.540s)
2023-01-11T21:56:30.5627265Z   test_qlinear_unpack (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (0.406s)
2023-01-11T21:56:30.5627470Z   test_qlinear_with_input_q_dq_qweight_dq_output_fp32 (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (0.281s)
2023-01-11T21:56:30.5627632Z   test_adaptive_avg_pool (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.623s)
2023-01-11T21:56:30.5627817Z   test_adaptive_avg_pool2d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.074s)
2023-01-11T21:56:30.5628001Z   test_adaptive_avg_pool3d_ndhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.070s)
2023-01-11T21:56:30.5628169Z   test_add_scalar_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.195s)
2023-01-11T21:56:30.5628335Z   test_advanced_indexing (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:30.5628476Z Verifies that the x[:, [0], :, :] syntax works for quantized tensors. ... ok (0.006s)
2023-01-11T21:56:30.5628628Z   test_avg_pool2d (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:30.5628809Z Note: we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (1.152s)
2023-01-11T21:56:30.5628965Z   test_avg_pool2d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:30.5629177Z Note: 1) we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (1.124s)
2023-01-11T21:56:30.5629327Z   test_avg_pool3d (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:30.5629504Z Note: we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (4.660s)
2023-01-11T21:56:30.5629659Z   test_avg_pool3d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:30.5629845Z Note: 1) we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (7.382s)
2023-01-11T21:56:30.5630011Z   test_batch_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.580s)
2023-01-11T21:56:30.5630181Z   test_batch_norm_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.641s)
2023-01-11T21:56:30.5630339Z   test_cat (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.358s)
2023-01-11T21:56:30.5630524Z   test_cat_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.265s)
2023-01-11T21:56:30.5630697Z   test_channel_shuffle (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (1.661s)
2023-01-11T21:56:30.5631307Z   test_custom_module_lstm (quantization.core.test_quantized_op.TestQuantizedOps) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:56:30.5631385Z   warnings.warn(
2023-01-11T21:56:30.5631450Z ok (38.856s)
2023-01-11T21:56:30.5632034Z   test_custom_module_multi_head_attention (quantization.core.test_quantized_op.TestQuantizedOps) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:1204: UserWarning: must run observer before calling calculate_qparams.                                    Returning default scale and zero point 
2023-01-11T21:56:30.5632114Z   warnings.warn(
2023-01-11T21:56:30.5632178Z ok (11.381s)
2023-01-11T21:56:30.5632681Z   test_empty_batch (quantization.core.test_quantized_op.TestQuantizedOps) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4022: UserWarning: nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.
2023-01-11T21:56:30.5632879Z   warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T21:56:30.5632932Z ok (0.025s)
2023-01-11T21:56:30.5633092Z   test_equal (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.182s)
2023-01-11T21:56:30.5633257Z   test_group_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (6.246s)
2023-01-11T21:56:30.5633421Z   test_hardswish (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.398s)
2023-01-11T21:56:30.5633580Z   test_hardtanh (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.485s)
2023-01-11T21:56:30.5633753Z   test_instance_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (1.499s)
2023-01-11T21:56:30.5633907Z   test_interpolate (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:30.5634048Z This test cover upsample_nearest2d and upsample_bilinear2d ... ok (3.004s)
2023-01-11T21:56:30.5634194Z   test_interpolate3d (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:30.5634304Z This test cover upsample_nearest3d ... ok (17.508s)
2023-01-11T21:56:30.5634467Z   test_leaky_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.019s)
2023-01-11T21:56:30.5634649Z   test_leaky_relu_observed_output (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.542s)
2023-01-11T21:56:30.5634821Z   test_linear_bias_unpack (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.003s)
2023-01-11T21:56:30.5634982Z   test_max_pool1d (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.233s)
2023-01-11T21:56:30.5635145Z   test_max_pool2d (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.545s)
2023-01-11T21:56:30.5635380Z   test_max_pool2d_cudnn (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: cudnn is not enabled. (0.005s)
2023-01-11T21:56:30.5635536Z   test_max_pool2d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.510s)
2023-01-11T21:56:30.5635698Z   test_mean (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.345s)
2023-01-11T21:56:30.5635863Z   test_mul_scalar_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.384s)
2023-01-11T21:56:30.5636451Z   test_qadd_broadcast (quantization.core.test_quantized_op.TestQuantizedOps) ... [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 4, 4], which does not match the required output shape [2, 1, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:30.5636522Z ok (0.002s)
2023-01-11T21:56:30.5636721Z   test_qadd_relu_cudnn (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: cudnn is not enabled. (0.001s)
2023-01-11T21:56:30.5636981Z   test_qadd_relu_cudnn_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: cudnn is not enabled. (0.001s)
2023-01-11T21:56:30.5637177Z   test_qadd_relu_different_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.005s)
2023-01-11T21:56:30.5637357Z   test_qadd_relu_same_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.004s)
2023-01-11T21:56:30.5637515Z   test_qcelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.129s)
2023-01-11T21:56:30.5637675Z   test_qclamp (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.633s)
2023-01-11T21:56:30.5637820Z   test_qelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.159s)
2023-01-11T21:56:30.5637983Z   test_qgelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.043s)
2023-01-11T21:56:30.5638152Z   test_qhardsigmoid (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.357s)
2023-01-11T21:56:30.5638313Z   test_qlayer_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.090s)
2023-01-11T21:56:30.5638476Z   test_qmatmul (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.181s)
2023-01-11T21:56:30.5638644Z   test_qmul_broadcast (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.002s)
2023-01-11T21:56:30.5638827Z   test_qmul_relu_different_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.005s)
2023-01-11T21:56:30.5639004Z   test_qmul_relu_same_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.391s)
2023-01-11T21:56:30.5639149Z   test_qprelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.042s)
2023-01-11T21:56:30.5654714Z   test_qrelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (1.710s)
2023-01-11T21:56:30.5654915Z   test_qrelu6 (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.220s)
2023-01-11T21:56:30.5655081Z   test_qsoftmax (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.141s)
2023-01-11T21:56:30.5655256Z   test_qsoftmax_qnnpack (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.174s)
2023-01-11T21:56:30.5655516Z   test_qtanh (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.005s)
2023-01-11T21:56:30.5655680Z   test_qthreshold (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.145s)
2023-01-11T21:56:30.5656118Z   test_qtopk (quantization.core.test_quantized_op.TestQuantizedOps) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_op.py:1999: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:30.5656328Z   indices = torch.tensor(X).long()
2023-01-11T21:56:30.5656393Z ok (0.147s)
2023-01-11T21:56:30.5656566Z   test_quantized_mean_qnnpack (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.013s)
2023-01-11T21:56:30.5656721Z   test_sigmoid (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.167s)
2023-01-11T21:56:30.5656896Z   test_sigmoid_non_observed (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.700s)
2023-01-11T21:56:30.5657055Z   test_std (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (1.439s)
2023-01-11T21:56:30.5657240Z   test_bfp16_quantize (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5657568Z   test_choose_qparams (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.004s)
2023-01-11T21:56:30.5657772Z   test_choose_qparams_optimized (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.011s)
2023-01-11T21:56:30.5657945Z   test_clone (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5658178Z   test_compare_per_channel_device_numerics (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.001s)
2023-01-11T21:56:30.5658399Z   test_compare_per_tensor_device_numerics (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.001s)
2023-01-11T21:56:30.5658633Z   test_cuda_quantization_does_not_pin_memory (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.000s)
2023-01-11T21:56:30.5658840Z   test_decomposed_dequantize_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5659049Z   test_decomposed_dequantize_per_tensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5659255Z   test_decomposed_dynamic_quant_pattern (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5659458Z   test_decomposed_quantize_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5659660Z   test_decomposed_quantize_per_tensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5659850Z   test_dequantize_fp16_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5660067Z   test_dequantize_fp16_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:30.5660474Z   test_fp16_saturate_op (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:1390: UserWarning: FOUND weight out of range  (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/quantized/cpu/QuantUtils.h:215.)
2023-01-11T21:56:30.5660560Z   y = torch._saturate_weight_to_fp16(x)
2023-01-11T21:56:30.5660625Z ok (0.001s)
2023-01-11T21:56:30.5660812Z   test_jit_serialization (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.008s)
2023-01-11T21:56:30.5661013Z   test_per_channel_qtensor_creation_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5661243Z   test_per_channel_qtensor_creation_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:30.5661447Z   test_per_channel_qtensor_to_memory_format (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5661663Z   test_per_channel_to_device (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.001s)
2023-01-11T21:56:30.5661898Z   test_per_tensor_qtensor_to_memory_format (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5662110Z   test_per_tensor_to_device (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.001s)
2023-01-11T21:56:30.5663093Z   test_pickle_checkpoint_qtensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:309: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5663173Z   device=storage.device,
2023-01-11T21:56:30.5663848Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:330: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5663936Z   device=storage.device,
2023-01-11T21:56:30.5664000Z ok (0.006s)
2023-01-11T21:56:30.5664182Z   test_qscheme_pickle (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.003s)
2023-01-11T21:56:30.5664383Z   test_qtensor_channel_float_assignment (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.078s)
2023-01-11T21:56:30.5664554Z   test_qtensor_copy (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.016s)
2023-01-11T21:56:30.5664734Z   test_qtensor_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.007s)
2023-01-11T21:56:30.5664914Z   test_qtensor_creation (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.005s)
2023-01-11T21:56:30.5665120Z   test_qtensor_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:30.5665293Z   test_qtensor_dtypes (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5665488Z   test_qtensor_fill_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.004s)
2023-01-11T21:56:30.5665684Z   test_qtensor_fill_per_channel_nhwc (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.008s)
2023-01-11T21:56:30.5665869Z   test_qtensor_fill_per_tensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.004s)
2023-01-11T21:56:30.5666057Z   test_qtensor_fill_per_tensor_nhwc (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.009s)
2023-01-11T21:56:30.5666248Z   test_qtensor_float_assignment (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.003s)
2023-01-11T21:56:30.5666433Z   test_qtensor_index_put_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.011s)
2023-01-11T21:56:30.5666656Z   test_qtensor_index_put_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:30.5666842Z   test_qtensor_index_select_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5667047Z   test_qtensor_index_select_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:30.5667224Z   test_qtensor_int_repr (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5667776Z   test_qtensor_legacy_new_failure (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:410: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5667940Z   self.assertRaises(RuntimeError, lambda: qr.new(r.storage()))
2023-01-11T21:56:30.5668001Z ok (0.006s)
2023-01-11T21:56:30.5668545Z   test_qtensor_load_save (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5668677Z   self.assertEqual(qr2.storage().data_ptr(), qrv2.storage().data_ptr())
2023-01-11T21:56:30.5668737Z ok (0.009s)
2023-01-11T21:56:30.5668930Z   test_qtensor_masked_fill_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.012s)
2023-01-11T21:56:30.5669177Z   test_qtensor_masked_fill_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:30.5669381Z   test_qtensor_per_channel_load_save (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.007s)
2023-01-11T21:56:30.5669567Z   test_qtensor_per_channel_permute (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.010s)
2023-01-11T21:56:30.5669744Z   test_qtensor_permute (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.007s)
2023-01-11T21:56:30.5669925Z   test_qtensor_quant_dequant (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:30.5670121Z   test_qtensor_quantize_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (9.031s)
2023-01-11T21:56:30.5670299Z   test_qtensor_reshape (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.003s)
2023-01-11T21:56:30.5670723Z   test_qtensor_resize (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:767: UserWarning: non-inplace resize is deprecated
2023-01-11T21:56:30.5670889Z   warnings.warn("non-inplace resize is deprecated")
2023-01-11T21:56:30.5670948Z ok (0.003s)
2023-01-11T21:56:30.5671501Z   test_qtensor_sub_byte_aligned_cols (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:290: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5671690Z   self.assertEqual(qr.storage().size(), rows * math.ceil(cols / elements_per_byte), f"with {dtype}, {elements_per_byte}")
2023-01-11T21:56:30.5672129Z /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:299: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5672318Z   self.assertEqual(q.storage().size(), math.ceil(num_elements / elements_per_byte), f"with {dtype}, {elements_per_byte}")
2023-01-11T21:56:30.5672372Z ok (0.006s)
2023-01-11T21:56:30.5672568Z   test_qtensor_sub_byte_not_aligned_cols (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.004s)
2023-01-11T21:56:30.5672747Z   test_qtensor_unsqueeze (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.013s)
2023-01-11T21:56:30.5672924Z   test_qtensor_view (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.008s)
2023-01-11T21:56:30.5673130Z   test_quant_pin_memory (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.000s)
2023-01-11T21:56:30.5673360Z   test_quantize_per_channel_float_qparams (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (9.028s)
2023-01-11T21:56:30.5673549Z   test_quantize_per_channel_sub_byte (quantization.core.test_quantized_tensor.TestQuantizedTensor)
2023-01-11T21:56:30.5673752Z Tests the per channel quantization scheme for 4-bit qtensors. ... ok (0.304s)
2023-01-11T21:56:30.5673920Z   test_repeat (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5674100Z   test_torch_qtensor_deepcopy (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:30.5674309Z   test_observer_scriptable (quantization.core.test_workflow_module.TestRecordHistogramObserver) ... ok (0.015s)
2023-01-11T21:56:30.5674505Z   test_record_observer (quantization.core.test_workflow_module.TestRecordHistogramObserver) ... ok (0.008s)
2023-01-11T21:56:30.5674678Z   test_rnn (quantization.core.test_quantized_module.TestReferenceQuantizedModule)
2023-01-11T21:56:30.5674854Z Checks the rnn reference quantized modules has correct numerics ... ok (0.011s)
2023-01-11T21:56:30.5675039Z   test_rnn_cell (quantization.core.test_quantized_module.TestReferenceQuantizedModule)
2023-01-11T21:56:30.5675189Z Checks the rnn cell reference quantized modules has correct numerics ... ok (0.007s)
2023-01-11T21:56:30.5675369Z   test_sparse (quantization.core.test_quantized_module.TestReferenceQuantizedModule)
2023-01-11T21:56:30.5675459Z Embedding and EmbeddingBag ... ok (0.004s)
2023-01-11T21:56:30.5675640Z   test_conv2d (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.061s)
2023-01-11T21:56:30.5675829Z   test_conv2d_graph (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.021s)
2023-01-11T21:56:30.5676016Z   test_conv2d_graph_v2 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.019s)
2023-01-11T21:56:30.5676208Z   test_conv2d_graph_v3 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.021s)
2023-01-11T21:56:30.5676397Z   test_conv2d_nobias (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.048s)
2023-01-11T21:56:30.5676590Z   test_conv2d_nobias_graph (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.020s)
2023-01-11T21:56:30.5676783Z   test_conv2d_nobias_graph_v2 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.018s)
2023-01-11T21:56:30.5677044Z   test_conv2d_nobias_graph_v3 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.019s)
2023-01-11T21:56:30.5677217Z   test_conv2d_relu (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.051s)
2023-01-11T21:56:30.5677399Z   test_conv3d (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.015s)
2023-01-11T21:56:30.5677583Z   test_conv3d_relu (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.015s)
2023-01-11T21:56:30.5677781Z   test_default_qat_qconfig (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.010s)
2023-01-11T21:56:30.5677963Z   test_linear (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.042s)
2023-01-11T21:56:30.5678151Z   test_linear_dynamic (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.063s)
2023-01-11T21:56:30.5678336Z   test_linear_relu (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.043s)
2023-01-11T21:56:30.5679183Z   test_linear_relu_package_quantization_transforms (quantization.bc.test_backward_compatibility.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5679289Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:56:30.5679797Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:1435: UserWarning: Passing a QConfig dictionary to prepare is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:30.5679871Z   warnings.warn(
2023-01-11T21:56:30.5680372Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node child_packed_weight_0 target child_packed_weight_0 child_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5680585Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5681068Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_0 target _packed_weight_0 _packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:30.5681308Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:30.5681378Z ok (0.379s)
2023-01-11T21:56:30.5681558Z   test_lstm (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.037s)
2023-01-11T21:56:30.5681760Z   test_per_channel_observer (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.004s)
2023-01-11T21:56:30.5681955Z   test_per_tensor_observer (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.003s)
2023-01-11T21:56:30.5682145Z   test_batch_norm2d (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5682262Z Tests the correctness of the batchnorm2d module. ... ok (0.002s)
2023-01-11T21:56:30.5682808Z   test_batch_norm2d_serialization (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:56:30.5682886Z   warnings.warn(
2023-01-11T21:56:30.5682953Z ok (0.008s)
2023-01-11T21:56:30.5683137Z   test_batch_norm3d (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5683263Z Tests the correctness of the batchnorm3d module. ... ok (0.002s)
2023-01-11T21:56:30.5683477Z   test_batch_norm3d_serialization (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.007s)
2023-01-11T21:56:30.5683664Z   test_channel_shuffle (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5683785Z Tests the correctness of the ChannelShuffle module. ... ok (0.007s)
2023-01-11T21:56:30.5683978Z   test_conv1d_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (5.744s)
2023-01-11T21:56:30.5684163Z   test_conv2d_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (4.305s)
2023-01-11T21:56:30.5684351Z   test_conv3d_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.545s)
2023-01-11T21:56:30.5684532Z   test_dropout (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5684655Z Tests the correctness of the dropout module. ... ok (0.001s)
2023-01-11T21:56:30.5684862Z   test_dropout_serialization (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.005s)
2023-01-11T21:56:30.5685036Z   test_elu (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5685151Z Tests the correctness of the ELU module. ... ok (0.002s)
2023-01-11T21:56:30.5685336Z   test_embedding_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (6.119s)
2023-01-11T21:56:30.5685523Z   test_embedding_bag_api (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5685697Z Test execution and serialization for dynamic quantized embedding_bag modules on int8 ... ok (6.288s)
2023-01-11T21:56:30.5685876Z   test_group_norm (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5686029Z Tests the correctness of the groupnorm module. ... ok (0.003s)
2023-01-11T21:56:30.5686210Z   test_instance_norm (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5686343Z Tests the correctness of the instancenorm{n}d modules. ... ok (0.007s)
2023-01-11T21:56:30.5686519Z   test_layer_norm (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5686630Z Tests the correctness of the layernorm module. ... ok (0.003s)
2023-01-11T21:56:30.5686820Z   test_leaky_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:30.5687679Z   test_linear (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:900: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5687794Z   storage_type_str = obj.pickle_storage_type()
2023-01-11T21:56:30.5688461Z /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:903: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:30.5688546Z   storage_numel = obj.size()
2023-01-11T21:56:30.5688612Z ok (22.240s)
2023-01-11T21:56:30.5688798Z   test_linear_leaky_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5688961Z test API functionality for nn.intrinsic.quantized.linear_leaky_relu ... ok (11.049s)
2023-01-11T21:56:30.5689159Z   test_linear_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (21.755s)
2023-01-11T21:56:30.5689331Z   test_linear_tanh (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5689487Z test API functionality for nn.intrinsic.quantized.linear_tanh ... ok (5.530s)
2023-01-11T21:56:30.5689665Z   test_pool_api (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:30.5689785Z Tests the correctness of the pool module. ... ok (0.013s)
2023-01-11T21:56:30.5689972Z   test_prelu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:30.5690173Z   test_quant_dequant_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:30.5690355Z   test_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.001s)
2023-01-11T21:56:30.5690543Z   test_sigmoid (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:30.5690756Z   test_subgraph_rewriter_annotations_int (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (5.136s)
2023-01-11T21:56:30.5690967Z   test_subgraph_rewriter_correct_output_replacement (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.437s)
2023-01-11T21:56:30.5691183Z   test_subgraph_rewriter_graph_argument_order (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.058s)
2023-01-11T21:56:30.5691436Z   test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.041s)
2023-01-11T21:56:30.5691651Z   test_subgraph_rewriter_multiple_pattern_match (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.054s)
2023-01-11T21:56:30.5691862Z   test_subgraph_rewriter_pattern_is_entire_graph (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.050s)
2023-01-11T21:56:30.5692118Z   test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.050s)
2023-01-11T21:56:30.5692363Z   test_subgraph_rewriter_placeholder_matching (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter)
2023-01-11T21:56:30.5692510Z This tests that a placeholder Node can be matched to a Node with ... ok (0.051s)
2023-01-11T21:56:30.5692715Z   test_subgraph_rewriter_preserves_logic (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.052s)
2023-01-11T21:56:30.5692940Z   test_subgraph_rewriter_replaces_referenced_submodules (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.040s)
2023-01-11T21:56:30.5693137Z   test_subgraph_rewriter_single_pattern_match (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.049s)
2023-01-11T21:56:30.5693347Z   test_subgraph_rewriter_traced_as_callable (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.049s)
2023-01-11T21:56:30.5693584Z   test_subgraph_rewriter_with_oneliner_pattern (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.047s)
2023-01-11T21:56:30.5693810Z   test_subgraph_writer_replace_consecutive_submodules (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.048s)
2023-01-11T21:56:30.5693975Z   test_get_fqn_to_example_inputs_complex_args (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:30.5694125Z Test that we can record complex example inputs such as lists and dicts ... ok (0.007s)
2023-01-11T21:56:30.5694292Z   test_get_fqn_to_example_inputs_default_kwargs (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:30.5694455Z Test that we can get example inputs for functions with default keyword arguments ... ok (0.007s)
2023-01-11T21:56:30.5694626Z   test_get_fqn_to_example_inputs_simple (quantization.core.test_utils.TestUtils) ... ok (0.007s)
2023-01-11T21:56:30.5694779Z   test_quantize_weight_clamping_per_channel (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:30.5694946Z Test quant_{min, max} from per channel observer is honored by `_quantize_weight` method ... ok (0.002s)
2023-01-11T21:56:30.5695112Z   test_quantize_weight_clamping_per_tensor (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:30.5695279Z Test quant_{min, max} from per tensor observer is honored by `_quantize_weight` method ... ok (0.002s)
2023-01-11T21:56:30.5695290Z 
2023-01-11T21:56:30.5695495Z ----------------------------------------------------------------------
2023-01-11T21:56:30.5695574Z Ran 1001 tests in 786.308s
2023-01-11T21:56:30.5695580Z 
2023-01-11T21:56:30.5695652Z OK (skipped=62)
2023-01-11T21:56:30.5695657Z 
2023-01-11T21:56:30.5695739Z Generating XML reports...
2023-01-11T21:56:30.5696153Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic-20230111214322.xml
2023-01-11T21:56:30.5696546Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized-20230111214322.xml
2023-01-11T21:56:30.5696962Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_quantization.TestAOMigrationQuantization-20230111214322.xml
2023-01-11T21:56:30.5697376Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx-20230111214322.xml
2023-01-11T21:56:30.5697747Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_backend_config.TestBackendConfig-20230111214322.xml
2023-01-11T21:56:30.5698143Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_bias_correction_eager.TestBiasCorrectionEager-20230111214322.xml
2023-01-11T21:56:30.5698495Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestComparatorOps-20230111214322.xml
2023-01-11T21:56:30.5698891Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized-20230111214322.xml
2023-01-11T21:56:30.5699278Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestDistributed-20230111214322.xml
2023-01-11T21:56:30.5699678Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_module.TestDynamicQuantizedModule-20230111214322.xml
2023-01-11T21:56:30.5700051Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestDynamicQuantizedOps-20230111214322.xml
2023-01-11T21:56:30.5700411Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_equalize_eager.TestEqualizeEager-20230111214322.xml
2023-01-11T21:56:30.5700775Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_equalize_fx.TestEqualizeFx-20230111214322.xml
2023-01-11T21:56:30.5701124Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher-20230111214322.xml
2023-01-11T21:56:30.5701495Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXGraphMatcherModels-20230111214322.xml
2023-01-11T21:56:30.5701879Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs-20230111214322.xml
2023-01-11T21:56:30.5702281Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels-20230111214322.xml
2023-01-11T21:56:30.5702778Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows-20230111214322.xml
2023-01-11T21:56:30.5703139Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestFakeQuantize-20230111214322.xml
2023-01-11T21:56:30.5703505Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_ops.TestFakeQuantizeOps-20230111214322.xml
2023-01-11T21:56:30.5703849Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_fuse_eager.TestFuseEager-20230111214322.xml
2023-01-11T21:56:30.5704177Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestFuseFx-20230111214322.xml
2023-01-11T21:56:30.5704538Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_ops.TestFusedObsFakeQuant-20230111214322.xml
2023-01-11T21:56:30.5704934Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestFusedObsFakeQuantModule-20230111214322.xml
2023-01-11T21:56:30.5705284Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_fusion_passes.TestFusionPasses-20230111214322.xml
2023-01-11T21:56:30.5705689Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization-20230111214322.xml
2023-01-11T21:56:30.5706046Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxDetectOutliers-20230111214322.xml
2023-01-11T21:56:30.5706408Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportClass-20230111214322.xml
2023-01-11T21:56:30.5706821Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportDetectDynamicStatic-20230111214322.xml
2023-01-11T21:56:30.5707197Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportDetector-20230111214322.xml
2023-01-11T21:56:30.5707619Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportObserver-20230111214322.xml
2023-01-11T21:56:30.5707999Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportVisualizer-20230111214322.xml
2023-01-11T21:56:30.5708369Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestHistogramObserver-20230111214322.xml
2023-01-11T21:56:30.5708754Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_model_numerics.TestModelNumericsEager-20230111214322.xml
2023-01-11T21:56:30.5709137Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager-20230111214322.xml
2023-01-11T21:56:30.5709519Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestObserver-20230111214322.xml
2023-01-11T21:56:30.5709852Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestPadding-20230111214322.xml
2023-01-11T21:56:30.5710197Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQNNPackOps-20230111214322.xml
2023-01-11T21:56:30.5710544Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_docs.TestQuantizationDocs-20230111214322.xml
2023-01-11T21:56:30.5710915Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps-20230111214322.xml
2023-01-11T21:56:30.5711298Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses-20230111214322.xml
2023-01-11T21:56:30.5711681Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps-20230111214322.xml
2023-01-11T21:56:30.5712079Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic-20230111214322.xml
2023-01-11T21:56:30.5712469Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic-20230111214322.xml
2023-01-11T21:56:30.5712837Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT-20230111214322.xml
2023-01-11T21:56:30.5713242Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics-20230111214322.xml
2023-01-11T21:56:30.5713582Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestQuantizeFx-20230111214322.xml
2023-01-11T21:56:30.5713931Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestQuantizeFxModels-20230111214322.xml
2023-01-11T21:56:30.5714285Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestQuantizeFxOps-20230111214322.xml
2023-01-11T21:56:30.5714634Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeJit-20230111214322.xml
2023-01-11T21:56:30.5714989Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeJitOps-20230111214322.xml
2023-01-11T21:56:30.5715350Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeJitPasses-20230111214322.xml
2023-01-11T21:56:30.5715722Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_pt2e.TestQuantizePT2EModels-20230111214322.xml
2023-01-11T21:56:30.5716108Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedConv-20230111214322.xml
2023-01-11T21:56:30.5716490Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedEmbeddingOps-20230111214322.xml
2023-01-11T21:56:30.5716959Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_functional.TestQuantizedFunctionalOps-20230111214322.xml
2023-01-11T21:56:30.5717326Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedLinear-20230111214322.xml
2023-01-11T21:56:30.5717674Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedOps-20230111214322.xml
2023-01-11T21:56:30.5718068Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_tensor.TestQuantizedTensor-20230111214322.xml
2023-01-11T21:56:30.5718468Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestRecordHistogramObserver-20230111214322.xml
2023-01-11T21:56:30.5718869Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_module.TestReferenceQuantizedModule-20230111214322.xml
2023-01-11T21:56:30.5719254Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.bc.test_backward_compatibility.TestSerialization-20230111214322.xml
2023-01-11T21:56:30.5719643Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_module.TestStaticQuantizedModule-20230111214322.xml
2023-01-11T21:56:30.5720031Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter-20230111214322.xml
2023-01-11T21:56:30.5720359Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_utils.TestUtils-20230111214322.xml
2023-01-11T21:56:30.5720366Z 
2023-01-11T21:56:30.5720752Z ##[endgroup]
2023-01-11T21:56:30.5721047Z FINISHED PRINTING LOG FILE of test_quantization (/var/lib/jenkins/workspace/test/test-reports/test_quantization_9fc5vzw9)
2023-01-11T21:56:30.5721053Z 
2023-01-11T21:56:30.8641442Z Running inductor/test_torchinductor_opinfo ... [2023-01-11 21:56:30.863728]
2023-01-11T21:56:32.5274089Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:32.5282531Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:32.6160078Z Ignoring disabled issues:  []
2023-01-11T21:56:32.6160372Z Ignoring disabled issues:  []
2023-01-11T21:56:32.6626705Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:56:32.662182]
2023-01-11T21:56:32.6627753Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:56:32.662212]
2023-01-11T21:56:42.0267341Z 
2023-01-11T21:56:42.0267953Z Expand the folded group to see the log file of inductor/test_torchinductor_opinfo
2023-01-11T21:56:42.0269062Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_oyr8io5w)
2023-01-11T21:56:42.0269988Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:42.0270712Z Test results will be stored in test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-484174d0a16e76ad.xml
2023-01-11T21:56:42.0271372Z ============================= test session starts ==============================
2023-01-11T21:56:42.0271747Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:56:42.0272005Z cachedir: .pytest_cache
2023-01-11T21:56:42.0272405Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:56:42.0272759Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:56:42.0273188Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:56:42.0273876Z collecting ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor_opinfo.py:0: PytestCollectionWarning: cannot collect test class 'TestExpect' because it has a __new__ constructor (from: test/inductor/test_torchinductor_opinfo.py)
2023-01-11T21:56:42.0274346Z collected 0 items
2023-01-11T21:56:42.0274538Z Running 0 items in this shard: 
2023-01-11T21:56:42.0274663Z 
2023-01-11T21:56:42.0274776Z =============================== warnings summary ===============================
2023-01-11T21:56:42.0275120Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:56:42.0275646Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:56:42.0275999Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:56:42.0276131Z 
2023-01-11T21:56:42.0276365Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:56:42.0276917Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-484174d0a16e76ad.xml -
2023-01-11T21:56:42.0277371Z ============================== 1 warning in 0.25s ==============================
2023-01-11T21:56:42.0277678Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:56:42.0277873Z 
2023-01-11T21:56:42.0278111Z ##[endgroup]
2023-01-11T21:56:42.0278559Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_oyr8io5w)
2023-01-11T21:56:42.0278823Z 
2023-01-11T21:56:47.2097450Z 
2023-01-11T21:56:47.2097892Z Expand the folded group to see the log file of inductor/test_torchinductor_opinfo
2023-01-11T21:56:47.2098848Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_bhl69r3u)
2023-01-11T21:56:47.2099537Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:47.2100116Z Test results will be stored in test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e42ebd2ecf6eaa23.xml
2023-01-11T21:56:47.2100554Z ============================= test session starts ==============================
2023-01-11T21:56:47.2100933Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:56:47.2101196Z cachedir: .pytest_cache
2023-01-11T21:56:47.2101708Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:56:47.2102150Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:56:47.2102783Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:56:47.2103539Z collecting ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor_opinfo.py:0: PytestCollectionWarning: cannot collect test class 'TestExpect' because it has a __new__ constructor (from: test/inductor/test_torchinductor_opinfo.py)
2023-01-11T21:56:47.2103948Z collected 0 items
2023-01-11T21:56:47.2104355Z Running 0 items in this shard: 
2023-01-11T21:56:47.2104478Z 
2023-01-11T21:56:47.2104593Z =============================== warnings summary ===============================
2023-01-11T21:56:47.2104991Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:56:47.2105559Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:56:47.2105925Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:56:47.2106058Z 
2023-01-11T21:56:47.2106336Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:56:47.2106915Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e42ebd2ecf6eaa23.xml -
2023-01-11T21:56:47.2107370Z ============================== 1 warning in 0.03s ==============================
2023-01-11T21:56:47.2107757Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:56:47.2107993Z 
2023-01-11T21:56:47.2108238Z ##[endgroup]
2023-01-11T21:56:47.2108748Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_bhl69r3u)
2023-01-11T21:56:47.2109008Z 
2023-01-11T21:56:47.5661153Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:56:47.565654]
2023-01-11T21:56:56.5191322Z 
2023-01-11T21:56:56.5191903Z Expand the folded group to see the log file of inductor/test_torchinductor_opinfo
2023-01-11T21:56:56.5192958Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_xb63g_n9)
2023-01-11T21:56:56.5193858Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:56.5194669Z Test results will be stored in test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-b7995a19757f5615.xml
2023-01-11T21:56:56.5195307Z ============================= test session starts ==============================
2023-01-11T21:56:56.5195930Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:56:56.5196349Z cachedir: .pytest_cache
2023-01-11T21:56:56.5197155Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:56:56.5197773Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:56:56.5198373Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:56:56.5199442Z collecting ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor_opinfo.py:0: PytestCollectionWarning: cannot collect test class 'TestExpect' because it has a __new__ constructor (from: test/inductor/test_torchinductor_opinfo.py)
2023-01-11T21:56:56.5200090Z collected 0 items
2023-01-11T21:56:56.5200377Z Running 0 items in this shard: 
2023-01-11T21:56:56.5200568Z 
2023-01-11T21:56:56.5200727Z =============================== warnings summary ===============================
2023-01-11T21:56:56.5201299Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:56:56.5202165Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:56:56.5202799Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:56:56.5203008Z 
2023-01-11T21:56:56.5203329Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:56:56.5204257Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-b7995a19757f5615.xml -
2023-01-11T21:56:56.5205151Z ============================== 1 warning in 0.03s ==============================
2023-01-11T21:56:56.5205718Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:56:56.5206074Z 
2023-01-11T21:56:56.5206547Z ##[endgroup]
2023-01-11T21:56:56.5207407Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_xb63g_n9)
2023-01-11T21:56:56.5207892Z 
2023-01-11T21:56:56.5208205Z Running test_autograd ... [2023-01-11 21:56:56.519783]
2023-01-11T21:56:56.5209039Z Executing ['/opt/conda/bin/python', '-bb', 'test_autograd.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:56:56.520037]
2023-01-11T21:57:16.0395655Z 
2023-01-11T21:57:16.0396415Z Expand the folded group to see the log file of test_autograd
2023-01-11T21:57:16.0397664Z ##[group]PRINTING LOG FILE of test_autograd (/var/lib/jenkins/workspace/test/test-reports/test_autograd_gulrsvtr)
2023-01-11T21:57:16.0398025Z 
2023-01-11T21:57:16.0398801Z Running tests...
2023-01-11T21:57:16.0399467Z ----------------------------------------------------------------------
2023-01-11T21:57:16.0400059Z Test results will be stored in test-reports/python-unittest/test_autograd
2023-01-11T21:57:16.0400547Z   test_backward_out_of_context (__main__.TestAllowMutationOnSaved) ... ok (0.003s)
2023-01-11T21:57:16.0400980Z   test_basic (__main__.TestAllowMutationOnSaved) ... ok (0.007s)
2023-01-11T21:57:16.0401393Z   test_disallow_nesting (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:16.0401871Z   test_double_backward (__main__.TestAllowMutationOnSaved) ... ok (0.002s)
2023-01-11T21:57:16.0402346Z   test_save_base_and_modify_view (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:16.0406658Z   test_save_view_modify_base (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:16.0407249Z   test_saved_but_not_anymore (__main__.TestAllowMutationOnSaved) ... ok (0.002s)
2023-01-11T21:57:16.0407776Z   test_saved_same_tensor_different_versions (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:16.0408202Z   test_saved_same_tensor_many_times (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:16.0408700Z   test_views (__main__.TestAllowMutationOnSaved) ... ok (0.007s)
2023-01-11T21:57:16.0409008Z   test_with_math_views (__main__.TestAllowMutationOnSaved) ... ok (0.002s)
2023-01-11T21:57:16.0409323Z   test_with_out_variant (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:16.0409840Z   test_access_saved_tensor_twice_without_recomputation_works (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0412009Z   test_accumulate_grad (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:197: UserWarning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/autograd/engine.cpp:1134.)
2023-01-11T21:57:16.0413186Z   Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
2023-01-11T21:57:16.0413715Z ok (0.002s)
2023-01-11T21:57:16.0414239Z   test_accumulate_grad_tensor_reference (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0414921Z   test_accumulate_grad_with_zero_numel_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0415991Z   test_anomaly_assign_parent_cleanup (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:4090: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
2023-01-11T21:57:16.0417072Z   with detect_anomaly():
2023-01-11T21:57:16.0417461Z ok (0.001s)
2023-01-11T21:57:16.0417928Z   test_anomaly_detect_nan (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0418524Z   test_anomaly_grad_warnings (__main__.TestAutograd) ... ok (0.013s)
2023-01-11T21:57:16.0419112Z   test_anomaly_mode_no_check_nan (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0419712Z   test_attribute_deletion (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0420394Z   test_autograd_function_extension_enabled_by_default (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0421089Z   test_autograd_function_extension_feature_flag (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0421765Z   test_autograd_inplace_view_of_view (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0422605Z   test_autograd_inplace_views_creation_meta (__main__.TestAutograd) ... ok (0.220s)
2023-01-11T21:57:16.0423222Z   test_autograd_inplace_views_cross_dtype (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0423709Z   test_autograd_multiple_views_python (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0424214Z   test_autograd_python_custom_function_inplace (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:16.0424751Z   test_autograd_simple_views_python (__main__.TestAutograd) ... ok (0.088s)
2023-01-11T21:57:16.0425272Z   test_autograd_views_codegen (__main__.TestAutograd) ... ok (0.017s)
2023-01-11T21:57:16.0425529Z   test_backward (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0425790Z   test_backward_badcalls (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0426057Z   test_backward_copy (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0426319Z   test_backward_create_graph_warns (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0426597Z   test_backward_no_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0426902Z   test_backward_twice_retained_graph_with_saved_values (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0427245Z   test_backward_twice_retained_graph_without_saved_values (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0427553Z   test_backward_twice_with_saved_values (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0428013Z   test_backward_twice_without_saved_values (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0428487Z   test_backward_with_inputs (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0428810Z   test_backward_with_nonleaf_inputs (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0430040Z   test_calculate_shape_util (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:57:16.0430914Z   return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)
2023-01-11T21:57:16.0431295Z ok (0.002s)
2023-01-11T21:57:16.0431637Z   test_callback_adds_callback (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0432072Z   test_cant_create_saved_tensors (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0432425Z   test_checkpoint_valid_reset_on_error (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0432788Z   test_checkpointing (__main__.TestAutograd) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:57:16.0433173Z   test_checkpointing_non_reentrant_autocast_cpu (__main__.TestAutograd)
2023-01-11T21:57:16.0433596Z Test that autocast args such as the dtype are preserved during non-reentrant ... ok (0.002s)
2023-01-11T21:57:16.0434088Z   test_checkpointing_non_reentrant_autocast_gpu (__main__.TestAutograd)
2023-01-11T21:57:16.0434730Z Test that autocast args/kwargs such as the dtype are preserved during ... skip: Test requires CUDA bf16 support (0.000s)
2023-01-11T21:57:16.0435305Z   test_checkpointing_without_reentrant_arbitrary_input_output (__main__.TestAutograd)
2023-01-11T21:57:16.0435643Z Ensures checkpointing without reentrant autograd works with functions ... ok (0.002s)
2023-01-11T21:57:16.0435969Z   test_checkpointing_without_reentrant_correct_grad (__main__.TestAutograd)
2023-01-11T21:57:16.0436268Z Verifies that correct gradients are calculated for checkpoint ... ok (0.002s)
2023-01-11T21:57:16.0436658Z   test_checkpointing_without_reentrant_custom_function_works (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0436993Z   test_checkpointing_without_reentrant_dataparallel (__main__.TestAutograd)
2023-01-11T21:57:16.0437598Z Verifies gradient correctness when checkpoint without reentrant autograd ... ok (0.002s)
2023-01-11T21:57:16.0438332Z   test_checkpointing_without_reentrant_detached_tensor_use_reentrant_False (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0439251Z   test_checkpointing_without_reentrant_detached_tensor_use_reentrant_True (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0440050Z   test_checkpointing_without_reentrant_input_requires_grad_False (__main__.TestAutograd)
2023-01-11T21:57:16.0440873Z Basic test for checkpoint without reentrant autograd. ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:57:16.0441703Z   test_checkpointing_without_reentrant_input_requires_grad_True (__main__.TestAutograd)
2023-01-11T21:57:16.0442506Z Basic test for checkpoint without reentrant autograd. ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:57:16.0443386Z   test_checkpointing_without_reentrant_memory_savings (__main__.TestAutograd) ... skip: Test requires CUDA (0.001s)
2023-01-11T21:57:16.0444145Z   test_checkpointing_without_reentrant_parameter_used_in_an_out (__main__.TestAutograd)
2023-01-11T21:57:16.0444849Z Ensures that gradient hooks are only called once per tensor. ... ok (0.001s)
2023-01-11T21:57:16.0445522Z   test_copy_slices_graph_task_updates (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0446179Z   test_create_graph_and_full_backward_hook_cycle (__main__.TestAutograd) ... ok (0.446s)
2023-01-11T21:57:16.0446855Z   test_current_graph_task_execution_order (__main__.TestAutograd) ... ok (0.011s)
2023-01-11T21:57:16.0447485Z   test_current_graph_task_id (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0448067Z   test_current_node (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0448646Z   test_custom_autograd_no_early_free (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:16.0449300Z   test_custom_autograd_repeated_grad_grad (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0449930Z   test_custom_function_cycle (__main__.TestAutograd) ... ok (0.143s)
2023-01-11T21:57:16.0450612Z   test_custom_function_error (__main__.TestAutograd) ... ok (0.068s)
2023-01-11T21:57:16.0451207Z   test_custom_function_exception (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0451887Z   test_custom_function_forward_mode_forward_is_no_op (__main__.TestAutograd) ... ok (0.021s)
2023-01-11T21:57:16.0452605Z   test_custom_function_forward_mode_inplace_checks (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0453335Z   test_custom_function_forward_mode_non_differentiable (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0454073Z   test_custom_function_forward_mode_non_tensor_before_tensor_args (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0454810Z   test_custom_function_forward_mode_view_checks (__main__.TestAutograd) ... ok (0.013s)
2023-01-11T21:57:16.0455507Z   test_custom_function_forward_mode_wrong_formula (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0456177Z   test_custom_function_local_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0456848Z   test_custom_function_mark_dirty_not_differentiable (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0457513Z   test_custom_function_no_tensors (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0458249Z   test_custom_function_non_tensor_inputs_outputs (__main__.TestAutograd) ... ok (0.009s)
2023-01-11T21:57:16.0458913Z   test_custom_function_return_view_in_nograd (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0459569Z   test_custom_function_save_for_forward (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0460217Z   test_custom_function_saved_tensors (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0460884Z   test_custom_function_setup_context_multi_input (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0461559Z   test_custom_function_setup_context_multi_output (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0462241Z   test_custom_function_setup_context_simple (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0462849Z   test_deep_reentrant (__main__.TestAutograd) ... ok (0.197s)
2023-01-11T21:57:16.0463309Z   test_default_saved_variable_hooks_double_backward (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0463853Z   test_dep_nograd (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0464283Z   test_dependent_backward (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0464692Z   test_detach (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0465045Z   test_detach_base (__main__.TestAutograd)
2023-01-11T21:57:16.0465430Z detaching base does not detach view ... ok (0.001s)
2023-01-11T21:57:16.0465869Z   test_detach_then_inplace_raises_in_autograd (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0466733Z   test_diagonal_expanded_v (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:2510: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:57:16.0467521Z   v_expanded = torch.tensor(value).expand(10)
2023-01-11T21:57:16.0467830Z ok (0.001s)
2023-01-11T21:57:16.0468144Z   test_dir (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0468529Z   test_disabling_saved_tensor_hooks (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0469058Z   test_disabling_saved_tensor_hooks_nested (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0469589Z   test_dont_materialize_grads (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0469865Z   test_duplicate_backward_root (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0470161Z   test_first_grad_fn_access_in_no_grad_mode (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0470447Z   test_free_deep_graph (__main__.TestAutograd) ... ok (1.759s)
2023-01-11T21:57:16.0470727Z   test_free_deep_graph_complicated (__main__.TestAutograd) ... ok (1.200s)
2023-01-11T21:57:16.0471008Z   test_free_deep_graph_pyfunction (__main__.TestAutograd) ... ok (1.707s)
2023-01-11T21:57:16.0471449Z   test_full_backward_hook_double_backward (__main__.TestAutograd) ... ok (0.067s)
2023-01-11T21:57:16.0471914Z   test_function (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0472380Z   test_function_returns_input (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0472921Z   test_function_returns_undefined_tensor (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0473420Z   test_gc_in_destructor (__main__.TestAutograd)
2023-01-11T21:57:16.0473926Z Previously, if a Function destructor triggered a garbage collection, ... ok (0.740s)
2023-01-11T21:57:16.0474454Z   test_grad (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0474816Z   test_grad_badcalls (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0475078Z   test_grad_batched_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0475335Z   test_grad_empty_inputs (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0475606Z   test_grad_fn_attr_bindings (__main__.TestAutograd) ... ok (0.017s)
2023-01-11T21:57:16.0475879Z   test_grad_fn_badcalls (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0476229Z   test_grad_fn_prehooks (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0476512Z   test_grad_fn_prehooks_multiple_outputs (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0476811Z   test_grad_fn_prehooks_remove_hooks (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0477103Z   test_grad_mode_class_decoration (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0477459Z   test_grad_mode_restored_reentrant (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0478779Z   test_grad_nonleaf (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:776: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:57:16.0479546Z   self.assertIsNone(x.grad)
2023-01-11T21:57:16.0479734Z ok (0.003s)
2023-01-11T21:57:16.0479950Z   test_grad_nonleaf_many_outputs (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0481185Z   test_grad_nonleaf_register_hook (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:828: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:57:16.0481949Z   self.assertIsNone(x_list[0].grad)
2023-01-11T21:57:16.0483050Z /var/lib/jenkins/workspace/test/test_autograd.py:835: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:57:16.0483771Z   self.assertIsNone(x_list[i].grad)
2023-01-11T21:57:16.0483960Z ok (0.002s)
2023-01-11T21:57:16.0484169Z   test_grad_unreachable (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0484454Z   test_grad_unreachable_discovery (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0484766Z   test_gradcheck_backward_mul_by_grad_output (__main__.TestAutograd) ... ok (0.020s)
2023-01-11T21:57:16.0485062Z   test_gradcheck_check_batched_grad (__main__.TestAutograd) ... ok (0.048s)
2023-01-11T21:57:16.0485359Z   test_gradcheck_check_forward_or_backward_only (__main__.TestAutograd)
2023-01-11T21:57:16.0485671Z Depending on settings for check_forward_ad and check_backward_ad, the ... ok (0.020s)
2023-01-11T21:57:16.0486483Z   test_gradcheck_check_no_differentiable_outputs (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:16.0486972Z   warnings.warn(
2023-01-11T21:57:16.0487143Z ok (0.003s)
2023-01-11T21:57:16.0487386Z   test_gradcheck_complex_non_complex_outputs (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:16.0488158Z   test_gradcheck_custom_error (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:16.0488659Z   warnings.warn(
2023-01-11T21:57:16.0488828Z ok (0.014s)
2023-01-11T21:57:16.0489069Z   test_gradcheck_dense_and_sparse_inputs (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0489352Z   test_gradcheck_forward_ad (__main__.TestAutograd) ... ok (0.108s)
2023-01-11T21:57:16.0489646Z   test_gradcheck_forward_ad_batched_grad (__main__.TestAutograd) ... ok (0.009s)
2023-01-11T21:57:16.0489968Z   test_gradcheck_forward_ad_respects_requires_grad (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0490300Z   test_gradcheck_forward_ad_runs_with_no_requires_grad (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0491346Z   test_gradcheck_get_analytical_jacobian (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:616: UserWarning: get_analytical_jacobian was part of PyTorch's private API and not meant to be exposed. We are deprecating it and it will be removed in a future version of PyTorch. If you have a specific use for this or feature request for this to be a stable API, please file us an issue at https://github.com/pytorch/pytorch/issues/new
2023-01-11T21:57:16.0492126Z   warnings.warn("get_analytical_jacobian was part of PyTorch's private API and not "
2023-01-11T21:57:16.0492372Z ok (0.011s)
2023-01-11T21:57:16.0493292Z   test_gradcheck_get_numerical_jacobian (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:209: UserWarning: get_numerical_jacobian was part of PyTorch's private API and not meant to be exposed. We are deprecating it and it will be removed in a future version of PyTorch. If you have a specific use for this or feature request for this to be a stable API, please file us an issue at https://github.com/pytorch/pytorch/issues/new
2023-01-11T21:57:16.0494276Z   warnings.warn("get_numerical_jacobian was part of PyTorch's private API and not "
2023-01-11T21:57:16.0494509Z ok (0.008s)
2023-01-11T21:57:16.0495213Z   test_gradcheck_jacobian_mismatch (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:16.0495686Z   warnings.warn(
2023-01-11T21:57:16.0495855Z ok (0.038s)
2023-01-11T21:57:16.0496556Z   test_gradcheck_multiple_mkldnn_inputs (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #1 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:16.0497039Z   warnings.warn(
2023-01-11T21:57:16.0497208Z ok (0.015s)
2023-01-11T21:57:16.0497444Z   test_gradcheck_nondeterministic (__main__.TestAutograd) ... ok (0.074s)
2023-01-11T21:57:16.0497829Z   test_gradcheck_output_shape_or_dtype_depend_on_values (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0498142Z   test_gradcheck_single_input (__main__.TestAutograd) ... ok (0.011s)
2023-01-11T21:57:16.0498779Z   test_gradcheck_sparse_bsc_input (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:4389: UserWarning: Sparse BSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:57:16.0499428Z   gradcheck(fn, torch.rand(4, 8, dtype=torch.double).to_sparse_bsc((2, 2)).requires_grad_(True),
2023-01-11T21:57:16.0499661Z ok (0.025s)
2023-01-11T21:57:16.0499938Z   test_gradcheck_sparse_bsr_input (__main__.TestAutograd) ... ok (0.019s)
2023-01-11T21:57:16.0500231Z   test_gradcheck_sparse_csc_input (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0500520Z   test_gradcheck_sparse_csr_input (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0500797Z   test_gradcheck_sparse_input (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0501570Z   test_gradcheck_test_outputs (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:16.0502045Z   warnings.warn(
2023-01-11T21:57:16.0502203Z ok (0.002s)
2023-01-11T21:57:16.0502581Z   test_gradcheck_undefined_grad (__main__.TestAutograd) ... ok (0.012s)
2023-01-11T21:57:16.0502996Z   test_gradcheck_validates_input_mkldnn (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:16.0503303Z   test_gradcheck_validates_inputs (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:16.0503573Z   test_graph_save_on_cpu (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0503873Z   test_graph_save_on_cpu_cuda (__main__.TestAutograd) ... skip: test requires CUDA (0.001s)
2023-01-11T21:57:16.0504169Z   test_hessian_vector (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0504409Z   test_hook_none (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0504664Z   test_hook_with_no_name (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0504913Z   test_hooks (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0505149Z   test_hooks_cpp (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0505428Z   test_index_backward_does_not_save_tensor (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0505704Z   test_indexing (__main__.TestAutograd) ... ok (0.018s)
2023-01-11T21:57:16.0505968Z   test_indexing_duplicates (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0506221Z   test_inplace (__main__.TestAutograd) ... ok (0.022s)
2023-01-11T21:57:16.0506488Z   test_inplace_not_requires_grad (__main__.TestAutograd) ... ok (0.010s)
2023-01-11T21:57:16.0506775Z   test_inplace_on_view_backward (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0507049Z   test_inplace_on_view_leaf_errors (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0507340Z   test_inplace_on_view_saved_output (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0507631Z   test_inplace_on_view_weak_grad_fn (__main__.TestAutograd) ... ok (0.092s)
2023-01-11T21:57:16.0507905Z   test_input_buffer_accum (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0508160Z   test_integer_outputs (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0508425Z   test_invalid_gradients (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0508692Z   test_isolated_node (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0508944Z   test_leaf_assignment (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0509240Z   test_legacy_function_deprecation_exception (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0510069Z   test_lobpcg (__main__.TestAutograd) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/80338 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.002s)
2023-01-11T21:57:16.0510613Z   test_mark_non_differentiable (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0510892Z   test_mark_non_differentiable_mixed (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0511188Z   test_mark_non_differentiable_none (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0511470Z   test_materialize_grads (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0511792Z   test_multi_backward (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0512050Z   test_multi_backward_no_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0512322Z   test_multi_grad_hooks (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0513195Z   test_named_tensor_for_complex_views (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:1114: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1816.)
2023-01-11T21:57:16.0513774Z   return super(Tensor, self).refine_names(names)
2023-01-11T21:57:16.0513963Z ok (0.001s)
2023-01-11T21:57:16.0514207Z   test_naughty_anomaly_access (__main__.TestAutograd) ... expected failure (0.002s)
2023-01-11T21:57:16.0514533Z   test_naughty_autograd_function_attribute_access (__main__.TestAutograd) ... ok (0.010s)
2023-01-11T21:57:16.0514896Z   test_naughty_autograd_function_stashing_ctx (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0515199Z   test_nested_anomaly_detect_nan (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0515494Z   test_nested_anomaly_printstack_cleanup (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0515767Z   test_next_functions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0516021Z   test_no_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0516275Z   test_no_grad_assignment (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0516539Z   test_no_grad_copy (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0517319Z   test_no_grad_copy_sparse (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2325: UserWarning: Argument order of nn.functional.embedding_bag was changed. Usage `embedding_bag(weight, input, ...)` is deprecated, and should now be `embedding_bag(input, weight, ...)`.
2023-01-11T21:57:16.0517792Z   warnings.warn(
2023-01-11T21:57:16.0517964Z ok (0.005s)
2023-01-11T21:57:16.0518177Z   test_no_grad_input (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0518437Z   test_no_grad_modifies_version (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0518705Z   test_no_grad_python_function (__main__.TestAutograd)
2023-01-11T21:57:16.0518966Z Python Functions should respect grad mode. ... ok (0.001s)
2023-01-11T21:57:16.0519228Z   test_no_requires_grad_inplace (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0519508Z   test_no_unnecessary_save (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0519790Z   test_no_unnecessary_unwrapping (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0520061Z   test_not_implemented_fwad (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0520334Z   test_not_implemented_grad (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0520606Z   test_numpy_requires_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0520880Z   test_once_differentiable (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0521169Z   test_out_variant_raises_when_inputs_require_grad (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0521498Z   test_pack_hook_with_inplace_modification_should_fail (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0521788Z   test_pickle (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0522039Z   test_pow_zero_tensor_gradient (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0522310Z   test_power_function (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0522772Z   test_profiler (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:06 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0523252Z STAGE:2023-01-11 21:57:06 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0523694Z STAGE:2023-01-11 21:57:06 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0524001Z ok (0.002s)
2023-01-11T21:57:16.0524231Z   test_profiler_aggregation_fake (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0524722Z   test_profiler_aggregation_lstm (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:06 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0525216Z STAGE:2023-01-11 21:57:06 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0525665Z STAGE:2023-01-11 21:57:06 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0525865Z 
2023-01-11T21:57:16.0525990Z ===================================================================================================================================================================
2023-01-11T21:57:16.0526175Z TEST
2023-01-11T21:57:16.0526670Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0527133Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:16.0527693Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0528103Z                    aten::lstm         0.91%     198.000us         4.79%       1.042ms       1.042ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0528395Z                    aten::lstm         0.91%     198.000us         4.89%       1.064ms       1.064ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0528688Z                    aten::lstm         0.90%     197.000us         4.80%       1.045ms       1.045ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0528975Z                    aten::lstm         0.88%     191.000us         4.88%       1.063ms       1.063ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0529255Z                    aten::lstm         0.87%     189.000us         4.89%       1.065ms       1.065ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0529532Z                    aten::lstm         0.86%     188.000us         4.82%       1.049ms       1.049ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0529792Z                    aten::lstm         0.84%     183.000us         4.84%       1.053ms       1.053ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0530079Z                    aten::lstm         0.83%     181.000us         4.93%       1.074ms       1.074ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0530362Z                    aten::lstm         0.82%     178.000us         4.80%       1.045ms       1.045ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0530639Z                    aten::lstm         0.80%     175.000us         4.90%       1.067ms       1.067ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0531144Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0531482Z Self CPU time total: 21.770ms
2023-01-11T21:57:16.0531603Z 
2023-01-11T21:57:16.0531964Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0532408Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:16.0532996Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0533386Z                    aten::lstm        16.11%       3.508ms        97.67%      21.262ms       1.063ms            20      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0533673Z                   aten::addmm        10.04%       2.186ms        12.62%       2.747ms      13.735us           200                 [[80], [3, 20], [20, 80], [], []]  
2023-01-11T21:57:16.0533961Z                aten::sigmoid_         9.26%       2.016ms         9.26%       2.016ms       3.360us           600                                         [[3, 20]]  
2023-01-11T21:57:16.0534246Z                     aten::mul         9.10%       1.981ms         9.10%       1.981ms       3.302us           600                                [[3, 20], [3, 20]]  
2023-01-11T21:57:16.0534567Z            aten::unsafe_split         6.29%       1.369ms        16.13%       3.512ms      17.560us           200                                 [[3, 80], [], []]  
2023-01-11T21:57:16.0534866Z                   aten::slice         4.97%       1.083ms         5.06%       1.102ms       1.377us           800                         [[3, 80], [], [], [], []]  
2023-01-11T21:57:16.0535141Z                  aten::narrow         4.78%       1.040ms         9.39%       2.044ms       2.555us           800                             [[3, 80], [], [], []]  
2023-01-11T21:57:16.0535425Z                   aten::tanh_         3.45%     752.000us         3.45%     752.000us       3.760us           200                                         [[3, 20]]  
2023-01-11T21:57:16.0535704Z                    aten::tanh         3.16%     689.000us         3.16%     689.000us       3.445us           200                                         [[3, 20]]  
2023-01-11T21:57:16.0535987Z                  aten::linear         3.03%     659.000us        18.97%       4.129ms      20.645us           200                         [[3, 20], [80, 20], [80]]  
2023-01-11T21:57:16.0536498Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0536832Z Self CPU time total: 21.770ms
2023-01-11T21:57:16.0536952Z 
2023-01-11T21:57:16.0537074Z ===================================================================================================================================================================
2023-01-11T21:57:16.0537271Z TEST
2023-01-11T21:57:16.0537481Z ===================================================================================================================================================================
2023-01-11T21:57:16.0537763Z This report only display top-level ops statistics
2023-01-11T21:57:16.0538267Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0538715Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:16.0539260Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0539648Z                    aten::lstm         0.91%     198.000us         4.79%       1.042ms       1.042ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0539932Z                    aten::lstm         0.91%     198.000us         4.89%       1.064ms       1.064ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0540215Z                    aten::lstm         0.90%     197.000us         4.80%       1.045ms       1.045ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0540528Z                    aten::lstm         0.88%     191.000us         4.88%       1.063ms       1.063ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0540802Z                    aten::lstm         0.87%     189.000us         4.89%       1.065ms       1.065ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0541066Z                    aten::lstm         0.86%     188.000us         4.82%       1.049ms       1.049ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0541339Z                    aten::lstm         0.84%     183.000us         4.84%       1.053ms       1.053ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0541621Z                    aten::lstm         0.83%     181.000us         4.93%       1.074ms       1.074ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0541932Z                    aten::lstm         0.82%     178.000us         4.80%       1.045ms       1.045ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0542214Z                    aten::lstm         0.80%     175.000us         4.90%       1.067ms       1.067ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0542848Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0543201Z Self CPU time total: 21.770ms
2023-01-11T21:57:16.0543322Z 
2023-01-11T21:57:16.0543588Z ERROR:2023-01-11 21:57:07 16151:16151 CudaDeviceProperties.cpp:27] cudaGetDeviceCount failed with code 35
2023-01-11T21:57:16.0543903Z ===================================================================================================================================================================
2023-01-11T21:57:16.0544190Z This report only display top-level ops statistics
2023-01-11T21:57:16.0544694Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0545140Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:16.0545692Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0546091Z                    aten::lstm        16.11%       3.508ms        97.67%      21.262ms       1.063ms            20      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:16.0546362Z                   aten::randn         1.03%     224.000us         2.33%     508.000us       8.467us            60                              [[], [], [], [], []]  
2023-01-11T21:57:16.0546877Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:16.0547224Z Self CPU time total: 21.770ms
2023-01-11T21:57:16.0547345Z 
2023-01-11T21:57:16.0547462Z Total time based on python measurements:  23.738ms
2023-01-11T21:57:16.0547704Z CPU time measurement python side overhead: 9.04%
2023-01-11T21:57:16.0547911Z ok (1.168s)
2023-01-11T21:57:16.0548128Z   test_profiler_aggregation_table (__main__.TestAutograd)
2023-01-11T21:57:16.0548614Z Test if the profiling result is aggregated for `str(prof)` ... STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0549107Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0549562Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0549892Z ok (0.002s)
2023-01-11T21:57:16.0550110Z   test_profiler_function_event_avg (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0550608Z   test_profiler_propagation (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0551095Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0551532Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0551792Z ok (0.046s)
2023-01-11T21:57:16.0552213Z   test_profiler_seq_nr (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0552691Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0553162Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0553692Z -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  
2023-01-11T21:57:16.0554100Z                                                    Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls  
2023-01-11T21:57:16.0554597Z -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  
2023-01-11T21:57:16.0554959Z                                               aten::add        15.73%      14.000us        15.73%      14.000us      14.000us             1  
2023-01-11T21:57:16.0555241Z                                               aten::sum        15.73%      14.000us        16.85%      15.000us      15.000us             1  
2023-01-11T21:57:16.0555526Z                                             aten::randn         8.99%       8.000us        20.22%      18.000us       9.000us             2  
2023-01-11T21:57:16.0555810Z                                           aten::normal_         7.87%       7.000us         7.87%       7.000us       3.500us             2  
2023-01-11T21:57:16.0556112Z                         torch::autograd::AccumulateGrad         7.87%       7.000us        19.10%      17.000us       8.500us             2  
2023-01-11T21:57:16.0556428Z autograd::engine::evaluate_function: torch::autograd...         6.74%       6.000us        20.22%      18.000us       9.000us             2  
2023-01-11T21:57:16.0556742Z                                             aten::copy_         6.74%       6.000us         6.74%       6.000us       3.000us             2  
2023-01-11T21:57:16.0557019Z                                            aten::expand         4.49%       4.000us         5.62%       5.000us       5.000us             1  
2023-01-11T21:57:16.0557373Z                                             aten::empty         3.37%       3.000us         3.37%       3.000us       1.500us             2  
2023-01-11T21:57:16.0557648Z                                        aten::empty_like         3.37%       3.000us         4.49%       4.000us       4.000us             1  
2023-01-11T21:57:16.0557959Z       autograd::engine::evaluate_function: SumBackward1         3.37%       3.000us        12.36%      11.000us      11.000us             1  
2023-01-11T21:57:16.0558266Z                                            SumBackward1         3.37%       3.000us         8.99%       8.000us       8.000us             1  
2023-01-11T21:57:16.0558552Z                                 aten::new_empty_strided         3.37%       3.000us         4.49%       4.000us       2.000us             2  
2023-01-11T21:57:16.0558840Z                                        aten::as_strided         2.25%       2.000us         2.25%       2.000us       1.000us             2  
2023-01-11T21:57:16.0559114Z                                         aten::ones_like         2.25%       2.000us         6.74%       6.000us       6.000us             1  
2023-01-11T21:57:16.0559433Z                                     aten::empty_strided         2.25%       2.000us         2.25%       2.000us       0.667us             3  
2023-01-11T21:57:16.0559758Z       autograd::engine::evaluate_function: AddBackward0         2.25%       2.000us         2.25%       2.000us       2.000us             1  
2023-01-11T21:57:16.0560064Z                                             aten::fill_         0.00%       0.000us         0.00%       0.000us       0.000us             2  
2023-01-11T21:57:16.0560326Z                                            AddBackward0         0.00%       0.000us         0.00%       0.000us       0.000us             1  
2023-01-11T21:57:16.0560807Z -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  
2023-01-11T21:57:16.0561143Z Self CPU time total: 89.000us
2023-01-11T21:57:16.0561263Z 
2023-01-11T21:57:16.0561335Z ok (0.004s)
2023-01-11T21:57:16.0561780Z   test_profiler_shapes (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0562267Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0562719Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0562923Z 
2023-01-11T21:57:16.0563260Z ----------------------  ------------  ------------  ------------  ------------  ------------  ------------  ---------------------------------------  
2023-01-11T21:57:16.0563676Z                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                             Input Shapes  
2023-01-11T21:57:16.0564201Z ----------------------  ------------  ------------  ------------  ------------  ------------  ------------  ---------------------------------------  
2023-01-11T21:57:16.0564594Z           aten::linear         3.27%       5.000us        50.98%      78.000us      78.000us             1              [[128, 20], [30, 20], [30]]  
2023-01-11T21:57:16.0564877Z                aten::t         6.54%      10.000us        11.76%      18.000us      18.000us             1                               [[30, 20]]  
2023-01-11T21:57:16.0565161Z        aten::transpose         3.92%       6.000us         3.92%       6.000us       6.000us             1                       [[30, 20], [], []]  
2023-01-11T21:57:16.0565441Z       aten::as_strided         1.31%       2.000us         1.31%       2.000us       2.000us             1                   [[30, 20], [], [], []]  
2023-01-11T21:57:16.0565729Z            aten::addmm        30.72%      47.000us        35.95%      55.000us      55.000us             1      [[30], [128, 20], [20, 30], [], []]  
2023-01-11T21:57:16.0566015Z           aten::expand         1.31%       2.000us         1.31%       2.000us       2.000us             1                           [[30], [], []]  
2023-01-11T21:57:16.0566303Z       aten::as_strided         0.00%       0.000us         0.00%       0.000us       0.000us             1                       [[30], [], [], []]  
2023-01-11T21:57:16.0566589Z            aten::copy_         3.92%       6.000us         3.92%       6.000us       6.000us             1               [[128, 30], [128, 30], []]  
2023-01-11T21:57:16.0566860Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 30]]  
2023-01-11T21:57:16.0567143Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 20]]  
2023-01-11T21:57:16.0567422Z           aten::linear         1.96%       3.000us        49.02%      75.000us      75.000us             1              [[128, 30], [40, 30], [40]]  
2023-01-11T21:57:16.0567701Z                aten::t         1.96%       3.000us         3.27%       5.000us       5.000us             1                               [[40, 30]]  
2023-01-11T21:57:16.0568017Z        aten::transpose         1.31%       2.000us         1.31%       2.000us       2.000us             1                       [[40, 30], [], []]  
2023-01-11T21:57:16.0568286Z       aten::as_strided         0.00%       0.000us         0.00%       0.000us       0.000us             1                   [[40, 30], [], [], []]  
2023-01-11T21:57:16.0568569Z            aten::addmm        40.52%      62.000us        43.79%      67.000us      67.000us             1      [[40], [128, 30], [30, 40], [], []]  
2023-01-11T21:57:16.0568846Z           aten::expand         0.65%       1.000us         0.65%       1.000us       1.000us             1                           [[40], [], []]  
2023-01-11T21:57:16.0569128Z       aten::as_strided         0.00%       0.000us         0.00%       0.000us       0.000us             1                       [[40], [], [], []]  
2023-01-11T21:57:16.0569440Z            aten::copy_         2.61%       4.000us         2.61%       4.000us       4.000us             1               [[128, 40], [128, 40], []]  
2023-01-11T21:57:16.0569717Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 40]]  
2023-01-11T21:57:16.0570003Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 30]]  
2023-01-11T21:57:16.0570485Z ----------------------  ------------  ------------  ------------  ------------  ------------  ------------  ---------------------------------------  
2023-01-11T21:57:16.0570817Z Self CPU time total: 153.000us
2023-01-11T21:57:16.0570939Z 
2023-01-11T21:57:16.0570995Z ok (0.003s)
2023-01-11T21:57:16.0571426Z   test_profiler_unboxed_only (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0571924Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0572373Z STAGE:2023-01-11 21:57:07 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0572622Z ok (0.001s)
2023-01-11T21:57:16.0572855Z   test_pynode_destruction_deadlock (__main__.TestAutograd) ... ok (1.374s)
2023-01-11T21:57:16.0573340Z   test_record_function (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0573817Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0574249Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0574686Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0575119Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0575549Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0575807Z ok (0.003s)
2023-01-11T21:57:16.0576240Z   test_record_function_callbacks (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0576723Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0577153Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0577413Z ok (0.002s)
2023-01-11T21:57:16.0577845Z   test_record_function_legacy (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:16.0578328Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:16.0578803Z STAGE:2023-01-11 21:57:09 16151:16151 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:16.0579063Z ok (0.002s)
2023-01-11T21:57:16.0579300Z   test_record_function_multithreaded (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0579581Z   test_reentrant_child_error (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0579856Z   test_reentrant_priority (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0580151Z   test_reentrant_with_callbacks_both_depths (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0580445Z   test_reentrant_with_callbacks_depth_0 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0580743Z   test_reentrant_with_callbacks_depth_1 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0581038Z   test_reentrant_with_leaf_variable_hook (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0581388Z   test_reentrant_with_non_leaf_variable_hook (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0581660Z   test_requires_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0581917Z   test_requires_grad_ (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0582187Z   test_requires_grad_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0582728Z   test_retain_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0582988Z   test_retain_grad_cycle (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0583257Z   test_retain_grad_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0583540Z   test_retain_grad_inplace_over_view (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0583807Z   test_return_duplicate (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0584082Z   test_return_duplicate_inplace (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0584351Z   test_return_leaf (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0584606Z   test_return_leaf_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0584879Z   test_save_none_for_backward (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0585159Z   test_save_on_cpu_and_checkpoint (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0585418Z   test_save_output_nr (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0585748Z   test_saved_variable_packing_unpacking_did_not_save_original_with_default_hooks (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0586132Z   test_saved_variable_packing_unpacking_did_not_save_original_with_hooks (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0586508Z   test_saved_variable_packing_unpacking_saved_original_with_default_hooks (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0586864Z   test_saved_variable_packing_unpacking_saved_original_with_hooks (__main__.TestAutograd) ... ok (0.012s)
2023-01-11T21:57:16.0587209Z   test_saved_variable_saved_original_inplace_detach (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0587527Z   test_saved_variable_version_counter (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0587821Z   test_saved_variables_deprecated (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0588094Z   test_saving_variable_to_disk (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0588368Z   test_select_expanded_v (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0588624Z   test_select_sum (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0588878Z   test_set_data_preserve_pyobj (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0589161Z   test_set_data_self_requires_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0589448Z   test_set_data_tensorimpl_type (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0589716Z   test_set_grad_coroutines (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0590009Z   test_set_grad_coroutines_benign_exceptions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0590391Z   test_set_grad_coroutines_critical_exceptions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0590692Z   test_set_grad_coroutines_exit (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0590955Z   test_set_grad_enabled (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0591232Z   test_set_grad_generator_functions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0591536Z   test_set_grad_generator_functions_recursive (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0591801Z   test_setitem (__main__.TestAutograd) ... ok (0.013s)
2023-01-11T21:57:16.0592050Z   test_setitem_mask (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0592356Z   test_setting_default_saved_variable_hooks_twice_should_not_fail (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0592709Z   test_setting_default_saved_variable_hooks_twice_should_use_inner (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0593032Z   test_shape (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0593286Z   test_sharded_grad (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:16.0593545Z   test_simple_reentrant (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0593796Z   test_slice_expanded_v (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0594071Z   test_sparse_gather_both_scalar (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0594347Z   test_sparse_gather_dim0 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0594612Z   test_sparse_gather_dim1 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0594873Z   test_sparse_gather_dim_neg (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0595152Z   test_sparse_gather_ind_scalar (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0595432Z   test_sparse_gather_x_scalar (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0595692Z   test_sparse_mm_backward (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:16.0596151Z   test_symeig_no_eigenvectors (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:4200: UserWarning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T21:57:16.0596686Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T21:57:16.0597005Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T21:57:16.0597266Z should be replaced with
2023-01-11T21:57:16.0597576Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T21:57:16.0597788Z and
2023-01-11T21:57:16.0597961Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T21:57:16.0598169Z should be replaced with
2023-01-11T21:57:16.0598633Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2910.)
2023-01-11T21:57:16.0598985Z   w, v = torch.symeig(A, eigenvectors=False)
2023-01-11T21:57:16.0599190Z ok (0.006s)
2023-01-11T21:57:16.0599413Z   test_tensor_grad_warnings (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0599684Z   test_thread_shutdown (__main__.TestAutograd) ... ok (1.357s)
2023-01-11T21:57:16.0599940Z   test_to_sparse_backward (__main__.TestAutograd) ... ok (0.046s)
2023-01-11T21:57:16.0600209Z   test_too_many_grads (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0600474Z   test_type_conversions (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:16.0600727Z   test_unrelated_inputs (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0600988Z   test_unused_output (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0601261Z   test_var_mean_differentiable (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0601525Z   test_variable_traverse (__main__.TestAutograd) ... ok (0.104s)
2023-01-11T21:57:16.0601789Z   test_version_counter (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0602054Z   test_view_func_replay (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0602365Z   test_volatile_deprecated (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0602634Z   test_will_engine_execute_node (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0602936Z   test_wrapped_number_saved_variable_hooks (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0603279Z   test_view_func_for_complex_views (autograd.test_complex.TestAutogradComplex) ... ok (0.003s)
2023-01-11T21:57:16.0603628Z   test_view_with_multi_output (autograd.test_complex.TestAutogradComplex) ... ok (0.007s)
2023-01-11T21:57:16.0604314Z   test_advanced_packing_unpacking (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:8092: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0604993Z   self.assertEqual(dual.storage().data_ptr(), foo.storage().data_ptr())
2023-01-11T21:57:16.0605569Z /var/lib/jenkins/workspace/test/test_autograd.py:8101: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0606134Z   self.assertEqual(dual_primal.storage().data_ptr(), foo.storage().data_ptr())
2023-01-11T21:57:16.0606700Z /var/lib/jenkins/workspace/test/test_autograd.py:8102: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0607260Z   self.assertEqual(dual_tangent.storage().data_ptr(), bar.storage().data_ptr())
2023-01-11T21:57:16.0607504Z ok (0.004s)
2023-01-11T21:57:16.0607758Z   test_backward_graph_destruction (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0608080Z   test_basic_packing_unpacking (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0608424Z   test_codegen_ignores_undefined_outputs (__main__.TestAutogradForwardMode) ... ok (0.007s)
2023-01-11T21:57:16.0609107Z   test_create_new_zeros_with_same_meta (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:8351: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0609747Z   self.assertEqual(len(result.storage()), len(target.storage()) * prod_of_t_bdims)
2023-01-11T21:57:16.0609978Z ok (0.007s)
2023-01-11T21:57:16.0610214Z   test_default_level (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0610530Z   test_detach_view_tracking (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0610853Z   test_forward_level_cleanup (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0611155Z   test_fwd_grad_enabled (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0611457Z   test_grad_cleanup (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0611777Z   test_make_dual_forbid_integral_dtype (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0612125Z   test_make_dual_inference_tensor_in_inference_mode (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0612470Z   test_make_dual_torch_dispatch (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:16.0612796Z   test_metadata_check_check_conj (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:16.0613181Z   test_metadata_check_checks_ignores_size_zero (__main__.TestAutogradForwardMode) ... ok (0.003s)
2023-01-11T21:57:16.0613853Z   test_metadata_check_checks_storage_numel (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:7790: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0614460Z   self.assertEqual(len(primal.storage()), 5)
2023-01-11T21:57:16.0614663Z ok (0.001s)
2023-01-11T21:57:16.0614948Z   test_metadata_check_ignore_storage_offset_for_zero_numel_tensor (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0615667Z   test_metadata_check_when_primal_has_conj_bit (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:7826: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0616274Z   self.assertEqual(len(a.storage()), len(b.storage()))
2023-01-11T21:57:16.0616480Z ok (0.001s)
2023-01-11T21:57:16.0617074Z   test_metadata_check_when_primal_has_neg_bit (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:7841: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0617675Z   self.assertEqual(len(a.storage()), len(b.storage()))
2023-01-11T21:57:16.0617866Z ok (0.001s)
2023-01-11T21:57:16.0618099Z   test_nested_level (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:16.0618408Z   test_non_differentiable (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0618702Z   test_out_variant (__main__.TestAutogradForwardMode) ... ok (0.004s)
2023-01-11T21:57:16.0618988Z   test_print (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:16.0619312Z   test_set_fw_grad_having_own_fw_grad_at_same_level (__main__.TestAutogradForwardMode) ... ok (0.004s)
2023-01-11T21:57:16.0619647Z   test_set_fwd_grad_enabled (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:16.0619937Z   test_size_check (__main__.TestAutogradForwardMode) ... ok (0.004s)
2023-01-11T21:57:16.0620256Z   test_view_inplace_always_creates_a_view (__main__.TestAutogradForwardMode) ... ok (0.003s)
2023-01-11T21:57:16.0620599Z   test_view_inplace_differentiable_views (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:16.0620937Z   test_view_inplace_non_differentiable_views (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:16.0621313Z   test_inplace_on_view_not_same_layout (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.001s)
2023-01-11T21:57:16.0621698Z   test_inplace_on_view_same_layout (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.001s)
2023-01-11T21:57:16.0622602Z   test_metadata_check_for_storage_numel_skipped (__main__.TestAutogradForwardModeBatchedGrad) ... /var/lib/jenkins/workspace/test/test_autograd.py:7716: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:16.0623248Z   self.assertEqual(len(primal.storage()), 5)
2023-01-11T21:57:16.0623439Z ok (0.004s)
2023-01-11T21:57:16.0623704Z   test_out_of_place_basic (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.007s)
2023-01-11T21:57:16.0624079Z   test_out_of_place_not_same_layout (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.001s)
2023-01-11T21:57:16.0624525Z   test_construct_standard_basis_for_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0624973Z   test_construct_standard_basis_for_cuda_base_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:16.0625446Z   test_construct_standard_basis_for_cuda_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:16.0625893Z   test_construct_standard_basis_for_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.011s)
2023-01-11T21:57:16.0626311Z   test_hessian_create_graph_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.135s)
2023-01-11T21:57:16.0626754Z   test_hessian_create_graph_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.813s)
2023-01-11T21:57:16.0627234Z   test_hessian_create_graph_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.071s)
2023-01-11T21:57:16.0627672Z   test_hessian_create_graph_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.363s)
2023-01-11T21:57:16.0628079Z   test_hessian_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0628490Z   test_hessian_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:16.0628908Z   test_hessian_err_check_strict_vectorize_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0629340Z   test_hessian_err_check_strict_vectorize_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0629759Z   test_hessian_err_check_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0630195Z   test_hessian_err_check_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.014s)
2023-01-11T21:57:16.0630625Z   test_hessian_err_check_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0631049Z   test_hessian_err_check_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:16.0631444Z   test_hessian_match_vhp_hvp_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0631844Z   test_hessian_match_vhp_hvp_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:16.0632239Z   test_hessian_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0632617Z   test_hessian_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:16.0633008Z   test_hessian_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0633396Z   test_hessian_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.012s)
2023-01-11T21:57:16.0633795Z   test_hessian_output_vectorized_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0634198Z   test_hessian_output_vectorized_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0634615Z   test_hessian_scalar_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0635034Z   test_hessian_scalar_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:16.0635455Z   test_hessian_scalar_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0635868Z   test_hessian_scalar_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0636331Z   test_hessian_vectorize_correctness_multi_input_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.014s)
2023-01-11T21:57:16.0636772Z   test_hessian_vectorize_correctness_multi_input_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.106s)
2023-01-11T21:57:16.0637278Z   test_hessian_vectorize_correctness_simple_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0637705Z   test_hessian_vectorize_correctness_simple_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.031s)
2023-01-11T21:57:16.0638155Z   test_hessian_vectorize_correctness_unrelated_outputs_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0638616Z   test_hessian_vectorize_correctness_unrelated_outputs_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.028s)
2023-01-11T21:57:16.0639102Z   test_hessian_vectorize_raises_no_warnings_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0639526Z   test_hessian_vectorize_raises_no_warnings_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0639941Z   test_hvp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.086s)
2023-01-11T21:57:16.0640337Z   test_hvp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.451s)
2023-01-11T21:57:16.0640732Z   test_hvp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0641169Z   test_hvp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:16.0641591Z   test_hvp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0641997Z   test_hvp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0642379Z   test_hvp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0642760Z   test_hvp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0643137Z   test_hvp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0643515Z   test_hvp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0643881Z   test_hvp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0644258Z   test_hvp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0644671Z   test_jacobian_create_graph_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.058s)
2023-01-11T21:57:16.0645115Z   test_jacobian_create_graph_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.326s)
2023-01-11T21:57:16.0645545Z   test_jacobian_create_graph_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.046s)
2023-01-11T21:57:16.0645980Z   test_jacobian_create_graph_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.233s)
2023-01-11T21:57:16.0646408Z   test_jacobian_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0646817Z   test_jacobian_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:16.0647222Z   test_jacobian_err_check_strict_vectorize_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0647651Z   test_jacobian_err_check_strict_vectorize_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0648125Z   test_jacobian_err_check_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0648555Z   test_jacobian_err_check_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:16.0648966Z   test_jacobian_err_check_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0649391Z   test_jacobian_err_check_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0649806Z   test_jacobian_match_vjp_jvp_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0650199Z   test_jacobian_match_vjp_jvp_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:16.0650595Z   test_jacobian_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0651019Z   test_jacobian_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0651417Z   test_jacobian_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0651799Z   test_jacobian_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:16.0652204Z   test_jacobian_output_vectorized_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0652620Z   test_jacobian_output_vectorized_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0653023Z   test_jacobian_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0653403Z   test_jacobian_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0653813Z   test_jacobian_scalar_vectorized_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0654232Z   test_jacobian_scalar_vectorized_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0654698Z   test_jacobian_vectorize_correctness_different_devices_base_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:16.0655194Z   test_jacobian_vectorize_correctness_different_devices_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:16.0655680Z   test_jacobian_vectorize_correctness_different_dtype_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0656137Z   test_jacobian_vectorize_correctness_different_dtype_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:16.0656589Z   test_jacobian_vectorize_correctness_multi_input_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0657031Z   test_jacobian_vectorize_correctness_multi_input_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.017s)
2023-01-11T21:57:16.0657490Z   test_jacobian_vectorize_correctness_multi_input_multi_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:16.0657959Z   test_jacobian_vectorize_correctness_multi_input_multi_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.057s)
2023-01-11T21:57:16.0658413Z   test_jacobian_vectorize_correctness_simple_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0658839Z   test_jacobian_vectorize_correctness_simple_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.019s)
2023-01-11T21:57:16.0659290Z   test_jacobian_vectorize_correctness_unrelated_outputs_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0659756Z   test_jacobian_vectorize_correctness_unrelated_outputs_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.021s)
2023-01-11T21:57:16.0660239Z   test_jacobian_vectorize_correctness_zero_dim_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0660670Z   test_jacobian_vectorize_correctness_zero_dim_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.033s)
2023-01-11T21:57:16.0661107Z   test_jacobian_vectorize_raises_no_warnings_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0661544Z   test_jacobian_vectorize_raises_no_warnings_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0661961Z   test_jvp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.060s)
2023-01-11T21:57:16.0665136Z   test_jvp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.317s)
2023-01-11T21:57:16.0665632Z   test_jvp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0666024Z   test_jvp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0666418Z   test_jvp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0666806Z   test_jvp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0667195Z   test_jvp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0667578Z   test_jvp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0667950Z   test_jvp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0668337Z   test_jvp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0668727Z   test_jvp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0669106Z   test_jvp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0669481Z   test_vhp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.077s)
2023-01-11T21:57:16.0669877Z   test_vhp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.415s)
2023-01-11T21:57:16.0670267Z   test_vhp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0670651Z   test_vhp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0671033Z   test_vhp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0671435Z   test_vhp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0671831Z   test_vhp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0672200Z   test_vhp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:16.0672585Z   test_vhp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0672971Z   test_vhp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0673355Z   test_vhp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0673723Z   test_vhp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0674118Z   test_vjp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.054s)
2023-01-11T21:57:16.0674511Z   test_vjp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.282s)
2023-01-11T21:57:16.0674943Z   test_vjp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0675316Z   test_vjp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0675713Z   test_vjp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0676116Z   test_vjp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0676492Z   test_vjp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0676878Z   test_vjp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:16.0677329Z   test_vjp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:16.0677747Z   test_vjp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:16.0678118Z   test_vjp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:16.0678499Z   test_vjp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:16.0678867Z   test_inference_mode_context_manager (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0679209Z   test_inference_mode_decorator (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0679552Z   test_inference_mode_existing_autograd_session (__main__.TestAutogradInferenceMode) ... ok (0.006s)
2023-01-11T21:57:16.0679923Z   test_inference_mode_handle_direct_view_on_rebase (__main__.TestAutogradInferenceMode) ... ok (0.007s)
2023-01-11T21:57:16.0680295Z   test_inference_mode_handle_indirect_view_on_rebase (__main__.TestAutogradInferenceMode) ... ok (0.005s)
2023-01-11T21:57:16.0680667Z   test_inference_mode_inf_tensor_in_inf_mode_functional_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0681051Z   test_inference_mode_inf_tensor_in_inf_mode_inplace_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0681423Z   test_inference_mode_inf_tensor_in_inf_mode_view_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0681808Z   test_inference_mode_inf_tensor_in_normal_mode_functional_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0682188Z   test_inference_mode_inf_tensor_in_normal_mode_inplace_op (__main__.TestAutogradInferenceMode) ... ok (0.007s)
2023-01-11T21:57:16.0682564Z   test_inference_mode_inf_tensor_in_normal_mode_view_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0682924Z   test_inference_mode_tensor_creation (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0683284Z   test_mix_inference_and_normal_tensor_functional_op (__main__.TestAutogradInferenceMode) ... ok (0.004s)
2023-01-11T21:57:16.0683645Z   test_mix_inference_and_normal_tensor_inplace_op (__main__.TestAutogradInferenceMode) ... ok (0.009s)
2023-01-11T21:57:16.0684013Z   test_mix_inference_and_normal_tensor_view_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0684383Z   test_normal_tensor_inplace_output_in_inference_mode (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0684758Z   test_normal_tensor_inplace_output_in_normal_mode (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0685113Z   test_normal_tensor_view_output_in_inference_mode (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:16.0685476Z   test_normal_tensor_view_output_in_normal_mode (__main__.TestAutogradInferenceMode) ... ok (0.004s)
2023-01-11T21:57:16.0685804Z   test_cat_stack_r_to_c (__main__.TestMultithreadAutograd) ... ok (0.106s)
2023-01-11T21:57:16.0686116Z   test_dataparallel_saved_tensors_hooks (__main__.TestMultithreadAutograd) ... ok (0.002s)
2023-01-11T21:57:16.0686441Z   test_fork_join_in_middle (__main__.TestMultithreadAutograd) ... ok (0.018s)
2023-01-11T21:57:16.0686778Z   test_multi_grad_hooks (__main__.TestMultithreadAutograd) ... ok (0.005s)
2023-01-11T21:57:16.0687114Z   test_multithreaded_exception_propagation (__main__.TestMultithreadAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0687438Z   test_preserve_backtrace (__main__.TestMultithreadAutograd) ... ok (0.001s)
2023-01-11T21:57:16.0687755Z   test_python_thread_in_middle (__main__.TestMultithreadAutograd) ... ok (0.021s)
2023-01-11T21:57:16.0688069Z   test_simple_backward (__main__.TestMultithreadAutograd) ... ok (0.006s)
2023-01-11T21:57:16.0688374Z   test_simple_backward_same_input (__main__.TestMultithreadAutograd) ... ok (0.009s)
2023-01-11T21:57:16.0688558Z 
2023-01-11T21:57:16.0688798Z ----------------------------------------------------------------------
2023-01-11T21:57:16.0689044Z Ran 478 tests in 16.842s
2023-01-11T21:57:16.0689158Z 
2023-01-11T21:57:16.0689252Z OK (skipped=11, expected failures=1)
2023-01-11T21:57:16.0689373Z 
2023-01-11T21:57:16.0689488Z Generating XML reports...
2023-01-11T21:57:16.0689929Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAllowMutationOnSaved-20230111215658.xml
2023-01-11T21:57:16.0690451Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutograd-20230111215658.xml
2023-01-11T21:57:16.0690997Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-autograd.test_complex.TestAutogradComplex-20230111215658.xml
2023-01-11T21:57:16.0691576Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutogradForwardMode-20230111215658.xml
2023-01-11T21:57:16.0692169Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutogradForwardModeBatchedGrad-20230111215658.xml
2023-01-11T21:57:16.0692803Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-autograd.test_functional.TestAutogradFunctional-20230111215658.xml
2023-01-11T21:57:16.0693385Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutogradInferenceMode-20230111215658.xml
2023-01-11T21:57:16.0693938Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestMultithreadAutograd-20230111215658.xml
2023-01-11T21:57:16.0694183Z 
2023-01-11T21:57:16.0694529Z ##[endgroup]
2023-01-11T21:57:16.0694904Z FINISHED PRINTING LOG FILE of test_autograd (/var/lib/jenkins/workspace/test/test-reports/test_autograd_gulrsvtr)
2023-01-11T21:57:16.0695105Z 
2023-01-11T21:57:16.0695283Z Running test_cpp_extensions_jit ... [2023-01-11 21:57:16.040753]
2023-01-11T21:57:16.0695769Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_jit.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:57:16.041004]
2023-01-11T21:57:52.5003526Z 
2023-01-11T21:57:52.5004186Z Expand the folded group to see the log file of test_cpp_extensions_jit
2023-01-11T21:57:52.5005003Z ##[group]PRINTING LOG FILE of test_cpp_extensions_jit (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_jit_jy4nl2_6)
2023-01-11T21:57:52.5005566Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:57:52.5005722Z 
2023-01-11T21:57:52.5005799Z Running tests...
2023-01-11T21:57:52.5006114Z ----------------------------------------------------------------------
2023-01-11T21:57:52.5007649Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_jit
2023-01-11T21:57:52.5007975Z   test_autograd_from_cpp (__main__.TestCppExtensionJIT) ... ok (1.546s)
2023-01-11T21:57:52.5008298Z   test_compilation_error_formatting (__main__.TestCppExtensionJIT) ... ok (13.744s)
2023-01-11T21:57:52.5008732Z   test_cpp_frontend_module_has_same_output_as_python (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5009179Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/cpp_frontend_extension...
2023-01-11T21:57:52.5009605Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/cpp_frontend_extension/build.ninja...
2023-01-11T21:57:52.5010109Z Building extension module cpp_frontend_extension...
2023-01-11T21:57:52.5010372Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5012383Z [1/2] c++ -MMD -MF cpp_frontend_extension.o.d -DTORCH_EXTENSION_NAME=cpp_frontend_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/workspace/test/cpp_extensions/cpp_frontend_extension.cpp -o cpp_frontend_extension.o 
2023-01-11T21:57:52.5015130Z [2/2] c++ cpp_frontend_extension.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o cpp_frontend_extension.so
2023-01-11T21:57:52.5015825Z Loading extension module cpp_frontend_extension...
2023-01-11T21:57:52.5016205Z ok (1.699s)
2023-01-11T21:57:52.5016825Z   test_cpp_frontend_module_has_up_to_date_attributes (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5017767Z No modifications detected for re-loaded extension module cpp_frontend_extension, skipping build step...
2023-01-11T21:57:52.5018295Z Loading extension module cpp_frontend_extension...
2023-01-11T21:57:52.5034545Z ok (0.002s)
2023-01-11T21:57:52.5035142Z   test_cpp_frontend_module_python_inter_op (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5036110Z No modifications detected for re-loaded extension module cpp_frontend_extension, skipping build step...
2023-01-11T21:57:52.5036704Z Loading extension module cpp_frontend_extension...
2023-01-11T21:57:52.5037054Z ok (0.008s)
2023-01-11T21:57:52.5037628Z   test_cpp_frontend_module_python_inter_op_with_cuda (__main__.TestCppExtensionJIT) ... skip: CUDA not found (0.001s)
2023-01-11T21:57:52.5038390Z   test_custom_compound_op_autograd (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5039136Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module...
2023-01-11T21:57:52.5039788Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/build.ninja...
2023-01-11T21:57:52.5040307Z Building extension module is_python_module...
2023-01-11T21:57:52.5040746Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5043150Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=is_python_module -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/main.cpp -o main.o 
2023-01-11T21:57:52.5045116Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o is_python_module.so
2023-01-11T21:57:52.5052155Z Loading extension module is_python_module...
2023-01-11T21:57:52.5054226Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:52.5056904Z   warnings.warn(
2023-01-11T21:57:52.5058037Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #1 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:52.5058788Z   warnings.warn(
2023-01-11T21:57:52.5059421Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:52.5059835Z   warnings.warn(
2023-01-11T21:57:52.5060535Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #1 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:52.5060973Z   warnings.warn(
2023-01-11T21:57:52.5061252Z ok (1.279s)
2023-01-11T21:57:52.5061523Z   test_half_support (__main__.TestCppExtensionJIT)
2023-01-11T21:57:52.5062086Z Checks for an issue with operator< ambiguity for half when certain ... skip: Temporarily disabled (0.001s)
2023-01-11T21:57:52.5062889Z   test_inline_jit_compile_custom_op_cuda (__main__.TestCppExtensionJIT) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:57:52.5063526Z   test_inline_jit_compile_extension_cuda (__main__.TestCppExtensionJIT) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:57:52.5064276Z   test_inline_jit_compile_extension_multiple_sources_and_no_functions (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5064738Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension...
2023-01-11T21:57:52.5065117Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension/build.ninja...
2023-01-11T21:57:52.5065421Z Building extension module inline_jit_extension...
2023-01-11T21:57:52.5065677Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5066996Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=inline_jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension/main.cpp -o main.o 
2023-01-11T21:57:52.5068060Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o inline_jit_extension.so
2023-01-11T21:57:52.5068457Z Loading extension module inline_jit_extension...
2023-01-11T21:57:52.5068669Z ok (1.253s)
2023-01-11T21:57:52.5068952Z   test_inline_jit_compile_extension_throws_when_functions_is_bad (__main__.TestCppExtensionJIT) ... ok (0.004s)
2023-01-11T21:57:52.5069393Z   test_inline_jit_compile_extension_with_functions_as_dict (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5069852Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_dict...
2023-01-11T21:57:52.5070274Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_dict/build.ninja...
2023-01-11T21:57:52.5070632Z Building extension module inline_jit_extension_with_functions_dict...
2023-01-11T21:57:52.5070995Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5072367Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=inline_jit_extension_with_functions_dict -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_dict/main.cpp -o main.o 
2023-01-11T21:57:52.5073544Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o inline_jit_extension_with_functions_dict.so
2023-01-11T21:57:52.5073946Z Loading extension module inline_jit_extension_with_functions_dict...
2023-01-11T21:57:52.5074175Z ok (1.224s)
2023-01-11T21:57:52.5074514Z   test_inline_jit_compile_extension_with_functions_as_list (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5074977Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_list...
2023-01-11T21:57:52.5075393Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_list/build.ninja...
2023-01-11T21:57:52.5075745Z Building extension module inline_jit_extension_with_functions_list...
2023-01-11T21:57:52.5076010Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5077460Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=inline_jit_extension_with_functions_list -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_list/main.cpp -o main.o 
2023-01-11T21:57:52.5078581Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o inline_jit_extension_with_functions_list.so
2023-01-11T21:57:52.5078978Z Loading extension module inline_jit_extension_with_functions_list...
2023-01-11T21:57:52.5079210Z ok (1.253s)
2023-01-11T21:57:52.5079524Z   test_jit_compile_extension (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5079934Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/jit_extension...
2023-01-11T21:57:52.5080305Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/jit_extension/build.ninja...
2023-01-11T21:57:52.5080607Z Building extension module jit_extension...
2023-01-11T21:57:52.5080845Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5082228Z [1/3] c++ -MMD -MF jit_extension.o.d -DTORCH_EXTENSION_NAME=jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/jit_extension.cpp -o jit_extension.o 
2023-01-11T21:57:52.5084320Z [2/3] c++ -MMD -MF jit_extension2.o.d -DTORCH_EXTENSION_NAME=jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/jit_extension2.cpp -o jit_extension2.o 
2023-01-11T21:57:52.5085501Z [3/3] c++ jit_extension.o jit_extension2.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o jit_extension.so
2023-01-11T21:57:52.5085861Z Loading extension module jit_extension...
2023-01-11T21:57:52.5086068Z ok (1.813s)
2023-01-11T21:57:52.5086325Z   test_jit_cuda_archflags (__main__.TestCppExtensionJIT) ... skip: CUDA not found (0.001s)
2023-01-11T21:57:52.5086650Z   test_jit_cuda_extension (__main__.TestCppExtensionJIT) ... skip: CUDA not found (0.000s)
2023-01-11T21:57:52.5086988Z   test_jit_cudnn_extension (__main__.TestCppExtensionJIT) ... skip: CuDNN not found (0.001s)
2023-01-11T21:57:52.5087409Z   test_lenient_flag_handling_in_jit_extensions (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5087833Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/lenient_flag_handling_extension...
2023-01-11T21:57:52.5088233Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/lenient_flag_handling_extension/build.ninja...
2023-01-11T21:57:52.5088575Z Building extension module lenient_flag_handling_extension...
2023-01-11T21:57:52.5088843Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5090280Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=lenient_flag_handling_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -O0 -Wall -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/lenient_flag_handling_extension/main.cpp -o main.o 
2023-01-11T21:57:52.5091421Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o lenient_flag_handling_extension.so
2023-01-11T21:57:52.5091784Z Loading extension module lenient_flag_handling_extension...
2023-01-11T21:57:52.5092003Z ok (1.480s)
2023-01-11T21:57:52.5092324Z   test_reload_jit_extension (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5092731Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension...
2023-01-11T21:57:52.5093111Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/build.ninja...
2023-01-11T21:57:52.5093433Z Building extension module reloaded_jit_extension...
2023-01-11T21:57:52.5093691Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5094997Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=reloaded_jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/main.cpp -o main.o 
2023-01-11T21:57:52.5096119Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o reloaded_jit_extension.so
2023-01-11T21:57:52.5096468Z Loading extension module reloaded_jit_extension...
2023-01-11T21:57:52.5096768Z Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5097332Z The input conditions for extension module reloaded_jit_extension have changed. Bumping to version 1 and re-building as reloaded_jit_extension_v1...
2023-01-11T21:57:52.5097746Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/build.ninja...
2023-01-11T21:57:52.5098069Z Building extension module reloaded_jit_extension_v1...
2023-01-11T21:57:52.5098329Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5099640Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=reloaded_jit_extension_v1 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/main.cpp -o main.o 
2023-01-11T21:57:52.5100721Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o reloaded_jit_extension_v1.so
2023-01-11T21:57:52.5101086Z Loading extension module reloaded_jit_extension_v1...
2023-01-11T21:57:52.5101373Z Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5101816Z No modifications detected for re-loaded extension module reloaded_jit_extension_v1, skipping build step...
2023-01-11T21:57:52.5102147Z Loading extension module reloaded_jit_extension_v1...
2023-01-11T21:57:52.5102613Z Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5103138Z The input conditions for extension module reloaded_jit_extension have changed. Bumping to version 2 and re-building as reloaded_jit_extension_v2...
2023-01-11T21:57:52.5103569Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/build.ninja...
2023-01-11T21:57:52.5103897Z Building extension module reloaded_jit_extension_v2...
2023-01-11T21:57:52.5104147Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5105473Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=reloaded_jit_extension_v2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/main.cpp -o main.o 
2023-01-11T21:57:52.5106605Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o reloaded_jit_extension_v2.so
2023-01-11T21:57:52.5106961Z Loading extension module reloaded_jit_extension_v2...
2023-01-11T21:57:52.5107182Z ok (3.817s)
2023-01-11T21:57:52.5107526Z   test_returns_shared_library_path_when_is_python_module_is_true (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5108114Z The input conditions for extension module is_python_module have changed. Bumping to version 1 and re-building as is_python_module_v1...
2023-01-11T21:57:52.5108526Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/build.ninja...
2023-01-11T21:57:52.5108840Z Building extension module is_python_module_v1...
2023-01-11T21:57:52.5109086Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5110424Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=is_python_module_v1 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/main.cpp -o main.o 
2023-01-11T21:57:52.5111477Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o is_python_module_v1.so
2023-01-11T21:57:52.5111826Z Loading extension module is_python_module_v1...
2023-01-11T21:57:52.5112022Z ok (1.417s)
2023-01-11T21:57:52.5112374Z   test_set_default_type_also_changes_aten_default_type (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:57:52.5112811Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/test_set_default_type...
2023-01-11T21:57:52.5113187Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/test_set_default_type/build.ninja...
2023-01-11T21:57:52.5113489Z Building extension module test_set_default_type...
2023-01-11T21:57:52.5113747Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:52.5115136Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=test_set_default_type -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/test_set_default_type/main.cpp -o main.o 
2023-01-11T21:57:52.5116202Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o test_set_default_type.so
2023-01-11T21:57:52.5116545Z Loading extension module test_set_default_type...
2023-01-11T21:57:52.5116741Z ok (1.337s)
2023-01-11T21:57:52.5117029Z   test_warning (__main__.TestCppExtensionJIT) ... [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:57:52.5117426Z [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:57:52.5117717Z [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:57:52.5117986Z [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:57:52.5118406Z UserWarning: Error with torch.DoubleTensor (Triggered internally at /var/lib/jenkins/.cache/torch_extensions/py310_cu117/warn_mod/main.cpp:12.)
2023-01-11T21:57:52.5118709Z ok (2.560s)
2023-01-11T21:57:52.5118816Z 
2023-01-11T21:57:52.5119009Z ----------------------------------------------------------------------
2023-01-11T21:57:52.5119255Z Ran 23 tests in 34.505s
2023-01-11T21:57:52.5119372Z 
2023-01-11T21:57:52.5119445Z OK (skipped=7)
2023-01-11T21:57:52.5119555Z 
2023-01-11T21:57:52.5119638Z Generating XML reports...
2023-01-11T21:57:52.5120063Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_jit/TEST-TestCppExtensionJIT-20230111215717.xml
2023-01-11T21:57:52.5120317Z 
2023-01-11T21:57:52.5120632Z ##[endgroup]
2023-01-11T21:57:52.5121043Z FINISHED PRINTING LOG FILE of test_cpp_extensions_jit (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_jit_jy4nl2_6)
2023-01-11T21:57:52.5121262Z 
2023-01-11T21:57:52.5121453Z Running test_cuda ... [2023-01-11 21:57:52.500689]
2023-01-11T21:57:52.5121914Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:57:52.500917]
2023-01-11T21:57:54.2760931Z 
2023-01-11T21:57:54.2761445Z Expand the folded group to see the log file of test_cuda
2023-01-11T21:57:54.2762406Z ##[group]PRINTING LOG FILE of test_cuda (/var/lib/jenkins/workspace/test/test-reports/test_cuda_4umde16w)
2023-01-11T21:57:54.2762932Z CUDA not available, skipping tests
2023-01-11T21:57:54.2763113Z 
2023-01-11T21:57:54.2763185Z Running tests...
2023-01-11T21:57:54.2763570Z ----------------------------------------------------------------------
2023-01-11T21:57:54.2763744Z 
2023-01-11T21:57:54.2763945Z ----------------------------------------------------------------------
2023-01-11T21:57:54.2764184Z Ran 0 tests in 0.000s
2023-01-11T21:57:54.2764306Z 
2023-01-11T21:57:54.2764356Z OK
2023-01-11T21:57:54.2764447Z 
2023-01-11T21:57:54.2764532Z Generating XML reports...
2023-01-11T21:57:54.2764863Z Test results will be stored in test-reports/python-unittest/test_cuda
2023-01-11T21:57:54.2765034Z 
2023-01-11T21:57:54.2765248Z ##[endgroup]
2023-01-11T21:57:54.2765612Z FINISHED PRINTING LOG FILE of test_cuda (/var/lib/jenkins/workspace/test/test-reports/test_cuda_4umde16w)
2023-01-11T21:57:54.2765814Z 
2023-01-11T21:57:54.2765980Z Running test_fake_tensor ... [2023-01-11 21:57:54.276111]
2023-01-11T21:57:54.2766450Z Executing ['/opt/conda/bin/python', '-bb', 'test_fake_tensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:57:54.276346]
2023-01-11T21:57:56.9222496Z 
2023-01-11T21:57:56.9223006Z Expand the folded group to see the log file of test_fake_tensor
2023-01-11T21:57:56.9223955Z ##[group]PRINTING LOG FILE of test_fake_tensor (/var/lib/jenkins/workspace/test/test-reports/test_fake_tensor_22j53fn3)
2023-01-11T21:57:56.9224363Z 
2023-01-11T21:57:56.9224486Z Running tests...
2023-01-11T21:57:56.9225113Z ----------------------------------------------------------------------
2023-01-11T21:57:56.9225696Z Test results will be stored in test-reports/python-unittest/test_fake_tensor
2023-01-11T21:57:56.9226271Z   test_aliased_const_write (__main__.FakeTensorConstHandling) ... ok (0.246s)
2023-01-11T21:57:56.9226854Z   test_constant_invalidation (__main__.FakeTensorConstHandling) ... ok (0.005s)
2023-01-11T21:57:56.9227448Z   test_fake_tensor_batch_norm_cpu (__main__.FakeTensorConstHandling) ... ok (0.081s)
2023-01-11T21:57:56.9228034Z   test_fake_tensor_in_intlist_repro (__main__.FakeTensorConstHandling) ... ok (0.008s)
2023-01-11T21:57:56.9228607Z   test_inplace_add (__main__.FakeTensorConstHandling) ... ok (0.002s)
2023-01-11T21:57:56.9229187Z   test_inplace_view_invalidation (__main__.FakeTensorConstHandling) ... ok (0.001s)
2023-01-11T21:57:56.9229759Z   test_shared_storage_invalidation (__main__.FakeTensorConstHandling) ... ok (0.005s)
2023-01-11T21:57:56.9230985Z   test_shared_storages (__main__.FakeTensorConstHandling) ... /var/lib/jenkins/workspace/test/test_fake_tensor.py:513: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:56.9232325Z   self.assertEqual(x.storage()._cdata, y.storage()._cdata)
2023-01-11T21:57:56.9233369Z /var/lib/jenkins/workspace/test/test_fake_tensor.py:514: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:56.9233988Z   self.assertEqual(x.constant.storage()._cdata, y.constant.storage()._cdata)
2023-01-11T21:57:56.9234216Z ok (0.001s)
2023-01-11T21:57:56.9234444Z   test_simple (__main__.FakeTensorConstHandling) ... ok (0.001s)
2023-01-11T21:57:56.9234799Z   test_dead_key (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:57:56.9235097Z   test_dead_weak_ref (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:57:56.9235407Z   test_memoized_conversion_from_meta (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:57:56.9235741Z   test_memoized_conversion_to_meta (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:57:56.9236061Z   test_no_active_mode (__main__.FakeTensorConverterTest) ... ok (0.008s)
2023-01-11T21:57:56.9236348Z   test_no_ref_cycle (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:57:56.9236658Z   test_separate_mode_error (__main__.FakeTensorConverterTest) ... ok (0.007s)
2023-01-11T21:57:56.9237398Z   test_separate_tensor_storages_non_view (__main__.FakeTensorConverterTest) ... /var/lib/jenkins/workspace/test/test_fake_tensor.py:602: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:56.9237964Z   y.set_(x.storage())
2023-01-11T21:57:56.9238125Z ok (0.001s)
2023-01-11T21:57:56.9238383Z   test_separate_tensor_storages_view (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:57:56.9238714Z   test_like_ops (__main__.FakeTensorOperatorInvariants) ... ok (0.015s)
2023-01-11T21:57:56.9239047Z   test_non_kwarg_only_device (__main__.FakeTensorOperatorInvariants) ... ok (0.055s)
2023-01-11T21:57:56.9239381Z   test_sparse_new (__main__.FakeTensorOperatorInvariants) ... expected failure (0.004s)
2023-01-11T21:57:56.9239755Z   test_tensor_constructors_all_have_kwarg_device (__main__.FakeTensorOperatorInvariants) ... ok (0.107s)
2023-01-11T21:57:56.9240620Z   test_fake_tensor_prop_on_nn_module (__main__.FakeTensorPropTest) ... /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py:564: UserWarning: Was not able to add assertion to guarantee correct input value to specialized function. It is up to the user to make sure that your inputs match the inputs you specialized the function with.
2023-01-11T21:57:56.9241135Z   warnings.warn(
2023-01-11T21:57:56.9241294Z ok (0.023s)
2023-01-11T21:57:56.9241507Z   test_basic (__main__.FakeTensorTest) ... ok (0.002s)
2023-01-11T21:57:56.9241779Z   test_binary_op_type_promotion (__main__.FakeTensorTest) ... ok (0.008s)
2023-01-11T21:57:56.9242060Z   test_constructor (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:57:56.9242332Z   test_cpu_fallback (__main__.FakeTensorTest) ... skip: requires cuda (0.001s)
2023-01-11T21:57:56.9242634Z   test_cuda_lstm (__main__.FakeTensorTest) ... skip: requires cuda (0.001s)
2023-01-11T21:57:56.9242947Z   test_cudnn_rnn_with_fallback (__main__.FakeTensorTest) ... skip: requires cuda (0.002s)
2023-01-11T21:57:56.9243262Z   test_cudnn_rnn_without_fallback (__main__.FakeTensorTest) ... skip: requires cuda (0.002s)
2023-01-11T21:57:56.9243621Z   test_data_dependent_operator (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:57:56.9244238Z   test_deepcopy (__main__.FakeTensorTest) ... /var/lib/jenkins/workspace/test/test_fake_tensor.py:466: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:56.9244851Z   self.assertEqual(mod_copied.b.storage()._cdata, mod_copied.a.storage()._cdata)
2023-01-11T21:57:56.9245080Z ok (0.005s)
2023-01-11T21:57:56.9245306Z   test_fake_dispatch_keys (__main__.FakeTensorTest) ... ok (0.008s)
2023-01-11T21:57:56.9245583Z   test_fake_grad_copy (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:57:56.9245857Z   test_fake_mode_error (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:57:56.9246177Z   test_fallback_memory_prop (__main__.FakeTensorTest) ... skip: requires cuda (0.001s)
2023-01-11T21:57:56.9246480Z   test_from_numpy (__main__.FakeTensorTest) ... ok (0.010s)
2023-01-11T21:57:56.9246772Z   test_index_cuda_with_cpu (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9247076Z   test_like_constructor (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9247354Z   test_mode (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:57:56.9247608Z   test_nan_to_num (__main__.FakeTensorTest) ... ok (0.013s)
2023-01-11T21:57:56.9247865Z   test_new (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9248166Z   test_non_kwarg_device (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9248475Z   test_non_overlapping_stride_zero (__main__.FakeTensorTest) ... ok (0.014s)
2023-01-11T21:57:56.9248768Z   test_non_parameter_grad (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:57:56.9249052Z   test_normalize_device (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9249364Z   test_parameter_instantiation (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:57:56.9249652Z   test_print_in_fake_mode (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:57:56.9249906Z   test_randperm (__main__.FakeTensorTest) ... ok (0.007s)
2023-01-11T21:57:56.9250178Z   test_recursive_invocation (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:57:56.9250453Z   test_scalar_inputs (__main__.FakeTensorTest) ... ok (0.005s)
2023-01-11T21:57:56.9250736Z   test_setitem (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9251030Z   test_shape_take_not_device (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9251334Z   test_throw (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9251624Z   test_type_as (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9251934Z   test_upsample_bilinear_small_channels (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9252259Z   test_zero_dim (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:57:56.9252421Z 
2023-01-11T21:57:56.9252632Z ----------------------------------------------------------------------
2023-01-11T21:57:56.9252878Z Ran 57 tests in 0.681s
2023-01-11T21:57:56.9252978Z 
2023-01-11T21:57:56.9253078Z OK (skipped=16, expected failures=1)
2023-01-11T21:57:56.9253210Z 
2023-01-11T21:57:56.9253296Z Generating XML reports...
2023-01-11T21:57:56.9253745Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorConstHandling-20230111215755.xml
2023-01-11T21:57:56.9254297Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorConverterTest-20230111215755.xml
2023-01-11T21:57:56.9254873Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorOperatorInvariants-20230111215755.xml
2023-01-11T21:57:56.9255425Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorPropTest-20230111215755.xml
2023-01-11T21:57:56.9255980Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorTest-20230111215755.xml
2023-01-11T21:57:56.9256206Z 
2023-01-11T21:57:56.9256463Z ##[endgroup]
2023-01-11T21:57:56.9256850Z FINISHED PRINTING LOG FILE of test_fake_tensor (/var/lib/jenkins/workspace/test/test-reports/test_fake_tensor_22j53fn3)
2023-01-11T21:57:56.9257066Z 
2023-01-11T21:57:56.9257218Z Running test_nn ... [2023-01-11 21:57:56.922523]
2023-01-11T21:57:56.9257651Z Executing ['/opt/conda/bin/python', '-bb', 'test_nn.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:57:56.922804]
2023-01-11T21:58:37.5245138Z 
2023-01-11T21:58:37.5245632Z Expand the folded group to see the log file of test_nn
2023-01-11T21:58:37.5249255Z ##[group]PRINTING LOG FILE of test_nn (/var/lib/jenkins/workspace/test/test-reports/test_nn_to8mjml2)
2023-01-11T21:58:37.5249586Z 
2023-01-11T21:58:37.5249697Z Running tests...
2023-01-11T21:58:37.5250501Z ----------------------------------------------------------------------
2023-01-11T21:58:37.5251020Z Test results will be stored in test-reports/python-unittest/test_nn
2023-01-11T21:58:37.5279287Z   test_add_relu (__main__.TestAddRelu) ... ok (0.002s)
2023-01-11T21:58:37.5279710Z   test_add_relu_broadcasting (__main__.TestAddRelu) ... ok (0.001s)
2023-01-11T21:58:37.5280016Z   test_constant_pad_nd (__main__.TestConstantPadNd) ... ok (0.001s)
2023-01-11T21:58:37.5280318Z   test_preserves_memory_format (__main__.TestConstantPadNd) ... ok (0.001s)
2023-01-11T21:58:37.5280618Z   test_pickle_softsign (__main__.TestFunctionalPickle) ... ok (0.000s)
2023-01-11T21:58:37.5283780Z   test_fuse_module_eval_numerics (__main__.TestFusionEval) ... ok (0.255s)
2023-01-11T21:58:37.5284379Z   test_fuse_conv_bn_requires_grad (__main__.TestFusionUtils) ... ok (0.002s)
2023-01-11T21:58:37.5284772Z   test_fuse_linear_bn_requires_grad (__main__.TestFusionUtils) ... ok (0.001s)
2023-01-11T21:58:37.5285341Z   test_AdaptiveAvgPool1d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5285994Z   test_AdaptiveAvgPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5286677Z   test_AdaptiveAvgPool1d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5287390Z   test_AdaptiveAvgPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5288101Z   test_AdaptiveAvgPool1d_one_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5288795Z   test_AdaptiveAvgPool1d_one_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5289510Z   test_AdaptiveAvgPool2d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5290225Z   test_AdaptiveAvgPool2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5303420Z   test_AdaptiveAvgPool2d_single (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5303886Z   test_AdaptiveAvgPool2d_single_1x1output (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5312471Z   test_AdaptiveAvgPool2d_single_1x1output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5313010Z   test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5313327Z   test_AdaptiveAvgPool2d_tuple (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5313632Z   test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5313952Z   test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5314276Z   test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5314593Z   test_AdaptiveAvgPool3d_last_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5314925Z   test_AdaptiveAvgPool3d_last_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5315337Z   test_AdaptiveAvgPool3d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5316076Z   test_AdaptiveAvgPool3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5317419Z   test_AdaptiveAvgPool3d_single (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5317743Z   test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5318062Z   test_AdaptiveAvgPool3d_tuple (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5318380Z   test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5318685Z   test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5319009Z   test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5319359Z   test_AdaptiveLogSoftmax (__main__.TestNN) ... ok (0.075s)
2023-01-11T21:58:37.5319947Z   test_AdaptiveLogSoftmax_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5320505Z   test_AdaptiveMaxPool1d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5321054Z   test_AdaptiveMaxPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5321640Z   test_AdaptiveMaxPool1d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5322108Z   test_AdaptiveMaxPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5322430Z   test_AdaptiveMaxPool2d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5322754Z   test_AdaptiveMaxPool2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5323061Z   test_AdaptiveMaxPool2d_single (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5323377Z   test_AdaptiveMaxPool2d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5323687Z   test_AdaptiveMaxPool2d_tuple (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5324048Z   test_AdaptiveMaxPool2d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5324351Z   test_AdaptiveMaxPool2d_tuple_none (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5324672Z   test_AdaptiveMaxPool2d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5324993Z   test_AdaptiveMaxPool3d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5325397Z   test_AdaptiveMaxPool3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5325922Z   test_AdaptiveMaxPool3d_single (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5326498Z   test_AdaptiveMaxPool3d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5326930Z   test_AdaptiveMaxPool3d_single_nonatomic (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5327366Z   test_AdaptiveMaxPool3d_single_nonatomic_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5327694Z   test_AdaptiveMaxPool3d_tuple (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5328121Z   test_AdaptiveMaxPool3d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5328612Z   test_AdaptiveMaxPool3d_tuple_nonatomic (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5329016Z   test_AdaptiveMaxPool3d_tuple_nonatomic_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5329635Z   test_AdaptiveMaxPool3d_tuple_none (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5330183Z   test_AdaptiveMaxPool3d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5330465Z   test_AvgPool1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5330742Z   test_AvgPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5331031Z   test_AvgPool1d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5331390Z   test_AvgPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5331683Z   test_AvgPool1d_stride (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5331975Z   test_AvgPool1d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5332264Z   test_AvgPool1d_stride_pad (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5332551Z   test_AvgPool1d_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5332832Z   test_AvgPool2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5333113Z   test_AvgPool2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5333386Z   test_AvgPool2d_divisor (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5333678Z   test_AvgPool2d_divisor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5334017Z   test_AvgPool2d_divisor_stride (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5334332Z   test_AvgPool2d_divisor_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5334630Z   test_AvgPool2d_divisor_stride_pad (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5334953Z   test_AvgPool2d_divisor_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5335285Z   test_AvgPool2d_divisor_stride_pad_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5335626Z   test_AvgPool2d_divisor_stride_pad_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5335969Z   test_AvgPool2d_divisor_stride_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5336315Z   test_AvgPool2d_divisor_stride_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5336646Z   test_AvgPool2d_divisor_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5336968Z   test_AvgPool2d_divisor_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5337279Z   test_AvgPool2d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5337585Z   test_AvgPool2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5337879Z   test_AvgPool2d_stride (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5338156Z   test_AvgPool2d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5338451Z   test_AvgPool2d_stride_pad (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5338751Z   test_AvgPool2d_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5339026Z   test_AvgPool3d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5339306Z   test_AvgPool3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5339592Z   test_AvgPool3d_divisor (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5339872Z   test_AvgPool3d_divisor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5340174Z   test_AvgPool3d_divisor_stride (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5340465Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5340812Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5341157Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5341532Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5341903Z   test_AvgPool3d_divisor_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5342215Z   test_AvgPool3d_divisor_stride_pad (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5342887Z   test_AvgPool3d_divisor_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5343459Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5344045Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5344637Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5345310Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5345995Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5346575Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5347256Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5347900Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5348515Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5349110Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5349702Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5350342Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5350951Z   test_AvgPool3d_divisor_stride_pad_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5351521Z   test_AvgPool3d_divisor_stride_pad_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5352061Z   test_AvgPool3d_divisor_stride_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5352614Z   test_AvgPool3d_divisor_stride_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5353161Z   test_AvgPool3d_divisor_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5353688Z   test_AvgPool3d_divisor_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5354179Z   test_AvgPool3d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5354672Z   test_AvgPool3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5355161Z   test_AvgPool3d_stride (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5355605Z   test_AvgPool3d_stride1_pad0_gpu_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5357135Z   test_AvgPool3d_stride1_pad0_gpu_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5357774Z   test_AvgPool3d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5358255Z   test_AvgPool3d_stride_pad (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5358750Z   test_AvgPool3d_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5359261Z   test_AvgPool3d_stride_pad_gpu_fixedkw_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5359826Z   test_AvgPool3d_stride_pad_gpu_fixedkw_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5360380Z   test_AvgPool3d_stride_pad_gpu_general_output (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5360918Z   test_AvgPool3d_stride_pad_gpu_general_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5361535Z   test_AvgPool3d_stride_pad_gpu_input_nooverlap (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5362275Z   test_AvgPool3d_stride_pad_gpu_input_nooverlap_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5362815Z   test_BCELoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5363245Z   test_BCELoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5363837Z   test_BCELoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5364210Z   test_BCELoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5364518Z   test_BCELoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5364792Z   test_BCELoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5365102Z   test_BCELoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5365494Z   test_BCELoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5365835Z   test_BCELoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5366133Z   test_BCELoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5366445Z   test_BCELoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5366793Z   test_BCELoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5367118Z   test_BCELoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5367415Z   test_BCELoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5367722Z   test_BCELoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5368050Z   test_BCELoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5368393Z   test_BCELoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5368688Z   test_BCELoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5368976Z   test_BCELoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5369257Z   test_BCELoss_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5369565Z   test_BCELoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5369865Z   test_BCELoss_scalar_weights (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5370175Z   test_BCELoss_scalar_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5370517Z   test_BCELoss_scalar_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5370867Z   test_BCELoss_scalar_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5371209Z   test_BCELoss_scalar_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5371491Z   test_BCELoss_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5371786Z   test_BCELoss_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5372121Z   test_BCELoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5372454Z   test_BCELoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5372776Z   test_BCELoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5373073Z   test_BCELoss_weights_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5373378Z   test_BCELoss_weights_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5373720Z   test_BCELoss_weights_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5374047Z   test_BCELoss_weights_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5374357Z   test_BCEWithLogitsLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5374667Z   test_BCEWithLogitsLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5374998Z   test_BCEWithLogitsLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5375340Z   test_BCEWithLogitsLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5376086Z   test_BCEWithLogitsLoss_legacy_enum (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:58:37.5376495Z   warnings.warn(warning.format(ret))
2023-01-11T21:58:37.5377033Z /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:58:37.5377390Z   warnings.warn(warning.format(ret))
2023-01-11T21:58:37.5377590Z ok (0.008s)
2023-01-11T21:58:37.5377851Z   test_BCEWithLogitsLoss_legacy_enum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5378186Z   test_BCEWithLogitsLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5378536Z   test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5378921Z   test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5379284Z   test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5379625Z   test_BCEWithLogitsLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5379967Z   test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5380350Z   test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5380708Z   test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5381046Z   test_BCEWithLogitsLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5381394Z   test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5381759Z   test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5382131Z   test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5382618Z   test_BCEWithLogitsLoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5382954Z   test_BCEWithLogitsLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5383269Z   test_BCEWithLogitsLoss_no_reduce_scalar (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5383615Z   test_BCEWithLogitsLoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5383946Z   test_BCEWithLogitsLoss_scalar_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5384287Z   test_BCEWithLogitsLoss_scalar_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5384650Z   test_BCEWithLogitsLoss_scalar_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5385027Z   test_BCEWithLogitsLoss_scalar_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5385426Z   test_BCEWithLogitsLoss_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5385737Z   test_BCEWithLogitsLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5386099Z   test_BCEWithLogitsLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5386462Z   test_BCEWithLogitsLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5386773Z   test_BatchNorm1d_3d_input (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5387060Z   test_BatchNorm1d_3d_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5387358Z   test_BatchNorm1d_3d_input_eval (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:37.5387668Z   test_BatchNorm1d_3d_input_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5387969Z   test_BatchNorm1d_3d_input_not_affine (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5388332Z   test_BatchNorm1d_3d_input_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5388655Z   test_BatchNorm1d_3d_input_not_affine_eval (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5388981Z   test_BatchNorm1d_3d_input_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5389273Z   test_BatchNorm1d_affine (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5389568Z   test_BatchNorm1d_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5389864Z   test_BatchNorm1d_affine_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5390155Z   test_BatchNorm1d_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5390466Z   test_BatchNorm1d_affine_simple_average (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5390800Z   test_BatchNorm1d_affine_simple_average_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5391134Z   test_BatchNorm1d_affine_simple_average_eval (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5391463Z   test_BatchNorm1d_affine_simple_average_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5391780Z   test_BatchNorm1d_not_affine (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5392079Z   test_BatchNorm1d_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5392382Z   test_BatchNorm1d_not_affine_eval (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5392687Z   test_BatchNorm1d_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5392994Z   test_BatchNorm1d_not_tracking_stats (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5393313Z   test_BatchNorm1d_not_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5393621Z   test_BatchNorm1d_not_tracking_stats_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5393954Z   test_BatchNorm1d_not_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5394262Z   test_BatchNorm1d_zero_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5394566Z   test_BatchNorm1d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5394853Z   test_BatchNorm1d_zero_batch_eval (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5395164Z   test_BatchNorm1d_zero_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5395451Z   test_BatchNorm2d (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5395705Z   test_BatchNorm2d_2d_simple_average (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5396022Z   test_BatchNorm2d_2d_simple_average_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5396336Z   test_BatchNorm2d_2d_simple_average_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5396706Z   test_BatchNorm2d_2d_simple_average_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5397029Z   test_BatchNorm2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5397311Z   test_BatchNorm2d_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5397665Z   test_BatchNorm2d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5397946Z   test_BatchNorm2d_momentum (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5398240Z   test_BatchNorm2d_momentum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5398539Z   test_BatchNorm2d_momentum_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5398850Z   test_BatchNorm2d_momentum_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5399138Z   test_BatchNorm2d_not_affine (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5399479Z   test_BatchNorm2d_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5399781Z   test_BatchNorm2d_not_affine_eval (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5400082Z   test_BatchNorm2d_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5400389Z   test_BatchNorm2d_not_tracking_stats (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5400711Z   test_BatchNorm2d_not_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5401029Z   test_BatchNorm2d_not_tracking_stats_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5401345Z   test_BatchNorm2d_not_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5401654Z   test_BatchNorm2d_zero_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5401955Z   test_BatchNorm2d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5402248Z   test_BatchNorm2d_zero_batch_eval (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5402557Z   test_BatchNorm2d_zero_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5402844Z   test_BatchNorm3d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5403115Z   test_BatchNorm3d_3d_simple_average (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5403418Z   test_BatchNorm3d_3d_simple_average_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5403732Z   test_BatchNorm3d_3d_simple_average_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5404059Z   test_BatchNorm3d_3d_simple_average_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5404377Z   test_BatchNorm3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5404662Z   test_BatchNorm3d_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5404954Z   test_BatchNorm3d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5405247Z   test_BatchNorm3d_momentum (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5405530Z   test_BatchNorm3d_momentum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5405828Z   test_BatchNorm3d_momentum_eval (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5406135Z   test_BatchNorm3d_momentum_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5406424Z   test_BatchNorm3d_not_affine (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5406723Z   test_BatchNorm3d_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5407028Z   test_BatchNorm3d_not_affine_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5407338Z   test_BatchNorm3d_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5407673Z   test_BatchNorm3d_not_tracking_stats (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5407992Z   test_BatchNorm3d_not_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5408314Z   test_BatchNorm3d_not_tracking_stats_eval (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5408628Z   test_BatchNorm3d_not_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5408936Z   test_BatchNorm3d_zero_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5409235Z   test_BatchNorm3d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5409538Z   test_BatchNorm3d_zero_batch_eval (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5409838Z   test_BatchNorm3d_zero_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5410114Z   test_CELU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5410407Z   test_CELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5410671Z   test_CELU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5410955Z   test_CELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5411226Z   test_CELU_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5411502Z   test_CELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5411797Z   test_CTCLoss_2d_int_target_lengths_intlists (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5412139Z   test_CTCLoss_2d_int_target_lengths_intlists_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5412514Z   test_CTCLoss_2d_int_target_lengths_intlists_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5412848Z   test_CTCLoss_2d_int_target_lengths_intlists_sum_reduction (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5413218Z   test_CTCLoss_2d_int_target_lengths_intlists_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5413618Z   test_CTCLoss_2d_int_target_lengths_intlists_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5413964Z   test_CTCLoss_2d_int_target_lengths_tensors (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5414289Z   test_CTCLoss_2d_int_target_lengths_tensors_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5414659Z   test_CTCLoss_2d_int_target_lengths_tensors_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5415008Z   test_CTCLoss_2d_int_target_lengths_tensors_sum_reduction (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5415368Z   test_CTCLoss_2d_int_target_lengths_tensors_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5415758Z   test_CTCLoss_2d_int_target_lengths_tensors_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5416089Z   test_CTCLoss_2d_lengths_tensors (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5416404Z   test_CTCLoss_2d_lengths_tensors_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5416753Z   test_CTCLoss_2d_lengths_tensors_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5417063Z   test_CTCLoss_2d_lengths_tensors_sum_reduction (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5417411Z   test_CTCLoss_2d_lengths_tensors_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5417789Z   test_CTCLoss_2d_lengths_tensors_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5418140Z   test_CTCLoss_critical_target_len (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5418462Z   test_CTCLoss_lengthchecks_cpu (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5418762Z   test_CTCLoss_lengthchecks_cuda (__main__.TestNN) ... skip: CUDA not available (0.000s)
2023-01-11T21:58:37.5419052Z   test_CTCLoss_lengths_intlists (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5419352Z   test_CTCLoss_lengths_intlists_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5419701Z   test_CTCLoss_lengths_intlists_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5420023Z   test_CTCLoss_lengths_intlists_sum_reduction (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5420362Z   test_CTCLoss_lengths_intlists_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5420727Z   test_CTCLoss_lengths_intlists_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5421077Z   test_CTCLoss_lengths_tensors (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5421390Z   test_CTCLoss_lengths_tensors_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5421722Z   test_CTCLoss_lengths_tensors_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5422037Z   test_CTCLoss_lengths_tensors_sum_reduction (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5422514Z   test_CTCLoss_lengths_tensors_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5422918Z   test_CTCLoss_lengths_tensors_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5423247Z   test_CTCLoss_long_targets (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5423531Z   test_CTCLoss_typechecks (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5423813Z   test_CTCLoss_zero_infinity (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5424084Z   test_ConstantPad1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5424345Z   test_ConstantPad1d_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5424643Z   test_ConstantPad1d_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5424948Z   test_ConstantPad1d_complex (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5425243Z   test_ConstantPad1d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5425576Z   test_ConstantPad1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5425861Z   test_ConstantPad2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5426108Z   test_ConstantPad2d_complex (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5426415Z   test_ConstantPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5426746Z   test_ConstantPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5427048Z   test_ConstantPad2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5427348Z   test_ConstantPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5427649Z   test_ConstantPad3d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5427910Z   test_ConstantPad3d_complex (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5428203Z   test_ConstantPad3d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5428532Z   test_ConstantPad3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5428833Z   test_ConstantPad3d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5429150Z   test_ConstantPad3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5429421Z   test_Conv1d (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5429686Z   test_Conv1d_circular_stride2_pad2 (__main__.TestNN) ... ok (0.027s)
2023-01-11T21:58:37.5430075Z   test_Conv1d_circular_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5430388Z   test_Conv1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5430662Z   test_Conv1d_dilated (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:37.5430943Z   test_Conv1d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5431222Z   test_Conv1d_groups (__main__.TestNN) ... ok (0.059s)
2023-01-11T21:58:37.5431485Z   test_Conv1d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5431758Z   test_Conv1d_pad1 (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5432036Z   test_Conv1d_pad1_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5432302Z   test_Conv1d_pad1size1 (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5432628Z   test_Conv1d_pad1size1_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5432906Z   test_Conv1d_pad2 (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5433167Z   test_Conv1d_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5433448Z   test_Conv1d_pad2size1 (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5433730Z   test_Conv1d_pad2size1_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5434011Z   test_Conv1d_pad_same (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5434848Z   test_Conv1d_pad_same2 (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:58:37.5435459Z   return F.conv1d(input, weight, bias, self.stride,
2023-01-11T21:58:37.5435674Z ok (0.031s)
2023-01-11T21:58:37.5435920Z   test_Conv1d_pad_same2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5436235Z   test_Conv1d_pad_same_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5436511Z   test_Conv1d_pad_same_dilated (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5436812Z   test_Conv1d_pad_same_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5437103Z   test_Conv1d_pad_valid (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5437438Z   test_Conv1d_pad_valid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5437737Z   test_Conv1d_reflect_stride2_pad2 (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5438046Z   test_Conv1d_reflect_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5438339Z   test_Conv1d_replicate_stride2_pad2 (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5438659Z   test_Conv1d_replicate_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5438952Z   test_Conv1d_stride (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5439228Z   test_Conv1d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5439495Z   test_Conv1d_zero_batch (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5439776Z   test_Conv1d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5440070Z   test_Conv1d_zeros_stride2_pad2 (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5440361Z   test_Conv1d_zeros_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5440637Z   test_Conv2d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5440886Z   test_Conv2d_circular_stride2_pad2 (__main__.TestNN) ... ok (0.032s)
2023-01-11T21:58:37.5441234Z   test_Conv2d_circular_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5441584Z   test_Conv2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5441856Z   test_Conv2d_depthwise (__main__.TestNN) ... ok (0.025s)
2023-01-11T21:58:37.5442140Z   test_Conv2d_depthwise_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5442417Z   test_Conv2d_depthwise_dilated (__main__.TestNN) ... ok (0.024s)
2023-01-11T21:58:37.5442722Z   test_Conv2d_depthwise_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5443021Z   test_Conv2d_depthwise_padded (__main__.TestNN) ... ok (0.026s)
2023-01-11T21:58:37.5443332Z   test_Conv2d_depthwise_padded_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5443621Z   test_Conv2d_depthwise_strided (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5443959Z   test_Conv2d_depthwise_strided_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5444272Z   test_Conv2d_depthwise_with_multiplier (__main__.TestNN) ... ok (0.026s)
2023-01-11T21:58:37.5444585Z   test_Conv2d_depthwise_with_multiplier_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5444879Z   test_Conv2d_dilated (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5445158Z   test_Conv2d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5445456Z   test_Conv2d_dilated_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5445760Z   test_Conv2d_dilated_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5446051Z   test_Conv2d_groups (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5446332Z   test_Conv2d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5446604Z   test_Conv2d_groups_thnn (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5446896Z   test_Conv2d_groups_thnn_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5447199Z   test_Conv2d_groups_thnn_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5447519Z   test_Conv2d_groups_thnn_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5447819Z   test_Conv2d_groups_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5448132Z   test_Conv2d_groups_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5448420Z   test_Conv2d_no_bias (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5448685Z   test_Conv2d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5448978Z   test_Conv2d_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5449292Z   test_Conv2d_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5449586Z   test_Conv2d_pad_same (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:37.5449855Z   test_Conv2d_pad_same_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5450139Z   test_Conv2d_pad_same_dilated (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5450438Z   test_Conv2d_pad_same_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5450716Z   test_Conv2d_pad_valid (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5450996Z   test_Conv2d_pad_valid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5451274Z   test_Conv2d_padding (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5451554Z   test_Conv2d_padding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5451837Z   test_Conv2d_padding_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5452158Z   test_Conv2d_padding_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5452494Z   test_Conv2d_reflect_stride2_pad2 (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5452791Z   test_Conv2d_reflect_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5453098Z   test_Conv2d_replicate_stride2_pad2 (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5453410Z   test_Conv2d_replicate_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5453700Z   test_Conv2d_strided (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5453967Z   test_Conv2d_strided_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5454264Z   test_Conv2d_strided_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5454577Z   test_Conv2d_strided_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5454863Z   test_Conv2d_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5455209Z   test_Conv2d_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5455496Z   test_Conv2d_zero_batch (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5455787Z   test_Conv2d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5456072Z   test_Conv2d_zero_batch_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5456391Z   test_Conv2d_zero_batch_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5456697Z   test_Conv2d_zeros_stride2_pad2 (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5456988Z   test_Conv2d_zeros_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5457262Z   test_Conv3d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5457504Z   test_Conv3d_1x1x1_no_bias (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5457800Z   test_Conv3d_1x1x1_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5458089Z   test_Conv3d_1x1x1_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5458409Z   test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5458718Z   test_Conv3d_circular_stride2_pad2 (__main__.TestNN) ... ok (0.042s)
2023-01-11T21:58:37.5459019Z   test_Conv3d_circular_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5459339Z   test_Conv3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5459614Z   test_Conv3d_dilated (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:37.5459896Z   test_Conv3d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5460172Z   test_Conv3d_dilated_strided (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5460471Z   test_Conv3d_dilated_strided_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5460760Z   test_Conv3d_groups (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5461029Z   test_Conv3d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5461322Z   test_Conv3d_groups_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5461634Z   test_Conv3d_groups_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5461921Z   test_Conv3d_no_bias (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5462187Z   test_Conv3d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5462819Z   test_Conv3d_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5463133Z   test_Conv3d_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5463406Z   test_Conv3d_pad_same (__main__.TestNN) ... ok (0.029s)
2023-01-11T21:58:37.5463764Z   test_Conv3d_pad_same_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5464052Z   test_Conv3d_pad_same_dilated (__main__.TestNN) ... ok (0.029s)
2023-01-11T21:58:37.5464352Z   test_Conv3d_pad_same_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5464629Z   test_Conv3d_pad_valid (__main__.TestNN) ... ok (0.025s)
2023-01-11T21:58:37.5464913Z   test_Conv3d_pad_valid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5465212Z   test_Conv3d_replicate_stride2_pad2 (__main__.TestNN) ... ok (0.025s)
2023-01-11T21:58:37.5465518Z   test_Conv3d_replicate_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5465813Z   test_Conv3d_stride (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:37.5466090Z   test_Conv3d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5466420Z   test_Conv3d_stride_padding (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5466707Z   test_Conv3d_stride_padding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5467019Z   test_Conv3d_stride_padding_with_long_tensor (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5467350Z   test_Conv3d_stride_padding_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5467655Z   test_Conv3d_stride_with_long_tensor (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5467967Z   test_Conv3d_stride_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5468268Z   test_Conv3d_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5468566Z   test_Conv3d_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5468840Z   test_Conv3d_zero_batch (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5469127Z   test_Conv3d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5469435Z   test_Conv3d_zero_batch_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5469747Z   test_Conv3d_zero_batch_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5470053Z   test_Conv3d_zeros_stride2_pad2 (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5470359Z   test_Conv3d_zeros_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5470660Z   test_ConvTranspose1d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5470941Z   test_ConvTranspose1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5471241Z   test_ConvTranspose1d_dilated (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5471549Z   test_ConvTranspose1d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5471842Z   test_ConvTranspose1d_groups (__main__.TestNN) ... ok (0.024s)
2023-01-11T21:58:37.5472153Z   test_ConvTranspose1d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5472458Z   test_ConvTranspose1d_no_bias (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5472767Z   test_ConvTranspose1d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5473046Z   test_ConvTranspose2d (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:37.5473343Z   test_ConvTranspose2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5473641Z   test_ConvTranspose2d_dilated (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5473936Z   test_ConvTranspose2d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5474255Z   test_ConvTranspose2d_dilated_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5474604Z   test_ConvTranspose2d_dilated_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5474964Z   test_ConvTranspose2d_groups (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5475256Z   test_ConvTranspose2d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5475581Z   test_ConvTranspose2d_groups_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5475924Z   test_ConvTranspose2d_groups_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5476230Z   test_ConvTranspose2d_no_bias (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5476538Z   test_ConvTranspose2d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5476854Z   test_ConvTranspose2d_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5477192Z   test_ConvTranspose2d_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5477620Z   test_ConvTranspose2d_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5477952Z   test_ConvTranspose2d_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5478257Z   test_ConvTranspose3d (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5478534Z   test_ConvTranspose3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5478829Z   test_ConvTranspose3d_dilated (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5479136Z   test_ConvTranspose3d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5479440Z   test_CosineEmbeddingLoss (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5479741Z   test_CosineEmbeddingLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5480097Z   test_CosineEmbeddingLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5480452Z   test_CosineEmbeddingLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5480767Z   test_CosineEmbeddingLoss_margin (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5481089Z   test_CosineEmbeddingLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5481461Z   test_CosineEmbeddingLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5481822Z   test_CosineEmbeddingLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5482147Z   test_CosineEmbeddingLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5482505Z   test_CosineEmbeddingLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5482898Z   test_CosineEmbeddingLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5483300Z   test_CosineEmbeddingLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5483633Z   test_CosineEmbeddingLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5483982Z   test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5484364Z   test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5484744Z   test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5485070Z   test_CosineEmbeddingLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5485415Z   test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5485798Z   test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5486216Z   test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5486543Z   test_CosineEmbeddingLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5486889Z   test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5487271Z   test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5487636Z   test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5487971Z   test_CosineEmbeddingLoss_sum_reduction (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5488315Z   test_CosineEmbeddingLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5488740Z   test_CosineEmbeddingLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5489106Z   test_CosineEmbeddingLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5489419Z   test_CrossEntropyLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5489677Z   test_CrossEntropyLoss_2d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5489986Z   test_CrossEntropyLoss_2d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5490323Z   test_CrossEntropyLoss_2d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5490667Z   test_CrossEntropyLoss_2d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5490983Z   test_CrossEntropyLoss_2d_ignore_index (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5491309Z   test_CrossEntropyLoss_2d_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5491676Z   test_CrossEntropyLoss_2d_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5492040Z   test_CrossEntropyLoss_2d_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5492378Z   test_CrossEntropyLoss_2d_indices_target_smoothing (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5492728Z   test_CrossEntropyLoss_2d_indices_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5493123Z   test_CrossEntropyLoss_2d_indices_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5493515Z   test_CrossEntropyLoss_2d_indices_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5493880Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5494258Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5494677Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5495091Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5495469Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5495842Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5496260Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5496676Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5497086Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5497449Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5497854Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5498261Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5498599Z   test_CrossEntropyLoss_2d_prob_target (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5498919Z   test_CrossEntropyLoss_2d_prob_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5499338Z   test_CrossEntropyLoss_2d_prob_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5499705Z   test_CrossEntropyLoss_2d_prob_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5500027Z   test_CrossEntropyLoss_2d_prob_target_smoothing (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5500380Z   test_CrossEntropyLoss_2d_prob_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5500763Z   test_CrossEntropyLoss_2d_prob_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5501145Z   test_CrossEntropyLoss_2d_prob_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5501493Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5501874Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5502294Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5502870Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5503225Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5503600Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5504002Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5504408Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5504761Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5505126Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5505518Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5505909Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5506240Z   test_CrossEntropyLoss_2d_prob_target_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5506590Z   test_CrossEntropyLoss_2d_prob_target_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5506976Z   test_CrossEntropyLoss_2d_prob_target_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5507361Z   test_CrossEntropyLoss_2d_prob_target_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5507764Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5508140Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5508557Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5508955Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5509300Z   test_CrossEntropyLoss_2d_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5509640Z   test_CrossEntropyLoss_2d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5510057Z   test_CrossEntropyLoss_2d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5510418Z   test_CrossEntropyLoss_2d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5510745Z   test_CrossEntropyLoss_2d_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5511072Z   test_CrossEntropyLoss_2d_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5511435Z   test_CrossEntropyLoss_2d_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5511780Z   test_CrossEntropyLoss_2d_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5512119Z   test_CrossEntropyLoss_3d_indices_target_smoothing (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5512484Z   test_CrossEntropyLoss_3d_indices_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5512880Z   test_CrossEntropyLoss_3d_indices_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5513265Z   test_CrossEntropyLoss_3d_indices_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5513627Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5514010Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5514425Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5514822Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5515200Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5515589Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5516009Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5516413Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5516805Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5517217Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5517719Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5518192Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5518556Z   test_CrossEntropyLoss_3d_prob_target (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5518893Z   test_CrossEntropyLoss_3d_prob_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5519246Z   test_CrossEntropyLoss_3d_prob_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5519611Z   test_CrossEntropyLoss_3d_prob_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5519947Z   test_CrossEntropyLoss_3d_prob_target_smoothing (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5520300Z   test_CrossEntropyLoss_3d_prob_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5520708Z   test_CrossEntropyLoss_3d_prob_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5521096Z   test_CrossEntropyLoss_3d_prob_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5521457Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5521840Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5522241Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5522653Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5523019Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5523382Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5523765Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5524159Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5524503Z   test_CrossEntropyLoss_3d_prob_target_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5524851Z   test_CrossEntropyLoss_3d_prob_target_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5525228Z   test_CrossEntropyLoss_3d_prob_target_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5525609Z   test_CrossEntropyLoss_3d_prob_target_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5525969Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5526351Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5526743Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5527155Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5527507Z   test_CrossEntropyLoss_4d_prob_target (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5527829Z   test_CrossEntropyLoss_4d_prob_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5528197Z   test_CrossEntropyLoss_4d_prob_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5528568Z   test_CrossEntropyLoss_4d_prob_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5528944Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5529298Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5529686Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5530075Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5530421Z   test_CrossEntropyLoss_4d_prob_target_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5530760Z   test_CrossEntropyLoss_4d_prob_target_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5531144Z   test_CrossEntropyLoss_4d_prob_target_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5531571Z   test_CrossEntropyLoss_4d_prob_target_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5531926Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5532293Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5532705Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5533118Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5533492Z   test_CrossEntropyLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5533818Z   test_CrossEntropyLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5534160Z   test_CrossEntropyLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5534464Z   test_CrossEntropyLoss_dim_is_3 (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5534768Z   test_CrossEntropyLoss_dim_is_3_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5535119Z   test_CrossEntropyLoss_dim_is_3_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5535472Z   test_CrossEntropyLoss_dim_is_3_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5535798Z   test_CrossEntropyLoss_dim_is_3_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5536129Z   test_CrossEntropyLoss_dim_is_3_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5536503Z   test_CrossEntropyLoss_dim_is_3_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5536886Z   test_CrossEntropyLoss_dim_is_3_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5537212Z   test_CrossEntropyLoss_higher_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5537526Z   test_CrossEntropyLoss_higher_dim_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5537891Z   test_CrossEntropyLoss_higher_dim_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5538246Z   test_CrossEntropyLoss_higher_dim_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5538578Z   test_CrossEntropyLoss_higher_dim_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5538920Z   test_CrossEntropyLoss_higher_dim_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5539314Z   test_CrossEntropyLoss_higher_dim_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5539737Z   test_CrossEntropyLoss_higher_dim_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5540049Z   test_CrossEntropyLoss_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5540366Z   test_CrossEntropyLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5540721Z   test_CrossEntropyLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5541073Z   test_CrossEntropyLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5541357Z   test_CrossMapLRN2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5541643Z   test_CrossMapLRN2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5541912Z   test_ELU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5542192Z   test_ELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5542609Z   test_ELU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5542892Z   test_ELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5543168Z   test_ELU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5543429Z   test_ELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5543702Z   test_Embedding (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5543964Z   test_EmbeddingBag_discontiguous (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5544275Z   test_EmbeddingBag_discontiguous_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5544575Z   test_EmbeddingBag_max (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5544863Z   test_EmbeddingBag_max_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5545170Z   test_EmbeddingBag_max_padding_idx (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5545484Z   test_EmbeddingBag_max_padding_idx_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5545782Z   test_EmbeddingBag_mean (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5546071Z   test_EmbeddingBag_mean_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5546361Z   test_EmbeddingBag_mean_padding_idx (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5546682Z   test_EmbeddingBag_mean_padding_idx_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5546985Z   test_EmbeddingBag_sparse (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5547280Z   test_EmbeddingBag_sparse_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5547554Z   test_EmbeddingBag_sum (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5547842Z   test_EmbeddingBag_sum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5548143Z   test_EmbeddingBag_sum_padding_idx (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5548447Z   test_EmbeddingBag_sum_padding_idx_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5548772Z   test_Embedding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5549056Z   test_Embedding_discontiguous (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5549362Z   test_Embedding_discontiguous_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5549640Z   test_Embedding_sparse (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5549927Z   test_Embedding_sparse_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5550198Z   test_Flatten (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5550452Z   test_Flatten_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5550728Z   test_Flatten_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5551089Z   test_Flatten_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5551346Z   test_Fold (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5551605Z   test_Fold_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5551873Z   test_Fold_int_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5552152Z   test_Fold_int_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5552427Z   test_Fold_no_batch_dim_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5552726Z   test_Fold_no_batch_dim_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5553027Z   test_Fold_no_batch_dim_int_input (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5553323Z   test_Fold_no_batch_dim_int_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5553680Z   test_FractionalMaxPool2d_ratio (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5554004Z   test_FractionalMaxPool2d_ratio_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5554331Z   test_FractionalMaxPool2d_ratio_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5554662Z   test_FractionalMaxPool2d_ratio_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5555020Z   test_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5555403Z   test_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5555760Z   test_FractionalMaxPool2d_ratio_return_indices (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5556097Z   test_FractionalMaxPool2d_ratio_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5556433Z   test_FractionalMaxPool2d_size (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5556747Z   test_FractionalMaxPool2d_size_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5557060Z   test_FractionalMaxPool2d_size_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5557457Z   test_FractionalMaxPool2d_size_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5557819Z   test_FractionalMaxPool2d_size_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5558199Z   test_FractionalMaxPool2d_size_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5558530Z   test_FractionalMaxPool3d_asymsize (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5558861Z   test_FractionalMaxPool3d_asymsize_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5559178Z   test_FractionalMaxPool3d_ratio (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5559487Z   test_FractionalMaxPool3d_ratio_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5559815Z   test_FractionalMaxPool3d_ratio_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5560161Z   test_FractionalMaxPool3d_ratio_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5560521Z   test_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5560887Z   test_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5561243Z   test_FractionalMaxPool3d_ratio_return_indices (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5561596Z   test_FractionalMaxPool3d_ratio_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5561921Z   test_FractionalMaxPool3d_size (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5562300Z   test_FractionalMaxPool3d_size_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5562625Z   test_FractionalMaxPool3d_size_no_batch_dim (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5562967Z   test_FractionalMaxPool3d_size_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5563315Z   test_FractionalMaxPool3d_size_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5563687Z   test_FractionalMaxPool3d_size_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5563994Z   test_GELU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5564256Z   test_GELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5564514Z   test_GELU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5564829Z   test_GELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5565105Z   test_GELU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5565372Z   test_GELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5565635Z   test_GLU (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5565889Z   test_GLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5566143Z   test_GLU_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5566393Z   test_GLU_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5566661Z   test_GLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5566941Z   test_GLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5567214Z   test_GroupNorm_1d_affine (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:37.5567472Z   test_GroupNorm_1d_affine_GN (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5567776Z   test_GroupNorm_1d_affine_GN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5568077Z   test_GroupNorm_1d_affine_GN_eval (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5568372Z   test_GroupNorm_1d_affine_GN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5568701Z   test_GroupNorm_1d_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5568990Z   test_GroupNorm_1d_affine_eval (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5569282Z   test_GroupNorm_1d_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5569619Z   test_GroupNorm_1d_affine_large_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5569968Z   test_GroupNorm_1d_affine_large_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5570278Z   test_GroupNorm_1d_no_affine_IN (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5570570Z   test_GroupNorm_1d_no_affine_IN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5570873Z   test_GroupNorm_1d_no_affine_IN_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5571195Z   test_GroupNorm_1d_no_affine_IN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5571486Z   test_GroupNorm_1d_no_affine_LN (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5571795Z   test_GroupNorm_1d_no_affine_LN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5572097Z   test_GroupNorm_1d_no_affine_LN_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5572409Z   test_GroupNorm_1d_no_affine_LN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5572698Z   test_GroupNorm_2d_affine (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5572996Z   test_GroupNorm_2d_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5573327Z   test_GroupNorm_2d_affine_eval (__main__.TestNN) ... ok (0.024s)
2023-01-11T21:58:37.5573623Z   test_GroupNorm_2d_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5573974Z   test_GroupNorm_2d_affine_large_feature_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5574336Z   test_GroupNorm_2d_affine_large_feature_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5574651Z   test_GroupNorm_2d_no_affine_IN (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5574943Z   test_GroupNorm_2d_no_affine_IN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5575248Z   test_GroupNorm_2d_no_affine_IN_eval (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5575563Z   test_GroupNorm_2d_no_affine_IN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5575904Z   test_GroupNorm_2d_no_affine_LN (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5576198Z   test_GroupNorm_2d_no_affine_LN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5576505Z   test_GroupNorm_2d_no_affine_LN_eval (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5576823Z   test_GroupNorm_2d_no_affine_LN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5577165Z   test_GroupNorm_2d_no_affine_large_feature_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5577533Z   test_GroupNorm_2d_no_affine_large_feature_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5577832Z   test_Hardshrink (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5578109Z   test_Hardshrink_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5578383Z   test_Hardshrink_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5578688Z   test_Hardshrink_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5578976Z   test_Hardshrink_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5579258Z   test_Hardshrink_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5579549Z   test_Hardsigmoid_no_batch_dim (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5579856Z   test_Hardsigmoid_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5580158Z   test_Hardswish_no_batch_dim (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5580442Z   test_Hardswish_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5580722Z   test_Hardtanh (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5580993Z   test_Hardtanh_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5581265Z   test_Hardtanh_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5581560Z   test_Hardtanh_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5581843Z   test_Hardtanh_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5582131Z   test_Hardtanh_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5582564Z   test_HingeEmbeddingLoss (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5582883Z   test_HingeEmbeddingLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5583234Z   test_HingeEmbeddingLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5583570Z   test_HingeEmbeddingLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5583882Z   test_HingeEmbeddingLoss_margin (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5584210Z   test_HingeEmbeddingLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5584643Z   test_HingeEmbeddingLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5584990Z   test_HingeEmbeddingLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5585319Z   test_HingeEmbeddingLoss_margin_no_reduce (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5585665Z   test_HingeEmbeddingLoss_margin_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5585993Z   test_HingeEmbeddingLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5586349Z   test_HingeEmbeddingLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5586745Z   test_HingeEmbeddingLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5587172Z   test_HingeEmbeddingLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5587505Z   test_HingeEmbeddingLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5587852Z   test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5588236Z   test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5588611Z   test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5588930Z   test_HingeEmbeddingLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5589273Z   test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5589646Z   test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5590031Z   test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5590350Z   test_HingeEmbeddingLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5590689Z   test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5591062Z   test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5591424Z   test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5591750Z   test_HingeEmbeddingLoss_no_reduce (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5592078Z   test_HingeEmbeddingLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5592396Z   test_HingeEmbeddingLoss_scalar_margin (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5592728Z   test_HingeEmbeddingLoss_scalar_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5593103Z   test_HingeEmbeddingLoss_scalar_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5593477Z   test_HingeEmbeddingLoss_scalar_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5593824Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5594179Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5594585Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5594985Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5595366Z   test_HingeEmbeddingLoss_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5595690Z   test_HingeEmbeddingLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5596065Z   test_HingeEmbeddingLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5596438Z   test_HingeEmbeddingLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5596727Z   test_HuberLoss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5597013Z   test_HuberLoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5597388Z   test_HuberLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5597714Z   test_HuberLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5598049Z   test_HuberLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5598331Z   test_HuberLoss_delta (__main__.TestNN) ... ok (0.075s)
2023-01-11T21:58:37.5598619Z   test_HuberLoss_delta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5598899Z   test_HuberLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5599217Z   test_HuberLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5599572Z   test_HuberLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5599923Z   test_HuberLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5600217Z   test_HuberLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5600531Z   test_HuberLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5600887Z   test_HuberLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5601234Z   test_HuberLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5601522Z   test_HuberLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5601836Z   test_HuberLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5602181Z   test_HuberLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5602513Z   test_HuberLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5602812Z   test_HuberLoss_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5603123Z   test_HuberLoss_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5603479Z   test_HuberLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5603814Z   test_HuberLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5604159Z   test_HuberLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5604453Z   test_InstanceNorm1d (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5604729Z   test_InstanceNorm1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5605019Z   test_InstanceNorm1d_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5605313Z   test_InstanceNorm1d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5605615Z   test_InstanceNorm1d_no_batch_dim (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5605915Z   test_InstanceNorm1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5606261Z   test_InstanceNorm1d_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5606583Z   test_InstanceNorm1d_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5606886Z   test_InstanceNorm1d_tracking_stats (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5607206Z   test_InstanceNorm1d_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5607524Z   test_InstanceNorm1d_tracking_stats_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5607855Z   test_InstanceNorm1d_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5608176Z   test_InstanceNorm1d_tracking_stats_no_batch_dim (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5608523Z   test_InstanceNorm1d_tracking_stats_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5608909Z   test_InstanceNorm1d_tracking_stats_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5609265Z   test_InstanceNorm1d_tracking_stats_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5609564Z   test_InstanceNorm2d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5609850Z   test_InstanceNorm2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5610138Z   test_InstanceNorm2d_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5610424Z   test_InstanceNorm2d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5610724Z   test_InstanceNorm2d_no_batch_dim (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5611036Z   test_InstanceNorm2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5611348Z   test_InstanceNorm2d_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5611656Z   test_InstanceNorm2d_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5611979Z   test_InstanceNorm2d_tracking_stats (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5612300Z   test_InstanceNorm2d_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5612610Z   test_InstanceNorm2d_tracking_stats_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5612936Z   test_InstanceNorm2d_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5613268Z   test_InstanceNorm2d_tracking_stats_no_batch_dim (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5613609Z   test_InstanceNorm2d_tracking_stats_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5613940Z   test_InstanceNorm2d_tracking_stats_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5614290Z   test_InstanceNorm2d_tracking_stats_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5614610Z   test_InstanceNorm3d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5614883Z   test_InstanceNorm3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5615170Z   test_InstanceNorm3d_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5615472Z   test_InstanceNorm3d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5615773Z   test_InstanceNorm3d_no_batch_dim (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5616072Z   test_InstanceNorm3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5635253Z   test_InstanceNorm3d_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5635706Z   test_InstanceNorm3d_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5636031Z   test_InstanceNorm3d_tracking_stats (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5636495Z   test_InstanceNorm3d_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5636814Z   test_InstanceNorm3d_tracking_stats_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5637143Z   test_InstanceNorm3d_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5637552Z   test_InstanceNorm3d_tracking_stats_no_batch_dim (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5637886Z   test_InstanceNorm3d_tracking_stats_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5638233Z   test_InstanceNorm3d_tracking_stats_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5638586Z   test_InstanceNorm3d_tracking_stats_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5639613Z   test_KLDivLoss (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5640129Z   warnings.warn(
2023-01-11T21:58:37.5640850Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5641327Z   warnings.warn(
2023-01-11T21:58:37.5642039Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5642492Z   warnings.warn(
2023-01-11T21:58:37.5642662Z ok (0.005s)
2023-01-11T21:58:37.5642879Z   test_KLDivLoss_batch_mean (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5643156Z   test_KLDivLoss_batch_mean_log_target (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5643451Z   test_KLDivLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5643775Z   test_KLDivLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5644096Z   test_KLDivLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5644937Z   test_KLDivLoss_log_target (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5645453Z   warnings.warn(
2023-01-11T21:58:37.5646163Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5646631Z   warnings.warn(
2023-01-11T21:58:37.5646797Z ok (0.005s)
2023-01-11T21:58:37.5647048Z   test_KLDivLoss_log_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5647390Z   test_KLDivLoss_log_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5647733Z   test_KLDivLoss_log_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5648077Z   test_KLDivLoss_log_target_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5648414Z   test_KLDivLoss_log_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5648790Z   test_KLDivLoss_log_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5649161Z   test_KLDivLoss_log_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5649468Z   test_KLDivLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5649787Z   test_KLDivLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5650142Z   test_KLDivLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5650494Z   test_KLDivLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5650824Z   test_KLDivLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5651145Z   test_KLDivLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5651498Z   test_KLDivLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5651835Z   test_KLDivLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5652139Z   test_KLDivLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5652450Z   test_KLDivLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5652803Z   test_KLDivLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5653139Z   test_KLDivLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5653441Z   test_KLDivLoss_no_reduce (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5653733Z   test_KLDivLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5654023Z   test_KLDivLoss_no_reduce_log_target (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5654341Z   test_KLDivLoss_no_reduce_log_target_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5654650Z   test_KLDivLoss_no_reduce_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5654959Z   test_KLDivLoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5655267Z   test_KLDivLoss_no_reduce_scalar_log_target (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5655599Z   test_KLDivLoss_no_reduce_scalar_log_target_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5656475Z   test_KLDivLoss_scalar (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5656993Z   warnings.warn(
2023-01-11T21:58:37.5657695Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5658169Z   warnings.warn(
2023-01-11T21:58:37.5658878Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5659378Z   warnings.warn(
2023-01-11T21:58:37.5659544Z ok (0.004s)
2023-01-11T21:58:37.5659789Z   test_KLDivLoss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5660131Z   test_KLDivLoss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5660467Z   test_KLDivLoss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5661324Z   test_KLDivLoss_scalar_log_target (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5661875Z   warnings.warn(
2023-01-11T21:58:37.5662713Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5663174Z   warnings.warn(
2023-01-11T21:58:37.5663342Z ok (0.005s)
2023-01-11T21:58:37.5663599Z   test_KLDivLoss_scalar_log_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5663961Z   test_KLDivLoss_scalar_log_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5664317Z   test_KLDivLoss_scalar_log_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5664634Z   test_KLDivLoss_scalar_log_target_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5664988Z   test_KLDivLoss_scalar_log_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5665372Z   test_KLDivLoss_scalar_log_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5665751Z   test_KLDivLoss_scalar_log_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5666069Z   test_KLDivLoss_scalar_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5666397Z   test_KLDivLoss_scalar_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5666762Z   test_KLDivLoss_scalar_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5667125Z   test_KLDivLoss_scalar_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5667427Z   test_KLDivLoss_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5667743Z   test_KLDivLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5668091Z   test_KLDivLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5668427Z   test_KLDivLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5668742Z   test_KLDivLoss_with_log_target_no_reduce (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5669068Z   test_KLDivLoss_with_log_target_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5669386Z   test_KLDivLoss_with_target_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5669693Z   test_KLDivLoss_with_target_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5669979Z   test_L1Loss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5670311Z   test_L1Loss_cuda_cdouble (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5670611Z   test_L1Loss_cuda_cfloat (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5670922Z   test_L1Loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5671239Z   test_L1Loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5671545Z   test_L1Loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5671815Z   test_L1Loss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5672119Z   test_L1Loss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5672465Z   test_L1Loss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5672847Z   test_L1Loss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5673137Z   test_L1Loss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5673442Z   test_L1Loss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5673784Z   test_L1Loss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5674113Z   test_L1Loss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5674408Z   test_L1Loss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5674711Z   test_L1Loss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5675054Z   test_L1Loss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5675380Z   test_L1Loss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5675674Z   test_L1Loss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5675932Z   test_L1Loss_no_reduce_complex (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5676223Z   test_L1Loss_no_reduce_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5676546Z   test_L1Loss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5676829Z   test_L1Loss_no_reduce_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5677127Z   test_L1Loss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5677458Z   test_L1Loss_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5677751Z   test_L1Loss_scalar_cuda_cdouble (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5678078Z   test_L1Loss_scalar_cuda_cfloat (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5678234Z   test_L1Loss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5678387Z   test_L1Loss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5678526Z   test_L1Loss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5678630Z   test_LPPool1d (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5678774Z   test_LPPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5678893Z   test_LPPool1d_no_batch_dim (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5679050Z   test_LPPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5679159Z   test_LPPool1d_norm (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5679307Z   test_LPPool1d_norm_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5679412Z   test_LPPool2d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5679582Z   test_LPPool2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5679690Z   test_LPPool2d_norm (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5679837Z   test_LPPool2d_norm_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5679942Z   test_LSTM_cell (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5680069Z   test_LSTM_cell_forward_hidden_size (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5680196Z   test_LSTM_cell_forward_input_size (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5680327Z   test_LayerNorm_1d_elementwise_affine (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5680487Z   test_LayerNorm_1d_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5680621Z   test_LayerNorm_1d_elementwise_affine_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5680828Z   test_LayerNorm_1d_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5680972Z   test_LayerNorm_1d_empty_elementwise_affine (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5681150Z   test_LayerNorm_1d_empty_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5681294Z   test_LayerNorm_1d_empty_elementwise_affine_eval (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5681475Z   test_LayerNorm_1d_empty_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5681607Z   test_LayerNorm_1d_no_elementwise_affine (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5681778Z   test_LayerNorm_1d_no_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5681906Z   test_LayerNorm_1d_no_elementwise_affine_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5682080Z   test_LayerNorm_1d_no_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5682216Z   test_LayerNorm_3d_elementwise_affine (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5682384Z   test_LayerNorm_3d_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5682517Z   test_LayerNorm_3d_elementwise_affine_eval (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5682690Z   test_LayerNorm_3d_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5682824Z   test_LayerNorm_3d_no_affine_large_feature (__main__.TestNN) ... ok (0.855s)
2023-01-11T21:58:37.5682994Z   test_LayerNorm_3d_no_affine_large_feature_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5683133Z   test_LayerNorm_3d_no_affine_large_feature_eval (__main__.TestNN) ... ok (0.791s)
2023-01-11T21:58:37.5683296Z   test_LayerNorm_3d_no_affine_large_feature_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5683433Z   test_LayerNorm_3d_no_elementwise_affine (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5683602Z   test_LayerNorm_3d_no_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5683743Z   test_LayerNorm_3d_no_elementwise_affine_eval (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5683916Z   test_LayerNorm_3d_no_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5684020Z   test_LeakyReLU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5684164Z   test_LeakyReLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5684282Z   test_LeakyReLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5684425Z   test_LeakyReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5684544Z   test_LeakyReLU_with_negval (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5684703Z   test_LeakyReLU_with_negval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5684861Z   test_LeakyReLU_with_negval_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5685024Z   test_LeakyReLU_with_negval_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5685148Z   test_LeakyReLU_with_zero_negval (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5685311Z   test_LeakyReLU_with_zero_negval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5685412Z   test_Linear (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5685537Z   test_Linear_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5685651Z   test_Linear_no_batch_dim (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5685800Z   test_Linear_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5685909Z   test_Linear_no_bias (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5686104Z   test_Linear_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5686227Z   test_LocalResponseNorm_1d (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5686382Z   test_LocalResponseNorm_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5686517Z   test_LocalResponseNorm_2d_uneven_pad (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5686677Z   test_LocalResponseNorm_2d_uneven_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5686812Z   test_LocalResponseNorm_3d_custom_params (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5686986Z   test_LocalResponseNorm_3d_custom_params_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5687089Z   test_LogSigmoid (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5687229Z   test_LogSigmoid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5687355Z   test_LogSigmoid_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5687513Z   test_LogSigmoid_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5687629Z   test_LogSigmoid_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5687768Z   test_LogSigmoid_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5687873Z   test_LogSoftmax (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5688017Z   test_LogSoftmax_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5688137Z   test_LogSoftmax_multiparam (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5688296Z   test_LogSoftmax_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5688425Z   test_LogSoftmax_multiparam_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5688592Z   test_LogSoftmax_multiparam_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5688714Z   test_LogSoftmax_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5688856Z   test_LogSoftmax_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5688954Z   test_MSELoss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5689100Z   test_MSELoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5689243Z   test_MSELoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5689382Z   test_MSELoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5689498Z   test_MSELoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5689660Z   test_MSELoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5689825Z   test_MSELoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5690018Z   test_MSELoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5690126Z   test_MSELoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5690285Z   test_MSELoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5690444Z   test_MSELoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5690603Z   test_MSELoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5690717Z   test_MSELoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5690880Z   test_MSELoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5691044Z   test_MSELoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5691235Z   test_MSELoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5691336Z   test_MSELoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5691482Z   test_MSELoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5691602Z   test_MSELoss_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5691760Z   test_MSELoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5691865Z   test_MSELoss_prec (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5692014Z   test_MSELoss_prec_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5692161Z   test_MSELoss_prec_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5692306Z   test_MSELoss_prec_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5692452Z   test_MSELoss_prec_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5692551Z   test_MSELoss_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5692707Z   test_MSELoss_scalar_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5692863Z   test_MSELoss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5693015Z   test_MSELoss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5693169Z   test_MSELoss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5693296Z   test_MSELoss_scalar_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5693470Z   test_MSELoss_scalar_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5693642Z   test_MSELoss_scalar_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5693802Z   test_MSELoss_scalar_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5693969Z   test_MSELoss_scalar_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5694087Z   test_MSELoss_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5694251Z   test_MSELoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5694412Z   test_MSELoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5694571Z   test_MSELoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5694691Z   test_MarginRankingLoss (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5694856Z   test_MarginRankingLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5695050Z   test_MarginRankingLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5695197Z   test_MarginRankingLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5695322Z   test_MarginRankingLoss_margin (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5695494Z   test_MarginRankingLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5695661Z   test_MarginRankingLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5695829Z   test_MarginRankingLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5695972Z   test_MarginRankingLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5696156Z   test_MarginRankingLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5696373Z   test_MarginRankingLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5696557Z   test_MarginRankingLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5696683Z   test_MarginRankingLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5696866Z   test_MarginRankingLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5697044Z   test_MarginRankingLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5697222Z   test_MarginRankingLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5697359Z   test_MarginRankingLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5697540Z   test_MarginRankingLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5697720Z   test_MarginRankingLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5697895Z   test_MarginRankingLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5698030Z   test_MarginRankingLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5698193Z   test_MarginRankingLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5698369Z   test_MarginRankingLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5698546Z   test_MarginRankingLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5698679Z   test_MarginRankingLoss_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5698858Z   test_MarginRankingLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5699037Z   test_MarginRankingLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5699214Z   test_MarginRankingLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5699319Z   test_MaxPool1d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5699450Z   test_MaxPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5699574Z   test_MaxPool1d_return_indices (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5699732Z   test_MaxPool1d_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5699845Z   test_MaxPool1d_stride (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5699994Z   test_MaxPool1d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5700146Z   test_MaxPool2d_3d_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5700297Z   test_MaxPool2d_3d_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5700410Z   test_MaxPool2d_4d_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5700544Z   test_MaxPool2d_4d_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5700665Z   test_MaxPool2d_return_indices (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5700824Z   test_MaxPool2d_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5700927Z   test_MaxPool3d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5701068Z   test_MaxPool3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5701189Z   test_MaxPool3d_return_indices (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5701348Z   test_MaxPool3d_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5701494Z   test_MaxPool3d_stride (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5701632Z   test_MaxPool3d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5701756Z   test_MaxPool3d_stride_padding (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5701915Z   test_MaxPool3d_stride_padding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5702028Z   test_MaxUnpool1d_net (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5702179Z   test_MaxUnpool1d_net_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5702305Z   test_MaxUnpool1d_net_no_batch_dim (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5702593Z   test_MaxUnpool1d_net_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5702705Z   test_MaxUnpool2d_net (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5702856Z   test_MaxUnpool2d_net_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5702972Z   test_MaxUnpool2d_net_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5703133Z   test_MaxUnpool2d_net_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5703244Z   test_MaxUnpool3d_net (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5703387Z   test_MaxUnpool3d_net_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5703510Z   test_MaxUnpool3d_net_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5703670Z   test_MaxUnpool3d_net_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5703766Z   test_Mish (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5703900Z   test_Mish_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5703997Z   test_Mish_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5704148Z   test_Mish_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5704255Z   test_Mish_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5704398Z   test_Mish_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5704503Z   test_ModuleDict (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5704606Z   test_ModuleList (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5704731Z   test_MultiLabelMarginLoss (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5704858Z   test_MultiLabelMarginLoss_0d_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5705034Z   test_MultiLabelMarginLoss_0d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5705159Z   test_MultiLabelMarginLoss_1d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5705333Z   test_MultiLabelMarginLoss_1d_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5705555Z   test_MultiLabelMarginLoss_1d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5705728Z   test_MultiLabelMarginLoss_1d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5705896Z   test_MultiLabelMarginLoss_1d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5706033Z   test_MultiLabelMarginLoss_1d_no_reduce (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5706207Z   test_MultiLabelMarginLoss_1d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5706337Z   test_MultiLabelMarginLoss_1d_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5706523Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5706709Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5706969Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5707154Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5707323Z   test_MultiLabelMarginLoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5707492Z   test_MultiLabelMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5707659Z   test_MultiLabelMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5707826Z   test_MultiLabelMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5707949Z   test_MultiLabelMarginLoss_index_neg (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5708122Z   test_MultiLabelMarginLoss_index_neg_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5708269Z   test_MultiLabelMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5708457Z   test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5708640Z   test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5708822Z   test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5708964Z   test_MultiLabelMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5709146Z   test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5709330Z   test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5709500Z   test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5709643Z   test_MultiLabelMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5709823Z   test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5710003Z   test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5710185Z   test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5710320Z   test_MultiLabelMarginLoss_no_reduce (__main__.TestNN) ... ok (0.024s)
2023-01-11T21:58:37.5710492Z   test_MultiLabelMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5710632Z   test_MultiLabelMarginLoss_sum_reduction (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:37.5710855Z   test_MultiLabelMarginLoss_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5711027Z   test_MultiLabelMarginLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5711208Z   test_MultiLabelMarginLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5711388Z   test_MultiLabelMarginLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5711522Z   test_MultiLabelSoftMarginLoss (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5711698Z   test_MultiLabelSoftMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5711875Z   test_MultiLabelSoftMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5712078Z   test_MultiLabelSoftMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5712234Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5712430Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5712611Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5712799Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5712948Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5713140Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5713327Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5713519Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5713670Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5713859Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5714047Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5714225Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5714368Z   test_MultiLabelSoftMarginLoss_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5714548Z   test_MultiLabelSoftMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5714693Z   test_MultiLabelSoftMarginLoss_weights (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5714881Z   test_MultiLabelSoftMarginLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5715066Z   test_MultiLabelSoftMarginLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5715250Z   test_MultiLabelSoftMarginLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5715402Z   test_MultiLabelSoftMarginLoss_weights_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5715592Z   test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5715737Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5715937Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5716174Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5716373Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5716488Z   test_MultiMarginLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5716606Z   test_MultiMarginLoss_1d (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5716770Z   test_MultiMarginLoss_1d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5716929Z   test_MultiMarginLoss_1d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5717088Z   test_MultiMarginLoss_1d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5717205Z   test_MultiMarginLoss_1d_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5717462Z   test_MultiMarginLoss_1d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5717602Z   test_MultiMarginLoss_1d_sum_reduction (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5717779Z   test_MultiMarginLoss_1d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5717957Z   test_MultiMarginLoss_1d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5718130Z   test_MultiMarginLoss_1d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5718289Z   test_MultiMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5718450Z   test_MultiMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5718603Z   test_MultiMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5718716Z   test_MultiMarginLoss_margin (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5718883Z   test_MultiMarginLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5719049Z   test_MultiMarginLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5719214Z   test_MultiMarginLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5719348Z   test_MultiMarginLoss_margin_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5719516Z   test_MultiMarginLoss_margin_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5719654Z   test_MultiMarginLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5719835Z   test_MultiMarginLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5720005Z   test_MultiMarginLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5720187Z   test_MultiMarginLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5720311Z   test_MultiMarginLoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5720471Z   test_MultiMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5720588Z   test_MultiMarginLoss_p (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5720751Z   test_MultiMarginLoss_p_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5720909Z   test_MultiMarginLoss_p_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5721066Z   test_MultiMarginLoss_p_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5721199Z   test_MultiMarginLoss_p_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5721353Z   test_MultiMarginLoss_p_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5721517Z   test_MultiMarginLoss_p_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5721698Z   test_MultiMarginLoss_p_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5721872Z   test_MultiMarginLoss_p_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5722049Z   test_MultiMarginLoss_p_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5722180Z   test_MultiMarginLoss_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5722352Z   test_MultiMarginLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5722525Z   test_MultiMarginLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5722739Z   test_MultiMarginLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5722851Z   test_MultiMarginLoss_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5723020Z   test_MultiMarginLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5723189Z   test_MultiMarginLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5723355Z   test_MultiMarginLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5723489Z   test_MultiMarginLoss_weights_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5723662Z   test_MultiMarginLoss_weights_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5723803Z   test_MultiMarginLoss_weights_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5723987Z   test_MultiMarginLoss_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5724158Z   test_MultiMarginLoss_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5724337Z   test_MultiMarginLoss_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5724439Z   test_NLLLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5724556Z   test_NLLLoss2d_no_reduce (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5724707Z   test_NLLLoss2d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5724838Z   test_NLLLoss2d_no_reduce_ignore_index (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5725005Z   test_NLLLoss2d_no_reduce_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5725131Z   test_NLLLoss2d_no_reduce_weights (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5725298Z   test_NLLLoss2d_no_reduce_weights_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5725401Z   test_NLLLossNd_no_reduce (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5725556Z   test_NLLLossNd_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5725688Z   test_NLLLossNd_no_reduce_ignore_index (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5725856Z   test_NLLLossNd_no_reduce_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5725982Z   test_NLLLossNd_no_reduce_weights (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5726144Z   test_NLLLossNd_no_reduce_weights_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5726248Z   test_NLLLoss_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5726399Z   test_NLLLoss_2d_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5726540Z   test_NLLLoss_2d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5726718Z   test_NLLLoss_2d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5726864Z   test_NLLLoss_2d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5726984Z   test_NLLLoss_2d_ignore_index (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5727151Z   test_NLLLoss_2d_ignore_index_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5727318Z   test_NLLLoss_2d_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5727481Z   test_NLLLoss_2d_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5727643Z   test_NLLLoss_2d_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5727753Z   test_NLLLoss_2d_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5727954Z   test_NLLLoss_2d_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5728120Z   test_NLLLoss_2d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5728285Z   test_NLLLoss_2d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5728448Z   test_NLLLoss_2d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5728564Z   test_NLLLoss_2d_weights (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5728726Z   test_NLLLoss_2d_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5728885Z   test_NLLLoss_2d_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5729040Z   test_NLLLoss_2d_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5729187Z   test_NLLLoss_2d_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5729335Z   test_NLLLoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5729482Z   test_NLLLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5729629Z   test_NLLLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5729774Z   test_NLLLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5729886Z   test_NLLLoss_dim_is_3 (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5730041Z   test_NLLLoss_dim_is_3_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5730190Z   test_NLLLoss_dim_is_3_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5730335Z   test_NLLLoss_dim_is_3_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5730492Z   test_NLLLoss_dim_is_3_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5730619Z   test_NLLLoss_dim_is_3_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5730792Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5730962Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5731130Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5731296Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5731412Z   test_NLLLoss_higher_dim (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5731575Z   test_NLLLoss_higher_dim_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5731755Z   test_NLLLoss_higher_dim_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5731913Z   test_NLLLoss_higher_dim_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5732065Z   test_NLLLoss_higher_dim_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5732196Z   test_NLLLoss_higher_dim_sum_reduction (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5732371Z   test_NLLLoss_higher_dim_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5732542Z   test_NLLLoss_higher_dim_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5732714Z   test_NLLLoss_higher_dim_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5732916Z   test_NLLLoss_higher_dim_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5733035Z   test_NLLLoss_ignore_index (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5733186Z   test_NLLLoss_ignore_index_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5733346Z   test_NLLLoss_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5733505Z   test_NLLLoss_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5733663Z   test_NLLLoss_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5733782Z   test_NLLLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5733946Z   test_NLLLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5734112Z   test_NLLLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5734275Z   test_NLLLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5734395Z   test_NLLLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5734550Z   test_NLLLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5734712Z   test_NLLLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5734874Z   test_NLLLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5734991Z   test_NLLLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5735151Z   test_NLLLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5735310Z   test_NLLLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5735476Z   test_NLLLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5735590Z   test_NLLLoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5735727Z   test_NLLLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5735856Z   test_NLLLoss_no_reduce_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5736020Z   test_NLLLoss_no_reduce_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5736142Z   test_NLLLoss_no_reduce_weights (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5736301Z   test_NLLLoss_no_reduce_weights_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5736437Z   test_NLLLoss_no_reduce_weights_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5736614Z   test_NLLLoss_no_reduce_weights_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5736789Z   test_NLLLoss_no_reduce_weights_ignore_index_neg (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5736964Z   test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5737068Z   test_NLLLoss_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5737228Z   test_NLLLoss_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5737390Z   test_NLLLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5737551Z   test_NLLLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5737710Z   test_NLLLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5737818Z   test_NLLLoss_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5738009Z   test_NLLLoss_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5738166Z   test_NLLLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5738310Z   test_NLLLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5738463Z   test_NLLLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5738588Z   test_NLLLoss_weights_ignore_index (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5738758Z   test_NLLLoss_weights_ignore_index_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5738929Z   test_NLLLoss_weights_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5739097Z   test_NLLLoss_weights_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5739270Z   test_NLLLoss_weights_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5739404Z   test_NLLLoss_weights_ignore_index_neg (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5739579Z   test_NLLLoss_weights_ignore_index_neg_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5739738Z   test_NLLLoss_weights_ignore_index_neg_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5739911Z   test_NLLLoss_weights_ignore_index_neg_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5740082Z   test_NLLLoss_weights_ignore_index_neg_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5740184Z   test_PReLU_1d (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5740324Z   test_PReLU_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5740444Z   test_PReLU_1d_multiparam (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5740601Z   test_PReLU_1d_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5740700Z   test_PReLU_2d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5740828Z   test_PReLU_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5740947Z   test_PReLU_2d_multiparam (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5741101Z   test_PReLU_2d_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5741202Z   test_PReLU_3d (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5741339Z   test_PReLU_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5741453Z   test_PReLU_3d_multiparam (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5741606Z   test_PReLU_3d_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5741742Z   test_PReLU_backward_requires_grad_false (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5741874Z   test_PReLU_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5742025Z   test_PReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5742131Z   test_PReLU_scalar (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5742277Z   test_PReLU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5742519Z   test_Padding122112_3dcircular (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5742681Z   test_Padding122112_3dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5742800Z   test_Padding1221_2dcircular (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5742958Z   test_Padding1221_2dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5743064Z   test_Padding12_1dcircular (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5743266Z   test_Padding12_1dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5743387Z   test_Padding2322_2dcircular (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5743544Z   test_Padding2322_2dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5743659Z   test_Padding31_1dcircular (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5743814Z   test_Padding31_1dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5743935Z   test_Padding322112_3dcircular (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5744093Z   test_Padding322112_3dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5744199Z   test_Padding332122_3dcircular (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5744350Z   test_Padding332122_3dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5744467Z   test_Padding3331_2dcircular (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5744625Z   test_Padding3331_2dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5744740Z   test_Padding33_1dcircular (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5744890Z   test_Padding33_1dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5745000Z   test_PairwiseDistance (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5745130Z   test_PairwiseDistance_broadcast_lhs (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5745299Z   test_PairwiseDistance_broadcast_lhs_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5745420Z   test_PairwiseDistance_broadcast_rhs (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5745594Z   test_PairwiseDistance_broadcast_rhs_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5745749Z   test_PairwiseDistance_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5745882Z   test_PairwiseDistance_no_batch_dim (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5746054Z   test_PairwiseDistance_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5746196Z   test_PairwiseDistance_with_non_default_args (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:37.5746372Z   test_PairwiseDistance_with_non_default_args_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5746484Z   test_ParameterDict (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5746597Z   test_ParameterDict_replication (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5746706Z   test_ParameterList (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5746823Z   test_ParameterList_meta (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5746950Z   test_ParameterList_replication (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5747063Z   test_PixelShuffle (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5747250Z   test_PixelShuffle_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5747361Z   test_PixelUnshuffle (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5747511Z   test_PixelUnshuffle_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5747621Z   test_PoissonNLLLoss_full_loss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5747794Z   test_PoissonNLLLoss_full_loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5747961Z   test_PoissonNLLLoss_full_loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5748124Z   test_PoissonNLLLoss_full_loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5748260Z   test_PoissonNLLLoss_full_loss_no_log_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5748470Z   test_PoissonNLLLoss_full_loss_no_log_input_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5748653Z   test_PoissonNLLLoss_full_loss_no_log_input_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5748832Z   test_PoissonNLLLoss_full_loss_no_log_input_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5748952Z   test_PoissonNLLLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5749130Z   test_PoissonNLLLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5749304Z   test_PoissonNLLLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5749476Z   test_PoissonNLLLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5749609Z   test_PoissonNLLLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5749792Z   test_PoissonNLLLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5749965Z   test_PoissonNLLLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5750137Z   test_PoissonNLLLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5750269Z   test_PoissonNLLLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5750428Z   test_PoissonNLLLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5750598Z   test_PoissonNLLLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5750771Z   test_PoissonNLLLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5750899Z   test_PoissonNLLLoss_no_full_loss (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5751074Z   test_PoissonNLLLoss_no_full_loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5751241Z   test_PoissonNLLLoss_no_full_loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5751406Z   test_PoissonNLLLoss_no_full_loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5751549Z   test_PoissonNLLLoss_no_full_loss_no_log_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5751728Z   test_PoissonNLLLoss_no_full_loss_no_log_input_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5751897Z   test_PoissonNLLLoss_no_full_loss_no_log_input_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5752073Z   test_PoissonNLLLoss_no_full_loss_no_log_input_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5752199Z   test_PoissonNLLLoss_no_reduce (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5752404Z   test_PoissonNLLLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5752505Z   test_RNN_cell (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5752629Z   test_RNN_cell_forward_hidden_size (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5752752Z   test_RNN_cell_forward_input_size (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5752880Z   test_RNN_cell_forward_zero_hidden_size (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5752987Z   test_RNN_cell_no_broadcasting (__main__.TestNN) ... ok (0.034s)
2023-01-11T21:58:37.5753123Z   test_RNN_change_dropout (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.001s)
2023-01-11T21:58:37.5753262Z   test_RNN_cpu_vs_cudnn_no_dropout (__main__.TestNN) ... skip: needs cudnn (0.000s)
2023-01-11T21:58:37.5753410Z   test_RNN_cpu_vs_cudnn_with_dropout (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.000s)
2023-01-11T21:58:37.5753583Z   test_RNN_cudnn_weight_norm (__main__.TestNN) ... skip: needs cudnn (0.001s)
2023-01-11T21:58:37.5753710Z   test_RNN_dropout (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.001s)
2023-01-11T21:58:37.5753843Z   test_RNN_dropout_state (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.001s)
2023-01-11T21:58:37.5753954Z   test_RNN_input_size_zero (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5754053Z   test_RNN_nonlinearity (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5754147Z   test_RReLU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5754284Z   test_RReLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5754396Z   test_RReLU_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5754546Z   test_RReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5754660Z   test_RReLU_with_up_down (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5754811Z   test_RReLU_with_up_down_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5754933Z   test_RReLU_with_up_down_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5755080Z   test_RReLU_with_up_down_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5755178Z   test_ReLU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5755277Z   test_ReLU6 (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5755414Z   test_ReLU6_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5755521Z   test_ReLU6_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5755668Z   test_ReLU6_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5755773Z   test_ReLU6_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5755917Z   test_ReLU6_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5756041Z   test_ReLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5756154Z   test_ReLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5756303Z   test_ReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5756408Z   test_ReLU_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5756551Z   test_ReLU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5756668Z   test_ReflectionPad1d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5756788Z   test_ReflectionPad1d_batch (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5756949Z   test_ReflectionPad1d_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5757060Z   test_ReflectionPad1d_complex (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5757223Z   test_ReflectionPad1d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5757438Z   test_ReflectionPad1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5757597Z   test_ReflectionPad2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5757719Z   test_ReflectionPad2d_complex (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5757879Z   test_ReflectionPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5758029Z   test_ReflectionPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5758159Z   test_ReflectionPad2d_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5758314Z   test_ReflectionPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5758428Z   test_ReflectionPad3d (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5758549Z   test_ReflectionPad3d_complex (__main__.TestNN) ... ok (0.027s)
2023-01-11T21:58:37.5758709Z   test_ReflectionPad3d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5758903Z   test_ReflectionPad3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5759033Z   test_ReflectionPad3d_no_batch_dim (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:37.5759197Z   test_ReflectionPad3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5759314Z   test_ReplicationPad1d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5759425Z   test_ReplicationPad1d_batch (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5759586Z   test_ReplicationPad1d_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5759710Z   test_ReplicationPad1d_complex (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5759876Z   test_ReplicationPad1d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5760028Z   test_ReplicationPad1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5760146Z   test_ReplicationPad2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5760269Z   test_ReplicationPad2d_complex (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5760433Z   test_ReplicationPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5760574Z   test_ReplicationPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5760702Z   test_ReplicationPad2d_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5760871Z   test_ReplicationPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5760987Z   test_ReplicationPad3d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5761112Z   test_ReplicationPad3d_complex (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5761274Z   test_ReplicationPad3d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5761430Z   test_ReplicationPad3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5761563Z   test_ReplicationPad3d_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5761732Z   test_ReplicationPad3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5761817Z   test_SELU (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5761953Z   test_SELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5762068Z   test_SELU_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5762216Z   test_SELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5762321Z   test_SELU_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5762464Z   test_SELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5762574Z   test_Sequential_add (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5762677Z   test_Sequential_append (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5762825Z   test_Sequential_delitem (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5762934Z   test_Sequential_extend (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5763048Z   test_Sequential_getitem (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5763158Z   test_Sequential_iadd (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5763268Z   test_Sequential_imul (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5763378Z   test_Sequential_insert (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5763503Z   test_Sequential_insert_fail_case (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5763600Z   test_Sequential_mul (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5763708Z   test_Sequential_pop (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5763816Z   test_Sequential_rmul (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5763928Z   test_Sequential_setitem (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5764077Z   test_Sequential_setitem_named (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5764177Z   test_SiLU (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5764312Z   test_SiLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5764411Z   test_SiLU_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5764558Z   test_SiLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5764663Z   test_SiLU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5764806Z   test_SiLU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5764906Z   test_Sigmoid (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5765045Z   test_Sigmoid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5765161Z   test_Sigmoid_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5765311Z   test_Sigmoid_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5765413Z   test_Sigmoid_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5765557Z   test_Sigmoid_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5765665Z   test_SmoothL1Loss (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5765782Z   test_SmoothL1Loss_beta (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5765932Z   test_SmoothL1Loss_beta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5766084Z   test_SmoothL1Loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5766234Z   test_SmoothL1Loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5766382Z   test_SmoothL1Loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5766498Z   test_SmoothL1Loss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5766671Z   test_SmoothL1Loss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5766845Z   test_SmoothL1Loss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5767012Z   test_SmoothL1Loss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5767139Z   test_SmoothL1Loss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5767307Z   test_SmoothL1Loss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5767474Z   test_SmoothL1Loss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5767642Z   test_SmoothL1Loss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5767769Z   test_SmoothL1Loss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5767960Z   test_SmoothL1Loss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5768130Z   test_SmoothL1Loss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5768297Z   test_SmoothL1Loss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5768420Z   test_SmoothL1Loss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5768576Z   test_SmoothL1Loss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5768702Z   test_SmoothL1Loss_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5768868Z   test_SmoothL1Loss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5768985Z   test_SmoothL1Loss_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5769174Z   test_SmoothL1Loss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5769326Z   test_SmoothL1Loss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5769482Z   test_SmoothL1Loss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5769611Z   test_SmoothL1Loss_scalar_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5769784Z   test_SmoothL1Loss_scalar_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5769958Z   test_SmoothL1Loss_scalar_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5770132Z   test_SmoothL1Loss_scalar_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5770257Z   test_SmoothL1Loss_sum_reduction (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5770422Z   test_SmoothL1Loss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5770590Z   test_SmoothL1Loss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5770743Z   test_SmoothL1Loss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5770861Z   test_SmoothL1Loss_zero_beta (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5771014Z   test_SmoothL1Loss_zero_beta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5771125Z   test_SoftMarginLoss (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5771282Z   test_SoftMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5771437Z   test_SoftMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5771589Z   test_SoftMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5771723Z   test_SoftMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5771888Z   test_SoftMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5772057Z   test_SoftMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5772227Z   test_SoftMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5772352Z   test_SoftMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5772517Z   test_SoftMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5772688Z   test_SoftMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5772860Z   test_SoftMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5773020Z   test_SoftMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5773186Z   test_SoftMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5773345Z   test_SoftMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5773507Z   test_SoftMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5773624Z   test_SoftMarginLoss_no_reduce (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5773782Z   test_SoftMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5773904Z   test_SoftMarginLoss_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5774075Z   test_SoftMarginLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5774276Z   test_SoftMarginLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5774442Z   test_SoftMarginLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5774531Z   test_Softmax (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5774632Z   test_Softmax2d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5774768Z   test_Softmax2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5774883Z   test_Softmax2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5775033Z   test_Softmax2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5775168Z   test_Softmax_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5775280Z   test_Softmax_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5775430Z   test_Softmax_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5775529Z   test_Softmax_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5775675Z   test_Softmax_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5776096Z   test_Softmin (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5776224Z   test_Softmin_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5776333Z   test_Softmin_multidim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5776478Z   test_Softmin_multidim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5776587Z   test_Softmin_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5776731Z   test_Softmin_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5776836Z   test_Softmin_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5776971Z   test_Softmin_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5777074Z   test_Softplus (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5777178Z   test_Softplus_beta (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5777318Z   test_Softplus_beta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5777437Z   test_Softplus_beta_threshold (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5777589Z   test_Softplus_beta_threshold_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5777710Z   test_Softplus_beta_threshold_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5777874Z   test_Softplus_beta_threshold_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5778002Z   test_Softplus_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5778115Z   test_Softplus_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5778265Z   test_Softplus_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5778401Z   test_Softshrink (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5778541Z   test_Softshrink_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5778653Z   test_Softshrink_lambda (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5778800Z   test_Softshrink_lambda_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5778914Z   test_Softshrink_lambda_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5779059Z   test_Softshrink_lambda_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5779174Z   test_Softshrink_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5779325Z   test_Softshrink_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5779422Z   test_Softsign (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5779589Z   test_Softsign_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5779703Z   test_Softsign_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5779851Z   test_Softsign_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5779958Z   test_Softsign_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5780095Z   test_Softsign_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5780189Z   test_Tanh (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5780321Z   test_Tanh_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5780430Z   test_Tanh_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5780571Z   test_Tanh_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5780669Z   test_Tanh_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5780811Z   test_Tanh_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5780915Z   test_Tanhshrink (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5781046Z   test_Tanhshrink_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5781165Z   test_Tanhshrink_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5781317Z   test_Tanhshrink_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5781426Z   test_Tanhshrink_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5781571Z   test_Tanhshrink_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5781684Z   test_Threshold_large_value (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5781837Z   test_Threshold_large_value_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5781952Z   test_Threshold_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5782099Z   test_Threshold_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5782220Z   test_Threshold_threshold_value (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5782497Z   test_Threshold_threshold_value_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5782627Z   test_Threshold_threshold_value_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5782790Z   test_Threshold_threshold_value_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5782942Z   test_TransformerDecoderLayer_gelu_activation (__main__.TestNN) ... ok (0.084s)
2023-01-11T21:58:37.5783127Z   test_TransformerDecoderLayer_gelu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5783274Z   test_TransformerDecoderLayer_relu_activation (__main__.TestNN) ... ok (0.084s)
2023-01-11T21:58:37.5783455Z   test_TransformerDecoderLayer_relu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5783654Z   test_TransformerEncoderLayer_gelu_activation (__main__.TestNN) ... ok (0.046s)
2023-01-11T21:58:37.5783836Z   test_TransformerEncoderLayer_gelu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5783983Z   test_TransformerEncoderLayer_relu_activation (__main__.TestNN) ... ok (0.043s)
2023-01-11T21:58:37.5784161Z   test_TransformerEncoderLayer_relu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5784271Z   test_Transformer_cell (__main__.TestNN) ... ok (0.642s)
2023-01-11T21:58:37.5784393Z   test_Transformer_multilayer_coder (__main__.TestNN) ... ok (0.484s)
2023-01-11T21:58:37.5784553Z   test_Transformer_multilayer_coder_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5784681Z   test_TripletMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5784898Z   test_TripletMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5785080Z   test_TripletMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5785256Z   test_TripletMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5785389Z   test_TripletMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5785569Z   test_TripletMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5785747Z   test_TripletMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5785916Z   test_TripletMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5786051Z   test_TripletMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5786219Z   test_TripletMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5786391Z   test_TripletMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5786564Z   test_TripletMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5786682Z   test_Unflatten_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5786836Z   test_Unflatten_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5786932Z   test_Unfold (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5787064Z   test_Unfold_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5787175Z   test_Unfold_int_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5787321Z   test_Unfold_int_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5787416Z   test_ZeroPad2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5787529Z   test_ZeroPad2d_complex (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5787679Z   test_ZeroPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5787819Z   test_ZeroPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5787941Z   test_ZeroPad2d_negative_dims (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5788099Z   test_ZeroPad2d_negative_dims_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5788215Z   test_ZeroPad2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5788368Z   test_ZeroPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5788474Z   test_adaptive_log_softmax (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5788572Z   test_add_module (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5788774Z   test_add_module_raises_error_if_attr_exists (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5788875Z   test_affine_grid (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5788976Z   test_affine_grid_3d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5789098Z   test_affine_grid_error_checking (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5789198Z   test_assignment (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5789335Z   test_batchnorm_buffer_update_when_stats_are_not_tracked (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5789476Z   test_batchnorm_cudnn_half (__main__.TestNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:58:37.5789613Z   test_batchnorm_cudnn_nhwc (__main__.TestNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:58:37.5789724Z   test_batchnorm_nhwc_cpu (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5789861Z   test_batchnorm_nhwc_cuda (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5790246Z   test_batchnorm_non_contig_cpu_bn_module_<class 'torch_nn_modules_batchnorm_BatchNorm2d'> (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5790526Z   test_batchnorm_non_contig_cpu_bn_module_<class 'torch_nn_modules_batchnorm_SyncBatchNorm'> (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5790686Z   test_batchnorm_nonaffine_cuda_half_input (__main__.TestNN) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:58:37.5790840Z   test_batchnorm_raises_error_if_bias_is_not_same_size_as_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5790983Z   test_batchnorm_raises_error_if_less_than_one_value_per_channel (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5791145Z   test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5791306Z   test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5791479Z   test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5791644Z   test_batchnorm_raises_error_if_weight_is_not_same_size_as_input (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5791768Z   test_bce_loss_always_nonnegative (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5791891Z   test_bce_loss_broadcasts_weights (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5792006Z   test_bce_loss_input_range (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5792111Z   test_bce_loss_size_mismatch (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5792249Z   test_bce_with_logits_broadcasts_pos_weights (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5792379Z   test_bce_with_logits_broadcasts_weights (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5792536Z   test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5792721Z   test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad (__main__.TestNN) ... ok (0.038s)
2023-01-11T21:58:37.5792861Z   test_bce_with_logits_has_correct_forward_grad (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5792998Z   test_bce_with_logits_has_correct_grad_at_zero (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5793151Z   test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5793315Z   test_bce_with_logits_raises_if_target_and_input_are_different_size (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5793425Z   test_bce_with_logits_stability (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5793580Z   test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5793681Z   test_bilinear (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5793801Z   test_bilinear_broadcasting (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5793911Z   test_bilinear_no_bias (__main__.TestNN) ... ok (0.032s)
2023-01-11T21:58:37.5794066Z   test_bilinear_non_contiguous (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5794314Z   test_broadcast_double_backwards_gpu (__main__.TestNN) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:58:37.5794529Z   test_broadcast_no_grad (__main__.TestNN) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:58:37.5794752Z   test_broadcast_not_requiring_grad (__main__.TestNN) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:58:37.5794871Z   test_buffer_not_persistent (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5794999Z   test_buffer_not_persistent_assign (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5795122Z   test_buffer_not_persistent_del (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5795245Z   test_buffer_not_persistent_load (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5795374Z   test_buffer_not_persistent_overwrite (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5795495Z   test_buffers_and_named_buffers (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5796281Z   test_call_supports_python_dict_output (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1296: UserWarning: Using non-full backward hooks on a Module that does not return a single Tensor or a tuple of Tensors is deprecated and will be removed in future versions. This hook will be missing some of the grad_output. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T21:58:37.5796501Z   warnings.warn("Using non-full backward hooks on a Module that does not return a "
2023-01-11T21:58:37.5796556Z ok (0.002s)
2023-01-11T21:58:37.5796668Z   test_channel_shuffle (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5796792Z   test_channel_shuffle_return_self (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5796893Z   test_children (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5797000Z   test_clip_grad_norm (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5797112Z   test_clip_grad_value (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5797223Z   test_container_copy (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5797416Z   test_convert_sync_batchnorm (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5797555Z   test_cosine_embedding_loss_invalid_shape (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5797692Z   test_cosine_embedding_loss_margin_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5797822Z   test_cosine_embedding_loss_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5797957Z   test_cosine_embedding_loss_with_diff_type (__main__.TestNN) ... ok (0.036s)
2023-01-11T21:58:37.5798069Z   test_cosine_similarity (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5798183Z   test_cross_entropy_loss (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5798310Z   test_cross_entropy_loss_precision (__main__.TestNN) ... ok (5.919s)
2023-01-11T21:58:37.5798420Z   test_cross_entropy_loss_zero_div (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5798684Z   test_cudnn_rnn_dropout_states_device (__main__.TestNN) ... skip: CUDNN or multi-gpu not available (0.000s)
2023-01-11T21:58:37.5798827Z   test_cudnn_weight_format (__main__.TestNN) ... skip: CUDNN not available (0.001s)
2023-01-11T21:58:37.5798968Z   test_cudnn_weight_tying (__main__.TestNN) ... skip: CUDNN not available (0.001s)
2023-01-11T21:58:37.5799062Z   test_dir (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5799166Z   test_dir_digit (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5799282Z   test_elu_inplace_gradgrad (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5799395Z   test_elu_inplace_on_view (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5799500Z   test_error_RNN_seq_len_zero (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5799603Z   test_extra_state (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5799736Z   test_extra_state_missing_get_extra_state (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5799869Z   test_extra_state_missing_set_extra_state (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5800022Z   test_extra_state_non_dict (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5800387Z   test_fb_fc_packed (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:2184: UserWarning: fbgemm_pack_gemm_matrix_fp16 is deprecated and will be removed in a future PyTorch release. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/QuantizedLinear.cpp:382.)
2023-01-11T21:58:37.5800518Z   packed_w_tensor = torch.fbgemm_pack_gemm_matrix_fp16(w_tensor)
2023-01-11T21:58:37.5800861Z /var/lib/jenkins/workspace/test/test_nn.py:2185: UserWarning: fbgemm_linear_fp16_weight_fp32_activation is deprecated and will be removed in a future PyTorch release. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/QuantizedLinear.cpp:417.)
2023-01-11T21:58:37.5801015Z   actual_output = torch.fbgemm_linear_fp16_weight(x_tensor, packed_w_tensor, b_tensor)
2023-01-11T21:58:37.5801069Z ok (0.002s)
2023-01-11T21:58:37.5801205Z   test_flatten (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5801318Z   test_fold_invalid_arg (__main__.TestNN) ... ok (0.028s)
2023-01-11T21:58:37.5801436Z   test_gaussian_nll_loss_args (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5801566Z   test_gaussian_nll_loss_broadcasting (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5801669Z   test_get_buffer (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5801791Z   test_get_buffer_from_submodules (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5801927Z   test_getattr_with_property (__main__.TestNN) ... expected failure (0.001s)
2023-01-11T21:58:37.5802020Z   test_grid_sample (__main__.TestNN) ... ok (0.906s)
2023-01-11T21:58:37.5802128Z   test_grid_sample_3d (__main__.TestNN) ... ok (0.080s)
2023-01-11T21:58:37.5802764Z   test_grid_sample_error_checking (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4235: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
2023-01-11T21:58:37.5802844Z   warnings.warn(
2023-01-11T21:58:37.5802908Z ok (0.034s)
2023-01-11T21:58:37.5803023Z   test_hardtanh_backward (__main__.TestNN) ... ok (0.111s)
2023-01-11T21:58:37.5803148Z   test_hardtanh_inplace_gradgrad (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5803267Z   test_huber_loss_invalid_delta (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:37.5803362Z   test_inplace_thnn (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5803469Z   test_interpolate (__main__.TestNN) ... ok (0.138s)
2023-01-11T21:58:37.5803588Z   test_interpolate_bicubic_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5803748Z   test_interpolate_bicubic_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5803876Z   test_interpolate_bicubic_2d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5804050Z   test_interpolate_bicubic_2d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5804177Z   test_interpolate_bicubic_scale_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5804342Z   test_interpolate_bicubic_scale_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5804473Z   test_interpolate_bicubic_scale_tuple_shared_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5804654Z   test_interpolate_bicubic_scale_tuple_shared_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5804796Z   test_interpolate_bicubic_scale_tuple_skewed_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5804955Z   test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5805150Z   test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5805362Z   test_interpolate_bicubic_scale_tuple_skewed_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5805491Z   test_interpolate_bicubic_tuple_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5805633Z   test_interpolate_bicubic_tuple_2d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5805812Z   test_interpolate_bicubic_tuple_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5805963Z   test_interpolate_bicubic_tuple_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5806083Z   test_interpolate_bilinear_2d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5806240Z   test_interpolate_bilinear_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5806371Z   test_interpolate_bilinear_2d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5806566Z   test_interpolate_bilinear_2d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5806697Z   test_interpolate_bilinear_scale_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5806861Z   test_interpolate_bilinear_scale_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5807006Z   test_interpolate_bilinear_scale_tuple_shared_2d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5807175Z   test_interpolate_bilinear_scale_tuple_shared_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5807315Z   test_interpolate_bilinear_scale_tuple_skewed_2d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5807473Z   test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5807669Z   test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5807850Z   test_interpolate_bilinear_scale_tuple_skewed_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5807983Z   test_interpolate_bilinear_tuple_2d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5808126Z   test_interpolate_bilinear_tuple_2d_align_corners (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5808305Z   test_interpolate_bilinear_tuple_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5808469Z   test_interpolate_bilinear_tuple_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5808586Z   test_interpolate_buffer_overflow (__main__.TestNN) ... ok (3.865s)
2023-01-11T21:58:37.5808741Z   test_interpolate_illegal_memory_access (__main__.TestNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:58:37.5808859Z   test_interpolate_linear_1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5808994Z   test_interpolate_linear_1d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5809167Z   test_interpolate_linear_1d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5809324Z   test_interpolate_linear_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5809452Z   test_interpolate_linear_1d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5809619Z   test_interpolate_linear_1d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5809732Z   test_interpolate_linear_scale_1d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5809871Z   test_interpolate_linear_scale_1d_align_corners (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5810050Z   test_interpolate_linear_scale_1d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5810211Z   test_interpolate_linear_scale_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5810337Z   test_interpolate_linear_tuple_1d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5810536Z   test_interpolate_linear_tuple_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5810657Z   test_interpolate_nearest_1d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5810816Z   test_interpolate_nearest_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5810946Z   test_interpolate_nearest_1d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5811101Z   test_interpolate_nearest_1d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5811220Z   test_interpolate_nearest_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5811380Z   test_interpolate_nearest_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5811518Z   test_interpolate_nearest_2d_launch_configs (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5811723Z   test_interpolate_nearest_2d_launch_configs_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5811855Z   test_interpolate_nearest_2d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5812021Z   test_interpolate_nearest_2d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5812138Z   test_interpolate_nearest_3d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5812282Z   test_interpolate_nearest_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5812411Z   test_interpolate_nearest_3d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5812578Z   test_interpolate_nearest_3d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5812704Z   test_interpolate_nearest_scale_1d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5812868Z   test_interpolate_nearest_scale_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5812995Z   test_interpolate_nearest_scale_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5813159Z   test_interpolate_nearest_scale_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5813284Z   test_interpolate_nearest_scale_3d (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5813445Z   test_interpolate_nearest_scale_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5813557Z   test_interpolate_nearest_tuple_1d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5813714Z   test_interpolate_nearest_tuple_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5813842Z   test_interpolate_nearest_tuple_2d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5814003Z   test_interpolate_nearest_tuple_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5814131Z   test_interpolate_nearest_tuple_3d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5814293Z   test_interpolate_nearest_tuple_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5814416Z   test_interpolate_trilinear_3d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5814576Z   test_interpolate_trilinear_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5814695Z   test_interpolate_trilinear_3d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5814864Z   test_interpolate_trilinear_3d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5814993Z   test_interpolate_trilinear_scale_3d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5815137Z   test_interpolate_trilinear_scale_3d_align_corners (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5815321Z   test_interpolate_trilinear_scale_3d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5815487Z   test_interpolate_trilinear_scale_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5815650Z   test_interpolate_trilinear_tuple_3d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5815794Z   test_interpolate_trilinear_tuple_3d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5815962Z   test_interpolate_trilinear_tuple_3d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5816126Z   test_interpolate_trilinear_tuple_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:37.5816250Z   test_kl_div_log_softmax_target (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5816937Z   test_kl_div_with_diff_type (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5817047Z   warnings.warn(
2023-01-11T21:58:37.5817115Z ok (0.002s)
2023-01-11T21:58:37.5817244Z   test_kl_div_with_diff_type_log_target (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5817354Z   test_l1_loss_correct (__main__.TestNN) ... ok (13.576s)
2023-01-11T21:58:37.5817493Z   test_layer_norm_grads_with_create_graph_flag (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5817624Z   test_linear_autograd_device_cpu_bias_weightCOO (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5818131Z   test_linear_autograd_device_cpu_bias_weightCSC (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:6609: UserWarning: Sparse CSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:58:37.5818268Z   module.weight = nn.Parameter(module.weight.to_sparse_csc())
2023-01-11T21:58:37.5818340Z ok (0.013s)
2023-01-11T21:58:37.5818482Z   test_linear_autograd_device_cpu_bias_weightCSR (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5818631Z   test_linear_autograd_device_cpu_bias_weightStrided (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5818776Z   test_linear_autograd_device_cpu_nobias_weightCOO (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5818915Z   test_linear_autograd_device_cpu_nobias_weightCSC (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5819051Z   test_linear_autograd_device_cpu_nobias_weightCSR (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5819201Z   test_linear_autograd_device_cpu_nobias_weightStrided (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5819307Z   test_linear_broadcasting (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5819418Z   test_load_state_dict (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5819532Z   test_load_state_dict_BC (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5819653Z   test_load_state_dict_child (__main__.TestNN) ... ok (0.189s)
2023-01-11T21:58:37.5819770Z   test_load_state_dict_custom (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5819889Z   test_load_state_dict_invalid (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5820010Z   test_load_state_dict_ref_cycle (__main__.TestNN) ... ok (0.169s)
2023-01-11T21:58:37.5820113Z   test_load_state_dict_type (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5820222Z   test_log_softmax_cpu (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5820332Z   test_log_softmax_dim0 (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5820482Z   test_log_softmax_dim0_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5820591Z   test_log_softmax_dim3 (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5820739Z   test_log_softmax_dim3_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5820854Z   test_log_softmax_lastdim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5821044Z   test_log_softmax_lastdim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5821144Z   test_log_softmax_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5821294Z   test_log_softmax_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5821411Z   test_log_softmax_spatial (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5821562Z   test_log_softmax_spatial_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5821687Z   test_log_softmax_spatial_special (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:37.5821850Z   test_log_softmax_spatial_special_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5822249Z   test_loss_equal_input_target_shape (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:2799: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5822544Z   'mse_loss': lambda x, y: F.mse_loss(x, y),
2023-01-11T21:58:37.5822868Z /var/lib/jenkins/workspace/test/test_nn.py:2800: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5823010Z   'l1_loss': lambda x, y: F.l1_loss(x, y),
2023-01-11T21:58:37.5823311Z /var/lib/jenkins/workspace/test/test_nn.py:2801: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5823475Z   'smooth_l1_loss': lambda x, y: F.smooth_l1_loss(x, y),
2023-01-11T21:58:37.5823784Z /var/lib/jenkins/workspace/test/test_nn.py:2802: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5823937Z   'huber_loss': lambda x, y: F.huber_loss(x, y),
2023-01-11T21:58:37.5824543Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:37.5824619Z   warnings.warn(
2023-01-11T21:58:37.5824685Z ok (0.023s)
2023-01-11T21:58:37.5824823Z   test_margin_ranking_loss_margin_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5824951Z   test_margin_ranking_loss_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5825063Z   test_module_apply_inplace_op (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5825182Z   test_module_backcompat (__main__.TestNN) ... ok (0.029s)
2023-01-11T21:58:37.5825298Z   test_module_to_argparse (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5825398Z   test_modules (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5825513Z   test_mse_loss_size_warning (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5825660Z   test_multimarginloss_1d_input_0d_target_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5825844Z   test_multimarginloss_1d_input_0d_target_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5825954Z   test_named_children (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5826049Z   test_named_modules (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5826181Z   test_named_parameters_remove_duplicate (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5826555Z   test_nested_tensor_from_mask (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:2204: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:58:37.5826718Z   nt = torch._nested_tensor_from_mask(input, mask)
2023-01-11T21:58:37.5826784Z ok (0.002s)
2023-01-11T21:58:37.5826911Z   test_nested_tensor_from_mask_error (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:37.5827011Z   test_no_grad (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:37.5827126Z   test_non_leaf_parameters (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5827216Z   test_normalize (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5827352Z   test_overwrite_module_params_on_conversion (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:37.5827507Z   test_pack_sequence_batch_sizes_throw (__main__.TestNN) ... skip: CUDA not available (0.000s)
2023-01-11T21:58:37.5827619Z   test_pad_scalar_error (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5827769Z   test_padding_list (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5827886Z   test_pairwise_distance (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5828003Z   test_parameter_assignment (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5828130Z   test_parameterlistdict_pickle (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5828261Z   test_parameterlistdict_setting_attributes (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5828394Z   test_parameters_and_named_parameters (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5828508Z   test_parameters_to_vector (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5828609Z   test_parse_to (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5828752Z   test_partial_flat_weights (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5828851Z   test_pdist (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:37.5829005Z   test_pdist_cpu_gradgrad_unimplemented (__main__.TestNN) ... expected failure (0.007s)
2023-01-11T21:58:37.5829165Z   test_pdist_cuda_gradgrad_unimplemented (__main__.TestNN) ... expected failure (0.009s)
2023-01-11T21:58:37.5829267Z   test_pdist_empty_col (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5829376Z   test_pdist_empty_row (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5829481Z   test_pdist_large (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5829569Z   test_pdist_zeros (__main__.TestNN)
2023-01-11T21:58:37.5829686Z Test that grad is still valid when dist is 0 ... ok (0.008s)
2023-01-11T21:58:37.5829803Z   test_pixel_shuffle_nhwc_cpu (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5829924Z   test_pixel_shuffle_unshuffle (__main__.TestNN) ... ok (0.462s)
2023-01-11T21:58:37.5830288Z   test_pointwise_loss_broadcast (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5830453Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:37.5830768Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5830936Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:37.5831248Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5831418Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:37.5831734Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5831935Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:37.5832245Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5832412Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:37.5832727Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5832892Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:37.5833248Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5833400Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:37.5833710Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5833875Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5834186Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5834355Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5834665Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5834828Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5835134Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5835295Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5835602Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5835766Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5836073Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5836222Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5836529Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5836689Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5837032Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5837219Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5837588Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5837778Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5838121Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5838308Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5838612Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5838799Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5839105Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5839276Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5839582Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5839771Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5840080Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5840263Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:37.5840570Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5840747Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:37.5841053Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5841227Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:37.5841533Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5841704Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:37.5842008Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5842211Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:37.5842504Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5842675Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:37.5842976Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5843148Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:37.5843488Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:37.5843662Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:37.5843729Z ok (0.038s)
2023-01-11T21:58:37.5843874Z   test_pointwise_loss_target_grad_none_reduction (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5844006Z   test_projections_errors_on_gru_and_rnn (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5844121Z   test_projections_lstm_args_check (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5844360Z   test_projections_lstm_check_device (__main__.TestNN) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:58:37.5844499Z   test_projections_lstm_initial_hidden_state (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:37.5844651Z   test_register_buffer_allows_overwriting_with_same_name (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5844797Z   test_register_buffer_raises_error_if_attr_exists (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5844946Z   test_register_buffer_raises_error_if_name_is_not_string (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5845086Z   test_register_buffer_raises_error_if_not_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5845241Z   test_register_parameter_allows_overwriting_with_same_name (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5845388Z   test_register_parameter_raises_error_if_attr_exists (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5845529Z   test_register_parameter_raises_error_if_name_is_not_string (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5845653Z   test_register_state_dict_pre_hook (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5845796Z   test_register_state_dict_pre_hook_backward_compat (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5845913Z   test_relu_inplace_on_view (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5846014Z   test_repr (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5846123Z   test_requires_grad_ (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5846229Z   test_rnn_args_check (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5846444Z   test_rnn_check_device (__main__.TestNN) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:58:37.5846553Z   test_rnn_initial_hidden_state (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5846662Z   test_rnn_weight_norm (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5847105Z   test_share_memory (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:181: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:37.5847215Z   self.assertFalse(p.storage().is_shared())
2023-01-11T21:58:37.5847652Z /var/lib/jenkins/workspace/test/test_nn.py:186: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:37.5847757Z   self.assertTrue(p.storage().is_shared())
2023-01-11T21:58:37.5847823Z ok (0.002s)
2023-01-11T21:58:37.5847953Z   test_smoothl1loss_intergral_target (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5848095Z   test_smoothl1loss_negative_beta_not_supported (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5848190Z   test_softmax_cpu (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5848309Z   test_softmax_functional_dim0 (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5848467Z   test_softmax_functional_dim0_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5848618Z   test_softmax_functional_dim3 (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5848776Z   test_softmax_functional_dim3_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5848897Z   test_softmax_functional_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5849057Z   test_softmax_functional_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5849169Z   test_softmax_lastdim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5849304Z   test_softmax_lastdim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5849422Z   test_softmax_lastdim_dtype (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5849573Z   test_softmax_lastdim_dtype_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5849684Z   test_softmax_spatial (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:37.5849830Z   test_softmax_spatial_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5849953Z   test_softmax_spatial_dtype (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5850106Z   test_softmax_spatial_dtype_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5850229Z   test_softmax_spatial_special (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:37.5850375Z   test_softmax_spatial_special_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:37.5850475Z   test_softmin (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5850925Z   test_spectral_norm (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:1899: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:37.5851058Z   self.assertEqual(m.weight_orig.storage(), m.weight.storage())
2023-01-11T21:58:37.5851734Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:37.5851825Z   device=typed_storage.device,
2023-01-11T21:58:37.5851890Z ok (0.024s)
2023-01-11T21:58:37.5852005Z   test_spectral_norm_dim (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5852125Z   test_spectral_norm_forward (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5852252Z   test_spectral_norm_load_state_dict (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:37.5852357Z   test_spectral_norm_pickle (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5852461Z   test_state_dict (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:37.5852615Z   test_sync_batchnorm_accuracy_cuda (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5852804Z   test_sync_batchnorm_backward_elemt (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:37.5852919Z   test_threshold_bfloat16 (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5853026Z   test_threshold_int (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5853120Z   test_to (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5853245Z   test_train_errors_for_invalid_mode (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5853353Z   test_transformer_args_check (__main__.TestNN) ... ok (0.174s)
2023-01-11T21:58:37.5853478Z   test_transformer_layer_args_check (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5853600Z   test_transformerdecoder (__main__.TestNN) ... ok (0.103s)
2023-01-11T21:58:37.5853729Z   test_transformerdecoderlayer (__main__.TestNN) ... ok (0.026s)
2023-01-11T21:58:37.5853862Z   test_transformerdecoderlayer_gelu (__main__.TestNN) ... ok (0.032s)
2023-01-11T21:58:37.5854009Z   test_triplet_margin_loss (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5854137Z   test_triplet_margin_loss_no_reduce (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5854246Z   test_triplet_margin_loss_swap (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5854377Z   test_triplet_margin_loss_swap_no_reduce (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:37.5854474Z   test_type (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5855154Z   test_unflatten (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:1114: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1816.)
2023-01-11T21:58:37.5855265Z   return super(Tensor, self).refine_names(names)
2023-01-11T21:58:37.5855331Z ok (0.002s)
2023-01-11T21:58:37.5855454Z   test_unflatten_invalid_arg (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5855571Z   test_unfold_invalid_arg (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:37.5855720Z   test_upsamplingBilinear2d_spatial_invariance (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5855828Z   test_upsamplingLinear1d (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:37.5855972Z   test_upsamplingLinear1d_spatial_invariance (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5856098Z   test_upsamplingTrilinear3d (__main__.TestNN) ... ok (0.044s)
2023-01-11T21:58:37.5856247Z   test_upsamplingTrilinear3d_spatial_invariance (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5856364Z   test_upsampling_bfloat16 (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:37.5856502Z   test_upsampling_not_recompute_scale_factor (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:37.5856621Z   test_upsampling_small_scale (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5856738Z   test_vector_to_parameters (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:37.5856836Z   test_weight_norm (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:37.5856950Z   test_weight_norm_pickle (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5857051Z   test_zero_grad (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:37.5857062Z 
2023-01-11T21:58:37.5857263Z ----------------------------------------------------------------------
2023-01-11T21:58:37.5857340Z Ran 2070 tests in 37.830s
2023-01-11T21:58:37.5857346Z 
2023-01-11T21:58:37.5857440Z OK (skipped=1111, expected failures=3)
2023-01-11T21:58:37.5857445Z 
2023-01-11T21:58:37.5857527Z Generating XML reports...
2023-01-11T21:58:37.5857790Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestAddRelu-20230111215758.xml
2023-01-11T21:58:37.5858060Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestConstantPadNd-20230111215758.xml
2023-01-11T21:58:37.5858348Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestFunctionalPickle-20230111215758.xml
2023-01-11T21:58:37.5858655Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestFusionEval-20230111215758.xml
2023-01-11T21:58:37.5858924Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestFusionUtils-20230111215758.xml
2023-01-11T21:58:37.5859163Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestNN-20230111215758.xml
2023-01-11T21:58:37.5859168Z 
2023-01-11T21:58:37.5859553Z ##[endgroup]
2023-01-11T21:58:37.5859810Z FINISHED PRINTING LOG FILE of test_nn (/var/lib/jenkins/workspace/test/test-reports/test_nn_to8mjml2)
2023-01-11T21:58:37.5859815Z 
2023-01-11T21:58:37.5859981Z Running test_overrides ... [2023-01-11 21:58:37.527449]
2023-01-11T21:58:37.5860303Z Executing ['/opt/conda/bin/python', '-bb', 'test_overrides.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:37.527649]
2023-01-11T21:58:40.5893256Z 
2023-01-11T21:58:40.5893803Z Expand the folded group to see the log file of test_overrides
2023-01-11T21:58:40.5895204Z ##[group]PRINTING LOG FILE of test_overrides (/var/lib/jenkins/workspace/test/test-reports/test_overrides_gz6u4dmy)
2023-01-11T21:58:40.5895511Z 
2023-01-11T21:58:40.5895628Z Running tests...
2023-01-11T21:58:40.5896244Z ----------------------------------------------------------------------
2023-01-11T21:58:40.5896882Z Test results will be stored in test-reports/python-unittest/test_overrides
2023-01-11T21:58:40.5897408Z   test_broadcast_all (__main__.TestBroadcastAllOverride) ... ok (0.229s)
2023-01-11T21:58:40.5897954Z   test_parameter_does_not_prevent_dispatch (__main__.TestDisabledTorchFunction) ... ok (0.001s)
2023-01-11T21:58:40.5898459Z   test_wrapper (__main__.TestEinsumOverride) ... ok (0.002s)
2023-01-11T21:58:40.5898927Z   test_gradcheck (__main__.TestGradCheckOverride) ... ok (0.015s)
2023-01-11T21:58:40.5899399Z   test_newones (__main__.TestGradNewOnesOverride) ... ok (0.001s)
2023-01-11T21:58:40.5899838Z   test_getitem (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:40.5900276Z   test_getitem_subclass (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:40.5900682Z   test_setitem (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:40.5901099Z   test_setitem_subclass (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:40.5901526Z   test_setitem_val (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:40.5901940Z   test_iterator (__main__.TestIterator) ... ok (0.001s)
2023-01-11T21:58:40.5902508Z   test_max (__main__.TestNamedTuple) ... ok (0.001s)
2023-01-11T21:58:40.5903430Z   test_pickle (__main__.TestPickle) ... ok (0.001s)
2023-01-11T21:58:40.5903839Z   test_rnn (__main__.TestRNN) ... ok (0.002s)
2023-01-11T21:58:40.5904252Z   test_resolve_name (__main__.TestResolveName) ... ok (0.083s)
2023-01-11T21:58:40.5904735Z   test_all_same_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5905247Z   test_basic (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5905722Z   test_disable_enable_subclass (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5906254Z   test_disable_subclass_not_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5906771Z   test_distributions_bernoulli (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5907294Z   test_error_using_class_method_on_mode (__main__.TestTorchFunctionMode) ... ok (0.002s)
2023-01-11T21:58:40.5907804Z   test_factory_override (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5908296Z   test_get_cur_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5908773Z   test_get_mode_stack (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5909272Z   test_mode_notimplemented_loop (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5909769Z   test_modes_handle_first (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5910320Z   test_modes_return_notimplemented (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5910957Z   test_nested_modes_with_python_has_torch_function (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5911724Z   test_nested_same_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5912225Z   test_nn_parse_to (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5912766Z   test_reentrant_mode_idiom (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5913323Z   test_restacking_with_ancestor (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5913861Z   test_subclass_hash (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5914360Z   test_with_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5914920Z   test_with_mode_created_separately (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5915476Z   test_with_nested_modes (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:40.5916025Z   test_Tensor___add__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5916653Z   test_Tensor___and__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5917203Z   test_Tensor___array__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5917816Z   test_Tensor___array_wrap__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5918285Z   test_Tensor___bool__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5918595Z   test_Tensor___complex__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5918899Z   test_Tensor___contains__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5919237Z   test_Tensor___cuda_array_interface_____get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5919571Z   test_Tensor___deepcopy__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5919867Z   test_Tensor___div__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5920177Z   test_Tensor___dlpack__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5920499Z   test_Tensor___dlpack_device__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5920812Z   test_Tensor___eq__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5921103Z   test_Tensor___float__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5921420Z   test_Tensor___floordiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5921733Z   test_Tensor___format__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5922024Z   test_Tensor___ge__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5922329Z   test_Tensor___getitem__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5922634Z   test_Tensor___gt__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5922935Z   test_Tensor___iadd__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5923229Z   test_Tensor___iand__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5923534Z   test_Tensor___idiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5923846Z   test_Tensor___ifloordiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5924151Z   test_Tensor___ilshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5924458Z   test_Tensor___imod__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5924759Z   test_Tensor___imul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5925062Z   test_Tensor___index__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5925353Z   test_Tensor___int__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5925656Z   test_Tensor___invert__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5925961Z   test_Tensor___ior__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5926310Z   test_Tensor___irshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5926616Z   test_Tensor___isub__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5926917Z   test_Tensor___ixor__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5927214Z   test_Tensor___le__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5927501Z   test_Tensor___len__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5927800Z   test_Tensor___long__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5928103Z   test_Tensor___lshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5928391Z   test_Tensor___lt__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5928697Z   test_Tensor___matmul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5929035Z   test_Tensor___mod__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5929339Z   test_Tensor___mul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5929623Z   test_Tensor___ne__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5929928Z   test_Tensor___nonzero__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5930230Z   test_Tensor___or__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5930519Z   test_Tensor___radd__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5930825Z   test_Tensor___rand__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5931127Z   test_Tensor___rdiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5931422Z   test_Tensor___reduce_ex__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5931731Z   test_Tensor___repr__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5932040Z   test_Tensor___reversed__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5932360Z   test_Tensor___rfloordiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5932661Z   test_Tensor___rlshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5932967Z   test_Tensor___rmatmul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5933272Z   test_Tensor___rmod__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5933558Z   test_Tensor___rmul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5933854Z   test_Tensor___ror__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5934156Z   test_Tensor___rpow__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5934463Z   test_Tensor___rrshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5934758Z   test_Tensor___rshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5935068Z   test_Tensor___rsub__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5935366Z   test_Tensor___rxor__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5935655Z   test_Tensor___setitem__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5935965Z   test_Tensor___setstate__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5936270Z   test_Tensor___sub__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5936575Z   test_Tensor___truediv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5936864Z   test_Tensor___xor__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5937190Z   test_Tensor__autocast_to_full_precision (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5937545Z   test_Tensor__autocast_to_reduced_precision (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5937874Z   test_Tensor__coalesced_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5938219Z   test_Tensor__dimI (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5938520Z   test_Tensor__dimV (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5938826Z   test_Tensor__indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5939117Z   test_Tensor__is_view (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5939437Z   test_Tensor__nested_tensor_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5939752Z   test_Tensor__nnz (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5940045Z   test_Tensor__to_dense (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5940361Z   test_Tensor__update_names (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5940675Z   test_Tensor__values (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5941012Z   test_Tensor_abs (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5941299Z   test_Tensor_abs_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5941601Z   test_Tensor_absolute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5941910Z   test_Tensor_absolute_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5942203Z   test_Tensor_acos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5942692Z   test_Tensor_acos_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5942997Z   test_Tensor_acosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5943300Z   test_Tensor_acosh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5943588Z   test_Tensor_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5943890Z   test_Tensor_add_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5944200Z   test_Tensor_addbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5944494Z   test_Tensor_addbmm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5944798Z   test_Tensor_addcdiv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5945103Z   test_Tensor_addcdiv_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5945397Z   test_Tensor_addcmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5945701Z   test_Tensor_addcmul_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5946010Z   test_Tensor_addmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5946313Z   test_Tensor_addmm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5946602Z   test_Tensor_addmv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5946905Z   test_Tensor_addmv_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5947215Z   test_Tensor_addr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5947503Z   test_Tensor_addr_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5947809Z   test_Tensor_adjoint (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5948118Z   test_Tensor_align_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5948426Z   test_Tensor_align_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5948711Z   test_Tensor_all (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5949016Z   test_Tensor_allclose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5949324Z   test_Tensor_amax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5949612Z   test_Tensor_amin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5949916Z   test_Tensor_aminmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5950295Z   test_Tensor_angle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5950596Z   test_Tensor_any (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5950890Z   test_Tensor_apply_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5951196Z   test_Tensor_arccos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5951502Z   test_Tensor_arccos_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5951797Z   test_Tensor_arccosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5952103Z   test_Tensor_arccosh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5952411Z   test_Tensor_arcsin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5952713Z   test_Tensor_arcsin_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5953005Z   test_Tensor_arcsinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5953357Z   test_Tensor_arcsinh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5953663Z   test_Tensor_arctan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5953953Z   test_Tensor_arctan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5954257Z   test_Tensor_arctan2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5954563Z   test_Tensor_arctan_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5954851Z   test_Tensor_arctanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5955157Z   test_Tensor_arctanh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5955464Z   test_Tensor_argmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5955766Z   test_Tensor_argmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5956059Z   test_Tensor_argsort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5956367Z   test_Tensor_argwhere (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5956680Z   test_Tensor_as_strided (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5956983Z   test_Tensor_as_strided_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5957308Z   test_Tensor_as_strided_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5957688Z   test_Tensor_asin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5957992Z   test_Tensor_asin_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5958282Z   test_Tensor_asinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5958585Z   test_Tensor_asinh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5958884Z   test_Tensor_atan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5959178Z   test_Tensor_atan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5959480Z   test_Tensor_atan2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5959779Z   test_Tensor_atan_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5960077Z   test_Tensor_atanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5960366Z   test_Tensor_atanh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5960672Z   test_Tensor_backward (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5960985Z   test_Tensor_baddbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5961282Z   test_Tensor_baddbmm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5961595Z   test_Tensor_bernoulli (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5961908Z   test_Tensor_bernoulli_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5962263Z   test_Tensor_bfloat16 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5962557Z   test_Tensor_bincount (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5962872Z   test_Tensor_bitwise_and (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5963191Z   test_Tensor_bitwise_and_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5963502Z   test_Tensor_bitwise_left_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5963833Z   test_Tensor_bitwise_left_shift_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5964155Z   test_Tensor_bitwise_not (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5964472Z   test_Tensor_bitwise_not_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5964773Z   test_Tensor_bitwise_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5965113Z   test_Tensor_bitwise_or_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5965446Z   test_Tensor_bitwise_right_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5965769Z   test_Tensor_bitwise_right_shift_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5966095Z   test_Tensor_bitwise_xor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5966407Z   test_Tensor_bitwise_xor_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5966715Z   test_Tensor_bmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5967004Z   test_Tensor_bool (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5967311Z   test_Tensor_broadcast_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5967620Z   test_Tensor_byte (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5967907Z   test_Tensor_cauchy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5968221Z   test_Tensor_ccol_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5968535Z   test_Tensor_cdouble (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5968951Z   test_Tensor_ceil (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5969255Z   test_Tensor_ceil_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5969562Z   test_Tensor_cfloat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5969865Z   test_Tensor_chalf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5970152Z   test_Tensor_char (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5970456Z   test_Tensor_cholesky (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5970775Z   test_Tensor_cholesky_inverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5971088Z   test_Tensor_cholesky_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5971408Z   test_Tensor_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5971713Z   test_Tensor_clamp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5972022Z   test_Tensor_clamp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5972320Z   test_Tensor_clamp_max (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5972630Z   test_Tensor_clamp_max_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5972942Z   test_Tensor_clamp_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5973239Z   test_Tensor_clamp_min_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5973546Z   test_Tensor_clip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5973844Z   test_Tensor_clip_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5974145Z   test_Tensor_clone (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5974480Z   test_Tensor_coalesce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5974794Z   test_Tensor_col_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5975104Z   test_Tensor_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5975402Z   test_Tensor_conj_physical (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5975723Z   test_Tensor_conj_physical_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5976042Z   test_Tensor_contiguous (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5976347Z   test_Tensor_copy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5976644Z   test_Tensor_copysign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5976956Z   test_Tensor_copysign_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5977296Z   test_Tensor_corrcoef (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5977588Z   test_Tensor_cos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5977887Z   test_Tensor_cos_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5978186Z   test_Tensor_cosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5978475Z   test_Tensor_cosh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5978787Z   test_Tensor_count_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5979092Z   test_Tensor_cov (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5979390Z   test_Tensor_cpu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5979678Z   test_Tensor_cross (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5979987Z   test_Tensor_crow_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5980301Z   test_Tensor_cuda (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5980593Z   test_Tensor_cummax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5980899Z   test_Tensor_cummin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5981205Z   test_Tensor_cumprod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5981515Z   test_Tensor_cumprod_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5981806Z   test_Tensor_cumsum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5982109Z   test_Tensor_cumsum_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5982515Z   test_Tensor_data_ptr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5982816Z   test_Tensor_deg2rad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5983124Z   test_Tensor_deg2rad_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5983440Z   test_Tensor_dense_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5983755Z   test_Tensor_dequantize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5984050Z   test_Tensor_det (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5984354Z   test_Tensor_detach (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5984661Z   test_Tensor_detach_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5984950Z   test_Tensor_diag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5985261Z   test_Tensor_diag_embed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5985575Z   test_Tensor_diagflat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5985875Z   test_Tensor_diagonal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5986190Z   test_Tensor_diagonal_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5986585Z   test_Tensor_diff (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5986889Z   test_Tensor_digamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5987183Z   test_Tensor_digamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5987485Z   test_Tensor_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5987783Z   test_Tensor_dist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5988069Z   test_Tensor_div (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5988366Z   test_Tensor_div_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5988669Z   test_Tensor_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5988976Z   test_Tensor_divide_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5989266Z   test_Tensor_dot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5989607Z   test_Tensor_double (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5989915Z   test_Tensor_dsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5990214Z   test_Tensor_element_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5990520Z   test_Tensor_eq (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5990819Z   test_Tensor_eq_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5991119Z   test_Tensor_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5991406Z   test_Tensor_erf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5991704Z   test_Tensor_erf_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5992002Z   test_Tensor_erfc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5992290Z   test_Tensor_erfc_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5992595Z   test_Tensor_erfinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5992898Z   test_Tensor_erfinv_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5993198Z   test_Tensor_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5993482Z   test_Tensor_exp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5993781Z   test_Tensor_exp2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5994079Z   test_Tensor_exp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5994369Z   test_Tensor_expand (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5994680Z   test_Tensor_expand_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5994987Z   test_Tensor_expm1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5995277Z   test_Tensor_expm1_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5995592Z   test_Tensor_exponential_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5995901Z   test_Tensor_fill_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5996215Z   test_Tensor_fill_diagonal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5996510Z   test_Tensor_fix (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5996808Z   test_Tensor_fix_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5997114Z   test_Tensor_flatten (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5997459Z   test_Tensor_flip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5997769Z   test_Tensor_fliplr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5998075Z   test_Tensor_flipud (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5998381Z   test_Tensor_float (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5998719Z   test_Tensor_float_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5999037Z   test_Tensor_float_power_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5999346Z   test_Tensor_floor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5999633Z   test_Tensor_floor_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.5999945Z   test_Tensor_floor_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6000269Z   test_Tensor_floor_divide_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6000577Z   test_Tensor_fmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6000864Z   test_Tensor_fmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6001164Z   test_Tensor_fmod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6001467Z   test_Tensor_fmod_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6001787Z   test_Tensor_frac (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6002091Z   test_Tensor_frac_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6002395Z   test_Tensor_frexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6002686Z   test_Tensor_gather (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6002991Z   test_Tensor_gcd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6003291Z   test_Tensor_gcd_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6003590Z   test_Tensor_ge (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6003876Z   test_Tensor_ge_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6004183Z   test_Tensor_geometric_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6004492Z   test_Tensor_geqrf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6004782Z   test_Tensor_ger (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6005089Z   test_Tensor_get_device (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6005401Z   test_Tensor_greater (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6005712Z   test_Tensor_greater_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6006018Z   test_Tensor_greater_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6006339Z   test_Tensor_greater_equal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6006652Z   test_Tensor_gt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6006936Z   test_Tensor_gt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6007239Z   test_Tensor_half (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6007549Z   test_Tensor_hardshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6007866Z   test_Tensor_has_names (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6008165Z   test_Tensor_heaviside (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6008478Z   test_Tensor_heaviside_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6008785Z   test_Tensor_histc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6009076Z   test_Tensor_histogram (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6009383Z   test_Tensor_hsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6009687Z   test_Tensor_hypot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6009989Z   test_Tensor_hypot_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6010273Z   test_Tensor_i0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6010569Z   test_Tensor_i0_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6010903Z   test_Tensor_igamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6011198Z   test_Tensor_igamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6011504Z   test_Tensor_igammac (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6011810Z   test_Tensor_igammac_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6012110Z   test_Tensor_index_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6012424Z   test_Tensor_index_add_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6012739Z   test_Tensor_index_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6013055Z   test_Tensor_index_copy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6013354Z   test_Tensor_index_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6013700Z   test_Tensor_index_fill_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6014017Z   test_Tensor_index_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6014317Z   test_Tensor_index_put_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6014634Z   test_Tensor_index_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6014950Z   test_Tensor_index_reduce_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6015269Z   test_Tensor_index_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6015567Z   test_Tensor_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6015872Z   test_Tensor_inner (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6016172Z   test_Tensor_int (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6016464Z   test_Tensor_int_repr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6016776Z   test_Tensor_inverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6017079Z   test_Tensor_ipu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6017389Z   test_Tensor_is_coalesced (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6017693Z   test_Tensor_is_complex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6018000Z   test_Tensor_is_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6018313Z   test_Tensor_is_contiguous (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6018626Z   test_Tensor_is_distributed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6018950Z   test_Tensor_is_floating_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6019277Z   test_Tensor_is_inference (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6019589Z   test_Tensor_is_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6019886Z   test_Tensor_is_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6020196Z   test_Tensor_is_pinned (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6020509Z   test_Tensor_is_same_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6020807Z   test_Tensor_is_set_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6021113Z   test_Tensor_is_shared (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6021421Z   test_Tensor_is_signed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6021727Z   test_Tensor_isclose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6022022Z   test_Tensor_isfinite (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6022326Z   test_Tensor_isinf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6022837Z   test_Tensor_isnan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6023131Z   test_Tensor_isneginf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6023441Z   test_Tensor_isposinf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6023744Z   test_Tensor_isreal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6024038Z   test_Tensor_istft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6024338Z   test_Tensor_item (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6024638Z   test_Tensor_kron (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6024943Z   test_Tensor_kthvalue (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6025234Z   test_Tensor_lcm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6025528Z   test_Tensor_lcm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6025873Z   test_Tensor_ldexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6026166Z   test_Tensor_ldexp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6026464Z   test_Tensor_le (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6026760Z   test_Tensor_le_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6027061Z   test_Tensor_lerp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6027348Z   test_Tensor_lerp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6027648Z   test_Tensor_less (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6027947Z   test_Tensor_less_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6028241Z   test_Tensor_less_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6028553Z   test_Tensor_less_equal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6028867Z   test_Tensor_lgamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6029173Z   test_Tensor_lgamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6029460Z   test_Tensor_log (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6029757Z   test_Tensor_log10 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6030060Z   test_Tensor_log10_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6030349Z   test_Tensor_log1p (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6030650Z   test_Tensor_log1p_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6030950Z   test_Tensor_log2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6031238Z   test_Tensor_log2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6031535Z   test_Tensor_log_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6031842Z   test_Tensor_log_normal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6032156Z   test_Tensor_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6032459Z   test_Tensor_logaddexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6032778Z   test_Tensor_logaddexp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6033099Z   test_Tensor_logcumsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6033399Z   test_Tensor_logdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6033710Z   test_Tensor_logical_and (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6034027Z   test_Tensor_logical_and_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6034343Z   test_Tensor_logical_not (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6034650Z   test_Tensor_logical_not_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6035000Z   test_Tensor_logical_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6035310Z   test_Tensor_logical_or_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6035606Z   test_Tensor_logical_xor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6035918Z   test_Tensor_logical_xor_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6036225Z   test_Tensor_logit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6036528Z   test_Tensor_logit_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6036825Z   test_Tensor_logsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6037133Z   test_Tensor_long (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6037486Z   test_Tensor_lt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6037805Z   test_Tensor_lt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6038106Z   test_Tensor_lu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6038408Z   test_Tensor_lu_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6038713Z   test_Tensor_map2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6039000Z   test_Tensor_map_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6039306Z   test_Tensor_masked_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6039622Z   test_Tensor_masked_fill_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6039926Z   test_Tensor_masked_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6040249Z   test_Tensor_masked_scatter_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6040574Z   test_Tensor_masked_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6040888Z   test_Tensor_matmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6041185Z   test_Tensor_matrix_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6041502Z   test_Tensor_matrix_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6041809Z   test_Tensor_max (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6042100Z   test_Tensor_maximum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6042403Z   test_Tensor_mean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6042705Z   test_Tensor_median (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6042994Z   test_Tensor_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6043300Z   test_Tensor_minimum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6043601Z   test_Tensor_mm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6043909Z   test_Tensor_mode (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6044201Z   test_Tensor_moveaxis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6044510Z   test_Tensor_movedim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6044815Z   test_Tensor_msort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6045099Z   test_Tensor_mul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6045399Z   test_Tensor_mul_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6045711Z   test_Tensor_multinomial (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6046029Z   test_Tensor_multiply (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6046328Z   test_Tensor_multiply_ (__main__.TestTorchFunctionOverride) ... ok (0.003s)
2023-01-11T21:58:40.6046630Z   test_Tensor_mv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6046971Z   test_Tensor_mvlgamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6047270Z   test_Tensor_mvlgamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6047583Z   test_Tensor_nan_to_num (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6047894Z   test_Tensor_nan_to_num_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6048202Z   test_Tensor_nanmean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6048501Z   test_Tensor_nanmedian (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6048818Z   test_Tensor_nanquantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6049130Z   test_Tensor_nansum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6049422Z   test_Tensor_narrow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6049764Z   test_Tensor_narrow_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6050085Z   test_Tensor_ndimension (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6050394Z   test_Tensor_ne (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6050678Z   test_Tensor_ne_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6050976Z   test_Tensor_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6051276Z   test_Tensor_neg_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6051570Z   test_Tensor_negative (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6051881Z   test_Tensor_negative_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6052184Z   test_Tensor_nelement (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6067299Z   test_Tensor_nextafter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6067801Z   test_Tensor_nextafter_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6068237Z   test_Tensor_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6068618Z   test_Tensor_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6068927Z   test_Tensor_normal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6069239Z   test_Tensor_not_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6069537Z   test_Tensor_not_equal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6069846Z   test_Tensor_numel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6070156Z   test_Tensor_numpy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6070445Z   test_Tensor_orgqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6070744Z   test_Tensor_ormqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6071049Z   test_Tensor_outer (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6071354Z   test_Tensor_permute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6071655Z   test_Tensor_pin_memory (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6071969Z   test_Tensor_pinverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6072281Z   test_Tensor_polygamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6072586Z   test_Tensor_polygamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6072899Z   test_Tensor_positive (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6073207Z   test_Tensor_pow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6073509Z   test_Tensor_pow_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6073798Z   test_Tensor_prelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6074106Z   test_Tensor_prod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6074521Z   test_Tensor_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6074808Z   test_Tensor_put_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6075122Z   test_Tensor_q_per_channel_axis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6075460Z   test_Tensor_q_per_channel_scales (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6075793Z   test_Tensor_q_per_channel_zero_points (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6076121Z   test_Tensor_q_scale (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6076432Z   test_Tensor_q_zero_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6076738Z   test_Tensor_qr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6077028Z   test_Tensor_qscheme (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6077496Z   test_Tensor_quantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6077816Z   test_Tensor_rad2deg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6078112Z   test_Tensor_rad2deg_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6078413Z   test_Tensor_random_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6078717Z   test_Tensor_ravel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6079031Z   test_Tensor_reciprocal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6079338Z   test_Tensor_reciprocal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6079658Z   test_Tensor_record_stream (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6079977Z   test_Tensor_refine_names (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6080288Z   test_Tensor_register_hook (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6080600Z   test_Tensor_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6080899Z   test_Tensor_relu_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6081208Z   test_Tensor_remainder (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6081508Z   test_Tensor_remainder_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6081817Z   test_Tensor_rename (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6082122Z   test_Tensor_rename_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6082412Z   test_Tensor_renorm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6082714Z   test_Tensor_renorm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6083022Z   test_Tensor_repeat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6083344Z   test_Tensor_repeat_interleave (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6083662Z   test_Tensor_requires_grad_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6083979Z   test_Tensor_reshape (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6084291Z   test_Tensor_reshape_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6084619Z   test_Tensor_resize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6084926Z   test_Tensor_resize_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6085232Z   test_Tensor_resize_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6085541Z   test_Tensor_resize_as_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6085845Z   test_Tensor_resize_as_sparse_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6086166Z   test_Tensor_resolve_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6086524Z   test_Tensor_resolve_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6086828Z   test_Tensor_retain_grad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6087132Z   test_Tensor_roll (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6087431Z   test_Tensor_rot90 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6087718Z   test_Tensor_round (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6088020Z   test_Tensor_round_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6088331Z   test_Tensor_row_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6088637Z   test_Tensor_rsqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6088925Z   test_Tensor_rsqrt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6089271Z   test_Tensor_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6089581Z   test_Tensor_scatter_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6089881Z   test_Tensor_scatter_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6090198Z   test_Tensor_scatter_add_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6090519Z   test_Tensor_scatter_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6090844Z   test_Tensor_scatter_reduce_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6091146Z   test_Tensor_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6091456Z   test_Tensor_select_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6091763Z   test_Tensor_set_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6092051Z   test_Tensor_sgn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6092354Z   test_Tensor_sgn_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6092661Z   test_Tensor_share_memory_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6092971Z   test_Tensor_short (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6093264Z   test_Tensor_sigmoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6093574Z   test_Tensor_sigmoid_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6093874Z   test_Tensor_sign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6094164Z   test_Tensor_sign_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6094464Z   test_Tensor_signbit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6094767Z   test_Tensor_sin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6095065Z   test_Tensor_sin_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6095352Z   test_Tensor_sinc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6095651Z   test_Tensor_sinc_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6095950Z   test_Tensor_sinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6096235Z   test_Tensor_sinh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6096530Z   test_Tensor_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6096840Z   test_Tensor_slice_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6097142Z   test_Tensor_slogdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6097442Z   test_Tensor_smm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6097747Z   test_Tensor_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6098048Z   test_Tensor_sort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6098383Z   test_Tensor_sparse_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6098699Z   test_Tensor_sparse_mask (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6099018Z   test_Tensor_sparse_resize_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6099339Z   test_Tensor_sparse_resize_and_clear_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6099660Z   test_Tensor_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6099972Z   test_Tensor_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6100282Z   test_Tensor_sqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6100567Z   test_Tensor_sqrt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6100868Z   test_Tensor_square (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6101206Z   test_Tensor_square_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6101501Z   test_Tensor_squeeze (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6101806Z   test_Tensor_squeeze_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6102115Z   test_Tensor_sspaddmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6102585Z   test_Tensor_std (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6102879Z   test_Tensor_stft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6103182Z   test_Tensor_storage (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6103496Z   test_Tensor_storage_offset (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6103806Z   test_Tensor_storage_type (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6104115Z   test_Tensor_sub (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6104413Z   test_Tensor_sub_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6104721Z   test_Tensor_subtract (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6105020Z   test_Tensor_subtract_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6105326Z   test_Tensor_sum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6105632Z   test_Tensor_sum_to_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6105922Z   test_Tensor_svd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6106227Z   test_Tensor_swapaxes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6106543Z   test_Tensor_swapaxes_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6106856Z   test_Tensor_swapdims (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6107155Z   test_Tensor_swapdims_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6107466Z   test_Tensor_symeig (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6107769Z   test_Tensor_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6108053Z   test_Tensor_t_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6108349Z   test_Tensor_take (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6108659Z   test_Tensor_take_along_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6108960Z   test_Tensor_tan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6109258Z   test_Tensor_tan_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6109553Z   test_Tensor_tanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6109855Z   test_Tensor_tanh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6110149Z   test_Tensor_tensor_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6110459Z   test_Tensor_tile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6110830Z   test_Tensor_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6111118Z   test_Tensor_to_dense (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6111425Z   test_Tensor_to_mkldnn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6111733Z   test_Tensor_to_sparse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6112037Z   test_Tensor_tolist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6112330Z   test_Tensor_topk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6112631Z   test_Tensor_trace (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6112939Z   test_Tensor_transpose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6113241Z   test_Tensor_transpose_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6113605Z   test_Tensor_triangular_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6113924Z   test_Tensor_tril (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6114223Z   test_Tensor_tril_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6114507Z   test_Tensor_triu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6114805Z   test_Tensor_triu_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6115113Z   test_Tensor_true_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6115418Z   test_Tensor_true_divide_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6115725Z   test_Tensor_trunc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6116025Z   test_Tensor_trunc_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6116315Z   test_Tensor_type (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6116622Z   test_Tensor_type_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6116928Z   test_Tensor_unbind (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6117230Z   test_Tensor_unfold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6117584Z   test_Tensor_uniform_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6117887Z   test_Tensor_unique (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6118202Z   test_Tensor_unique_consecutive (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6118514Z   test_Tensor_unsafe_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6118830Z   test_Tensor_unsafe_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6119155Z   test_Tensor_unsafe_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6119485Z   test_Tensor_unsqueeze (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6119788Z   test_Tensor_unsqueeze_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6120107Z   test_Tensor_untyped_storage (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6120419Z   test_Tensor_values (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6120708Z   test_Tensor_var (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6121012Z   test_Tensor_vdot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6121313Z   test_Tensor_view (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6121617Z   test_Tensor_view_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6121906Z   test_Tensor_vsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6122208Z   test_Tensor_where (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6122514Z   test_Tensor_xlogy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6122845Z   test_Tensor_xlogy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6123146Z   test_Tensor_xpu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6123446Z   test_Tensor_zero_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6123755Z   test__TensorBase_H___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6124063Z   test__TensorBase_T___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6124397Z   test__TensorBase__backward_hooks___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6124733Z   test__TensorBase__base___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6125050Z   test__TensorBase__cdata___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6125407Z   test__TensorBase__grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6125744Z   test__TensorBase__grad_fn___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6126076Z   test__TensorBase__version___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6126394Z   test__TensorBase_data___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6126721Z   test__TensorBase_device___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6127049Z   test__TensorBase_dtype___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6127364Z   test__TensorBase_grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6127689Z   test__TensorBase_grad_fn___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6128014Z   test__TensorBase_imag___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6128339Z   test__TensorBase_is_cpu___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6128659Z   test__TensorBase_is_cuda___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6128987Z   test__TensorBase_is_ipu___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6129313Z   test__TensorBase_is_leaf___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6129628Z   test__TensorBase_is_meta___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6129957Z   test__TensorBase_is_mkldnn___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6130285Z   test__TensorBase_is_mps___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6130614Z   test__TensorBase_is_nested___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6130926Z   test__TensorBase_is_ort___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6131259Z   test__TensorBase_is_quantized___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6131596Z   test__TensorBase_is_sparse___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6131932Z   test__TensorBase_is_sparse_csr___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6132257Z   test__TensorBase_is_vulkan___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6132585Z   test__TensorBase_is_xpu___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6132910Z   test__TensorBase_layout___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6133220Z   test__TensorBase_mH___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6133535Z   test__TensorBase_mT___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6133854Z   test__TensorBase_name___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6134212Z   test__TensorBase_names___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6134521Z   test__TensorBase_ndim___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6134847Z   test__TensorBase_output_nr___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6135173Z   test__TensorBase_real___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6135496Z   test__TensorBase_requires_grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6135834Z   test__TensorBase_retains_grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6136165Z   test__TensorBase_shape___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6136490Z   test__TensorBase_volatile___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6136785Z   test_base (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6137109Z   test_grad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6137432Z   test_has_torch_function_non_sequence (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6137730Z   test_mean_semantics (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:40.6138017Z Test that a function with one argument can be overrided ... ok (0.001s)
2023-01-11T21:58:40.6138301Z   test_mm_semantics (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:40.6138593Z Test that a function with multiple arguments can be overrided ... ok (0.002s)
2023-01-11T21:58:40.6138880Z   test_pow_rpow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6139178Z   test_precedence_semantics (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:40.6139478Z Test semantics for __torch_function__ for functions that take ... ok (0.003s)
2023-01-11T21:58:40.6139774Z   test_tensor_subclass_propagation (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:40.6140080Z this test exercises the functionality described in ... ok (0.001s)
2023-01-11T21:58:40.6140385Z   test_torch__C__fft_fft_fft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6140692Z   test_torch__C__fft_fft_fft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6141006Z   test_torch__C__fft_fft_fftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6141327Z   test_torch__C__fft_fft_fftshift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6141647Z   test_torch__C__fft_fft_hfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6141953Z   test_torch__C__fft_fft_hfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6142269Z   test_torch__C__fft_fft_hfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6142719Z   test_torch__C__fft_fft_ifft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6143023Z   test_torch__C__fft_fft_ifft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6143339Z   test_torch__C__fft_fft_ifftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6143664Z   test_torch__C__fft_fft_ifftshift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6143986Z   test_torch__C__fft_fft_ihfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6144293Z   test_torch__C__fft_fft_ihfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6144611Z   test_torch__C__fft_fft_ihfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6144929Z   test_torch__C__fft_fft_irfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6145232Z   test_torch__C__fft_fft_irfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6145550Z   test_torch__C__fft_fft_irfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6145871Z   test_torch__C__fft_fft_rfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6146249Z   test_torch__C__fft_fft_rfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6146552Z   test_torch__C__fft_fft_rfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6146884Z   test_torch__C__linalg_linalg_cholesky (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6147231Z   test_torch__C__linalg_linalg_cholesky_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6147571Z   test_torch__C__linalg_linalg_cond (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6147896Z   test_torch__C__linalg_linalg_cross (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6148234Z   test_torch__C__linalg_linalg_det (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6148572Z   test_torch__C__linalg_linalg_diagonal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6148935Z   test_torch__C__linalg_linalg_eig (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6149273Z   test_torch__C__linalg_linalg_eigh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6149613Z   test_torch__C__linalg_linalg_eigvals (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6149956Z   test_torch__C__linalg_linalg_eigvalsh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6150304Z   test_torch__C__linalg_linalg_householder_product (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6150653Z   test_torch__C__linalg_linalg_inv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6150986Z   test_torch__C__linalg_linalg_inv_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6151314Z   test_torch__C__linalg_linalg_ldl_factor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6151667Z   test_torch__C__linalg_linalg_ldl_factor_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6152017Z   test_torch__C__linalg_linalg_ldl_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6152352Z   test_torch__C__linalg_linalg_lstsq (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6152670Z   test_torch__C__linalg_linalg_lu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6153009Z   test_torch__C__linalg_linalg_lu_factor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6153354Z   test_torch__C__linalg_linalg_lu_factor_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6153695Z   test_torch__C__linalg_linalg_lu_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6154019Z   test_torch__C__linalg_linalg_matmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6154362Z   test_torch__C__linalg_linalg_matrix_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6154712Z   test_torch__C__linalg_linalg_matrix_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6155046Z   test_torch__C__linalg_linalg_matrix_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6155392Z   test_torch__C__linalg_linalg_matrix_rank (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6155737Z   test_torch__C__linalg_linalg_multi_dot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6156076Z   test_torch__C__linalg_linalg_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6156394Z   test_torch__C__linalg_linalg_pinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6156721Z   test_torch__C__linalg_linalg_qr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6157058Z   test_torch__C__linalg_linalg_slogdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6157436Z   test_torch__C__linalg_linalg_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6157812Z   test_torch__C__linalg_linalg_solve_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6158158Z   test_torch__C__linalg_linalg_solve_triangular (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6158503Z   test_torch__C__linalg_linalg_svd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6158824Z   test_torch__C__linalg_linalg_svdvals (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6159165Z   test_torch__C__linalg_linalg_tensorinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6159510Z   test_torch__C__linalg_linalg_tensorsolve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6159853Z   test_torch__C__linalg_linalg_vander (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6160223Z   test_torch__C__linalg_linalg_vecdot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6160567Z   test_torch__C__linalg_linalg_vector_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6160902Z   test_torch__C__nn_avg_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6161213Z   test_torch__C__nn_avg_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6161528Z   test_torch__C__nn_gelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6161840Z   test_torch__C__nn_linear (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6162160Z   test_torch__C__nn_log_sigmoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6162464Z   test_torch__C__nn_one_hot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6162778Z   test_torch__C__nn_softplus (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6163099Z   test_torch__C__nn_softshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6163428Z   test_torch__C__special_special_airy_ai (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6163773Z   test_torch__C__special_special_bessel_j0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6164119Z   test_torch__C__special_special_bessel_j1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6164462Z   test_torch__C__special_special_bessel_y0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6164793Z   test_torch__C__special_special_bessel_y1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6165154Z   test_torch__C__special_special_chebyshev_polynomial_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6165538Z   test_torch__C__special_special_chebyshev_polynomial_u (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6165911Z   test_torch__C__special_special_chebyshev_polynomial_v (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6166276Z   test_torch__C__special_special_chebyshev_polynomial_w (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6166639Z   test_torch__C__special_special_digamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6166981Z   test_torch__C__special_special_entr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6167308Z   test_torch__C__special_special_erf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6167643Z   test_torch__C__special_special_erfc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6167982Z   test_torch__C__special_special_erfcx (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6168326Z   test_torch__C__special_special_erfinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6168654Z   test_torch__C__special_special_exp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6169026Z   test_torch__C__special_special_expit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6169365Z   test_torch__C__special_special_expm1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6169693Z   test_torch__C__special_special_gammainc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6170046Z   test_torch__C__special_special_gammaincc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6170394Z   test_torch__C__special_special_gammaln (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6170755Z   test_torch__C__special_special_hermite_polynomial_h (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6171121Z   test_torch__C__special_special_hermite_polynomial_he (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6171474Z   test_torch__C__special_special_i0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6171841Z   test_torch__C__special_special_i0e (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6172175Z   test_torch__C__special_special_i1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6172492Z   test_torch__C__special_special_i1e (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6172847Z   test_torch__C__special_special_laguerre_polynomial_l (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6173229Z   test_torch__C__special_special_legendre_polynomial_p (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6173575Z   test_torch__C__special_special_log1p (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6173920Z   test_torch__C__special_special_log_ndtr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6174270Z   test_torch__C__special_special_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6174617Z   test_torch__C__special_special_logit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6174956Z   test_torch__C__special_special_logsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6175315Z   test_torch__C__special_special_modified_bessel_i0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6175685Z   test_torch__C__special_special_modified_bessel_i1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6176047Z   test_torch__C__special_special_modified_bessel_k0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6176401Z   test_torch__C__special_special_modified_bessel_k1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6176764Z   test_torch__C__special_special_multigammaln (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6177116Z   test_torch__C__special_special_ndtr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6177446Z   test_torch__C__special_special_ndtri (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6177796Z   test_torch__C__special_special_polygamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6178141Z   test_torch__C__special_special_psi (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6178480Z   test_torch__C__special_special_round (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6178830Z   test_torch__C__special_special_scaled_modified_bessel_k0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6179213Z   test_torch__C__special_special_scaled_modified_bessel_k1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6179602Z   test_torch__C__special_special_shifted_chebyshev_polynomial_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6179999Z   test_torch__C__special_special_shifted_chebyshev_polynomial_u (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6180420Z   test_torch__C__special_special_shifted_chebyshev_polynomial_v (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6180813Z   test_torch__C__special_special_shifted_chebyshev_polynomial_w (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6181179Z   test_torch__C__special_special_sinc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6181511Z   test_torch__C__special_special_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6181872Z   test_torch__C__special_special_spherical_bessel_j0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6182230Z   test_torch__C__special_special_xlog1py (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6182711Z   test_torch__C__special_special_xlogy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6183042Z   test_torch__C__special_special_zeta (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6183438Z   test_torch__assert_async (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6183755Z   test_torch__conj_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6184076Z   test_torch__fw_primal_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6184385Z   test_torch__indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6184706Z   test_torch__lobpcg_lobpcg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6185038Z   test_torch__lowrank_pca_lowrank (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6185357Z   test_torch__lowrank_svd_lowrank (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6185682Z   test_torch__make_dual_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6186012Z   test_torch__native_batch_norm_legit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6186347Z   test_torch__neg_view_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6187002Z   test_torch__reshape_alias_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6187326Z   test_torch__rowwise_prune (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6187657Z   test_torch__sparse_broadcast_to_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6187977Z   test_torch__values_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6188285Z   test_torch_abs (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6188586Z   test_torch_absolute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6188890Z   test_torch_acos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6189178Z   test_torch_acosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6189501Z   test_torch_adaptive_avg_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6189838Z   test_torch_adaptive_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6190136Z   test_torch_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6190438Z   test_torch_addbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6190744Z   test_torch_addcdiv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6191051Z   test_torch_addcmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6191344Z   test_torch_addmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6191644Z   test_torch_addmv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6191943Z   test_torch_addr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6192234Z   test_torch_adjoint (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6192561Z   test_torch_affine_grid_generator (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6192940Z   test_torch_alias_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6193246Z   test_torch_all (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6193536Z   test_torch_allclose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6193853Z   test_torch_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6194163Z   test_torch_amax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6194449Z   test_torch_amin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6194748Z   test_torch_aminmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6195052Z   test_torch_angle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6195337Z   test_torch_any (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6195666Z   test_torch_arccos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6195974Z   test_torch_arccosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6196280Z   test_torch_arcsin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6196572Z   test_torch_arcsinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6196875Z   test_torch_arctan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6197177Z   test_torch_arctan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6197522Z   test_torch_arctanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6197825Z   test_torch_argmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6198129Z   test_torch_argmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6198432Z   test_torch_argsort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6198730Z   test_torch_argwhere (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6199050Z   test_torch_as_strided_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6199384Z   test_torch_as_strided_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6199687Z   test_torch_asin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6199988Z   test_torch_asinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6200289Z   test_torch_atan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6200588Z   test_torch_atan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6200877Z   test_torch_atanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6201184Z   test_torch_avg_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6201495Z   test_torch_baddbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6201795Z   test_torch_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6202120Z   test_torch_batch_norm_backward_elemt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6202464Z   test_torch_batch_norm_backward_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6202796Z   test_torch_batch_norm_elemt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6203114Z   test_torch_batch_norm_gather_stats (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6203468Z   test_torch_batch_norm_gather_stats_with_counts (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6203808Z   test_torch_batch_norm_stats (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6204127Z   test_torch_batch_norm_update_stats (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6204452Z   test_torch_bernoulli (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6204805Z   test_torch_bilinear (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6205140Z   test_torch_binary_cross_entropy_with_logits (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6205457Z   test_torch_bincount (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6205762Z   test_torch_binomial (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6206071Z   test_torch_bitwise_and (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6206379Z   test_torch_bitwise_left_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6206697Z   test_torch_bitwise_not (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6207006Z   test_torch_bitwise_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6207327Z   test_torch_bitwise_right_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6207668Z   test_torch_bitwise_xor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6207971Z   test_torch_bmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6208279Z   test_torch_broadcast_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6208583Z   test_torch_bucketize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6208884Z   test_torch_cat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6209198Z   test_torch_ccol_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6209511Z   test_torch_ceil (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6209799Z   test_torch_celu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6210112Z   test_torch_channel_shuffle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6210425Z   test_torch_cholesky (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6210734Z   test_torch_cholesky_inverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6211056Z   test_torch_cholesky_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6211388Z   test_torch_choose_qparams_optimized (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6211713Z   test_torch_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6212001Z   test_torch_clamp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6212305Z   test_torch_clamp_max (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6212613Z   test_torch_clamp_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6212899Z   test_torch_clip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6213200Z   test_torch_clone (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6213514Z   test_torch_col_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6213823Z   test_torch_column_stack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6214138Z   test_torch_combinations (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6214447Z   test_torch_complex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6214750Z   test_torch_concat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6215050Z   test_torch_concatenate (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6215355Z   test_torch_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6215663Z   test_torch_conj_physical (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6215970Z   test_torch_constant_pad_nd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6216282Z   test_torch_conv1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6216583Z   test_torch_conv2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6216924Z   test_torch_conv3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6217215Z   test_torch_conv_tbc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6217531Z   test_torch_conv_transpose1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6217856Z   test_torch_conv_transpose2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6218166Z   test_torch_conv_transpose3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6218484Z   test_torch_convolution (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6218796Z   test_torch_copysign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6219104Z   test_torch_corrcoef (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6219392Z   test_torch_cos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6219749Z   test_torch_cosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6220070Z   test_torch_cosine_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6220391Z   test_torch_cosine_similarity (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6220714Z   test_torch_count_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6221016Z   test_torch_cov (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6221317Z   test_torch_cross (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6221618Z   test_torch_crow_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6221934Z   test_torch_ctc_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6222237Z   test_torch_cummax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6222674Z   test_torch_cummin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6222983Z   test_torch_cumprod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6223291Z   test_torch_cumsum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6223610Z   test_torch_cumulative_trapezoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6223920Z   test_torch_deg2rad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6224230Z   test_torch_dequantize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6224538Z   test_torch_det (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6224825Z   test_torch_detach (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6225138Z   test_torch_detach_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6225443Z   test_torch_diag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6225741Z   test_torch_diag_embed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6226055Z   test_torch_diagflat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6226363Z   test_torch_diagonal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6226676Z   test_torch_diagonal_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6226990Z   test_torch_diagonal_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6227301Z   test_torch_diff (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6227604Z   test_torch_digamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6227892Z   test_torch_dist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6228187Z   test_torch_div (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6228491Z   test_torch_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6228793Z   test_torch_dot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6229138Z   test_torch_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6229442Z   test_torch_dsmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6229742Z   test_torch_dsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6230033Z   test_torch_dstack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6230340Z   test_torch_embedding (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6230657Z   test_torch_embedding_bag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6230977Z   test_torch_empty_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6231267Z   test_torch_eq (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6231565Z   test_torch_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6231906Z   test_torch_erf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6232194Z   test_torch_erfc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6232491Z   test_torch_erfinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6232789Z   test_torch_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6233075Z   test_torch_exp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6233382Z   test_torch_expand_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6233689Z   test_torch_expm1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6234028Z   test_torch_fake_quantize_per_channel_affine (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6234376Z   test_torch_fake_quantize_per_tensor_affine (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6234728Z   test_torch_fbgemm_linear_fp16_weight (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6235097Z   test_torch_fbgemm_linear_fp16_weight_fp32_activation (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6235453Z   test_torch_fbgemm_linear_int8_weight (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6235795Z   test_torch_fbgemm_linear_int8_weight_fp32_activation (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6236161Z   test_torch_fbgemm_linear_quantize_weight (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6236513Z   test_torch_fbgemm_pack_gemm_matrix_fp16 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6236851Z   test_torch_fbgemm_pack_quantized_matrix (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6237194Z   test_torch_feature_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6237581Z   test_torch_feature_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6237901Z   test_torch_fix (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6238187Z   test_torch_flatten (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6238488Z   test_torch_flip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6238789Z   test_torch_fliplr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6239082Z   test_torch_flipud (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6239393Z   test_torch_float_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6239697Z   test_torch_floor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6240002Z   test_torch_floor_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6240295Z   test_torch_fmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6240594Z   test_torch_fmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6240934Z   test_torch_fmod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6241217Z   test_torch_frac (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6241517Z   test_torch_frexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6241831Z   test_torch_frobenius_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6241973Z   test_torch_full_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6242130Z   test_torch_functional_atleast_1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6242275Z   test_torch_functional_atleast_2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6242431Z   test_torch_functional_atleast_3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6242589Z   test_torch_functional_block_diag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6242802Z   test_torch_functional_broadcast_tensors (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6242969Z   test_torch_functional_cartesian_prod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6243122Z   test_torch_functional_cdist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6243281Z   test_torch_functional_chain_matmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6243435Z   test_torch_functional_einsum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6243571Z   test_torch_functional_lu (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6243730Z   test_torch_functional_meshgrid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6243880Z   test_torch_functional_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6244032Z   test_torch_functional_split (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6244185Z   test_torch_functional_stft (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6244343Z   test_torch_functional_tensordot (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6244495Z   test_torch_functional_unique (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6244663Z   test_torch_functional_unique_consecutive (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6244832Z   test_torch_fused_moving_avg_obs_fake_quant (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6244961Z   test_torch_gather (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6245096Z   test_torch_gcd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6245231Z   test_torch_ge (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6245369Z   test_torch_geqrf (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6245507Z   test_torch_ger (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6245653Z   test_torch_gradient (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6245795Z   test_torch_greater (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6245942Z   test_torch_greater_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6246079Z   test_torch_grid_sampler (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6246230Z   test_torch_grid_sampler_2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6246379Z   test_torch_grid_sampler_3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6246522Z   test_torch_group_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6246657Z   test_torch_gru (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6246798Z   test_torch_gru_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6246935Z   test_torch_gt (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6247111Z   test_torch_hardshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6247241Z   test_torch_heaviside (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6247399Z   test_torch_hinge_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6247538Z   test_torch_histc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6247680Z   test_torch_histogram (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6247829Z   test_torch_histogramdd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6247965Z   test_torch_hsmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6248105Z   test_torch_hsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6248245Z   test_torch_hstack (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6248404Z   test_torch_hypot (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6248542Z   test_torch_i0 (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6248682Z   test_torch_igamma (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6248825Z   test_torch_igammac (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6248961Z   test_torch_imag (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6249103Z   test_torch_index_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6249249Z   test_torch_index_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6249392Z   test_torch_index_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6249519Z   test_torch_index_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6249666Z   test_torch_index_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6249818Z   test_torch_index_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6249963Z   test_torch_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6250102Z   test_torch_inner (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6250251Z   test_torch_instance_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6250395Z   test_torch_int_repr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6250534Z   test_torch_inverse (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6250665Z   test_torch_is_complex (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6250803Z   test_torch_is_conj (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6250952Z   test_torch_is_distributed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6251108Z   test_torch_is_floating_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6251255Z   test_torch_is_inference (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6251395Z   test_torch_is_neg (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6251537Z   test_torch_is_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6251680Z   test_torch_is_same_size (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6251810Z   test_torch_is_signed (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6251948Z   test_torch_isclose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6252090Z   test_torch_isfinite (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6252226Z   test_torch_isin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6252363Z   test_torch_isinf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6252504Z   test_torch_isnan (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6252677Z   test_torch_isneginf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6252818Z   test_torch_isposinf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6252946Z   test_torch_isreal (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6253083Z   test_torch_istft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6253222Z   test_torch_kl_div (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6253357Z   test_torch_kron (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6253498Z   test_torch_kthvalue (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6253642Z   test_torch_layer_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6253776Z   test_torch_lcm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6253932Z   test_torch_ldexp (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6254074Z   test_torch_le (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6254210Z   test_torch_lerp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6254346Z   test_torch_less (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6254487Z   test_torch_less_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6254624Z   test_torch_lgamma (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6254758Z   test_torch_log (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6254894Z   test_torch_log10 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6255018Z   test_torch_log1p (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6255154Z   test_torch_log2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6255301Z   test_torch_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6255448Z   test_torch_logaddexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6255594Z   test_torch_logaddexp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6255743Z   test_torch_logcumsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6255881Z   test_torch_logdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6256026Z   test_torch_logical_and (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6256157Z   test_torch_logical_not (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6256301Z   test_torch_logical_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6256443Z   test_torch_logical_xor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6256582Z   test_torch_logit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6256727Z   test_torch_logsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6256866Z   test_torch_lstm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6257006Z   test_torch_lstm_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6257140Z   test_torch_lt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6257269Z   test_torch_lu_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6257409Z   test_torch_lu_unpack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6257564Z   test_torch_margin_ranking_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6257709Z   test_torch_masked_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6257862Z   test_torch_masked_scatter (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6258008Z   test_torch_masked_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6258182Z   test_torch_matmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6258325Z   test_torch_matrix_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6258459Z   test_torch_matrix_power (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6258596Z   test_torch_max (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6258736Z   test_torch_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6258896Z   test_torch_max_pool1d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6259040Z   test_torch_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6259183Z   test_torch_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6259323Z   test_torch_maximum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6259461Z   test_torch_mean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6259618Z   test_torch_median (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6259754Z   test_torch_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6259894Z   test_torch_minimum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6260046Z   test_torch_miopen_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6260202Z   test_torch_miopen_convolution (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6260367Z   test_torch_miopen_convolution_add_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6260529Z   test_torch_miopen_convolution_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6260697Z   test_torch_miopen_convolution_transpose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6260865Z   test_torch_miopen_depthwise_convolution (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6261004Z   test_torch_miopen_rnn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6261141Z   test_torch_mode (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6261284Z   test_torch_moveaxis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6261427Z   test_torch_movedim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6261567Z   test_torch_msort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6261703Z   test_torch_mul (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6261850Z   test_torch_multinomial (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6261994Z   test_torch_multiply (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6262118Z   test_torch_mv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6262258Z   test_torch_mvlgamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6262569Z   test_torch_nan_to_num (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6262716Z   test_torch_nanmean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6262861Z   test_torch_nanmedian (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6263009Z   test_torch_nanquantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6263149Z   test_torch_nansum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6263278Z   test_torch_narrow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6263423Z   test_torch_narrow_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6263574Z   test_torch_native_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6263733Z   test_torch_native_channel_shuffle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6263886Z   test_torch_native_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6264098Z   test_torch_native_group_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6264248Z   test_torch_native_layer_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6264391Z   test_torch_native_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6264524Z   test_torch_ne (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6264649Z   test_torch_neg (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6264789Z   test_torch_negative (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6264931Z   test_torch_nextafter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6265090Z   test_torch_nn_functional__threshold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6265301Z   test_torch_nn_functional_adaptive_avg_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6265475Z   test_torch_nn_functional_adaptive_avg_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6265641Z   test_torch_nn_functional_adaptive_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6265830Z   test_torch_nn_functional_adaptive_max_pool1d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6265989Z   test_torch_nn_functional_adaptive_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6266176Z   test_torch_nn_functional_adaptive_max_pool2d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6266348Z   test_torch_nn_functional_adaptive_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6266535Z   test_torch_nn_functional_adaptive_max_pool3d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6266699Z   test_torch_nn_functional_affine_grid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6266871Z   test_torch_nn_functional_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6267031Z   test_torch_nn_functional_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6267205Z   test_torch_nn_functional_binary_cross_entropy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6267388Z   test_torch_nn_functional_binary_cross_entropy_with_logits (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6267527Z   test_torch_nn_functional_celu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6267702Z   test_torch_nn_functional_cosine_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6267865Z   test_torch_nn_functional_cross_entropy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6268025Z   test_torch_nn_functional_ctc_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6268186Z   test_torch_nn_functional_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6268344Z   test_torch_nn_functional_dropout1d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6268504Z   test_torch_nn_functional_dropout2d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6268662Z   test_torch_nn_functional_dropout3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6268801Z   test_torch_nn_functional_elu (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6268960Z   test_torch_nn_functional_embedding (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6269122Z   test_torch_nn_functional_embedding_bag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6269298Z   test_torch_nn_functional_feature_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6269493Z   test_torch_nn_functional_fold (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6269665Z   test_torch_nn_functional_fractional_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6269855Z   test_torch_nn_functional_fractional_max_pool2d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6270030Z   test_torch_nn_functional_fractional_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6270217Z   test_torch_nn_functional_fractional_max_pool3d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6270371Z   test_torch_nn_functional_gaussian_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6270523Z   test_torch_nn_functional_glu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6270686Z   test_torch_nn_functional_grid_sample (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6270879Z   test_torch_nn_functional_group_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6271047Z   test_torch_nn_functional_gumbel_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6271206Z   test_torch_nn_functional_hardtanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6271380Z   test_torch_nn_functional_hinge_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6271537Z   test_torch_nn_functional_huber_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6271698Z   test_torch_nn_functional_instance_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6271847Z   test_torch_nn_functional_interpolate (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6272001Z   test_torch_nn_functional_kl_div (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6272160Z   test_torch_nn_functional_l1_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6272322Z   test_torch_nn_functional_layer_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6272480Z   test_torch_nn_functional_leaky_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6272652Z   test_torch_nn_functional_local_response_norm (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6272814Z   test_torch_nn_functional_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6272974Z   test_torch_nn_functional_lp_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6273122Z   test_torch_nn_functional_lp_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6273295Z   test_torch_nn_functional_margin_ranking_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6273454Z   test_torch_nn_functional_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6273634Z   test_torch_nn_functional_max_pool1d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6273792Z   test_torch_nn_functional_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6273966Z   test_torch_nn_functional_max_pool2d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6274126Z   test_torch_nn_functional_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6274301Z   test_torch_nn_functional_max_pool3d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6274463Z   test_torch_nn_functional_max_unpool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6274614Z   test_torch_nn_functional_max_unpool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6274774Z   test_torch_nn_functional_max_unpool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6274967Z   test_torch_nn_functional_mish (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6275125Z   test_torch_nn_functional_mse_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6275307Z   test_torch_nn_functional_multi_head_attention_forward (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6275476Z   test_torch_nn_functional_multi_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6275652Z   test_torch_nn_functional_multilabel_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6275831Z   test_torch_nn_functional_multilabel_soft_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6275986Z   test_torch_nn_functional_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6276135Z   test_torch_nn_functional_normalize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6276317Z   test_torch_nn_functional_pad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6276487Z   test_torch_nn_functional_poisson_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6276639Z   test_torch_nn_functional_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6276793Z   test_torch_nn_functional_relu6 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6276943Z   test_torch_nn_functional_rrelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6277092Z   test_torch_nn_functional_selu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6277238Z   test_torch_nn_functional_silu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6277443Z   test_torch_nn_functional_smooth_l1_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6277611Z   test_torch_nn_functional_soft_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6277773Z   test_torch_nn_functional_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6277928Z   test_torch_nn_functional_softmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6278084Z   test_torch_nn_functional_softsign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6278243Z   test_torch_nn_functional_tanhshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6278414Z   test_torch_nn_functional_triplet_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6278599Z   test_torch_nn_functional_triplet_margin_with_distance_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6278754Z   test_torch_nn_functional_unfold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6278893Z   test_torch_nn_init_constant_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6279052Z   test_torch_nn_init_kaiming_uniform_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6279203Z   test_torch_nn_init_normal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6279353Z   test_torch_nn_init_uniform_ (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6279492Z   test_torch_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6279642Z   test_torch_norm_except_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6279786Z   test_torch_not_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6279932Z   test_torch_nuclear_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6280057Z   test_torch_numel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6280197Z   test_torch_ones_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6280334Z   test_torch_orgqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6280507Z   test_torch_ormqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6280644Z   test_torch_outer (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6280799Z   test_torch_pairwise_distance (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6280937Z   test_torch_pdist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6281077Z   test_torch_permute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6281212Z   test_torch_permute_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6281353Z   test_torch_pinverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6281501Z   test_torch_pixel_shuffle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6281649Z   test_torch_pixel_unshuffle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6281831Z   test_torch_poisson (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6281986Z   test_torch_poisson_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6282124Z   test_torch_polar (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6282272Z   test_torch_polygamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6282402Z   test_torch_positive (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6282539Z   test_torch_pow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6282675Z   test_torch_prelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6282812Z   test_torch_prod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6282946Z   test_torch_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6283098Z   test_torch_q_per_channel_axis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6283256Z   test_torch_q_per_channel_scales (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6283418Z   test_torch_q_per_channel_zero_points (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6283546Z   test_torch_q_scale (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6283691Z   test_torch_q_zero_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6283826Z   test_torch_qr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6283967Z   test_torch_quantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6284121Z   test_torch_quantize_per_channel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6284277Z   test_torch_quantize_per_tensor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6284444Z   test_torch_quantize_per_tensor_dynamic (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6284603Z   test_torch_quantized_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6284749Z   test_torch_quantized_gru_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6284904Z   test_torch_quantized_lstm_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6285060Z   test_torch_quantized_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6285213Z   test_torch_quantized_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6285372Z   test_torch_quantized_rnn_relu_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6285530Z   test_torch_quantized_rnn_tanh_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6285672Z   test_torch_rad2deg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6285814Z   test_torch_rand_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6285950Z   test_torch_randint_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6286129Z   test_torch_randn_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6286269Z   test_torch_ravel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6286407Z   test_torch_real (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6286552Z   test_torch_reciprocal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6286690Z   test_torch_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6286835Z   test_torch_remainder (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6286976Z   test_torch_renorm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6287117Z   test_torch_repeat_interleave (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6287258Z   test_torch_reshape (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6287434Z   test_torch_resolve_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6287582Z   test_torch_resolve_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6287723Z   test_torch_rnn_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6287868Z   test_torch_rnn_relu_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288008Z   test_torch_rnn_tanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288154Z   test_torch_rnn_tanh_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288278Z   test_torch_roll (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288413Z   test_torch_rot90 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288551Z   test_torch_round (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288703Z   test_torch_row_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288850Z   test_torch_row_stack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6288990Z   test_torch_rrelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6289123Z   test_torch_rsqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6289262Z   test_torch_rsub (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6289389Z   test_torch_saddmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6289532Z   test_torch_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6289680Z   test_torch_scatter_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6289829Z   test_torch_scatter_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6289976Z   test_torch_searchsorted (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6290124Z   test_torch_segment_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6290269Z   test_torch_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6290414Z   test_torch_select_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6290550Z   test_torch_select_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6290687Z   test_torch_selu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6290822Z   test_torch_sgn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6290962Z   test_torch_sigmoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6291098Z   test_torch_sign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6291237Z   test_torch_signbit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6291373Z   test_torch_sin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6291510Z   test_torch_sinc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6291667Z   test_torch_sinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6291813Z   test_torch_slice_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6291961Z   test_torch_slice_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6292104Z   test_torch_slogdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6292238Z   test_torch_smm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6292377Z   test_torch_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6292513Z   test_torch_sort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6292658Z   test_torch_split_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6292796Z   test_torch_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6292983Z   test_torch_split_with_sizes_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6293123Z   test_torch_sqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6293262Z   test_torch_square (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6293400Z   test_torch_squeeze (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6293546Z   test_torch_squeeze_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6293684Z   test_torch_stack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6293820Z   test_torch_std (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6293948Z   test_torch_std_mean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6294080Z   test_torch_sub (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6294222Z   test_torch_subtract (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6294356Z   test_torch_sum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6294491Z   test_torch_svd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6294632Z   test_torch_swapaxes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6294772Z   test_torch_swapdims (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6294912Z   test_torch_symeig (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6295034Z   test_torch_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6295171Z   test_torch_t_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6295308Z   test_torch_take (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6295456Z   test_torch_take_along_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6295591Z   test_torch_tan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6295727Z   test_torch_tanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6295878Z   test_torch_tensor_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296024Z   test_torch_threshold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296146Z   test_torch_tile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296281Z   test_torch_topk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296419Z   test_torch_trace (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296559Z   test_torch_transpose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296711Z   test_torch_transpose_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296854Z   test_torch_trapezoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6296991Z   test_torch_trapz (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6297146Z   test_torch_triangular_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6297300Z   test_torch_tril (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6297458Z   test_torch_triplet_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6297593Z   test_torch_triu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6297739Z   test_torch_true_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6297874Z   test_torch_trunc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6298013Z   test_torch_unbind (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6298159Z   test_torch_unbind_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6298303Z   test_torch_unflatten (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6298434Z   test_torch_unfold_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6298611Z   test_torch_unsafe_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6298763Z   test_torch_unsafe_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6298922Z   test_torch_unsafe_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6299065Z   test_torch_unsqueeze (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6299214Z   test_torch_unsqueeze_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6299360Z   test_torch_values_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6299495Z   test_torch_var (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6299624Z   test_torch_var_mean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6299761Z   test_torch_vdot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6299909Z   test_torch_view_as_complex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6300070Z   test_torch_view_as_complex_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6300214Z   test_torch_view_as_real (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6300365Z   test_torch_view_as_real_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6300506Z   test_torch_view_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6300648Z   test_torch_vsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6300776Z   test_torch_vstack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6300913Z   test_torch_where (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6301052Z   test_torch_xlogy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:40.6301195Z   test_torch_zeros_like (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:40.6301342Z   test_user_implementation_raises (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:40.6301499Z Test that errors raised in user implementations propagate correctly ... ok (0.001s)
2023-01-11T21:58:40.6301657Z   test_warn_on_invalid_torch_function (__main__.TestTorchFunctionWarning) ... ok (0.009s)
2023-01-11T21:58:40.6301798Z   test_wrap_torch_function (__main__.TestWrapTorchFunction) ... ok (0.001s)
2023-01-11T21:58:40.6301809Z 
2023-01-11T21:58:40.6302091Z ----------------------------------------------------------------------
2023-01-11T21:58:40.6302171Z Ran 1416 tests in 1.182s
2023-01-11T21:58:40.6302176Z 
2023-01-11T21:58:40.6302237Z OK
2023-01-11T21:58:40.6302243Z 
2023-01-11T21:58:40.6302325Z Generating XML reports...
2023-01-11T21:58:40.6302815Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestBroadcastAllOverride-20230111215838.xml
2023-01-11T21:58:40.6303141Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestDisabledTorchFunction-20230111215838.xml
2023-01-11T21:58:40.6303501Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestEinsumOverride-20230111215838.xml
2023-01-11T21:58:40.6303805Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestGradCheckOverride-20230111215838.xml
2023-01-11T21:58:40.6304112Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestGradNewOnesOverride-20230111215838.xml
2023-01-11T21:58:40.6304375Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestIndexing-20230111215838.xml
2023-01-11T21:58:40.6304647Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestIterator-20230111215838.xml
2023-01-11T21:58:40.6304921Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestNamedTuple-20230111215838.xml
2023-01-11T21:58:40.6305185Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestPickle-20230111215838.xml
2023-01-11T21:58:40.6305438Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestRNN-20230111215838.xml
2023-01-11T21:58:40.6305764Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestResolveName-20230111215838.xml
2023-01-11T21:58:40.6306065Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionMode-20230111215838.xml
2023-01-11T21:58:40.6306378Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionOverride-20230111215838.xml
2023-01-11T21:58:40.6306686Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionWarning-20230111215838.xml
2023-01-11T21:58:40.6306972Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestWrapTorchFunction-20230111215838.xml
2023-01-11T21:58:40.6306990Z 
2023-01-11T21:58:40.6307322Z ##[endgroup]
2023-01-11T21:58:40.6307607Z FINISHED PRINTING LOG FILE of test_overrides (/var/lib/jenkins/workspace/test/test-reports/test_overrides_gz6u4dmy)
2023-01-11T21:58:40.6307612Z 
2023-01-11T21:58:40.6307778Z Running test_sparse_csr ... [2023-01-11 21:58:40.590824]
2023-01-11T21:58:40.6308112Z Executing ['/opt/conda/bin/python', '-bb', 'test_sparse_csr.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:40.591265]
2023-01-11T21:58:43.6077628Z 
2023-01-11T21:58:43.6078343Z Expand the folded group to see the log file of test_sparse_csr
2023-01-11T21:58:43.6079230Z ##[group]PRINTING LOG FILE of test_sparse_csr (/var/lib/jenkins/workspace/test/test-reports/test_sparse_csr_4s1dozzd)
2023-01-11T21:58:43.6079451Z 
2023-01-11T21:58:43.6079545Z Running tests...
2023-01-11T21:58:43.6080033Z ----------------------------------------------------------------------
2023-01-11T21:58:43.6080509Z Test results will be stored in test-reports/python-unittest/test_sparse_csr
2023-01-11T21:58:43.6080859Z   test_make_crow_indices (__main__.TestSparseCSRSampler) ... ok (0.381s)
2023-01-11T21:58:43.6081039Z 
2023-01-11T21:58:43.6081290Z ----------------------------------------------------------------------
2023-01-11T21:58:43.6081548Z Ran 1 test in 0.381s
2023-01-11T21:58:43.6081660Z 
2023-01-11T21:58:43.6081720Z OK
2023-01-11T21:58:43.6081810Z 
2023-01-11T21:58:43.6081893Z Generating XML reports...
2023-01-11T21:58:43.6082395Z Generated XML report: test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRSampler-20230111215842.xml
2023-01-11T21:58:43.6082627Z 
2023-01-11T21:58:43.6082904Z ##[endgroup]
2023-01-11T21:58:43.6083336Z FINISHED PRINTING LOG FILE of test_sparse_csr (/var/lib/jenkins/workspace/test/test-reports/test_sparse_csr_4s1dozzd)
2023-01-11T21:58:43.6083603Z 
2023-01-11T21:58:43.6083770Z Running test_torch ... [2023-01-11 21:58:43.607909]
2023-01-11T21:58:43.6084215Z Executing ['/opt/conda/bin/python', '-bb', 'test_torch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:43.608155]
2023-01-11T21:58:47.3479059Z 
2023-01-11T21:58:47.3479988Z Expand the folded group to see the log file of test_torch
2023-01-11T21:58:47.3481040Z ##[group]PRINTING LOG FILE of test_torch (/var/lib/jenkins/workspace/test/test-reports/test_torch_r7omzqvk)
2023-01-11T21:58:47.3481821Z 
2023-01-11T21:58:47.3482470Z Running tests...
2023-01-11T21:58:47.3483258Z ----------------------------------------------------------------------
2023-01-11T21:58:47.3484018Z Test results will be stored in test-reports/python-unittest/test_torch
2023-01-11T21:58:47.3484618Z   test_basic_vitals (__main__.TestBasicVitalSigns) ... ok (0.001s)
2023-01-11T21:58:47.3485492Z   test_basic_vitals_read_write (__main__.TestBasicVitalSigns) ... ok (0.001s)
2023-01-11T21:58:47.3485956Z   test_dataloader_vitals (__main__.TestBasicVitalSigns) ... ok (0.001s)
2023-01-11T21:58:47.3486499Z   test_RNGState (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3486963Z   test_RNGStateAliasing (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3487333Z   test_RNG_after_pickle (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3487735Z   test_Size (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3488313Z   test_Size_iter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3488724Z   test_Size_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3489143Z   test_add_meta_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3489605Z   test_allow_tensor_metadata_change (__main__.TestTorch) ... ok (0.000s)
2023-01-11T21:58:47.3490043Z   test_apply (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3490426Z   test_as_subclass (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:47.3490762Z   test_assert_async (__main__.TestTorch) ... ok (0.018s)
2023-01-11T21:58:47.3491123Z   test_backward_hooks_traverse (__main__.TestTorch) ... ok (0.071s)
2023-01-11T21:58:47.3491521Z   test_batch_norm_cpu_inference (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:47.3492693Z   test_bmm_multithreaded (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3493757Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3494749Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3495398Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3496056Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3496848Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3497520Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 23, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3498240Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3498897Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3499513Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3500206Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3500803Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3501457Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3502064Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3502952Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3503589Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3504244Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3504871Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3505515Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3506136Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3506793Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 23, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3507468Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3508133Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3508753Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3509438Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3510060Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3510715Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:47.3511350Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:47.3511542Z ok (0.657s)
2023-01-11T21:58:47.3511761Z   test_boxMullerState (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3512070Z   test_c10_layer_norm (__main__.TestTorch) ... skip: Pytorch is compiled without Caffe2 (0.001s)
2023-01-11T21:58:47.3512366Z   test_cat_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3512617Z   test_chunk_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:47.3512857Z   test_conj_neg_tolist (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3513103Z   test_contains (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3513353Z   test_copy_broadcast (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:58:47.3513591Z   test_copy_dtypes (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3513840Z   test_copy_float16 (__main__.TestTorch) ... ok (0.088s)
2023-01-11T21:58:47.3514090Z   test_copy_many_to_one (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:58:47.3514830Z   test_copy_transpose (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7754: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:58:47.3515310Z   x = torch.arange(100 * 100).reshape(100, 100).to(dtype=torch.complex32).t()
2023-01-11T21:58:47.3515545Z ok (0.003s)
2023-01-11T21:58:47.3515897Z   test_cuda_not_built (__main__.TestTorch) ... skip: CUDA is built, can't test CUDA not built error (0.001s)
2023-01-11T21:58:47.3516186Z   test_cummax_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3516436Z   test_cummin_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3516689Z   test_cumprod_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3516942Z   test_cumsum_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3517168Z   test_cxx_flags (__main__.TestTorch) ... ok (0.000s)
2023-01-11T21:58:47.3518058Z   test_dead_weak_ref (__main__.TestTorch) ... [W python_variable.cpp:319] Warning: Deallocating Tensor that still has live PyObject references.  This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it.  Subsequent accesses to this tensor via the PyObject will now fail. (function decref)
2023-01-11T21:58:47.3518552Z ok (0.004s)
2023-01-11T21:58:47.3518761Z   test_deepcopy_gradient (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3519032Z   test_deepcopy_parameter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3519299Z   test_deterministic_flag (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:47.3519552Z   test_device (__main__.TestTorch) ... ok (0.018s)
2023-01-11T21:58:47.3519772Z   test_dir (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3520001Z   test_doc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3520226Z   test_doc_template (__main__.TestTorch)
2023-01-11T21:58:47.3520530Z Test that all public API doc strings use the same standard template for ... ok (0.020s)
2023-01-11T21:58:47.3520824Z   test_dot_data_use (__main__.TestTorch) ... ok (0.012s)
2023-01-11T21:58:47.3521075Z   test_dtype_is_signed (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:47.3521668Z   test_element_size (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5939: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3522210Z   byte = torch.ByteStorage().element_size()
2023-01-11T21:58:47.3522753Z /var/lib/jenkins/workspace/test/test_torch.py:5940: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3523271Z   char = torch.CharStorage().element_size()
2023-01-11T21:58:47.3523811Z /var/lib/jenkins/workspace/test/test_torch.py:5941: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3524322Z   short = torch.ShortStorage().element_size()
2023-01-11T21:58:47.3524848Z /var/lib/jenkins/workspace/test/test_torch.py:5942: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3525353Z   int = torch.IntStorage().element_size()
2023-01-11T21:58:47.3525881Z /var/lib/jenkins/workspace/test/test_torch.py:5943: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3526383Z   long = torch.LongStorage().element_size()
2023-01-11T21:58:47.3526917Z /var/lib/jenkins/workspace/test/test_torch.py:5944: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3527415Z   float = torch.FloatStorage().element_size()
2023-01-11T21:58:47.3527948Z /var/lib/jenkins/workspace/test/test_torch.py:5945: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3528495Z   double = torch.DoubleStorage().element_size()
2023-01-11T21:58:47.3529031Z /var/lib/jenkins/workspace/test/test_torch.py:5946: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3529544Z   bool = torch.BoolStorage().element_size()
2023-01-11T21:58:47.3530093Z /var/lib/jenkins/workspace/test/test_torch.py:5947: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3530618Z   bfloat16 = torch.BFloat16Storage().element_size()
2023-01-11T21:58:47.3531157Z /var/lib/jenkins/workspace/test/test_torch.py:5948: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3531698Z   complexfloat = torch.ComplexFloatStorage().element_size()
2023-01-11T21:58:47.3532242Z /var/lib/jenkins/workspace/test/test_torch.py:5949: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3532783Z   complexdouble = torch.ComplexDoubleStorage().element_size()
2023-01-11T21:58:47.3533013Z ok (0.002s)
2023-01-11T21:58:47.3533222Z   test_empty_meta (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:58:47.3533461Z   test_empty_storage_view (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3533703Z   test_equal (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3533959Z   test_error_msg_type_translation (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:58:47.3534211Z   test_fill_diagonal (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3534468Z   test_fix_weakref_no_leak (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3534725Z   test_format_scalar_meta (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3535310Z   test_from_buffer (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6322: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3535887Z   self.assertEqual(torch.ByteStorage.from_buffer(a).tolist(), [1, 2, 3, 4])
2023-01-11T21:58:47.3536702Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3537202Z   return list(self)
2023-01-11T21:58:47.3537938Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:649: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3538529Z   return iter(map(lambda i: self[i], range(self.size())))
2023-01-11T21:58:47.3539059Z /var/lib/jenkins/workspace/test/test_torch.py:6323: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3539632Z   shorts = torch.ShortStorage.from_buffer(a, 'big')
2023-01-11T21:58:47.3540184Z /var/lib/jenkins/workspace/test/test_torch.py:6324: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3540686Z   self.assertEqual(shorts.size(), 2)
2023-01-11T21:58:47.3541253Z /var/lib/jenkins/workspace/test/test_torch.py:6325: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3541747Z   self.assertEqual(shorts.tolist(), [258, 772])
2023-01-11T21:58:47.3542277Z /var/lib/jenkins/workspace/test/test_torch.py:6326: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3543030Z   ints = torch.IntStorage.from_buffer(a, 'little')
2023-01-11T21:58:47.3543581Z /var/lib/jenkins/workspace/test/test_torch.py:6327: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3544081Z   self.assertEqual(ints.size(), 1)
2023-01-11T21:58:47.3544593Z /var/lib/jenkins/workspace/test/test_torch.py:6328: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3545083Z   self.assertEqual(ints[0], 67305985)
2023-01-11T21:58:47.3545609Z /var/lib/jenkins/workspace/test/test_torch.py:6330: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3546176Z   floats = torch.FloatStorage.from_buffer(f, 'big')
2023-01-11T21:58:47.3546720Z /var/lib/jenkins/workspace/test/test_torch.py:6331: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3547205Z   self.assertEqual(floats.size(), 1)
2023-01-11T21:58:47.3547732Z /var/lib/jenkins/workspace/test/test_torch.py:6332: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3548222Z   self.assertEqual(floats[0], 2.25)
2023-01-11T21:58:47.3548745Z /var/lib/jenkins/workspace/test/test_torch.py:6335: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3549372Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:58:47.3549895Z /var/lib/jenkins/workspace/test/test_torch.py:6336: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3550389Z   self.assertEqual(bools.size(), 8)
2023-01-11T21:58:47.3550957Z /var/lib/jenkins/workspace/test/test_torch.py:6337: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3551513Z   self.assertEqual(bools.tolist(), [False, True, True, True, True, True, True, True])
2023-01-11T21:58:47.3552082Z /var/lib/jenkins/workspace/test/test_torch.py:6338: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3552641Z   self.assertEqual(bools.type(), 'torch.BoolStorage')
2023-01-11T21:58:47.3553430Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3554010Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:58:47.3554780Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3555382Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:58:47.3556148Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:978: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3556722Z   return (cls_device == instance.device.type) and (cls.dtype == instance.dtype)
2023-01-11T21:58:47.3557583Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3558113Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:58:47.3558652Z /var/lib/jenkins/workspace/test/test_torch.py:6342: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3559218Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:58:47.3559775Z /var/lib/jenkins/workspace/test/test_torch.py:6343: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3560336Z   self.assertEqual(bools.size(), 19)
2023-01-11T21:58:47.3560870Z /var/lib/jenkins/workspace/test/test_torch.py:6346: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3561433Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:58:47.3561987Z /var/lib/jenkins/workspace/test/test_torch.py:6347: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3562481Z   self.assertEqual(bools.size(), 4)
2023-01-11T21:58:47.3563008Z /var/lib/jenkins/workspace/test/test_torch.py:6348: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3563538Z   self.assertEqual(bools.tolist(), [False, True, True, True])
2023-01-11T21:58:47.3564084Z /var/lib/jenkins/workspace/test/test_torch.py:6349: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3564591Z   bytes = torch.ByteStorage.from_buffer(a)
2023-01-11T21:58:47.3565118Z /var/lib/jenkins/workspace/test/test_torch.py:6350: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3565612Z   self.assertEqual(bytes.nbytes(), 4)
2023-01-11T21:58:47.3566134Z /var/lib/jenkins/workspace/test/test_torch.py:6351: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3566646Z   self.assertEqual(bytes.tolist(), [1, 2, 3, 4])
2023-01-11T21:58:47.3566834Z ok (0.004s)
2023-01-11T21:58:47.3567370Z   test_from_file (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6663: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3567931Z   s1 = torch.FloatStorage.from_file(filename, True, size)
2023-01-11T21:58:47.3568713Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:899: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3569230Z   storage = cls(wrap_storage=untyped_storage)
2023-01-11T21:58:47.3569765Z /var/lib/jenkins/workspace/test/test_torch.py:6665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3570346Z   self.assertEqual(s1.data_ptr(), torch.FloatTensor(s1).data_ptr())
2023-01-11T21:58:47.3570907Z /var/lib/jenkins/workspace/test/test_torch.py:6668: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3571439Z   s2 = torch.FloatStorage.from_file(filename, True, size)
2023-01-11T21:58:47.3571642Z ok (0.004s)
2023-01-11T21:58:47.3571853Z   test_gather_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3572104Z   test_generator_cpu (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3572383Z   test_has_internal_overlap (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3573089Z   test_has_storage (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7615: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3573644Z   self.assertIsNotNone(torch.tensor([]).storage())
2023-01-11T21:58:47.3574193Z /var/lib/jenkins/workspace/test/test_torch.py:7616: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3574715Z   self.assertIsNotNone(torch.empty(0).storage())
2023-01-11T21:58:47.3575247Z /var/lib/jenkins/workspace/test/test_torch.py:7617: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3575777Z   self.assertIsNotNone(torch.tensor([]).clone().storage())
2023-01-11T21:58:47.3576328Z /var/lib/jenkins/workspace/test/test_torch.py:7618: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3576861Z   self.assertIsNotNone(torch.tensor([0, 0, 0]).nonzero().storage())
2023-01-11T21:58:47.3577419Z /var/lib/jenkins/workspace/test/test_torch.py:7619: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3577929Z   self.assertIsNotNone(torch.tensor([]).new().storage())
2023-01-11T21:58:47.3578143Z ok (0.001s)
2023-01-11T21:58:47.3578349Z   test_index_add (__main__.TestTorch) ... ok (0.015s)
2023-01-11T21:58:47.3578595Z   test_index_add_all_dtypes (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:47.3579457Z   test_index_add_correctness (__main__.TestTorch) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91184 for platform(s) linux, rocm, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.002s)
2023-01-11T21:58:47.3580026Z   test_index_add_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3580327Z   test_index_copy_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3580577Z   test_index_fill_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3580836Z   test_index_select_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3581106Z   test_invalid_generator_raises (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3581369Z   test_is_nonzero (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:47.3581857Z   test_is_same_size (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5821: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:58:47.3582509Z   nt1 = torch.nested.nested_tensor([torch.ones(2, 4), torch.ones(3, 4), torch.ones(5, 4)])
2023-01-11T21:58:47.3582770Z ok (0.006s)
2023-01-11T21:58:47.3583025Z   test_iter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3583261Z   test_kthvalue_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3583524Z   test_logcumsumexp_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3583782Z   test_manual_seed (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:47.3584007Z   test_map (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3584240Z   test_map2 (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3584481Z   test_max_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3584712Z   test_mean_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3584957Z   test_median_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3585205Z   test_memory_format (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3585496Z   test_memory_format_contiguous_returns_same_tensor_if_already_satisfies (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3585809Z   test_memory_format_empty (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:58:47.3586061Z   test_min_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3586304Z   test_mode_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3586625Z   test_multinomial_invalid_probs (__main__.TestTorch) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:58:47.3586953Z   test_nanmedian_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3587205Z   test_narrow_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3587426Z   test_ndim (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3587990Z   test_new (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7016: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3588551Z   self.assertEqual(x.new(y.storage()).data_ptr(), y.data_ptr())
2023-01-11T21:58:47.3589100Z /var/lib/jenkins/workspace/test/test_torch.py:7022: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3589644Z   self.assertRaises(RuntimeError, lambda: x.new(z.storage()))
2023-01-11T21:58:47.3589850Z ok (0.005s)
2023-01-11T21:58:47.3590074Z   test_newaxis_numpy_comparison (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:47.3590334Z   test_newindex (__main__.TestTorch) ... ok (0.010s)
2023-01-11T21:58:47.3590715Z   test_no_cuda_monkeypatch (__main__.TestTorch) ... skip: Skipped for cuda-enabled build (0.000s)
2023-01-11T21:58:47.3591009Z   test_norm_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3591254Z   test_normal_shape (__main__.TestTorch) ... ok (0.026s)
2023-01-11T21:58:47.3591539Z   test_numel (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3591767Z   test_parallel_info (__main__.TestTorch) ... ok (0.000s)
2023-01-11T21:58:47.3592018Z   test_parsing_double (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3592264Z   test_parsing_int64 (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3592795Z   test_parsing_intlist (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6302: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
2023-01-11T21:58:47.3593503Z Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
2023-01-11T21:58:47.3593888Z   self.assertRaises(TypeError, lambda: torch.ones((np.float(3.), torch.tensor(4))))
2023-01-11T21:58:47.3594177Z ok (0.014s)
2023-01-11T21:58:47.3594368Z   test_permute (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3594608Z   test_pickle (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3594852Z   test_pickle_dtype (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3595092Z   test_pickle_function (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3595348Z   test_pickle_parameter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3595624Z   test_pickle_parameter_no_requires_grad (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3595894Z   test_pickle_size (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3596124Z   test_pin_memory (__main__.TestTorch) ... ok (0.013s)
2023-01-11T21:58:47.3596359Z   test_print (__main__.TestTorch) ... ok (0.040s)
2023-01-11T21:58:47.3596597Z   test_prod_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3596835Z   test_pyobj_preserved (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3597084Z   test_qengine (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3597325Z   test_renorm_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3597639Z   test_resurrected_weak_ref (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3597898Z   test_reversed (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3598145Z   test_scatter_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3598395Z   test_select_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3598677Z   test_set_flush_denormal (__main__.TestTorch) ... skip: flush_denormal not supported (0.001s)
2023-01-11T21:58:47.3598982Z   test_setting_real_imag_to_a_number (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3599249Z   test_show_config (__main__.TestTorch) ... ok (0.000s)
2023-01-11T21:58:47.3599481Z   test_size_neg_dim (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3600065Z   test_sizeof (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6980: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3600629Z   sizeof_empty = torch.randn(0).storage().__sizeof__()
2023-01-11T21:58:47.3601411Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3601964Z   return super(TypedStorage, self).__sizeof__() + self.nbytes()
2023-01-11T21:58:47.3602502Z /var/lib/jenkins/workspace/test/test_torch.py:6981: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3603062Z   sizeof_10 = torch.randn(10).storage().__sizeof__()
2023-01-11T21:58:47.3603593Z /var/lib/jenkins/workspace/test/test_torch.py:6982: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3604108Z   sizeof_100 = torch.randn(100).storage().__sizeof__()
2023-01-11T21:58:47.3604682Z /var/lib/jenkins/workspace/test/test_torch.py:6986: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3605204Z   sizeof_empty = torch.randn(0).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:58:47.3605754Z /var/lib/jenkins/workspace/test/test_torch.py:6987: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3606277Z   sizeof_10 = torch.randn(10).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:58:47.3606825Z /var/lib/jenkins/workspace/test/test_torch.py:6988: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3607348Z   sizeof_100 = torch.randn(100).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:58:47.3607553Z ok (0.001s)
2023-01-11T21:58:47.3607750Z   test_slice (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3608058Z   test_slow_test (__main__.TestTorch) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:58:47.3608357Z   test_sobolengine_bounds (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3608631Z   test_sobolengine_bounds_scrambled (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:47.3608909Z   test_sobolengine_continuing (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3609192Z   test_sobolengine_continuing_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3609469Z   test_sobolengine_distribution (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:47.3609757Z   test_sobolengine_distribution_scrambled (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:47.3610035Z   test_sobolengine_draw (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3610288Z   test_sobolengine_draw_base2 (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3610565Z   test_sobolengine_draw_base2_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3610849Z   test_sobolengine_draw_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3611111Z   test_sobolengine_fast_forward (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3611397Z   test_sobolengine_fast_forward_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3611679Z   test_sobolengine_first_point (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3611954Z   test_sobolengine_high_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:47.3612206Z   test_sobolengine_raise (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3612464Z   test_sobolengine_reset (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3612733Z   test_sobolengine_reset_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3612986Z   test_sort_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3613267Z   test_split_neg_dim (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:47.3613517Z   test_squeeze_neg_dim (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:47.3613750Z   test_std_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3614333Z   test_storage_casts (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6470: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3614942Z   storage = torch.IntStorage([-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3615509Z /var/lib/jenkins/workspace/test/test_torch.py:6471: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3616015Z   self.assertEqual(storage.size(), 6)
2023-01-11T21:58:47.3616532Z /var/lib/jenkins/workspace/test/test_torch.py:6472: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3617104Z   self.assertEqual(storage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3617877Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3618383Z   return list(self)
2023-01-11T21:58:47.3619118Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:649: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3619640Z   return iter(map(lambda i: self[i], range(self.size())))
2023-01-11T21:58:47.3620182Z /var/lib/jenkins/workspace/test/test_torch.py:6473: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3620764Z   self.assertEqual(storage.type(), 'torch.IntStorage')
2023-01-11T21:58:47.3621543Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3622112Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:58:47.3623014Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3623622Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:58:47.3624181Z /var/lib/jenkins/workspace/test/test_torch.py:6476: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3624744Z   floatStorage = storage.float()
2023-01-11T21:58:47.3625506Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:808: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3626087Z   storage = torch.tensor([], dtype=self.dtype, device=self.device).set_(self).to(dtype)._typed_storage()
2023-01-11T21:58:47.3626948Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:809: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3627485Z   if storage.data_ptr() == self.data_ptr():
2023-01-11T21:58:47.3628017Z /var/lib/jenkins/workspace/test/test_torch.py:6477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3628529Z   self.assertEqual(floatStorage.size(), 6)
2023-01-11T21:58:47.3629061Z /var/lib/jenkins/workspace/test/test_torch.py:6478: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3629656Z   self.assertEqual(floatStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3630210Z /var/lib/jenkins/workspace/test/test_torch.py:6479: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3630808Z   self.assertEqual(floatStorage.type(), 'torch.FloatStorage')
2023-01-11T21:58:47.3631378Z /var/lib/jenkins/workspace/test/test_torch.py:6480: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3631957Z   self.assertEqual(floatStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3632513Z /var/lib/jenkins/workspace/test/test_torch.py:6483: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3633009Z   halfStorage = storage.half()
2023-01-11T21:58:47.3633521Z /var/lib/jenkins/workspace/test/test_torch.py:6484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3634033Z   self.assertEqual(halfStorage.size(), 6)
2023-01-11T21:58:47.3634556Z /var/lib/jenkins/workspace/test/test_torch.py:6485: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3635192Z   self.assertEqual(halfStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3635733Z /var/lib/jenkins/workspace/test/test_torch.py:6486: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3636321Z   self.assertEqual(halfStorage.type(), 'torch.HalfStorage')
2023-01-11T21:58:47.3636910Z /var/lib/jenkins/workspace/test/test_torch.py:6487: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3637545Z   self.assertEqual(halfStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3638112Z /var/lib/jenkins/workspace/test/test_torch.py:6490: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3638621Z   bfloat16Storage = storage.bfloat16()
2023-01-11T21:58:47.3639151Z /var/lib/jenkins/workspace/test/test_torch.py:6491: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3639664Z   self.assertEqual(bfloat16Storage.size(), 6)
2023-01-11T21:58:47.3640194Z /var/lib/jenkins/workspace/test/test_torch.py:6492: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3640788Z   self.assertEqual(bfloat16Storage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3641342Z /var/lib/jenkins/workspace/test/test_torch.py:6493: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3641951Z   self.assertEqual(bfloat16Storage.type(), 'torch.BFloat16Storage')
2023-01-11T21:58:47.3642520Z /var/lib/jenkins/workspace/test/test_torch.py:6494: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3643111Z   self.assertEqual(bfloat16Storage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3643664Z /var/lib/jenkins/workspace/test/test_torch.py:6497: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3644153Z   longStorage = storage.long()
2023-01-11T21:58:47.3644668Z /var/lib/jenkins/workspace/test/test_torch.py:6498: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3645242Z   self.assertEqual(longStorage.size(), 6)
2023-01-11T21:58:47.3645758Z /var/lib/jenkins/workspace/test/test_torch.py:6499: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3646341Z   self.assertEqual(longStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3646890Z /var/lib/jenkins/workspace/test/test_torch.py:6500: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3647510Z   self.assertEqual(longStorage.type(), 'torch.LongStorage')
2023-01-11T21:58:47.3648071Z /var/lib/jenkins/workspace/test/test_torch.py:6501: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3648650Z   self.assertEqual(longStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3649198Z /var/lib/jenkins/workspace/test/test_torch.py:6504: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3649695Z   shortStorage = storage.short()
2023-01-11T21:58:47.3650209Z /var/lib/jenkins/workspace/test/test_torch.py:6505: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3650723Z   self.assertEqual(shortStorage.size(), 6)
2023-01-11T21:58:47.3651246Z /var/lib/jenkins/workspace/test/test_torch.py:6506: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3651825Z   self.assertEqual(shortStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3652375Z /var/lib/jenkins/workspace/test/test_torch.py:6507: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3652964Z   self.assertEqual(shortStorage.type(), 'torch.ShortStorage')
2023-01-11T21:58:47.3653524Z /var/lib/jenkins/workspace/test/test_torch.py:6508: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3654100Z   self.assertEqual(shortStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3654650Z /var/lib/jenkins/workspace/test/test_torch.py:6511: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3655181Z   doubleStorage = storage.double()
2023-01-11T21:58:47.3655705Z /var/lib/jenkins/workspace/test/test_torch.py:6512: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3656220Z   self.assertEqual(doubleStorage.size(), 6)
2023-01-11T21:58:47.3656737Z /var/lib/jenkins/workspace/test/test_torch.py:6513: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3657375Z   self.assertEqual(doubleStorage.tolist(), [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
2023-01-11T21:58:47.3657934Z /var/lib/jenkins/workspace/test/test_torch.py:6514: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3658527Z   self.assertEqual(doubleStorage.type(), 'torch.DoubleStorage')
2023-01-11T21:58:47.3659094Z /var/lib/jenkins/workspace/test/test_torch.py:6515: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3659677Z   self.assertEqual(doubleStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3660225Z /var/lib/jenkins/workspace/test/test_torch.py:6518: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3660705Z   charStorage = storage.char()
2023-01-11T21:58:47.3661215Z /var/lib/jenkins/workspace/test/test_torch.py:6519: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3661714Z   self.assertEqual(charStorage.size(), 6)
2023-01-11T21:58:47.3662231Z /var/lib/jenkins/workspace/test/test_torch.py:6520: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3662981Z   self.assertEqual(charStorage.tolist(), [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
2023-01-11T21:58:47.3663531Z /var/lib/jenkins/workspace/test/test_torch.py:6521: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3664125Z   self.assertEqual(charStorage.type(), 'torch.CharStorage')
2023-01-11T21:58:47.3664685Z /var/lib/jenkins/workspace/test/test_torch.py:6522: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3665324Z   self.assertEqual(charStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3666000Z /var/lib/jenkins/workspace/test/test_torch.py:6525: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3666483Z   byteStorage = storage.byte()
2023-01-11T21:58:47.3666996Z /var/lib/jenkins/workspace/test/test_torch.py:6526: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3667504Z   self.assertEqual(byteStorage.size(), 6)
2023-01-11T21:58:47.3668080Z /var/lib/jenkins/workspace/test/test_torch.py:6527: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3668611Z   self.assertEqual(byteStorage.tolist(), [255, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3669140Z /var/lib/jenkins/workspace/test/test_torch.py:6528: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3669729Z   self.assertEqual(byteStorage.type(), 'torch.ByteStorage')
2023-01-11T21:58:47.3670282Z /var/lib/jenkins/workspace/test/test_torch.py:6529: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3670800Z   self.assertEqual(byteStorage.int().tolist(), [255, 0, 1, 2, 3, 4])
2023-01-11T21:58:47.3671345Z /var/lib/jenkins/workspace/test/test_torch.py:6532: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3671817Z   boolStorage = storage.bool()
2023-01-11T21:58:47.3672329Z /var/lib/jenkins/workspace/test/test_torch.py:6533: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3672827Z   self.assertEqual(boolStorage.size(), 6)
2023-01-11T21:58:47.3673361Z /var/lib/jenkins/workspace/test/test_torch.py:6534: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3673904Z   self.assertEqual(boolStorage.tolist(), [True, False, True, True, True, True])
2023-01-11T21:58:47.3674466Z /var/lib/jenkins/workspace/test/test_torch.py:6535: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3675086Z   self.assertEqual(boolStorage.type(), 'torch.BoolStorage')
2023-01-11T21:58:47.3675634Z /var/lib/jenkins/workspace/test/test_torch.py:6536: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3676150Z   self.assertEqual(boolStorage.int().tolist(), [1, 0, 1, 1, 1, 1])
2023-01-11T21:58:47.3676693Z /var/lib/jenkins/workspace/test/test_torch.py:6539: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3677304Z   complexfloat_storage = torch.ComplexFloatStorage([-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:58:47.3677973Z /var/lib/jenkins/workspace/test/test_torch.py:6540: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3678500Z   self.assertEqual(complexfloat_storage.size(), 6)
2023-01-11T21:58:47.3679047Z /var/lib/jenkins/workspace/test/test_torch.py:6541: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3679673Z   self.assertEqual(complexfloat_storage.tolist(), [-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:58:47.3680226Z /var/lib/jenkins/workspace/test/test_torch.py:6542: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3680854Z   self.assertEqual(complexfloat_storage.type(), 'torch.ComplexFloatStorage')
2023-01-11T21:58:47.3681440Z /var/lib/jenkins/workspace/test/test_torch.py:6545: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3681971Z   complexdouble_storage = complexfloat_storage.complex_double()
2023-01-11T21:58:47.3682527Z /var/lib/jenkins/workspace/test/test_torch.py:6546: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3683043Z   self.assertEqual(complexdouble_storage.size(), 6)
2023-01-11T21:58:47.3683578Z /var/lib/jenkins/workspace/test/test_torch.py:6547: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3684186Z   self.assertEqual(complexdouble_storage.tolist(), [-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:58:47.3684750Z /var/lib/jenkins/workspace/test/test_torch.py:6548: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3685407Z   self.assertEqual(complexdouble_storage.type(), 'torch.ComplexDoubleStorage')
2023-01-11T21:58:47.3685648Z ok (0.011s)
2023-01-11T21:58:47.3686187Z   test_storage_error (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6364: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3686721Z   torch.storage._LegacyStorage()
2023-01-11T21:58:47.3687503Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3688034Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:58:47.3688552Z /var/lib/jenkins/workspace/test/test_torch.py:6378: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3689068Z   storage_class(device='cpu')
2023-01-11T21:58:47.3689582Z /var/lib/jenkins/workspace/test/test_torch.py:6381: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3690078Z   storage_class(dtype=torch.float)
2023-01-11T21:58:47.3690598Z /var/lib/jenkins/workspace/test/test_torch.py:6387: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3691061Z   storage_class(0, 0)
2023-01-11T21:58:47.3691557Z /var/lib/jenkins/workspace/test/test_torch.py:6390: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3692060Z   storage_class('string')
2023-01-11T21:58:47.3692569Z /var/lib/jenkins/workspace/test/test_torch.py:6393: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3693061Z   storage_class(torch.tensor([]))
2023-01-11T21:58:47.3693564Z /var/lib/jenkins/workspace/test/test_torch.py:6395: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3694027Z   s = storage_class()
2023-01-11T21:58:47.3694528Z /var/lib/jenkins/workspace/test/test_torch.py:6398: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3695064Z   storage_class(0, wrap_storage=s.untyped())
2023-01-11T21:58:47.3695577Z /var/lib/jenkins/workspace/test/test_torch.py:6401: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3696058Z   storage_class(wrap_storage=s)
2023-01-11T21:58:47.3696562Z /var/lib/jenkins/workspace/test/test_torch.py:6420: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3697092Z   torch.TypedStorage(0, wrap_storage=s.untyped(), dtype=dtype)
2023-01-11T21:58:47.3697670Z /var/lib/jenkins/workspace/test/test_torch.py:6423: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3698177Z   torch.TypedStorage(wrap_storage=s.untyped())
2023-01-11T21:58:47.3698704Z /var/lib/jenkins/workspace/test/test_torch.py:6426: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3699218Z   torch.TypedStorage(wrap_storage=s.untyped(), dtype=0)
2023-01-11T21:58:47.3699769Z /var/lib/jenkins/workspace/test/test_torch.py:6429: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3700310Z   torch.TypedStorage(wrap_storage=s.untyped(), dtype=dtype, device=device)
2023-01-11T21:58:47.3700858Z /var/lib/jenkins/workspace/test/test_torch.py:6432: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3701376Z   torch.TypedStorage(wrap_storage=s, dtype=dtype)
2023-01-11T21:58:47.3701914Z /var/lib/jenkins/workspace/test/test_torch.py:6435: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3702612Z   torch.TypedStorage(dtype=dtype, device='xla')
2023-01-11T21:58:47.3703148Z /var/lib/jenkins/workspace/test/test_torch.py:6443: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3703673Z   torch.TypedStorage(torch.tensor([]), dtype=dtype, device=device)
2023-01-11T21:58:47.3704233Z /var/lib/jenkins/workspace/test/test_torch.py:6446: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3704751Z   torch.TypedStorage(0, 0, dtype=dtype, device=device)
2023-01-11T21:58:47.3715943Z /var/lib/jenkins/workspace/test/test_torch.py:6449: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3716586Z   s_other = torch.TypedStorage([1, 2, 3, 4], device=device, dtype=dtype)
2023-01-11T21:58:47.3717150Z /var/lib/jenkins/workspace/test/test_torch.py:6452: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3717700Z   s.fill_(s_other)
2023-01-11T21:58:47.3717872Z ok (0.041s)
2023-01-11T21:58:47.3718544Z   test_storage_error_no_attribute (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6461: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3719084Z   storage_class.from_buffer()
2023-01-11T21:58:47.3719257Z ok (0.001s)
2023-01-11T21:58:47.3719471Z   test_structseq_repr (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3719734Z   test_subclass_preserved (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3719989Z   test_subclass_tensors (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3720243Z   test_sum_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3720492Z   test_t_not_2d_error (__main__.TestTorch) ... ok (0.009s)
2023-01-11T21:58:47.3720728Z   test_tensor_base_init (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3720984Z   test_tensor_base_new (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3721244Z   test_tensor_ctor_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3721503Z   test_tensor_cycle_via_dict (__main__.TestTorch) ... ok (0.108s)
2023-01-11T21:58:47.3721755Z   test_tensor_cycle_via_slots (__main__.TestTorch) ... ok (0.049s)
2023-01-11T21:58:47.3722017Z   test_tensor_dict_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3722285Z   test_tensor_finalizer_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3722874Z   test_tensor_set (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5839: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3723439Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:47.3723994Z /var/lib/jenkins/workspace/test/test_torch.py:5841: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3724481Z   t1.set_(t2.storage(), 0, size)
2023-01-11T21:58:47.3724996Z /var/lib/jenkins/workspace/test/test_torch.py:5843: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3725477Z   t1.set_(t2.storage(), 0, tuple(size))
2023-01-11T21:58:47.3726001Z /var/lib/jenkins/workspace/test/test_torch.py:5847: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3726535Z   t1.set_(t2.storage(), 0, size, stride)
2023-01-11T21:58:47.3727047Z /var/lib/jenkins/workspace/test/test_torch.py:5849: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3727548Z   t1.set_(t2.storage(), 0, size=size, stride=stride)
2023-01-11T21:58:47.3728104Z /var/lib/jenkins/workspace/test/test_torch.py:5857: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3728633Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:47.3729176Z /var/lib/jenkins/workspace/test/test_torch.py:5859: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3729651Z   t1.set_(source=t2.storage())
2023-01-11T21:58:47.3730160Z /var/lib/jenkins/workspace/test/test_torch.py:5860: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3730667Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:47.3731212Z /var/lib/jenkins/workspace/test/test_torch.py:5862: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3731736Z   t1.set_(source=t2.storage(), storage_offset=0, size=size, stride=stride)
2023-01-11T21:58:47.3732285Z /var/lib/jenkins/workspace/test/test_torch.py:5869: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3732813Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:47.3733009Z ok (0.003s)
2023-01-11T21:58:47.3733555Z   test_tensor_set_errors (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5876: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3734151Z   self.assertRaises(RuntimeError, lambda: f_cpu.set_(d_cpu.storage()))
2023-01-11T21:58:47.3734715Z /var/lib/jenkins/workspace/test/test_torch.py:5878: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3735237Z   lambda: f_cpu.set_(d_cpu.storage(), 0, d_cpu.size(), d_cpu.stride()))
2023-01-11T21:58:47.3735459Z ok (0.004s)
2023-01-11T21:58:47.3735675Z   test_tensor_slot_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3735985Z   test_tensor_weakref_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3736252Z   test_tensoriterator_output_setup (__main__.TestTorch) ... ok (0.114s)
2023-01-11T21:58:47.3736863Z   test_to (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7842: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:58:47.3737416Z   a = torch.tensor([[0, 1, 2], [2, 0, 3]]).to_sparse_csr()
2023-01-11T21:58:47.3737613Z ok (0.004s)
2023-01-11T21:58:47.3737804Z   test_to_with_tensor (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3738048Z   test_topk_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3738296Z   test_torch_from_file (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:47.3738573Z   test_transpose_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:47.3738818Z   test_type (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3739051Z   test_type_alias (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3739305Z   test_type_conversion_via_dtype_name (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3739932Z   test_typed_storage_deprecation_warning (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6624: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3740487Z   s0 = torch.FloatStorage(10)
2023-01-11T21:58:47.3740669Z ok (0.002s)
2023-01-11T21:58:47.3741223Z   test_typed_storage_internal_no_warning (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6554: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3741747Z   s0 = torch.FloatStorage(10)
2023-01-11T21:58:47.3742266Z /var/lib/jenkins/workspace/test/test_torch.py:6555: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:47.3742929Z   s0_untyped = s0.untyped()
2023-01-11T21:58:47.3743108Z ok (0.002s)
2023-01-11T21:58:47.3743305Z   test_unbind_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3743553Z   test_unflatten (__main__.TestTorch) ... ok (0.021s)
2023-01-11T21:58:47.3743802Z   test_unfold_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3744044Z   test_unsqueeze_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3744311Z   test_upsample_nearest1d_meta (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:58:47.3744583Z   test_upsample_nearest2d_meta (__main__.TestTorch) ... ok (0.013s)
2023-01-11T21:58:47.3744839Z   test_var_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:47.3745068Z   test_warn_types (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3745321Z   test_wildcard_import (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:47.3745467Z 
2023-01-11T21:58:47.3745713Z ----------------------------------------------------------------------
2023-01-11T21:58:47.3745943Z Ran 181 tests in 1.700s
2023-01-11T21:58:47.3746057Z 
2023-01-11T21:58:47.3746129Z OK (skipped=7)
2023-01-11T21:58:47.3746235Z 
2023-01-11T21:58:47.3746318Z Generating XML reports...
2023-01-11T21:58:47.3746724Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestBasicVitalSigns-20230111215845.xml
2023-01-11T21:58:47.3747287Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestTorch-20230111215845.xml
2023-01-11T21:58:47.3747591Z [TORCH_VITAL] Dataloader.enabled		 True
2023-01-11T21:58:47.3747854Z [TORCH_VITAL] Dataloader.basic_unit_test		 TEST_VALUE_STRING
2023-01-11T21:58:47.3748079Z [TORCH_VITAL] CUDA.used		 False
2023-01-11T21:58:47.3748201Z 
2023-01-11T21:58:47.3748539Z ##[endgroup]
2023-01-11T21:58:47.3748903Z FINISHED PRINTING LOG FILE of test_torch (/var/lib/jenkins/workspace/test/test-reports/test_torch_r7omzqvk)
2023-01-11T21:58:47.3749099Z 
2023-01-11T21:58:47.3749294Z Running distributions/test_distributions ... [2023-01-11 21:58:47.349048]
2023-01-11T21:58:47.3749816Z Executing ['/opt/conda/bin/python', '-bb', 'distributions/test_distributions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:47.349306]
2023-01-11T21:59:30.0896920Z 
2023-01-11T21:59:30.0898185Z Expand the folded group to see the log file of distributions/test_distributions
2023-01-11T21:59:30.0899076Z ##[group]PRINTING LOG FILE of distributions/test_distributions (/var/lib/jenkins/workspace/test/test-reports/distributions-test_distributions_q4k_g_kn)
2023-01-11T21:59:30.0899531Z 
2023-01-11T21:59:30.0899779Z Running tests...
2023-01-11T21:59:30.0900366Z ----------------------------------------------------------------------
2023-01-11T21:59:30.0901117Z Test results will be stored in test-reports/python-unittest/distributions.test_distributions
2023-01-11T21:59:30.0906590Z   test_cdf (__main__.TestAgainstScipy) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:59:30.0907312Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:59:30.0907810Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.0908142Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.0908337Z ok (0.041s)
2023-01-11T21:59:30.0908553Z   test_icdf (__main__.TestAgainstScipy) ... ok (0.023s)
2023-01-11T21:59:30.0908807Z   test_mean (__main__.TestAgainstScipy) ... ok (1.140s)
2023-01-11T21:59:30.0909900Z   test_variance_stddev (__main__.TestAgainstScipy) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_comparison.py:679: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:210.)
2023-01-11T21:59:30.0910630Z   return torch.as_tensor(tensor_like)
2023-01-11T21:59:30.0910811Z ok (0.034s)
2023-01-11T21:59:30.0913165Z   test_params_constraints (__main__.TestConstraints) ... ok (0.055s)
2023-01-11T21:59:30.0913683Z   test_support_constraints (__main__.TestConstraints) ... ok (0.071s)
2023-01-11T21:59:30.0914230Z   test_bernoulli_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0914810Z   test_bernoulli_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0915328Z   test_beta_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0915874Z   test_beta_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0916396Z   test_binomial_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0916935Z   test_binomial_shape_vectorized_n (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0917491Z   test_categorical_shape (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:59:30.0918119Z   test_cauchy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.001s)
2023-01-11T21:59:30.0918880Z   test_cauchy_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0919432Z   test_chi2_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0919997Z   test_chi2_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0920647Z   test_continuous_bernoulli_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0921287Z   test_continuous_bernoulli_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0921873Z   test_dirichlet_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0922410Z   test_entropy_shape (__main__.TestDistributionShapes) ... ok (0.021s)
2023-01-11T21:59:30.0922982Z   test_exponential_shape_scalar_param (__main__.TestDistributionShapes) ... ok (0.001s)
2023-01-11T21:59:30.0923695Z   test_exponential_shape_tensor_param (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0924313Z   test_gamma_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0924901Z   test_gamma_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0925506Z   test_geometric_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0926090Z   test_geometric_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0926686Z   test_gumbel_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0927303Z   test_halfcauchy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0927888Z   test_halfcauchy_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0928472Z   test_kumaraswamy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0929059Z   test_laplace_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0929646Z   test_laplace_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0930215Z   test_mixture_same_family_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0930770Z   test_multinomial_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0931334Z   test_normal_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.001s)
2023-01-11T21:59:30.0931905Z   test_normal_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0932467Z   test_one_hot_categorical_shape (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:59:30.0933056Z   test_pareto_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0933661Z   test_studentT_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0934209Z   test_studentT_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0934808Z   test_uniform_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.001s)
2023-01-11T21:59:30.0935410Z   test_uniform_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0935982Z   test_vonmises_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0936567Z   test_vonmises_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0937152Z   test_weibull_scale_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:30.0937737Z   test_wishart_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:59:30.0938279Z   test_wishart_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.007s)
2023-01-11T21:59:30.0938824Z   test_argmax_relaxed_categorical (__main__.TestDistributions) ... ok (0.020s)
2023-01-11T21:59:30.0939334Z   test_bernoulli (__main__.TestDistributions) ... ok (0.073s)
2023-01-11T21:59:30.0939916Z   test_bernoulli_3d (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0940419Z   test_bernoulli_enumerate_support (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0940937Z   test_beta_log_prob (__main__.TestDistributions) ... ok (0.063s)
2023-01-11T21:59:30.0941428Z   test_beta_sample (__main__.TestDistributions) ... ok (0.287s)
2023-01-11T21:59:30.0941893Z   test_beta_shape (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:30.0942585Z   test_beta_underflow (__main__.TestDistributions) ... ok (0.034s)
2023-01-11T21:59:30.0943119Z   test_beta_underflow_gpu (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:30.0943613Z   test_binomial (__main__.TestDistributions) ... ok (0.024s)
2023-01-11T21:59:30.0944114Z   test_binomial_enumerate_support (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0944650Z   test_binomial_extreme_vals (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:30.0945321Z   test_binomial_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.057s)
2023-01-11T21:59:30.0945890Z   test_binomial_log_prob_vectorized_count (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:59:30.0946426Z   test_binomial_sample (__main__.TestDistributions) ... ok (0.054s)
2023-01-11T21:59:30.0946925Z   test_binomial_stable (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0947574Z   test_binomial_vectorized_count (__main__.TestDistributions) ... ok (0.076s)
2023-01-11T21:59:30.0948092Z   test_categorical_1d (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:30.0948572Z   test_categorical_2d (__main__.TestDistributions) ... ok (0.008s)
2023-01-11T21:59:30.0949100Z   test_categorical_enumerate_support (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:30.0949608Z   test_cauchy (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:30.0950594Z   test_cdf_icdf_inverse (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.0951250Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.0951610Z ok (0.195s)
2023-01-11T21:59:30.0951998Z   test_cdf_log_prob (__main__.TestDistributions) ... ok (0.086s)
2023-01-11T21:59:30.0952469Z   test_chi2_sample (__main__.TestDistributions) ... ok (0.092s)
2023-01-11T21:59:30.0952919Z   test_chi2_shape (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:30.0953407Z   test_continuous_bernoulli (__main__.TestDistributions) ... ok (0.014s)
2023-01-11T21:59:30.0953925Z   test_continuous_bernoulli_3d (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0954445Z   test_dirichlet_log_prob (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0954879Z   test_dirichlet_mode (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0955360Z   test_dirichlet_sample (__main__.TestDistributions) ... ok (0.038s)
2023-01-11T21:59:30.0955844Z   test_dirichlet_shape (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0956798Z   test_distribution_expand (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.0957427Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.0957823Z ok (0.554s)
2023-01-11T21:59:30.0958186Z   test_distribution_subclass_expand (__main__.TestDistributions) ... ok (0.142s)
2023-01-11T21:59:30.0958697Z   test_enumerate_support_type (__main__.TestDistributions) ... ok (0.026s)
2023-01-11T21:59:30.0959222Z   test_exponential (__main__.TestDistributions) ... ok (0.031s)
2023-01-11T21:59:30.0959711Z   test_exponential_sample (__main__.TestDistributions) ... ok (0.089s)
2023-01-11T21:59:30.0960218Z   test_fishersnedecor (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:30.0960723Z   test_fishersnedecor_sample (__main__.TestDistributions) ... ok (0.883s)
2023-01-11T21:59:30.0961390Z   test_gamma_gpu_sample (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:30.0961945Z   test_gamma_gpu_shape (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:30.0962480Z   test_gamma_log_prob_at_boundary (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:59:30.0963000Z   test_gamma_sample (__main__.TestDistributions) ... ok (0.269s)
2023-01-11T21:59:30.0963453Z   test_gamma_shape (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:30.0963927Z   test_geometric (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:30.0964443Z   test_geometric_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:30.0964970Z   test_geometric_sample (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:30.0965454Z   test_gumbel (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:30.0965913Z   test_gumbel_sample (__main__.TestDistributions) ... ok (0.521s)
2023-01-11T21:59:30.0966476Z   test_halfcauchy (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:59:30.0966955Z   test_halfnormal (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:59:30.0967454Z   test_halfnormal_logprob (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:30.0967973Z   test_halfnormal_sample (__main__.TestDistributions) ... ok (0.089s)
2023-01-11T21:59:30.0968465Z   test_has_examples (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:30.0969434Z   test_independent_expand (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.0970058Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.0970404Z ok (0.669s)
2023-01-11T21:59:30.0970804Z   test_independent_shape (__main__.TestDistributions) ... ok (0.264s)
2023-01-11T21:59:30.0971348Z   test_invalid_parameter_broadcasting (__main__.TestDistributions) ... ok (0.033s)
2023-01-11T21:59:30.0971910Z   test_kumaraswamy_mean_variance (__main__.TestDistributions) ... ok (0.030s)
2023-01-11T21:59:30.0972423Z   test_kumaraswamy_shape (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:30.0972911Z   test_laplace (__main__.TestDistributions) ... ok (0.018s)
2023-01-11T21:59:30.0973393Z   test_laplace_sample (__main__.TestDistributions) ... ok (0.267s)
2023-01-11T21:59:30.0973885Z   test_lazy_property_grad (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0974387Z   test_lkj_cholesky_log_prob (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:30.0974884Z   test_logisticnormal (__main__.TestDistributions) ... ok (0.023s)
2023-01-11T21:59:30.0975384Z   test_logisticnormal_logprob (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:30.0975925Z   test_logisticnormal_sample (__main__.TestDistributions) ... ok (0.307s)
2023-01-11T21:59:30.0976432Z   test_lognormal (__main__.TestDistributions) ... ok (0.017s)
2023-01-11T21:59:30.0976923Z   test_lognormal_logprob (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:30.0977417Z   test_lognormal_sample (__main__.TestDistributions) ... ok (0.264s)
2023-01-11T21:59:30.0977951Z   test_lowrank_multivariate_normal_log_prob (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:30.0978614Z   test_lowrank_multivariate_normal_moments (__main__.TestDistributions) ... ok (0.036s)
2023-01-11T21:59:30.0979187Z   test_lowrank_multivariate_normal_properties (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:30.0979762Z   test_lowrank_multivariate_normal_sample (__main__.TestDistributions) ... ok (0.038s)
2023-01-11T21:59:30.0980338Z   test_lowrank_multivariate_normal_shape (__main__.TestDistributions) ... ok (0.047s)
2023-01-11T21:59:30.0980903Z   test_mixture_same_family_log_prob (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:30.0981438Z   test_mixture_same_family_sample (__main__.TestDistributions) ... ok (0.045s)
2023-01-11T21:59:30.0981970Z   test_mixture_same_family_shape (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:30.0983184Z   test_mode (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.0983817Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.0984164Z ok (0.111s)
2023-01-11T21:59:30.0984541Z   test_multinomial_1d (__main__.TestDistributions) ... ok (0.011s)
2023-01-11T21:59:30.0985053Z   test_multinomial_1d_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:59:30.0985580Z   test_multinomial_2d (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:30.0986090Z   test_multivariate_normal_log_prob (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:30.0986641Z   test_multivariate_normal_moments (__main__.TestDistributions) ... ok (0.026s)
2023-01-11T21:59:30.0987174Z   test_multivariate_normal_properties (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0987727Z   test_multivariate_normal_sample (__main__.TestDistributions) ... ok (0.101s)
2023-01-11T21:59:30.0988396Z   test_multivariate_normal_shape (__main__.TestDistributions) ... ok (0.067s)
2023-01-11T21:59:30.0988980Z   test_multivariate_normal_stable_with_precision_matrix (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:30.0989536Z   test_negative_binomial (__main__.TestDistributions) ... ok (0.018s)
2023-01-11T21:59:30.0990061Z   test_negative_binomial_log_prob (__main__.TestDistributions) ... ok (0.052s)
2023-01-11T21:59:30.0990645Z   test_negative_binomial_log_prob_vectorized_count (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:30.0991170Z   test_normal (__main__.TestDistributions) ... ok (0.017s)
2023-01-11T21:59:30.0991634Z   test_normal_sample (__main__.TestDistributions) ... ok (0.264s)
2023-01-11T21:59:30.0992151Z   test_one_hot_categorical_1d (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:30.0992654Z   test_one_hot_categorical_2d (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:30.0993200Z   test_one_hot_categorical_enumerate_support (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.0993719Z   test_pareto (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:30.0994183Z   test_pareto_sample (__main__.TestDistributions) ... ok (0.264s)
2023-01-11T21:59:30.0994677Z   test_poisson_forward_ad (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:30.0995199Z   test_poisson_gpu_sample (__main__.TestDistributions) ... skip: CUDA not found (0.000s)
2023-01-11T21:59:30.0995727Z   test_poisson_log_prob (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:30.0996238Z   test_poisson_sample (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:30.0996704Z   test_poisson_shape (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:30.0997188Z   test_relaxed_bernoulli (__main__.TestDistributions) ... ok (0.015s)
2023-01-11T21:59:30.0997800Z   test_relaxed_one_hot_categorical_1d (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:59:30.0998351Z   test_relaxed_one_hot_categorical_2d (__main__.TestDistributions) ... ok (0.014s)
2023-01-11T21:59:30.0998856Z   test_repr (__main__.TestDistributions) ... ok (0.019s)
2023-01-11T21:59:30.0999354Z   test_rounded_relaxed_bernoulli (__main__.TestDistributions) ... ok (0.020s)
2023-01-11T21:59:30.1000369Z   test_rsample_requires_grad (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1001050Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1001400Z ok (0.017s)
2023-01-11T21:59:30.1001800Z   test_sample_detached (__main__.TestDistributions) ... ok (0.018s)
2023-01-11T21:59:30.1002273Z   test_studentT (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:30.1002753Z   test_studentT_log_prob (__main__.TestDistributions) ... ok (0.091s)
2023-01-11T21:59:30.1003222Z   test_studentT_sample (__main__.TestDistributions) ... ok (1.110s)
2023-01-11T21:59:30.1003714Z   test_support_attributes (__main__.TestDistributions) ... ok (0.023s)
2023-01-11T21:59:30.1004323Z   test_uniform (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:30.1004847Z   test_valid_parameter_broadcasting (__main__.TestDistributions) ... ok (0.026s)
2023-01-11T21:59:30.1005385Z   test_vonmises_logprob (__main__.TestDistributions) ... ok (0.017s)
2023-01-11T21:59:30.1005875Z   test_vonmises_sample (__main__.TestDistributions) ... ok (6.787s)
2023-01-11T21:59:30.1007056Z   test_wishart_log_prob (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:59:30.1008114Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:59:30.1008985Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1009638Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1010011Z ok (0.075s)
2023-01-11T21:59:30.1010403Z   test_wishart_moments (__main__.TestDistributions) ... ok (1.518s)
2023-01-11T21:59:30.1010912Z   test_wishart_properties (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:30.1011428Z   test_wishart_sample (__main__.TestDistributions) ... ok (0.295s)
2023-01-11T21:59:30.1012357Z   test_wishart_shape (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1012970Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1013718Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1014274Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1015017Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1015583Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1016322Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1016894Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1017643Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1018188Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1018941Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1019492Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1019826Z ok (0.127s)
2023-01-11T21:59:30.1020256Z   test_wishart_stable_with_precision_matrix (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:30.1020850Z   test_zero_excluded_binomial (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:30.1021356Z   test_cat_event_dim (__main__.TestFunctors) ... ok (0.001s)
2023-01-11T21:59:30.1021803Z   test_cat_transform (__main__.TestFunctors) ... ok (0.003s)
2023-01-11T21:59:30.1022289Z   test_cat_transform_non_uniform (__main__.TestFunctors) ... ok (0.003s)
2023-01-11T21:59:30.1022939Z   test_stack_transform (__main__.TestFunctors) ... ok (0.003s)
2023-01-11T21:59:30.1023763Z   test_cdf (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:30.1024345Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:30.1024677Z ok (2.505s)
2023-01-11T21:59:30.1025546Z   test_entropy (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:59:30.1026563Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:59:30.1027152Z ok (3.315s)
2023-01-11T21:59:30.1027486Z   test_enumerate_support (__main__.TestJit) ... ok (0.228s)
2023-01-11T21:59:30.1027865Z   test_log_prob (__main__.TestJit) ... ok (5.717s)
2023-01-11T21:59:30.1028226Z   test_mean (__main__.TestJit) ... ok (1.532s)
2023-01-11T21:59:30.1028604Z   test_rsample (__main__.TestJit) ... ok (0.382s)
2023-01-11T21:59:30.1028973Z   test_sample (__main__.TestJit) ... ok (0.515s)
2023-01-11T21:59:30.1029346Z   test_variance (__main__.TestJit) ... ok (2.607s)
2023-01-11T21:59:30.1029793Z   test_entropy_exponential_family (__main__.TestKL) ... ok (0.038s)
2023-01-11T21:59:30.1030262Z   test_entropy_monte_carlo (__main__.TestKL) ... ok (2.669s)
2023-01-11T21:59:30.1030696Z   test_kl_edgecases (__main__.TestKL) ... ok (0.012s)
2023-01-11T21:59:30.1031114Z   test_kl_exponential_family (__main__.TestKL) ... ok (0.021s)
2023-01-11T21:59:30.1031477Z   test_kl_infinite (__main__.TestKL) ... ok (0.015s)
2023-01-11T21:59:30.1032004Z   test_kl_lowrank_multivariate_normal (__main__.TestKL) ... ok (0.024s)
2023-01-11T21:59:30.1032537Z   test_kl_lowrank_multivariate_normal_batched (__main__.TestKL) ... ok (0.017s)
2023-01-11T21:59:30.1032999Z   test_kl_monte_carlo (__main__.TestKL) ... ok (0.705s)
2023-01-11T21:59:30.1033415Z   test_kl_multivariate_normal (__main__.TestKL) ... ok (0.029s)
2023-01-11T21:59:30.1033883Z   test_kl_multivariate_normal_batched (__main__.TestKL) ... ok (0.014s)
2023-01-11T21:59:30.1034407Z   test_kl_multivariate_normal_batched_broadcasted (__main__.TestKL) ... ok (0.014s)
2023-01-11T21:59:30.1034857Z   test_kl_shape (__main__.TestKL) ... ok (0.045s)
2023-01-11T21:59:30.1035259Z   test_kl_transformed (__main__.TestKL) ... ok (0.011s)
2023-01-11T21:59:30.1035763Z   test_lazy_logits_initialization (__main__.TestLazyLogitsInitialization) ... ok (0.004s)
2023-01-11T21:59:30.1036399Z   test_lazy_probs_initialization (__main__.TestLazyLogitsInitialization) ... ok (0.002s)
2023-01-11T21:59:30.1036976Z   test_bernoulli_gradient (__main__.TestNumericalStability) ... ok (0.014s)
2023-01-11T21:59:30.1037542Z   test_bernoulli_with_logits_overflow (__main__.TestNumericalStability) ... ok (0.003s)
2023-01-11T21:59:30.1038196Z   test_bernoulli_with_logits_underflow (__main__.TestNumericalStability) ... ok (0.003s)
2023-01-11T21:59:30.1038753Z   test_categorical_log_prob (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:30.1039314Z   test_categorical_log_prob_with_logits (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:30.1039906Z   test_continuous_bernoulli_gradient (__main__.TestNumericalStability) ... ok (0.028s)
2023-01-11T21:59:30.1040502Z   test_continuous_bernoulli_with_logits_overflow (__main__.TestNumericalStability) ... ok (0.004s)
2023-01-11T21:59:30.1041125Z   test_continuous_bernoulli_with_logits_underflow (__main__.TestNumericalStability) ... ok (0.004s)
2023-01-11T21:59:30.1041708Z   test_multinomial_log_prob (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:30.1042297Z   test_multinomial_log_prob_with_logits (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:30.1042806Z   test_beta_wrt_alpha (__main__.TestRsample) ... ok (0.043s)
2023-01-11T21:59:30.1043252Z   test_beta_wrt_beta (__main__.TestRsample) ... ok (0.042s)
2023-01-11T21:59:30.1043663Z   test_chi2 (__main__.TestRsample) ... ok (0.023s)
2023-01-11T21:59:30.1044117Z   test_dirichlet_multivariate (__main__.TestRsample) ... ok (0.503s)
2023-01-11T21:59:30.1044601Z   test_dirichlet_on_diagonal (__main__.TestRsample) ... ok (0.045s)
2023-01-11T21:59:30.1045097Z   test_dirichlet_tangent_field (__main__.TestRsample) ... ok (0.083s)
2023-01-11T21:59:30.1045538Z   test_gamma (__main__.TestRsample) ... ok (0.023s)
2023-01-11T21:59:30.1045962Z   test_invalid (__main__.TestValidation) ... ok (0.019s)
2023-01-11T21:59:30.1046441Z   test_invalid_log_probs_arg (__main__.TestValidation) ... ok (0.277s)
2023-01-11T21:59:30.1046920Z   test_valid (__main__.TestValidation) ... ok (0.012s)
2023-01-11T21:59:30.1047413Z   test_warning_unimplemented_constraints (__main__.TestValidation) ... ok (0.008s)
2023-01-11T21:59:30.1047838Z 
2023-01-11T21:59:30.1048230Z ----------------------------------------------------------------------
2023-01-11T21:59:30.1048658Z Ran 219 tests in 40.211s
2023-01-11T21:59:30.1048857Z 
2023-01-11T21:59:30.1048980Z OK (skipped=5)
2023-01-11T21:59:30.1049156Z 
2023-01-11T21:59:30.1049300Z Generating XML reports...
2023-01-11T21:59:30.1050109Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestAgainstScipy-20230111215849.xml
2023-01-11T21:59:30.1051151Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestConstraints-20230111215849.xml
2023-01-11T21:59:30.1052185Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributionShapes-20230111215849.xml
2023-01-11T21:59:30.1053258Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributions-20230111215849.xml
2023-01-11T21:59:30.1054377Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestFunctors-20230111215849.xml
2023-01-11T21:59:30.1055353Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestJit-20230111215849.xml
2023-01-11T21:59:30.1056239Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestKL-20230111215849.xml
2023-01-11T21:59:30.1057305Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestLazyLogitsInitialization-20230111215849.xml
2023-01-11T21:59:30.1058406Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestNumericalStability-20230111215849.xml
2023-01-11T21:59:30.1059443Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestRsample-20230111215849.xml
2023-01-11T21:59:30.1060444Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestValidation-20230111215849.xml
2023-01-11T21:59:30.1060909Z 
2023-01-11T21:59:30.1061403Z ##[endgroup]
2023-01-11T21:59:30.1062527Z FINISHED PRINTING LOG FILE of distributions/test_distributions (/var/lib/jenkins/workspace/test/test-reports/distributions-test_distributions_q4k_g_kn)
2023-01-11T21:59:30.1062979Z 
2023-01-11T21:59:30.1063295Z Running nn/test_convolution ... [2023-01-11 21:59:30.090060]
2023-01-11T21:59:30.1064108Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_convolution.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:59:30.090336]
2023-01-11T21:59:38.2256044Z 
2023-01-11T21:59:38.2256638Z Expand the folded group to see the log file of nn/test_convolution
2023-01-11T21:59:38.2257512Z ##[group]PRINTING LOG FILE of nn/test_convolution (/var/lib/jenkins/workspace/test/test-reports/nn-test_convolution_wt79iwbk)
2023-01-11T21:59:38.2257778Z 
2023-01-11T21:59:38.2257885Z Running tests...
2023-01-11T21:59:38.2258487Z ----------------------------------------------------------------------
2023-01-11T21:59:38.2259046Z Test results will be stored in test-reports/python-unittest/nn.test_convolution
2023-01-11T21:59:38.2260487Z   test_Conv1d_module_same_padding (__main__.TestConvolutionNN) ... /var/lib/jenkins/workspace/test/nn/test_convolution.py:152: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:59:38.2261512Z   expect = F.conv1d(x, module.weight, module.bias, padding='same')
2023-01-11T21:59:38.2261893Z ok (0.005s)
2023-01-11T21:59:38.2262287Z   test_Conv2d_1x1 (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:38.2262954Z   test_Conv2d_OneDNN (__main__.TestConvolutionNN) ... ok (0.018s)
2023-01-11T21:59:38.2263428Z   test_Conv2d_backward_twice (__main__.TestConvolutionNN) ... ok (0.007s)
2023-01-11T21:59:38.2263739Z   test_Conv2d_groups_nobias (__main__.TestConvolutionNN) ... ok (0.041s)
2023-01-11T21:59:38.2264290Z   test_Conv2d_groups_nobias_v2 (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:38.2264595Z   test_Conv2d_inconsistent_types (__main__.TestConvolutionNN) ... ok (0.008s)
2023-01-11T21:59:38.2264936Z   test_Conv2d_inconsistent_types_on_GPU_with_cudnn (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:59:38.2265330Z   test_Conv2d_inconsistent_types_on_GPU_without_cudnn (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:59:38.2265672Z   test_Conv2d_missing_argument (__main__.TestConvolutionNN) ... ok (0.002s)
2023-01-11T21:59:38.2265977Z   test_Conv2d_module_same_padding (__main__.TestConvolutionNN) ... ok (0.003s)
2023-01-11T21:59:38.2266263Z   test_Conv3d_groups_nobias (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:38.2266554Z   test_Conv3d_groups_wbias (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:38.2266916Z   test_Conv3d_module_same_padding (__main__.TestConvolutionNN) ... ok (0.003s)
2023-01-11T21:59:38.2267250Z   test_ConvTranspose2d_half_cublas_gemm (__main__.TestConvolutionNN) ... skip: CUDA not available (0.000s)
2023-01-11T21:59:38.2267587Z   test_ConvTranspose2d_output_size (__main__.TestConvolutionNN) ... ok (0.003s)
2023-01-11T21:59:38.2267925Z   test_ConvTranspose2d_output_size_downsample_upsample (__main__.TestConvolutionNN) ... ok (4.115s)
2023-01-11T21:59:38.2268301Z   test_ConvTranspose3d_correct_output_size (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:59:38.2268611Z   test_conv2d_discontiguous_weight (__main__.TestConvolutionNN) ... ok (0.039s)
2023-01-11T21:59:38.2268903Z   test_conv_backcompat (__main__.TestConvolutionNN) ... ok (0.010s)
2023-01-11T21:59:38.2269228Z   test_conv_cudnn_memory_layout_dominance (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.004s)
2023-01-11T21:59:38.2269552Z   test_conv_invalid_groups (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:59:38.2269864Z   test_conv_modules_raise_error_on_incorrect_input_size (__main__.TestConvolutionNN) ... ok (0.094s)
2023-01-11T21:59:38.2270191Z   test_conv_padding_mode (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:59:38.2271216Z   test_conv_shapecheck (__main__.TestConvolutionNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1120: UserWarning: Complex modules are a new feature under active development whose design may change, and some modules might not work as expected when using complex tensors as parameters or buffers. Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml if a complex module does not work as expected.
2023-01-11T21:59:38.2271837Z   warnings.warn(
2023-01-11T21:59:38.2271996Z ok (0.179s)
2023-01-11T21:59:38.2272214Z   test_conv_tbc (__main__.TestConvolutionNN) ... ok (0.009s)
2023-01-11T21:59:38.2272522Z   test_cudnn_non_contiguous (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:59:38.2272867Z   test_cudnn_noncontiguous_weight (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:38.2273189Z   test_functional_grad_conv (__main__.TestConvolutionNN) ... ok (0.004s)
2023-01-11T21:59:38.2273487Z   test_functional_grad_conv2d (__main__.TestConvolutionNN) ... ok (0.158s)
2023-01-11T21:59:38.2273782Z   test_grad_conv1d_input (__main__.TestConvolutionNN) ... ok (0.058s)
2023-01-11T21:59:38.2274057Z   test_grad_conv1d_weight (__main__.TestConvolutionNN) ... ok (0.042s)
2023-01-11T21:59:38.2274344Z   test_grad_conv2d_input (__main__.TestConvolutionNN) ... ok (0.064s)
2023-01-11T21:59:38.2274628Z   test_grad_conv2d_weight (__main__.TestConvolutionNN) ... ok (0.062s)
2023-01-11T21:59:38.2274902Z   test_grad_conv3d_input (__main__.TestConvolutionNN) ... ok (0.082s)
2023-01-11T21:59:38.2275187Z   test_grad_conv3d_weight (__main__.TestConvolutionNN) ... ok (0.060s)
2023-01-11T21:59:38.2275517Z   test_grouped_conv_cudnn_nhwc_support (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:38.2276587Z   test_invalid_conv1d (__main__.TestConvolutionNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1120: UserWarning: Complex modules are a new feature under active development whose design may change, and some modules might not work as expected when using complex tensors as parameters or buffers. Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml if a complex module does not work as expected.
2023-01-11T21:59:38.2277195Z   warnings.warn(
2023-01-11T21:59:38.2277357Z ok (0.080s)
2023-01-11T21:59:38.2277580Z   test_invalid_conv2d (__main__.TestConvolutionNN) ... ok (0.158s)
2023-01-11T21:59:38.2277989Z   test_invalid_conv3d (__main__.TestConvolutionNN) ... ok (0.079s)
2023-01-11T21:59:38.2278263Z   test_mismatch_shape_conv2d (__main__.TestConvolutionNN) ... ok (0.007s)
2023-01-11T21:59:38.2278545Z   test_nnpack_conv (__main__.TestConvolutionNN) ... ok (0.363s)
2023-01-11T21:59:38.2278915Z   test_thnn_conv_strided_padded_dilated (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:59:38.2279123Z 
2023-01-11T21:59:38.2279316Z ----------------------------------------------------------------------
2023-01-11T21:59:38.2279558Z Ran 43 tests in 5.789s
2023-01-11T21:59:38.2279672Z 
2023-01-11T21:59:38.2279742Z OK (skipped=8)
2023-01-11T21:59:38.2279847Z 
2023-01-11T21:59:38.2279934Z Generating XML reports...
2023-01-11T21:59:38.2280349Z Generated XML report: test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNN-20230111215931.xml
2023-01-11T21:59:38.2280591Z 
2023-01-11T21:59:38.2280889Z ##[endgroup]
2023-01-11T21:59:38.2281288Z FINISHED PRINTING LOG FILE of nn/test_convolution (/var/lib/jenkins/workspace/test/test-reports/nn-test_convolution_wt79iwbk)
2023-01-11T21:59:38.2281513Z 
2023-01-11T21:59:38.2281664Z Running nn/test_pooling ... [2023-01-11 21:59:38.225729]
2023-01-11T21:59:38.2282141Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_pooling.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:59:38.226004]
2023-01-11T21:59:40.9086760Z 
2023-01-11T21:59:40.9087282Z Expand the folded group to see the log file of nn/test_pooling
2023-01-11T21:59:40.9088354Z ##[group]PRINTING LOG FILE of nn/test_pooling (/var/lib/jenkins/workspace/test/test-reports/nn-test_pooling_i3n92shm)
2023-01-11T21:59:40.9088912Z 
2023-01-11T21:59:40.9089030Z Running tests...
2023-01-11T21:59:40.9089839Z ----------------------------------------------------------------------
2023-01-11T21:59:40.9090500Z Test results will be stored in test-reports/python-unittest/nn.test_pooling
2023-01-11T21:59:40.9091049Z   test_avg_pool1d_ceil_mode (__main__.TestAvgPool) ... ok (0.002s)
2023-01-11T21:59:40.9091497Z   test_avg_pool2d_ceil_mode (__main__.TestAvgPool) ... ok (0.001s)
2023-01-11T21:59:40.9091833Z   test_avg_pool3d_ceil_mode (__main__.TestAvgPool) ... ok (0.001s)
2023-01-11T21:59:40.9092353Z   test_doubletensor_avg_pool2d (__main__.TestAvgPool) ... ok (0.008s)
2023-01-11T21:59:40.9092760Z   test_doubletensor_avg_pool2d_with_divisor (__main__.TestAvgPool) ... ok (0.005s)
2023-01-11T21:59:40.9093066Z   test_doubletensor_avg_pool3d (__main__.TestAvgPool) ... ok (0.047s)
2023-01-11T21:59:40.9093363Z   test_doubletensor_avg_pool3d_with_divisor (__main__.TestAvgPool) ... ok (0.160s)
2023-01-11T21:59:40.9093669Z   test_MaxUnpool2d_output_size (__main__.TestPoolingNN) ... ok (0.009s)
2023-01-11T21:59:40.9093951Z   test_adaptive_pooling_avg_nhwc (__main__.TestPoolingNN) ... ok (0.003s)
2023-01-11T21:59:40.9094301Z   test_adaptive_pooling_avg_nhwc_launch_config_backward (__main__.TestPoolingNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:40.9094690Z   test_adaptive_pooling_avg_nhwc_launch_config_forward (__main__.TestPoolingNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:40.9095034Z   test_adaptive_pooling_avg_nhwc_non_contiguous (__main__.TestPoolingNN) ... ok (0.002s)
2023-01-11T21:59:40.9095344Z   test_adaptive_pooling_bfloat16 (__main__.TestPoolingNN) ... ok (0.006s)
2023-01-11T21:59:40.9095857Z   test_adaptive_pooling_input_size (__main__.TestPoolingNN) ... ok (0.001s)
2023-01-11T21:59:40.9096157Z   test_adaptive_pooling_size_none (__main__.TestPoolingNN) ... ok (0.002s)
2023-01-11T21:59:40.9096441Z   test_adaptive_pooling_size_overflow (__main__.TestPoolingNN) ... ok (0.009s)
2023-01-11T21:59:40.9096721Z   test_max_unpool (__main__.TestPoolingNN) ... ok (0.225s)
2023-01-11T21:59:40.9096991Z   test_max_unpool2d_nhwc_cpu (__main__.TestPoolingNN) ... ok (0.002s)
2023-01-11T21:59:40.9097151Z 
2023-01-11T21:59:40.9097350Z ----------------------------------------------------------------------
2023-01-11T21:59:40.9097596Z Ran 18 tests in 0.486s
2023-01-11T21:59:40.9097709Z 
2023-01-11T21:59:40.9097782Z OK (skipped=2)
2023-01-11T21:59:40.9097889Z 
2023-01-11T21:59:40.9097975Z Generating XML reports...
2023-01-11T21:59:40.9098368Z Generated XML report: test-reports/python-unittest/nn.test_pooling/TEST-TestAvgPool-20230111215939.xml
2023-01-11T21:59:40.9098945Z Generated XML report: test-reports/python-unittest/nn.test_pooling/TEST-TestPoolingNN-20230111215939.xml
2023-01-11T21:59:40.9099171Z 
2023-01-11T21:59:40.9099442Z ##[endgroup]
2023-01-11T21:59:40.9099819Z FINISHED PRINTING LOG FILE of nn/test_pooling (/var/lib/jenkins/workspace/test/test-reports/nn-test_pooling_i3n92shm)
2023-01-11T21:59:40.9100035Z 
2023-01-11T21:59:40.9100209Z Running test_cpp_api_parity ... [2023-01-11 21:59:40.908760]
2023-01-11T21:59:40.9100685Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_api_parity.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:59:40.909009]
2023-01-11T21:59:54.4080537Z 
2023-01-11T21:59:54.4081083Z Expand the folded group to see the log file of test_cpp_api_parity
2023-01-11T21:59:54.4082147Z ##[group]PRINTING LOG FILE of test_cpp_api_parity (/var/lib/jenkins/workspace/test/test-reports/test_cpp_api_parity_ip7klvi_)
2023-01-11T21:59:54.4082932Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:59:54.4083195Z 
2023-01-11T21:59:54.4083310Z Running tests...
2023-01-11T21:59:54.4083792Z ----------------------------------------------------------------------
2023-01-11T21:59:54.4084462Z Test results will be stored in test-reports/python-unittest/test_cpp_api_parity
2023-01-11T21:59:54.4085047Z   test_torch_nn_AdaptiveAvgPool1d (__main__.TestCppApiParity) ... ok (0.032s)
2023-01-11T21:59:54.4085546Z   test_torch_nn_AdaptiveAvgPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4086125Z   test_torch_nn_AdaptiveAvgPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4086704Z   test_torch_nn_AdaptiveAvgPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4087199Z   test_torch_nn_AdaptiveAvgPool1d_one_output (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4087786Z   test_torch_nn_AdaptiveAvgPool1d_one_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4088421Z   test_torch_nn_AdaptiveAvgPool2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4089055Z   test_torch_nn_AdaptiveAvgPool2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4089686Z   test_torch_nn_AdaptiveAvgPool2d_single (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4090292Z   test_torch_nn_AdaptiveAvgPool2d_single_1x1output (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4090994Z   test_torch_nn_AdaptiveAvgPool2d_single_1x1output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4091567Z   test_torch_nn_AdaptiveAvgPool2d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4092115Z   test_torch_nn_AdaptiveAvgPool2d_tuple (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4092678Z   test_torch_nn_AdaptiveAvgPool2d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4093515Z   test_torch_nn_AdaptiveAvgPool2d_tuple_none (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4093950Z   test_torch_nn_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4094444Z   test_torch_nn_AdaptiveAvgPool3d_last_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4095045Z   test_torch_nn_AdaptiveAvgPool3d_last_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4095631Z   test_torch_nn_AdaptiveAvgPool3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4096256Z   test_torch_nn_AdaptiveAvgPool3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4096882Z   test_torch_nn_AdaptiveAvgPool3d_single (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4097555Z   test_torch_nn_AdaptiveAvgPool3d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4098054Z   test_torch_nn_AdaptiveAvgPool3d_tuple (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4098415Z   test_torch_nn_AdaptiveAvgPool3d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4098916Z   test_torch_nn_AdaptiveAvgPool3d_tuple_none (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4099344Z   test_torch_nn_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4099683Z   test_torch_nn_AdaptiveMaxPool1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4100077Z   test_torch_nn_AdaptiveMaxPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4100429Z   test_torch_nn_AdaptiveMaxPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4100796Z   test_torch_nn_AdaptiveMaxPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4101416Z   test_torch_nn_AdaptiveMaxPool2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4102053Z   test_torch_nn_AdaptiveMaxPool2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4102666Z   test_torch_nn_AdaptiveMaxPool2d_single (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4103031Z   test_torch_nn_AdaptiveMaxPool2d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4103370Z   test_torch_nn_AdaptiveMaxPool2d_tuple (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4103724Z   test_torch_nn_AdaptiveMaxPool2d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4104079Z   test_torch_nn_AdaptiveMaxPool2d_tuple_none (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4104427Z   test_torch_nn_AdaptiveMaxPool2d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4104794Z   test_torch_nn_AdaptiveMaxPool3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4105160Z   test_torch_nn_AdaptiveMaxPool3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4105583Z   test_torch_nn_AdaptiveMaxPool3d_single (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4105941Z   test_torch_nn_AdaptiveMaxPool3d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4106301Z   test_torch_nn_AdaptiveMaxPool3d_single_nonatomic (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4106665Z   test_torch_nn_AdaptiveMaxPool3d_single_nonatomic_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4107026Z   test_torch_nn_AdaptiveMaxPool3d_tuple (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4107380Z   test_torch_nn_AdaptiveMaxPool3d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4107838Z   test_torch_nn_AdaptiveMaxPool3d_tuple_nonatomic (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4108203Z   test_torch_nn_AdaptiveMaxPool3d_tuple_nonatomic_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4108569Z   test_torch_nn_AdaptiveMaxPool3d_tuple_none (__main__.TestCppApiParity) ... ok (0.020s)
2023-01-11T21:59:54.4108934Z   test_torch_nn_AdaptiveMaxPool3d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4109256Z   test_torch_nn_AvgPool1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4109572Z   test_torch_nn_AvgPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4109899Z   test_torch_nn_AvgPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4110246Z   test_torch_nn_AvgPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4110616Z   test_torch_nn_AvgPool1d_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4110954Z   test_torch_nn_AvgPool1d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4111290Z   test_torch_nn_AvgPool1d_stride_pad (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4111621Z   test_torch_nn_AvgPool1d_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4111942Z   test_torch_nn_AvgPool2d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4112256Z   test_torch_nn_AvgPool2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4112578Z   test_torch_nn_AvgPool2d_divisor (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4112902Z   test_torch_nn_AvgPool2d_divisor_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4113239Z   test_torch_nn_AvgPool2d_divisor_stride (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4113593Z   test_torch_nn_AvgPool2d_divisor_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4113939Z   test_torch_nn_AvgPool2d_divisor_stride_pad (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4114281Z   test_torch_nn_AvgPool2d_divisor_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4114630Z   test_torch_nn_AvgPool2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4114970Z   test_torch_nn_AvgPool2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4115289Z   test_torch_nn_AvgPool2d_stride (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4115619Z   test_torch_nn_AvgPool2d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4115951Z   test_torch_nn_AvgPool2d_stride_pad (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4116295Z   test_torch_nn_AvgPool2d_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4116604Z   test_torch_nn_AvgPool3d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4116923Z   test_torch_nn_AvgPool3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4117243Z   test_torch_nn_AvgPool3d_divisor (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4117566Z   test_torch_nn_AvgPool3d_divisor_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4117978Z   test_torch_nn_AvgPool3d_divisor_stride (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4118318Z   test_torch_nn_AvgPool3d_divisor_stride1_pad0_gpu_input (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4118697Z   test_torch_nn_AvgPool3d_divisor_stride1_pad0_gpu_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4119074Z   test_torch_nn_AvgPool3d_divisor_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4119460Z   test_torch_nn_AvgPool3d_divisor_stride_pad (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4119816Z   test_torch_nn_AvgPool3d_divisor_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4120188Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4120570Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4120958Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_general_output (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4121353Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_general_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4121781Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4122168Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4122532Z   test_torch_nn_AvgPool3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4122980Z   test_torch_nn_AvgPool3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4123301Z   test_torch_nn_AvgPool3d_stride (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4123624Z   test_torch_nn_AvgPool3d_stride1_pad0_gpu_input (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4123987Z   test_torch_nn_AvgPool3d_stride1_pad0_gpu_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4124358Z   test_torch_nn_AvgPool3d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4124684Z   test_torch_nn_AvgPool3d_stride_pad (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4125024Z   test_torch_nn_AvgPool3d_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4125383Z   test_torch_nn_AvgPool3d_stride_pad_gpu_fixedkw_output (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4125768Z   test_torch_nn_AvgPool3d_stride_pad_gpu_fixedkw_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4126133Z   test_torch_nn_AvgPool3d_stride_pad_gpu_general_output (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4126513Z   test_torch_nn_AvgPool3d_stride_pad_gpu_general_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4126895Z   test_torch_nn_AvgPool3d_stride_pad_gpu_input_nooverlap (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4127278Z   test_torch_nn_AvgPool3d_stride_pad_gpu_input_nooverlap_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4127607Z   test_torch_nn_BCELoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4127919Z   test_torch_nn_BCELoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4128260Z   test_torch_nn_BCELoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4128608Z   test_torch_nn_BCELoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4128966Z   test_torch_nn_BCELoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4129323Z   test_torch_nn_BCELoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4129677Z   test_torch_nn_BCELoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4130025Z   test_torch_nn_BCELoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4130407Z   test_torch_nn_BCELoss_scalar_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4130751Z   test_torch_nn_BCELoss_scalar_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4131081Z   test_torch_nn_BCELoss_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4131396Z   test_torch_nn_BCELoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4131729Z   test_torch_nn_BCEWithLogitsLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4132073Z   test_torch_nn_BCEWithLogitsLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4132433Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4132853Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4133244Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4133631Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4134004Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4134390Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4134759Z   test_torch_nn_BCEWithLogitsLoss_scalar_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4135132Z   test_torch_nn_BCEWithLogitsLoss_scalar_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4135483Z   test_torch_nn_BCEWithLogitsLoss_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4135846Z   test_torch_nn_BCEWithLogitsLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4136192Z   test_torch_nn_BatchNorm1d_3d_input (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4136523Z   test_torch_nn_BatchNorm1d_3d_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4136868Z   test_torch_nn_BatchNorm1d_3d_input_not_affine (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4137226Z   test_torch_nn_BatchNorm1d_3d_input_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4137569Z   test_torch_nn_BatchNorm1d_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4137893Z   test_torch_nn_BatchNorm1d_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4138244Z   test_torch_nn_BatchNorm1d_affine_simple_average (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4138613Z   test_torch_nn_BatchNorm1d_affine_simple_average_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4138969Z   test_torch_nn_BatchNorm1d_not_affine (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4139302Z   test_torch_nn_BatchNorm1d_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4139650Z   test_torch_nn_BatchNorm1d_not_tracking_stats (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4140011Z   test_torch_nn_BatchNorm1d_not_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4140341Z   test_torch_nn_BatchNorm1d_zero_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4140683Z   test_torch_nn_BatchNorm1d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4141011Z   test_torch_nn_BatchNorm2d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4141323Z   test_torch_nn_BatchNorm2d_2d_simple_average (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4141704Z   test_torch_nn_BatchNorm2d_2d_simple_average_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4142066Z   test_torch_nn_BatchNorm2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4142523Z   test_torch_nn_BatchNorm2d_momentum (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4142889Z   test_torch_nn_BatchNorm2d_momentum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4143384Z   test_torch_nn_BatchNorm2d_not_affine (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4143957Z   test_torch_nn_BatchNorm2d_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4144545Z   test_torch_nn_BatchNorm2d_not_tracking_stats (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4145251Z   test_torch_nn_BatchNorm2d_not_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4145871Z   test_torch_nn_BatchNorm2d_zero_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4146453Z   test_torch_nn_BatchNorm2d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4146989Z   test_torch_nn_BatchNorm3d (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4147486Z   test_torch_nn_BatchNorm3d_3d_simple_average (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4148102Z   test_torch_nn_BatchNorm3d_3d_simple_average_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4148705Z   test_torch_nn_BatchNorm3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4149261Z   test_torch_nn_BatchNorm3d_momentum (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4149797Z   test_torch_nn_BatchNorm3d_momentum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4150385Z   test_torch_nn_BatchNorm3d_not_affine (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4150967Z   test_torch_nn_BatchNorm3d_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4151574Z   test_torch_nn_BatchNorm3d_not_tracking_stats (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4152204Z   test_torch_nn_BatchNorm3d_not_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4152782Z   test_torch_nn_BatchNorm3d_zero_batch (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4153344Z   test_torch_nn_BatchNorm3d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4153843Z   test_torch_nn_CELU (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4154342Z   test_torch_nn_CELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4154864Z   test_torch_nn_CELU_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4155394Z   test_torch_nn_CELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4155938Z   test_torch_nn_CTCLoss_2d_int_target_lengths_tensors (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4156549Z   test_torch_nn_CTCLoss_2d_int_target_lengths_tensors_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4157123Z   test_torch_nn_CTCLoss_2d_lengths_tensors (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4157756Z   test_torch_nn_CTCLoss_2d_lengths_tensors_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4158373Z   test_torch_nn_CTCLoss_lengths_tensors (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4159008Z   test_torch_nn_CTCLoss_lengths_tensors_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4159715Z   test_torch_nn_ConstantPad1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4160265Z   test_torch_nn_ConstantPad1d_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4160824Z   test_torch_nn_ConstantPad1d_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4161328Z   test_torch_nn_ConstantPad1d_complex (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4161967Z   test_torch_nn_ConstantPad1d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4162402Z   test_torch_nn_ConstantPad1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4162729Z   test_torch_nn_ConstantPad2d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4163039Z   test_torch_nn_ConstantPad2d_complex (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4163737Z   test_torch_nn_ConstantPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4164173Z   test_torch_nn_ConstantPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4164515Z   test_torch_nn_ConstantPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4164870Z   test_torch_nn_ConstantPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4165193Z   test_torch_nn_ConstantPad3d (__main__.TestCppApiParity) ... ok (0.033s)
2023-01-11T21:59:54.4165504Z   test_torch_nn_ConstantPad3d_complex (__main__.TestCppApiParity) ... ok (0.041s)
2023-01-11T21:59:54.4165941Z   test_torch_nn_ConstantPad3d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4166479Z   test_torch_nn_ConstantPad3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4167095Z   test_torch_nn_ConstantPad3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.022s)
2023-01-11T21:59:54.4167456Z   test_torch_nn_ConstantPad3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4167779Z   test_torch_nn_Conv1d (__main__.TestCppApiParity) ... ok (0.032s)
2023-01-11T21:59:54.4168069Z   test_torch_nn_Conv1d_circular_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4168420Z   test_torch_nn_Conv1d_circular_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4168772Z   test_torch_nn_Conv1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4169087Z   test_torch_nn_Conv1d_dilated (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4169402Z   test_torch_nn_Conv1d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4169725Z   test_torch_nn_Conv1d_groups (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4170050Z   test_torch_nn_Conv1d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4170366Z   test_torch_nn_Conv1d_pad1 (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4170671Z   test_torch_nn_Conv1d_pad1_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4170992Z   test_torch_nn_Conv1d_pad1size1 (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4171320Z   test_torch_nn_Conv1d_pad1size1_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4171626Z   test_torch_nn_Conv1d_pad2 (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4171942Z   test_torch_nn_Conv1d_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4172259Z   test_torch_nn_Conv1d_pad2size1 (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4172583Z   test_torch_nn_Conv1d_pad2size1_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4172896Z   test_torch_nn_Conv1d_pad_same (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4173877Z   test_torch_nn_Conv1d_pad_same2 (__main__.TestCppApiParity) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:59:54.4174447Z   return F.conv1d(input, weight, bias, self.stride,
2023-01-11T21:59:54.4174650Z ok (0.014s)
2023-01-11T21:59:54.4174903Z   test_torch_nn_Conv1d_pad_same2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4175255Z   test_torch_nn_Conv1d_pad_same_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4175588Z   test_torch_nn_Conv1d_pad_same_dilated (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4175992Z   test_torch_nn_Conv1d_pad_same_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4176314Z   test_torch_nn_Conv1d_pad_valid (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4176640Z   test_torch_nn_Conv1d_pad_valid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4176978Z   test_torch_nn_Conv1d_reflect_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4177315Z   test_torch_nn_Conv1d_reflect_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4177665Z   test_torch_nn_Conv1d_replicate_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4178024Z   test_torch_nn_Conv1d_replicate_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4178360Z   test_torch_nn_Conv1d_stride (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4178668Z   test_torch_nn_Conv1d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4178994Z   test_torch_nn_Conv1d_zero_batch (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4179322Z   test_torch_nn_Conv1d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4179653Z   test_torch_nn_Conv1d_zeros_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4179987Z   test_torch_nn_Conv1d_zeros_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4180305Z   test_torch_nn_Conv2d (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4180607Z   test_torch_nn_Conv2d_circular_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4180949Z   test_torch_nn_Conv2d_circular_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4181301Z   test_torch_nn_Conv2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4181623Z   test_torch_nn_Conv2d_depthwise (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4181956Z   test_torch_nn_Conv2d_depthwise_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4182279Z   test_torch_nn_Conv2d_depthwise_dilated (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4182822Z   test_torch_nn_Conv2d_depthwise_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4183169Z   test_torch_nn_Conv2d_depthwise_padded (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4183505Z   test_torch_nn_Conv2d_depthwise_padded_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4183849Z   test_torch_nn_Conv2d_depthwise_strided (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4184198Z   test_torch_nn_Conv2d_depthwise_strided_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4184554Z   test_torch_nn_Conv2d_depthwise_with_multiplier (__main__.TestCppApiParity) ... ok (0.023s)
2023-01-11T21:59:54.4184971Z   test_torch_nn_Conv2d_depthwise_with_multiplier_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4185312Z   test_torch_nn_Conv2d_dilated (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4185639Z   test_torch_nn_Conv2d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4185963Z   test_torch_nn_Conv2d_groups (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4186274Z   test_torch_nn_Conv2d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4186598Z   test_torch_nn_Conv2d_groups_thnn (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4186931Z   test_torch_nn_Conv2d_groups_thnn_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4187241Z   test_torch_nn_Conv2d_no_bias (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4187606Z   test_torch_nn_Conv2d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4187933Z   test_torch_nn_Conv2d_pad_same (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4188260Z   test_torch_nn_Conv2d_pad_same_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4188579Z   test_torch_nn_Conv2d_pad_same_dilated (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4188925Z   test_torch_nn_Conv2d_pad_same_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4189260Z   test_torch_nn_Conv2d_pad_valid (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4189583Z   test_torch_nn_Conv2d_pad_valid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4189907Z   test_torch_nn_Conv2d_padding (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4190238Z   test_torch_nn_Conv2d_padding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4190576Z   test_torch_nn_Conv2d_reflect_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4190913Z   test_torch_nn_Conv2d_reflect_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4191262Z   test_torch_nn_Conv2d_replicate_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4191623Z   test_torch_nn_Conv2d_replicate_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4191956Z   test_torch_nn_Conv2d_strided (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4192267Z   test_torch_nn_Conv2d_strided_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4192591Z   test_torch_nn_Conv2d_zero_batch (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4192919Z   test_torch_nn_Conv2d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4193243Z   test_torch_nn_Conv2d_zeros_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4193588Z   test_torch_nn_Conv2d_zeros_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4193905Z   test_torch_nn_Conv3d (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4194196Z   test_torch_nn_Conv3d_1x1x1_no_bias (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4194514Z   test_torch_nn_Conv3d_1x1x1_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4194853Z   test_torch_nn_Conv3d_circular_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T21:59:54.4195205Z   test_torch_nn_Conv3d_circular_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4195545Z   test_torch_nn_Conv3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4195858Z   test_torch_nn_Conv3d_dilated (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4196219Z   test_torch_nn_Conv3d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4196547Z   test_torch_nn_Conv3d_dilated_strided (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4196874Z   test_torch_nn_Conv3d_dilated_strided_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4197202Z   test_torch_nn_Conv3d_groups (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4197523Z   test_torch_nn_Conv3d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4197929Z   test_torch_nn_Conv3d_no_bias (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4198238Z   test_torch_nn_Conv3d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4198556Z   test_torch_nn_Conv3d_pad_same (__main__.TestCppApiParity) ... ok (0.028s)
2023-01-11T21:59:54.4198924Z   test_torch_nn_Conv3d_pad_same_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4199248Z   test_torch_nn_Conv3d_pad_same_dilated (__main__.TestCppApiParity) ... ok (0.026s)
2023-01-11T21:59:54.4199591Z   test_torch_nn_Conv3d_pad_same_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4199921Z   test_torch_nn_Conv3d_pad_valid (__main__.TestCppApiParity) ... ok (0.019s)
2023-01-11T21:59:54.4200246Z   test_torch_nn_Conv3d_pad_valid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4200573Z   test_torch_nn_Conv3d_replicate_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.017s)
2023-01-11T21:59:54.4200930Z   test_torch_nn_Conv3d_replicate_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4201263Z   test_torch_nn_Conv3d_stride (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4201574Z   test_torch_nn_Conv3d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4201907Z   test_torch_nn_Conv3d_stride_padding (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T21:59:54.4202245Z   test_torch_nn_Conv3d_stride_padding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4202575Z   test_torch_nn_Conv3d_zero_batch (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4202892Z   test_torch_nn_Conv3d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4203222Z   test_torch_nn_Conv3d_zeros_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.017s)
2023-01-11T21:59:54.4203567Z   test_torch_nn_Conv3d_zeros_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4203896Z   test_torch_nn_ConvTranspose1d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4204219Z   test_torch_nn_ConvTranspose1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4204563Z   test_torch_nn_ConvTranspose1d_dilated (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4204912Z   test_torch_nn_ConvTranspose1d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4205246Z   test_torch_nn_ConvTranspose1d_groups (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4205593Z   test_torch_nn_ConvTranspose1d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4205934Z   test_torch_nn_ConvTranspose1d_no_bias (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4206278Z   test_torch_nn_ConvTranspose1d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4206599Z   test_torch_nn_ConvTranspose2d (__main__.TestCppApiParity) ... ok (0.024s)
2023-01-11T21:59:54.4206932Z   test_torch_nn_ConvTranspose2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4207309Z   test_torch_nn_ConvTranspose2d_dilated (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4207645Z   test_torch_nn_ConvTranspose2d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4207991Z   test_torch_nn_ConvTranspose2d_groups (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4208336Z   test_torch_nn_ConvTranspose2d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4208675Z   test_torch_nn_ConvTranspose2d_no_bias (__main__.TestCppApiParity) ... ok (0.023s)
2023-01-11T21:59:54.4209003Z   test_torch_nn_ConvTranspose2d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4209335Z   test_torch_nn_ConvTranspose3d (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T21:59:54.4209669Z   test_torch_nn_ConvTranspose3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4210035Z   test_torch_nn_ConvTranspose3d_dilated (__main__.TestCppApiParity) ... ok (0.023s)
2023-01-11T21:59:54.4210376Z   test_torch_nn_ConvTranspose3d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4210718Z   test_torch_nn_CosineEmbeddingLoss (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4211067Z   test_torch_nn_CosineEmbeddingLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4211409Z   test_torch_nn_CosineEmbeddingLoss_margin (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4211770Z   test_torch_nn_CosineEmbeddingLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4212139Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4212522Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4212890Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4213274Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4213648Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4214027Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4214365Z   test_torch_nn_CrossEntropyLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4214677Z   test_torch_nn_CrossEntropyLoss_2d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4215019Z   test_torch_nn_CrossEntropyLoss_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4215358Z   test_torch_nn_CrossEntropyLoss_2d_ignore_index (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4215732Z   test_torch_nn_CrossEntropyLoss_2d_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4216109Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4216498Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4216891Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4217306Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4217721Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4218142Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4218583Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_weight (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4218990Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4219369Z   test_torch_nn_CrossEntropyLoss_2d_prob_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4219734Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4220095Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4220478Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4220872Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4221321Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4221712Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_weight (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4222107Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4222695Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4223077Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4223429Z   test_torch_nn_CrossEntropyLoss_2d_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4223787Z   test_torch_nn_CrossEntropyLoss_2d_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4224166Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4224546Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4224948Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4225360Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4225773Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4226179Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4226604Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4227049Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4227446Z   test_torch_nn_CrossEntropyLoss_3d_prob_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4227799Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4228168Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4228550Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4228945Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4229347Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4229806Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4230187Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4230554Z   test_torch_nn_CrossEntropyLoss_4d_prob_target (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4230906Z   test_torch_nn_CrossEntropyLoss_4d_prob_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4231273Z   test_torch_nn_CrossEntropyLoss_4d_prob_target_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4231652Z   test_torch_nn_CrossEntropyLoss_4d_prob_target_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4232071Z   test_torch_nn_CrossEntropyLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4232402Z   test_torch_nn_CrossEntropyLoss_dim_is_3 (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4232753Z   test_torch_nn_CrossEntropyLoss_dim_is_3_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4233107Z   test_torch_nn_CrossEntropyLoss_higher_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4233466Z   test_torch_nn_CrossEntropyLoss_higher_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4233804Z   test_torch_nn_CrossEntropyLoss_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4234160Z   test_torch_nn_CrossEntropyLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4234498Z   test_torch_nn_CrossMapLRN2d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4234820Z   test_torch_nn_CrossMapLRN2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4235141Z   test_torch_nn_ELU (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4235445Z   test_torch_nn_ELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4235754Z   test_torch_nn_ELU_scalar (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4236062Z   test_torch_nn_ELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4236377Z   test_torch_nn_Embedding (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4236690Z   test_torch_nn_EmbeddingBag_discontiguous (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4237038Z   test_torch_nn_EmbeddingBag_discontiguous_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4237379Z   test_torch_nn_EmbeddingBag_max (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4237772Z   test_torch_nn_EmbeddingBag_max_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4238125Z   test_torch_nn_EmbeddingBag_max_padding_idx (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4238471Z   test_torch_nn_EmbeddingBag_max_padding_idx_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4238812Z   test_torch_nn_EmbeddingBag_mean (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4239154Z   test_torch_nn_EmbeddingBag_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4239493Z   test_torch_nn_EmbeddingBag_mean_padding_idx (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4239853Z   test_torch_nn_EmbeddingBag_mean_padding_idx_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4240197Z   test_torch_nn_EmbeddingBag_sparse (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4240540Z   test_torch_nn_EmbeddingBag_sparse_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4240903Z   test_torch_nn_EmbeddingBag_sum (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4241238Z   test_torch_nn_EmbeddingBag_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4241580Z   test_torch_nn_EmbeddingBag_sum_padding_idx (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4241941Z   test_torch_nn_EmbeddingBag_sum_padding_idx_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4242286Z   test_torch_nn_Embedding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4242617Z   test_torch_nn_Embedding_discontiguous (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4242969Z   test_torch_nn_Embedding_discontiguous_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4243295Z   test_torch_nn_Embedding_sparse (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4243658Z   test_torch_nn_Embedding_sparse_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4243982Z   test_torch_nn_Flatten (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4244293Z   test_torch_nn_Flatten_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4244603Z   test_torch_nn_Flatten_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4244940Z   test_torch_nn_Flatten_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4245250Z   test_torch_nn_Fold (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4245542Z   test_torch_nn_Fold_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4245850Z   test_torch_nn_Fold_int_input (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4246174Z   test_torch_nn_Fold_int_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4246512Z   test_torch_nn_Fold_no_batch_dim_input (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4246841Z   test_torch_nn_Fold_no_batch_dim_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4247180Z   test_torch_nn_Fold_no_batch_dim_int_input (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4247525Z   test_torch_nn_Fold_no_batch_dim_int_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4247874Z   test_torch_nn_FractionalMaxPool2d_ratio (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4248224Z   test_torch_nn_FractionalMaxPool2d_ratio_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4248595Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4248979Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4249367Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4249790Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4250178Z   test_torch_nn_FractionalMaxPool2d_size (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4250537Z   test_torch_nn_FractionalMaxPool2d_size_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4250890Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4251270Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4251671Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4252133Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4252514Z   test_torch_nn_FractionalMaxPool3d_asymsize (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T21:59:54.4252879Z   test_torch_nn_FractionalMaxPool3d_asymsize_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4253239Z   test_torch_nn_FractionalMaxPool3d_ratio (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4253603Z   test_torch_nn_FractionalMaxPool3d_ratio_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4253959Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4254341Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4254787Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4255213Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4255588Z   test_torch_nn_FractionalMaxPool3d_size (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T21:59:54.4255947Z   test_torch_nn_FractionalMaxPool3d_size_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4256311Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4256679Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4257080Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4257505Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4257869Z   test_torch_nn_GELU (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4258160Z   test_torch_nn_GELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4258472Z   test_torch_nn_GELU_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4258793Z   test_torch_nn_GELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4259100Z   test_torch_nn_GLU (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4259385Z   test_torch_nn_GLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4259687Z   test_torch_nn_GLU_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4259997Z   test_torch_nn_GLU_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4260304Z   test_torch_nn_GroupNorm_1d_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4260614Z   test_torch_nn_GroupNorm_1d_affine_GN (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4260955Z   test_torch_nn_GroupNorm_1d_affine_GN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4261316Z   test_torch_nn_GroupNorm_1d_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4261646Z   test_torch_nn_GroupNorm_1d_affine_large_batch (__main__.TestCppApiParity) ... ok (0.020s)
2023-01-11T21:59:54.4262006Z   test_torch_nn_GroupNorm_1d_affine_large_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4262500Z   test_torch_nn_GroupNorm_1d_no_affine_IN (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4262845Z   test_torch_nn_GroupNorm_1d_no_affine_IN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4263181Z   test_torch_nn_GroupNorm_1d_no_affine_LN (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4263591Z   test_torch_nn_GroupNorm_1d_no_affine_LN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4263932Z   test_torch_nn_GroupNorm_2d_affine (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4264260Z   test_torch_nn_GroupNorm_2d_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4264607Z   test_torch_nn_GroupNorm_2d_affine_large_feature (__main__.TestCppApiParity) ... ok (0.026s)
2023-01-11T21:59:54.4264969Z   test_torch_nn_GroupNorm_2d_affine_large_feature_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4265321Z   test_torch_nn_GroupNorm_2d_no_affine_IN (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4265652Z   test_torch_nn_GroupNorm_2d_no_affine_IN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4265988Z   test_torch_nn_GroupNorm_2d_no_affine_LN (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4266371Z   test_torch_nn_GroupNorm_2d_no_affine_LN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4266711Z   test_torch_nn_GroupNorm_2d_no_affine_large_feature (__main__.TestCppApiParity) ... ok (0.103s)
2023-01-11T21:59:54.4267078Z   test_torch_nn_GroupNorm_2d_no_affine_large_feature_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4267414Z   test_torch_nn_Hardshrink (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4267733Z   test_torch_nn_Hardshrink_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4268047Z   test_torch_nn_Hardshrink_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4268382Z   test_torch_nn_Hardshrink_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4268701Z   test_torch_nn_Hardtanh (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4269022Z   test_torch_nn_Hardtanh_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4269328Z   test_torch_nn_Hardtanh_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4269656Z   test_torch_nn_Hardtanh_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4269991Z   test_torch_nn_HingeEmbeddingLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4270328Z   test_torch_nn_HingeEmbeddingLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4270678Z   test_torch_nn_HingeEmbeddingLoss_margin (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4271037Z   test_torch_nn_HingeEmbeddingLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4271417Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4271797Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4272191Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4272579Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4272969Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4273350Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4273719Z   test_torch_nn_HingeEmbeddingLoss_scalar_margin (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4274094Z   test_torch_nn_HingeEmbeddingLoss_scalar_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4274417Z   test_torch_nn_HuberLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4274777Z   test_torch_nn_HuberLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4275096Z   test_torch_nn_InstanceNorm1d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4275432Z   test_torch_nn_InstanceNorm1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4275757Z   test_torch_nn_InstanceNorm1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4276114Z   test_torch_nn_InstanceNorm1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4276468Z   test_torch_nn_InstanceNorm1d_tracking_stats (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4276829Z   test_torch_nn_InstanceNorm1d_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4277185Z   test_torch_nn_InstanceNorm1d_tracking_stats_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4277614Z   test_torch_nn_InstanceNorm1d_tracking_stats_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4278030Z   test_torch_nn_InstanceNorm2d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4278352Z   test_torch_nn_InstanceNorm2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4278692Z   test_torch_nn_InstanceNorm2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4279045Z   test_torch_nn_InstanceNorm2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4279398Z   test_torch_nn_InstanceNorm2d_tracking_stats (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4279747Z   test_torch_nn_InstanceNorm2d_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4280117Z   test_torch_nn_InstanceNorm2d_tracking_stats_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4280501Z   test_torch_nn_InstanceNorm2d_tracking_stats_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4280850Z   test_torch_nn_InstanceNorm3d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4281168Z   test_torch_nn_InstanceNorm3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4281509Z   test_torch_nn_InstanceNorm3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4281870Z   test_torch_nn_InstanceNorm3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4282211Z   test_torch_nn_InstanceNorm3d_tracking_stats (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4282572Z   test_torch_nn_InstanceNorm3d_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4282944Z   test_torch_nn_InstanceNorm3d_tracking_stats_no_batch_dim (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4283330Z   test_torch_nn_InstanceNorm3d_tracking_stats_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4284324Z   test_torch_nn_KLDivLoss (__main__.TestCppApiParity) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:59:54.4284859Z   warnings.warn(
2023-01-11T21:59:54.4285744Z /var/lib/jenkins/workspace/test/cpp_api_parity/module_impl_check.py:149: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/api/include/torch/nn/functional/loss.h:57.)
2023-01-11T21:59:54.4286474Z   cpp_test_fn(arg_dict_file_path, module_file_path, forward_output_file_path, backward_grad_dict_file_path)
2023-01-11T21:59:54.4286736Z ok (0.008s)
2023-01-11T21:59:54.4286986Z   test_torch_nn_KLDivLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4287318Z   test_torch_nn_KLDivLoss_log_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4287657Z   test_torch_nn_KLDivLoss_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4287993Z   test_torch_nn_KLDivLoss_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4288320Z   test_torch_nn_KLDivLoss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4288660Z   test_torch_nn_KLDivLoss_scalar_log_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4289055Z   test_torch_nn_KLDivLoss_scalar_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4289372Z   test_torch_nn_L1Loss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4289681Z   test_torch_nn_L1Loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4289992Z   test_torch_nn_L1Loss_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4290318Z   test_torch_nn_L1Loss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4290623Z   test_torch_nn_LPPool1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4290943Z   test_torch_nn_LPPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4291270Z   test_torch_nn_LPPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4291602Z   test_torch_nn_LPPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4291934Z   test_torch_nn_LPPool1d_norm (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4292257Z   test_torch_nn_LPPool1d_norm_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4292574Z   test_torch_nn_LPPool2d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4292878Z   test_torch_nn_LPPool2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4293194Z   test_torch_nn_LPPool2d_norm (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4293520Z   test_torch_nn_LPPool2d_norm_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4293865Z   test_torch_nn_LayerNorm_1d_elementwise_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4294216Z   test_torch_nn_LayerNorm_1d_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4294589Z   test_torch_nn_LayerNorm_1d_empty_elementwise_affine (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4294964Z   test_torch_nn_LayerNorm_1d_empty_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4295318Z   test_torch_nn_LayerNorm_1d_no_elementwise_affine (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4295687Z   test_torch_nn_LayerNorm_1d_no_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4296049Z   test_torch_nn_LayerNorm_3d_elementwise_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4296412Z   test_torch_nn_LayerNorm_3d_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4296764Z   test_torch_nn_LayerNorm_3d_no_affine_large_feature (__main__.TestCppApiParity) ... ok (0.055s)
2023-01-11T21:59:54.4297133Z   test_torch_nn_LayerNorm_3d_no_affine_large_feature_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4297538Z   test_torch_nn_LayerNorm_3d_no_elementwise_affine (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4297904Z   test_torch_nn_LayerNorm_3d_no_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4298228Z   test_torch_nn_LeakyReLU (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4298547Z   test_torch_nn_LeakyReLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4298872Z   test_torch_nn_LeakyReLU_with_negval (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4299205Z   test_torch_nn_LeakyReLU_with_negval_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4299551Z   test_torch_nn_LeakyReLU_with_negval_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4299906Z   test_torch_nn_LeakyReLU_with_negval_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4300301Z   test_torch_nn_LeakyReLU_with_zero_negval (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4300639Z   test_torch_nn_LeakyReLU_with_zero_negval_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4300962Z   test_torch_nn_Linear (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4301271Z   test_torch_nn_Linear_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4301594Z   test_torch_nn_Linear_no_batch_dim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4301921Z   test_torch_nn_Linear_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4302247Z   test_torch_nn_Linear_no_bias (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4302746Z   test_torch_nn_Linear_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4303075Z   test_torch_nn_LocalResponseNorm_1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4303429Z   test_torch_nn_LocalResponseNorm_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4303784Z   test_torch_nn_LocalResponseNorm_2d_uneven_pad (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4304152Z   test_torch_nn_LocalResponseNorm_2d_uneven_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4304508Z   test_torch_nn_LocalResponseNorm_3d_custom_params (__main__.TestCppApiParity) ... ok (0.028s)
2023-01-11T21:59:54.4304885Z   test_torch_nn_LocalResponseNorm_3d_custom_params_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4305225Z   test_torch_nn_LogSigmoid (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4305548Z   test_torch_nn_LogSigmoid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4305862Z   test_torch_nn_LogSigmoid_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4306204Z   test_torch_nn_LogSigmoid_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4306523Z   test_torch_nn_LogSoftmax (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4306831Z   test_torch_nn_LogSoftmax_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4307162Z   test_torch_nn_LogSoftmax_multiparam (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4307508Z   test_torch_nn_LogSoftmax_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4307859Z   test_torch_nn_LogSoftmax_multiparam_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4308203Z   test_torch_nn_LogSoftmax_multiparam_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4308548Z   test_torch_nn_LogSoftmax_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4308968Z   test_torch_nn_LogSoftmax_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4309276Z   test_torch_nn_MSELoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4309586Z   test_torch_nn_MSELoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4309900Z   test_torch_nn_MSELoss_prec (__main__.TestCppApiParity) ... ok (0.038s)
2023-01-11T21:59:54.4310223Z   test_torch_nn_MSELoss_prec_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4310530Z   test_torch_nn_MSELoss_scalar (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4310854Z   test_torch_nn_MSELoss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4311184Z   test_torch_nn_MarginRankingLoss (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4311555Z   test_torch_nn_MarginRankingLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4311906Z   test_torch_nn_MarginRankingLoss_margin (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4312261Z   test_torch_nn_MarginRankingLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4312621Z   test_torch_nn_MarginRankingLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4312985Z   test_torch_nn_MarginRankingLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4313357Z   test_torch_nn_MarginRankingLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4313733Z   test_torch_nn_MarginRankingLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4314099Z   test_torch_nn_MarginRankingLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4314459Z   test_torch_nn_MarginRankingLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4314798Z   test_torch_nn_MaxPool1d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4315115Z   test_torch_nn_MaxPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4315425Z   test_torch_nn_MaxPool1d_stride (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4315758Z   test_torch_nn_MaxPool1d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4316085Z   test_torch_nn_MaxPool2d_3d_input (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4316419Z   test_torch_nn_MaxPool2d_3d_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4316736Z   test_torch_nn_MaxPool2d_4d_input (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4317066Z   test_torch_nn_MaxPool2d_4d_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4317386Z   test_torch_nn_MaxPool3d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4317761Z   test_torch_nn_MaxPool3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4318075Z   test_torch_nn_MaxPool3d_stride (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4318408Z   test_torch_nn_MaxPool3d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4318745Z   test_torch_nn_MaxPool3d_stride_padding (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4319086Z   test_torch_nn_MaxPool3d_stride_padding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4319406Z   test_torch_nn_Mish (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4319709Z   test_torch_nn_Mish_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4320016Z   test_torch_nn_Mish_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4320363Z   test_torch_nn_Mish_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4320701Z   test_torch_nn_MultiLabelMarginLoss (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4321029Z   test_torch_nn_MultiLabelMarginLoss_1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4321372Z   test_torch_nn_MultiLabelMarginLoss_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4321749Z   test_torch_nn_MultiLabelMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4322117Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4322499Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4322869Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4323288Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4323670Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4324052Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4324410Z   test_torch_nn_MultiLabelSoftMarginLoss (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4324781Z   test_torch_nn_MultiLabelSoftMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4325162Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4325550Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4325949Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4326347Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4326742Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4327124Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4327509Z   test_torch_nn_MultiLabelSoftMarginLoss_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4327894Z   test_torch_nn_MultiLabelSoftMarginLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4328246Z   test_torch_nn_MultiMarginLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4328541Z   test_torch_nn_MultiMarginLoss_1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4328884Z   test_torch_nn_MultiMarginLoss_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4329244Z   test_torch_nn_MultiMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4329583Z   test_torch_nn_MultiMarginLoss_margin (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4329918Z   test_torch_nn_MultiMarginLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4330257Z   test_torch_nn_MultiMarginLoss_p (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4330596Z   test_torch_nn_MultiMarginLoss_p_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4330924Z   test_torch_nn_MultiMarginLoss_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4331277Z   test_torch_nn_MultiMarginLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4331642Z   test_torch_nn_NLLLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4331925Z   test_torch_nn_NLLLoss_2d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4332225Z   test_torch_nn_NLLLoss_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4332552Z   test_torch_nn_NLLLoss_2d_ignore_index (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4332896Z   test_torch_nn_NLLLoss_2d_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4333218Z   test_torch_nn_NLLLoss_2d_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4333553Z   test_torch_nn_NLLLoss_2d_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4333899Z   test_torch_nn_NLLLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4334215Z   test_torch_nn_NLLLoss_dim_is_3 (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4334582Z   test_torch_nn_NLLLoss_dim_is_3_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4334913Z   test_torch_nn_NLLLoss_higher_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4335250Z   test_torch_nn_NLLLoss_higher_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4335586Z   test_torch_nn_NLLLoss_ignore_index (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4350561Z   test_torch_nn_NLLLoss_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4351072Z   test_torch_nn_NLLLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.015s)
2023-01-11T21:59:54.4351439Z   test_torch_nn_NLLLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4351808Z   test_torch_nn_NLLLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.011s)
2023-01-11T21:59:54.4352186Z   test_torch_nn_NLLLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4352546Z   test_torch_nn_NLLLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.011s)
2023-01-11T21:59:54.4352894Z   test_torch_nn_NLLLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4353226Z   test_torch_nn_NLLLoss_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4353561Z   test_torch_nn_NLLLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4353888Z   test_torch_nn_NLLLoss_weights_ignore_index (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4354241Z   test_torch_nn_NLLLoss_weights_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4354593Z   test_torch_nn_NLLLoss_weights_ignore_index_neg (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4354961Z   test_torch_nn_NLLLoss_weights_ignore_index_neg_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4355283Z   test_torch_nn_PReLU_1d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4355598Z   test_torch_nn_PReLU_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4355923Z   test_torch_nn_PReLU_1d_multiparam (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4356265Z   test_torch_nn_PReLU_1d_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4356574Z   test_torch_nn_PReLU_2d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4356887Z   test_torch_nn_PReLU_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4357207Z   test_torch_nn_PReLU_2d_multiparam (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4357538Z   test_torch_nn_PReLU_2d_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4358076Z   test_torch_nn_PReLU_3d (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T21:59:54.4358389Z   test_torch_nn_PReLU_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4358713Z   test_torch_nn_PReLU_3d_multiparam (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T21:59:54.4359038Z   test_torch_nn_PReLU_3d_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4359362Z   test_torch_nn_PReLU_scalar (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4359686Z   test_torch_nn_PReLU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4359999Z   test_torch_nn_PairwiseDistance (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4360323Z   test_torch_nn_PairwiseDistance_broadcast_lhs (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4360746Z   test_torch_nn_PairwiseDistance_broadcast_lhs_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4361116Z   test_torch_nn_PairwiseDistance_broadcast_rhs (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4361474Z   test_torch_nn_PairwiseDistance_broadcast_rhs_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4361853Z   test_torch_nn_PairwiseDistance_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4362199Z   test_torch_nn_PairwiseDistance_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4362564Z   test_torch_nn_PairwiseDistance_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4362920Z   test_torch_nn_PairwiseDistance_with_non_default_args (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4363304Z   test_torch_nn_PairwiseDistance_with_non_default_args_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4363660Z   test_torch_nn_PixelShuffle (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4363976Z   test_torch_nn_PixelShuffle_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4364301Z   test_torch_nn_PixelUnshuffle (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4364633Z   test_torch_nn_PixelUnshuffle_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4364972Z   test_torch_nn_PoissonNLLLoss_full_loss (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4365311Z   test_torch_nn_PoissonNLLLoss_full_loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4365668Z   test_torch_nn_PoissonNLLLoss_full_loss_no_log_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4366040Z   test_torch_nn_PoissonNLLLoss_full_loss_no_log_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4366403Z   test_torch_nn_PoissonNLLLoss_no_full_loss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4366748Z   test_torch_nn_PoissonNLLLoss_no_full_loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4367112Z   test_torch_nn_PoissonNLLLoss_no_full_loss_no_log_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4367491Z   test_torch_nn_PoissonNLLLoss_no_full_loss_no_log_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4367815Z   test_torch_nn_RReLU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4368130Z   test_torch_nn_RReLU_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4368454Z   test_torch_nn_RReLU_with_up_down (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4368792Z   test_torch_nn_RReLU_with_up_down_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4369122Z   test_torch_nn_RReLU_with_up_down_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4369509Z   test_torch_nn_RReLU_with_up_down_scalar_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4369830Z   test_torch_nn_ReLU (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4370092Z   test_torch_nn_ReLU6 (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4370395Z   test_torch_nn_ReLU6_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4370705Z   test_torch_nn_ReLU6_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4371025Z   test_torch_nn_ReLU6_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4371348Z   test_torch_nn_ReLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4371659Z   test_torch_nn_ReLU_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4372011Z   test_torch_nn_ReLU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4372329Z   test_torch_nn_ReflectionPad1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4372640Z   test_torch_nn_ReflectionPad1d_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4372990Z   test_torch_nn_ReflectionPad1d_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4373337Z   test_torch_nn_ReflectionPad1d_complex (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4373676Z   test_torch_nn_ReflectionPad1d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4374048Z   test_torch_nn_ReflectionPad1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4374378Z   test_torch_nn_ReflectionPad2d (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T21:59:54.4374694Z   test_torch_nn_ReflectionPad2d_complex (__main__.TestCppApiParity) ... ok (0.026s)
2023-01-11T21:59:54.4375041Z   test_torch_nn_ReflectionPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4375407Z   test_torch_nn_ReflectionPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4375749Z   test_torch_nn_ReflectionPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4376094Z   test_torch_nn_ReflectionPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4376438Z   test_torch_nn_ReflectionPad3d (__main__.TestCppApiParity) ... ok (0.031s)
2023-01-11T21:59:54.4376751Z   test_torch_nn_ReflectionPad3d_complex (__main__.TestCppApiParity) ... ok (0.049s)
2023-01-11T21:59:54.4377103Z   test_torch_nn_ReflectionPad3d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4377458Z   test_torch_nn_ReflectionPad3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4377806Z   test_torch_nn_ReflectionPad3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.020s)
2023-01-11T21:59:54.4378165Z   test_torch_nn_ReflectionPad3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4378509Z   test_torch_nn_ReplicationPad1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4378814Z   test_torch_nn_ReplicationPad1d_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4379168Z   test_torch_nn_ReplicationPad1d_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4379521Z   test_torch_nn_ReplicationPad1d_complex (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4379869Z   test_torch_nn_ReplicationPad1d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4380241Z   test_torch_nn_ReplicationPad1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4380619Z   test_torch_nn_ReplicationPad2d (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T21:59:54.4380937Z   test_torch_nn_ReplicationPad2d_complex (__main__.TestCppApiParity) ... ok (0.019s)
2023-01-11T21:59:54.4381280Z   test_torch_nn_ReplicationPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4381655Z   test_torch_nn_ReplicationPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4382006Z   test_torch_nn_ReplicationPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4382487Z   test_torch_nn_ReplicationPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4382859Z   test_torch_nn_ReplicationPad3d (__main__.TestCppApiParity) ... ok (0.019s)
2023-01-11T21:59:54.4383177Z   test_torch_nn_ReplicationPad3d_complex (__main__.TestCppApiParity) ... ok (0.029s)
2023-01-11T21:59:54.4383587Z   test_torch_nn_ReplicationPad3d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4383951Z   test_torch_nn_ReplicationPad3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4384299Z   test_torch_nn_ReplicationPad3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T21:59:54.4384661Z   test_torch_nn_ReplicationPad3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4384989Z   test_torch_nn_SELU (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4385279Z   test_torch_nn_SELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4385588Z   test_torch_nn_SELU_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4385906Z   test_torch_nn_SELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4386227Z   test_torch_nn_SampleModule_has_parity (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4386582Z   test_torch_nn_SampleModule_has_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4386942Z   test_torch_nn_SampleModule_no_parity (__main__.TestCppApiParity) ... expected failure (0.011s)
2023-01-11T21:59:54.4387303Z   test_torch_nn_SampleModule_no_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4387610Z   test_torch_nn_SiLU (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4387913Z   test_torch_nn_SiLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4388221Z   test_torch_nn_SiLU_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4388527Z   test_torch_nn_SiLU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4388836Z   test_torch_nn_Sigmoid (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4389144Z   test_torch_nn_Sigmoid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4389461Z   test_torch_nn_Sigmoid_scalar (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4389773Z   test_torch_nn_Sigmoid_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4390095Z   test_torch_nn_SmoothL1Loss (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4390418Z   test_torch_nn_SmoothL1Loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4390730Z   test_torch_nn_SmoothL1Loss_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4391065Z   test_torch_nn_SmoothL1Loss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4391394Z   test_torch_nn_SoftMarginLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4391722Z   test_torch_nn_SoftMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4392073Z   test_torch_nn_SoftMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4392504Z   test_torch_nn_SoftMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4392882Z   test_torch_nn_SoftMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4393260Z   test_torch_nn_SoftMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4393623Z   test_torch_nn_SoftMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T21:59:54.4393995Z   test_torch_nn_SoftMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4394324Z   test_torch_nn_Softmax (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4394597Z   test_torch_nn_Softmax2d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4394944Z   test_torch_nn_Softmax2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4395270Z   test_torch_nn_Softmax2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4395608Z   test_torch_nn_Softmax2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4395947Z   test_torch_nn_Softmax_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4396266Z   test_torch_nn_Softmax_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4396599Z   test_torch_nn_Softmax_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4396920Z   test_torch_nn_Softmax_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4397232Z   test_torch_nn_Softmax_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4397545Z   test_torch_nn_Softmin (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4397914Z   test_torch_nn_Softmin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4398218Z   test_torch_nn_Softmin_multidim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4398548Z   test_torch_nn_Softmin_multidim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4398874Z   test_torch_nn_Softmin_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4399205Z   test_torch_nn_Softmin_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4399513Z   test_torch_nn_Softmin_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4399842Z   test_torch_nn_Softmin_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4400154Z   test_torch_nn_Softplus (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4400434Z   test_torch_nn_Softplus_beta (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4400759Z   test_torch_nn_Softplus_beta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4401092Z   test_torch_nn_Softplus_beta_threshold (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4401437Z   test_torch_nn_Softplus_beta_threshold_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4401771Z   test_torch_nn_Softplus_beta_threshold_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4402131Z   test_torch_nn_Softplus_beta_threshold_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4402487Z   test_torch_nn_Softplus_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4402781Z   test_torch_nn_Softshrink (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4403099Z   test_torch_nn_Softshrink_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4403463Z   test_torch_nn_Softshrink_lambda (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4403798Z   test_torch_nn_Softshrink_lambda_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4404125Z   test_torch_nn_Softshrink_lambda_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4404474Z   test_torch_nn_Softshrink_lambda_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4404796Z   test_torch_nn_Softsign (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4405106Z   test_torch_nn_Softsign_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4405406Z   test_torch_nn_Softsign_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4405732Z   test_torch_nn_Softsign_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4406050Z   test_torch_nn_Tanh (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4406375Z   test_torch_nn_Tanh_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4406684Z   test_torch_nn_Tanh_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4407001Z   test_torch_nn_Tanh_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4407312Z   test_torch_nn_Tanhshrink (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4407618Z   test_torch_nn_Tanhshrink_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4407937Z   test_torch_nn_Tanhshrink_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4408265Z   test_torch_nn_Tanhshrink_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4408749Z   test_torch_nn_Threshold_large_value (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4409092Z   test_torch_nn_Threshold_large_value_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4409436Z   test_torch_nn_Threshold_threshold_value (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4409787Z   test_torch_nn_Threshold_threshold_value_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4410126Z   test_torch_nn_Threshold_threshold_value_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4410488Z   test_torch_nn_Threshold_threshold_value_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4410867Z   test_torch_nn_TransformerDecoderLayer_gelu_activation (__main__.TestCppApiParity) ... ok (0.052s)
2023-01-11T21:59:54.4411267Z   test_torch_nn_TransformerDecoderLayer_gelu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4411650Z   test_torch_nn_TransformerDecoderLayer_relu_activation (__main__.TestCppApiParity) ... ok (0.052s)
2023-01-11T21:59:54.4412051Z   test_torch_nn_TransformerDecoderLayer_relu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4412447Z   test_torch_nn_TransformerEncoderLayer_gelu_activation (__main__.TestCppApiParity) ... ok (0.037s)
2023-01-11T21:59:54.4412830Z   test_torch_nn_TransformerEncoderLayer_gelu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4413223Z   test_torch_nn_TransformerEncoderLayer_relu_activation (__main__.TestCppApiParity) ... ok (0.039s)
2023-01-11T21:59:54.4413618Z   test_torch_nn_TransformerEncoderLayer_relu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4413992Z   test_torch_nn_Transformer_multilayer_coder (__main__.TestCppApiParity) ... ok (0.173s)
2023-01-11T21:59:54.4414338Z   test_torch_nn_Transformer_multilayer_coder_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4414698Z   test_torch_nn_TripletMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4415117Z   test_torch_nn_TripletMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4415486Z   test_torch_nn_TripletMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4415848Z   test_torch_nn_TripletMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4416216Z   test_torch_nn_TripletMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4416583Z   test_torch_nn_TripletMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4416932Z   test_torch_nn_Unflatten_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4417264Z   test_torch_nn_Unflatten_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4417616Z   test_torch_nn_Unfold (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4417930Z   test_torch_nn_Unfold_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4418233Z   test_torch_nn_Unfold_int_input (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4418562Z   test_torch_nn_Unfold_int_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4418882Z   test_torch_nn_ZeroPad2d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T21:59:54.4419180Z   test_torch_nn_ZeroPad2d_complex (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4419504Z   test_torch_nn_ZeroPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4419854Z   test_torch_nn_ZeroPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4420184Z   test_torch_nn_ZeroPad2d_negative_dims (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4420528Z   test_torch_nn_ZeroPad2d_negative_dims_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4420871Z   test_torch_nn_ZeroPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4421215Z   test_torch_nn_ZeroPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4421564Z   test_torch_nn_functional_BCELoss_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4421906Z   test_torch_nn_functional_BCELoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4422266Z   test_torch_nn_functional_BCELoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4422749Z   test_torch_nn_functional_BCELoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4423118Z   test_torch_nn_functional_BCELoss_weights_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4423481Z   test_torch_nn_functional_BCELoss_weights_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4423855Z   test_torch_nn_functional_BCELoss_weights_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4424240Z   test_torch_nn_functional_BCELoss_weights_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4425058Z   test_torch_nn_functional_BCEWithLogitsLoss_legacy_enum (__main__.TestCppApiParity) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:59:54.4425499Z   warnings.warn(warning.format(ret))
2023-01-11T21:59:54.4425692Z ok (0.006s)
2023-01-11T21:59:54.4425986Z   test_torch_nn_functional_BCEWithLogitsLoss_legacy_enum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4426360Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4426837Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4427221Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4427615Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4428000Z   test_torch_nn_functional_HingeEmbeddingLoss_margin_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4428402Z   test_torch_nn_functional_HingeEmbeddingLoss_margin_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4428790Z   test_torch_nn_functional_HingeEmbeddingLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4429216Z   test_torch_nn_functional_HingeEmbeddingLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4429570Z   test_torch_nn_functional_HuberLoss_delta (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4429926Z   test_torch_nn_functional_HuberLoss_delta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4430278Z   test_torch_nn_functional_KLDivLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4430642Z   test_torch_nn_functional_KLDivLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4431001Z   test_torch_nn_functional_KLDivLoss_no_reduce_log_target (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4431382Z   test_torch_nn_functional_KLDivLoss_no_reduce_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4431756Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4432125Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4432510Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar_log_target (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4432908Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4433299Z   test_torch_nn_functional_KLDivLoss_with_log_target_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4433677Z   test_torch_nn_functional_KLDivLoss_with_log_target_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4434057Z   test_torch_nn_functional_KLDivLoss_with_target_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4434441Z   test_torch_nn_functional_KLDivLoss_with_target_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4434809Z   test_torch_nn_functional_L1Loss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4435131Z   test_torch_nn_functional_L1Loss_no_reduce_complex (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4435498Z   test_torch_nn_functional_L1Loss_no_reduce_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4435883Z   test_torch_nn_functional_L1Loss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4436237Z   test_torch_nn_functional_L1Loss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4436590Z   test_torch_nn_functional_L1Loss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4436946Z   test_torch_nn_functional_MSELoss_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4437303Z   test_torch_nn_functional_MSELoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4437757Z   test_torch_nn_functional_MSELoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4438116Z   test_torch_nn_functional_MSELoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4438500Z   test_torch_nn_functional_MultiLabelMarginLoss_0d_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4438902Z   test_torch_nn_functional_MultiLabelMarginLoss_0d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4439282Z   test_torch_nn_functional_MultiLabelMarginLoss_1d_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4439678Z   test_torch_nn_functional_MultiLabelMarginLoss_1d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4440071Z   test_torch_nn_functional_MultiLabelMarginLoss_index_neg (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4440506Z   test_torch_nn_functional_MultiLabelMarginLoss_index_neg_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4440885Z   test_torch_nn_functional_MultiLabelMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4441283Z   test_torch_nn_functional_MultiLabelMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4441677Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4442089Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4442492Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_weights_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4442924Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_weights_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4443327Z   test_torch_nn_functional_MultiMarginLoss_1d_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4443708Z   test_torch_nn_functional_MultiMarginLoss_1d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4444082Z   test_torch_nn_functional_MultiMarginLoss_margin_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4444472Z   test_torch_nn_functional_MultiMarginLoss_margin_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4444848Z   test_torch_nn_functional_MultiMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4445224Z   test_torch_nn_functional_MultiMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4445583Z   test_torch_nn_functional_MultiMarginLoss_p_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4445967Z   test_torch_nn_functional_MultiMarginLoss_p_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4446351Z   test_torch_nn_functional_MultiMarginLoss_weights_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4446742Z   test_torch_nn_functional_MultiMarginLoss_weights_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4447100Z   test_torch_nn_functional_NLLLoss2d_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4447460Z   test_torch_nn_functional_NLLLoss2d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4447831Z   test_torch_nn_functional_NLLLoss2d_no_reduce_ignore_index (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4448203Z   test_torch_nn_functional_NLLLoss2d_no_reduce_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4448623Z   test_torch_nn_functional_NLLLoss2d_no_reduce_weights (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4449000Z   test_torch_nn_functional_NLLLoss2d_no_reduce_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4449362Z   test_torch_nn_functional_NLLLossNd_no_reduce (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4449713Z   test_torch_nn_functional_NLLLossNd_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4450083Z   test_torch_nn_functional_NLLLossNd_no_reduce_ignore_index (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4450469Z   test_torch_nn_functional_NLLLossNd_no_reduce_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4450847Z   test_torch_nn_functional_NLLLossNd_no_reduce_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4451243Z   test_torch_nn_functional_NLLLossNd_no_reduce_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4451606Z   test_torch_nn_functional_NLLLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4451962Z   test_torch_nn_functional_NLLLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4452331Z   test_torch_nn_functional_NLLLoss_no_reduce_ignore_index (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4452696Z   test_torch_nn_functional_NLLLoss_no_reduce_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4453068Z   test_torch_nn_functional_NLLLoss_no_reduce_weights (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4453438Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4453805Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4454204Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4454598Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_neg (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4454999Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_neg_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4455369Z   test_torch_nn_functional_Padding122112_3dcircular (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4455738Z   test_torch_nn_functional_Padding122112_3dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4456100Z   test_torch_nn_functional_Padding1221_2dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4456464Z   test_torch_nn_functional_Padding1221_2dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4456821Z   test_torch_nn_functional_Padding12_1dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4457184Z   test_torch_nn_functional_Padding12_1dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4457541Z   test_torch_nn_functional_Padding2322_2dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4457899Z   test_torch_nn_functional_Padding2322_2dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4458244Z   test_torch_nn_functional_Padding31_1dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4458603Z   test_torch_nn_functional_Padding31_1dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4458961Z   test_torch_nn_functional_Padding322112_3dcircular (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4459327Z   test_torch_nn_functional_Padding322112_3dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4459710Z   test_torch_nn_functional_Padding332122_3dcircular (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4460073Z   test_torch_nn_functional_Padding332122_3dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4460436Z   test_torch_nn_functional_Padding3331_2dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4460786Z   test_torch_nn_functional_Padding3331_2dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4461147Z   test_torch_nn_functional_Padding33_1dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4461509Z   test_torch_nn_functional_Padding33_1dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4461871Z   test_torch_nn_functional_PoissonNLLLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4462266Z   test_torch_nn_functional_PoissonNLLLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4462768Z   test_torch_nn_functional_SmoothL1Loss_beta (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4463124Z   test_torch_nn_functional_SmoothL1Loss_beta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4463478Z   test_torch_nn_functional_SmoothL1Loss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4463828Z   test_torch_nn_functional_SmoothL1Loss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4464201Z   test_torch_nn_functional_SmoothL1Loss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4464580Z   test_torch_nn_functional_SmoothL1Loss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4464945Z   test_torch_nn_functional_SmoothL1Loss_zero_beta (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4465296Z   test_torch_nn_functional_SmoothL1Loss_zero_beta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4465662Z   test_torch_nn_functional_SoftMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4466031Z   test_torch_nn_functional_SoftMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4466382Z   test_torch_nn_functional_interpolate_bicubic_2d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4466743Z   test_torch_nn_functional_interpolate_bicubic_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4467111Z   test_torch_nn_functional_interpolate_bicubic_2d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4467487Z   test_torch_nn_functional_interpolate_bicubic_2d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4467855Z   test_torch_nn_functional_interpolate_bicubic_scale_2d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4468229Z   test_torch_nn_functional_interpolate_bicubic_scale_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4468617Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4469020Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4469406Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4469793Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4470227Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4470727Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4471101Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4471465Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4471870Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4472287Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4472640Z   test_torch_nn_functional_interpolate_bilinear_2d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4473057Z   test_torch_nn_functional_interpolate_bilinear_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4473433Z   test_torch_nn_functional_interpolate_bilinear_2d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4473815Z   test_torch_nn_functional_interpolate_bilinear_2d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4474183Z   test_torch_nn_functional_interpolate_bilinear_scale_2d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4474557Z   test_torch_nn_functional_interpolate_bilinear_scale_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4474945Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4475346Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4475736Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4476129Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4476559Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4476766Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4476937Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4477106Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4477315Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4477512Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4477728Z   test_torch_nn_functional_interpolate_linear_1d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4477907Z   test_torch_nn_functional_interpolate_linear_1d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4478107Z   test_torch_nn_functional_interpolate_linear_1d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4478290Z   test_torch_nn_functional_interpolate_linear_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4478458Z   test_torch_nn_functional_interpolate_linear_1d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4478649Z   test_torch_nn_functional_interpolate_linear_1d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4478815Z   test_torch_nn_functional_interpolate_linear_scale_1d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4479020Z   test_torch_nn_functional_interpolate_linear_scale_1d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4479223Z   test_torch_nn_functional_interpolate_linear_scale_1d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4479418Z   test_torch_nn_functional_interpolate_linear_scale_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4479582Z   test_torch_nn_functional_interpolate_linear_tuple_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4479775Z   test_torch_nn_functional_interpolate_linear_tuple_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4479932Z   test_torch_nn_functional_interpolate_nearest_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4480151Z   test_torch_nn_functional_interpolate_nearest_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4480322Z   test_torch_nn_functional_interpolate_nearest_1d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4480517Z   test_torch_nn_functional_interpolate_nearest_1d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4480662Z   test_torch_nn_functional_interpolate_nearest_2d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4480847Z   test_torch_nn_functional_interpolate_nearest_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4481023Z   test_torch_nn_functional_interpolate_nearest_2d_launch_configs (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T21:59:54.4481226Z   test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4481397Z   test_torch_nn_functional_interpolate_nearest_2d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4481594Z   test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4481752Z   test_torch_nn_functional_interpolate_nearest_3d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4481942Z   test_torch_nn_functional_interpolate_nearest_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4482109Z   test_torch_nn_functional_interpolate_nearest_3d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4482289Z   test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4482452Z   test_torch_nn_functional_interpolate_nearest_scale_1d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4482646Z   test_torch_nn_functional_interpolate_nearest_scale_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4482813Z   test_torch_nn_functional_interpolate_nearest_scale_2d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T21:59:54.4483002Z   test_torch_nn_functional_interpolate_nearest_scale_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4483165Z   test_torch_nn_functional_interpolate_nearest_scale_3d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4483356Z   test_torch_nn_functional_interpolate_nearest_scale_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4483520Z   test_torch_nn_functional_interpolate_nearest_tuple_1d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4483709Z   test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4483876Z   test_torch_nn_functional_interpolate_nearest_tuple_2d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4484096Z   test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4484261Z   test_torch_nn_functional_interpolate_nearest_tuple_3d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4484450Z   test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4484609Z   test_torch_nn_functional_interpolate_trilinear_3d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T21:59:54.4484797Z   test_torch_nn_functional_interpolate_trilinear_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4484966Z   test_torch_nn_functional_interpolate_trilinear_3d_zero_dim (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4485162Z   test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4485374Z   test_torch_nn_functional_interpolate_trilinear_scale_3d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4485560Z   test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T21:59:54.4485758Z   test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4485954Z   test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4486120Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T21:59:54.4486299Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T21:59:54.4486508Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4486703Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4486856Z   test_torch_nn_functional_log_softmax_dim0 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4487034Z   test_torch_nn_functional_log_softmax_dim0_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4487183Z   test_torch_nn_functional_log_softmax_dim3 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4487349Z   test_torch_nn_functional_log_softmax_dim3_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4487502Z   test_torch_nn_functional_log_softmax_lastdim (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4487684Z   test_torch_nn_functional_log_softmax_lastdim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4487836Z   test_torch_nn_functional_log_softmax_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4488021Z   test_torch_nn_functional_log_softmax_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4488174Z   test_torch_nn_functional_log_softmax_spatial (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4488357Z   test_torch_nn_functional_log_softmax_spatial_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4488521Z   test_torch_nn_functional_log_softmax_spatial_special (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4488711Z   test_torch_nn_functional_log_softmax_spatial_special_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4488880Z   test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4489089Z   test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4489320Z   test_torch_nn_functional_sample_functional_has_parity (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4489514Z   test_torch_nn_functional_sample_functional_has_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4489696Z   test_torch_nn_functional_sample_functional_no_parity (__main__.TestCppApiParity) ... expected failure (0.005s)
2023-01-11T21:59:54.4489889Z   test_torch_nn_functional_sample_functional_no_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4490047Z   test_torch_nn_functional_softmax_functional_dim0 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4490241Z   test_torch_nn_functional_softmax_functional_dim0_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4490398Z   test_torch_nn_functional_softmax_functional_dim3 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4490623Z   test_torch_nn_functional_softmax_functional_dim3_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4490781Z   test_torch_nn_functional_softmax_functional_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T21:59:54.4490978Z   test_torch_nn_functional_softmax_functional_scalar_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4491130Z   test_torch_nn_functional_softmax_lastdim (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4491311Z   test_torch_nn_functional_softmax_lastdim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4491468Z   test_torch_nn_functional_softmax_lastdim_dtype (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T21:59:54.4491660Z   test_torch_nn_functional_softmax_lastdim_dtype_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4491813Z   test_torch_nn_functional_softmax_spatial (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4491995Z   test_torch_nn_functional_softmax_spatial_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4492153Z   test_torch_nn_functional_softmax_spatial_dtype (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4492331Z   test_torch_nn_functional_softmax_spatial_dtype_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:59:54.4492491Z   test_torch_nn_functional_softmax_spatial_special (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T21:59:54.4492679Z   test_torch_nn_functional_softmax_spatial_special_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:54.4492692Z 
2023-01-11T21:59:54.4492970Z ----------------------------------------------------------------------
2023-01-11T21:59:54.4493050Z Ran 1100 tests in 6.108s
2023-01-11T21:59:54.4493056Z 
2023-01-11T21:59:54.4493153Z OK (skipped=550, expected failures=17)
2023-01-11T21:59:54.4493160Z 
2023-01-11T21:59:54.4493245Z Generating XML reports...
2023-01-11T21:59:54.4493566Z Generated XML report: test-reports/python-unittest/test_cpp_api_parity/TEST-TestCppApiParity-20230111215947.xml
2023-01-11T21:59:54.4493571Z 
2023-01-11T21:59:54.4493928Z ##[endgroup]
2023-01-11T21:59:54.4494230Z FINISHED PRINTING LOG FILE of test_cpp_api_parity (/var/lib/jenkins/workspace/test/test-reports/test_cpp_api_parity_ip7klvi_)
2023-01-11T21:59:54.4494235Z 
2023-01-11T21:59:54.4494422Z Running test_cpp_extensions_aot_ninja ... [2023-01-11 21:59:54.409813]
2023-01-11T21:59:56.0132634Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:59:56.1191575Z running install
2023-01-11T21:59:56.1192260Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T21:59:56.1192643Z   warnings.warn(
2023-01-11T21:59:56.1279991Z running build
2023-01-11T21:59:56.1280416Z running build_py
2023-01-11T21:59:56.1328765Z creating build
2023-01-11T21:59:56.1329242Z creating build/lib.linux-x86_64-cpython-310
2023-01-11T21:59:56.1329760Z creating build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T21:59:56.1330208Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T21:59:56.1331621Z running build_ext
2023-01-11T21:59:56.1638174Z building 'torch_test_cpp_extension.cpp' extension
2023-01-11T21:59:56.1638646Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310
2023-01-11T21:59:56.1931556Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T21:59:56.1931932Z Compiling objects...
2023-01-11T21:59:56.1932152Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:59:57.3472993Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/extension.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T21:59:57.3476164Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/Exceptions.h:14:0,
2023-01-11T21:59:57.3477101Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2023-01-11T21:59:57.3478039Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/extension.h:6,
2023-01-11T21:59:57.3478679Z                  from /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp:1:
2023-01-11T21:59:57.3482409Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2023-01-11T21:59:57.3483200Z /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp:40:53:   required from here
2023-01-11T21:59:57.3484760Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than the type of its field ‘pybind11::class_<MatrixMultiplier>::<anonymous>’ [-Wattributes]
2023-01-11T21:59:57.3485709Z  class class_ : public detail::generic_type {
2023-01-11T21:59:57.3486088Z        ^~~~~~
2023-01-11T21:59:57.3487341Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2023-01-11T21:59:57.3567884Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so
2023-01-11T21:59:57.7341233Z building 'torch_test_cpp_extension.ort' extension
2023-01-11T21:59:57.7624926Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T21:59:57.7654423Z Compiling objects...
2023-01-11T21:59:57.7654748Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:59:58.8787223Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/ort_extension.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/ort_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/ort_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=ort -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T21:59:58.8831225Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/ort_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so
2023-01-11T21:59:59.2082302Z building 'torch_test_cpp_extension.rng' extension
2023-01-11T21:59:59.2365198Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T21:59:59.2365571Z Compiling objects...
2023-01-11T21:59:59.2365801Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:00.3900088Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/rng_extension.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:00.3901777Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8:0,
2023-01-11T22:00:00.3902215Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2023-01-11T22:00:00.3902848Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2023-01-11T22:00:00.3903317Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:8,
2023-01-11T22:00:00.3903675Z                  from /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp:6:
2023-01-11T22:00:00.3904156Z /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1008:0: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2023-01-11T22:00:00.3904638Z  # pragma unroll
2023-01-11T22:00:00.3904804Z  
2023-01-11T22:00:00.3946635Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/rng_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:00.7636574Z running install_lib
2023-01-11T22:00:00.7677049Z creating install
2023-01-11T22:00:00.7677387Z creating install/opt
2023-01-11T22:00:00.7677774Z creating install/opt/conda
2023-01-11T22:00:00.7678158Z creating install/opt/conda/lib
2023-01-11T22:00:00.7678442Z creating install/opt/conda/lib/python3.10
2023-01-11T22:00:00.7678895Z creating install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:00.7679484Z creating install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:00.7680080Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:00.7680804Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:00.7752897Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:00.7825138Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:00.7902863Z byte-compiling ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-310.pyc
2023-01-11T22:00:00.7903950Z running install_egg_info
2023-01-11T22:00:00.7998747Z running egg_info
2023-01-11T22:00:00.7999231Z creating torch_test_cpp_extension.egg-info
2023-01-11T22:00:00.8032059Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2023-01-11T22:00:00.8033785Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2023-01-11T22:00:00.8036244Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2023-01-11T22:00:00.8037116Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:00.8071791Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:00.8077095Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:00.8077787Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension-0.0.0-py3.10.egg-info
2023-01-11T22:00:00.8081599Z running install_scripts
2023-01-11T22:00:02.6568406Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:02.6762114Z running install
2023-01-11T22:00:02.6763368Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T22:00:02.6764066Z   warnings.warn(
2023-01-11T22:00:02.6849095Z running build
2023-01-11T22:00:02.6849312Z running build_ext
2023-01-11T22:00:02.7141696Z building 'no_python_abi_suffix_test' extension
2023-01-11T22:00:02.7142024Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build
2023-01-11T22:00:02.7142691Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310
2023-01-11T22:00:02.7420610Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T22:00:02.7421229Z Compiling objects...
2023-01-11T22:00:02.7421500Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:02.8216663Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:02.8254274Z creating build/lib.linux-x86_64-cpython-310
2023-01-11T22:00:02.8255757Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so
2023-01-11T22:00:02.8833391Z running install_lib
2023-01-11T22:00:02.8870571Z creating install
2023-01-11T22:00:02.8870874Z creating install/opt
2023-01-11T22:00:02.8871228Z creating install/opt/conda
2023-01-11T22:00:02.8871655Z creating install/opt/conda/lib
2023-01-11T22:00:02.8871945Z creating install/opt/conda/lib/python3.10
2023-01-11T22:00:02.8872369Z creating install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:02.8872831Z copying build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so -> ./install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:02.8876957Z running install_egg_info
2023-01-11T22:00:02.8967663Z running egg_info
2023-01-11T22:00:02.8968187Z creating no_python_abi_suffix_test.egg-info
2023-01-11T22:00:02.9000728Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2023-01-11T22:00:02.9002627Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2023-01-11T22:00:02.9005228Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2023-01-11T22:00:02.9005884Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:02.9040217Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:02.9045390Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:02.9046135Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/lib/python3.10/site-packages/no_python_abi_suffix_test-0.0.0-py3.10.egg-info
2023-01-11T22:00:02.9049503Z running install_scripts
2023-01-11T22:00:03.2054957Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:03.205091]
2023-01-11T22:00:05.4185614Z 
2023-01-11T22:00:05.4186194Z Expand the folded group to see the log file of test_cpp_extensions_aot_ninja
2023-01-11T22:00:05.4187285Z ##[group]PRINTING LOG FILE of test_cpp_extensions_aot_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_ninja_1uthn5zt)
2023-01-11T22:00:05.4188140Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:05.4188356Z 
2023-01-11T22:00:05.4188453Z Running tests...
2023-01-11T22:00:05.4189160Z ----------------------------------------------------------------------
2023-01-11T22:00:05.4189822Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_aot_ninja
2023-01-11T22:00:05.4190327Z   test_backward (__main__.TestCppExtensionAOT) ... ok (0.011s)
2023-01-11T22:00:05.4190807Z   test_cublas_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:05.4191347Z   test_cuda_dlink_libs (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:05.4191855Z   test_cuda_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:05.4192412Z   test_cusolver_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:05.4192938Z   test_extension_function (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:05.4193429Z   test_extension_module (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:05.4194095Z   test_no_python_abi_suffix_sets_the_correct_library_name (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:05.4194646Z   test_optional (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:05.4195063Z   test_add (__main__.TestORTTensor) ... ok (0.003s)
2023-01-11T22:00:05.4195476Z   test_conv_backend_override (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:05.4195934Z   test_unregistered (__main__.TestORTTensor) ... ok (0.006s)
2023-01-11T22:00:05.4196350Z   test_zeros (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:05.4196801Z   test_pybind_return_types (__main__.TestPybindTypeCasters) ... ok (0.001s)
2023-01-11T22:00:05.4197248Z   test_rng (__main__.TestRNGExtension) ... ok (0.002s)
2023-01-11T22:00:05.4197793Z   test_torch_library (__main__.TestTorchLibrary) ... skip: CUDA not found (0.001s)
2023-01-11T22:00:05.4198085Z 
2023-01-11T22:00:05.4198439Z ----------------------------------------------------------------------
2023-01-11T22:00:05.4198811Z Ran 16 tests in 0.031s
2023-01-11T22:00:05.4199001Z 
2023-01-11T22:00:05.4199117Z OK (skipped=5)
2023-01-11T22:00:05.4199283Z 
2023-01-11T22:00:05.4199422Z Generating XML reports...
2023-01-11T22:00:05.4200183Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestCppExtensionAOT-20230111220005.xml
2023-01-11T22:00:05.4201115Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestORTTensor-20230111220005.xml
2023-01-11T22:00:05.4202075Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestPybindTypeCasters-20230111220005.xml
2023-01-11T22:00:05.4203016Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestRNGExtension-20230111220005.xml
2023-01-11T22:00:05.4203932Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestTorchLibrary-20230111220005.xml
2023-01-11T22:00:05.4204345Z 
2023-01-11T22:00:05.4204746Z ##[endgroup]
2023-01-11T22:00:05.4205478Z FINISHED PRINTING LOG FILE of test_cpp_extensions_aot_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_ninja_1uthn5zt)
2023-01-11T22:00:05.4205889Z 
2023-01-11T22:00:05.4206224Z Running test_cpp_extensions_aot_no_ninja ... [2023-01-11 22:00:05.418831]
2023-01-11T22:00:06.9114236Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:07.0122390Z running install
2023-01-11T22:00:07.0123214Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T22:00:07.0123601Z   warnings.warn(
2023-01-11T22:00:07.0207508Z running build
2023-01-11T22:00:07.0207728Z running build_py
2023-01-11T22:00:07.0245388Z creating build
2023-01-11T22:00:07.0246018Z creating build/lib.linux-x86_64-cpython-310
2023-01-11T22:00:07.0246450Z creating build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T22:00:07.0246909Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T22:00:07.0248467Z running build_ext
2023-01-11T22:00:07.0259579Z building 'torch_test_cpp_extension.cpp' extension
2023-01-11T22:00:07.0260199Z creating build/temp.linux-x86_64-cpython-310
2023-01-11T22:00:07.0263559Z gcc -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c extension.cpp -o build/temp.linux-x86_64-cpython-310/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:08.0347767Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/Exceptions.h:14:0,
2023-01-11T22:00:08.0348317Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2023-01-11T22:00:08.0348798Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/extension.h:6,
2023-01-11T22:00:08.0349086Z                  from extension.cpp:1:
2023-01-11T22:00:08.0349725Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2023-01-11T22:00:08.0350117Z extension.cpp:40:53:   required from here
2023-01-11T22:00:08.0350857Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than the type of its field ‘pybind11::class_<MatrixMultiplier>::<anonymous>’ [-Wattributes]
2023-01-11T22:00:08.0351342Z  class class_ : public detail::generic_type {
2023-01-11T22:00:08.0351559Z        ^~~~~~
2023-01-11T22:00:08.0352195Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2023-01-11T22:00:08.0355100Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib build/temp.linux-x86_64-cpython-310/extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:08.3825090Z building 'torch_test_cpp_extension.ort' extension
2023-01-11T22:00:08.3827056Z gcc -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c ort_extension.cpp -o build/temp.linux-x86_64-cpython-310/ort_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=ort -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:09.3388394Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib build/temp.linux-x86_64-cpython-310/ort_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:09.6802444Z building 'torch_test_cpp_extension.rng' extension
2023-01-11T22:00:09.6804781Z gcc -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-310/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:10.7431234Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8:0,
2023-01-11T22:00:10.7431721Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2023-01-11T22:00:10.7432154Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2023-01-11T22:00:10.7432625Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:8,
2023-01-11T22:00:10.7432933Z                  from rng_extension.cpp:6:
2023-01-11T22:00:10.7433377Z /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1008:0: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2023-01-11T22:00:10.7433676Z  # pragma unroll
2023-01-11T22:00:10.7433846Z  
2023-01-11T22:00:10.7438566Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib build/temp.linux-x86_64-cpython-310/rng_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:11.1171101Z running install_lib
2023-01-11T22:00:11.1210981Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:11.1297286Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:11.1383473Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:11.1474242Z running install_egg_info
2023-01-11T22:00:11.1568026Z running egg_info
2023-01-11T22:00:11.1599713Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2023-01-11T22:00:11.1610815Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2023-01-11T22:00:11.1621394Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2023-01-11T22:00:11.1665343Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:11.1670602Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:11.1679650Z removing './install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension-0.0.0-py3.10.egg-info' (and everything under it)
2023-01-11T22:00:11.1681378Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension-0.0.0-py3.10.egg-info
2023-01-11T22:00:11.1684604Z running install_scripts
2023-01-11T22:00:12.9793153Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:12.9985686Z running install
2023-01-11T22:00:12.9987168Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T22:00:12.9987648Z   warnings.warn(
2023-01-11T22:00:13.0072200Z running build
2023-01-11T22:00:13.0072412Z running build_ext
2023-01-11T22:00:13.0364026Z building 'no_python_abi_suffix_test' extension
2023-01-11T22:00:13.0635066Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T22:00:13.0642707Z Compiling objects...
2023-01-11T22:00:13.0643024Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:13.0887809Z ninja: no work to do.
2023-01-11T22:00:13.0922976Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so
2023-01-11T22:00:13.1488566Z running install_lib
2023-01-11T22:00:13.1526121Z copying build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so -> ./install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:13.1530511Z running install_egg_info
2023-01-11T22:00:13.1622302Z running egg_info
2023-01-11T22:00:13.1654647Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2023-01-11T22:00:13.1679598Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2023-01-11T22:00:13.1689768Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2023-01-11T22:00:13.1734408Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:13.1739687Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:13.1751450Z removing './install/opt/conda/lib/python3.10/site-packages/no_python_abi_suffix_test-0.0.0-py3.10.egg-info' (and everything under it)
2023-01-11T22:00:13.1753156Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/lib/python3.10/site-packages/no_python_abi_suffix_test-0.0.0-py3.10.egg-info
2023-01-11T22:00:13.1756501Z running install_scripts
2023-01-11T22:00:13.4660472Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:13.465684]
2023-01-11T22:00:15.7426668Z 
2023-01-11T22:00:15.7427155Z Expand the folded group to see the log file of test_cpp_extensions_aot_no_ninja
2023-01-11T22:00:15.7428260Z ##[group]PRINTING LOG FILE of test_cpp_extensions_aot_no_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_no_ninja_mvc4iqfa)
2023-01-11T22:00:15.7428841Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:15.7429031Z 
2023-01-11T22:00:15.7429105Z Running tests...
2023-01-11T22:00:15.7429417Z ----------------------------------------------------------------------
2023-01-11T22:00:15.7429968Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_aot_no_ninja
2023-01-11T22:00:15.7430360Z   test_backward (__main__.TestCppExtensionAOT) ... ok (0.011s)
2023-01-11T22:00:15.7430704Z   test_cublas_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:15.7431074Z   test_cuda_dlink_libs (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:15.7431561Z   test_cuda_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:15.7431999Z   test_cusolver_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:15.7432566Z   test_extension_function (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:15.7432866Z   test_extension_module (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:15.7433249Z   test_no_python_abi_suffix_sets_the_correct_library_name (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:15.7433551Z   test_optional (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:15.7433856Z   test_add (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:15.7434120Z   test_conv_backend_override (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:15.7434386Z   test_unregistered (__main__.TestORTTensor) ... ok (0.006s)
2023-01-11T22:00:15.7434693Z   test_zeros (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:15.7434971Z   test_pybind_return_types (__main__.TestPybindTypeCasters) ... ok (0.001s)
2023-01-11T22:00:15.7435290Z   test_rng (__main__.TestRNGExtension) ... ok (0.002s)
2023-01-11T22:00:15.7435645Z   test_torch_library (__main__.TestTorchLibrary) ... skip: CUDA not found (0.001s)
2023-01-11T22:00:15.7435825Z 
2023-01-11T22:00:15.7436091Z ----------------------------------------------------------------------
2023-01-11T22:00:15.7436335Z Ran 16 tests in 0.029s
2023-01-11T22:00:15.7436435Z 
2023-01-11T22:00:15.7436506Z OK (skipped=5)
2023-01-11T22:00:15.7436613Z 
2023-01-11T22:00:15.7436707Z Generating XML reports...
2023-01-11T22:00:15.7437213Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestCppExtensionAOT-20230111220015.xml
2023-01-11T22:00:15.7437877Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestORTTensor-20230111220015.xml
2023-01-11T22:00:15.7438496Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestPybindTypeCasters-20230111220015.xml
2023-01-11T22:00:15.7439107Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestRNGExtension-20230111220015.xml
2023-01-11T22:00:15.7439676Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestTorchLibrary-20230111220015.xml
2023-01-11T22:00:15.7439973Z 
2023-01-11T22:00:15.7440219Z ##[endgroup]
2023-01-11T22:00:15.7440713Z FINISHED PRINTING LOG FILE of test_cpp_extensions_aot_no_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_no_ninja_mvc4iqfa)
2023-01-11T22:00:15.7440962Z 
2023-01-11T22:00:15.7441195Z Running test_cpp_extensions_open_device_registration ... [2023-01-11 22:00:15.742843]
2023-01-11T22:00:15.7441797Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_open_device_registration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:15.743064]
2023-01-11T22:00:39.3143532Z 
2023-01-11T22:00:39.3144164Z Expand the folded group to see the log file of test_cpp_extensions_open_device_registration
2023-01-11T22:00:39.3145036Z ##[group]PRINTING LOG FILE of test_cpp_extensions_open_device_registration (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_open_device_registration_33_5dxtm)
2023-01-11T22:00:39.3145937Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:39.3146221Z 
2023-01-11T22:00:39.3146337Z Running tests...
2023-01-11T22:00:39.3146678Z ----------------------------------------------------------------------
2023-01-11T22:00:39.3147472Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_open_device_registration
2023-01-11T22:00:39.3147992Z   test_open_device_registration (__main__.TestCppExtensionOpenRgistration) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T22:00:39.3148437Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/custom_device_extension...
2023-01-11T22:00:39.3148810Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/custom_device_extension/build.ninja...
2023-01-11T22:00:39.3149292Z Building extension module custom_device_extension...
2023-01-11T22:00:39.3149956Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:39.3151458Z [1/2] c++ -MMD -MF open_registration_extension.o.d -DTORCH_EXTENSION_NAME=custom_device_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension.cpp -o open_registration_extension.o 
2023-01-11T22:00:39.3152754Z [2/2] c++ open_registration_extension.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o custom_device_extension.so
2023-01-11T22:00:39.3153131Z Loading extension module custom_device_extension...
2023-01-11T22:00:39.3153330Z ok (21.630s)
2023-01-11T22:00:39.3153433Z 
2023-01-11T22:00:39.3153633Z ----------------------------------------------------------------------
2023-01-11T22:00:39.3153873Z Ran 1 test in 21.642s
2023-01-11T22:00:39.3153973Z 
2023-01-11T22:00:39.3154033Z OK
2023-01-11T22:00:39.3154123Z 
2023-01-11T22:00:39.3154209Z Generating XML reports...
2023-01-11T22:00:39.3154719Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_open_device_registration/TEST-TestCppExtensionOpenRgistration-20230111220017.xml
2023-01-11T22:00:39.3155025Z 
2023-01-11T22:00:39.3155255Z ##[endgroup]
2023-01-11T22:00:39.3155736Z FINISHED PRINTING LOG FILE of test_cpp_extensions_open_device_registration (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_open_device_registration_33_5dxtm)
2023-01-11T22:00:39.3156011Z 
2023-01-11T22:00:39.3156208Z Running test_cuda_nvml_based_avail ... [2023-01-11 22:00:39.314495]
2023-01-11T22:00:39.3156728Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '-v', '--subprocess', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:39.314745]
2023-01-11T22:00:40.8673451Z 
2023-01-11T22:00:40.8673980Z Expand the folded group to see the log file of test_cuda_nvml_based_avail
2023-01-11T22:00:40.8675313Z ##[group]PRINTING LOG FILE of test_cuda_nvml_based_avail (/var/lib/jenkins/workspace/test/test-reports/test_cuda_nvml_based_avail_3xzfsqj4)
2023-01-11T22:00:40.8675884Z CUDA not available, skipping tests
2023-01-11T22:00:40.8676024Z 
2023-01-11T22:00:40.8676232Z ##[endgroup]
2023-01-11T22:00:40.8677032Z FINISHED PRINTING LOG FILE of test_cuda_nvml_based_avail (/var/lib/jenkins/workspace/test/test-reports/test_cuda_nvml_based_avail_3xzfsqj4)
2023-01-11T22:00:40.8677405Z 
2023-01-11T22:00:40.8677598Z Running test_cuda_primary_ctx ... [2023-01-11 22:00:40.867458]
2023-01-11T22:00:40.8679051Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_primary_ctx.py', '-v', '--subprocess', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:40.867697]
2023-01-11T22:00:42.3949284Z 
2023-01-11T22:00:42.3949926Z Expand the folded group to see the log file of test_cuda_primary_ctx
2023-01-11T22:00:42.3950908Z ##[group]PRINTING LOG FILE of test_cuda_primary_ctx (/var/lib/jenkins/workspace/test/test-reports/test_cuda_primary_ctx_365vsslp)
2023-01-11T22:00:42.3951238Z CUDA not available, skipping tests
2023-01-11T22:00:42.3951370Z 
2023-01-11T22:00:42.3951631Z ##[endgroup]
2023-01-11T22:00:42.3952126Z FINISHED PRINTING LOG FILE of test_cuda_primary_ctx (/var/lib/jenkins/workspace/test/test-reports/test_cuda_primary_ctx_365vsslp)
2023-01-11T22:00:42.3952340Z 
2023-01-11T22:00:42.3952543Z Running test_cuda_trace ... [2023-01-11 22:00:42.394991]
2023-01-11T22:00:42.3954046Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_trace.py', '-v', '--subprocess', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:42.395222]
2023-01-11T22:00:43.9320158Z 
2023-01-11T22:00:43.9320678Z Expand the folded group to see the log file of test_cuda_trace
2023-01-11T22:00:43.9321333Z ##[group]PRINTING LOG FILE of test_cuda_trace (/var/lib/jenkins/workspace/test/test-reports/test_cuda_trace_yr1b68oh)
2023-01-11T22:00:43.9321662Z CUDA not available, skipping tests
2023-01-11T22:00:43.9321794Z 
2023-01-11T22:00:43.9322006Z ##[endgroup]
2023-01-11T22:00:43.9322541Z FINISHED PRINTING LOG FILE of test_cuda_trace (/var/lib/jenkins/workspace/test/test-reports/test_cuda_trace_yr1b68oh)
2023-01-11T22:00:43.9322894Z 
2023-01-11T22:00:43.9323160Z Running test_dispatch ... [2023-01-11 22:00:43.932111]
2023-01-11T22:00:43.9325064Z Executing ['/opt/conda/bin/python', '-bb', 'test_dispatch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:43.932325]
2023-01-11T22:01:08.9405323Z 
2023-01-11T22:01:08.9405707Z Expand the folded group to see the log file of test_dispatch
2023-01-11T22:01:08.9406612Z ##[group]PRINTING LOG FILE of test_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_dispatch_tl3w7n2s)
2023-01-11T22:01:08.9407766Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:01:08.9408052Z 
2023-01-11T22:01:08.9408194Z Running tests...
2023-01-11T22:01:08.9409911Z ----------------------------------------------------------------------
2023-01-11T22:01:08.9410566Z Test results will be stored in test-reports/python-unittest/test_dispatch
2023-01-11T22:01:08.9411083Z   test_all_invariants (__main__.TestDispatch) ... ok (0.233s)
2023-01-11T22:01:08.9411448Z   test_computed_table (__main__.TestDispatch) ... ok (7.012s)
2023-01-11T22:01:08.9411784Z   test_computed_table_with_ambiguous_autogradother (__main__.TestDispatch) ... ok (0.011s)
2023-01-11T22:01:08.9412102Z   test_computed_table_with_autograd (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:08.9412433Z   test_computed_table_with_cpu_autograd_defaultbackend (__main__.TestDispatch) ... ok (0.197s)
2023-01-11T22:01:08.9412770Z   test_computed_table_with_cpu_autograd_math (__main__.TestDispatch) ... ok (0.211s)
2023-01-11T22:01:08.9413095Z   test_computed_table_with_cpu_autograd_math_defaultbackend (__main__.TestDispatch) ... ok (6.608s)
2023-01-11T22:01:08.9413430Z   test_computed_table_with_cpu_defaultbackend (__main__.TestDispatch) ... ok (0.010s)
2023-01-11T22:01:08.9413738Z   test_computed_table_with_cpu_math (__main__.TestDispatch) ... ok (0.011s)
2023-01-11T22:01:08.9414059Z   test_computed_table_with_cpu_math_autogradcpu_fallthrough (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:08.9414379Z   test_computed_table_with_math (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:08.9414639Z   test_def (__main__.TestDispatch) ... ok (6.688s)
2023-01-11T22:01:08.9414903Z   test_def_impl_schema_mismatch (__main__.TestDispatch) ... ok (0.011s)
2023-01-11T22:01:08.9415155Z   test_def_only (__main__.TestDispatch) ... ok (0.001s)
2023-01-11T22:01:08.9415425Z   test_def_with_explicit_alias (__main__.TestDispatch) ... ok (0.001s)
2023-01-11T22:01:08.9415702Z   test_def_with_inference (__main__.TestDispatch) ... ok (0.223s)
2023-01-11T22:01:08.9416007Z   test_dispatch_print_registrations_for_dispatch_key_invalid (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:08.9416320Z   test_find_dangling_impls (__main__.TestDispatch) ... ok (0.001s)
2023-01-11T22:01:08.9416801Z   test_find_dangling_impls_ext (__main__.TestDispatch) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T22:01:08.9417207Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/dangling_impl_extension...
2023-01-11T22:01:08.9417580Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/dangling_impl_extension/build.ninja...
2023-01-11T22:01:08.9417901Z Building extension module dangling_impl_extension...
2023-01-11T22:01:08.9418158Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:01:08.9419702Z [1/2] c++ -MMD -MF dangling_impl_extension.o.d -DTORCH_EXTENSION_NAME=dangling_impl_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/dangling_impl_extension.cpp -o dangling_impl_extension.o 
2023-01-11T22:01:08.9420872Z [2/2] c++ dangling_impl_extension.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o dangling_impl_extension.so
2023-01-11T22:01:08.9421246Z Loading extension module dangling_impl_extension...
2023-01-11T22:01:08.9421496Z ok (1.526s)
2023-01-11T22:01:08.9421715Z   test_impl_only (__main__.TestDispatch) ... ok (0.213s)
2023-01-11T22:01:08.9421993Z   test_multiple_def_alias_defaulting (__main__.TestDispatch) ... ok (0.008s)
2023-01-11T22:01:08.9422277Z   test_multiple_def_alias_mismatch (__main__.TestDispatch) ... ok (0.008s)
2023-01-11T22:01:08.9422741Z   test_multiple_def_error (__main__.TestDispatch) ... ok (0.008s)
2023-01-11T22:01:08.9423011Z   test_multiple_fallback (__main__.TestDispatch) ... ok (0.009s)
2023-01-11T22:01:08.9423407Z   test_overwrite_math (__main__.TestDispatch) ... [W OperatorEntry.cpp:159] Warning: Overriding a previously registered kernel for the same operator and the same dispatch key
2023-01-11T22:01:08.9423752Z   operator: __test45643__::foo
2023-01-11T22:01:08.9423937Z     no debug info
2023-01-11T22:01:08.9424117Z   dispatch key: (catch all)
2023-01-11T22:01:08.9424295Z   previous kernel: fn1
2023-01-11T22:01:08.9424512Z        new kernel: fn2 (function registerKernel)
2023-01-11T22:01:08.9424857Z [W OperatorEntry.cpp:159] Warning: Overriding a previously registered kernel for the same operator and the same dispatch key
2023-01-11T22:01:08.9425144Z   operator: __test45644__::foo
2023-01-11T22:01:08.9425325Z     no debug info
2023-01-11T22:01:08.9425505Z   dispatch key: (catch all)
2023-01-11T22:01:08.9425682Z   previous kernel: fn1
2023-01-11T22:01:08.9425896Z        new kernel: fn2 (function registerKernel)
2023-01-11T22:01:08.9426096Z ok (0.001s)
2023-01-11T22:01:08.9426319Z   test_autogradother (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:08.9426606Z   test_basic (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:08.9426909Z   test_defaultbackend_autogradcpu (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:08.9427228Z   test_defaultbackend_math (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:08.9427530Z   test_duplicate_registrations (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:08.9427842Z   test_math_autogradcpu (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:08.9428169Z   test_quantized_structured_not_implemented (__main__.TestPythonDispatcher) ... ok (0.024s)
2023-01-11T22:01:08.9428360Z 
2023-01-11T22:01:08.9428571Z ----------------------------------------------------------------------
2023-01-11T22:01:08.9428805Z Ran 32 tests in 23.029s
2023-01-11T22:01:08.9428920Z 
2023-01-11T22:01:08.9428981Z OK
2023-01-11T22:01:08.9429069Z 
2023-01-11T22:01:08.9429153Z Generating XML reports...
2023-01-11T22:01:08.9429538Z Generated XML report: test-reports/python-unittest/test_dispatch/TEST-TestDispatch-20230111220045.xml
2023-01-11T22:01:08.9430061Z Generated XML report: test-reports/python-unittest/test_dispatch/TEST-TestPythonDispatcher-20230111220045.xml
2023-01-11T22:01:08.9430299Z 
2023-01-11T22:01:08.9430572Z ##[endgroup]
2023-01-11T22:01:08.9430939Z FINISHED PRINTING LOG FILE of test_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_dispatch_tl3w7n2s)
2023-01-11T22:01:08.9431228Z 
2023-01-11T22:01:08.9431383Z Running test_fx ... [2023-01-11 22:01:08.940718]
2023-01-11T22:01:08.9431839Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:01:08.940977]
2023-01-11T22:05:43.9983982Z 
2023-01-11T22:05:43.9984903Z Expand the folded group to see the log file of test_fx
2023-01-11T22:05:43.9985810Z ##[group]PRINTING LOG FILE of test_fx (/var/lib/jenkins/workspace/test/test-reports/test_fx_jso06ja3)
2023-01-11T22:05:43.9986135Z 
2023-01-11T22:05:43.9990619Z Running tests...
2023-01-11T22:05:43.9991406Z ----------------------------------------------------------------------
2023-01-11T22:05:43.9991873Z Test results will be stored in test-reports/python-unittest/test_fx
2023-01-11T22:05:43.9992259Z   test_annotate (fx.test_gradual_type.AnnotationsTest) ... ok (0.018s)
2023-01-11T22:05:43.9992689Z   test_annotations (fx.test_gradual_type.AnnotationsTest)
2023-01-11T22:05:43.9993199Z Test type annotations in the forward function. ... ok (0.003s)
2023-01-11T22:05:43.9993623Z   test_broadcasting1 (fx.test_gradual_type.AnnotationsTest) ... ok (0.001s)
2023-01-11T22:05:43.9994619Z   test_broadcasting2 (fx.test_gradual_type.AnnotationsTest) ... ok (0.001s)
2023-01-11T22:05:43.9994920Z   test_broadcasting3 (fx.test_gradual_type.AnnotationsTest) ... ok (0.001s)
2023-01-11T22:05:43.9995316Z   test_consistency (fx.test_gradual_type.AnnotationsTest)
2023-01-11T22:05:43.9995749Z Test the consistency relation. ... ok (0.001s)
2023-01-11T22:05:43.9996171Z   test_precision (fx.test_gradual_type.AnnotationsTest)
2023-01-11T22:05:43.9996488Z Test the consistency relation. ... ok (0.001s)
2023-01-11T22:05:43.9996798Z   test_banned_list (fx.test_cse_pass.TestCSEPass) ... ok (0.061s)
2023-01-11T22:05:43.9997071Z   test_empty (fx.test_cse_pass.TestCSEPass) ... ok (0.007s)
2023-01-11T22:05:43.9997439Z   test_immutable_list_multiple_entries (fx.test_cse_pass.TestCSEPass) ... ok (0.059s)
2023-01-11T22:05:43.9997863Z   test_immutable_list_type (fx.test_cse_pass.TestCSEPass) ... ok (0.059s)
2023-01-11T22:05:43.9998298Z   test_kwarg (fx.test_cse_pass.TestCSEPass) ... ok (0.027s)
2023-01-11T22:05:43.9998812Z   test_nested_immutable_list_type (fx.test_cse_pass.TestCSEPass) ... ok (0.030s)
2023-01-11T22:05:43.9999298Z   test_nochange (fx.test_cse_pass.TestCSEPass) ... ok (0.038s)
2023-01-11T22:05:43.9999863Z   test_rand_like (fx.test_cse_pass.TestCSEPass) ... ok (0.026s)
2023-01-11T22:05:44.0000319Z   test_rand_n (fx.test_cse_pass.TestCSEPass) ... ok (0.026s)
2023-01-11T22:05:44.0000709Z   test_random (fx.test_cse_pass.TestCSEPass) ... ok (0.081s)
2023-01-11T22:05:44.0000989Z   test_simple (fx.test_cse_pass.TestCSEPass) ... ok (0.045s)
2023-01-11T22:05:44.0001252Z   test_simple_2 (fx.test_cse_pass.TestCSEPass) ... ok (0.059s)
2023-01-11T22:05:44.0001637Z   test_simple_multiple_same_ops (fx.test_cse_pass.TestCSEPass) ... ok (0.058s)
2023-01-11T22:05:44.0002103Z   test_two_args (fx.test_cse_pass.TestCSEPass) ... ok (0.061s)
2023-01-11T22:05:44.0002552Z   test_two_args_default (fx.test_cse_pass.TestCSEPass) ... ok (0.058s)
2023-01-11T22:05:44.0002942Z   test_correctness_CSEPass_MutationInput_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.030s)
2023-01-11T22:05:44.0003372Z   test_correctness_CSEPass_MutationMetadata_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.016s)
2023-01-11T22:05:44.0003775Z   test_correctness_CSEPass_MutationTorchTensorCall_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.028s)
2023-01-11T22:05:44.0004556Z   test_correctness_CSEPass_Mutation_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.029s)
2023-01-11T22:05:44.0005164Z   test_correctness_CSEPass_ReturnList_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.041s)
2023-01-11T22:05:44.0005734Z   test_correctness_CSEPass_TakeList_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.015s)
2023-01-11T22:05:44.0006350Z   test_correctness_factory_CSEPass_FactoryFunctionCall_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.020s)
2023-01-11T22:05:44.0007093Z   test_correctness_factory_CSEPass_MutationFactory_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.028s)
2023-01-11T22:05:44.0007891Z   test_check_inline_non_const (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0009284Z Perform constant folding conversion and check that the non-const module is inlined ... /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py:248: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
2023-01-11T22:05:44.0010002Z   new_node = root_const_gm.graph.get_attr(in_node.target)
2023-01-11T22:05:44.0010219Z ok (0.008s)
2023-01-11T22:05:44.0010468Z   test_check_inline_non_const_mult_return (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0010876Z Perform constant folding conversion and check that the non-const module is inlined ... ok (0.006s)
2023-01-11T22:05:44.0011282Z   test_check_skip_folding_quant_dequant_pattern (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0011663Z Set up skip_folding_quant_dequant function to skip quant/dequant pattern. ... ok (0.022s)
2023-01-11T22:05:44.0011998Z   test_const_fold_basic_one_attr_name_collision (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0012328Z Perform constant folding conversion, from original mod to split constant folding ... ok (0.007s)
2023-01-11T22:05:44.0012740Z   test_const_fold_basic_one_attr_no_name_collision (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0013221Z Perform constant folding conversion, from original mod to split constant folding ... ok (0.006s)
2023-01-11T22:05:44.0013566Z   test_const_fold_basic_placeholder_reordered (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0013878Z Test code path where placeholder comes after normal op node in FX ... ok (0.003s)
2023-01-11T22:05:44.0014189Z   test_const_fold_basic_two_attr (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0014505Z Perform constant folding conversion, from original mod to split constant ... ok (0.005s)
2023-01-11T22:05:44.0014820Z   test_const_fold_basic_two_attr_three_input (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0015146Z Perform constant folding conversion, from original mod to split constant ... ok (0.005s)
2023-01-11T22:05:44.0015493Z   test_const_fold_has_inlined_call_module_node (fx.test_fx_const_fold.TestConstFold) ... ok (0.006s)
2023-01-11T22:05:44.0016061Z   test_const_fold_module_attr (fx.test_fx_const_fold.TestConstFold) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:973: UserWarning: Failed to fetch module mod!
2023-01-11T22:05:44.0016428Z   warnings.warn(f"Failed to fetch module {module_path}!")
2023-01-11T22:05:44.0016638Z ok (0.117s)
2023-01-11T22:05:44.0016880Z   test_const_fold_multi_const_folded_attrs (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0017197Z Perform constant folding conversion, from original mod to split constant ... ok (0.008s)
2023-01-11T22:05:44.0017499Z   test_const_fold_noop (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0017790Z Check that a graph with no constant folding is handled correctly. ... ok (0.003s)
2023-01-11T22:05:44.0018096Z   test_const_fold_submod_hierarchy (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0018646Z Perform constant folding conversion, from original mod to split constant folding ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:973: UserWarning: Failed to fetch module my_mod!
2023-01-11T22:05:44.0019039Z   warnings.warn(f"Failed to fetch module {module_path}!")
2023-01-11T22:05:44.0019362Z ok (0.005s)
2023-01-11T22:05:44.0019597Z   test_const_fold_tensor_meta (fx.test_fx_const_fold.TestConstFold) ... ok (0.011s)
2023-01-11T22:05:44.0019925Z   test_const_fold_unused_placeholder (fx.test_fx_const_fold.TestConstFold) ... ok (0.005s)
2023-01-11T22:05:44.0020237Z   test_dict_output (fx.test_fx_const_fold.TestConstFold) ... ok (0.005s)
2023-01-11T22:05:44.0020575Z   test_fold_module (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0020835Z Perform constant folding with a call_module node. ... ok (0.005s)
2023-01-11T22:05:44.0021112Z   test_retain_node_meta (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:44.0021421Z Perform constant folding conversion, and validate that node meta is retained. ... ok (0.006s)
2023-01-11T22:05:44.0021725Z   test_three_outputs (fx.test_fx_const_fold.TestConstFold) ... ok (0.005s)
2023-01-11T22:05:44.0022022Z   test_two_outputs (fx.test_fx_const_fold.TestConstFold) ... ok (0.005s)
2023-01-11T22:05:44.0022547Z   test_param_dim_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.006s)
2023-01-11T22:05:44.0022978Z   test_param_ndim_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:44.0023533Z   test_param_nelement_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:44.0024038Z   test_param_numel_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:44.0024454Z   test_param_shape_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:44.0024869Z   test_param_size_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:44.0025178Z   test_dead_chain (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:44.0025533Z Tests that a chain of two nodes in the graph are DCE'd correctly. ... graph():
2023-01-11T22:05:44.0025796Z     %x : [#users=2] = placeholder[target=x]
2023-01-11T22:05:44.0026054Z     %add : [#users=1] = call_function[target=operator.add](args = (%x, 1), kwargs = {})
2023-01-11T22:05:44.0026362Z     %mul : [#users=0] = call_function[target=operator.mul](args = (%add, 7), kwargs = {})
2023-01-11T22:05:44.0026628Z     %attr_1 : [#users=1] = get_attr[target=attr_1]
2023-01-11T22:05:44.0026961Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, %attr_1), kwargs = {})
2023-01-11T22:05:44.0027232Z     return add_1
2023-01-11T22:05:44.0027396Z ok (0.003s)
2023-01-11T22:05:44.0027599Z   test_dead_getattr (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:44.0027908Z Tests that a getatrr in the graph is DCE'd correctly. ... graph():
2023-01-11T22:05:44.0028159Z     %x : [#users=2] = placeholder[target=x]
2023-01-11T22:05:44.0028425Z     %add : [#users=1] = call_function[target=operator.add](args = (%x, 1), kwargs = {})
2023-01-11T22:05:44.0028680Z     %attr_1 : [#users=1] = get_attr[target=attr_1]
2023-01-11T22:05:44.0029070Z     %mul : [#users=0] = call_function[target=operator.mul](args = (%add, %attr_1), kwargs = {})
2023-01-11T22:05:44.0029415Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, 11), kwargs = {})
2023-01-11T22:05:44.0029637Z     return add_1
2023-01-11T22:05:44.0029801Z ok (0.003s)
2023-01-11T22:05:44.0030011Z   test_dead_placeholder (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:44.0030385Z Tests that a placeholder in the graph is not DCE'd, as that would change ... graph():
2023-01-11T22:05:44.0030784Z     %x : [#users=1] = placeholder[target=x]
2023-01-11T22:05:44.0031082Z     %y : [#users=0] = placeholder[target=y]
2023-01-11T22:05:44.0031495Z     %add : [#users=1] = call_function[target=operator.add](args = (%x, 7), kwargs = {})
2023-01-11T22:05:44.0031869Z     return add
2023-01-11T22:05:44.0032121Z ok (0.002s)
2023-01-11T22:05:44.0032502Z   test_dead_placeholder_with_user (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:44.0033146Z Tests that a placeholder in the graph is not DCE'd, as that would change ... graph():
2023-01-11T22:05:44.0033569Z     %x : [#users=1] = placeholder[target=x]
2023-01-11T22:05:44.0033929Z     %y : [#users=1] = placeholder[target=y]
2023-01-11T22:05:44.0034289Z     %add : [#users=0] = call_function[target=operator.add](args = (%y, 2), kwargs = {})
2023-01-11T22:05:44.0034791Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, 7), kwargs = {})
2023-01-11T22:05:44.0035305Z     return add_1
2023-01-11T22:05:44.0035568Z ok (0.003s)
2023-01-11T22:05:44.0035919Z   test_keep_module_with_side_effects (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:44.0036550Z Test that DCE doesn't remove a module if it's specified as having side effects. ... graph():
2023-01-11T22:05:44.0037037Z     %a : torch.Tensor [#users=2] = placeholder[target=a]
2023-01-11T22:05:44.0037477Z     %relu : [#users=0] = call_module[target=relu](args = (%a,), kwargs = {})
2023-01-11T22:05:44.0037971Z     %mul : [#users=1] = call_function[target=operator.mul](args = (%a, 2), kwargs = {})
2023-01-11T22:05:44.0038400Z     return mul
2023-01-11T22:05:44.0038675Z ok (0.003s)
2023-01-11T22:05:44.0039107Z   test_keep_torch_assert (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:44.0039763Z Test that DCE doesn't remove torch._assert since it has side effects. ... graph():
2023-01-11T22:05:44.0040259Z     %a : torch.Tensor [#users=2] = placeholder[target=a]
2023-01-11T22:05:44.0040896Z     %equal : [#users=1] = call_function[target=torch.equal](args = (%a, %a), kwargs = {})
2023-01-11T22:05:44.0041235Z     %_assert : [#users=0] = call_function[target=torch._assert](args = (%equal, a must equal a), kwargs = {})
2023-01-11T22:05:44.0041561Z     %mul : [#users=1] = call_function[target=operator.mul](args = (%a, 2), kwargs = {})
2023-01-11T22:05:44.0041780Z     return mul
2023-01-11T22:05:44.0041946Z ok (0.003s)
2023-01-11T22:05:44.0042143Z   test_simple (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:44.0042457Z Tests that a single node in the graph is DCE'd correctly. ... graph():
2023-01-11T22:05:44.0042715Z     %x : [#users=2] = placeholder[target=x]
2023-01-11T22:05:44.0042980Z     %add : [#users=0] = call_function[target=operator.add](args = (%x, 1), kwargs = {})
2023-01-11T22:05:44.0043242Z     %attr_1 : [#users=1] = get_attr[target=attr_1]
2023-01-11T22:05:44.0043510Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, %attr_1), kwargs = {})
2023-01-11T22:05:44.0043759Z     return add_1
2023-01-11T22:05:44.0043924Z ok (0.003s)
2023-01-11T22:05:44.0044117Z   test_all_input_nodes (__main__.TestFX) ... ok (0.013s)
2023-01-11T22:05:44.0044373Z   test_annotation_with_future (__main__.TestFX) ... ok (0.009s)
2023-01-11T22:05:44.0045158Z   test_annotations_empty_tuple (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T22:05:44.0045775Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T22:05:44.0045981Z ok (0.027s)
2023-01-11T22:05:44.0046215Z   test_annotations_with_forward_references (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0046519Z   test_annotations_with_no_forward_references (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0046847Z   test_annotations_with_non_torch_reference_and_internal_forward_references (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0047211Z   test_annotations_with_non_torch_reference_and_no_internal_forward_references (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0047506Z   test_args_kwargs (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0047748Z   test_args_kwargs_no_self (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0048081Z   test_assert (__main__.TestFX) ... skip: Does not work on Python-3.10 (0.000s)
2023-01-11T22:05:44.0048584Z   test_ast_rewriter_reassigns_submodules (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0048861Z   test_ast_rewriter_rewrites_assert (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0049136Z   test_ast_rewriter_rewrites_assert_with_message (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0049406Z   test_ast_rewriter_wrap (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0049667Z   test_ast_rewriter_wrap_fn_directly (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0049993Z   test_ast_rewriter_wrap_with_submodule (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0050264Z   test_ast_rewriter_wrapped_via_decorator (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0050572Z   test_ast_rewriter_wrapped_via_decorator_and_transformed (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0050865Z   test_autowrap_functions (__main__.TestFX) ... ok (0.061s)
2023-01-11T22:05:44.0051110Z   test_concrete_arg_none_assert (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0051367Z   test_construct_root_dict (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0051604Z   test_copy_it (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0051836Z   test_copy_no_remap (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0052056Z   test_ctx_mgr (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0052291Z   test_custom_codegen (__main__.TestFX) ... ok (0.021s)
2023-01-11T22:05:44.0052553Z   test_custom_codegen_with_transformer (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0052835Z   test_custom_import (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0053096Z   test_custom_proxy_dynamic_value (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0053388Z   test_custom_proxy_input_dependent_control_flow (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0053655Z   test_custom_proxy_type (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0053914Z   test_custom_proxy_type_literal (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0054229Z   test_custom_traceback_not_raised_when_exception_source_is_submodule (__main__.TestFX) ... ok (0.009s)
2023-01-11T22:05:44.0054580Z   test_custom_traceback_raised_when_exception_source_is_graphmodule (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:44.0054887Z   test_deepcopy_graph_with_tracer_cls (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0055186Z   test_deepcopy_graphmodule_with_transform (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0055477Z   test_deepcopy_recursion_depth (__main__.TestFX) ... ok (0.059s)
2023-01-11T22:05:44.0055724Z   test_deepcopy_tracer (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0055989Z   test_deepcopy_with_submods_params (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0056275Z   test_delete_unused_submodules_leaf (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0056523Z   test_dict (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0056746Z   test_direct_param_use (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0057001Z   test_disallow_override (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0057244Z   test_ellipsis (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0057477Z   test_empty_graph_codegen (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0057726Z   test_erase_node_error (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0057973Z   test_example_shape_prop (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0058199Z   test_find_uses (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0058456Z   test_fn_type_annotation_empty (__main__.TestFX) ... ok (0.019s)
2023-01-11T22:05:44.0058715Z   test_fn_type_annotations (__main__.TestFX) ... ok (0.027s)
2023-01-11T22:05:44.0058946Z   test_fx_and_or (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0059184Z   test_fx_create_arg (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0059421Z   test_fx_shifts (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0059650Z   test_fx_stateless (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0059889Z   test_get_torch_func_signature (__main__.TestFX) ... ok (0.101s)
2023-01-11T22:05:44.0060182Z   test_getitem (__main__.TestFX) ... skip: Will be checked in test_getitem_subproc (0.000s)
2023-01-11T22:05:44.0060465Z   test_getitem_subproc (__main__.TestFX) ... ok (0.031s)
2023-01-11T22:05:44.0060705Z   test_graph_edit_with_proxy (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:44.0073562Z   test_graph_fns (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0073912Z   test_graph_module (__main__.TestFX) ... ok (0.037s)
2023-01-11T22:05:44.0074316Z   test_graph_module_init_buffer_param_copied_dict_init (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0074648Z   test_graph_module_init_buffer_param_copied_mod_init (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0074959Z   test_graph_module_replicate_for_dp (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0075220Z   test_graph_unique_names (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0075479Z   test_graph_unique_names_manual (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0075738Z   test_immutable_dict_pytree_ops (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0075989Z   test_immutable_list_pytree_ops (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0076234Z   test_imul_code_print (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0076460Z   test_inf_nan (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0076681Z   test_inf_nan_kwds (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0076952Z   test_inline_graph (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:44.0077199Z   test_insertion_point (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0077445Z   test_interpreter (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0077693Z   test_interpreter_default_args (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0077959Z   test_interpreter_gc_values (__main__.TestFX) ... ok (0.353s)
2023-01-11T22:05:44.0078227Z   test_interpreter_noop_resnet18 (__main__.TestFX) ... ok (0.418s)
2023-01-11T22:05:44.0078495Z   test_interpreter_not_enough_args (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0078768Z   test_interpreter_onthefly_swap (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0079138Z   test_interpreter_partial_eval (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0079420Z   test_interpreter_run_node_override (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0079680Z   test_interpreter_star_args (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0079951Z   test_interpreter_with_codegen (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0080201Z   test_layout (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0080419Z   test_leaf_module (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0080658Z   test_matmul_tracing (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0080919Z   test_module_deepcopy_edit_nodes (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0081162Z   test_move_before (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0081410Z   test_multi_insert_point (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0081669Z   test_multiple_default_args (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:44.0081924Z   test_named_tuple_inlined (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0082175Z   test_namedtuple_return_qualname (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0082444Z   test_namedtuple_return_trace (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0082698Z   test_native_callable (__main__.TestFX) ... ok (0.025s)
2023-01-11T22:05:44.0082927Z   test_no_mutation (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0083164Z   test_node_tagging (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0083410Z   test_nonetype_annotation (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0084176Z   test_partial_trace (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py:564: UserWarning: Was not able to add assertion to guarantee correct input f to specialized function. It is up to the user to make sure that your inputs match the inputs you specialized the function with.
2023-01-11T22:05:44.0084630Z   warnings.warn(
2023-01-11T22:05:44.0084744Z 
2023-01-11T22:05:44.0084748Z 
2023-01-11T22:05:44.0084752Z 
2023-01-11T22:05:44.0084833Z def forward(self, x, y_1):
2023-01-11T22:05:44.0085032Z     eq = y_1 == True;  y_1 = None
2023-01-11T22:05:44.0085387Z     _assert = torch._assert(eq, 'y has been specialized to have value True but got another value');  eq = None
2023-01-11T22:05:44.0085649Z     mul = 2 * x;  x = None
2023-01-11T22:05:44.0085863Z     return mul
2023-01-11T22:05:44.0086023Z     
2023-01-11T22:05:44.0086162Z ok (0.007s)
2023-01-11T22:05:44.0086373Z   test_pickle_custom_import (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0086633Z   test_pickle_graphmodule (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:44.0086884Z   test_pickle_nonetype_annotation (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:44.0087151Z   test_pickle_torch_custom_ops (__main__.TestFX) ... ok (0.007s)
2023-01-11T22:05:44.0087692Z   test_prepend_self (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/fx/node.py:244: UserWarning: Trying to prepend a node to itself. This behavior has no effect on the graph.
2023-01-11T22:05:44.0088115Z   warnings.warn("Trying to prepend a node to itself. This behavior has no effect on the graph.")
2023-01-11T22:05:44.0088364Z ok (0.001s)
2023-01-11T22:05:44.0088567Z   test_pretty_print (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0088845Z   test_pretty_print_graph (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0089086Z   test_pretty_print_node (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0089337Z   test_pretty_print_targets (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0089602Z   test_profiler_ranges_side_effect (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0089838Z   test_pytree (__main__.TestFX) ... ok (0.101s)
2023-01-11T22:05:44.0090074Z   test_pytree_concrete (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0090326Z   test_reassign_args_kwargs_uses (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0090581Z   test_regular_and_default_args (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0090827Z   test_remove_uses (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0091082Z   test_remove_uses_with_custom_filter (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0091339Z   test_replace_input (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0091564Z   test_replace_uses (__main__.TestFX) ... ok (0.236s)
2023-01-11T22:05:44.0091793Z   test_reserved_getattr (__main__.TestFX)
2023-01-11T22:05:44.0092066Z Ensure that we do not name any nodes with a reserved builtin like `getattr` ... ok (0.003s)
2023-01-11T22:05:44.0092335Z   test_return_tuple (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0092580Z   test_return_type_exists (__main__.TestFX) ... ok (0.018s)
2023-01-11T22:05:44.0092827Z   test_script_method_trace (__main__.TestFX) ... ok (0.009s)
2023-01-11T22:05:44.0093069Z   test_script_tensor_constant (__main__.TestFX) ... ok (0.018s)
2023-01-11T22:05:44.0093312Z   test_sequential (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0093561Z   test_shape_prop_aggregate (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0093813Z   test_shape_prop_layout (__main__.TestFX) ... ok (0.036s)
2023-01-11T22:05:44.0094049Z   test_shape_prop_layout_3d (__main__.TestFX) ... ok (0.684s)
2023-01-11T22:05:44.0094299Z   test_single_default_arg (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0094539Z   test_snake_case (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0094753Z   test_sqrt (__main__.TestFX) ... ok (0.008s)
2023-01-11T22:05:44.0094976Z   test_stack_traces (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0095232Z   test_stack_traces_with_transformer (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0095487Z   test_string_literal_return (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0095754Z   test_submodule_manipulation_API (__main__.TestFX) ... ok (0.019s)
2023-01-11T22:05:44.0096019Z   test_symbolic_trace_assert (__main__.TestFX) ... ok (0.010s)
2023-01-11T22:05:44.0096279Z   test_symbolic_trace_sequential (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0096521Z   test_tensor_attribute (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:44.0096779Z   test_tensor_attribute_coalseced (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0097036Z   test_tensor_constant (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0097268Z   test_throw_out_variant (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0097546Z   test_torch_custom_ops (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0097788Z   test_torch_fx_getattr (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0098536Z   test_torch_fx_len (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T22:05:44.0099129Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T22:05:44.0099349Z ok (0.027s)
2023-01-11T22:05:44.0099559Z   test_torch_op_overloads (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0099815Z   test_torchbind_class_attribute_in_fx (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0100109Z   test_torchbind_class_attribute_in_fx_tensor_arg (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0100454Z   test_trace_buffer_slice (__main__.TestFX) ... skip: Hotfix for SEV remediation (0.001s)
2023-01-11T22:05:44.0100743Z   test_trace_dict_int_keys (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0100982Z   test_trace_dict_proxy_keys (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0101232Z   test_trace_fn_constant (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0101472Z   test_trace_function (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0101716Z   test_trace_multiple_funcs (__main__.TestFX) ... 2.0.0a0+git8419ddd
2023-01-11T22:05:44.0101926Z ok (0.007s)
2023-01-11T22:05:44.0102166Z   test_tracing_graphmodules_as_leaf_submodules (__main__.TestFX) ... ok (0.015s)
2023-01-11T22:05:44.0102600Z   test_transformer_multi_outputs (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0102864Z   test_transformer_noop (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0103116Z   test_transformer_op_swap (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0103376Z   test_tuple_no_subscript (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:44.0103612Z   test_typename_print (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0103849Z   test_unpack (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0104095Z   test_unpack_dict_better_error (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0104346Z   test_unpack_list_better_error (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0104603Z   test_update_args_api (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0104865Z   test_update_args_kwargs_yells_at_you (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0105117Z   test_update_kwargs_api (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:44.0105397Z   test_user_friendly_call_provenance_with_function (__main__.TestFX) ... ok (0.017s)
2023-01-11T22:05:44.0105703Z   test_user_friendly_call_provenance_with_module (__main__.TestFX) ... ok (0.017s)
2023-01-11T22:05:44.0105962Z   test_wrap (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0106196Z   test_wrap_decorated_function (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0106457Z   test_wrap_fn_directly (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0106705Z   test_wrap_with_submodule (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0106939Z   test_wrapped_method (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0107185Z   test_wrapped_retrace (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:44.0107442Z   test_wrapped_via_decorator (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0107710Z   test_wrapped_via_decorator_and_transformed (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:44.0107986Z   test_wrong_target_type (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0108224Z   test_wrong_topo (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:44.0108508Z   test_class_member_back_compat (__main__.TestFXAPIBackwardCompatibility)
2023-01-11T22:05:44.0108813Z Test backward compatibility for members of classes with ... ok (0.002s)
2023-01-11T22:05:44.0109122Z   test_function_back_compat (__main__.TestFXAPIBackwardCompatibility)
2023-01-11T22:05:44.0109499Z Test backward compatibility for function signatures with ... ok (0.008s)
2023-01-11T22:05:44.0109812Z   test_public_api_surface (__main__.TestFXAPIBackwardCompatibility) ... ok (0.002s)
2023-01-11T22:05:44.0110156Z   test_nn_functional_adaptive_avg_pool1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0110492Z   test_nn_functional_adaptive_avg_pool2d (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0110825Z   test_nn_functional_adaptive_avg_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0111147Z   test_nn_functional_adaptive_max_pool1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0111495Z   test_nn_functional_adaptive_max_pool1d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0111842Z   test_nn_functional_adaptive_max_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0112213Z   test_nn_functional_adaptive_max_pool2d_with_indices (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0112561Z   test_nn_functional_adaptive_max_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0112912Z   test_nn_functional_adaptive_max_pool3d_with_indices (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0113316Z   test_nn_functional_affine_grid (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0113629Z   test_nn_functional_alpha_dropout (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0113954Z   test_nn_functional_avg_pool1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0114275Z   test_nn_functional_avg_pool2d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0114576Z   test_nn_functional_avg_pool3d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0114888Z   test_nn_functional_batch_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0115205Z   test_nn_functional_bilinear (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0115536Z   test_nn_functional_binary_cross_entropy (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0115875Z   test_nn_functional_binary_cross_entropy_with_logits (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0116208Z   test_nn_functional_celu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0116511Z   test_nn_functional_celu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0116816Z   test_nn_functional_channel_shuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0117135Z   test_nn_functional_conv1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0117442Z   test_nn_functional_conv2d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0117746Z   test_nn_functional_conv3d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0118037Z   test_nn_functional_conv_tbc (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0118363Z   test_nn_functional_conv_transpose1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0118694Z   test_nn_functional_conv_transpose2d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0119012Z   test_nn_functional_conv_transpose3d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0119405Z   test_nn_functional_cosine_embedding_loss (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0119744Z   test_nn_functional_cosine_similarity (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0120075Z   test_nn_functional_cross_entropy (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0120379Z   test_nn_functional_ctc_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0120689Z   test_nn_functional_dropout (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0121006Z   test_nn_functional_dropout1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0121349Z   test_nn_functional_dropout2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0121663Z   test_nn_functional_dropout3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0121964Z   test_nn_functional_elu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0122261Z   test_nn_functional_elu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0122552Z   test_nn_functional_embedding (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0122870Z   test_nn_functional_embedding_bag (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0123199Z   test_nn_functional_feature_alpha_dropout (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0123504Z   test_nn_functional_fold (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0123820Z   test_nn_functional_fractional_max_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0124204Z   test_nn_functional_fractional_max_pool2d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0124554Z   test_nn_functional_fractional_max_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0124891Z   test_nn_functional_fractional_max_pool3d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0125234Z   test_nn_functional_gaussian_nll_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0125543Z   test_nn_functional_gelu (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0125831Z   test_nn_functional_glu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0126137Z   test_nn_functional_grid_sample (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0126452Z   test_nn_functional_group_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0126769Z   test_nn_functional_gumbel_softmax (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0127081Z   test_nn_functional_hardshrink (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0127399Z   test_nn_functional_hardsigmoid (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0127713Z   test_nn_functional_hardswish (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0128010Z   test_nn_functional_hardtanh (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0128322Z   test_nn_functional_hardtanh_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0128647Z   test_nn_functional_hinge_embedding_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0128969Z   test_nn_functional_huber_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0129270Z   test_nn_functional_instance_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0129586Z   test_nn_functional_interpolate (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0129901Z   test_nn_functional_kl_div (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0130193Z   test_nn_functional_l1_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0130500Z   test_nn_functional_layer_norm (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0130809Z   test_nn_functional_leaky_relu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0131118Z   test_nn_functional_leaky_relu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0131411Z   test_nn_functional_linear (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0131725Z   test_nn_functional_local_response_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0132046Z   test_nn_functional_log_softmax (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0132346Z   test_nn_functional_logsigmoid (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0132691Z   test_nn_functional_lp_pool1d (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0133000Z   test_nn_functional_lp_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0133321Z   test_nn_functional_margin_ranking_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0133628Z   test_nn_functional_max_pool1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0133952Z   test_nn_functional_max_pool1d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0134275Z   test_nn_functional_max_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0134597Z   test_nn_functional_max_pool2d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0134908Z   test_nn_functional_max_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0135233Z   test_nn_functional_max_pool3d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0135609Z   test_nn_functional_max_unpool1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0135916Z   test_nn_functional_max_unpool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0136228Z   test_nn_functional_max_unpool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0136535Z   test_nn_functional_mish (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0136837Z   test_nn_functional_mse_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0137153Z   test_nn_functional_multi_head_attention_forward (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0137494Z   test_nn_functional_multi_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0137826Z   test_nn_functional_multilabel_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0138157Z   test_nn_functional_multilabel_soft_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0138504Z   test_nn_functional_native_channel_shuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0138825Z   test_nn_functional_nll_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0139133Z   test_nn_functional_normalize (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:44.0139428Z   test_nn_functional_one_hot (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0139730Z   test_nn_functional_pad (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0140043Z   test_nn_functional_pairwise_distance (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0140348Z   test_nn_functional_pdist (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0140660Z   test_nn_functional_pixel_shuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0140982Z   test_nn_functional_pixel_unshuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0141309Z   test_nn_functional_poisson_nll_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0141607Z   test_nn_functional_prelu (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0141906Z   test_nn_functional_relu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0142201Z   test_nn_functional_relu6 (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0142614Z   test_nn_functional_relu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0142912Z   test_nn_functional_rrelu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0143211Z   test_nn_functional_rrelu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0143513Z   test_nn_functional_selu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0143798Z   test_nn_functional_selu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0144096Z   test_nn_functional_silu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0144468Z   test_nn_functional_smooth_l1_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0144779Z   test_nn_functional_soft_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0145098Z   test_nn_functional_softmax (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0145405Z   test_nn_functional_softmin (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0145713Z   test_nn_functional_softplus (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0146012Z   test_nn_functional_softshrink (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0146327Z   test_nn_functional_threshold (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0146642Z   test_nn_functional_threshold_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:44.0146950Z   test_nn_functional_triplet_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0147338Z   test_nn_functional_triplet_margin_with_distance_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0147673Z   test_nn_functional_unfold (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:44.0148346Z   test_nn_functional_upsample (__main__.TestFunctionalTracing) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:3736: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
2023-01-11T22:05:44.0148854Z   warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T22:05:44.0149128Z ok (0.002s)
2023-01-11T22:05:44.0149724Z   test_nn_functional_upsample_bilinear (__main__.TestFunctionalTracing) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4078: UserWarning: nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.
2023-01-11T22:05:44.0150269Z   warnings.warn("nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T22:05:44.0150537Z ok (0.002s)
2023-01-11T22:05:44.0151125Z   test_nn_functional_upsample_nearest (__main__.TestFunctionalTracing) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4022: UserWarning: nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.
2023-01-11T22:05:44.0151655Z   warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T22:05:44.0151933Z ok (0.002s)
2023-01-11T22:05:44.0152142Z   test_pass_manager (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:44.0152431Z Tests that the pass manager runs the passes correctly. ... ok (0.007s)
2023-01-11T22:05:44.0152732Z   test_pass_manager_bad_checks (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:44.0153039Z Checks that we error if we pass in a check function with the wrong parameters ... ok (0.001s)
2023-01-11T22:05:44.0153352Z   test_pass_manager_checks (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:44.0153647Z Tests that users can add in check functions correctly ... ok (0.002s)
2023-01-11T22:05:44.0153925Z   test_pass_manager_error (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:44.0154177Z Tests error catching + debug ... ok (0.004s)
2023-01-11T22:05:44.0154446Z   test_this_before_that_pass_constraint (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:44.0154732Z Tests the construction of constraints ... ok (0.001s)
2023-01-11T22:05:44.0154989Z   test_topological_sort (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:44.0155286Z Tests that passes are correctly ordered based on contraints. ... ok (0.002s)
2023-01-11T22:05:44.0155639Z   test_matching_pattern_with_list_type_arg (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:44.0156019Z   test_replace_pattern_with_filters (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.015s)
2023-01-11T22:05:44.0156410Z   test_subgraph_rewriter_annotations_int (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.004s)
2023-01-11T22:05:44.0156836Z   test_subgraph_rewriter_call_method (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:44.0157234Z   test_subgraph_rewriter_correct_output_replacement (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0157628Z   test_subgraph_rewriter_graph_argument_order (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0158066Z   test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0158492Z   test_subgraph_rewriter_local_revert (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.010s)
2023-01-11T22:05:44.0158883Z   test_subgraph_rewriter_multiple_pattern_match (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.010s)
2023-01-11T22:05:44.0159338Z   test_subgraph_rewriter_nodes_with_kwargs (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:44.0159772Z   test_subgraph_rewriter_pattern_is_entire_graph (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0160220Z   test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0160652Z   test_subgraph_rewriter_placeholder_matching (fx.test_subgraph_rewriter.TestSubgraphRewriter)
2023-01-11T22:05:44.0160987Z This tests that a placeholder Node can be matched to a Node with ... ok (0.008s)
2023-01-11T22:05:44.0161341Z   test_subgraph_rewriter_preserves_logic (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0161748Z   test_subgraph_rewriter_replace_consecutive_submodules (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0162160Z   test_subgraph_rewriter_replace_with_duplicated_outputs (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0162582Z   test_subgraph_rewriter_replace_with_multiple_outputs (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.009s)
2023-01-11T22:05:44.0163007Z   test_subgraph_rewriter_replaces_referenced_submodules (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.009s)
2023-01-11T22:05:44.0163424Z   test_subgraph_rewriter_single_pattern_match (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.009s)
2023-01-11T22:05:44.0163810Z   test_subgraph_rewriter_traced_as_callable (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.009s)
2023-01-11T22:05:44.0164211Z   test_subgraph_rewriter_with_oneliner_pattern (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0164616Z   test_subgraph_rewriter_with_overlapping_matches (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0165020Z   test_subgraph_rewriter_with_unused_args (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:44.0165358Z   test_torchvision_models_alexnet (__main__.TestVisionTracing) ... ok (0.503s)
2023-01-11T22:05:44.0165678Z   test_torchvision_models_convnext_base (__main__.TestVisionTracing) ... ok (2.298s)
2023-01-11T22:05:44.0166112Z   test_torchvision_models_convnext_large (__main__.TestVisionTracing) ... ok (3.997s)
2023-01-11T22:05:44.0166419Z   test_torchvision_models_convnext_small (__main__.TestVisionTracing) ... ok (1.447s)
2023-01-11T22:05:44.0166735Z   test_torchvision_models_convnext_tiny (__main__.TestVisionTracing) ... ok (0.791s)
2023-01-11T22:05:44.0167052Z   test_torchvision_models_densenet121 (__main__.TestVisionTracing) ... ok (1.650s)
2023-01-11T22:05:44.0167367Z   test_torchvision_models_densenet161 (__main__.TestVisionTracing) ... ok (2.824s)
2023-01-11T22:05:44.0167660Z   test_torchvision_models_densenet169 (__main__.TestVisionTracing) ... ok (2.291s)
2023-01-11T22:05:44.0167977Z   test_torchvision_models_densenet201 (__main__.TestVisionTracing) ... ok (2.970s)
2023-01-11T22:05:44.0168793Z   test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_320_fpn (__main__.TestVisionTracing) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.
2023-01-11T22:05:44.0169315Z   warnings.warn(
2023-01-11T22:05:44.0169963Z /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=None`.
2023-01-11T22:05:44.0170412Z   warnings.warn(msg)
2023-01-11T22:05:44.0170592Z ok (0.232s)
2023-01-11T22:05:44.0170877Z   test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_fpn (__main__.TestVisionTracing) ... ok (0.229s)
2023-01-11T22:05:44.0171269Z   test_torchvision_models_detection_fasterrcnn_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.610s)
2023-01-11T22:05:44.0171640Z   test_torchvision_models_detection_fasterrcnn_resnet50_fpn_v2 (__main__.TestVisionTracing) ... ok (0.661s)
2023-01-11T22:05:44.0172001Z   test_torchvision_models_detection_fcos_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.626s)
2023-01-11T22:05:44.0172347Z   test_torchvision_models_detection_keypointrcnn_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.955s)
2023-01-11T22:05:44.0172711Z   test_torchvision_models_detection_maskrcnn_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.662s)
2023-01-11T22:05:44.0173066Z   test_torchvision_models_detection_maskrcnn_resnet50_fpn_v2 (__main__.TestVisionTracing) ... ok (0.714s)
2023-01-11T22:05:44.0173425Z   test_torchvision_models_detection_retinanet_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.629s)
2023-01-11T22:05:44.0173769Z   test_torchvision_models_detection_retinanet_resnet50_fpn_v2 (__main__.TestVisionTracing) ... ok (0.670s)
2023-01-11T22:05:44.0174110Z   test_torchvision_models_detection_ssd300_vgg16 (__main__.TestVisionTracing) ... ok (3.108s)
2023-01-11T22:05:44.0174467Z   test_torchvision_models_detection_ssdlite320_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (0.160s)
2023-01-11T22:05:44.0174812Z   test_torchvision_models_efficientnet_b0 (__main__.TestVisionTracing) ... ok (1.081s)
2023-01-11T22:05:44.0175122Z   test_torchvision_models_efficientnet_b1 (__main__.TestVisionTracing) ... ok (1.518s)
2023-01-11T22:05:44.0175442Z   test_torchvision_models_efficientnet_b2 (__main__.TestVisionTracing) ... ok (1.494s)
2023-01-11T22:05:44.0175757Z   test_torchvision_models_efficientnet_b3 (__main__.TestVisionTracing) ... ok (1.796s)
2023-01-11T22:05:44.0176065Z   test_torchvision_models_efficientnet_b4 (__main__.TestVisionTracing) ... ok (2.426s)
2023-01-11T22:05:44.0176381Z   test_torchvision_models_efficientnet_b5 (__main__.TestVisionTracing) ... ok (3.047s)
2023-01-11T22:05:44.0176699Z   test_torchvision_models_efficientnet_b6 (__main__.TestVisionTracing) ... ok (3.903s)
2023-01-11T22:05:44.0177017Z   test_torchvision_models_efficientnet_b7 (__main__.TestVisionTracing) ... ok (5.062s)
2023-01-11T22:05:44.0177326Z   test_torchvision_models_efficientnet_v2_l (__main__.TestVisionTracing) ... ok (7.592s)
2023-01-11T22:05:44.0177647Z   test_torchvision_models_efficientnet_v2_m (__main__.TestVisionTracing) ... ok (4.915s)
2023-01-11T22:05:44.0177968Z   test_torchvision_models_efficientnet_v2_s (__main__.TestVisionTracing) ... ok (3.074s)
2023-01-11T22:05:44.0178862Z   test_torchvision_models_googlenet (__main__.TestVisionTracing) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/googlenet.py:47: FutureWarning: The default weight initialization of GoogleNet will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11299), please set init_weights=True.
2023-01-11T22:05:44.0179395Z   warnings.warn(
2023-01-11T22:05:44.0179563Z ok (1.020s)
2023-01-11T22:05:44.0180416Z   test_torchvision_models_inception_v3 (__main__.TestVisionTracing) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/inception.py:43: FutureWarning: The default weight initialization of inception_v3 will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11299), please set init_weights=True.
2023-01-11T22:05:44.0180960Z   warnings.warn(
2023-01-11T22:05:44.0181118Z ok (1.548s)
2023-01-11T22:05:44.0181822Z   test_torchvision_models_maxvit_t (__main__.TestVisionTracing) ... /opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorShape.cpp:3452.)
2023-01-11T22:05:44.0182557Z   return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
2023-01-11T22:05:44.0182785Z ok (3.429s)
2023-01-11T22:05:44.0183073Z   test_torchvision_models_mnasnet0_5 (__main__.TestVisionTracing) ... ok (0.732s)
2023-01-11T22:05:44.0183384Z   test_torchvision_models_mnasnet0_75 (__main__.TestVisionTracing) ... ok (0.743s)
2023-01-11T22:05:44.0183693Z   test_torchvision_models_mnasnet1_0 (__main__.TestVisionTracing) ... ok (0.742s)
2023-01-11T22:05:44.0184004Z   test_torchvision_models_mnasnet1_3 (__main__.TestVisionTracing) ... ok (0.852s)
2023-01-11T22:05:44.0184300Z   test_torchvision_models_mobilenet_v2 (__main__.TestVisionTracing) ... ok (0.816s)
2023-01-11T22:05:44.0184620Z   test_torchvision_models_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (1.182s)
2023-01-11T22:05:44.0184945Z   test_torchvision_models_mobilenet_v3_small (__main__.TestVisionTracing) ... ok (0.785s)
2023-01-11T22:05:44.0185253Z   test_torchvision_models_regnet_x_16gf (__main__.TestVisionTracing) ... ok (2.516s)
2023-01-11T22:05:44.0185568Z   test_torchvision_models_regnet_x_1_6gf (__main__.TestVisionTracing) ... ok (1.283s)
2023-01-11T22:05:44.0185886Z   test_torchvision_models_regnet_x_32gf (__main__.TestVisionTracing) ... ok (4.136s)
2023-01-11T22:05:44.0186200Z   test_torchvision_models_regnet_x_3_2gf (__main__.TestVisionTracing) ... ok (1.902s)
2023-01-11T22:05:44.0186496Z   test_torchvision_models_regnet_x_400mf (__main__.TestVisionTracing) ... ok (1.416s)
2023-01-11T22:05:44.0186803Z   test_torchvision_models_regnet_x_800mf (__main__.TestVisionTracing) ... ok (1.215s)
2023-01-11T22:05:44.0187111Z   test_torchvision_models_regnet_x_8gf (__main__.TestVisionTracing) ... ok (2.266s)
2023-01-11T22:05:44.0187408Z   test_torchvision_models_regnet_y_128gf (__main__.TestVisionTracing) ... ok (18.645s)
2023-01-11T22:05:44.0187721Z   test_torchvision_models_regnet_y_16gf (__main__.TestVisionTracing) ... ok (3.483s)
2023-01-11T22:05:44.0188027Z   test_torchvision_models_regnet_y_1_6gf (__main__.TestVisionTracing) ... ok (2.482s)
2023-01-11T22:05:44.0188335Z   test_torchvision_models_regnet_y_32gf (__main__.TestVisionTracing) ... ok (5.491s)
2023-01-11T22:05:44.0188635Z   test_torchvision_models_regnet_y_3_2gf (__main__.TestVisionTracing) ... ok (2.199s)
2023-01-11T22:05:44.0188943Z   test_torchvision_models_regnet_y_400mf (__main__.TestVisionTracing) ... ok (1.510s)
2023-01-11T22:05:44.0189244Z   test_torchvision_models_regnet_y_800mf (__main__.TestVisionTracing) ... ok (1.539s)
2023-01-11T22:05:44.0189535Z   test_torchvision_models_regnet_y_8gf (__main__.TestVisionTracing) ... ok (2.392s)
2023-01-11T22:05:44.0189841Z   test_torchvision_models_resnet101 (__main__.TestVisionTracing) ... ok (2.286s)
2023-01-11T22:05:44.0190146Z   test_torchvision_models_resnet152 (__main__.TestVisionTracing) ... ok (3.252s)
2023-01-11T22:05:44.0190451Z   test_torchvision_models_resnet18 (__main__.TestVisionTracing) ... ok (0.598s)
2023-01-11T22:05:44.0190742Z   test_torchvision_models_resnet34 (__main__.TestVisionTracing) ... ok (1.151s)
2023-01-11T22:05:44.0191042Z   test_torchvision_models_resnet50 (__main__.TestVisionTracing) ... ok (1.176s)
2023-01-11T22:05:44.0191424Z   test_torchvision_models_resnext101_32x8d (__main__.TestVisionTracing) ... ok (3.232s)
2023-01-11T22:05:44.0191730Z   test_torchvision_models_resnext101_64x4d (__main__.TestVisionTracing) ... ok (3.104s)
2023-01-11T22:05:44.0192044Z   test_torchvision_models_resnext50_32x4d (__main__.TestVisionTracing) ... ok (1.188s)
2023-01-11T22:05:44.0192400Z   test_torchvision_models_segmentation_deeplabv3_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (1.269s)
2023-01-11T22:05:44.0192773Z   test_torchvision_models_segmentation_deeplabv3_resnet101 (__main__.TestVisionTracing) ... ok (2.471s)
2023-01-11T22:05:44.0193120Z   test_torchvision_models_segmentation_deeplabv3_resnet50 (__main__.TestVisionTracing) ... ok (1.347s)
2023-01-11T22:05:44.0193468Z   test_torchvision_models_segmentation_fcn_resnet101 (__main__.TestVisionTracing) ... ok (2.157s)
2023-01-11T22:05:44.0193819Z   test_torchvision_models_segmentation_fcn_resnet50 (__main__.TestVisionTracing) ... ok (1.288s)
2023-01-11T22:05:44.0194207Z   test_torchvision_models_segmentation_lraspp_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (1.209s)
2023-01-11T22:05:44.0194541Z   test_torchvision_models_shufflenet_v2_x0_5 (__main__.TestVisionTracing) ... ok (1.135s)
2023-01-11T22:05:44.0194861Z   test_torchvision_models_shufflenet_v2_x1_0 (__main__.TestVisionTracing) ... ok (1.059s)
2023-01-11T22:05:44.0195176Z   test_torchvision_models_shufflenet_v2_x1_5 (__main__.TestVisionTracing) ... ok (1.063s)
2023-01-11T22:05:44.0195483Z   test_torchvision_models_shufflenet_v2_x2_0 (__main__.TestVisionTracing) ... ok (1.132s)
2023-01-11T22:05:44.0195798Z   test_torchvision_models_squeezenet1_0 (__main__.TestVisionTracing) ... ok (0.354s)
2023-01-11T22:05:44.0196113Z   test_torchvision_models_squeezenet1_1 (__main__.TestVisionTracing) ... ok (0.310s)
2023-01-11T22:05:44.0196420Z   test_torchvision_models_swin_b (__main__.TestVisionTracing) ... ok (3.584s)
2023-01-11T22:05:44.0196707Z   test_torchvision_models_swin_s (__main__.TestVisionTracing) ... ok (3.036s)
2023-01-11T22:05:44.0197007Z   test_torchvision_models_swin_t (__main__.TestVisionTracing) ... ok (1.493s)
2023-01-11T22:05:44.0197308Z   test_torchvision_models_swin_v2_b (__main__.TestVisionTracing) ... ok (4.276s)
2023-01-11T22:05:44.0197597Z   test_torchvision_models_swin_v2_s (__main__.TestVisionTracing) ... ok (3.652s)
2023-01-11T22:05:44.0197896Z   test_torchvision_models_swin_v2_t (__main__.TestVisionTracing) ... ok (1.839s)
2023-01-11T22:05:44.0198193Z   test_torchvision_models_vgg11 (__main__.TestVisionTracing) ... ok (2.800s)
2023-01-11T22:05:44.0198492Z   test_torchvision_models_vgg11_bn (__main__.TestVisionTracing) ... ok (2.991s)
2023-01-11T22:05:44.0198781Z   test_torchvision_models_vgg13 (__main__.TestVisionTracing) ... ok (3.049s)
2023-01-11T22:05:44.0199138Z   test_torchvision_models_vgg13_bn (__main__.TestVisionTracing) ... ok (2.891s)
2023-01-11T22:05:44.0199445Z   test_torchvision_models_vgg16 (__main__.TestVisionTracing) ... ok (3.203s)
2023-01-11T22:05:44.0199729Z   test_torchvision_models_vgg16_bn (__main__.TestVisionTracing) ... ok (3.084s)
2023-01-11T22:05:44.0200027Z   test_torchvision_models_vgg19 (__main__.TestVisionTracing) ... ok (3.352s)
2023-01-11T22:05:44.0200325Z   test_torchvision_models_vgg19_bn (__main__.TestVisionTracing) ... ok (3.466s)
2023-01-11T22:05:44.0200628Z   test_torchvision_models_video_mc3_18 (__main__.TestVisionTracing) ... ok (1.139s)
2023-01-11T22:05:44.0200929Z   test_torchvision_models_video_mvit_v1_b (__main__.TestVisionTracing) ... ok (5.543s)
2023-01-11T22:05:44.0201248Z   test_torchvision_models_video_mvit_v2_s (__main__.TestVisionTracing) ... ok (5.992s)
2023-01-11T22:05:44.0201570Z   test_torchvision_models_video_r2plus1d_18 (__main__.TestVisionTracing) ... ok (1.577s)
2023-01-11T22:05:44.0201874Z   test_torchvision_models_video_r3d_18 (__main__.TestVisionTracing) ... ok (1.441s)
2023-01-11T22:05:44.0202179Z   test_torchvision_models_video_s3d (__main__.TestVisionTracing) ... ok (2.587s)
2023-01-11T22:05:44.0202491Z   test_torchvision_models_video_swin3d_b (__main__.TestVisionTracing) ... ok (3.519s)
2023-01-11T22:05:44.0202846Z   test_torchvision_models_video_swin3d_s (__main__.TestVisionTracing) ... ok (2.786s)
2023-01-11T22:05:44.0203139Z   test_torchvision_models_video_swin3d_t (__main__.TestVisionTracing) ... ok (1.467s)
2023-01-11T22:05:44.0203444Z   test_torchvision_models_vit_b_16 (__main__.TestVisionTracing) ... ok (2.194s)
2023-01-11T22:05:44.0203742Z   test_torchvision_models_vit_b_32 (__main__.TestVisionTracing) ... ok (1.838s)
2023-01-11T22:05:44.0204029Z   test_torchvision_models_vit_h_14 (__main__.TestVisionTracing) ... ok (11.818s)
2023-01-11T22:05:44.0204328Z   test_torchvision_models_vit_l_16 (__main__.TestVisionTracing) ... ok (6.593s)
2023-01-11T22:05:44.0204624Z   test_torchvision_models_vit_l_32 (__main__.TestVisionTracing) ... ok (6.242s)
2023-01-11T22:05:44.0204933Z   test_torchvision_models_wide_resnet101_2 (__main__.TestVisionTracing) ... ok (4.098s)
2023-01-11T22:05:44.0205263Z   test_torchvision_models_wide_resnet50_2 (__main__.TestVisionTracing) ... ok (2.182s)
2023-01-11T22:05:44.0205585Z   test_flatten_fully_static (fx.test_gradual_type.TypeCheckerTest) ... ok (0.017s)
2023-01-11T22:05:44.0205893Z   test_resnet50 (fx.test_gradual_type.TypeCheckerTest) ... ok (1.852s)
2023-01-11T22:05:44.0206195Z   test_symbolic_add_with_broadcast (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:44.0206532Z   test_symbolic_add_with_broadcast_2 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0206858Z   test_type_check_add_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0207173Z   test_type_check_add_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0207488Z   test_type_check_add_with_broadcast (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0207827Z   test_type_check_add_with_scalar (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0208158Z   test_type_check_batch_norm_2D (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:44.0208489Z   test_type_check_batch_norm_2D_broadcast (fx.test_gradual_type.TypeCheckerTest) ... ok (0.007s)
2023-01-11T22:05:44.0208830Z   test_type_check_batch_norm_2D_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0209167Z   test_type_check_batch_norm_symbolic (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:44.0209490Z   test_type_check_conv2D (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:44.0209788Z   test_type_check_conv2D_2 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.007s)
2023-01-11T22:05:44.0210113Z   test_type_check_conv2D_2_fully_static (fx.test_gradual_type.TypeCheckerTest) ... ok (0.028s)
2023-01-11T22:05:44.0210461Z   test_type_check_conv2D_maxpool2d_flatten (fx.test_gradual_type.TypeCheckerTest) ... ok (0.005s)
2023-01-11T22:05:44.0210787Z   test_type_check_conv2D_types (fx.test_gradual_type.TypeCheckerTest) ... ok (0.008s)
2023-01-11T22:05:44.0211109Z   test_type_check_flatten (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0211426Z   test_type_check_flatten3 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0211740Z   test_type_check_flatten_2 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:44.0212054Z   test_type_check_reshape_dyn_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:44.0212388Z   test_type_check_reshape_dyn_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:44.0212734Z   test_type_check_reshape_dyn_true_param_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:44.0213075Z   test_type_check_reshape_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0213387Z   test_type_check_reshape_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0213751Z   test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten (fx.test_gradual_type.TypeCheckerTest) ... ok (0.019s)
2023-01-11T22:05:44.0214150Z   test_type_check_transpose_False (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0214473Z   test_type_check_transpose_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0214808Z   test_type_maxpool2d_fully_static (fx.test_gradual_type.TypeCheckerTest) ... ok (0.025s)
2023-01-11T22:05:44.0215158Z   test_type_typechecl_maxpool2d_3dinput (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:44.0215500Z   test_typecheck_basicblock (fx.test_gradual_type.TypeCheckerTest) ... ok (0.006s)
2023-01-11T22:05:44.0215671Z 
2023-01-11T22:05:44.0215899Z ----------------------------------------------------------------------
2023-01-11T22:05:44.0216142Z Ran 526 tests in 269.381s
2023-01-11T22:05:44.0216258Z 
2023-01-11T22:05:44.0216331Z OK (skipped=3)
2023-01-11T22:05:44.0216439Z 
2023-01-11T22:05:44.0216509Z Generating XML reports...
2023-01-11T22:05:44.0216982Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.AnnotationsTest-20230111220111.xml
2023-01-11T22:05:44.0217513Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_cse_pass.TestCSEPass-20230111220111.xml
2023-01-11T22:05:44.0218051Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_common_passes.TestCommonPass-20230111220111.xml
2023-01-11T22:05:44.0218567Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_fx_const_fold.TestConstFold-20230111220111.xml
2023-01-11T22:05:44.0219193Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow-20230111220111.xml
2023-01-11T22:05:44.0219770Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_dce_pass.TestDCE-20230111220111.xml
2023-01-11T22:05:44.0220229Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestFX-20230111220111.xml
2023-01-11T22:05:44.0220733Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestFXAPIBackwardCompatibility-20230111220111.xml
2023-01-11T22:05:44.0221280Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestFunctionalTracing-20230111220111.xml
2023-01-11T22:05:44.0221815Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_pass_infra.TestPassManager-20230111220111.xml
2023-01-11T22:05:44.0222535Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_subgraph_rewriter.TestSubgraphRewriter-20230111220111.xml
2023-01-11T22:05:44.0223065Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestVisionTracing-20230111220111.xml
2023-01-11T22:05:44.0223585Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.TypeCheckerTest-20230111220111.xml
2023-01-11T22:05:44.0223821Z 
2023-01-11T22:05:44.0224171Z ##[endgroup]
2023-01-11T22:05:44.0224520Z FINISHED PRINTING LOG FILE of test_fx (/var/lib/jenkins/workspace/test/test-reports/test_fx_jso06ja3)
2023-01-11T22:05:44.0224720Z 
2023-01-11T22:05:44.0224883Z Running test_indexing ... [2023-01-11 22:05:44.002579]
2023-01-11T22:05:44.0225345Z Executing ['/opt/conda/bin/python', '-bb', 'test_indexing.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:44.002821]
2023-01-11T22:05:45.9732667Z 
2023-01-11T22:05:45.9733982Z Expand the folded group to see the log file of test_indexing
2023-01-11T22:05:45.9735596Z ##[group]PRINTING LOG FILE of test_indexing (/var/lib/jenkins/workspace/test/test-reports/test_indexing_o2r311vl)
2023-01-11T22:05:45.9736008Z 
2023-01-11T22:05:45.9736095Z Running tests...
2023-01-11T22:05:45.9736508Z ----------------------------------------------------------------------
2023-01-11T22:05:45.9736681Z 
2023-01-11T22:05:45.9736935Z ----------------------------------------------------------------------
2023-01-11T22:05:45.9737248Z Ran 0 tests in 0.000s
2023-01-11T22:05:45.9737388Z 
2023-01-11T22:05:45.9737451Z OK
2023-01-11T22:05:45.9737571Z 
2023-01-11T22:05:45.9737668Z Generating XML reports...
2023-01-11T22:05:45.9738487Z Test results will be stored in test-reports/python-unittest/test_indexing
2023-01-11T22:05:45.9738793Z 
2023-01-11T22:05:45.9739033Z ##[endgroup]
2023-01-11T22:05:45.9739427Z FINISHED PRINTING LOG FILE of test_indexing (/var/lib/jenkins/workspace/test/test-reports/test_indexing_o2r311vl)
2023-01-11T22:05:45.9739643Z 
2023-01-11T22:05:45.9739819Z Running test_jit_cuda_fuser ... [2023-01-11 22:05:45.973350]
2023-01-11T22:05:45.9740288Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_cuda_fuser.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:45.973641]
2023-01-11T22:05:48.7644758Z 
2023-01-11T22:05:48.7645206Z Expand the folded group to see the log file of test_jit_cuda_fuser
2023-01-11T22:05:48.7645973Z ##[group]PRINTING LOG FILE of test_jit_cuda_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_cuda_fuser_nfdw1hv2)
2023-01-11T22:05:48.7646342Z 
2023-01-11T22:05:48.7646456Z Running tests...
2023-01-11T22:05:48.7647412Z ----------------------------------------------------------------------
2023-01-11T22:05:48.7647801Z Test results will be stored in test-reports/python-unittest/test_jit_cuda_fuser
2023-01-11T22:05:48.7648129Z   test__softmax_function (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7648464Z   test__softmax_function_half_to_float (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7648781Z   test_addcmul_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7649335Z   test_alias_pass_fix (__main__.TestCudaFuser) ... skip: skipping this test since unsqueeze is disabled now (0.001s)
2023-01-11T22:05:48.7649899Z   test_autocast_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7650392Z   test_autocast_1_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7650925Z   test_autocast_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7651379Z   test_autocast_2_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7651725Z   test_backward_type (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7652055Z   test_batch_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7652394Z   test_batch_norm_impl_index_correctness (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7652740Z   test_batch_norm_impl_index_inner_bcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7653090Z   test_bfloat (__main__.TestCudaFuser) ... skip: device does not support BFloat16 (0.001s)
2023-01-11T22:05:48.7653604Z   test_binary_bitwise (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7654108Z   test_binary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7654706Z   test_binary_ops_channels_last_with_bcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7655408Z   test_binary_ops_complex (__main__.TestCudaFuser) ... skip: see issue https://github.com/csarofeen/pytorch/issues/1730 (0.001s)
2023-01-11T22:05:48.7656058Z   test_binary_ops_permutation (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7656609Z   test_branches (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7657125Z   test_broadcasting_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7657411Z   test_broadcasting_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7657703Z   test_broadcasting_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7657999Z   test_broadcasting_3 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7658467Z   test_broadcasting_multiple_output (__main__.TestCudaFuser) ... skip: broadcast on branches can't be resolved yet (0.001s)
2023-01-11T22:05:48.7658874Z   test_broadcasting_multiple_output_shape (__main__.TestCudaFuser) ... skip: Broadcast with different output not supported yet (0.001s)
2023-01-11T22:05:48.7659366Z   test_broadcasting_partition_logic_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7659705Z   test_broadcasting_partition_logic_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7660042Z   test_build_shape_expression_native_dropout (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7660372Z   test_category_rule (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T22:05:48.7660696Z   test_channels_last_with_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T22:05:48.7661005Z   test_chunk (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7661276Z   test_clamp (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7661580Z   test_clamp_reversed_bound (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7661938Z   test_clean_profile_ivalue (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7662234Z   test_const (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7662728Z   test_contiguous_on_broadcasted (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7663046Z   test_conv2d_bias (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7663359Z   test_conv2d_symbolic_shapes (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7663655Z   test_cpu_scalar (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7663959Z   test_cuda_fusion_guard (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7664341Z   test_cuda_fusion_guard_backward (__main__.TestCudaFuser) ... skip: requires NVFuser (0.001s)
2023-01-11T22:05:48.7664658Z   test_device_constant (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7665012Z   test_disable_const_chunk_propagation_for_normalization (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7665362Z   test_disable_sibling_fuse (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7665667Z   test_dropout_inference_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7666008Z   test_dropout_train_nograd_fusion (__main__.TestCudaFuser) ... skip: not enough memory (0.001s)
2023-01-11T22:05:48.7666347Z   test_dropout_train_nograd_prob_check (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7666669Z   test_dropout_training_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7666995Z   test_dropout_training_prob_check (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7667309Z   test_dynamic_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7667603Z   test_expand (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7667900Z   test_fix_shape_expression_bn (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7668242Z   test_flatten (__main__.TestCudaFuser) ... skip: skipping this test since flatten is disabled now (0.001s)
2023-01-11T22:05:48.7668561Z   test_gelu (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7668839Z   test_grad_sum_to_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7669168Z   test_graph_for_with_missing_optimized_engine (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7669491Z   test_graph_rng (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7669775Z   test_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7670053Z   test_high_rank_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7670361Z   test_inf_quick_patch (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7670721Z   test_inplace_removal (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7671029Z   test_input_output_passthrough (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7671347Z   test_int_tensor_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7671648Z   test_issue1445_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7671944Z   test_issue_1785 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7672232Z   test_layer_norm_autodiff (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7672548Z   test_layer_norm_parser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7672874Z   test_layer_norm_trivial_reduce_dim (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7673169Z   test_linear (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7673516Z   test_linear_symbolic_shapes (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7673839Z   test_multiple_device_pw (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7674157Z   test_native_batch_norm_backward (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7674463Z   test_native_layer_norm (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7674774Z   test_native_layer_norm_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7675092Z   test_native_layer_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7675424Z   test_nested_view (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.000s)
2023-01-11T22:05:48.7675755Z   test_no_tensor_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7676047Z   test_norm (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7676339Z   test_norm_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7676626Z   test_norm_channels_last (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7676927Z   test_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7677222Z   test_norm_half_layer (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7677506Z   test_norm_large (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7677814Z   test_normalization_partition (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7678163Z   test_nvfuser_comparison_callbacks_with_fallback (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7678533Z   test_nvfuser_comparison_callbacks_without_fallback (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7678863Z   test_overlapped_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7679250Z   test_permutation_preservation (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7679598Z   test_permutation_preservation_edge_case_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7679965Z   test_permutation_preservation_edge_case_1_broken (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7680311Z   test_permutation_preservation_edge_case_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7680634Z   test_permute (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7680950Z   test_pointwise_reference_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7681256Z   test_profile_ivalue (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7681585Z   test_profile_ivalue_multiple_profiles (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7681978Z   test_profiling_node (__main__.TestCudaFuser) ... skip: Skipped due to rand_like behavior change (0.000s)
2023-01-11T22:05:48.7682329Z   test_pw_single_reduction_partition (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7682633Z   test_random_topo (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7682927Z   test_reduction (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7683232Z   test_reduction_dtypes_axis (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7683534Z   test_reduction_empty_axes (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7683852Z   test_reduction_multiple_output (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7684178Z   test_reduction_permutation (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7684536Z   test_reduction_sizes_op (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7684853Z   test_remove_output_used_only_in_dtype (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7685161Z   test_rsub (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7685456Z   test_scalar_cuda_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7685748Z   test_scalar_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7686043Z   test_scalar_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7686350Z   test_scalar_tensor_permuted (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7686689Z   test_scheduler_with_polymorphic_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7687059Z   test_shape_expression (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T22:05:48.7687419Z   test_sibling_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7687741Z   test_sibling_fusion_no_scalar_inputs (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7688074Z   test_single_reduction_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7688377Z   test_singleton_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7688673Z   test_skip_parser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7688965Z   test_softmax (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7689247Z   test_softmax_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7689544Z   test_softmax_dtype (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7689840Z   test_softmax_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7690142Z   test_softplus_fuser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7690473Z   test_squeeze (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.000s)
2023-01-11T22:05:48.7690869Z   test_squeeze_negative_dim (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.000s)
2023-01-11T22:05:48.7691264Z   test_squeeze_zero (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T22:05:48.7691591Z   test_strict_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7691885Z   test_sum_to_one (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7692175Z   test_sum_to_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7692465Z   test_ternary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T22:05:48.7692775Z   test_ternary_ops_integer_compatibility (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7693150Z   test_ternary_ops_type_promotion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7693458Z   test_to_boolean (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7693731Z   test_to_copy (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7694026Z   test_to_dtype_bf16_to_bf16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7694327Z   test_to_dtype_bf16_to_fp32 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7694632Z   test_to_dtype_fp16_to_fp16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7694920Z   test_to_dtype_fp16_to_fp32 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7695219Z   test_to_dtype_fp32_to_bf16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7695552Z   test_to_dtype_fp32_to_fp16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7695853Z   test_transpose (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7696146Z   test_transpose_default (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7696454Z   test_trivial_reduction (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7696752Z   test_type_as_op (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7697035Z   test_type_inference (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7697332Z   test_unary_bitwise (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7697621Z   test_unary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7697959Z   test_unsqueeze (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.000s)
2023-01-11T22:05:48.7698283Z   test_variance (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7698586Z   test_variance_profiling (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7698916Z   test_view (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.000s)
2023-01-11T22:05:48.7699227Z   test_view_before_permute (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7699582Z   test_view_copy_graph_guard (__main__.TestCudaFuser) ... skip: skipping this test since reshape is disabled now (0.000s)
2023-01-11T22:05:48.7699980Z   test_view_copy_graph_guard_double_fusion (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.001s)
2023-01-11T22:05:48.7700343Z   test_can_be_enabled_nvfuser (__main__.TestEnableDisableCudaFuser) ... ok (0.001s)
2023-01-11T22:05:48.7700680Z   test_context_manager_test (__main__.TestEnableDisableCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:48.7701038Z   test_register_fuser (__main__.TestEnableDisableCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:48.7701373Z   test_register_fuser_cpu (__main__.TestEnableDisableCudaFuser) ... ok (0.005s)
2023-01-11T22:05:48.7701684Z   test_autodiff_fallback (jit.test_fuser_common.TestFuserCommon) ... ok (0.054s)
2023-01-11T22:05:48.7701863Z 
2023-01-11T22:05:48.7702101Z ----------------------------------------------------------------------
2023-01-11T22:05:48.7702462Z Ran 158 tests in 0.168s
2023-01-11T22:05:48.7702638Z 
2023-01-11T22:05:48.7702714Z OK (skipped=155)
2023-01-11T22:05:48.7702824Z 
2023-01-11T22:05:48.7702896Z Generating XML reports...
2023-01-11T22:05:48.7703370Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestEnableDisableCudaFuser-20230111220548.xml
2023-01-11T22:05:48.7703959Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-jit.test_fuser_common.TestFuserCommon-20230111220548.xml
2023-01-11T22:05:48.7704498Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestCudaFuser-20230111220548.xml
2023-01-11T22:05:48.7704780Z 
2023-01-11T22:05:48.7705078Z ##[endgroup]
2023-01-11T22:05:48.7705477Z FINISHED PRINTING LOG FILE of test_jit_cuda_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_cuda_fuser_nfdw1hv2)
2023-01-11T22:05:48.7705700Z 
2023-01-11T22:05:48.7705871Z Running test_jit_disabled ... [2023-01-11 22:05:48.764936]
2023-01-11T22:05:48.7706333Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_disabled.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:48.765443]
2023-01-11T22:05:50.7294047Z 
2023-01-11T22:05:50.7294671Z Expand the folded group to see the log file of test_jit_disabled
2023-01-11T22:05:50.7295694Z ##[group]PRINTING LOG FILE of test_jit_disabled (/var/lib/jenkins/workspace/test/test-reports/test_jit_disabled_ttoqxyt1)
2023-01-11T22:05:50.7296012Z 
2023-01-11T22:05:50.7296134Z Running tests...
2023-01-11T22:05:50.7296607Z ----------------------------------------------------------------------
2023-01-11T22:05:50.7297251Z Test results will be stored in test-reports/python-unittest/test_jit_disabled
2023-01-11T22:05:50.7297662Z   test_attribute (__main__.TestJitDisabled) ... ok (0.256s)
2023-01-11T22:05:50.7298001Z   test_recursive_script (__main__.TestJitDisabled) ... ok (0.032s)
2023-01-11T22:05:50.7298300Z   test_script_module_construction (__main__.TestJitDisabled) ... ok (0.032s)
2023-01-11T22:05:50.7298472Z 
2023-01-11T22:05:50.7298666Z ----------------------------------------------------------------------
2023-01-11T22:05:50.7298909Z Ran 3 tests in 0.321s
2023-01-11T22:05:50.7299024Z 
2023-01-11T22:05:50.7299085Z OK
2023-01-11T22:05:50.7299176Z 
2023-01-11T22:05:50.7299245Z Generating XML reports...
2023-01-11T22:05:50.7299668Z Generated XML report: test-reports/python-unittest/test_jit_disabled/TEST-TestJitDisabled-20230111220550.xml
2023-01-11T22:05:50.7299902Z 
2023-01-11T22:05:50.7300144Z ##[endgroup]
2023-01-11T22:05:50.7300524Z FINISHED PRINTING LOG FILE of test_jit_disabled (/var/lib/jenkins/workspace/test/test-reports/test_jit_disabled_ttoqxyt1)
2023-01-11T22:05:50.7300748Z 
2023-01-11T22:05:50.7300915Z Running test_linalg ... [2023-01-11 22:05:50.729439]
2023-01-11T22:05:50.7301372Z Executing ['/opt/conda/bin/python', '-bb', 'test_linalg.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:50.729670]
2023-01-11T22:05:52.6343737Z 
2023-01-11T22:05:52.6344263Z Expand the folded group to see the log file of test_linalg
2023-01-11T22:05:52.6345193Z ##[group]PRINTING LOG FILE of test_linalg (/var/lib/jenkins/workspace/test/test-reports/test_linalg_l0999tk3)
2023-01-11T22:05:52.6345407Z 
2023-01-11T22:05:52.6345482Z Running tests...
2023-01-11T22:05:52.6345903Z ----------------------------------------------------------------------
2023-01-11T22:05:52.6346075Z 
2023-01-11T22:05:52.6346301Z ----------------------------------------------------------------------
2023-01-11T22:05:52.6346579Z Ran 0 tests in 0.000s
2023-01-11T22:05:52.6346691Z 
2023-01-11T22:05:52.6346753Z OK
2023-01-11T22:05:52.6346859Z 
2023-01-11T22:05:52.6346944Z Generating XML reports...
2023-01-11T22:05:52.6347255Z Test results will be stored in test-reports/python-unittest/test_linalg
2023-01-11T22:05:52.6347431Z 
2023-01-11T22:05:52.6347647Z ##[endgroup]
2023-01-11T22:05:52.6348120Z FINISHED PRINTING LOG FILE of test_linalg (/var/lib/jenkins/workspace/test/test-reports/test_linalg_l0999tk3)
2023-01-11T22:05:52.6348352Z 
2023-01-11T22:05:52.6348545Z Running test_mobile_optimizer ... [2023-01-11 22:05:52.634506]
2023-01-11T22:05:52.6349035Z Executing ['/opt/conda/bin/python', '-bb', 'test_mobile_optimizer.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:52.634725]
2023-01-11T22:05:56.8808894Z 
2023-01-11T22:05:56.8809694Z Expand the folded group to see the log file of test_mobile_optimizer
2023-01-11T22:05:56.8810993Z ##[group]PRINTING LOG FILE of test_mobile_optimizer (/var/lib/jenkins/workspace/test/test-reports/test_mobile_optimizer_2kec7tf5)
2023-01-11T22:05:56.8811738Z 
2023-01-11T22:05:56.8811872Z Running tests...
2023-01-11T22:05:56.8812288Z ----------------------------------------------------------------------
2023-01-11T22:05:56.8812770Z Test results will be stored in test-reports/python-unittest/test_mobile_optimizer
2023-01-11T22:05:56.8813086Z   test_clone_module_with_class (__main__.TestOptimizer) ... ok (0.241s)
2023-01-11T22:05:56.8814134Z   test_generate_mobile_module_lints (__main__.TestOptimizer) ... /opt/conda/lib/python3.10/site-packages/torch/utils/bundled_inputs.py:394: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:56.8814865Z   if arg._typed_storage().size() <= MAX_RAW_TENSOR_SIZE or skip_size_check:
2023-01-11T22:05:56.8815104Z ok (0.029s)
2023-01-11T22:05:56.8815424Z   test_hoist_conv_packed_params (__main__.TestOptimizer) ... ok (0.464s)
2023-01-11T22:05:56.8815769Z   test_mobilenet_optimize_for_mobile (__main__.TestOptimizer) ... ok (1.348s)
2023-01-11T22:05:56.8816059Z   test_optimize_for_mobile (__main__.TestOptimizer) ... ok (0.172s)
2023-01-11T22:05:56.8816393Z   test_preserve_bundled_inputs_methods (__main__.TestOptimizer) ... ok (0.037s)
2023-01-11T22:05:56.8816701Z   test_quantized_conv_no_asan_failures (__main__.TestOptimizer) ... ok (0.130s)
2023-01-11T22:05:56.8816858Z 
2023-01-11T22:05:56.8817080Z ----------------------------------------------------------------------
2023-01-11T22:05:56.8817353Z Ran 7 tests in 2.423s
2023-01-11T22:05:56.8817468Z 
2023-01-11T22:05:56.8817529Z OK
2023-01-11T22:05:56.8817619Z 
2023-01-11T22:05:56.8817690Z Generating XML reports...
2023-01-11T22:05:56.8818162Z Generated XML report: test-reports/python-unittest/test_mobile_optimizer/TEST-TestOptimizer-20230111220554.xml
2023-01-11T22:05:56.8818395Z 
2023-01-11T22:05:56.8818685Z ##[endgroup]
2023-01-11T22:05:56.8819088Z FINISHED PRINTING LOG FILE of test_mobile_optimizer (/var/lib/jenkins/workspace/test/test-reports/test_mobile_optimizer_2kec7tf5)
2023-01-11T22:05:56.8819318Z 
2023-01-11T22:05:56.8819532Z Running test_modules ... [2023-01-11 22:05:56.881047]
2023-01-11T22:05:56.8819990Z Executing ['/opt/conda/bin/python', '-bb', 'test_modules.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:56.881305]
2023-01-11T22:05:59.3453926Z 
2023-01-11T22:05:59.3454481Z Expand the folded group to see the log file of test_modules
2023-01-11T22:05:59.3455410Z ##[group]PRINTING LOG FILE of test_modules (/var/lib/jenkins/workspace/test/test-reports/test_modules_y75r8rd8)
2023-01-11T22:05:59.3455681Z 
2023-01-11T22:05:59.3455745Z Running tests...
2023-01-11T22:05:59.3456246Z ----------------------------------------------------------------------
2023-01-11T22:05:59.3456504Z 
2023-01-11T22:05:59.3456709Z ----------------------------------------------------------------------
2023-01-11T22:05:59.3456960Z Ran 0 tests in 0.000s
2023-01-11T22:05:59.3457120Z 
2023-01-11T22:05:59.3457182Z OK
2023-01-11T22:05:59.3457274Z 
2023-01-11T22:05:59.3457362Z Generating XML reports...
2023-01-11T22:05:59.3457709Z Test results will be stored in test-reports/python-unittest/test_modules
2023-01-11T22:05:59.3457930Z 
2023-01-11T22:05:59.3458146Z ##[endgroup]
2023-01-11T22:05:59.3458609Z FINISHED PRINTING LOG FILE of test_modules (/var/lib/jenkins/workspace/test/test-reports/test_modules_y75r8rd8)
2023-01-11T22:05:59.3458822Z 
2023-01-11T22:05:59.3459013Z Running test_multiprocessing ... [2023-01-11 22:05:59.345526]
2023-01-11T22:05:59.3459495Z Executing ['/opt/conda/bin/python', '-bb', 'test_multiprocessing.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:59.345742]
2023-01-11T22:06:11.3333412Z 
2023-01-11T22:06:11.3333964Z Expand the folded group to see the log file of test_multiprocessing
2023-01-11T22:06:11.3335044Z ##[group]PRINTING LOG FILE of test_multiprocessing (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_p7p9y9kt)
2023-01-11T22:06:11.3335669Z 
2023-01-11T22:06:11.3335765Z Running tests...
2023-01-11T22:06:11.3336313Z ----------------------------------------------------------------------
2023-01-11T22:06:11.3337385Z Test results will be stored in test-reports/python-unittest/test_multiprocessing
2023-01-11T22:06:11.3337843Z   test_autograd_errors (__main__.TestMultiprocessing) ... ok (0.246s)
2023-01-11T22:06:11.3338727Z   test_autograd_fine_with_spawn (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:11.3339309Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:11.3340029Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:11.3340662Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:11.3341505Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:11.3342039Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:11.3342956Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:11.3343533Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:11.3344385Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:11.3345027Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:11.3345760Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:11.3346360Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:11.3346745Z ok (1.373s)
2023-01-11T22:06:11.3347203Z   test_cuda_bad_call (__main__.TestMultiprocessing) ... skip: CUDA not available (0.001s)
2023-01-11T22:06:11.3347772Z   test_cuda_ipc_deadlock (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3348378Z   test_cuda_memory_allocation (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3349012Z   test_cuda_parameter_sharing (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:06:11.3349629Z   test_cuda_send_many (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3350175Z   test_cuda_simple (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:06:11.3350748Z   test_cuda_small_tensors (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3351354Z   test_cuda_variable_sharing (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:06:11.3351877Z   test_empty_shared (__main__.TestMultiprocessing) ... ok (0.001s)
2023-01-11T22:06:11.3352364Z   test_empty_tensor_sharing (__main__.TestMultiprocessing) ... ok (0.003s)
2023-01-11T22:06:11.3352919Z   test_empty_tensor_sharing_cuda (__main__.TestMultiprocessing) ... skip: CUDA not available (0.000s)
2023-01-11T22:06:11.3353495Z   test_event (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3354061Z   test_event_handle_exporter (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:06:11.3354676Z   test_event_handle_importer (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3355310Z   test_event_handle_multi_gpu (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3355932Z   test_event_multiprocess (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3356475Z   test_fd_pool (__main__.TestMultiprocessing) ... ok (0.614s)
2023-01-11T22:06:11.3357793Z   test_fd_preserve_sharing (__main__.TestMultiprocessing) ... /var/lib/jenkins/workspace/test/test_multiprocessing.py:304: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:06:11.3358857Z   data = [x.storage(), x, x[2], x[:, 1]]
2023-01-11T22:06:11.3360444Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:06:11.3361424Z   device=typed_storage.device,
2023-01-11T22:06:11.3362494Z /var/lib/jenkins/workspace/test/test_multiprocessing.py:312: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:06:11.3363515Z   self.assertEqual(t.storage()._cdata, storage_cdata)
2023-01-11T22:06:11.3363887Z ok (0.145s)
2023-01-11T22:06:11.3364879Z   test_fd_sharing (__main__.TestMultiprocessing) ... /var/lib/jenkins/workspace/test/test_multiprocessing.py:284: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:06:11.3365831Z   s1 = t1.storage()
2023-01-11T22:06:11.3366786Z /var/lib/jenkins/workspace/test/test_multiprocessing.py:285: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:06:11.3367671Z   s2 = t2.storage()
2023-01-11T22:06:11.3368611Z /var/lib/jenkins/workspace/test/test_multiprocessing.py:287: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:06:11.3369579Z   self.assertEqual(s1.data_ptr(), s1.data_ptr())
2023-01-11T22:06:11.3369918Z ok (0.638s)
2023-01-11T22:06:11.3370944Z   test_fs (__main__.TestMultiprocessing) ... /var/lib/jenkins/workspace/test/test_multiprocessing.py:378: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:06:11.3371951Z   x = torch.DoubleStorage(4)
2023-01-11T22:06:11.3372265Z ok (5.006s)
2023-01-11T22:06:11.3372664Z   test_fs_is_shared (__main__.TestMultiprocessing) ... ok (0.001s)
2023-01-11T22:06:11.3373149Z   test_fs_pool (__main__.TestMultiprocessing) ... ok (0.501s)
2023-01-11T22:06:11.3373658Z   test_fs_preserve_sharing (__main__.TestMultiprocessing) ... ok (0.099s)
2023-01-11T22:06:11.3375247Z   test_fs_sharing (__main__.TestMultiprocessing) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/67002 for platform(s) windows, mac, linux, dynamo, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T22:06:11.3376425Z   test_inherit_tensor (__main__.TestMultiprocessing) ... ok (0.011s)
2023-01-11T22:06:11.3377498Z   test_integer_parameter_serialization_cpu (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:11.3378240Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:11.3378987Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:11.3379602Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:11.3380006Z ok (1.564s)
2023-01-11T22:06:11.3380523Z   test_integer_parameter_serialization_cuda (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:06:11.3381103Z   test_is_shared (__main__.TestMultiprocessing) ... ok (0.001s)
2023-01-11T22:06:11.3381644Z   test_is_shared_cuda (__main__.TestMultiprocessing) ... skip: CUDA not available (0.000s)
2023-01-11T22:06:11.3382325Z   test_leaf_variable_sharing (__main__.TestMultiprocessing) ... ok (0.015s)
2023-01-11T22:06:11.3383037Z   test_mixed_types_cuda_sharing (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:06:11.3383638Z   test_non_leaf_variable_sharing (__main__.TestMultiprocessing) ... ok (0.002s)
2023-01-11T22:06:11.3385297Z   test_parameter_sharing (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/utils/hooks.py:87: UserWarning: backward hook <function TestMultiprocessing._test_autograd_sharing.<locals>.hook at 0x7fad44f14c10> on tensor will not be serialized.  If this is expected, you can decorate the function with @torch.utils.hooks.unserializable_hook to suppress this warning
2023-01-11T22:06:11.3386324Z   warnings.warn("backward hook {} on tensor will not be "
2023-01-11T22:06:11.3386684Z ok (0.010s)
2023-01-11T22:06:11.3388222Z   test_variable_sharing (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/utils/hooks.py:87: UserWarning: backward hook <function TestMultiprocessing._test_autograd_sharing.<locals>.hook at 0x7fad44f157e0> on tensor will not be serialized.  If this is expected, you can decorate the function with @torch.utils.hooks.unserializable_hook to suppress this warning
2023-01-11T22:06:11.3389306Z   warnings.warn("backward hook {} on tensor will not be "
2023-01-11T22:06:11.3389676Z ok (0.018s)
2023-01-11T22:06:11.3390110Z   test_wrong_cuda_fork (__main__.TestMultiprocessing) ... skip: CUDA not available (0.001s)
2023-01-11T22:06:11.3390397Z 
2023-01-11T22:06:11.3390753Z ----------------------------------------------------------------------
2023-01-11T22:06:11.3391165Z Ran 37 tests in 10.260s
2023-01-11T22:06:11.3391365Z 
2023-01-11T22:06:11.3391485Z OK (skipped=19)
2023-01-11T22:06:11.3391652Z 
2023-01-11T22:06:11.3391791Z Generating XML reports...
2023-01-11T22:06:11.3392551Z Generated XML report: test-reports/python-unittest/test_multiprocessing/TEST-TestMultiprocessing-20230111220600.xml
2023-01-11T22:06:11.3392968Z 
2023-01-11T22:06:11.3393408Z ##[endgroup]
2023-01-11T22:06:11.3394036Z FINISHED PRINTING LOG FILE of test_multiprocessing (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_p7p9y9kt)
2023-01-11T22:06:11.3394466Z 
2023-01-11T22:06:11.3394830Z Running test_multiprocessing_spawn ... [2023-01-11 22:06:11.333528]
2023-01-11T22:06:11.3395708Z Executing ['/opt/conda/bin/python', '-bb', 'test_multiprocessing_spawn.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:11.333808]
2023-01-11T22:06:29.9797656Z 
2023-01-11T22:06:29.9798188Z Expand the folded group to see the log file of test_multiprocessing_spawn
2023-01-11T22:06:29.9799354Z ##[group]PRINTING LOG FILE of test_multiprocessing_spawn (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_spawn_jml9v8n9)
2023-01-11T22:06:29.9799806Z 
2023-01-11T22:06:29.9799915Z Running tests...
2023-01-11T22:06:29.9800506Z ----------------------------------------------------------------------
2023-01-11T22:06:29.9801594Z Test results will be stored in test-reports/python-unittest/test_multiprocessing_spawn
2023-01-11T22:06:29.9802062Z   test_errors_pickleable (__main__.ErrorTest) ... ok (0.225s)
2023-01-11T22:06:29.9802451Z   test_exception_all (__main__.ForkTest) ... ok (0.052s)
2023-01-11T22:06:29.9802831Z   test_exception_single (__main__.ForkTest) ... ok (0.021s)
2023-01-11T22:06:29.9803204Z   test_first_argument_index (__main__.ForkTest) ... ok (0.010s)
2023-01-11T22:06:29.9803686Z   test_success (__main__.ForkTest) ... ok (0.009s)
2023-01-11T22:06:29.9804123Z   test_success_first_then_exception (__main__.ForkTest) ... ok (0.112s)
2023-01-11T22:06:29.9804558Z   test_success_non_blocking (__main__.ForkTest) ... ok (0.011s)
2023-01-11T22:06:29.9804949Z   test_terminate_exit (__main__.ForkTest) ... ok (0.009s)
2023-01-11T22:06:29.9805336Z   test_terminate_signal (__main__.ForkTest) ... ok (0.233s)
2023-01-11T22:06:29.9806395Z   test_exception_all (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9807003Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9807747Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9808351Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9809175Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9809817Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9810657Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9811327Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9811733Z ok (1.632s)
2023-01-11T22:06:29.9812506Z   test_exception_raises (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9813283Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9814077Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9814676Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9815066Z ok (1.563s)
2023-01-11T22:06:29.9815875Z   test_exception_single (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9816532Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9817370Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9818006Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9818814Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9819413Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9820249Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9820894Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9821704Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9822254Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9823502Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9824021Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9824962Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9825481Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9826302Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9826878Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9827267Z ok (3.281s)
2023-01-11T22:06:29.9828088Z   test_first_argument_index (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9828771Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9829404Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9829825Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9830268Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9830603Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9831036Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9831362Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9831589Z ok (1.622s)
2023-01-11T22:06:29.9831803Z   test_signal_raises (__main__.SpawnTest) ... ok (0.002s)
2023-01-11T22:06:29.9832263Z   test_success (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9832621Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9833052Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9833398Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9833814Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9834144Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9834571Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9834901Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9835125Z ok (1.659s)
2023-01-11T22:06:29.9835588Z   test_success_first_then_exception (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9835968Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9836388Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9836731Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9837160Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9837513Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9837929Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9838270Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9838493Z ok (1.725s)
2023-01-11T22:06:29.9838943Z   test_success_non_blocking (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9839368Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9839864Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9840215Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9840641Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9840973Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9841404Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9841747Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9841960Z ok (1.629s)
2023-01-11T22:06:29.9842404Z   test_terminate_exit (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9842811Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9843232Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9843573Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9844008Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9844337Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9844750Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9845090Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9845312Z ok (1.595s)
2023-01-11T22:06:29.9845748Z   test_terminate_signal (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9846114Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9846545Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9846884Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9847304Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:06:29.9847628Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:06:29.9848056Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:06:29.9848397Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:06:29.9848607Z ok (1.550s)
2023-01-11T22:06:29.9848708Z 
2023-01-11T22:06:29.9848911Z ----------------------------------------------------------------------
2023-01-11T22:06:29.9849160Z Ran 19 tests in 16.941s
2023-01-11T22:06:29.9849274Z 
2023-01-11T22:06:29.9849322Z OK
2023-01-11T22:06:29.9849412Z 
2023-01-11T22:06:29.9849496Z Generating XML reports...
2023-01-11T22:06:29.9849909Z Generated XML report: test-reports/python-unittest/test_multiprocessing_spawn/TEST-ErrorTest-20230111220612.xml
2023-01-11T22:06:29.9850428Z Generated XML report: test-reports/python-unittest/test_multiprocessing_spawn/TEST-ForkTest-20230111220612.xml
2023-01-11T22:06:29.9850921Z Generated XML report: test-reports/python-unittest/test_multiprocessing_spawn/TEST-SpawnTest-20230111220612.xml
2023-01-11T22:06:29.9851146Z 
2023-01-11T22:06:29.9851457Z ##[endgroup]
2023-01-11T22:06:29.9851886Z FINISHED PRINTING LOG FILE of test_multiprocessing_spawn (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_spawn_jml9v8n9)
2023-01-11T22:06:29.9852115Z 
2023-01-11T22:06:29.9852300Z Running test_namedtuple_return_api ... [2023-01-11 22:06:29.979984]
2023-01-11T22:06:29.9852844Z Executing ['/opt/conda/bin/python', '-bb', 'test_namedtuple_return_api.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:29.980275]
2023-01-11T22:06:32.9954491Z 
2023-01-11T22:06:32.9954973Z Expand the folded group to see the log file of test_namedtuple_return_api
2023-01-11T22:06:32.9955923Z ##[group]PRINTING LOG FILE of test_namedtuple_return_api (/var/lib/jenkins/workspace/test/test-reports/test_namedtuple_return_api_v4dh998s)
2023-01-11T22:06:32.9956300Z 
2023-01-11T22:06:32.9956416Z Running tests...
2023-01-11T22:06:32.9957037Z ----------------------------------------------------------------------
2023-01-11T22:06:32.9957762Z Test results will be stored in test-reports/python-unittest/test_namedtuple_return_api
2023-01-11T22:06:32.9958361Z   test_import_return_types (__main__.TestNamedTupleAPI) ... ok (0.210s)
2023-01-11T22:06:32.9959196Z   test_namedtuple_return (__main__.TestNamedTupleAPI) ... /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.qr is deprecated in favor of torch.linalg.qr and will be removed in a future PyTorch release.
2023-01-11T22:06:32.9960083Z The boolean parameter 'some' has been replaced with a string parameter 'mode'.
2023-01-11T22:06:32.9960440Z Q, R = torch.qr(A, some)
2023-01-11T22:06:32.9960711Z should be replaced with
2023-01-11T22:06:32.9961516Z Q, R = torch.linalg.qr(A, 'reduced' if some else 'complete') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2459.)
2023-01-11T22:06:32.9962084Z   ret1 = func(a, *op.input)
2023-01-11T22:06:32.9962668Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:155: UserWarning: torch.qr is deprecated in favor of torch.linalg.qr and will be removed in a future PyTorch release.
2023-01-11T22:06:32.9963163Z The boolean parameter 'some' has been replaced with a string parameter 'mode'.
2023-01-11T22:06:32.9963417Z Q, R = torch.qr(A, some)
2023-01-11T22:06:32.9963596Z should be replaced with
2023-01-11T22:06:32.9964081Z Q, R = torch.linalg.qr(A, 'reduced' if some else 'complete') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2471.)
2023-01-11T22:06:32.9964449Z   ret2 = func(a, *op.input, out=tuple(ret1))
2023-01-11T22:06:32.9964839Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T22:06:32.9965321Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T22:06:32.9965647Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T22:06:32.9965852Z should be replaced with
2023-01-11T22:06:32.9966132Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T22:06:32.9966344Z and
2023-01-11T22:06:32.9966531Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T22:06:32.9966728Z should be replaced with
2023-01-11T22:06:32.9967198Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2910.)
2023-01-11T22:06:32.9967537Z   ret1 = func(a, *op.input)
2023-01-11T22:06:32.9967894Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:155: UserWarning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T22:06:32.9968377Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T22:06:32.9968694Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T22:06:32.9968898Z should be replaced with
2023-01-11T22:06:32.9969173Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T22:06:32.9969385Z and
2023-01-11T22:06:32.9969571Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T22:06:32.9969767Z should be replaced with
2023-01-11T22:06:32.9970230Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2928.)
2023-01-11T22:06:32.9970714Z   ret2 = func(a, *op.input, out=tuple(ret1))
2023-01-11T22:06:32.9971125Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.
2023-01-11T22:06:32.9971604Z torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.
2023-01-11T22:06:32.9971934Z X = torch.triangular_solve(B, A).solution
2023-01-11T22:06:32.9972146Z should be replaced with
2023-01-11T22:06:32.9972474Z X = torch.linalg.solve_triangular(A, B). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2225.)
2023-01-11T22:06:32.9972798Z   ret1 = func(a, *op.input)
2023-01-11T22:06:32.9973240Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.lu is deprecated in favor of torch.linalg.lu_factor / torch.linalg.lu_factor_ex and will be removed in a future PyTorch release.
2023-01-11T22:06:32.9973642Z LU, pivots = torch.lu(A, compute_pivots)
2023-01-11T22:06:32.9973839Z should be replaced with
2023-01-11T22:06:32.9974070Z LU, pivots = torch.linalg.lu_factor(A, compute_pivots)
2023-01-11T22:06:32.9974280Z and
2023-01-11T22:06:32.9974481Z LU, pivots, info = torch.lu(A, compute_pivots, get_infos=True)
2023-01-11T22:06:32.9974712Z should be replaced with
2023-01-11T22:06:32.9975071Z LU, pivots, info = torch.linalg.lu_factor_ex(A, compute_pivots) (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2029.)
2023-01-11T22:06:32.9975412Z   ret1 = func(a, *op.input)
2023-01-11T22:06:32.9975581Z ok (0.039s)
2023-01-11T22:06:32.9975820Z   test_native_functions_yaml (__main__.TestNamedTupleAPI) ... ok (1.117s)
2023-01-11T22:06:32.9975993Z 
2023-01-11T22:06:32.9976199Z ----------------------------------------------------------------------
2023-01-11T22:06:32.9976435Z Ran 3 tests in 1.366s
2023-01-11T22:06:32.9976551Z 
2023-01-11T22:06:32.9976613Z OK
2023-01-11T22:06:32.9976704Z 
2023-01-11T22:06:32.9976787Z Generating XML reports...
2023-01-11T22:06:32.9977212Z Generated XML report: test-reports/python-unittest/test_namedtuple_return_api/TEST-TestNamedTupleAPI-20230111220631.xml
2023-01-11T22:06:32.9977461Z 
2023-01-11T22:06:32.9977742Z ##[endgroup]
2023-01-11T22:06:32.9978168Z FINISHED PRINTING LOG FILE of test_namedtuple_return_api (/var/lib/jenkins/workspace/test/test-reports/test_namedtuple_return_api_v4dh998s)
2023-01-11T22:06:32.9978409Z 
2023-01-11T22:06:32.9978562Z Running test_ops ... [2023-01-11 22:06:32.995544]
2023-01-11T22:06:34.5481026Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:34.5661498Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:34.6173331Z Ignoring disabled issues:  []
2023-01-11T22:06:34.6305913Z Ignoring disabled issues:  []
2023-01-11T22:06:34.8046269Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:34.804174]
2023-01-11T22:06:34.8047861Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:34.804276]
2023-01-11T22:06:37.9685924Z 
2023-01-11T22:06:37.9686426Z Expand the folded group to see the log file of test_ops
2023-01-11T22:06:37.9687170Z ##[group]PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_p3qr_v_u)
2023-01-11T22:06:37.9687737Z Test results will be stored in test-reports/python-pytest/test_ops/test_ops-b5301370d4336bb2.xml
2023-01-11T22:06:37.9688058Z ============================= test session starts ==============================
2023-01-11T22:06:37.9688642Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:37.9688903Z cachedir: .pytest_cache
2023-01-11T22:06:37.9689319Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:37.9689674Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:37.9690086Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:37.9690382Z collecting ... collected 0 items
2023-01-11T22:06:37.9690588Z Running 0 items in this shard: 
2023-01-11T22:06:37.9690710Z 
2023-01-11T22:06:37.9690810Z =============================== warnings summary ===============================
2023-01-11T22:06:37.9691158Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:37.9691759Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:37.9692121Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:37.9692240Z 
2023-01-11T22:06:37.9692473Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:37.9692952Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops/test_ops-b5301370d4336bb2.xml -
2023-01-11T22:06:37.9693280Z ============================== 1 warning in 0.04s ==============================
2023-01-11T22:06:37.9693581Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:37.9693759Z 
2023-01-11T22:06:37.9693986Z ##[endgroup]
2023-01-11T22:06:37.9694341Z FINISHED PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_p3qr_v_u)
2023-01-11T22:06:37.9694539Z 
2023-01-11T22:06:38.0000064Z 
2023-01-11T22:06:38.0000436Z Expand the folded group to see the log file of test_ops
2023-01-11T22:06:38.0001175Z ##[group]PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_12evdp8v)
2023-01-11T22:06:38.0002089Z Test results will be stored in test-reports/python-pytest/test_ops/test_ops-b489e73817dffd05.xml
2023-01-11T22:06:38.0002477Z ============================= test session starts ==============================
2023-01-11T22:06:38.0002969Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:38.0003439Z cachedir: .pytest_cache
2023-01-11T22:06:38.0003974Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:38.0004332Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:38.0004745Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:38.0005048Z collecting ... collected 0 items
2023-01-11T22:06:38.0005264Z Running 0 items in this shard: 
2023-01-11T22:06:38.0005390Z 
2023-01-11T22:06:38.0005523Z =============================== warnings summary ===============================
2023-01-11T22:06:38.0006010Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:38.0006804Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:38.0007169Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:38.0007301Z 
2023-01-11T22:06:38.0007529Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:38.0008007Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops/test_ops-b489e73817dffd05.xml -
2023-01-11T22:06:38.0008342Z ============================== 1 warning in 0.04s ==============================
2023-01-11T22:06:38.0008645Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:38.0008995Z 
2023-01-11T22:06:38.0009259Z ##[endgroup]
2023-01-11T22:06:38.0009619Z FINISHED PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_12evdp8v)
2023-01-11T22:06:38.0009820Z 
2023-01-11T22:06:38.3380869Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:38.337669]
2023-01-11T22:06:41.3317242Z 
2023-01-11T22:06:41.3317717Z Expand the folded group to see the log file of test_ops
2023-01-11T22:06:41.3318622Z ##[group]PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_h_1vzg1z)
2023-01-11T22:06:41.3319220Z Test results will be stored in test-reports/python-pytest/test_ops/test_ops-a5a4f1a02e3ee27c.xml
2023-01-11T22:06:41.3319816Z ============================= test session starts ==============================
2023-01-11T22:06:41.3320196Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:41.3320451Z cachedir: .pytest_cache
2023-01-11T22:06:41.3332249Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:41.3333098Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:41.3333932Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:41.3334432Z collecting ... collected 0 items
2023-01-11T22:06:41.3334793Z Running 0 items in this shard: 
2023-01-11T22:06:41.3335001Z 
2023-01-11T22:06:41.3335184Z =============================== warnings summary ===============================
2023-01-11T22:06:41.3335791Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:41.3336669Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:41.3337288Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:41.3337505Z 
2023-01-11T22:06:41.3337896Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:41.3338694Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops/test_ops-a5a4f1a02e3ee27c.xml -
2023-01-11T22:06:41.3339243Z ============================== 1 warning in 0.12s ==============================
2023-01-11T22:06:41.3339734Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:41.3340059Z 
2023-01-11T22:06:41.3340490Z ##[endgroup]
2023-01-11T22:06:41.3341075Z FINISHED PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_h_1vzg1z)
2023-01-11T22:06:41.3341399Z 
2023-01-11T22:06:41.3341737Z Running test_ops_fwd_gradients ... [2023-01-11 22:06:41.332143]
2023-01-11T22:06:42.8888939Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:42.8946588Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:42.9545820Z Ignoring disabled issues:  []
2023-01-11T22:06:42.9597054Z Ignoring disabled issues:  []
2023-01-11T22:06:42.9733948Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:42.973127]
2023-01-11T22:06:42.9783555Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:42.978069]
2023-01-11T22:06:45.7198762Z 
2023-01-11T22:06:45.7199359Z Expand the folded group to see the log file of test_ops_fwd_gradients
2023-01-11T22:06:45.7200671Z ##[group]PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_iw077jfx)
2023-01-11T22:06:45.7201795Z Test results will be stored in test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-b9403dcd25081305.xml
2023-01-11T22:06:45.7202398Z ============================= test session starts ==============================
2023-01-11T22:06:45.7203037Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:45.7203462Z cachedir: .pytest_cache
2023-01-11T22:06:45.7204205Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:45.7204834Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:45.7205568Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:45.7206199Z collecting ... collected 0 items
2023-01-11T22:06:45.7206549Z Running 0 items in this shard: 
2023-01-11T22:06:45.7206767Z 
2023-01-11T22:06:45.7206959Z =============================== warnings summary ===============================
2023-01-11T22:06:45.7207569Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:45.7208530Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:45.7209163Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:45.7209400Z 
2023-01-11T22:06:45.7209785Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:45.7210735Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-b9403dcd25081305.xml -
2023-01-11T22:06:45.7211385Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:45.7211927Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:45.7212275Z 
2023-01-11T22:06:45.7212668Z ##[endgroup]
2023-01-11T22:06:45.7213399Z FINISHED PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_iw077jfx)
2023-01-11T22:06:45.7213817Z 
2023-01-11T22:06:45.7662540Z 
2023-01-11T22:06:45.7663073Z Expand the folded group to see the log file of test_ops_fwd_gradients
2023-01-11T22:06:45.7664109Z ##[group]PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_qo6xd6ys)
2023-01-11T22:06:45.7665041Z Test results will be stored in test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-1e0b07ffbf426a50.xml
2023-01-11T22:06:45.7665524Z ============================= test session starts ==============================
2023-01-11T22:06:45.7665995Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:45.7666277Z cachedir: .pytest_cache
2023-01-11T22:06:45.7666694Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:45.7667128Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:45.7667788Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:45.7668211Z collecting ... collected 0 items
2023-01-11T22:06:45.7668452Z Running 0 items in this shard: 
2023-01-11T22:06:45.7668578Z 
2023-01-11T22:06:45.7668691Z =============================== warnings summary ===============================
2023-01-11T22:06:45.7669040Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:45.7669572Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:45.7670118Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:45.7670251Z 
2023-01-11T22:06:45.7670488Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:45.7671012Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-1e0b07ffbf426a50.xml -
2023-01-11T22:06:45.7671380Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:45.7671682Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:45.7671878Z 
2023-01-11T22:06:45.7672131Z ##[endgroup]
2023-01-11T22:06:45.7672538Z FINISHED PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_qo6xd6ys)
2023-01-11T22:06:45.7672768Z 
2023-01-11T22:06:46.1155147Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:46.115058]
2023-01-11T22:06:48.7607551Z 
2023-01-11T22:06:48.7608171Z Expand the folded group to see the log file of test_ops_fwd_gradients
2023-01-11T22:06:48.7609352Z ##[group]PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_04gf6wqu)
2023-01-11T22:06:48.7610396Z Test results will be stored in test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-27a816f975c007fa.xml
2023-01-11T22:06:48.7610745Z ============================= test session starts ==============================
2023-01-11T22:06:48.7611103Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:48.7611364Z cachedir: .pytest_cache
2023-01-11T22:06:48.7611793Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:48.7612150Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:48.7612562Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:48.7612993Z collecting ... collected 0 items
2023-01-11T22:06:48.7613203Z Running 0 items in this shard: 
2023-01-11T22:06:48.7613327Z 
2023-01-11T22:06:48.7613428Z =============================== warnings summary ===============================
2023-01-11T22:06:48.7613882Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:48.7614540Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:48.7614956Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:48.7615087Z 
2023-01-11T22:06:48.7615351Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:48.7615926Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-27a816f975c007fa.xml -
2023-01-11T22:06:48.7616333Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:48.7616665Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:48.7616846Z 
2023-01-11T22:06:48.7617127Z ##[endgroup]
2023-01-11T22:06:48.7617538Z FINISHED PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_04gf6wqu)
2023-01-11T22:06:48.7617766Z 
2023-01-11T22:06:48.7617944Z Running test_ops_gradients ... [2023-01-11 22:06:48.761213]
2023-01-11T22:06:50.3324668Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:50.3375816Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:50.4023874Z Ignoring disabled issues:  []
2023-01-11T22:06:50.4181696Z Ignoring disabled issues:  []
2023-01-11T22:06:50.4211371Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:50.420845]
2023-01-11T22:06:50.4368803Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:50.436568]
2023-01-11T22:06:53.2078808Z 
2023-01-11T22:06:53.2079693Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T22:06:53.2084077Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_wjsvgr44)
2023-01-11T22:06:53.2084930Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-57f53faeab72d232.xml
2023-01-11T22:06:53.2085285Z ============================= test session starts ==============================
2023-01-11T22:06:53.2085699Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:53.2085975Z cachedir: .pytest_cache
2023-01-11T22:06:53.2086395Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:53.2086789Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:53.2087263Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:53.2087563Z collecting ... collected 0 items
2023-01-11T22:06:53.2087758Z Running 0 items in this shard: 
2023-01-11T22:06:53.2087883Z 
2023-01-11T22:06:53.2088040Z =============================== warnings summary ===============================
2023-01-11T22:06:53.2088406Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:53.2088978Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:53.2089320Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:53.2089467Z 
2023-01-11T22:06:53.2089736Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:53.2090295Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-57f53faeab72d232.xml -
2023-01-11T22:06:53.2090663Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:53.2090958Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:53.2091195Z 
2023-01-11T22:06:53.2091435Z ##[endgroup]
2023-01-11T22:06:53.2091887Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_wjsvgr44)
2023-01-11T22:06:53.2092105Z 
2023-01-11T22:06:53.2092109Z 
2023-01-11T22:06:53.2092243Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T22:06:53.2092780Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients__bi2bxls)
2023-01-11T22:06:53.2093330Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-fac064cd5b4e0e08.xml
2023-01-11T22:06:53.2093660Z ============================= test session starts ==============================
2023-01-11T22:06:53.2094037Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:53.2094320Z cachedir: .pytest_cache
2023-01-11T22:06:53.2094732Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:53.2095128Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:53.2095702Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:53.2095998Z collecting ... collected 0 items
2023-01-11T22:06:53.2096193Z Running 0 items in this shard: 
2023-01-11T22:06:53.2096348Z 
2023-01-11T22:06:53.2096476Z =============================== warnings summary ===============================
2023-01-11T22:06:53.2096832Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:53.2097418Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:53.2097762Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:53.2097944Z 
2023-01-11T22:06:53.2098171Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:53.2098784Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-fac064cd5b4e0e08.xml -
2023-01-11T22:06:53.2099142Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:53.2099499Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:53.2099694Z 
2023-01-11T22:06:53.2099913Z ##[endgroup]
2023-01-11T22:06:53.2100347Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients__bi2bxls)
2023-01-11T22:06:53.2100572Z 
2023-01-11T22:06:53.5558389Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:53.555453]
2023-01-11T22:06:56.2452960Z 
2023-01-11T22:06:56.2453537Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T22:06:56.2454860Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_h20d9p2f)
2023-01-11T22:06:56.2455549Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-94cc7b66651244d1.xml
2023-01-11T22:06:56.2455891Z ============================= test session starts ==============================
2023-01-11T22:06:56.2456245Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:56.2456505Z cachedir: .pytest_cache
2023-01-11T22:06:56.2456920Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:56.2457282Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:56.2457755Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:56.2458054Z collecting ... collected 0 items
2023-01-11T22:06:56.2458314Z Running 0 items in this shard: 
2023-01-11T22:06:56.2458446Z 
2023-01-11T22:06:56.2458576Z =============================== warnings summary ===============================
2023-01-11T22:06:56.2458937Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:56.2459460Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:56.2459814Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:56.2459934Z 
2023-01-11T22:06:56.2460169Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:56.2460686Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-94cc7b66651244d1.xml -
2023-01-11T22:06:56.2461042Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:56.2461352Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:56.2461718Z 
2023-01-11T22:06:56.2461961Z ##[endgroup]
2023-01-11T22:06:56.2462566Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_h20d9p2f)
2023-01-11T22:06:56.2462796Z 
2023-01-11T22:06:56.2462971Z Running test_ops_jit ... [2023-01-11 22:06:56.245722]
2023-01-11T22:06:57.8450104Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:57.8526576Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:57.9186510Z Ignoring disabled issues:  []
2023-01-11T22:06:57.9305645Z Ignoring disabled issues:  []
2023-01-11T22:06:57.9377955Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_jit.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:57.937400]
2023-01-11T22:06:57.9493812Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_jit.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:57.949058]
2023-01-11T22:07:00.7614698Z 
2023-01-11T22:07:00.7615218Z Expand the folded group to see the log file of test_ops_jit
2023-01-11T22:07:00.7616235Z ##[group]PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_a0yd3h9f)
2023-01-11T22:07:00.7617270Z Test results will be stored in test-reports/python-pytest/test_ops_jit/test_ops_jit-fe16d0ecf9be8ab0.xml
2023-01-11T22:07:00.7617824Z ============================= test session starts ==============================
2023-01-11T22:07:00.7618335Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:07:00.7618692Z cachedir: .pytest_cache
2023-01-11T22:07:00.7619133Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:07:00.7619489Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:07:00.7619918Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:07:00.7620211Z collecting ... collected 0 items
2023-01-11T22:07:00.7620402Z Running 0 items in this shard: 
2023-01-11T22:07:00.7620523Z 
2023-01-11T22:07:00.7620635Z =============================== warnings summary ===============================
2023-01-11T22:07:00.7620986Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:07:00.7621512Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:07:00.7621855Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:07:00.7621985Z 
2023-01-11T22:07:00.7622222Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:07:00.7622910Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_jit/test_ops_jit-fe16d0ecf9be8ab0.xml -
2023-01-11T22:07:00.7623261Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:07:00.7623555Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:07:00.7623748Z 
2023-01-11T22:07:00.7624003Z ##[endgroup]
2023-01-11T22:07:00.7624379Z FINISHED PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_a0yd3h9f)
2023-01-11T22:07:00.7624575Z 
2023-01-11T22:07:00.7843417Z 
2023-01-11T22:07:00.7843811Z Expand the folded group to see the log file of test_ops_jit
2023-01-11T22:07:00.7844791Z ##[group]PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_huxx9xsf)
2023-01-11T22:07:00.7845508Z Test results will be stored in test-reports/python-pytest/test_ops_jit/test_ops_jit-f889cd96ee3679f1.xml
2023-01-11T22:07:00.7846026Z ============================= test session starts ==============================
2023-01-11T22:07:00.7846383Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:07:00.7846639Z cachedir: .pytest_cache
2023-01-11T22:07:00.7847056Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:07:00.7847462Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:07:00.7847938Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:07:00.7848355Z collecting ... collected 0 items
2023-01-11T22:07:00.7848651Z Running 0 items in this shard: 
2023-01-11T22:07:00.7848805Z 
2023-01-11T22:07:00.7848957Z =============================== warnings summary ===============================
2023-01-11T22:07:00.7849788Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:07:00.7851124Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:07:00.7851971Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:07:00.7852293Z 
2023-01-11T22:07:00.7852825Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:07:00.7854037Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_jit/test_ops_jit-f889cd96ee3679f1.xml -
2023-01-11T22:07:00.7854856Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:07:00.7855533Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:07:00.7856001Z 
2023-01-11T22:07:00.7856554Z ##[endgroup]
2023-01-11T22:07:00.7857467Z FINISHED PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_huxx9xsf)
2023-01-11T22:07:00.7857978Z 
2023-01-11T22:07:01.1353301Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_jit.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:07:01.134932]
2023-01-11T22:07:03.9056376Z 
2023-01-11T22:07:03.9057061Z Expand the folded group to see the log file of test_ops_jit
2023-01-11T22:07:03.9058091Z ##[group]PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_82kkahjm)
2023-01-11T22:07:03.9058691Z Test results will be stored in test-reports/python-pytest/test_ops_jit/test_ops_jit-0627f0d222682e84.xml
2023-01-11T22:07:03.9059011Z ============================= test session starts ==============================
2023-01-11T22:07:03.9059381Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:07:03.9059627Z cachedir: .pytest_cache
2023-01-11T22:07:03.9060067Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:07:03.9060422Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:07:03.9060847Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:07:03.9061133Z collecting ... collected 0 items
2023-01-11T22:07:03.9061338Z Running 0 items in this shard: 
2023-01-11T22:07:03.9061505Z 
2023-01-11T22:07:03.9061653Z =============================== warnings summary ===============================
2023-01-11T22:07:03.9061997Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:07:03.9062679Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:07:03.9063043Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:07:03.9063380Z 
2023-01-11T22:07:03.9063664Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:07:03.9064182Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_jit/test_ops_jit-0627f0d222682e84.xml -
2023-01-11T22:07:03.9064523Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:07:03.9064829Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:07:03.9065024Z 
2023-01-11T22:07:03.9065250Z ##[endgroup]
2023-01-11T22:07:03.9065618Z FINISHED PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_82kkahjm)
2023-01-11T22:07:03.9065850Z 
2023-01-11T22:07:03.9066097Z Running test_prims ... [2023-01-11 22:07:03.906066]
2023-01-11T22:07:03.9066604Z Executing ['/opt/conda/bin/python', '-bb', 'test_prims.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:07:03.906301]
2023-01-11T22:07:06.4744881Z 
2023-01-11T22:07:06.4745500Z Expand the folded group to see the log file of test_prims
2023-01-11T22:07:06.4746278Z ##[group]PRINTING LOG FILE of test_prims (/var/lib/jenkins/workspace/test/test-reports/test_prims_v_ku9jcy)
2023-01-11T22:07:06.4746562Z 
2023-01-11T22:07:06.4746641Z Running tests...
2023-01-11T22:07:06.4747047Z ----------------------------------------------------------------------
2023-01-11T22:07:06.4747460Z Test results will be stored in test-reports/python-unittest/test_prims
2023-01-11T22:07:06.4747755Z   test_mul_complex (__main__.TestPrimsBasic) ... ok (0.002s)
2023-01-11T22:07:06.4748032Z   test_torch_ops (__main__.TestPrimsBasic) ... ok (0.002s)
2023-01-11T22:07:06.4748225Z 
2023-01-11T22:07:06.4748432Z ----------------------------------------------------------------------
2023-01-11T22:07:06.4748666Z Ran 2 tests in 0.004s
2023-01-11T22:07:06.4748783Z 
2023-01-11T22:07:06.4748874Z OK
2023-01-11T22:07:06.4749005Z 
2023-01-11T22:07:06.4749123Z Generating XML reports...
2023-01-11T22:07:06.4749549Z Generated XML report: test-reports/python-unittest/test_prims/TEST-TestPrimsBasic-20230111220706.xml
2023-01-11T22:07:06.4749779Z 
2023-01-11T22:07:06.4750013Z ##[endgroup]
2023-01-11T22:07:06.4750385Z FINISHED PRINTING LOG FILE of test_prims (/var/lib/jenkins/workspace/test/test-reports/test_prims_v_ku9jcy)
2023-01-11T22:07:06.4750591Z 
2023-01-11T22:07:06.4750752Z Running test_reductions ... [2023-01-11 22:07:06.474567]
2023-01-11T22:07:06.4751228Z Executing ['/opt/conda/bin/python', '-bb', 'test_reductions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:07:06.474776]
2023-01-11T22:07:09.0237879Z 
2023-01-11T22:07:09.0238421Z Expand the folded group to see the log file of test_reductions
2023-01-11T22:07:09.0239429Z ##[group]PRINTING LOG FILE of test_reductions (/var/lib/jenkins/workspace/test/test-reports/test_reductions_s4gn_tll)
2023-01-11T22:07:09.0239804Z 
2023-01-11T22:07:09.0239898Z Running tests...
2023-01-11T22:07:09.0240402Z ----------------------------------------------------------------------
2023-01-11T22:07:09.0240635Z 
2023-01-11T22:07:09.0240867Z ----------------------------------------------------------------------
2023-01-11T22:07:09.0241114Z Ran 0 tests in 0.000s
2023-01-11T22:07:09.0241230Z 
2023-01-11T22:07:09.0241293Z OK
2023-01-11T22:07:09.0241385Z 
2023-01-11T22:07:09.0241517Z Generating XML reports...
2023-01-11T22:07:09.0241870Z Test results will be stored in test-reports/python-unittest/test_reductions
2023-01-11T22:07:09.0242148Z 
2023-01-11T22:07:09.0242454Z ##[endgroup]
2023-01-11T22:07:09.0242900Z FINISHED PRINTING LOG FILE of test_reductions (/var/lib/jenkins/workspace/test/test-reports/test_reductions_s4gn_tll)
2023-01-11T22:07:09.0243174Z 
2023-01-11T22:07:09.0243352Z Running test_show_pickle ... [2023-01-11 22:07:09.023845]
2023-01-11T22:07:09.0243873Z Executing ['/opt/conda/bin/python', '-bb', 'test_show_pickle.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:07:09.024140]
2023-01-11T22:07:10.8703101Z 
2023-01-11T22:07:10.8703801Z Expand the folded group to see the log file of test_show_pickle
2023-01-11T22:07:10.8704785Z ##[group]PRINTING LOG FILE of test_show_pickle (/var/lib/jenkins/workspace/test/test-reports/test_show_pickle_11w5apkv)
2023-01-11T22:07:10.8705197Z 
2023-01-11T22:07:10.8705275Z Running tests...
2023-01-11T22:07:10.8705741Z ----------------------------------------------------------------------
2023-01-11T22:07:10.8706204Z Test results will be stored in test-reports/python-unittest/test_show_pickle
2023-01-11T22:07:10.8706501Z   test_scripted_model (__main__.TestShowPickle) ... ok (0.227s)
2023-01-11T22:07:10.8706660Z 
2023-01-11T22:07:10.8706874Z ----------------------------------------------------------------------
2023-01-11T22:07:10.8707151Z Ran 1 test in 0.227s
2023-01-11T22:07:10.8707262Z 
2023-01-11T22:07:10.8707325Z OK
2023-01-11T22:07:10.8707402Z 
2023-01-11T22:07:10.8707522Z Generating XML reports...
2023-01-11T22:07:10.8708246Z Generated XML report: test-reports/python-unittest/test_show_pickle/TEST-TestShowPickle-20230111220710.xml
2023-01-11T22:07:10.8708529Z 
2023-01-11T22:07:10.8708771Z ##[endgroup]
2023-01-11T22:07:10.8709146Z FINISHED PRINTING LOG FILE of test_show_pickle (/var/lib/jenkins/workspace/test/test-reports/test_show_pickle_11w5apkv)
2023-01-11T22:07:10.8709415Z 
2023-01-11T22:07:10.8709596Z Running test_spectral_ops ... [2023-01-11 22:07:10.870405]
2023-01-11T22:07:10.8710131Z Executing ['/opt/conda/bin/python', '-bb', 'test_spectral_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:07:10.870643]
2023-01-11T22:07:14.2568019Z 
2023-01-11T22:07:14.2568616Z Expand the folded group to see the log file of test_spectral_ops
2023-01-11T22:07:14.2569719Z ##[group]PRINTING LOG FILE of test_spectral_ops (/var/lib/jenkins/workspace/test/test-reports/test_spectral_ops_glwj31_w)
2023-01-11T22:07:14.2570584Z /var/lib/jenkins/workspace/test/test_spectral_ops.py:44: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
2023-01-11T22:07:14.2571312Z   if LooseVersion(np.__version__) >= '1.20.0' and (
2023-01-11T22:07:14.2571680Z /var/lib/jenkins/workspace/test/test_spectral_ops.py:45: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
2023-01-11T22:07:14.2572217Z   not has_scipy_fft or LooseVersion(scipy.__version__) >= '1.6.0')
2023-01-11T22:07:14.2572364Z 
2023-01-11T22:07:14.2572436Z Running tests...
2023-01-11T22:07:14.2572745Z ----------------------------------------------------------------------
2023-01-11T22:07:14.2572918Z 
2023-01-11T22:07:14.2573112Z ----------------------------------------------------------------------
2023-01-11T22:07:14.2573336Z Ran 0 tests in 0.000s
2023-01-11T22:07:14.2573448Z 
2023-01-11T22:07:14.2573508Z OK
2023-01-11T22:07:14.2573600Z 
2023-01-11T22:07:14.2573684Z Generating XML reports...
2023-01-11T22:07:14.2574018Z Test results will be stored in test-reports/python-unittest/test_spectral_ops
2023-01-11T22:07:14.2574193Z 
2023-01-11T22:07:14.2574419Z ##[endgroup]
2023-01-11T22:07:14.2574811Z FINISHED PRINTING LOG FILE of test_spectral_ops (/var/lib/jenkins/workspace/test/test-reports/test_spectral_ops_glwj31_w)
2023-01-11T22:07:14.2575034Z 
2023-01-11T22:07:14.2575220Z Running test_tensor_creation_ops ... [2023-01-11 22:07:14.256836]
2023-01-11T22:07:14.2575699Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensor_creation_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:07:14.257128]
2023-01-11T22:07:16.1915977Z 
2023-01-11T22:07:16.1916636Z Expand the folded group to see the log file of test_tensor_creation_ops
2023-01-11T22:07:16.1917531Z ##[group]PRINTING LOG FILE of test_tensor_creation_ops (/var/lib/jenkins/workspace/test/test-reports/test_tensor_creation_ops_3zjs2c7_)
2023-01-11T22:07:16.1917786Z 
2023-01-11T22:07:16.1917860Z Running tests...
2023-01-11T22:07:16.1918333Z ----------------------------------------------------------------------
2023-01-11T22:07:16.1918716Z 
2023-01-11T22:07:16.1918919Z ----------------------------------------------------------------------
2023-01-11T22:07:16.1919142Z Ran 0 tests in 0.000s
2023-01-11T22:07:16.1919257Z 
2023-01-11T22:07:16.1919318Z OK
2023-01-11T22:07:16.1919468Z 
2023-01-11T22:07:16.1919582Z Generating XML reports...
2023-01-11T22:07:16.1919966Z Test results will be stored in test-reports/python-unittest/test_tensor_creation_ops
2023-01-11T22:07:16.1920161Z 
2023-01-11T22:07:16.1920444Z ##[endgroup]
2023-01-11T22:07:16.1920865Z FINISHED PRINTING LOG FILE of test_tensor_creation_ops (/var/lib/jenkins/workspace/test/test-reports/test_tensor_creation_ops_3zjs2c7_)
2023-01-11T22:07:16.1921097Z 
2023-01-11T22:07:16.1921255Z Running test_tensorexpr ... [2023-01-11 22:07:16.191635]
2023-01-11T22:07:16.1921736Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorexpr.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:07:16.191865]
2023-01-11T22:08:02.2324020Z 
2023-01-11T22:08:02.2324649Z Expand the folded group to see the log file of test_tensorexpr
2023-01-11T22:08:02.2325445Z ##[group]PRINTING LOG FILE of test_tensorexpr (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_48q3w91m)
2023-01-11T22:08:02.2327274Z 
2023-01-11T22:08:02.2327672Z Running tests...
2023-01-11T22:08:02.2328320Z ----------------------------------------------------------------------
2023-01-11T22:08:02.2328986Z Test results will be stored in test-reports/python-unittest/test_tensorexpr
2023-01-11T22:08:02.2329309Z   test_add_const_rhs (__main__.TestTensorExprFuser) ... ok (0.263s)
2023-01-11T22:08:02.2329629Z   test_add_sub (__main__.TestTensorExprFuser) ... ok (0.041s)
2023-01-11T22:08:02.2330082Z   test_alias_analysis_input_and_module (__main__.TestTensorExprFuser) ... ok (0.046s)
2023-01-11T22:08:02.2330398Z   test_alias_analysis_inputs (__main__.TestTensorExprFuser) ... ok (0.010s)
2023-01-11T22:08:02.2330710Z   test_alias_analysis_module (__main__.TestTensorExprFuser) ... ok (0.128s)
2023-01-11T22:08:02.2331008Z   test_all_combos (__main__.TestTensorExprFuser) ... ok (0.039s)
2023-01-11T22:08:02.2331356Z   test_alpha (__main__.TestTensorExprFuser) ... ok (0.026s)
2023-01-11T22:08:02.2331718Z   test_binary_ops (__main__.TestTensorExprFuser) ... ok (1.458s)
2023-01-11T22:08:02.2332145Z   test_bitwise_ops (__main__.TestTensorExprFuser) ... ok (0.164s)
2023-01-11T22:08:02.2332661Z   test_broadcast (__main__.TestTensorExprFuser) ... ok (0.039s)
2023-01-11T22:08:02.2333088Z   test_broadcast3 (__main__.TestTensorExprFuser) ... ok (0.146s)
2023-01-11T22:08:02.2333425Z   test_broadcast_2 (__main__.TestTensorExprFuser) ... ok (0.033s)
2023-01-11T22:08:02.2333812Z   test_broadcast_big2 (__main__.TestTensorExprFuser) ... ok (0.047s)
2023-01-11T22:08:02.2334295Z   test_cat (__main__.TestTensorExprFuser) ... ok (3.256s)
2023-01-11T22:08:02.2334800Z   test_cat_empty_tensors (__main__.TestTensorExprFuser) ... ok (0.115s)
2023-01-11T22:08:02.2335251Z   test_cat_negative_dim (__main__.TestTensorExprFuser) ... ok (0.069s)
2023-01-11T22:08:02.2335943Z   test_cat_only (__main__.TestTensorExprFuser) ... skip: cat is broken with fusion group inlining disabled (0.001s)
2023-01-11T22:08:02.2336302Z   test_cat_promote_inputs (__main__.TestTensorExprFuser) ... ok (0.131s)
2023-01-11T22:08:02.2336795Z   test_cat_with_constant_dim (__main__.TestTensorExprFuser) ... ok (0.047s)
2023-01-11T22:08:02.2337279Z   test_char (__main__.TestTensorExprFuser) ... ok (0.029s)
2023-01-11T22:08:02.2337766Z   test_chunk (__main__.TestTensorExprFuser) ... ok (0.213s)
2023-01-11T22:08:02.2338050Z   test_clamp (__main__.TestTensorExprFuser) ... ok (0.038s)
2023-01-11T22:08:02.2338304Z   test_constant (__main__.TestTensorExprFuser) ... ok (0.027s)
2023-01-11T22:08:02.2338576Z   test_double (__main__.TestTensorExprFuser) ... ok (0.026s)
2023-01-11T22:08:02.2338860Z   test_double_intrinsics (__main__.TestTensorExprFuser) ... ok (0.033s)
2023-01-11T22:08:02.2339199Z   test_dynamic_shape (__main__.TestTensorExprFuser) ... skip: dynamic shapes are not quite there yet (0.001s)
2023-01-11T22:08:02.2339661Z   test_easy (__main__.TestTensorExprFuser) ... ok (0.038s)
2023-01-11T22:08:02.2339920Z   test_eq (__main__.TestTensorExprFuser) ... ok (0.064s)
2023-01-11T22:08:02.2340185Z   test_exp_pow (__main__.TestTensorExprFuser) ... ok (0.060s)
2023-01-11T22:08:02.2340443Z   test_four_arg (__main__.TestTensorExprFuser) ... ok (0.039s)
2023-01-11T22:08:02.2340719Z   test_ge (__main__.TestTensorExprFuser) ... ok (0.064s)
2023-01-11T22:08:02.2340974Z   test_gt (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:08:02.2341266Z   test_guard_fails (__main__.TestTensorExprFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:08:02.2341552Z   test_half_bn_relu (__main__.TestTensorExprFuser) ... ok (0.001s)
2023-01-11T22:08:02.2341834Z   test_half_gelu (__main__.TestTensorExprFuser) ... ok (0.003s)
2023-01-11T22:08:02.2342115Z   test_int64_promotion (__main__.TestTensorExprFuser) ... ok (0.027s)
2023-01-11T22:08:02.2342616Z   test_int_output (__main__.TestTensorExprFuser) ... ok (0.027s)
2023-01-11T22:08:02.2342977Z   test_le (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:08:02.2343241Z   test_loop (__main__.TestTensorExprFuser) ... ok (0.075s)
2023-01-11T22:08:02.2343489Z   test_lt (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:08:02.2343749Z   test_mask (__main__.TestTensorExprFuser) ... ok (0.026s)
2023-01-11T22:08:02.2344012Z   test_min_max (__main__.TestTensorExprFuser) ... ok (0.039s)
2023-01-11T22:08:02.2344292Z   test_min_max_reduction (__main__.TestTensorExprFuser) ... ok (0.026s)
2023-01-11T22:08:02.2344577Z   test_min_max_reduction2 (__main__.TestTensorExprFuser) ... ok (0.026s)
2023-01-11T22:08:02.2344876Z   test_min_max_reduction_dim1 (__main__.TestTensorExprFuser) ... ok (0.029s)
2023-01-11T22:08:02.2345180Z   test_min_max_reduction_dim1_2 (__main__.TestTensorExprFuser) ... ok (0.041s)
2023-01-11T22:08:02.2345459Z   test_multi_rand (__main__.TestTensorExprFuser) ... ok (0.052s)
2023-01-11T22:08:02.2345739Z   test_multioutput (__main__.TestTensorExprFuser) ... ok (0.036s)
2023-01-11T22:08:02.2346035Z   test_multiple_outputs (__main__.TestTensorExprFuser) ... ok (0.179s)
2023-01-11T22:08:02.2346316Z   test_nans (__main__.TestTensorExprFuser) ... ok (0.101s)
2023-01-11T22:08:02.2346562Z   test_ne (__main__.TestTensorExprFuser) ... ok (0.064s)
2023-01-11T22:08:02.2346828Z   test_promotion (__main__.TestTensorExprFuser) ... ok (0.033s)
2023-01-11T22:08:02.2383326Z   test_propagated_mem_layout (__main__.TestTensorExprFuser) ... ok (30.894s)
2023-01-11T22:08:02.2383790Z   test_rand_like (__main__.TestTensorExprFuser) ... ok (0.066s)
2023-01-11T22:08:02.2384277Z   test_rank_two (__main__.TestTensorExprFuser) ... ok (0.044s)
2023-01-11T22:08:02.2384754Z   test_relu (__main__.TestTensorExprFuser) ... ok (0.037s)
2023-01-11T22:08:02.2385232Z   test_remainder (__main__.TestTensorExprFuser) ... ok (0.261s)
2023-01-11T22:08:02.2385722Z   test_reps (__main__.TestTensorExprFuser) ... ok (0.043s)
2023-01-11T22:08:02.2386201Z   test_scalar (__main__.TestTensorExprFuser) ... ok (0.119s)
2023-01-11T22:08:02.2386696Z   test_short (__main__.TestTensorExprFuser) ... ok (0.028s)
2023-01-11T22:08:02.2387134Z   test_simple_add (__main__.TestTensorExprFuser) ... ok (0.017s)
2023-01-11T22:08:02.2387408Z   test_sin_pow (__main__.TestTensorExprFuser) ... ok (0.313s)
2023-01-11T22:08:02.2387677Z   test_slice (__main__.TestTensorExprFuser) ... ok (0.058s)
2023-01-11T22:08:02.2387939Z   test_sliced_stride (__main__.TestTensorExprFuser) ... ok (0.108s)
2023-01-11T22:08:02.2388220Z   test_softmax_cpu (__main__.TestTensorExprFuser) ... ok (0.657s)
2023-01-11T22:08:02.2388522Z   test_softmax_cuda (__main__.TestTensorExprFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:08:02.2388832Z   test_strided_output_preserved (__main__.TestTensorExprFuser) ... ok (0.112s)
2023-01-11T22:08:02.2389127Z   test_three_arg (__main__.TestTensorExprFuser) ... ok (0.037s)
2023-01-11T22:08:02.2389398Z   test_three_arg2 (__main__.TestTensorExprFuser) ... ok (0.040s)
2023-01-11T22:08:02.2389674Z   test_transpose (__main__.TestTensorExprFuser) ... ok (0.162s)
2023-01-11T22:08:02.2390059Z   test_unary_ops (__main__.TestTensorExprFuser) ... ok (3.221s)
2023-01-11T22:08:02.2390427Z   test_unsqueeze (__main__.TestTensorExprFuser) ... ok (0.061s)
2023-01-11T22:08:02.2390696Z   test_where (__main__.TestTensorExprFuser) ... ok (0.162s)
2023-01-11T22:08:02.2390845Z 
2023-01-11T22:08:02.2391096Z ----------------------------------------------------------------------
2023-01-11T22:08:02.2391339Z Ran 73 tests in 44.111s
2023-01-11T22:08:02.2391453Z 
2023-01-11T22:08:02.2391523Z OK (skipped=4)
2023-01-11T22:08:02.2391629Z 
2023-01-11T22:08:02.2391714Z Generating XML reports...
2023-01-11T22:08:02.2392137Z Generated XML report: test-reports/python-unittest/test_tensorexpr/TEST-TestTensorExprFuser-20230111220717.xml
2023-01-11T22:08:02.2392380Z 
2023-01-11T22:08:02.2392740Z ##[endgroup]
2023-01-11T22:08:02.2393133Z FINISHED PRINTING LOG FILE of test_tensorexpr (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_48q3w91m)
2023-01-11T22:08:02.2393423Z 
2023-01-11T22:08:02.2393568Z Running doctests ... [2023-01-11 22:08:02.232620]
2023-01-11T22:08:02.2652999Z Start doctest_module('/opt/conda/lib/python3.10/site-packages/torch')
2023-01-11T22:08:02.2653397Z Listing tests
2023-01-11T22:08:08.3517125Z gathering tests
2023-01-11T22:08:08.3531665Z running 663 test(s)
2023-01-11T22:08:08.3572897Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::is_tensor:0, line 429 <- wrt source file
2023-01-11T22:08:08.3579990Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::is_tensor:0
2023-01-11T22:08:08.3580540Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_tensor_type:0, line 458 <- wrt source file
2023-01-11T22:08:08.3582563Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_tensor_type:0
2023-01-11T22:08:08.3583114Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_dtype:0, line 496 <- wrt source file
2023-01-11T22:08:08.3585722Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_dtype:0
2023-01-11T22:08:08.3586251Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 629 <- wrt source file
2023-01-11T22:08:08.3588615Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::use_deterministic_algorithms:0
2023-01-11T22:08:08.3589589Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::compile:0, line 1221 <- wrt source file
2023-01-11T22:08:08.3590406Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::compile:0
2023-01-11T22:08:08.3591381Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file
2023-01-11T22:08:08.3592401Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::Generator:0
2023-01-11T22:08:08.3593466Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file
2023-01-11T22:08:08.3594529Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::_LinAlgError:0
2023-01-11T22:08:08.3595520Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_namedtensor_internals.py::update_names:0, line 125 <- wrt source file
2023-01-11T22:08:08.3596520Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_namedtensor_internals.py::update_names:0
2023-01-11T22:08:08.3597514Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 508 <- wrt source file
2023-01-11T22:08:08.3606763Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.register_hook:0
2023-01-11T22:08:08.3608159Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1096 <- wrt source file
2023-01-11T22:08:08.3865661Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.refine_names:0
2023-01-11T22:08:08.3869997Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1141 <- wrt source file
2023-01-11T22:08:08.3876512Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.align_to:0
2023-01-11T22:08:08.3877456Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.rename:0, line 1214 <- wrt source file
2023-01-11T22:08:08.3886101Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.rename:0
2023-01-11T22:08:08.3887272Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1244 <- wrt source file
2023-01-11T22:08:08.3895480Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0
2023-01-11T22:08:08.3896415Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py::set_printoptions:0, line 49 <- wrt source file
2023-01-11T22:08:08.3923085Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py::set_printoptions:0
2023-01-11T22:08:08.3924079Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_tensors:0, line 61 <- wrt source file
2023-01-11T22:08:08.3931580Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_tensors:0
2023-01-11T22:08:08.3932163Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_shapes:0, line 89 <- wrt source file
2023-01-11T22:08:08.3935331Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_shapes:0
2023-01-11T22:08:08.3935851Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::split:0, line 161 <- wrt source file
2023-01-11T22:08:08.3948812Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::split:0
2023-01-11T22:08:08.3949423Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::einsum:0, line 269 <- wrt source file
2023-01-11T22:08:08.4158156Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::einsum:0
2023-01-11T22:08:08.4158860Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::meshgrid:0, line 450 <- wrt source file
2023-01-11T22:08:08.4194643Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::meshgrid:0
2023-01-11T22:08:08.4195208Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_impl:0, line 764 <- wrt source file
2023-01-11T22:08:08.4209422Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_impl:0
2023-01-11T22:08:08.4210046Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 842 <- wrt source file
2023-01-11T22:08:08.4222755Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_consecutive_impl:0
2023-01-11T22:08:08.4223286Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::tensordot:0, line 1040 <- wrt source file
2023-01-11T22:08:08.4236437Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::tensordot:0
2023-01-11T22:08:08.4236979Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::cartesian_prod:0, line 1118 <- wrt source file
2023-01-11T22:08:08.4244972Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::cartesian_prod:0
2023-01-11T22:08:08.4245504Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::block_diag:0, line 1152 <- wrt source file
2023-01-11T22:08:08.4256192Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::block_diag:0
2023-01-11T22:08:08.4256719Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::cdist:0, line 1203 <- wrt source file
2023-01-11T22:08:08.4272333Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::cdist:0
2023-01-11T22:08:08.4272850Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_1d:0, line 1243 <- wrt source file
2023-01-11T22:08:08.4290152Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_1d:0
2023-01-11T22:08:08.4290794Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_2d:0, line 1279 <- wrt source file
2023-01-11T22:08:08.4309292Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_2d:0
2023-01-11T22:08:08.4309808Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_3d:0, line 1317 <- wrt source file
2023-01-11T22:08:08.4333232Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_3d:0
2023-01-11T22:08:08.4333725Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::norm:0, line 1455 <- wrt source file
2023-01-11T22:08:08.4371660Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::norm:0
2023-01-11T22:08:08.4372258Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::chain_matmul:0, line 1606 <- wrt source file
2023-01-11T22:08:08.4373335Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/functional.py::chain_matmul:0
2023-01-11T22:08:08.4373922Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::_lu_impl:0, line 1706 <- wrt source file
2023-01-11T22:08:08.4376188Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/functional.py::_lu_impl:0
2023-01-11T22:08:08.4377099Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::list:0, line 391 <- wrt source file
2023-01-11T22:08:08.4377933Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::list:0
2023-01-11T22:08:08.4378667Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::help:0, line 444 <- wrt source file
2023-01-11T22:08:08.4379316Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::help:0
2023-01-11T22:08:08.4379850Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::load:0, line 524 <- wrt source file
2023-01-11T22:08:08.4380504Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::load:0
2023-01-11T22:08:08.4381212Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::_load_local:0, line 563 <- wrt source file
2023-01-11T22:08:08.4381914Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::_load_local:0
2023-01-11T22:08:08.4382768Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::download_url_to_file:0, line 592 <- wrt source file
2023-01-11T22:08:08.4383287Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::download_url_to_file:0
2023-01-11T22:08:08.4383793Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::load_state_dict_from_url:0, line 701 <- wrt source file
2023-01-11T22:08:08.4384298Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::load_state_dict_from_url:0
2023-01-11T22:08:08.4384950Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.define:0, line 61 <- wrt source file
2023-01-11T22:08:08.4385448Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.define:0
2023-01-11T22:08:08.4385950Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.impl:0, line 81 <- wrt source file
2023-01-11T22:08:08.4386444Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.impl:0
2023-01-11T22:08:08.4387010Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_ignored_functions:0, line 67 <- wrt source file
2023-01-11T22:08:08.4387779Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_ignored_functions:0
2023-01-11T22:08:08.4388301Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_testing_overrides:0, line 336 <- wrt source file
2023-01-11T22:08:08.4426687Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_testing_overrides:0
2023-01-11T22:08:08.4427237Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::wrap_torch_function:0, line 1391 <- wrt source file
2023-01-11T22:08:08.4430805Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::wrap_torch_function:0
2023-01-11T22:08:08.4431369Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::handle_torch_function:0, line 1508 <- wrt source file
2023-01-11T22:08:08.4434469Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::handle_torch_function:0
2023-01-11T22:08:08.4435093Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1732 <- wrt source file
2023-01-11T22:08:08.4477084Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_method_or_property:0
2023-01-11T22:08:08.4477639Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_like:0, line 1750 <- wrt source file
2023-01-11T22:08:08.4485648Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_like:0
2023-01-11T22:08:08.4486346Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/quasirandom.py::SobolEngine:0, line 37 <- wrt source file
2023-01-11T22:08:08.4487326Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/quasirandom.py::SobolEngine:0
2023-01-11T22:08:08.4488115Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/serialization.py::save:0, line 429 <- wrt source file
2023-01-11T22:08:08.4488610Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/serialization.py::save:0
2023-01-11T22:08:08.4489120Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/serialization.py::load:0, line 754 <- wrt source file
2023-01-11T22:08:08.4492185Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/serialization.py::load:0
2023-01-11T22:08:08.4492843Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/torch_version.py::TorchVersion:0, line 49 <- wrt source file
2023-01-11T22:08:08.4493437Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/torch_version.py::TorchVersion:0
2023-01-11T22:08:08.4494013Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1495 <- wrt source file
2023-01-11T22:08:08.4499601Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0
2023-01-11T22:08:08.4500307Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.then:0, line 147 <- wrt source file
2023-01-11T22:08:08.4501062Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.then:0
2023-01-11T22:08:08.4501605Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 195 <- wrt source file
2023-01-11T22:08:08.4504651Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.add_done_callback:0
2023-01-11T22:08:08.4505384Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_result:0, line 228 <- wrt source file
2023-01-11T22:08:08.4506297Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_result:0
2023-01-11T22:08:08.4507061Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 257 <- wrt source file
2023-01-11T22:08:08.4507948Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_exception:0
2023-01-11T22:08:08.4508828Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::collect_all:0, line 288 <- wrt source file
2023-01-11T22:08:08.4509751Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::collect_all:0
2023-01-11T22:08:08.4510639Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::annotate:0, line 103 <- wrt source file
2023-01-11T22:08:08.4511505Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::annotate:0
2023-01-11T22:08:08.4512018Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::strict_fusion:0, line 202 <- wrt source file
2023-01-11T22:08:08.4512505Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::strict_fusion:0
2023-01-11T22:08:08.4513043Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 21 <- wrt source file
2023-01-11T22:08:08.4528115Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0
2023-01-11T22:08:08.4528788Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 39 <- wrt source file
2023-01-11T22:08:08.4543842Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py::as_nested_tensor:0
2023-01-11T22:08:08.4544546Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/sparse/__init__.py::sum:0, line 175 <- wrt source file
2023-01-11T22:08:08.4583179Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/sparse/__init__.py::sum:0
2023-01-11T22:08:08.4583766Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 2139 <- wrt source file
2023-01-11T22:08:08.5828087Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py::aot_function:0
2023-01-11T22:08:08.5828720Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 162 <- wrt source file
2023-01-11T22:08:08.5829295Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0
2023-01-11T22:08:08.5829858Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 195 <- wrt source file
2023-01-11T22:08:08.5909123Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::vjp:0
2023-01-11T22:08:08.5909679Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 382 <- wrt source file
2023-01-11T22:08:08.6065278Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacrev:0
2023-01-11T22:08:08.6065846Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 882 <- wrt source file
2023-01-11T22:08:08.6736881Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jvp:0
2023-01-11T22:08:08.6737474Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1024 <- wrt source file
2023-01-11T22:08:08.6889873Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0
2023-01-11T22:08:08.6890452Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1173 <- wrt source file
2023-01-11T22:08:08.6936275Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::hessian:0
2023-01-11T22:08:08.6936926Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::grad:0, line 1290 <- wrt source file
2023-01-11T22:08:08.6937585Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::grad:0
2023-01-11T22:08:08.6938141Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1441 <- wrt source file
2023-01-11T22:08:08.6943008Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::functionalize:0
2023-01-11T22:08:08.6944023Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 72 <- wrt source file
2023-01-11T22:08:08.6944620Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/fx_minifier.py::minifier:0
2023-01-11T22:08:08.6945142Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/vmap.py::vmap:0, line 306 <- wrt source file
2023-01-11T22:08:08.6986611Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/vmap.py::vmap:0
2023-01-11T22:08:08.6987339Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::NvfuserPrimsMode:0, line 90 <- wrt source file
2023-01-11T22:08:08.6988033Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::NvfuserPrimsMode:0
2023-01-11T22:08:08.6988747Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 141 <- wrt source file
2023-01-11T22:08:08.6989417Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::TorchRefsMode:0
2023-01-11T22:08:08.6990179Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 21 <- wrt source file
2023-01-11T22:08:08.6990941Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0
2023-01-11T22:08:08.6991760Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 21 <- wrt source file
2023-01-11T22:08:08.6992560Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0
2023-01-11T22:08:08.6993370Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file
2023-01-11T22:08:08.6994142Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0
2023-01-11T22:08:08.6994958Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 59 <- wrt source file
2023-01-11T22:08:08.6995922Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0
2023-01-11T22:08:08.6996716Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 126 <- wrt source file
2023-01-11T22:08:08.6997494Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0
2023-01-11T22:08:08.6998257Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 24 <- wrt source file
2023-01-11T22:08:08.7018687Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0
2023-01-11T22:08:08.7020067Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 274 <- wrt source file
2023-01-11T22:08:08.7053667Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0
2023-01-11T22:08:08.7056143Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 166 <- wrt source file
2023-01-11T22:08:08.7057049Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0
2023-01-11T22:08:08.7057607Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 226 <- wrt source file
2023-01-11T22:08:08.7058684Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0
2023-01-11T22:08:08.7059439Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 287 <- wrt source file
2023-01-11T22:08:08.7059977Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0
2023-01-11T22:08:08.7060531Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 74 <- wrt source file
2023-01-11T22:08:08.7065910Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0
2023-01-11T22:08:08.7066480Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 114 <- wrt source file
2023-01-11T22:08:08.7072453Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0
2023-01-11T22:08:08.7073426Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 34 <- wrt source file
2023-01-11T22:08:08.7074077Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0
2023-01-11T22:08:08.7074835Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 105 <- wrt source file
2023-01-11T22:08:08.7075518Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0
2023-01-11T22:08:08.7076271Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 170 <- wrt source file
2023-01-11T22:08:08.7076961Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0
2023-01-11T22:08:08.7077716Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 236 <- wrt source file
2023-01-11T22:08:08.7078699Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0
2023-01-11T22:08:08.7079699Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 297 <- wrt source file
2023-01-11T22:08:08.7080545Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0
2023-01-11T22:08:08.7081525Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 358 <- wrt source file
2023-01-11T22:08:08.7082372Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0
2023-01-11T22:08:08.7083426Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 28 <- wrt source file
2023-01-11T22:08:08.7084170Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0
2023-01-11T22:08:08.7084732Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 391 <- wrt source file
2023-01-11T22:08:08.7085284Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0
2023-01-11T22:08:08.7085853Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 618 <- wrt source file
2023-01-11T22:08:08.7086688Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0
2023-01-11T22:08:08.7087487Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 940 <- wrt source file
2023-01-11T22:08:08.7088329Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0
2023-01-11T22:08:08.7089193Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 993 <- wrt source file
2023-01-11T22:08:08.7089952Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0
2023-01-11T22:08:08.7090737Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1036 <- wrt source file
2023-01-11T22:08:08.7091462Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0
2023-01-11T22:08:08.7092263Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 31 <- wrt source file
2023-01-11T22:08:08.7092892Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0
2023-01-11T22:08:08.7093603Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv1d:0, line 295 <- wrt source file
2023-01-11T22:08:08.7094259Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv1d:0
2023-01-11T22:08:08.7094811Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 403 <- wrt source file
2023-01-11T22:08:08.7095342Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0
2023-01-11T22:08:08.7095889Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 502 <- wrt source file
2023-01-11T22:08:08.7096409Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0
2023-01-11T22:08:08.7097064Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 685 <- wrt source file
2023-01-11T22:08:08.7097629Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0
2023-01-11T22:08:08.7098221Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 775 <- wrt source file
2023-01-11T22:08:08.7098769Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0
2023-01-11T22:08:08.7099350Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 869 <- wrt source file
2023-01-11T22:08:08.7099940Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0
2023-01-11T22:08:08.7100765Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 84 <- wrt source file
2023-01-11T22:08:08.7119850Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0
2023-01-11T22:08:08.7120769Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 209 <- wrt source file
2023-01-11T22:08:08.7144423Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0
2023-01-11T22:08:08.7145380Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 21 <- wrt source file
2023-01-11T22:08:08.7150383Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0
2023-01-11T22:08:08.7151196Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 141 <- wrt source file
2023-01-11T22:08:08.7154552Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0
2023-01-11T22:08:08.7155538Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 117 <- wrt source file
2023-01-11T22:08:08.7156279Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0
2023-01-11T22:08:08.7156991Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/rnn.py::LSTM:0, line 20 <- wrt source file
2023-01-11T22:08:08.7157776Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/rnn.py::LSTM:0
2023-01-11T22:08:08.7158669Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 59 <- wrt source file
2023-01-11T22:08:08.7159666Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0
2023-01-11T22:08:08.7160639Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 92 <- wrt source file
2023-01-11T22:08:08.7192616Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0
2023-01-11T22:08:08.7193971Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 54 <- wrt source file
2023-01-11T22:08:08.7195373Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0
2023-01-11T22:08:08.7196479Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 19 <- wrt source file
2023-01-11T22:08:08.7197714Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0
2023-01-11T22:08:08.7198801Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 45 <- wrt source file
2023-01-11T22:08:08.7200013Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0
2023-01-11T22:08:08.7201358Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier.squash_mask:0, line 237 <- wrt source file
2023-01-11T22:08:08.7205792Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier.squash_mask:0
2023-01-11T22:08:08.7206894Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 143 <- wrt source file
2023-01-11T22:08:08.7207880Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0
2023-01-11T22:08:08.7208957Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 27 <- wrt source file
2023-01-11T22:08:08.7217019Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0
2023-01-11T22:08:08.7218133Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 64 <- wrt source file
2023-01-11T22:08:08.7225194Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0
2023-01-11T22:08:08.7226328Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 111 <- wrt source file
2023-01-11T22:08:08.7232246Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0
2023-01-11T22:08:08.7233417Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 139 <- wrt source file
2023-01-11T22:08:08.7239696Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0
2023-01-11T22:08:08.7240824Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 85 <- wrt source file
2023-01-11T22:08:08.7241721Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_args:0
2023-01-11T22:08:08.7242773Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 106 <- wrt source file
2023-01-11T22:08:08.7243812Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0
2023-01-11T22:08:08.7244877Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 242 <- wrt source file
2023-01-11T22:08:08.7245862Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0
2023-01-11T22:08:08.7248529Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 301 <- wrt source file
2023-01-11T22:08:08.7249718Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0
2023-01-11T22:08:08.7252237Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 439 <- wrt source file
2023-01-11T22:08:08.7253176Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0
2023-01-11T22:08:08.7253977Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 604 <- wrt source file
2023-01-11T22:08:08.7254590Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0
2023-01-11T22:08:08.7255384Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 663 <- wrt source file
2023-01-11T22:08:08.7256128Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0
2023-01-11T22:08:08.7256814Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 715 <- wrt source file
2023-01-11T22:08:08.7257649Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0
2023-01-11T22:08:08.7258464Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 408 <- wrt source file
2023-01-11T22:08:08.7259126Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0
2023-01-11T22:08:08.7259682Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 429 <- wrt source file
2023-01-11T22:08:08.7260392Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0
2023-01-11T22:08:08.7261035Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 442 <- wrt source file
2023-01-11T22:08:08.7261575Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0
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2023-01-11T22:08:08.7288553Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0
2023-01-11T22:08:08.7289274Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 215 <- wrt source file
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2023-01-11T22:08:08.7336287Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0
2023-01-11T22:08:08.7336842Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 406 <- wrt source file
2023-01-11T22:08:08.7354512Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0
2023-01-11T22:08:08.7355518Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/autograd/profiler.py::profile:0, line 123 <- wrt source file
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2023-01-11T22:08:08.7585097Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py::RemoteModule:0, line 646 <- wrt source file
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2023-01-11T22:08:08.7587362Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 27 <- wrt source file
2023-01-11T22:08:08.7588563Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0
2023-01-11T22:08:08.7589730Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 38 <- wrt source file
2023-01-11T22:08:08.7590823Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0
2023-01-11T22:08:08.7591952Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py::DistributedOptimizer:0, line 160 <- wrt source file
2023-01-11T22:08:08.7593211Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py::DistributedOptimizer:0
2023-01-11T22:08:08.7594404Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py::PostLocalSGDOptimizer:0, line 18 <- wrt source file
2023-01-11T22:08:08.7595614Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py::PostLocalSGDOptimizer:0
2023-01-11T22:08:08.7596675Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 35 <- wrt source file
2023-01-11T22:08:08.7597684Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0
2023-01-11T22:08:08.7598971Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py::ZeroRedundancyOptimizer:0, line 325 <- wrt source file
2023-01-11T22:08:08.7600323Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py::ZeroRedundancyOptimizer:0
2023-01-11T22:08:08.7601434Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::WithDevice:0, line 152 <- wrt source file
2023-01-11T22:08:08.7602430Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::WithDevice:0
2023-01-11T22:08:08.7603460Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::Pipe:0, line 274 <- wrt source file
2023-01-11T22:08:08.7604402Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::Pipe:0
2023-01-11T22:08:08.7605406Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 160 <- wrt source file
2023-01-11T22:08:08.7606289Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::_wait_all:0
2023-01-11T22:08:08.7607151Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 333 <- wrt source file
2023-01-11T22:08:08.7607914Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::shutdown:0
2023-01-11T22:08:08.7608692Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::remote:0, line 582 <- wrt source file
2023-01-11T22:08:08.7609464Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::remote:0
2023-01-11T22:08:08.7610373Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 766 <- wrt source file
2023-01-11T22:08:08.7611160Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_sync:0
2023-01-11T22:08:08.7612071Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 858 <- wrt source file
2023-01-11T22:08:08.7612975Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_async:0
2023-01-11T22:08:08.7613968Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/functions.py::async_execution:0, line 33 <- wrt source file
2023-01-11T22:08:08.7614910Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/functions.py::async_execution:0
2023-01-11T22:08:08.7615871Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/options.py::TensorPipeRpcBackendOptions.set_device_map:0, line 117 <- wrt source file
2023-01-11T22:08:08.7616986Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/options.py::TensorPipeRpcBackendOptions.set_device_map:0
2023-01-11T22:08:08.7618101Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0, line 58 <- wrt source file
2023-01-11T22:08:08.7619211Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0
2023-01-11T22:08:08.7620156Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_input_validate:0, line 33 <- wrt source file
2023-01-11T22:08:08.7621169Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_input_validate:0
2023-01-11T22:08:08.7622503Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_output_validate:0, line 78 <- wrt source file
2023-01-11T22:08:08.7623595Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_output_validate:0
2023-01-11T22:08:08.7624618Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 63 <- wrt source file
2023-01-11T22:08:08.7625530Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0
2023-01-11T22:08:08.7626418Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 21 <- wrt source file
2023-01-11T22:08:08.7627280Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/bernoulli.py::Bernoulli:0
2023-01-11T22:08:08.7628142Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/beta.py::Beta:0, line 16 <- wrt source file
2023-01-11T22:08:08.7629098Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/beta.py::Beta:0
2023-01-11T22:08:08.7630047Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/binomial.py::Binomial:0, line 20 <- wrt source file
2023-01-11T22:08:08.7634690Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/binomial.py::Binomial:0
2023-01-11T22:08:08.7635681Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/categorical.py::Categorical:0, line 37 <- wrt source file
2023-01-11T22:08:08.7643134Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/categorical.py::Categorical:0
2023-01-11T22:08:08.7644269Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 19 <- wrt source file
2023-01-11T22:08:08.7649782Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/cauchy.py::Cauchy:0
2023-01-11T22:08:08.7650731Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/chi2.py::Chi2:0, line 12 <- wrt source file
2023-01-11T22:08:08.7656218Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/chi2.py::Chi2:0
2023-01-11T22:08:08.7657262Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 152 <- wrt source file
2023-01-11T22:08:08.7658313Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/constraints.py::_DependentProperty:0
2023-01-11T22:08:08.7659337Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 24 <- wrt source file
2023-01-11T22:08:08.7666402Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0
2023-01-11T22:08:08.7667129Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 35 <- wrt source file
2023-01-11T22:08:08.7672196Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/dirichlet.py::Dirichlet:0
2023-01-11T22:08:08.7672766Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/exponential.py::Exponential:0, line 15 <- wrt source file
2023-01-11T22:08:08.7677528Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/exponential.py::Exponential:0
2023-01-11T22:08:08.7678109Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 16 <- wrt source file
2023-01-11T22:08:08.7684807Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0
2023-01-11T22:08:08.7685463Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/gamma.py::Gamma:0, line 19 <- wrt source file
2023-01-11T22:08:08.7689899Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/gamma.py::Gamma:0
2023-01-11T22:08:08.7690432Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/geometric.py::Geometric:0, line 21 <- wrt source file
2023-01-11T22:08:08.7695613Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/geometric.py::Geometric:0
2023-01-11T22:08:08.7696186Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 17 <- wrt source file
2023-01-11T22:08:08.7703612Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/gumbel.py::Gumbel:0
2023-01-11T22:08:08.7704196Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 20 <- wrt source file
2023-01-11T22:08:08.7709831Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0
2023-01-11T22:08:08.7710398Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 20 <- wrt source file
2023-01-11T22:08:08.7715549Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/half_normal.py::HalfNormal:0
2023-01-11T22:08:08.7716118Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/independent.py::Independent:0, line 18 <- wrt source file
2023-01-11T22:08:08.7728654Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/independent.py::Independent:0
2023-01-11T22:08:08.7729416Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 25 <- wrt source file
2023-01-11T22:08:08.7735697Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0
2023-01-11T22:08:08.7736432Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/laplace.py::Laplace:0, line 14 <- wrt source file
2023-01-11T22:08:08.7741595Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/laplace.py::Laplace:0
2023-01-11T22:08:08.7742296Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 38 <- wrt source file
2023-01-11T22:08:08.7754022Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0
2023-01-11T22:08:08.7754660Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 17 <- wrt source file
2023-01-11T22:08:08.7760861Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/log_normal.py::LogNormal:0
2023-01-11T22:08:08.7761558Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 22 <- wrt source file
2023-01-11T22:08:08.7770139Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0
2023-01-11T22:08:08.7770929Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 56 <- wrt source file
2023-01-11T22:08:08.7772947Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0
2023-01-11T22:08:08.7773575Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/mixture_same_family.py::MixtureSameFamily:0, line 19 <- wrt source file
2023-01-11T22:08:08.7776824Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/mixture_same_family.py::MixtureSameFamily:0
2023-01-11T22:08:08.7777803Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 34 <- wrt source file
2023-01-11T22:08:08.7778670Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/multinomial.py::Multinomial:0
2023-01-11T22:08:08.7779278Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 94 <- wrt source file
2023-01-11T22:08:08.7779870Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0
2023-01-11T22:08:08.7780432Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/normal.py::Normal:0, line 18 <- wrt source file
2023-01-11T22:08:08.7784369Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/normal.py::Normal:0
2023-01-11T22:08:08.7784972Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 27 <- wrt source file
2023-01-11T22:08:08.7793008Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0
2023-01-11T22:08:08.7793583Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/pareto.py::Pareto:0, line 14 <- wrt source file
2023-01-11T22:08:08.7800224Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/pareto.py::Pareto:0
2023-01-11T22:08:08.7800910Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/poisson.py::Poisson:0, line 20 <- wrt source file
2023-01-11T22:08:08.7801726Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/poisson.py::Poisson:0
2023-01-11T22:08:08.7802322Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_bernoulli.py::RelaxedBernoulli:0, line 102 <- wrt source file
2023-01-11T22:08:08.7809819Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_bernoulli.py::RelaxedBernoulli:0
2023-01-11T22:08:08.7810453Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_categorical.py::RelaxedOneHotCategorical:0, line 96 <- wrt source file
2023-01-11T22:08:08.7820105Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_categorical.py::RelaxedOneHotCategorical:0
2023-01-11T22:08:08.7820683Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/studentT.py::StudentT:0, line 17 <- wrt source file
2023-01-11T22:08:08.7828532Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/studentT.py::StudentT:0
2023-01-11T22:08:08.7829840Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1002 <- wrt source file
2023-01-11T22:08:08.7830947Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CatTransform:0
2023-01-11T22:08:08.7831787Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1105 <- wrt source file
2023-01-11T22:08:08.7832845Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::StackTransform:0
2023-01-11T22:08:08.7833564Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1178 <- wrt source file
2023-01-11T22:08:08.7834190Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0
2023-01-11T22:08:08.7834836Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/uniform.py::Uniform:0, line 17 <- wrt source file
2023-01-11T22:08:08.7837082Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/uniform.py::Uniform:0
2023-01-11T22:08:08.7837697Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/von_mises.py::VonMises:0, line 79 <- wrt source file
2023-01-11T22:08:08.7846045Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/von_mises.py::VonMises:0
2023-01-11T22:08:08.7846645Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/weibull.py::Weibull:0, line 16 <- wrt source file
2023-01-11T22:08:08.7853063Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/weibull.py::Weibull:0
2023-01-11T22:08:08.7853785Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py::Wishart:0, line 36 <- wrt source file
2023-01-11T22:08:08.7854492Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py::Wishart:0
2023-01-11T22:08:08.7855200Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::_snake_case:0, line 79 <- wrt source file
2023-01-11T22:08:08.7855806Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::_snake_case:0
2023-01-11T22:08:08.7856447Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1363 <- wrt source file
2023-01-11T22:08:08.7857021Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0
2023-01-11T22:08:08.7858645Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1431 <- wrt source file
2023-01-11T22:08:08.7859214Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.on_generate_code:0
2023-01-11T22:08:08.7860191Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py::Interpreter:0, line 37 <- wrt source file
2023-01-11T22:08:08.7860798Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py::Interpreter:0
2023-01-11T22:08:08.7861641Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py::Transformer:0, line 380 <- wrt source file
2023-01-11T22:08:08.7862181Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py::Transformer:0
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2023-01-11T22:08:08.7981023Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0
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2023-01-11T22:08:08.8001007Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0
2023-01-11T22:08:08.8001667Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 15 <- wrt source file
2023-01-11T22:08:08.8005078Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0
2023-01-11T22:08:08.8005716Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 38 <- wrt source file
2023-01-11T22:08:08.8009117Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0
2023-01-11T22:08:08.8009754Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 66 <- wrt source file
2023-01-11T22:08:08.8012837Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0
2023-01-11T22:08:08.8013568Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 85 <- wrt source file
2023-01-11T22:08:08.8017148Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0
2023-01-11T22:08:08.8017766Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 115 <- wrt source file
2023-01-11T22:08:08.8021002Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0
2023-01-11T22:08:08.8021811Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file
2023-01-11T22:08:08.8022765Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0
2023-01-11T22:08:08.8023419Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 80 <- wrt source file
2023-01-11T22:08:08.8025156Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0
2023-01-11T22:08:08.8026344Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 76 <- wrt source file
2023-01-11T22:08:08.8026872Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0
2023-01-11T22:08:08.8028960Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/passes/split_module.py::split_module:0, line 68 <- wrt source file
2023-01-11T22:08:08.8029980Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/passes/split_module.py::split_module:0
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2023-01-11T22:08:08.8037576Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 23 <- wrt source file
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2023-01-11T22:08:08.8039078Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_model_bytecode_version:0, line 89 <- wrt source file
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2023-01-11T22:08:08.8040378Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 119 <- wrt source file
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2023-01-11T22:08:08.8055396Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 199 <- wrt source file
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2023-01-11T22:08:08.8056511Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 22 <- wrt source file
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2023-01-11T22:08:08.8057633Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 452 <- wrt source file
2023-01-11T22:08:08.8161809Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0
2023-01-11T22:08:08.8162702Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 553 <- wrt source file
2023-01-11T22:08:09.0025600Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0
2023-01-11T22:08:09.0041160Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 1878 <- wrt source file
2023-01-11T22:08:09.0052146Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::gumbel_softmax:0
2023-01-11T22:08:09.0053400Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding:0, line 2149 <- wrt source file
2023-01-11T22:08:09.0064105Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding:0
2023-01-11T22:08:09.0065077Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding_bag:0, line 2286 <- wrt source file
2023-01-11T22:08:09.0077507Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding_bag:0
2023-01-11T22:08:09.0078535Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::ctc_loss:0, line 2615 <- wrt source file
2023-01-11T22:08:09.0102250Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::ctc_loss:0
2023-01-11T22:08:09.0103413Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::nll_loss:0, line 2681 <- wrt source file
2023-01-11T22:08:09.0110425Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::nll_loss:0
2023-01-11T22:08:09.0111735Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::cross_entropy:0, line 3000 <- wrt source file
2023-01-11T22:08:09.0120823Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::cross_entropy:0
2023-01-11T22:08:09.0121874Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3066 <- wrt source file
2023-01-11T22:08:09.0128745Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy:0
2023-01-11T22:08:09.0129799Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3138 <- wrt source file
2023-01-11T22:08:09.0136891Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0
2023-01-11T22:08:09.0138264Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_input:0, line 23 <- wrt source file
2023-01-11T22:08:09.0147728Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_input:0
2023-01-11T22:08:09.0148875Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_weight:0, line 53 <- wrt source file
2023-01-11T22:08:09.0154950Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_weight:0
2023-01-11T22:08:09.0155634Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_input:0, line 86 <- wrt source file
2023-01-11T22:08:09.0164215Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_input:0
2023-01-11T22:08:09.0164886Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_weight:0, line 116 <- wrt source file
2023-01-11T22:08:09.0170666Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_weight:0
2023-01-11T22:08:09.0171360Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_input:0, line 149 <- wrt source file
2023-01-11T22:08:09.0205088Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_input:0
2023-01-11T22:08:09.0205992Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_weight:0, line 179 <- wrt source file
2023-01-11T22:08:09.0230511Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_weight:0
2023-01-11T22:08:09.0231406Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::calculate_gain:0, line 96 <- wrt source file
2023-01-11T22:08:09.0235216Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::calculate_gain:0
2023-01-11T22:08:09.0235888Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::uniform_:0, line 132 <- wrt source file
2023-01-11T22:08:09.0239521Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::uniform_:0
2023-01-11T22:08:09.0240180Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::normal_:0, line 150 <- wrt source file
2023-01-11T22:08:09.0243954Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::normal_:0
2023-01-11T22:08:09.0244620Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::trunc_normal_:0, line 173 <- wrt source file
2023-01-11T22:08:09.0248966Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::trunc_normal_:0
2023-01-11T22:08:09.0249625Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::constant_:0, line 187 <- wrt source file
2023-01-11T22:08:09.0254914Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::constant_:0
2023-01-11T22:08:09.0256197Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::ones_:0, line 202 <- wrt source file
2023-01-11T22:08:09.0260333Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::ones_:0
2023-01-11T22:08:09.0261689Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::zeros_:0, line 215 <- wrt source file
2023-01-11T22:08:09.0265815Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::zeros_:0
2023-01-11T22:08:09.0266915Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::eye_:0, line 230 <- wrt source file
2023-01-11T22:08:09.0271148Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::eye_:0
2023-01-11T22:08:09.0271727Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::dirac_:0, line 251 <- wrt source file
2023-01-11T22:08:09.0276560Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::dirac_:0
2023-01-11T22:08:09.0277092Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_uniform_:0, line 320 <- wrt source file
2023-01-11T22:08:09.0280616Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_uniform_:0
2023-01-11T22:08:09.0281121Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_normal_:0, line 347 <- wrt source file
2023-01-11T22:08:09.0284459Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_normal_:0
2023-01-11T22:08:09.0284978Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 392 <- wrt source file
2023-01-11T22:08:09.0288576Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_uniform_:0
2023-01-11T22:08:09.0289090Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_normal_:0, line 441 <- wrt source file
2023-01-11T22:08:09.0292845Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_normal_:0
2023-01-11T22:08:09.0293504Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::orthogonal_:0, line 466 <- wrt source file
2023-01-11T22:08:09.0294302Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::orthogonal_:0
2023-01-11T22:08:09.0294818Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::sparse_:0, line 512 <- wrt source file
2023-01-11T22:08:09.0299619Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::sparse_:0
2023-01-11T22:08:09.0300156Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Threshold:0, line 40 <- wrt source file
2023-01-11T22:08:09.0304892Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Threshold:0
2023-01-11T22:08:09.0305441Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU:0, line 83 <- wrt source file
2023-01-11T22:08:09.0312288Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU:0
2023-01-11T22:08:09.0312834Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::RReLU:0, line 142 <- wrt source file
2023-01-11T22:08:09.0317080Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::RReLU:0
2023-01-11T22:08:09.0317701Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 202 <- wrt source file
2023-01-11T22:08:09.0322156Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardtanh:0
2023-01-11T22:08:09.0322924Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 260 <- wrt source file
2023-01-11T22:08:09.0326614Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU6:0
2023-01-11T22:08:09.0327261Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 288 <- wrt source file
2023-01-11T22:08:09.0330797Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Sigmoid:0
2023-01-11T22:08:09.0331532Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 320 <- wrt source file
2023-01-11T22:08:09.0335229Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0
2023-01-11T22:08:09.0336002Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanh:0, line 352 <- wrt source file
2023-01-11T22:08:09.0339616Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanh:0
2023-01-11T22:08:09.0340293Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SiLU:0, line 383 <- wrt source file
2023-01-11T22:08:09.0344943Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SiLU:0
2023-01-11T22:08:09.0345609Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Mish:0, line 419 <- wrt source file
2023-01-11T22:08:09.0349493Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Mish:0
2023-01-11T22:08:09.0350183Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 461 <- wrt source file
2023-01-11T22:08:09.0353954Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardswish:0
2023-01-11T22:08:09.0354612Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ELU:0, line 502 <- wrt source file
2023-01-11T22:08:09.0358532Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ELU:0
2023-01-11T22:08:09.0359215Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::CELU:0, line 543 <- wrt source file
2023-01-11T22:08:09.0363001Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::CELU:0
2023-01-11T22:08:09.0363668Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SELU:0, line 595 <- wrt source file
2023-01-11T22:08:09.0367383Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SELU:0
2023-01-11T22:08:09.0368074Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GLU:0, line 631 <- wrt source file
2023-01-11T22:08:09.0372165Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GLU:0
2023-01-11T22:08:09.0372851Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GELU:0, line 672 <- wrt source file
2023-01-11T22:08:09.0378517Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GELU:0
2023-01-11T22:08:09.0379230Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 714 <- wrt source file
2023-01-11T22:08:09.0383925Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardshrink:0
2023-01-11T22:08:09.0384655Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 761 <- wrt source file
2023-01-11T22:08:09.0388825Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LeakyReLU:0
2023-01-11T22:08:09.0389553Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 796 <- wrt source file
2023-01-11T22:08:09.0393672Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSigmoid:0
2023-01-11T22:08:09.0394384Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softplus:0, line 827 <- wrt source file
2023-01-11T22:08:09.0398558Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softplus:0
2023-01-11T22:08:09.0399328Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 869 <- wrt source file
2023-01-11T22:08:09.0403705Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softshrink:0
2023-01-11T22:08:09.0404537Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 938 <- wrt source file
2023-01-11T22:08:09.0405283Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0
2023-01-11T22:08:09.0405997Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1274 <- wrt source file
2023-01-11T22:08:09.0410705Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::PReLU:0
2023-01-11T22:08:09.0411420Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1309 <- wrt source file
2023-01-11T22:08:09.0415884Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softsign:0
2023-01-11T22:08:09.0416597Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1332 <- wrt source file
2023-01-11T22:08:09.0421080Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanhshrink:0
2023-01-11T22:08:09.0421799Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1366 <- wrt source file
2023-01-11T22:08:09.0427358Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmin:0
2023-01-11T22:08:09.0428089Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1421 <- wrt source file
2023-01-11T22:08:09.0432510Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax:0
2023-01-11T22:08:09.0433241Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1461 <- wrt source file
2023-01-11T22:08:09.0437048Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax2d:0
2023-01-11T22:08:09.0437607Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1493 <- wrt source file
2023-01-11T22:08:09.0441780Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSoftmax:0
2023-01-11T22:08:09.0442336Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 290 <- wrt source file
2023-01-11T22:08:09.0450239Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0
2023-01-11T22:08:09.0450967Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 399 <- wrt source file
2023-01-11T22:08:09.0972388Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0
2023-01-11T22:08:09.0973180Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 505 <- wrt source file
2023-01-11T22:08:09.6331674Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0
2023-01-11T22:08:09.6447348Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm:0, line 627 <- wrt source file
2023-01-11T22:08:09.6450013Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm:0
2023-01-11T22:08:09.6451029Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm.convert_sync_batchnorm:0, line 782 <- wrt source file
2023-01-11T22:08:09.6452371Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm.convert_sync_batchnorm:0
2023-01-11T22:08:09.6453294Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 17 <- wrt source file
2023-01-11T22:08:09.6471189Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0
2023-01-11T22:08:09.6473068Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::Sequential:0, line 63 <- wrt source file
2023-01-11T22:08:09.6473768Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::Sequential:0
2023-01-11T22:08:09.6474486Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleList:0, line 261 <- wrt source file
2023-01-11T22:08:09.6475160Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleList:0
2023-01-11T22:08:09.6475900Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 433 <- wrt source file
2023-01-11T22:08:09.6476587Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleDict:0
2023-01-11T22:08:09.6477313Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterList:0, line 567 <- wrt source file
2023-01-11T22:08:09.6478038Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterList:0
2023-01-11T22:08:09.6478755Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 707 <- wrt source file
2023-01-11T22:08:09.6479506Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterDict:0
2023-01-11T22:08:09.6480239Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 36 <- wrt source file
2023-01-11T22:08:09.6488838Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0
2023-01-11T22:08:09.6489559Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 72 <- wrt source file
2023-01-11T22:08:09.6500581Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0
2023-01-11T22:08:09.6501328Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 49 <- wrt source file
2023-01-11T22:08:09.6507061Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout:0
2023-01-11T22:08:09.6507584Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 91 <- wrt source file
2023-01-11T22:08:09.6515099Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout1d:0
2023-01-11T22:08:09.6515636Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 140 <- wrt source file
2023-01-11T22:08:09.6569541Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout2d:0
2023-01-11T22:08:09.6570256Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 182 <- wrt source file
2023-01-11T22:08:09.6764585Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout3d:0
2023-01-11T22:08:09.6765319Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 225 <- wrt source file
2023-01-11T22:08:09.6770767Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0
2023-01-11T22:08:09.6771562Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 272 <- wrt source file
2023-01-11T22:08:09.6969651Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0
2023-01-11T22:08:09.6970411Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 24 <- wrt source file
2023-01-11T22:08:09.6977533Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Flatten:0
2023-01-11T22:08:09.6978244Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Unflatten:0, line 76 <- wrt source file
2023-01-11T22:08:09.6996899Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Unflatten:0
2023-01-11T22:08:09.6997865Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file
2023-01-11T22:08:09.7004516Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Fold:0
2023-01-11T22:08:09.7005447Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Unfold:0, line 253 <- wrt source file
2023-01-11T22:08:09.7020518Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Unfold:0
2023-01-11T22:08:09.7021903Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 135 <- wrt source file
2023-01-11T22:08:09.7042577Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0
2023-01-11T22:08:09.7043633Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 251 <- wrt source file
2023-01-11T22:08:09.7520494Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0
2023-01-11T22:08:09.7521521Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 367 <- wrt source file
2023-01-11T22:08:10.2876339Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0
2023-01-11T22:08:10.2992346Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file
2023-01-11T22:08:10.2997595Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0
2023-01-11T22:08:10.2998467Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Identity:0, line 33 <- wrt source file
2023-01-11T22:08:10.3006893Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Identity:0
2023-01-11T22:08:10.3008120Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Linear:0, line 78 <- wrt source file
2023-01-11T22:08:10.3017528Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Linear:0
2023-01-11T22:08:10.3018666Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 164 <- wrt source file
2023-01-11T22:08:10.3042756Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Bilinear:0
2023-01-11T22:08:10.3044112Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 88 <- wrt source file
2023-01-11T22:08:10.3051785Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::L1Loss:0
2023-01-11T22:08:10.3052646Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 184 <- wrt source file
2023-01-11T22:08:10.3076975Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::NLLLoss:0
2023-01-11T22:08:10.3077926Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 271 <- wrt source file
2023-01-11T22:08:10.3085870Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0
2023-01-11T22:08:10.3086623Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 343 <- wrt source file
2023-01-11T22:08:10.3102658Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0
2023-01-11T22:08:10.3103362Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 451 <- wrt source file
2023-01-11T22:08:10.3113268Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::KLDivLoss:0
2023-01-11T22:08:10.3113957Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 523 <- wrt source file
2023-01-11T22:08:10.3120958Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MSELoss:0
2023-01-11T22:08:10.3121459Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 605 <- wrt source file
2023-01-11T22:08:10.3128392Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCELoss:0
2023-01-11T22:08:10.3128927Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 668 <- wrt source file
2023-01-11T22:08:10.3141462Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0
2023-01-11T22:08:10.3142048Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 831 <- wrt source file
2023-01-11T22:08:10.3151549Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0
2023-01-11T22:08:10.3152113Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1149 <- wrt source file
2023-01-11T22:08:10.3161853Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0
2023-01-11T22:08:10.3162557Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1317 <- wrt source file
2023-01-11T22:08:10.3169856Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0
2023-01-11T22:08:10.3170597Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1388 <- wrt source file
2023-01-11T22:08:10.3179176Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0
2023-01-11T22:08:10.3179905Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1468 <- wrt source file
2023-01-11T22:08:10.3195516Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0
2023-01-11T22:08:10.3196282Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginWithDistanceLoss:0, line 1559 <- wrt source file
2023-01-11T22:08:10.3228821Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginWithDistanceLoss:0
2023-01-11T22:08:10.3229571Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1670 <- wrt source file
2023-01-11T22:08:10.3263144Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CTCLoss:0
2023-01-11T22:08:10.3263896Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 491 <- wrt source file
2023-01-11T22:08:10.3264636Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.register_buffer:0
2023-01-11T22:08:10.3265368Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.apply:0, line 847 <- wrt source file
2023-01-11T22:08:10.3280428Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.apply:0
2023-01-11T22:08:10.3281157Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.to:0, line 1072 <- wrt source file
2023-01-11T22:08:10.3289621Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.to:0
2023-01-11T22:08:10.3290350Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 1763 <- wrt source file
2023-01-11T22:08:10.3291059Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.state_dict:0
2023-01-11T22:08:10.3291781Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2050 <- wrt source file
2023-01-11T22:08:10.3292490Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.parameters:0
2023-01-11T22:08:10.3293246Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2082 <- wrt source file
2023-01-11T22:08:10.3293962Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_parameters:0
2023-01-11T22:08:10.3294688Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2107 <- wrt source file
2023-01-11T22:08:10.3295379Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.buffers:0
2023-01-11T22:08:10.3296098Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2133 <- wrt source file
2023-01-11T22:08:10.3296804Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_buffers:0
2023-01-11T22:08:10.3297526Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2163 <- wrt source file
2023-01-11T22:08:10.3298232Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_children:0
2023-01-11T22:08:10.3298965Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2187 <- wrt source file
2023-01-11T22:08:10.3310559Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.modules:0
2023-01-11T22:08:10.3311193Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2221 <- wrt source file
2023-01-11T22:08:10.3316843Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_modules:0
2023-01-11T22:08:10.3317466Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 34 <- wrt source file
2023-01-11T22:08:10.3384476Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0
2023-01-11T22:08:10.3385375Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 140 <- wrt source file
2023-01-11T22:08:10.3395087Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LayerNorm:0
2023-01-11T22:08:10.3395820Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 230 <- wrt source file
2023-01-11T22:08:10.3404890Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::GroupNorm:0
2023-01-11T22:08:10.3405612Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 48 <- wrt source file
2023-01-11T22:08:10.3414083Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0
2023-01-11T22:08:10.3414815Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 101 <- wrt source file
2023-01-11T22:08:10.3420868Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0
2023-01-11T22:08:10.3421589Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 157 <- wrt source file
2023-01-11T22:08:10.3470436Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0
2023-01-11T22:08:10.3471174Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 201 <- wrt source file
2023-01-11T22:08:10.3477381Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0
2023-01-11T22:08:10.3478113Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 244 <- wrt source file
2023-01-11T22:08:10.3485131Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0
2023-01-11T22:08:10.3486519Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 301 <- wrt source file
2023-01-11T22:08:10.3490709Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0
2023-01-11T22:08:10.3491930Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 358 <- wrt source file
2023-01-11T22:08:10.3497978Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0
2023-01-11T22:08:10.3498752Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 401 <- wrt source file
2023-01-11T22:08:10.3505621Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0
2023-01-11T22:08:10.3506380Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 458 <- wrt source file
2023-01-11T22:08:11.5091406Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0
2023-01-11T22:08:11.5319715Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 494 <- wrt source file
2023-01-11T22:08:11.5327605Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0
2023-01-11T22:08:11.5328185Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 36 <- wrt source file
2023-01-11T22:08:11.5335642Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0
2023-01-11T22:08:11.5336702Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 86 <- wrt source file
2023-01-11T22:08:11.5342260Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0
2023-01-11T22:08:11.5343470Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 76 <- wrt source file
2023-01-11T22:08:11.5350624Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0
2023-01-11T22:08:11.5351624Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 148 <- wrt source file
2023-01-11T22:08:11.5445266Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0
2023-01-11T22:08:11.5446221Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 226 <- wrt source file
2023-01-11T22:08:11.9548520Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0
2023-01-11T22:08:11.9591769Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 293 <- wrt source file
2023-01-11T22:08:11.9606302Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0
2023-01-11T22:08:11.9607074Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool2d:0, line 366 <- wrt source file
2023-01-11T22:08:11.9627869Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool2d:0
2023-01-11T22:08:11.9628610Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 451 <- wrt source file
2023-01-11T22:08:12.1145992Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0
2023-01-11T22:08:12.1146605Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 524 <- wrt source file
2023-01-11T22:08:12.1157067Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0
2023-01-11T22:08:12.1157803Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 600 <- wrt source file
2023-01-11T22:08:12.1243867Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0
2023-01-11T22:08:12.1244620Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 686 <- wrt source file
2023-01-11T22:08:12.4942304Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0
2023-01-11T22:08:12.4984187Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 749 <- wrt source file
2023-01-11T22:08:12.5080051Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0
2023-01-11T22:08:12.5080834Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 819 <- wrt source file
2023-01-11T22:08:12.6483002Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0
2023-01-11T22:08:12.6483786Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 909 <- wrt source file
2023-01-11T22:08:12.6494607Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool1d:0
2023-01-11T22:08:12.6495321Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 960 <- wrt source file
2023-01-11T22:08:12.6603091Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool2d:0
2023-01-11T22:08:12.6603844Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1011 <- wrt source file
2023-01-11T22:08:12.6608606Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0
2023-01-11T22:08:12.6609370Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1045 <- wrt source file
2023-01-11T22:08:12.6619945Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0
2023-01-11T22:08:12.6620690Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1088 <- wrt source file
2023-01-11T22:08:12.6696770Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0
2023-01-11T22:08:12.6697553Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1135 <- wrt source file
2023-01-11T22:08:12.6702032Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0
2023-01-11T22:08:12.6702915Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1166 <- wrt source file
2023-01-11T22:08:12.6713487Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0
2023-01-11T22:08:12.6714247Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1205 <- wrt source file
2023-01-11T22:08:12.6749544Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0
2023-01-11T22:08:12.6750300Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNN:0, line 436 <- wrt source file
2023-01-11T22:08:12.6762447Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNN:0
2023-01-11T22:08:12.6763234Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 702 <- wrt source file
2023-01-11T22:08:12.6781491Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTM:0
2023-01-11T22:08:12.6782742Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRU:0, line 933 <- wrt source file
2023-01-11T22:08:12.6800816Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRU:0
2023-01-11T22:08:12.6802419Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1106 <- wrt source file
2023-01-11T22:08:12.6813798Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNNCell:0
2023-01-11T22:08:12.6814356Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1207 <- wrt source file
2023-01-11T22:08:12.6825988Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTMCell:0
2023-01-11T22:08:12.6826522Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1300 <- wrt source file
2023-01-11T22:08:12.6841698Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRUCell:0
2023-01-11T22:08:12.6842700Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 67 <- wrt source file
2023-01-11T22:08:12.6857562Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding:0
2023-01-11T22:08:12.6858311Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 200 <- wrt source file
2023-01-11T22:08:12.6864064Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0
2023-01-11T22:08:12.6864626Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag:0, line 278 <- wrt source file
2023-01-11T22:08:12.6881427Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag:0
2023-01-11T22:08:12.6882174Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 429 <- wrt source file
2023-01-11T22:08:12.6888937Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0
2023-01-11T22:08:12.6889701Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 42 <- wrt source file
2023-01-11T22:08:13.3906248Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer:0
2023-01-11T22:08:13.3920616Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 134 <- wrt source file
2023-01-11T22:08:13.3921607Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0
2023-01-11T22:08:13.3922386Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 181 <- wrt source file
2023-01-11T22:08:13.4599725Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0
2023-01-11T22:08:13.4604019Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 325 <- wrt source file
2023-01-11T22:08:13.5972786Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0
2023-01-11T22:08:13.5979648Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 391 <- wrt source file
2023-01-11T22:08:13.6242792Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0
2023-01-11T22:08:13.6243639Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 608 <- wrt source file
2023-01-11T22:08:13.6672649Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0
2023-01-11T22:08:13.6673471Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 74 <- wrt source file
2023-01-11T22:08:13.6699917Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::Upsample:0
2023-01-11T22:08:13.6700661Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 196 <- wrt source file
2023-01-11T22:08:13.6714088Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0
2023-01-11T22:08:13.6714713Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 242 <- wrt source file
2023-01-11T22:08:13.6723701Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0
2023-01-11T22:08:13.6724752Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 116 <- wrt source file
2023-01-11T22:08:13.6725824Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0
2023-01-11T22:08:13.6726752Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 534 <- wrt source file
2023-01-11T22:08:13.6727498Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0
2023-01-11T22:08:13.6728376Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1051 <- wrt source file
2023-01-11T22:08:13.6729160Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0
2023-01-11T22:08:13.6730047Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.join:0, line 1377 <- wrt source file
2023-01-11T22:08:13.6730686Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.join:0
2023-01-11T22:08:13.6731348Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1550 <- wrt source file
2023-01-11T22:08:13.6731990Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0
2023-01-11T22:08:13.6732660Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1560 <- wrt source file
2023-01-11T22:08:13.6733309Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1
2023-01-11T22:08:13.6733981Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 1594 <- wrt source file
2023-01-11T22:08:13.6734631Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0
2023-01-11T22:08:13.6735348Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_fused_optim:0, line 1652 <- wrt source file
2023-01-11T22:08:13.6736003Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_fused_optim:0
2023-01-11T22:08:13.6736626Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 32 <- wrt source file
2023-01-11T22:08:13.6737194Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0
2023-01-11T22:08:13.6737836Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/init.py::skip_init:0, line 30 <- wrt source file
2023-01-11T22:08:13.6742660Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/init.py::skip_init:0
2023-01-11T22:08:13.6743350Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/memory_format.py::convert_conv2d_weight_memory_format:0, line 54 <- wrt source file
2023-01-11T22:08:13.6744018Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/memory_format.py::convert_conv2d_weight_memory_format:0
2023-01-11T22:08:13.6744621Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 245 <- wrt source file
2023-01-11T22:08:13.6745651Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0
2023-01-11T22:08:13.6746439Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 462 <- wrt source file
2023-01-11T22:08:13.6747383Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0
2023-01-11T22:08:13.6748002Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 463 <- wrt source file
2023-01-11T22:08:13.6753192Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0
2023-01-11T22:08:13.6754031Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::identity:0, line 846 <- wrt source file
2023-01-11T22:08:13.6754758Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::identity:0
2023-01-11T22:08:13.6755400Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 880 <- wrt source file
2023-01-11T22:08:13.6756140Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_unstructured:0
2023-01-11T22:08:13.6756758Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 921 <- wrt source file
2023-01-11T22:08:13.6757414Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::l1_unstructured:0
2023-01-11T22:08:13.6758146Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_structured:0, line 959 <- wrt source file
2023-01-11T22:08:13.6758675Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_structured:0
2023-01-11T22:08:13.6759267Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::ln_structured:0, line 1005 <- wrt source file
2023-01-11T22:08:13.6771277Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::ln_structured:0
2023-01-11T22:08:13.6771893Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::global_unstructured:0, line 1058 <- wrt source file
2023-01-11T22:08:13.6788083Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::global_unstructured:0
2023-01-11T22:08:13.6788692Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::custom_from_mask:0, line 1160 <- wrt source file
2023-01-11T22:08:13.6798236Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::custom_from_mask:0
2023-01-11T22:08:13.6798814Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::remove:0, line 1188 <- wrt source file
2023-01-11T22:08:13.6805271Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::remove:0
2023-01-11T22:08:13.6805959Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1215 <- wrt source file
2023-01-11T22:08:13.6814795Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::is_pruned:0
2023-01-11T22:08:13.6815332Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 282 <- wrt source file
2023-01-11T22:08:13.6831004Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0
2023-01-11T22:08:13.6831521Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 359 <- wrt source file
2023-01-11T22:08:13.6837643Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_sequence:0
2023-01-11T22:08:13.6838274Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 412 <- wrt source file
2023-01-11T22:08:13.6852872Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0
2023-01-11T22:08:13.6853570Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 467 <- wrt source file
2023-01-11T22:08:13.6861305Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pack_sequence:0
2023-01-11T22:08:13.6862013Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 495 <- wrt source file
2023-01-11T22:08:13.6879997Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0
2023-01-11T22:08:13.6881014Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 267 <- wrt source file
2023-01-11T22:08:13.6888519Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0
2023-01-11T22:08:13.6889596Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 294 <- wrt source file
2023-01-11T22:08:13.6896811Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0
2023-01-11T22:08:13.6898014Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 123 <- wrt source file
2023-01-11T22:08:13.6901980Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/stateless.py::functional_call:0
2023-01-11T22:08:13.6903195Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 99 <- wrt source file
2023-01-11T22:08:13.6910940Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0
2023-01-11T22:08:13.6911982Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 121 <- wrt source file
2023-01-11T22:08:13.6917884Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0
2023-01-11T22:08:13.6919286Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 203 <- wrt source file
2023-01-11T22:08:13.6920593Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0
2023-01-11T22:08:13.6921967Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 108 <- wrt source file
2023-01-11T22:08:13.6943573Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0
2023-01-11T22:08:13.6944783Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/onnx/_type_utils.py::JitScalarType:0, line 66 <- wrt source file
2023-01-11T22:08:13.6945822Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/onnx/_type_utils.py::JitScalarType:0
2023-01-11T22:08:13.6946815Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/onnx/verification.py::find_mismatch:0, line 1746 <- wrt source file
2023-01-11T22:08:13.6947802Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/onnx/verification.py::find_mismatch:0
2023-01-11T22:08:13.6948863Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/engine.py::DiagnosticEngine:0, line 20 <- wrt source file
2023-01-11T22:08:13.6950141Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/engine.py::DiagnosticEngine:0
2023-01-11T22:08:13.6951164Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 200 <- wrt source file
2023-01-11T22:08:13.6952486Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0
2023-01-11T22:08:13.6953595Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 286 <- wrt source file
2023-01-11T22:08:13.6954769Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0
2023-01-11T22:08:13.6956119Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 369 <- wrt source file
2023-01-11T22:08:13.6957034Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::StepLR:0
2023-01-11T22:08:13.6958007Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 418 <- wrt source file
2023-01-11T22:08:13.6958978Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0
2023-01-11T22:08:13.6960021Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 467 <- wrt source file
2023-01-11T22:08:13.6960928Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0
2023-01-11T22:08:13.6961900Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 529 <- wrt source file
2023-01-11T22:08:13.6962827Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LinearLR:0
2023-01-11T22:08:13.6963752Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 621 <- wrt source file
2023-01-11T22:08:13.6964711Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0
2023-01-11T22:08:13.6965684Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 729 <- wrt source file
2023-01-11T22:08:13.6966612Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0
2023-01-11T22:08:13.6967688Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 849 <- wrt source file
2023-01-11T22:08:13.6968631Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0
2023-01-11T22:08:13.6969638Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 953 <- wrt source file
2023-01-11T22:08:13.6970607Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0
2023-01-11T22:08:13.6971561Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1168 <- wrt source file
2023-01-11T22:08:13.6972540Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0
2023-01-11T22:08:13.6973627Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1389 <- wrt source file
2023-01-11T22:08:13.6974742Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0
2023-01-11T22:08:13.6975926Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1405 <- wrt source file
2023-01-11T22:08:13.6977387Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1
2023-01-11T22:08:13.6978652Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1547 <- wrt source file
2023-01-11T22:08:13.6979473Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0
2023-01-11T22:08:13.6980319Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/sgd.py::SGD:0, line 58 <- wrt source file
2023-01-11T22:08:13.6981176Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/sgd.py::SGD:0
2023-01-11T22:08:13.6982129Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:0, line 38 <- wrt source file
2023-01-11T22:08:13.6983247Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:0
2023-01-11T22:08:13.6984176Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:1, line 64 <- wrt source file
2023-01-11T22:08:13.6985050Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:1
2023-01-11T22:08:13.6985894Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::update_bn:0, line 161 <- wrt source file
2023-01-11T22:08:13.6986693Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::update_bn:0
2023-01-11T22:08:13.6987535Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::SWALR:0, line 222 <- wrt source file
2023-01-11T22:08:13.6988414Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::SWALR:0
2023-01-11T22:08:13.6989394Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/package/glob_group.py::GlobGroup:0, line 19 <- wrt source file
2023-01-11T22:08:13.6990296Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/package/glob_group.py::GlobGroup:0
2023-01-11T22:08:13.6991170Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/profiler/profiler.py::profile:0, line 363 <- wrt source file
2023-01-11T22:08:13.6992062Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/profiler/profiler.py::profile:0
2023-01-11T22:08:13.6992941Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_comparison.py::assert_close:0, line 1395 <- wrt source file
2023-01-11T22:08:13.7032184Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/testing/_comparison.py::assert_close:0
2023-01-11T22:08:13.7033201Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_creation.py::make_tensor:0, line 93 <- wrt source file
2023-01-11T22:08:13.7034116Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_creation.py::make_tensor:0
2023-01-11T22:08:13.7035293Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 305 <- wrt source file
2023-01-11T22:08:13.7036309Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::parametrize:0
2023-01-11T22:08:13.7037369Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 3447 <- wrt source file
2023-01-11T22:08:13.7038460Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0
2023-01-11T22:08:13.7039652Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 3461 <- wrt source file
2023-01-11T22:08:13.7040724Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0
2023-01-11T22:08:13.7041935Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 3491 <- wrt source file
2023-01-11T22:08:13.7043008Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0
2023-01-11T22:08:13.7044142Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 57 <- wrt source file
2023-01-11T22:08:13.7045319Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0
2023-01-11T22:08:13.7046446Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 24 <- wrt source file
2023-01-11T22:08:13.7047505Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0
2023-01-11T22:08:13.7048523Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 306 <- wrt source file
2023-01-11T22:08:13.7049419Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0
2023-01-11T22:08:13.7050401Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CppExtension:0, line 912 <- wrt source file
2023-01-11T22:08:13.7051196Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CppExtension:0
2023-01-11T22:08:13.7052131Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:0, line 960 <- wrt source file
2023-01-11T22:08:13.7053003Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:0
2023-01-11T22:08:13.7053973Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:1, line 1037 <- wrt source file
2023-01-11T22:08:13.7054882Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:1
2023-01-11T22:08:13.7055848Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load:0, line 1273 <- wrt source file
2023-01-11T22:08:13.7056729Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load:0
2023-01-11T22:08:13.7057690Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load_inline:0, line 1364 <- wrt source file
2023-01-11T22:08:13.7058624Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load_inline:0
2023-01-11T22:08:13.7059544Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 71 <- wrt source file
2023-01-11T22:08:13.7067398Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/dlpack.py::from_dlpack:0
2023-01-11T22:08:13.7068473Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/throughput_benchmark.py::ThroughputBenchmark:0, line 77 <- wrt source file
2023-01-11T22:08:13.7069573Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/throughput_benchmark.py::ThroughputBenchmark:0
2023-01-11T22:08:13.7070630Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 84 <- wrt source file
2023-01-11T22:08:13.7077594Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::IterableDataset:0
2023-01-11T22:08:13.7078641Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::random_split:0, line 320 <- wrt source file
2023-01-11T22:08:13.7079792Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::random_split:0
2023-01-11T22:08:13.7080821Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/distributed.py::DistributedSampler:0, line 51 <- wrt source file
2023-01-11T22:08:13.7081820Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/distributed.py::DistributedSampler:0
2023-01-11T22:08:13.7082730Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 172 <- wrt source file
2023-01-11T22:08:13.7088188Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0
2023-01-11T22:08:13.7089416Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 220 <- wrt source file
2023-01-11T22:08:13.7094771Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::BatchSampler:0
2023-01-11T22:08:13.7095831Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 36 <- wrt source file
2023-01-11T22:08:13.7099358Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_convert:0
2023-01-11T22:08:13.7100412Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 102 <- wrt source file
2023-01-11T22:08:13.7105990Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::collate:0
2023-01-11T22:08:13.7107061Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 231 <- wrt source file
2023-01-11T22:08:13.7112842Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_collate:0
2023-01-11T22:08:13.7117236Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 84 <- wrt source file
2023-01-11T22:08:13.7118262Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0
2023-01-11T22:08:13.7119420Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 232 <- wrt source file
2023-01-11T22:08:13.7120466Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0
2023-01-11T22:08:13.7121546Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 46 <- wrt source file
2023-01-11T22:08:13.7122930Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0
2023-01-11T22:08:13.7124248Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 187 <- wrt source file
2023-01-11T22:08:13.7126575Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0
2023-01-11T22:08:13.7127606Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 80 <- wrt source file
2023-01-11T22:08:13.7128633Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0
2023-01-11T22:08:13.7129760Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 33 <- wrt source file
2023-01-11T22:08:13.7154407Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0
2023-01-11T22:08:13.7155583Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 75 <- wrt source file
2023-01-11T22:08:13.7156710Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0
2023-01-11T22:08:13.7157860Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 250 <- wrt source file
2023-01-11T22:08:13.7158964Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0
2023-01-11T22:08:13.7160174Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 329 <- wrt source file
2023-01-11T22:08:13.7161353Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0
2023-01-11T22:08:13.7162508Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 507 <- wrt source file
2023-01-11T22:08:13.7163912Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0
2023-01-11T22:08:13.7164751Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 572 <- wrt source file
2023-01-11T22:08:13.7165810Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0
2023-01-11T22:08:13.7166938Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 29 <- wrt source file
2023-01-11T22:08:13.7167868Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0
2023-01-11T22:08:13.7168872Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 33 <- wrt source file
2023-01-11T22:08:13.7169740Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0
2023-01-11T22:08:13.7170542Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 102 <- wrt source file
2023-01-11T22:08:13.7171141Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0
2023-01-11T22:08:13.7171760Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 159 <- wrt source file
2023-01-11T22:08:13.7172480Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0
2023-01-11T22:08:13.7173100Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 226 <- wrt source file
2023-01-11T22:08:13.7175628Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0
2023-01-11T22:08:13.7176401Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 34 <- wrt source file
2023-01-11T22:08:13.7177462Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0
2023-01-11T22:08:13.7178627Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 20 <- wrt source file
2023-01-11T22:08:13.7179720Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0
2023-01-11T22:08:13.7180371Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 23 <- wrt source file
2023-01-11T22:08:13.7181018Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0
2023-01-11T22:08:13.7182064Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 30 <- wrt source file
2023-01-11T22:08:13.7182967Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0
2023-01-11T22:08:13.7183974Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 31 <- wrt source file
2023-01-11T22:08:13.7185111Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0
2023-01-11T22:08:13.7185946Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 24 <- wrt source file
2023-01-11T22:08:13.7186907Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0
2023-01-11T22:08:13.7187538Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 66 <- wrt source file
2023-01-11T22:08:13.7188585Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0
2023-01-11T22:08:13.7189437Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 23 <- wrt source file
2023-01-11T22:08:13.7190015Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0
2023-01-11T22:08:13.7190639Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 23 <- wrt source file
2023-01-11T22:08:13.7191325Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0
2023-01-11T22:08:13.7192105Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 33 <- wrt source file
2023-01-11T22:08:13.7193051Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0
2023-01-11T22:08:13.7194159Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 415 <- wrt source file
2023-01-11T22:08:13.7195176Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0
2023-01-11T22:08:13.7196241Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 511 <- wrt source file
2023-01-11T22:08:13.7197110Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0
2023-01-11T22:08:13.7198166Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 213 <- wrt source file
2023-01-11T22:08:13.7199360Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0
2023-01-11T22:08:13.7200177Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 320 <- wrt source file
2023-01-11T22:08:13.7201051Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0
2023-01-11T22:08:13.7202019Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 368 <- wrt source file
2023-01-11T22:08:13.7203037Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0
2023-01-11T22:08:13.7203985Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 404 <- wrt source file
2023-01-11T22:08:13.7204820Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0
2023-01-11T22:08:13.7205717Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 462 <- wrt source file
2023-01-11T22:08:13.7206363Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0
2023-01-11T22:08:13.7207101Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 519 <- wrt source file
2023-01-11T22:08:13.7207877Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0
2023-01-11T22:08:13.7208529Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 585 <- wrt source file
2023-01-11T22:08:13.7209162Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0
2023-01-11T22:08:13.7209777Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 638 <- wrt source file
2023-01-11T22:08:13.7210480Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0
2023-01-11T22:08:13.7211116Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 810 <- wrt source file
2023-01-11T22:08:13.7211669Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0
2023-01-11T22:08:13.7212263Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 896 <- wrt source file
2023-01-11T22:08:13.7212920Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0
2023-01-11T22:08:13.7213587Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 1001 <- wrt source file
2023-01-11T22:08:13.7214274Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0
2023-01-11T22:08:13.7215031Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1076 <- wrt source file
2023-01-11T22:08:13.7215672Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0
2023-01-11T22:08:13.7216363Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1095 <- wrt source file
2023-01-11T22:08:13.7217262Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0
2023-01-11T22:08:13.7217987Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1117 <- wrt source file
2023-01-11T22:08:13.7218956Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0
2023-01-11T22:08:13.7219603Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1161 <- wrt source file
2023-01-11T22:08:13.7220169Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0
2023-01-11T22:08:13.7220476Z ============
2023-01-11T22:08:13.7220663Z Finished doctests
2023-01-11T22:08:13.7220834Z 287 / 663 passed
2023-01-11T22:08:13.7220999Z 
2023-01-11T22:08:13.7221232Z === Found 3 run-time warnings ===
2023-01-11T22:08:13.7221505Z --- Runtime Warning: 1 / 3 ---
2023-01-11T22:08:13.7221765Z example = <DocTest(torch._tensor Tensor.refine_names:0 ln 1096) at 0x7f4b33a051e0>
2023-01-11T22:08:13.7222872Z     /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:1114: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1816.)
2023-01-11T22:08:13.7223427Z       return super(Tensor, self).refine_names(names)
2023-01-11T22:08:13.7223625Z     
2023-01-11T22:08:13.7223853Z --- Runtime Warning: 2 / 3 ---
2023-01-11T22:08:13.7224124Z example = <DocTest(torch.nested as_nested_tensor:0 ln 39) at 0x7f4b314b96f0>
2023-01-11T22:08:13.7224818Z     /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T22:08:13.7225325Z       return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)
2023-01-11T22:08:13.7225549Z     
2023-01-11T22:08:13.7225782Z --- Runtime Warning: 3 / 3 ---
2023-01-11T22:08:13.7226062Z example = <DocTest(torch._functorch.aot_autograd aot_function:0 ln 2139) at 0x7f4b31346fe0>
2023-01-11T22:08:13.7226939Z     /opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py:921: UserWarning: Your compiler for AOTAutograd is returning a a function that doesn't take boxed arguments. Please wrap it with functorch.compile.make_boxed_func or handle the boxed arguments yourself. See https://github.com/pytorch/pytorch/pull/83137#issuecomment-1211320670 for rationale.
2023-01-11T22:08:13.7227543Z       warnings.warn(
2023-01-11T22:08:13.7227712Z     
2023-01-11T22:08:13.7227995Z === 287 passed, 376 skipped, 3 warnings in 11.46 seconds ===
2023-01-11T22:08:14.0861670Z 
2023-01-11T22:08:14.0862155Z real	45m47.839s
2023-01-11T22:08:14.0862696Z user	87m51.939s
2023-01-11T22:08:14.0863024Z sys	10m9.710s
2023-01-11T22:08:14.0863232Z + assert_git_not_dirty
2023-01-11T22:08:14.0863633Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:08:14.0863957Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:08:14.0866241Z ++ git status --porcelain
2023-01-11T22:08:23.8491185Z + git_status=
2023-01-11T22:08:23.8491530Z + [[ -n '' ]]
2023-01-11T22:08:23.8504323Z + test_aten
2023-01-11T22:08:23.8505066Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:08:23.8505721Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:08:23.8506493Z + echo 'Running ATen tests with pytorch lib'
2023-01-11T22:08:23.8506908Z Running ATen tests with pytorch lib
2023-01-11T22:08:23.8507284Z + [[ -n '' ]]
2023-01-11T22:08:23.8507685Z + echo 'Running test with the build folder'
2023-01-11T22:08:23.8508052Z Running test with the build folder
2023-01-11T22:08:23.8508370Z + TEST_BASE_DIR=build/bin
2023-01-11T22:08:23.8509277Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10d_cuda_test.so build/bin
2023-01-11T22:08:23.8607523Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libcaffe2_nvrtc.so build/bin
2023-01-11T22:08:23.8616498Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libmkldnn*' build/bin
2023-01-11T22:08:23.8626478Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libnccl*' build/bin
2023-01-11T22:08:23.8635735Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda_linalg.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorchbind_test.so build/bin
2023-01-11T22:08:23.8643875Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libtbb*' build/bin
2023-01-11T22:08:23.8651943Z + ls build/bin
2023-01-11T22:08:23.8737636Z  CppSignature_test
2023-01-11T22:08:23.8738044Z  Dict_test
2023-01-11T22:08:23.8738315Z  Dimname_test
2023-01-11T22:08:23.8738617Z  FileStoreTest
2023-01-11T22:08:23.8738899Z  HashStoreTest
2023-01-11T22:08:23.8739177Z  IListRef_test
2023-01-11T22:08:23.8739488Z  KernelFunction_test
2023-01-11T22:08:23.8739779Z  List_test
2023-01-11T22:08:23.8740060Z  MaybeOwned_test
2023-01-11T22:08:23.8740379Z  NamedTensor_test
2023-01-11T22:08:23.8740720Z  ProcessGroupGlooAsyncTest
2023-01-11T22:08:23.8741107Z  ProcessGroupGlooTest
2023-01-11T22:08:23.8741435Z  ProcessGroupMPITest
2023-01-11T22:08:23.8741630Z  ProcessGroupNCCLErrorsTest
2023-01-11T22:08:23.8741856Z  ProcessGroupNCCLTest
2023-01-11T22:08:23.8742149Z  ProcessGroupUCCTest
2023-01-11T22:08:23.8742511Z  TCPStoreTest
2023-01-11T22:08:23.8742767Z  aot_model_compiler_test
2023-01-11T22:08:23.8742953Z  apply_utils_test
2023-01-11T22:08:23.8743106Z  atest
2023-01-11T22:08:23.8743270Z  backend_fallback_test
2023-01-11T22:08:23.8743439Z  basic
2023-01-11T22:08:23.8743582Z  broadcast_test
2023-01-11T22:08:23.8743752Z  c10_Array_test
2023-01-11T22:08:23.8743917Z  c10_Bitset_test
2023-01-11T22:08:23.8744070Z  c10_C++17_test
2023-01-11T22:08:23.8744270Z  c10_CompileTimeFunctionPointer_test
2023-01-11T22:08:23.8744487Z  c10_ConstexprCrc_test
2023-01-11T22:08:23.8744666Z  c10_DeadlockDetection_test
2023-01-11T22:08:23.8744857Z  c10_DeviceGuard_test
2023-01-11T22:08:23.8745031Z  c10_Device_test
2023-01-11T22:08:23.8745376Z  c10_DispatchKeySet_test
2023-01-11T22:08:23.8745558Z  c10_Half_test
2023-01-11T22:08:23.8745738Z  c10_InlineDeviceGuard_test
2023-01-11T22:08:23.8745927Z  c10_InlineStreamGuard_test
2023-01-11T22:08:23.8746117Z  c10_LeftRight_test
2023-01-11T22:08:23.8746307Z  c10_Metaprogramming_test
2023-01-11T22:08:23.8746546Z  c10_SizesAndStrides_test
2023-01-11T22:08:23.8746719Z  c10_SmallVectorTest
2023-01-11T22:08:23.8746904Z  c10_StreamGuard_test
2023-01-11T22:08:23.8747075Z  c10_SymInt_test
2023-01-11T22:08:23.8747303Z  c10_Synchronized_test
2023-01-11T22:08:23.8747580Z  c10_ThreadLocal_test
2023-01-11T22:08:23.8747849Z  c10_TypeIndex_test
2023-01-11T22:08:23.8748079Z  c10_TypeList_test
2023-01-11T22:08:23.8748333Z  c10_TypeTraits_test
2023-01-11T22:08:23.8748612Z  c10_accumulate_test
2023-01-11T22:08:23.8748873Z  c10_bfloat16_test
2023-01-11T22:08:23.8749163Z  c10_complex_math_test
2023-01-11T22:08:23.8749462Z  c10_complex_test
2023-01-11T22:08:23.8749764Z  c10_cuda_CUDAAssertionsTest_1_var_test
2023-01-11T22:08:23.8750375Z  c10_cuda_CUDAAssertionsTest_catches_stream
2023-01-11T22:08:23.8750903Z  c10_cuda_CUDAAssertionsTest_catches_thread_and_block_and_device
2023-01-11T22:08:23.8751321Z  c10_cuda_CUDAAssertionsTest_from_2_processes
2023-01-11T22:08:23.8751781Z  c10_cuda_CUDAAssertionsTest_multiple_writes_from_blocks_and_threads
2023-01-11T22:08:23.8752318Z  c10_cuda_CUDAAssertionsTest_multiple_writes_from_multiple_blocks
2023-01-11T22:08:23.8752810Z  c10_cuda_CUDAAssertionsTest_multiple_writes_from_same_block
2023-01-11T22:08:23.8753161Z  c10_cuda_CUDATest
2023-01-11T22:08:23.8753441Z  c10_either_test
2023-01-11T22:08:23.8753732Z  c10_exception_test
2023-01-11T22:08:23.8753994Z  c10_flags_test
2023-01-11T22:08:23.8754308Z  c10_intrusive_ptr_benchmark
2023-01-11T22:08:23.8754638Z  c10_intrusive_ptr_test
2023-01-11T22:08:23.8754901Z  c10_irange_test
2023-01-11T22:08:23.8755163Z  c10_logging_test
2023-01-11T22:08:23.8755424Z  c10_optional_test
2023-01-11T22:08:23.8755727Z  c10_ordered_preserving_dict_test
2023-01-11T22:08:23.8756050Z  c10_registry_test
2023-01-11T22:08:23.8756342Z  c10_string_view_test
2023-01-11T22:08:23.8756641Z  c10_tempfile_test
2023-01-11T22:08:23.8756936Z  c10_typeid_test
2023-01-11T22:08:23.8757243Z  cpu_generator_test
2023-01-11T22:08:23.8757568Z  cpu_profiling_allocator_test
2023-01-11T22:08:23.8757883Z  cpu_rng_test
2023-01-11T22:08:23.8758157Z  cuda_apply_test
2023-01-11T22:08:23.8758461Z  cuda_atomic_ops_test
2023-01-11T22:08:23.8758801Z  cuda_caching_host_allocator_test
2023-01-11T22:08:23.8759229Z  cuda_complex_math_test
2023-01-11T22:08:23.8759532Z  cuda_complex_test
2023-01-11T22:08:23.8759809Z  cuda_cub_test
2023-01-11T22:08:23.8760056Z  cuda_cudnn_test
2023-01-11T22:08:23.8760322Z  cuda_device_test
2023-01-11T22:08:23.8760622Z  cuda_distributions_test
2023-01-11T22:08:23.8760931Z  cuda_dlconvertor_test
2023-01-11T22:08:23.8761204Z  cuda_generator_test
2023-01-11T22:08:23.8761507Z  cuda_half_test
2023-01-11T22:08:23.8761819Z  cuda_integer_divider_test
2023-01-11T22:08:23.8762149Z  cuda_optional_test
2023-01-11T22:08:23.8762492Z  cuda_packedtensoraccessor_test
2023-01-11T22:08:23.8762837Z  cuda_reportMemoryUsage_test
2023-01-11T22:08:23.8763139Z  cuda_stream_test
2023-01-11T22:08:23.8763424Z  cuda_vectorized_test
2023-01-11T22:08:23.8763732Z  dispatch_key_set_test
2023-01-11T22:08:23.8764023Z  dlconvertor_test
2023-01-11T22:08:23.8764333Z  example_allreduce
2023-01-11T22:08:23.8764651Z  extension_backend_test
2023-01-11T22:08:23.8764900Z  half_test
2023-01-11T22:08:23.8765165Z  inline_container_test
2023-01-11T22:08:23.8765450Z  ivalue_test
2023-01-11T22:08:23.8765745Z  kernel_function_legacy_test
2023-01-11T22:08:23.8766061Z  kernel_function_test
2023-01-11T22:08:23.8766358Z  kernel_lambda_legacy_test
2023-01-11T22:08:23.8766632Z  kernel_lambda_test
2023-01-11T22:08:23.8766913Z  kernel_stackbased_test
2023-01-11T22:08:23.8767171Z  lazy_tensor_test
2023-01-11T22:08:23.8767334Z  legacy_vmap_test
2023-01-11T22:08:23.8767505Z  libc10.so
2023-01-11T22:08:23.8767771Z  libc10_cuda.so
2023-01-11T22:08:23.8767933Z  libc10d_cuda_test.so
2023-01-11T22:08:23.8768110Z  libcaffe2_nvrtc.so
2023-01-11T22:08:23.8768381Z 'libmkldnn*'
2023-01-11T22:08:23.8768554Z 'libnccl*'
2023-01-11T22:08:23.8768730Z 'libtbb*'
2023-01-11T22:08:23.8768887Z  libtorch.so
2023-01-11T22:08:23.8769044Z  libtorch_cpu.so
2023-01-11T22:08:23.8769220Z  libtorch_cuda.so
2023-01-11T22:08:23.8769447Z  libtorch_cuda_linalg.so
2023-01-11T22:08:23.8769661Z  libtorch_global_deps.so
2023-01-11T22:08:23.8769843Z  libtorch_python.so
2023-01-11T22:08:23.8770024Z  libtorchbind_test.so
2023-01-11T22:08:23.8770212Z  make_boxed_from_unboxed_functor_test
2023-01-11T22:08:23.8770406Z  math_kernel_test
2023-01-11T22:08:23.8770578Z  memory_format_test
2023-01-11T22:08:23.8770750Z  memory_overlapping_test
2023-01-11T22:08:23.8770941Z  mobile_memory_cleanup
2023-01-11T22:08:23.8771114Z  native_test
2023-01-11T22:08:23.8771270Z  op_allowlist_test
2023-01-11T22:08:23.8771449Z  op_registration_test
2023-01-11T22:08:23.8771634Z  operator_name_test
2023-01-11T22:08:23.8771841Z  operators_test
2023-01-11T22:08:23.8772037Z  packedtensoraccessor_test
2023-01-11T22:08:23.8772233Z  parallel_benchmark
2023-01-11T22:08:23.8772387Z  pow_test
2023-01-11T22:08:23.8772541Z  protoc
2023-01-11T22:08:23.8772745Z  protoc-3.13.0.0
2023-01-11T22:08:23.8772909Z  quantized_test
2023-01-11T22:08:23.8773078Z  reduce_ops_test
2023-01-11T22:08:23.8773261Z  reportMemoryUsage_test
2023-01-11T22:08:23.8773435Z  scalar_tensor_test
2023-01-11T22:08:23.8773605Z  scalar_test
2023-01-11T22:08:23.8773783Z  stride_properties_test
2023-01-11T22:08:23.8773956Z  tensor_iterator_test
2023-01-11T22:08:23.8774125Z  test_api
2023-01-11T22:08:23.8774282Z  test_cpp_rpc
2023-01-11T22:08:23.8774437Z  test_dist_autograd
2023-01-11T22:08:23.8774620Z  test_edge_op_registration
2023-01-11T22:08:23.8774796Z  test_jit
2023-01-11T22:08:23.8774935Z  test_lazy
2023-01-11T22:08:23.8775093Z  test_mobile_nnc
2023-01-11T22:08:23.8775258Z  test_parallel
2023-01-11T22:08:23.8775412Z  test_tensorexpr
2023-01-11T22:08:23.8775586Z  thread_init_test
2023-01-11T22:08:23.8775757Z  torch_shm_manager
2023-01-11T22:08:23.8775919Z  tutorial_tensorexpr
2023-01-11T22:08:23.8776093Z  type_ptr_test
2023-01-11T22:08:23.8776256Z  type_test
2023-01-11T22:08:23.8776412Z  undefined_tensor_test
2023-01-11T22:08:23.8776584Z  variant_test
2023-01-11T22:08:23.8776758Z  vec_test_all_types_AVX2
2023-01-11T22:08:23.8776935Z  vec_test_all_types_DEFAULT
2023-01-11T22:08:23.8777123Z  verify_api_visibility
2023-01-11T22:08:23.8777296Z  weakref_test
2023-01-11T22:08:23.8777447Z  wrapdim_test
2023-01-11T22:08:23.8777615Z  xla_tensor_test
2023-01-11T22:08:23.8777804Z + aten/tools/run_tests.sh build/bin
2023-01-11T22:08:23.8778003Z + set -e
2023-01-11T22:08:23.8778183Z ++ dirname aten/tools/run_tests.sh
2023-01-11T22:08:23.8797779Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup
2023-01-11T22:08:23.8798168Z + pushd build/bin
2023-01-11T22:08:23.8798475Z ~/workspace/build/bin ~/workspace
2023-01-11T22:08:23.8798724Z + VALGRIND=ON
2023-01-11T22:08:23.8798890Z + ./basic
2023-01-11T22:08:25.2508169Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:25.2508792Z [==========] Running 5 tests from 1 test suite.
2023-01-11T22:08:25.2509107Z [----------] Global test environment set-up.
2023-01-11T22:08:25.2509406Z [----------] 5 tests from BasicTest
2023-01-11T22:08:25.2509677Z [ RUN      ] BasicTest.BasicTestCPU
2023-01-11T22:08:25.3802702Z    5 ms
2023-01-11T22:08:25.4557109Z    72 ms
2023-01-11T22:08:25.5677098Z    109 ms
2023-01-11T22:08:25.6332647Z [       OK ] BasicTest.BasicTestCPU (382 ms)
2023-01-11T22:08:25.6333117Z [ RUN      ] BasicTest.BasicTestHalfCPU
2023-01-11T22:08:25.6385820Z    2 ms
2023-01-11T22:08:25.7333272Z    93 ms
2023-01-11T22:08:25.8653249Z    131 ms
2023-01-11T22:08:25.8786895Z [       OK ] BasicTest.BasicTestHalfCPU (245 ms)
2023-01-11T22:08:25.8787256Z [ RUN      ] BasicTest.BasicTestCUDA
2023-01-11T22:08:25.8787781Z [       OK ] BasicTest.BasicTestCUDA (0 ms)
2023-01-11T22:08:25.8788094Z [ RUN      ] BasicTest.FactoryMethodsTest
2023-01-11T22:08:25.8855049Z [       OK ] BasicTest.FactoryMethodsTest (6 ms)
2023-01-11T22:08:25.8855506Z [ RUN      ] BasicTest.BasicStdTestCPU
2023-01-11T22:08:25.8856190Z Simple example: called once
2023-01-11T22:08:25.8859753Z throw: call_once will retry
2023-01-11T22:08:25.8861402Z throw: call_once will retry
2023-01-11T22:08:25.8863542Z Didn't throw, call_once will not attempt again
2023-01-11T22:08:25.8864125Z [       OK ] BasicTest.BasicStdTestCPU (0 ms)
2023-01-11T22:08:25.8864697Z [----------] 5 tests from BasicTest (635 ms total)
2023-01-11T22:08:25.8864851Z 
2023-01-11T22:08:25.8865030Z [----------] Global test environment tear-down
2023-01-11T22:08:25.8865352Z [==========] 5 tests from 1 test suite ran. (635 ms total)
2023-01-11T22:08:25.8865780Z [  PASSED  ] 5 tests.
2023-01-11T22:08:26.1254871Z + ./atest
2023-01-11T22:08:26.4484116Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:26.4488616Z [==========] Running 16 tests from 1 test suite.
2023-01-11T22:08:26.4488981Z [----------] Global test environment set-up.
2023-01-11T22:08:26.4489277Z [----------] 16 tests from atest
2023-01-11T22:08:26.4489585Z [ RUN      ] atest.operators
2023-01-11T22:08:26.4492192Z [       OK ] atest.operators (0 ms)
2023-01-11T22:08:26.4492547Z [ RUN      ] atest.logical_and_operators
2023-01-11T22:08:26.4493779Z [       OK ] atest.logical_and_operators (0 ms)
2023-01-11T22:08:26.4494181Z [ RUN      ] atest.logical_or_operators
2023-01-11T22:08:26.4494523Z [       OK ] atest.logical_or_operators (0 ms)
2023-01-11T22:08:26.4494813Z [ RUN      ] atest.logical_xor_operators
2023-01-11T22:08:26.4495106Z [       OK ] atest.logical_xor_operators (0 ms)
2023-01-11T22:08:26.4495377Z [ RUN      ] atest.lt_operators
2023-01-11T22:08:26.4503026Z [       OK ] atest.lt_operators (0 ms)
2023-01-11T22:08:26.4503518Z [ RUN      ] atest.le_operators
2023-01-11T22:08:26.4503996Z [       OK ] atest.le_operators (0 ms)
2023-01-11T22:08:26.4504496Z [ RUN      ] atest.gt_operators
2023-01-11T22:08:26.4504954Z [       OK ] atest.gt_operators (0 ms)
2023-01-11T22:08:26.4505437Z [ RUN      ] atest.ge_operators
2023-01-11T22:08:26.4505749Z [       OK ] atest.ge_operators (0 ms)
2023-01-11T22:08:26.4505997Z [ RUN      ] atest.eq_operators
2023-01-11T22:08:26.4506263Z [       OK ] atest.eq_operators (0 ms)
2023-01-11T22:08:26.4506522Z [ RUN      ] atest.ne_operators
2023-01-11T22:08:26.4506790Z [       OK ] atest.ne_operators (0 ms)
2023-01-11T22:08:26.4507038Z [ RUN      ] atest.add_operators
2023-01-11T22:08:26.4507330Z [       OK ] atest.add_operators (0 ms)
2023-01-11T22:08:26.4507597Z [ RUN      ] atest.max_operators
2023-01-11T22:08:26.4516665Z [       OK ] atest.max_operators (1 ms)
2023-01-11T22:08:26.4517162Z [ RUN      ] atest.min_operators
2023-01-11T22:08:26.4517801Z [       OK ] atest.min_operators (0 ms)
2023-01-11T22:08:26.4518236Z [ RUN      ] atest.sigmoid_backward_operator
2023-01-11T22:08:26.4518575Z [       OK ] atest.sigmoid_backward_operator (0 ms)
2023-01-11T22:08:26.4518874Z [ RUN      ] atest.fmod_tensor_operators
2023-01-11T22:08:26.4519709Z [       OK ] atest.fmod_tensor_operators (0 ms)
2023-01-11T22:08:26.4519974Z [ RUN      ] atest.atest
2023-01-11T22:08:26.4642999Z [       OK ] atest.atest (12 ms)
2023-01-11T22:08:26.4643408Z [----------] 16 tests from atest (15 ms total)
2023-01-11T22:08:26.4643561Z 
2023-01-11T22:08:26.4643744Z [----------] Global test environment tear-down
2023-01-11T22:08:26.4644223Z [==========] 16 tests from 1 test suite ran. (15 ms total)
2023-01-11T22:08:26.4644472Z [  PASSED  ] 16 tests.
2023-01-11T22:08:26.5397284Z + ./scalar_test
2023-01-11T22:08:26.8644234Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:26.8645040Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:08:26.8645364Z [----------] Global test environment set-up.
2023-01-11T22:08:26.8645654Z [----------] 4 tests from TestScalar
2023-01-11T22:08:26.8645916Z [ RUN      ] TestScalar.TestScalar
2023-01-11T22:08:26.8646116Z H2: 3 257 3 1
2023-01-11T22:08:26.8782060Z [       OK ] TestScalar.TestScalar (13 ms)
2023-01-11T22:08:26.8782648Z [ RUN      ] TestScalar.TestConj
2023-01-11T22:08:26.8783039Z [       OK ] TestScalar.TestConj (0 ms)
2023-01-11T22:08:26.8783568Z [ RUN      ] TestScalar.TestEqual
2023-01-11T22:08:26.8783983Z [       OK ] TestScalar.TestEqual (0 ms)
2023-01-11T22:08:26.8784512Z [ RUN      ] TestScalar.TestFormatting
2023-01-11T22:08:26.8785069Z [       OK ] TestScalar.TestFormatting (0 ms)
2023-01-11T22:08:26.8785403Z [----------] 4 tests from TestScalar (13 ms total)
2023-01-11T22:08:26.8785579Z 
2023-01-11T22:08:26.8785783Z [----------] Global test environment tear-down
2023-01-11T22:08:26.8786102Z [==========] 4 tests from 1 test suite ran. (13 ms total)
2023-01-11T22:08:26.8786364Z [  PASSED  ] 4 tests.
2023-01-11T22:08:26.9525429Z + ./broadcast_test
2023-01-11T22:08:27.2754713Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:27.2755207Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:08:27.2756125Z [----------] Global test environment set-up.
2023-01-11T22:08:27.2756457Z [----------] 1 test from BroadcastTest
2023-01-11T22:08:27.2756751Z [ RUN      ] BroadcastTest.Broadcast
2023-01-11T22:08:27.3421611Z [       OK ] BroadcastTest.Broadcast (66 ms)
2023-01-11T22:08:27.3421992Z [----------] 1 test from BroadcastTest (66 ms total)
2023-01-11T22:08:27.3422201Z 
2023-01-11T22:08:27.3422549Z [----------] Global test environment tear-down
2023-01-11T22:08:27.3422939Z [==========] 1 test from 1 test suite ran. (66 ms total)
2023-01-11T22:08:27.3423204Z [  PASSED  ] 1 test.
2023-01-11T22:08:27.4145609Z + ./wrapdim_test
2023-01-11T22:08:27.7404980Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:27.7405510Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:08:27.7405817Z [----------] Global test environment set-up.
2023-01-11T22:08:27.7406101Z [----------] 1 test from TestWrapdim
2023-01-11T22:08:27.7406394Z [ RUN      ] TestWrapdim.TestWrapdim
2023-01-11T22:08:27.7410716Z [       OK ] TestWrapdim.TestWrapdim (0 ms)
2023-01-11T22:08:27.7411249Z [----------] 1 test from TestWrapdim (0 ms total)
2023-01-11T22:08:27.7411538Z 
2023-01-11T22:08:27.7411784Z [----------] Global test environment tear-down
2023-01-11T22:08:27.7412092Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:08:27.7412345Z [  PASSED  ] 1 test.
2023-01-11T22:08:27.8114998Z + ./apply_utils_test
2023-01-11T22:08:28.1336709Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:28.1337434Z [==========] Running 6 tests from 1 test suite.
2023-01-11T22:08:28.1337894Z [----------] Global test environment set-up.
2023-01-11T22:08:28.1338385Z [----------] 6 tests from ApplyUtilsTest
2023-01-11T22:08:28.1338903Z [ RUN      ] ApplyUtilsTest.Contiguous2D
2023-01-11T22:08:28.1369149Z [       OK ] ApplyUtilsTest.Contiguous2D (3 ms)
2023-01-11T22:08:28.1370084Z [ RUN      ] ApplyUtilsTest.Small2D
2023-01-11T22:08:28.1370663Z [       OK ] ApplyUtilsTest.Small2D (0 ms)
2023-01-11T22:08:28.1371206Z [ RUN      ] ApplyUtilsTest._2D
2023-01-11T22:08:28.1382779Z [       OK ] ApplyUtilsTest._2D (1 ms)
2023-01-11T22:08:28.1383336Z [ RUN      ] ApplyUtilsTest._3D
2023-01-11T22:08:28.1385379Z [       OK ] ApplyUtilsTest._3D (0 ms)
2023-01-11T22:08:28.1385918Z [ RUN      ] ApplyUtilsTest.Medium3D
2023-01-11T22:08:28.1399882Z [       OK ] ApplyUtilsTest.Medium3D (1 ms)
2023-01-11T22:08:28.1400201Z [ RUN      ] ApplyUtilsTest._10D
2023-01-11T22:08:28.2240467Z [       OK ] ApplyUtilsTest._10D (83 ms)
2023-01-11T22:08:28.2240842Z [----------] 6 tests from ApplyUtilsTest (90 ms total)
2023-01-11T22:08:28.2241040Z 
2023-01-11T22:08:28.2241245Z [----------] Global test environment tear-down
2023-01-11T22:08:28.2241834Z [==========] 6 tests from 1 test suite ran. (90 ms total)
2023-01-11T22:08:28.2242105Z [  PASSED  ] 6 tests.
2023-01-11T22:08:28.2976888Z + ./dlconvertor_test
2023-01-11T22:08:28.6207606Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:28.6208091Z [==========] Running 2 tests from 1 test suite.
2023-01-11T22:08:28.6208389Z [----------] Global test environment set-up.
2023-01-11T22:08:28.6208691Z [----------] 2 tests from TestDlconvertor
2023-01-11T22:08:28.6209005Z [ RUN      ] TestDlconvertor.TestDlconvertor
2023-01-11T22:08:28.6211459Z [       OK ] TestDlconvertor.TestDlconvertor (0 ms)
2023-01-11T22:08:28.6212135Z [ RUN      ] TestDlconvertor.TestDlconvertorNoStrides
2023-01-11T22:08:28.6212611Z [       OK ] TestDlconvertor.TestDlconvertorNoStrides (0 ms)
2023-01-11T22:08:28.6212980Z [----------] 2 tests from TestDlconvertor (0 ms total)
2023-01-11T22:08:28.6213161Z 
2023-01-11T22:08:28.6213329Z [----------] Global test environment tear-down
2023-01-11T22:08:28.6213627Z [==========] 2 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:08:28.6213886Z [  PASSED  ] 2 tests.
2023-01-11T22:08:28.6920572Z + ./native_test
2023-01-11T22:08:29.0146877Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:29.0147646Z [==========] Running 2 tests from 1 test suite.
2023-01-11T22:08:29.0147960Z [----------] Global test environment set-up.
2023-01-11T22:08:29.0148257Z [----------] 2 tests from TestNative
2023-01-11T22:08:29.0148539Z [ RUN      ] TestNative.NativeTestCPU
2023-01-11T22:08:29.0341388Z [W TensorCompare.cpp:493] Warning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (function operator())
2023-01-11T22:08:29.0350374Z [       OK ] TestNative.NativeTestCPU (20 ms)
2023-01-11T22:08:29.0350927Z [ RUN      ] TestNative.NativeTestGPU
2023-01-11T22:08:29.0351361Z [       OK ] TestNative.NativeTestGPU (0 ms)
2023-01-11T22:08:29.0351674Z [----------] 2 tests from TestNative (20 ms total)
2023-01-11T22:08:29.0351828Z 
2023-01-11T22:08:29.0351997Z [----------] Global test environment tear-down
2023-01-11T22:08:29.0352313Z [==========] 2 tests from 1 test suite ran. (20 ms total)
2023-01-11T22:08:29.0352557Z [  PASSED  ] 2 tests.
2023-01-11T22:08:29.1090531Z + ./scalar_tensor_test
2023-01-11T22:08:29.4345014Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:29.4345726Z [==========] Running 3 tests from 1 test suite.
2023-01-11T22:08:29.4346032Z [----------] Global test environment set-up.
2023-01-11T22:08:29.4346341Z [----------] 3 tests from TestScalarTensor
2023-01-11T22:08:29.4346815Z [ RUN      ] TestScalarTensor.TestScalarTensorCPU
2023-01-11T22:08:29.6593109Z [       OK ] TestScalarTensor.TestScalarTensorCPU (224 ms)
2023-01-11T22:08:29.6593840Z [ RUN      ] TestScalarTensor.TestScalarTensorCUDA
2023-01-11T22:08:29.6594284Z [       OK ] TestScalarTensor.TestScalarTensorCUDA (0 ms)
2023-01-11T22:08:29.6594653Z [ RUN      ] TestScalarTensor.TestScalarTensorMPS
2023-01-11T22:08:29.6595018Z [       OK ] TestScalarTensor.TestScalarTensorMPS (0 ms)
2023-01-11T22:08:29.6595372Z [----------] 3 tests from TestScalarTensor (224 ms total)
2023-01-11T22:08:29.6595538Z 
2023-01-11T22:08:29.6595706Z [----------] Global test environment tear-down
2023-01-11T22:08:29.6596021Z [==========] 3 tests from 1 test suite ran. (224 ms total)
2023-01-11T22:08:29.6596278Z [  PASSED  ] 3 tests.
2023-01-11T22:08:29.7339046Z + [[ -x ./tensor_interop_test ]]
2023-01-11T22:08:29.7339620Z + ./undefined_tensor_test
2023-01-11T22:08:30.0632047Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:30.0632871Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:08:30.0633183Z [----------] Global test environment set-up.
2023-01-11T22:08:30.0633461Z [----------] 1 test from TestUndefined
2023-01-11T22:08:30.0633754Z [ RUN      ] TestUndefined.UndefinedTest
2023-01-11T22:08:30.0962216Z [       OK ] TestUndefined.UndefinedTest (33 ms)
2023-01-11T22:08:30.0962828Z [----------] 1 test from TestUndefined (33 ms total)
2023-01-11T22:08:30.0963098Z 
2023-01-11T22:08:30.0963276Z [----------] Global test environment tear-down
2023-01-11T22:08:30.0963587Z [==========] 1 test from 1 test suite ran. (33 ms total)
2023-01-11T22:08:30.0963844Z [  PASSED  ] 1 test.
2023-01-11T22:08:30.1718928Z + ./extension_backend_test
2023-01-11T22:08:30.5005169Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:30.5009962Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:08:30.5010464Z [----------] Global test environment set-up.
2023-01-11T22:08:30.5010937Z [----------] 1 test from BackendExtensionTest
2023-01-11T22:08:30.5011423Z [ RUN      ] BackendExtensionTest.TestRegisterOp
2023-01-11T22:08:30.5011894Z [       OK ] BackendExtensionTest.TestRegisterOp (0 ms)
2023-01-11T22:08:30.5012379Z [----------] 1 test from BackendExtensionTest (0 ms total)
2023-01-11T22:08:30.5012654Z 
2023-01-11T22:08:30.5012917Z [----------] Global test environment tear-down
2023-01-11T22:08:30.5013405Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:08:30.5013783Z [  PASSED  ] 1 test.
2023-01-11T22:08:30.5755490Z + ./lazy_tensor_test
2023-01-11T22:08:30.9026094Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:30.9026836Z [==========] Running 2 tests from 2 test suites.
2023-01-11T22:08:30.9027299Z [----------] Global test environment set-up.
2023-01-11T22:08:30.9027764Z [----------] 1 test from XlaTensorTest
2023-01-11T22:08:30.9028240Z [ RUN      ] XlaTensorTest.TestNoStorage
2023-01-11T22:08:30.9028775Z [       OK ] XlaTensorTest.TestNoStorage (0 ms)
2023-01-11T22:08:30.9029319Z [----------] 1 test from XlaTensorTest (0 ms total)
2023-01-11T22:08:30.9029560Z 
2023-01-11T22:08:30.9029798Z [----------] 1 test from LazyTensorTest
2023-01-11T22:08:30.9030275Z [ RUN      ] LazyTensorTest.TestNoStorage
2023-01-11T22:08:30.9030795Z [       OK ] LazyTensorTest.TestNoStorage (0 ms)
2023-01-11T22:08:30.9031300Z [----------] 1 test from LazyTensorTest (0 ms total)
2023-01-11T22:08:30.9031545Z 
2023-01-11T22:08:30.9031823Z [----------] Global test environment tear-down
2023-01-11T22:08:30.9032603Z [==========] 2 tests from 2 test suites ran. (0 ms total)
2023-01-11T22:08:30.9032997Z [  PASSED  ] 2 tests.
2023-01-11T22:08:30.9773111Z + ./tensor_iterator_test
2023-01-11T22:08:31.3042377Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:31.3044473Z [==========] Running 65 tests from 1 test suite.
2023-01-11T22:08:31.3044774Z [----------] Global test environment set-up.
2023-01-11T22:08:31.3045083Z [----------] 65 tests from TensorIteratorTest
2023-01-11T22:08:31.3045391Z [ RUN      ] TensorIteratorTest.CPUScalar
2023-01-11T22:08:31.3045703Z [       OK ] TensorIteratorTest.CPUScalar (0 ms)
2023-01-11T22:08:31.3046038Z [ RUN      ] TensorIteratorTest.CPUScalarInputs
2023-01-11T22:08:31.3046391Z [       OK ] TensorIteratorTest.CPUScalarInputs (0 ms)
2023-01-11T22:08:31.3046962Z [ RUN      ] TensorIteratorTest.MixedDevices
2023-01-11T22:08:31.3047302Z [       OK ] TensorIteratorTest.MixedDevices (0 ms)
2023-01-11T22:08:31.3047656Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Byte
2023-01-11T22:08:31.3067236Z [       OK ] TensorIteratorTest.SerialLoopUnary_Byte (2 ms)
2023-01-11T22:08:31.3067862Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Char
2023-01-11T22:08:31.3085008Z [       OK ] TensorIteratorTest.SerialLoopUnary_Char (1 ms)
2023-01-11T22:08:31.3085620Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Short
2023-01-11T22:08:31.3102261Z [       OK ] TensorIteratorTest.SerialLoopUnary_Short (1 ms)
2023-01-11T22:08:31.3103110Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Int
2023-01-11T22:08:31.3119804Z [       OK ] TensorIteratorTest.SerialLoopUnary_Int (1 ms)
2023-01-11T22:08:31.3120408Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Long
2023-01-11T22:08:31.3137018Z [       OK ] TensorIteratorTest.SerialLoopUnary_Long (1 ms)
2023-01-11T22:08:31.3137612Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Float
2023-01-11T22:08:31.3154957Z [       OK ] TensorIteratorTest.SerialLoopUnary_Float (1 ms)
2023-01-11T22:08:31.3155558Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Double
2023-01-11T22:08:31.3172437Z [       OK ] TensorIteratorTest.SerialLoopUnary_Double (1 ms)
2023-01-11T22:08:31.3173038Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Byte
2023-01-11T22:08:31.3189731Z [       OK ] TensorIteratorTest.SerialLoopBinary_Byte (1 ms)
2023-01-11T22:08:31.3190304Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Char
2023-01-11T22:08:31.3207020Z [       OK ] TensorIteratorTest.SerialLoopBinary_Char (1 ms)
2023-01-11T22:08:31.3207606Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Short
2023-01-11T22:08:31.3224419Z [       OK ] TensorIteratorTest.SerialLoopBinary_Short (1 ms)
2023-01-11T22:08:31.3225001Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Int
2023-01-11T22:08:31.3241640Z [       OK ] TensorIteratorTest.SerialLoopBinary_Int (1 ms)
2023-01-11T22:08:31.3242242Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Long
2023-01-11T22:08:31.3258788Z [       OK ] TensorIteratorTest.SerialLoopBinary_Long (1 ms)
2023-01-11T22:08:31.3259414Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Float
2023-01-11T22:08:31.3276177Z [       OK ] TensorIteratorTest.SerialLoopBinary_Float (1 ms)
2023-01-11T22:08:31.3276798Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Double
2023-01-11T22:08:31.3293545Z [       OK ] TensorIteratorTest.SerialLoopBinary_Double (1 ms)
2023-01-11T22:08:31.3294169Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Byte
2023-01-11T22:08:31.3310925Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Byte (1 ms)
2023-01-11T22:08:31.3311641Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Char
2023-01-11T22:08:31.3328153Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Char (1 ms)
2023-01-11T22:08:31.3328759Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Short
2023-01-11T22:08:31.3345461Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Short (1 ms)
2023-01-11T22:08:31.3346054Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Int
2023-01-11T22:08:31.3362683Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Int (1 ms)
2023-01-11T22:08:31.3363291Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Long
2023-01-11T22:08:31.3379831Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Long (1 ms)
2023-01-11T22:08:31.3380440Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Float
2023-01-11T22:08:31.3397133Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Float (1 ms)
2023-01-11T22:08:31.3397959Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Double
2023-01-11T22:08:31.3414512Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Double (1 ms)
2023-01-11T22:08:31.3415146Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Byte
2023-01-11T22:08:31.3415842Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Byte (0 ms)
2023-01-11T22:08:31.3416519Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Char
2023-01-11T22:08:31.3417190Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Char (0 ms)
2023-01-11T22:08:31.3417945Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Short
2023-01-11T22:08:31.3418729Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Short (0 ms)
2023-01-11T22:08:31.3419423Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Int
2023-01-11T22:08:31.3420063Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Int (0 ms)
2023-01-11T22:08:31.3420658Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Long
2023-01-11T22:08:31.3421265Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Long (0 ms)
2023-01-11T22:08:31.3421796Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Float
2023-01-11T22:08:31.3422603Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Float (0 ms)
2023-01-11T22:08:31.3423261Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Double
2023-01-11T22:08:31.3424034Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Double (0 ms)
2023-01-11T22:08:31.3424784Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Byte
2023-01-11T22:08:31.3425348Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Byte (0 ms)
2023-01-11T22:08:31.3425762Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Char
2023-01-11T22:08:31.3426182Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Char (0 ms)
2023-01-11T22:08:31.3426640Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Short
2023-01-11T22:08:31.3427187Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Short (0 ms)
2023-01-11T22:08:31.3427869Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Int
2023-01-11T22:08:31.3428587Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Int (0 ms)
2023-01-11T22:08:31.3429004Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Long
2023-01-11T22:08:31.3429420Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Long (0 ms)
2023-01-11T22:08:31.3429824Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Float
2023-01-11T22:08:31.3430244Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Float (0 ms)
2023-01-11T22:08:31.3430662Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Double
2023-01-11T22:08:31.3431089Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Double (0 ms)
2023-01-11T22:08:31.3431619Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Byte
2023-01-11T22:08:31.3432053Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Byte (0 ms)
2023-01-11T22:08:31.3432479Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Char
2023-01-11T22:08:31.3432898Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Char (0 ms)
2023-01-11T22:08:31.3433327Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Short
2023-01-11T22:08:31.3433763Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Short (0 ms)
2023-01-11T22:08:31.3434191Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Int
2023-01-11T22:08:31.3434608Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Int (0 ms)
2023-01-11T22:08:31.3435034Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Long
2023-01-11T22:08:31.3435515Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Long (0 ms)
2023-01-11T22:08:31.3435940Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Float
2023-01-11T22:08:31.3436373Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Float (0 ms)
2023-01-11T22:08:31.3436817Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Double
2023-01-11T22:08:31.3437256Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Double (0 ms)
2023-01-11T22:08:31.3437655Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Byte
2023-01-11T22:08:31.3438053Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Byte (0 ms)
2023-01-11T22:08:31.3438444Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Char
2023-01-11T22:08:31.3438824Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Char (0 ms)
2023-01-11T22:08:31.3439284Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Short
2023-01-11T22:08:31.3439687Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Short (0 ms)
2023-01-11T22:08:31.3440080Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Int
2023-01-11T22:08:31.3440460Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Int (0 ms)
2023-01-11T22:08:31.3440851Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Long
2023-01-11T22:08:31.3441247Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Long (0 ms)
2023-01-11T22:08:31.3441629Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Float
2023-01-11T22:08:31.3442027Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Float (0 ms)
2023-01-11T22:08:31.3442423Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Double
2023-01-11T22:08:31.3442828Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Double (0 ms)
2023-01-11T22:08:31.3443209Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Bool
2023-01-11T22:08:31.3443610Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Bool (0 ms)
2023-01-11T22:08:31.3444000Z [ RUN      ] TensorIteratorTest.SerialLoopSingleThread
2023-01-11T22:08:31.3503245Z [       OK ] TensorIteratorTest.SerialLoopSingleThread (4 ms)
2023-01-11T22:08:31.3503873Z [ RUN      ] TensorIteratorTest.InputDType
2023-01-11T22:08:31.3504522Z [       OK ] TensorIteratorTest.InputDType (0 ms)
2023-01-11T22:08:31.3504953Z [ RUN      ] TensorIteratorTest.ComputeCommonDTypeInputOnly
2023-01-11T22:08:31.3505373Z [       OK ] TensorIteratorTest.ComputeCommonDTypeInputOnly (0 ms)
2023-01-11T22:08:31.3505817Z [ RUN      ] TensorIteratorTest.DoNotComputeCommonDTypeInputOnly
2023-01-11T22:08:31.3506276Z [       OK ] TensorIteratorTest.DoNotComputeCommonDTypeInputOnly (0 ms)
2023-01-11T22:08:31.3506691Z [ RUN      ] TensorIteratorTest.FailNonPromotingBinaryOp
2023-01-11T22:08:31.3507101Z [       OK ] TensorIteratorTest.FailNonPromotingBinaryOp (0 ms)
2023-01-11T22:08:31.3507633Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Byte
2023-01-11T22:08:31.3508060Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Byte (0 ms)
2023-01-11T22:08:31.3508467Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Char
2023-01-11T22:08:31.3519886Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Char (0 ms)
2023-01-11T22:08:31.3520531Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Short
2023-01-11T22:08:31.3521069Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Short (0 ms)
2023-01-11T22:08:31.3521649Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Int
2023-01-11T22:08:31.3522290Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Int (0 ms)
2023-01-11T22:08:31.3522946Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Long
2023-01-11T22:08:31.3523648Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Long (0 ms)
2023-01-11T22:08:31.3524514Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Float
2023-01-11T22:08:31.3525263Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Float (0 ms)
2023-01-11T22:08:31.3526002Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Double
2023-01-11T22:08:31.3526724Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Double (0 ms)
2023-01-11T22:08:31.3527390Z [----------] 65 tests from TensorIteratorTest (44 ms total)
2023-01-11T22:08:31.3527691Z 
2023-01-11T22:08:31.3527976Z [----------] Global test environment tear-down
2023-01-11T22:08:31.3528481Z [==========] 65 tests from 1 test suite ran. (44 ms total)
2023-01-11T22:08:31.3528919Z [  PASSED  ] 65 tests.
2023-01-11T22:08:31.4224936Z + ./Dimname_test
2023-01-11T22:08:31.7506716Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:31.7513014Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:08:31.7513598Z [----------] Global test environment set-up.
2023-01-11T22:08:31.7514108Z [----------] 4 tests from DimnameTest
2023-01-11T22:08:31.7514616Z [ RUN      ] DimnameTest.isValidIdentifier
2023-01-11T22:08:31.7515195Z [       OK ] DimnameTest.isValidIdentifier (0 ms)
2023-01-11T22:08:31.7515729Z [ RUN      ] DimnameTest.wildcardName
2023-01-11T22:08:31.7516242Z [       OK ] DimnameTest.wildcardName (0 ms)
2023-01-11T22:08:31.7516779Z [ RUN      ] DimnameTest.createNormalName
2023-01-11T22:08:31.7533026Z [       OK ] DimnameTest.createNormalName (1 ms)
2023-01-11T22:08:31.7533524Z [ RUN      ] DimnameTest.unifyAndMatch
2023-01-11T22:08:31.7533995Z [       OK ] DimnameTest.unifyAndMatch (0 ms)
2023-01-11T22:08:31.7534505Z [----------] 4 tests from DimnameTest (2 ms total)
2023-01-11T22:08:31.7534734Z 
2023-01-11T22:08:31.7535010Z [----------] Global test environment tear-down
2023-01-11T22:08:31.7535467Z [==========] 4 tests from 1 test suite ran. (2 ms total)
2023-01-11T22:08:31.7535855Z [  PASSED  ] 4 tests.
2023-01-11T22:08:31.8238731Z + ./Dict_test
2023-01-11T22:08:32.1488738Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:32.1489487Z [==========] Running 47 tests from 2 test suites.
2023-01-11T22:08:32.1490073Z [----------] Global test environment set-up.
2023-01-11T22:08:32.1490594Z [----------] 46 tests from DictTest
2023-01-11T22:08:32.1491142Z [ RUN      ] DictTest.givenEmptyDict_whenCallingEmpty_thenReturnsTrue
2023-01-11T22:08:32.1491878Z [       OK ] DictTest.givenEmptyDict_whenCallingEmpty_thenReturnsTrue (0 ms)
2023-01-11T22:08:32.1492660Z [ RUN      ] DictTest.givenNonemptyDict_whenCallingEmpty_thenReturnsFalse
2023-01-11T22:08:32.1493246Z [       OK ] DictTest.givenNonemptyDict_whenCallingEmpty_thenReturnsFalse (0 ms)
2023-01-11T22:08:32.1494420Z [ RUN      ] DictTest.givenEmptyDict_whenCallingSize_thenReturnsZero
2023-01-11T22:08:32.1495502Z [       OK ] DictTest.givenEmptyDict_whenCallingSize_thenReturnsZero (0 ms)
2023-01-11T22:08:32.1496561Z [ RUN      ] DictTest.givenNonemptyDict_whenCallingSize_thenReturnsNumberOfElements
2023-01-11T22:08:32.1497583Z [       OK ] DictTest.givenNonemptyDict_whenCallingSize_thenReturnsNumberOfElements (0 ms)
2023-01-11T22:08:32.1498511Z [ RUN      ] DictTest.givenNonemptyDict_whenCallingClear_thenIsEmpty
2023-01-11T22:08:32.1499374Z [       OK ] DictTest.givenNonemptyDict_whenCallingClear_thenIsEmpty (0 ms)
2023-01-11T22:08:32.1500337Z [ RUN      ] DictTest.whenInsertingNewKey_thenReturnsTrueAndIteratorToNewElement
2023-01-11T22:08:32.1501397Z [       OK ] DictTest.whenInsertingNewKey_thenReturnsTrueAndIteratorToNewElement (0 ms)
2023-01-11T22:08:32.1502777Z [ RUN      ] DictTest.whenInsertingExistingKey_thenReturnsFalseAndIteratorToExistingElement
2023-01-11T22:08:32.1504009Z [       OK ] DictTest.whenInsertingExistingKey_thenReturnsFalseAndIteratorToExistingElement (0 ms)
2023-01-11T22:08:32.1505054Z [ RUN      ] DictTest.whenInsertingExistingKey_thenDoesNotModifyDict
2023-01-11T22:08:32.1505995Z [       OK ] DictTest.whenInsertingExistingKey_thenDoesNotModifyDict (0 ms)
2023-01-11T22:08:32.1507035Z [ RUN      ] DictTest.whenInsertOrAssigningNewKey_thenReturnsTrueAndIteratorToNewElement
2023-01-11T22:08:32.1508196Z [       OK ] DictTest.whenInsertOrAssigningNewKey_thenReturnsTrueAndIteratorToNewElement (0 ms)
2023-01-11T22:08:32.1509430Z [ RUN      ] DictTest.whenInsertOrAssigningExistingKey_thenReturnsFalseAndIteratorToChangedElement
2023-01-11T22:08:32.1510717Z [       OK ] DictTest.whenInsertOrAssigningExistingKey_thenReturnsFalseAndIteratorToChangedElement (0 ms)
2023-01-11T22:08:32.1511810Z [ RUN      ] DictTest.whenInsertOrAssigningExistingKey_thenDoesModifyDict
2023-01-11T22:08:32.1512814Z [       OK ] DictTest.whenInsertOrAssigningExistingKey_thenDoesModifyDict (0 ms)
2023-01-11T22:08:32.1513720Z [ RUN      ] DictTest.givenEmptyDict_whenIterating_thenBeginIsEnd
2023-01-11T22:08:32.1514635Z [       OK ] DictTest.givenEmptyDict_whenIterating_thenBeginIsEnd (0 ms)
2023-01-11T22:08:32.1515476Z [ RUN      ] DictTest.givenMutableDict_whenIterating_thenFindsElements
2023-01-11T22:08:32.1516366Z [       OK ] DictTest.givenMutableDict_whenIterating_thenFindsElements (0 ms)
2023-01-11T22:08:32.1517317Z [ RUN      ] DictTest.givenMutableDict_whenIteratingWithForeach_thenFindsElements
2023-01-11T22:08:32.1518313Z [       OK ] DictTest.givenMutableDict_whenIteratingWithForeach_thenFindsElements (0 ms)
2023-01-11T22:08:32.1519265Z [ RUN      ] DictTest.givenConstDict_whenIterating_thenFindsElements
2023-01-11T22:08:32.1520150Z [       OK ] DictTest.givenConstDict_whenIterating_thenFindsElements (0 ms)
2023-01-11T22:08:32.1521066Z [ RUN      ] DictTest.givenConstDict_whenIteratingWithForeach_thenFindsElements
2023-01-11T22:08:32.1522030Z [       OK ] DictTest.givenConstDict_whenIteratingWithForeach_thenFindsElements (0 ms)
2023-01-11T22:08:32.1522877Z [ RUN      ] DictTest.givenIterator_thenCanModifyValue
2023-01-11T22:08:32.1523653Z [       OK ] DictTest.givenIterator_thenCanModifyValue (0 ms)
2023-01-11T22:08:32.1524547Z [ RUN      ] DictTest.givenOneElementDict_whenErasingByIterator_thenDictIsEmpty
2023-01-11T22:08:32.1525513Z [       OK ] DictTest.givenOneElementDict_whenErasingByIterator_thenDictIsEmpty (0 ms)
2023-01-11T22:08:32.1526521Z [ RUN      ] DictTest.givenOneElementDict_whenErasingByKey_thenReturnsOneAndDictIsEmpty
2023-01-11T22:08:32.1527594Z [       OK ] DictTest.givenOneElementDict_whenErasingByKey_thenReturnsOneAndDictIsEmpty (0 ms)
2023-01-11T22:08:32.1528838Z [ RUN      ] DictTest.givenOneElementDict_whenErasingByNonexistingKey_thenReturnsZeroAndDictIsUnchanged
2023-01-11T22:08:32.1530027Z [       OK ] DictTest.givenOneElementDict_whenErasingByNonexistingKey_thenReturnsZeroAndDictIsUnchanged (0 ms)
2023-01-11T22:08:32.1531104Z [ RUN      ] DictTest.whenCallingAtWithExistingKey_thenReturnsCorrectElement
2023-01-11T22:08:32.1532114Z [       OK ] DictTest.whenCallingAtWithExistingKey_thenReturnsCorrectElement (0 ms)
2023-01-11T22:08:32.1533108Z [ RUN      ] DictTest.whenCallingAtWithNonExistingKey_thenReturnsCorrectElement
2023-01-11T22:08:32.1534169Z [       OK ] DictTest.whenCallingAtWithNonExistingKey_thenReturnsCorrectElement (0 ms)
2023-01-11T22:08:32.1535237Z [ RUN      ] DictTest.givenMutableDict_whenCallingFindOnExistingKey_thenFindsCorrectElement
2023-01-11T22:08:32.1536395Z [       OK ] DictTest.givenMutableDict_whenCallingFindOnExistingKey_thenFindsCorrectElement (0 ms)
2023-01-11T22:08:32.1537426Z [ RUN      ] DictTest.givenMutableDict_whenCallingFindOnNonExistingKey_thenReturnsEnd
2023-01-11T22:08:32.1538458Z [       OK ] DictTest.givenMutableDict_whenCallingFindOnNonExistingKey_thenReturnsEnd (0 ms)
2023-01-11T22:08:32.1539495Z [ RUN      ] DictTest.givenConstDict_whenCallingFindOnExistingKey_thenFindsCorrectElement
2023-01-11T22:08:32.1540569Z [       OK ] DictTest.givenConstDict_whenCallingFindOnExistingKey_thenFindsCorrectElement (0 ms)
2023-01-11T22:08:32.1541571Z [ RUN      ] DictTest.givenConstDict_whenCallingFindOnNonExistingKey_thenReturnsEnd
2023-01-11T22:08:32.1542699Z [       OK ] DictTest.givenConstDict_whenCallingFindOnNonExistingKey_thenReturnsEnd (0 ms)
2023-01-11T22:08:32.1543691Z [ RUN      ] DictTest.whenCallingContainsWithExistingKey_thenReturnsTrue
2023-01-11T22:08:32.1544664Z [       OK ] DictTest.whenCallingContainsWithExistingKey_thenReturnsTrue (0 ms)
2023-01-11T22:08:32.1545678Z [ RUN      ] DictTest.whenCallingContainsWithNonExistingKey_thenReturnsFalse
2023-01-11T22:08:32.1546723Z [       OK ] DictTest.whenCallingContainsWithNonExistingKey_thenReturnsFalse (0 ms)
2023-01-11T22:08:32.1547614Z [ RUN      ] DictTest.whenCallingReserve_thenDoesntCrash
2023-01-11T22:08:32.1548391Z [       OK ] DictTest.whenCallingReserve_thenDoesntCrash (0 ms)
2023-01-11T22:08:32.1549206Z [ RUN      ] DictTest.whenCopyConstructingDict_thenAreEqual
2023-01-11T22:08:32.1550048Z [       OK ] DictTest.whenCopyConstructingDict_thenAreEqual (0 ms)
2023-01-11T22:08:32.1550837Z [ RUN      ] DictTest.whenCopyAssigningDict_thenAreEqual
2023-01-11T22:08:32.1551643Z [       OK ] DictTest.whenCopyAssigningDict_thenAreEqual (0 ms)
2023-01-11T22:08:32.1552401Z [ RUN      ] DictTest.whenCopyingDict_thenAreEqual
2023-01-11T22:08:32.1553140Z [       OK ] DictTest.whenCopyingDict_thenAreEqual (0 ms)
2023-01-11T22:08:32.1553927Z [ RUN      ] DictTest.whenMoveConstructingDict_thenNewIsCorrect
2023-01-11T22:08:32.1554802Z [       OK ] DictTest.whenMoveConstructingDict_thenNewIsCorrect (0 ms)
2023-01-11T22:08:32.1555638Z [ RUN      ] DictTest.whenMoveAssigningDict_thenNewIsCorrect
2023-01-11T22:08:32.1556443Z [       OK ] DictTest.whenMoveAssigningDict_thenNewIsCorrect (0 ms)
2023-01-11T22:08:32.1557293Z [ RUN      ] DictTest.whenMoveConstructingDict_thenOldIsUnchanged
2023-01-11T22:08:32.1558179Z [       OK ] DictTest.whenMoveConstructingDict_thenOldIsUnchanged (0 ms)
2023-01-11T22:08:32.1559063Z [ RUN      ] DictTest.whenMoveAssigningDict_thenOldIsUnchanged
2023-01-11T22:08:32.1559923Z [       OK ] DictTest.whenMoveAssigningDict_thenOldIsUnchanged (0 ms)
2023-01-11T22:08:32.1560949Z [ RUN      ] DictTest.givenIterator_whenPostfixIncrementing_thenMovesToNextAndReturnsOldPosition
2023-01-11T22:08:32.1562214Z [       OK ] DictTest.givenIterator_whenPostfixIncrementing_thenMovesToNextAndReturnsOldPosition (0 ms)
2023-01-11T22:08:32.1563328Z [ RUN      ] DictTest.givenIterator_whenPrefixIncrementing_thenMovesToNextAndReturnsNewPosition
2023-01-11T22:08:32.1564488Z [       OK ] DictTest.givenIterator_whenPrefixIncrementing_thenMovesToNextAndReturnsNewPosition (0 ms)
2023-01-11T22:08:32.1565412Z [ RUN      ] DictTest.givenEqualIterators_thenAreEqual
2023-01-11T22:08:32.1566212Z [       OK ] DictTest.givenEqualIterators_thenAreEqual (0 ms)
2023-01-11T22:08:32.1567008Z [ RUN      ] DictTest.givenDifferentIterators_thenAreNotEqual
2023-01-11T22:08:32.1567887Z [       OK ] DictTest.givenDifferentIterators_thenAreNotEqual (0 ms)
2023-01-11T22:08:32.1568817Z [ RUN      ] DictTest.givenIterator_whenDereferencing_thenPointsToCorrectElement
2023-01-11T22:08:32.1569871Z [       OK ] DictTest.givenIterator_whenDereferencing_thenPointsToCorrectElement (0 ms)
2023-01-11T22:08:32.1570798Z [ RUN      ] DictTest.givenIterator_whenWritingToValue_thenChangesValue
2023-01-11T22:08:32.1571703Z [       OK ] DictTest.givenIterator_whenWritingToValue_thenChangesValue (0 ms)
2023-01-11T22:08:32.1572391Z [ RUN      ] DictTest.isReferenceType
2023-01-11T22:08:32.1572816Z [       OK ] DictTest.isReferenceType (0 ms)
2023-01-11T22:08:32.1573384Z [ RUN      ] DictTest.copyHasSeparateStorage
2023-01-11T22:08:32.1573866Z [       OK ] DictTest.copyHasSeparateStorage (0 ms)
2023-01-11T22:08:32.1574176Z [ RUN      ] DictTest.dictTensorAsKey
2023-01-11T22:08:32.1574478Z [       OK ] DictTest.dictTensorAsKey (0 ms)
2023-01-11T22:08:32.1574768Z [ RUN      ] DictTest.dictEquality
2023-01-11T22:08:32.1575044Z [       OK ] DictTest.dictEquality (0 ms)
2023-01-11T22:08:32.1575358Z [----------] 46 tests from DictTest (0 ms total)
2023-01-11T22:08:32.1575519Z 
2023-01-11T22:08:32.1575697Z [----------] 1 test from ListTest_IValueBasedList
2023-01-11T22:08:32.1576127Z [ RUN      ] ListTest_IValueBasedList.givenIterator_whenWritingToValueFromIterator_thenChangesValue
2023-01-11T22:08:32.1576639Z [       OK ] ListTest_IValueBasedList.givenIterator_whenWritingToValueFromIterator_thenChangesValue (0 ms)
2023-01-11T22:08:32.1577067Z [----------] 1 test from ListTest_IValueBasedList (0 ms total)
2023-01-11T22:08:32.1577236Z 
2023-01-11T22:08:32.1577404Z [----------] Global test environment tear-down
2023-01-11T22:08:32.1577705Z [==========] 47 tests from 2 test suites ran. (0 ms total)
2023-01-11T22:08:32.1577967Z [  PASSED  ] 47 tests.
2023-01-11T22:08:32.2206953Z + ./NamedTensor_test
2023-01-11T22:08:32.5457564Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:32.5461749Z [==========] Running 10 tests from 1 test suite.
2023-01-11T22:08:32.5462273Z [----------] Global test environment set-up.
2023-01-11T22:08:32.5462957Z [----------] 10 tests from NamedTensorTest
2023-01-11T22:08:32.5463408Z [ RUN      ] NamedTensorTest.isNamed
2023-01-11T22:08:32.5464168Z [W TensorImpl.h:1816] Warning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (function operator())
2023-01-11T22:08:32.5465129Z [       OK ] NamedTensorTest.isNamed (0 ms)
2023-01-11T22:08:32.5465663Z [ RUN      ] NamedTensorTest.attachMetadata
2023-01-11T22:08:32.5466244Z [       OK ] NamedTensorTest.attachMetadata (0 ms)
2023-01-11T22:08:32.5466871Z [ RUN      ] NamedTensorTest.internalSetNamesInplace
2023-01-11T22:08:32.5467542Z [       OK ] NamedTensorTest.internalSetNamesInplace (0 ms)
2023-01-11T22:08:32.5468410Z [ RUN      ] NamedTensorTest.empty
2023-01-11T22:08:32.5501572Z [       OK ] NamedTensorTest.empty (3 ms)
2023-01-11T22:08:32.5502181Z [ RUN      ] NamedTensorTest.dimnameToPosition
2023-01-11T22:08:32.5507640Z [       OK ] NamedTensorTest.dimnameToPosition (0 ms)
2023-01-11T22:08:32.5508284Z [ RUN      ] NamedTensorTest.unifyFromRight
2023-01-11T22:08:32.5576615Z [       OK ] NamedTensorTest.unifyFromRight (6 ms)
2023-01-11T22:08:32.5577165Z [ RUN      ] NamedTensorTest.alias
2023-01-11T22:08:32.5577725Z [       OK ] NamedTensorTest.alias (0 ms)
2023-01-11T22:08:32.5578047Z [ RUN      ] NamedTensorTest.NoNamesGuard
2023-01-11T22:08:32.5578374Z [       OK ] NamedTensorTest.NoNamesGuard (0 ms)
2023-01-11T22:08:32.5578687Z [ RUN      ] NamedTensorTest.TensorNamePrint
2023-01-11T22:08:32.5579030Z [       OK ] NamedTensorTest.TensorNamePrint (0 ms)
2023-01-11T22:08:32.5579574Z [ RUN      ] NamedTensorTest.TensorNamesCheckUnique
2023-01-11T22:08:32.5583002Z [       OK ] NamedTensorTest.TensorNamesCheckUnique (0 ms)
2023-01-11T22:08:32.5583653Z [----------] 10 tests from NamedTensorTest (12 ms total)
2023-01-11T22:08:32.5583969Z 
2023-01-11T22:08:32.5584243Z [----------] Global test environment tear-down
2023-01-11T22:08:32.5584564Z [==========] 10 tests from 1 test suite ran. (12 ms total)
2023-01-11T22:08:32.5584811Z [  PASSED  ] 10 tests.
2023-01-11T22:08:32.6304726Z + ./cpu_generator_test
2023-01-11T22:08:32.9538853Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:32.9544772Z [==========] Running 15 tests from 1 test suite.
2023-01-11T22:08:32.9545214Z [----------] Global test environment set-up.
2023-01-11T22:08:32.9545537Z [----------] 15 tests from CPUGeneratorImpl
2023-01-11T22:08:32.9546024Z [ RUN      ] CPUGeneratorImpl.TestGeneratorDynamicCast
2023-01-11T22:08:32.9546641Z [       OK ] CPUGeneratorImpl.TestGeneratorDynamicCast (0 ms)
2023-01-11T22:08:32.9547094Z [ RUN      ] CPUGeneratorImpl.TestDefaultGenerator
2023-01-11T22:08:32.9547458Z [       OK ] CPUGeneratorImpl.TestDefaultGenerator (0 ms)
2023-01-11T22:08:32.9547850Z [ RUN      ] CPUGeneratorImpl.TestCloning
2023-01-11T22:08:32.9548363Z [       OK ] CPUGeneratorImpl.TestCloning (0 ms)
2023-01-11T22:08:32.9548817Z [ RUN      ] CPUGeneratorImpl.TestMultithreadingGetEngineOperator
2023-01-11T22:08:32.9549295Z [       OK ] CPUGeneratorImpl.TestMultithreadingGetEngineOperator (0 ms)
2023-01-11T22:08:32.9549710Z [ RUN      ] CPUGeneratorImpl.TestGetSetCurrentSeed
2023-01-11T22:08:32.9550089Z [       OK ] CPUGeneratorImpl.TestGetSetCurrentSeed (0 ms)
2023-01-11T22:08:32.9550494Z [ RUN      ] CPUGeneratorImpl.TestMultithreadingGetSetCurrentSeed
2023-01-11T22:08:32.9552514Z [       OK ] CPUGeneratorImpl.TestMultithreadingGetSetCurrentSeed (0 ms)
2023-01-11T22:08:32.9552915Z [ RUN      ] CPUGeneratorImpl.TestRNGForking
2023-01-11T22:08:32.9573572Z [       OK ] CPUGeneratorImpl.TestRNGForking (2 ms)
2023-01-11T22:08:32.9574085Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineReproducibility
2023-01-11T22:08:32.9574554Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineReproducibility (0 ms)
2023-01-11T22:08:32.9574970Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineOffset1
2023-01-11T22:08:32.9575352Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineOffset1 (0 ms)
2023-01-11T22:08:32.9575735Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineOffset2
2023-01-11T22:08:32.9576123Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineOffset2 (0 ms)
2023-01-11T22:08:32.9576493Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineOffset3
2023-01-11T22:08:32.9576969Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineOffset3 (0 ms)
2023-01-11T22:08:32.9577505Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineIndex
2023-01-11T22:08:32.9578017Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineIndex (0 ms)
2023-01-11T22:08:32.9578683Z [ RUN      ] CPUGeneratorImpl.TestMT19937EngineReproducibility
2023-01-11T22:08:32.9579129Z [       OK ] CPUGeneratorImpl.TestMT19937EngineReproducibility (0 ms)
2023-01-11T22:08:32.9579578Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineReproducibilityRandN
2023-01-11T22:08:32.9580042Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineReproducibilityRandN (0 ms)
2023-01-11T22:08:32.9580465Z [ RUN      ] CPUGeneratorImpl.TestPhiloxDeterministic
2023-01-11T22:08:32.9580854Z [       OK ] CPUGeneratorImpl.TestPhiloxDeterministic (0 ms)
2023-01-11T22:08:32.9581226Z [----------] 15 tests from CPUGeneratorImpl (3 ms total)
2023-01-11T22:08:32.9581376Z 
2023-01-11T22:08:32.9581541Z [----------] Global test environment tear-down
2023-01-11T22:08:32.9581925Z [==========] 15 tests from 1 test suite ran. (3 ms total)
2023-01-11T22:08:32.9582185Z [  PASSED  ] 15 tests.
2023-01-11T22:08:33.0283236Z + ./legacy_vmap_test
2023-01-11T22:08:33.3625353Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:33.3630805Z [==========] Running 23 tests from 1 test suite.
2023-01-11T22:08:33.3631230Z [----------] Global test environment set-up.
2023-01-11T22:08:33.3631506Z [----------] 23 tests from VmapTest
2023-01-11T22:08:33.3631798Z [ RUN      ] VmapTest.TestBatchedTensor
2023-01-11T22:08:33.3654828Z [       OK ] VmapTest.TestBatchedTensor (2 ms)
2023-01-11T22:08:33.3655365Z [ RUN      ] VmapTest.TestBatchedTensorMaxLevel
2023-01-11T22:08:33.3669723Z [       OK ] VmapTest.TestBatchedTensorMaxLevel (1 ms)
2023-01-11T22:08:33.3670128Z [ RUN      ] VmapTest.TestBatchedTensorActualDim
2023-01-11T22:08:33.3694565Z [       OK ] VmapTest.TestBatchedTensorActualDim (2 ms)
2023-01-11T22:08:33.3694947Z [ RUN      ] VmapTest.TestMultiBatchVmapTransform
2023-01-11T22:08:33.3704848Z [       OK ] VmapTest.TestMultiBatchVmapTransform (1 ms)
2023-01-11T22:08:33.3705281Z [ RUN      ] VmapTest.TestVmapPhysicalViewGetPhysicalDim
2023-01-11T22:08:33.3714442Z [       OK ] VmapTest.TestVmapPhysicalViewGetPhysicalDim (0 ms)
2023-01-11T22:08:33.3714900Z [ RUN      ] VmapTest.TestVmapPhysicalViewGetPhysicalDims
2023-01-11T22:08:33.3724225Z [       OK ] VmapTest.TestVmapPhysicalViewGetPhysicalDims (0 ms)
2023-01-11T22:08:33.3724690Z [ RUN      ] VmapTest.TestVmapPhysicalViewNewLogicalFromPhysical
2023-01-11T22:08:33.3725141Z [       OK ] VmapTest.TestVmapPhysicalViewNewLogicalFromPhysical (0 ms)
2023-01-11T22:08:33.3725529Z [ RUN      ] VmapTest.TestBatchedTensorSum
2023-01-11T22:08:33.3728419Z [       OK ] VmapTest.TestBatchedTensorSum (0 ms)
2023-01-11T22:08:33.3728874Z [ RUN      ] VmapTest.TestBroadcastingVmapTransformBatchedBatched
2023-01-11T22:08:33.3734798Z [       OK ] VmapTest.TestBroadcastingVmapTransformBatchedBatched (0 ms)
2023-01-11T22:08:33.3735275Z [ RUN      ] VmapTest.TestBroadcastingVmapTransformBatchedUnbatched
2023-01-11T22:08:33.3740269Z [       OK ] VmapTest.TestBroadcastingVmapTransformBatchedUnbatched (0 ms)
2023-01-11T22:08:33.3740714Z [ RUN      ] VmapTest.TestBroadcastingVmapTransformMaxLevels
2023-01-11T22:08:33.3744271Z [       OK ] VmapTest.TestBroadcastingVmapTransformMaxLevels (0 ms)
2023-01-11T22:08:33.3744650Z [ RUN      ] VmapTest.TestBatchedTensorMul
2023-01-11T22:08:33.3746494Z [       OK ] VmapTest.TestBatchedTensorMul (0 ms)
2023-01-11T22:08:33.3746813Z [ RUN      ] VmapTest.TestBatchedTensorSize
2023-01-11T22:08:33.3751746Z [       OK ] VmapTest.TestBatchedTensorSize (0 ms)
2023-01-11T22:08:33.3752622Z [ RUN      ] VmapTest.TestVmapPhysicalViewGetPhysicalShape
2023-01-11T22:08:33.3753052Z [       OK ] VmapTest.TestVmapPhysicalViewGetPhysicalShape (0 ms)
2023-01-11T22:08:33.3753416Z [ RUN      ] VmapTest.TestBatchedTensorExpand
2023-01-11T22:08:33.3894330Z [       OK ] VmapTest.TestBatchedTensorExpand (14 ms)
2023-01-11T22:08:33.3894928Z [ RUN      ] VmapTest.TestBatchedTensorUnsqueeze
2023-01-11T22:08:33.3895387Z [       OK ] VmapTest.TestBatchedTensorUnsqueeze (0 ms)
2023-01-11T22:08:33.3895804Z [ RUN      ] VmapTest.TestBatchedTensorSqueeze
2023-01-11T22:08:33.3896672Z [       OK ] VmapTest.TestBatchedTensorSqueeze (0 ms)
2023-01-11T22:08:33.3897086Z [ RUN      ] VmapTest.TestBatchedTensorTranspose
2023-01-11T22:08:33.3899291Z [       OK ] VmapTest.TestBatchedTensorTranspose (0 ms)
2023-01-11T22:08:33.3899648Z [ RUN      ] VmapTest.TestBatchedTensorPermute
2023-01-11T22:08:33.3901719Z [       OK ] VmapTest.TestBatchedTensorPermute (0 ms)
2023-01-11T22:08:33.3902129Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformBatchedBatched
2023-01-11T22:08:33.3928064Z [       OK ] VmapTest.TestMultiBatchVmapTransformBatchedBatched (2 ms)
2023-01-11T22:08:33.3928895Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformBatchedUnbatched
2023-01-11T22:08:33.3934604Z [       OK ] VmapTest.TestMultiBatchVmapTransformBatchedUnbatched (0 ms)
2023-01-11T22:08:33.3935399Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformMaxLevels
2023-01-11T22:08:33.3939681Z [       OK ] VmapTest.TestMultiBatchVmapTransformMaxLevels (0 ms)
2023-01-11T22:08:33.3940472Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformMultipleTensors
2023-01-11T22:08:33.3944942Z [       OK ] VmapTest.TestMultiBatchVmapTransformMultipleTensors (0 ms)
2023-01-11T22:08:33.3945523Z [----------] 23 tests from VmapTest (31 ms total)
2023-01-11T22:08:33.3945756Z 
2023-01-11T22:08:33.3946011Z [----------] Global test environment tear-down
2023-01-11T22:08:33.3946474Z [==========] 23 tests from 1 test suite ran. (31 ms total)
2023-01-11T22:08:33.3946850Z [  PASSED  ] 23 tests.
2023-01-11T22:08:33.4691807Z + ./operators_test
2023-01-11T22:08:33.7954234Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:33.7954752Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:08:33.7955066Z [----------] Global test environment set-up.
2023-01-11T22:08:33.7955375Z [----------] 4 tests from OperatorsTest
2023-01-11T22:08:33.7955684Z [ RUN      ] OperatorsTest.TestFunctionDecltype
2023-01-11T22:08:33.7960153Z [       OK ] OperatorsTest.TestFunctionDecltype (0 ms)
2023-01-11T22:08:33.7960624Z [ RUN      ] OperatorsTest.TestMethodOnlyDecltype
2023-01-11T22:08:33.7961368Z [       OK ] OperatorsTest.TestMethodOnlyDecltype (0 ms)
2023-01-11T22:08:33.7961718Z [ RUN      ] OperatorsTest.Test_ATEN_FN
2023-01-11T22:08:33.7992047Z [       OK ] OperatorsTest.Test_ATEN_FN (2 ms)
2023-01-11T22:08:33.7992635Z [ RUN      ] OperatorsTest.TestOutVariantIsFaithful
2023-01-11T22:08:33.7993015Z [       OK ] OperatorsTest.TestOutVariantIsFaithful (0 ms)
2023-01-11T22:08:33.7993385Z [----------] 4 tests from OperatorsTest (3 ms total)
2023-01-11T22:08:33.7993540Z 
2023-01-11T22:08:33.7993709Z [----------] Global test environment tear-down
2023-01-11T22:08:33.7994025Z [==========] 4 tests from 1 test suite ran. (3 ms total)
2023-01-11T22:08:33.7994269Z [  PASSED  ] 4 tests.
2023-01-11T22:08:33.8728254Z + [[ -x ./cudnn_test ]]
2023-01-11T22:08:33.8728528Z + [[ -x ./cuda_generator_test ]]
2023-01-11T22:08:33.8728716Z + ./cuda_generator_test
2023-01-11T22:08:34.1937126Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:34.1937904Z [==========] Running 11 tests from 1 test suite.
2023-01-11T22:08:34.1938231Z [----------] Global test environment set-up.
2023-01-11T22:08:34.1938591Z [----------] 11 tests from CUDAGeneratorImpl
2023-01-11T22:08:34.1938965Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineReproducibility
2023-01-11T22:08:34.1939478Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineReproducibility (0 ms)
2023-01-11T22:08:34.1939953Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineOffset1
2023-01-11T22:08:34.1940339Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineOffset1 (0 ms)
2023-01-11T22:08:34.1940788Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineOffset2
2023-01-11T22:08:34.1941182Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineOffset2 (0 ms)
2023-01-11T22:08:34.1941610Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineOffset3
2023-01-11T22:08:34.1942077Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineOffset3 (0 ms)
2023-01-11T22:08:34.1942706Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineIndex
2023-01-11T22:08:34.1943155Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineIndex (0 ms)
2023-01-11T22:08:34.1943529Z [ RUN      ] CUDAGeneratorImpl.TestGeneratorDynamicCast
2023-01-11T22:08:34.1943993Z [       OK ] CUDAGeneratorImpl.TestGeneratorDynamicCast (0 ms)
2023-01-11T22:08:34.1944381Z [ RUN      ] CUDAGeneratorImpl.TestDefaultGenerator
2023-01-11T22:08:34.1944805Z [       OK ] CUDAGeneratorImpl.TestDefaultGenerator (0 ms)
2023-01-11T22:08:34.1945149Z [ RUN      ] CUDAGeneratorImpl.TestCloning
2023-01-11T22:08:34.1945534Z [       OK ] CUDAGeneratorImpl.TestCloning (0 ms)
2023-01-11T22:08:34.1945922Z [ RUN      ] CUDAGeneratorImpl.TestMultithreadingGetSetCurrentSeed
2023-01-11T22:08:34.1946457Z [       OK ] CUDAGeneratorImpl.TestMultithreadingGetSetCurrentSeed (0 ms)
2023-01-11T22:08:34.1946914Z [ RUN      ] CUDAGeneratorImpl.TestRNGForking
2023-01-11T22:08:34.1947260Z [       OK ] CUDAGeneratorImpl.TestRNGForking (0 ms)
2023-01-11T22:08:34.1947623Z [ RUN      ] CUDAGeneratorImpl.TestMultithreadRNG
2023-01-11T22:08:34.1948013Z [       OK ] CUDAGeneratorImpl.TestMultithreadRNG (0 ms)
2023-01-11T22:08:34.1948393Z [----------] 11 tests from CUDAGeneratorImpl (0 ms total)
2023-01-11T22:08:34.1948593Z 
2023-01-11T22:08:34.1948752Z [----------] Global test environment tear-down
2023-01-11T22:08:34.1949061Z [==========] 11 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:08:34.1949375Z [  PASSED  ] 11 tests.
2023-01-11T22:08:34.2658485Z + [[ -x ./apply_test ]]
2023-01-11T22:08:34.2658865Z + [[ -x ./stream_test ]]
2023-01-11T22:08:34.2659092Z + [[ -x ./cuda_half_test ]]
2023-01-11T22:08:34.2659282Z + ./cuda_half_test
2023-01-11T22:08:34.5857742Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:34.5858602Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:08:34.5859153Z [----------] Global test environment set-up.
2023-01-11T22:08:34.5859669Z [----------] 1 test from HalfCuda
2023-01-11T22:08:34.5860121Z [ RUN      ] HalfCuda.HalfCuda
2023-01-11T22:08:34.5860585Z [       OK ] HalfCuda.HalfCuda (0 ms)
2023-01-11T22:08:34.5860895Z [----------] 1 test from HalfCuda (0 ms total)
2023-01-11T22:08:34.5861029Z 
2023-01-11T22:08:34.5861199Z [----------] Global test environment tear-down
2023-01-11T22:08:34.5861507Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:08:34.5861766Z [  PASSED  ] 1 test.
2023-01-11T22:08:34.6551381Z + [[ -x ./cuda_vectorized_test ]]
2023-01-11T22:08:34.6551807Z + ./cuda_vectorized_test
2023-01-11T22:08:34.9726619Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:34.9727689Z [==========] Running 2 tests from 1 test suite.
2023-01-11T22:08:34.9728005Z [----------] Global test environment set-up.
2023-01-11T22:08:34.9728344Z [----------] 2 tests from TestVectorizedMemoryAccess
2023-01-11T22:08:34.9728711Z [ RUN      ] TestVectorizedMemoryAccess.CanVectorizeUpTo
2023-01-11T22:08:34.9729122Z [       OK ] TestVectorizedMemoryAccess.CanVectorizeUpTo (0 ms)
2023-01-11T22:08:34.9729494Z [ RUN      ] TestVectorizedMemoryAccess.CopyKernel
2023-01-11T22:08:34.9729866Z [       OK ] TestVectorizedMemoryAccess.CopyKernel (0 ms)
2023-01-11T22:08:34.9730257Z [----------] 2 tests from TestVectorizedMemoryAccess (0 ms total)
2023-01-11T22:08:34.9730440Z 
2023-01-11T22:08:34.9730607Z [----------] Global test environment tear-down
2023-01-11T22:08:34.9730907Z [==========] 2 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:08:34.9731247Z [  PASSED  ] 2 tests.
2023-01-11T22:08:35.0450647Z + [[ -x ./cuda_distributions_test ]]
2023-01-11T22:08:35.0450889Z + ./cuda_distributions_test
2023-01-11T22:08:35.3697670Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:35.3698430Z [==========] Running 4 tests from 2 test suites.
2023-01-11T22:08:35.3699123Z [----------] Global test environment set-up.
2023-01-11T22:08:35.3699560Z [----------] 3 tests from DistributionsTest
2023-01-11T22:08:35.3699959Z [ RUN      ] DistributionsTest.TestPhiloxIncrementSmallUniformTensor
2023-01-11T22:08:35.3700447Z [       OK ] DistributionsTest.TestPhiloxIncrementSmallUniformTensor (0 ms)
2023-01-11T22:08:35.3700909Z [ RUN      ] DistributionsTest.TestPhiloxIncrementBigUniformTensor
2023-01-11T22:08:35.3701382Z [       OK ] DistributionsTest.TestPhiloxIncrementBigUniformTensor (0 ms)
2023-01-11T22:08:35.3701882Z [ RUN      ] DistributionsTest.TestPhiloxIncrementSmallMultinomialTensor
2023-01-11T22:08:35.3702660Z [       OK ] DistributionsTest.TestPhiloxIncrementSmallMultinomialTensor (0 ms)
2023-01-11T22:08:35.3703090Z [----------] 3 tests from DistributionsTest (0 ms total)
2023-01-11T22:08:35.3703253Z 
2023-01-11T22:08:35.3703430Z [----------] 1 test from RandomPermutationTest
2023-01-11T22:08:35.3703790Z [ RUN      ] RandomPermutationTest.TestIslandShuffle
2023-01-11T22:08:35.3704161Z [       OK ] RandomPermutationTest.TestIslandShuffle (0 ms)
2023-01-11T22:08:35.3704535Z [----------] 1 test from RandomPermutationTest (0 ms total)
2023-01-11T22:08:35.3704702Z 
2023-01-11T22:08:35.3704869Z [----------] Global test environment tear-down
2023-01-11T22:08:35.3705180Z [==========] 4 tests from 2 test suites ran. (0 ms total)
2023-01-11T22:08:35.3705426Z [  PASSED  ] 4 tests.
2023-01-11T22:08:35.4417030Z + [[ -x ./cuda_optional_test ]]
2023-01-11T22:08:35.4417274Z + ./cuda_optional_test
2023-01-11T22:08:35.7591200Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:35.7591763Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:08:35.7592082Z [----------] Global test environment set-up.
2023-01-11T22:08:35.7592380Z [----------] 1 test from OptionalTest
2023-01-11T22:08:35.7592748Z [ RUN      ] OptionalTest.OptionalTestCUDA
2023-01-11T22:08:35.7593084Z [       OK ] OptionalTest.OptionalTestCUDA (0 ms)
2023-01-11T22:08:35.7593462Z [----------] 1 test from OptionalTest (0 ms total)
2023-01-11T22:08:35.7593613Z 
2023-01-11T22:08:35.7593780Z [----------] Global test environment tear-down
2023-01-11T22:08:35.7594118Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:08:35.7594393Z [  PASSED  ] 1 test.
2023-01-11T22:08:35.8288453Z + [[ -x ./cuda_tensor_interop_test ]]
2023-01-11T22:08:35.8288983Z + [[ -x ./cuda_complex_test ]]
2023-01-11T22:08:35.8289182Z + ./cuda_complex_test
2023-01-11T22:08:36.1515002Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:36.1520288Z [==========] Running 11 tests from 7 test suites.
2023-01-11T22:08:36.1520892Z [----------] Global test environment set-up.
2023-01-11T22:08:36.1521435Z [----------] 2 tests from TestMemory
2023-01-11T22:08:36.1521879Z [ RUN      ] TestMemory.ReinterpretCast
2023-01-11T22:08:36.1522189Z [       OK ] TestMemory.ReinterpretCast (0 ms)
2023-01-11T22:08:36.1522518Z [ RUN      ] TestMemory.ThrustReinterpretCast
2023-01-11T22:08:36.1522863Z [       OK ] TestMemory.ThrustReinterpretCast (0 ms)
2023-01-11T22:08:36.1523185Z [----------] 2 tests from TestMemory (0 ms total)
2023-01-11T22:08:36.1523333Z 
2023-01-11T22:08:36.1523491Z [----------] 2 tests from TestConstructors
2023-01-11T22:08:36.1523985Z [ RUN      ] TestConstructors.FromThrust
2023-01-11T22:08:36.1524299Z [       OK ] TestConstructors.FromThrust (0 ms)
2023-01-11T22:08:36.1524627Z [ RUN      ] TestConstructors.UnorderedMap
2023-01-11T22:08:36.1524957Z [       OK ] TestConstructors.UnorderedMap (0 ms)
2023-01-11T22:08:36.1525299Z [----------] 2 tests from TestConstructors (0 ms total)
2023-01-11T22:08:36.1525446Z 
2023-01-11T22:08:36.1525596Z [----------] 1 test from TestAssignment
2023-01-11T22:08:36.1525889Z [ RUN      ] TestAssignment.FromThrust
2023-01-11T22:08:36.1526346Z [       OK ] TestAssignment.FromThrust (0 ms)
2023-01-11T22:08:36.1526658Z [----------] 1 test from TestAssignment (0 ms total)
2023-01-11T22:08:36.1526811Z 
2023-01-11T22:08:36.1527007Z [----------] 1 test from TestArithmeticIntScalar
2023-01-11T22:08:36.1527528Z [ RUN      ] TestArithmeticIntScalar.All
2023-01-11T22:08:36.1528118Z [       OK ] TestArithmeticIntScalar.All (0 ms)
2023-01-11T22:08:36.1528575Z [----------] 1 test from TestArithmeticIntScalar (0 ms total)
2023-01-11T22:08:36.1528757Z 
2023-01-11T22:08:36.1528894Z [----------] 1 test from TestIO
2023-01-11T22:08:36.1529135Z [ RUN      ] TestIO.All
2023-01-11T22:08:36.1529379Z [       OK ] TestIO.All (0 ms)
2023-01-11T22:08:36.1529658Z [----------] 1 test from TestIO (0 ms total)
2023-01-11T22:08:36.1529801Z 
2023-01-11T22:08:36.1529988Z [----------] 1 test from TestStd
2023-01-11T22:08:36.1530259Z [ RUN      ] TestStd.BasicFunctions
2023-01-11T22:08:36.1530538Z [       OK ] TestStd.BasicFunctions (0 ms)
2023-01-11T22:08:36.1530842Z [----------] 1 test from TestStd (0 ms total)
2023-01-11T22:08:36.1530982Z 
2023-01-11T22:08:36.1531127Z [----------] 3 tests from DeviceTests
2023-01-11T22:08:36.1531405Z [ RUN      ] DeviceTests.ThrustConversion
2023-01-11T22:08:36.1531734Z [       OK ] DeviceTests.ThrustConversion (0 ms)
2023-01-11T22:08:36.1532039Z [ RUN      ] DeviceTests.StdFunctions
2023-01-11T22:08:36.1532333Z [       OK ] DeviceTests.StdFunctions (0 ms)
2023-01-11T22:08:36.1532644Z [ RUN      ] DeviceTests.ReinterpretCast
2023-01-11T22:08:36.1532963Z [       OK ] DeviceTests.ReinterpretCast (0 ms)
2023-01-11T22:08:36.1533287Z [----------] 3 tests from DeviceTests (0 ms total)
2023-01-11T22:08:36.1533424Z 
2023-01-11T22:08:36.1533590Z [----------] Global test environment tear-down
2023-01-11T22:08:36.1533907Z [==========] 11 tests from 7 test suites ran. (0 ms total)
2023-01-11T22:08:36.1534171Z [  PASSED  ] 11 tests.
2023-01-11T22:08:36.2253698Z + [[ -x ./cuda_complex_math_test ]]
2023-01-11T22:08:36.2253983Z + ./cuda_complex_math_test
2023-01-11T22:08:36.5510669Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:36.5511678Z [==========] Running 30 tests from 24 test suites.
2023-01-11T22:08:36.5512155Z [----------] Global test environment set-up.
2023-01-11T22:08:36.5512660Z [----------] 2 tests from TestExponentialDevice
2023-01-11T22:08:36.5513133Z [ RUN      ] TestExponentialDevice.IPi
2023-01-11T22:08:36.5513865Z [       OK ] TestExponentialDevice.IPi (0 ms)
2023-01-11T22:08:36.5514427Z [ RUN      ] TestExponentialDevice.EulerFormula
2023-01-11T22:08:36.5514964Z [       OK ] TestExponentialDevice.EulerFormula (0 ms)
2023-01-11T22:08:36.5515561Z [----------] 2 tests from TestExponentialDevice (0 ms total)
2023-01-11T22:08:36.5515799Z 
2023-01-11T22:08:36.5516053Z [----------] 1 test from TestLogDevice
2023-01-11T22:08:36.5516505Z [ RUN      ] TestLogDevice.Definition
2023-01-11T22:08:36.5517012Z [       OK ] TestLogDevice.Definition (0 ms)
2023-01-11T22:08:36.5517827Z [----------] 1 test from TestLogDevice (0 ms total)
2023-01-11T22:08:36.5518121Z 
2023-01-11T22:08:36.5518375Z [----------] 1 test from TestLog10Device
2023-01-11T22:08:36.5518874Z [ RUN      ] TestLog10Device.Rev
2023-01-11T22:08:36.5519288Z [       OK ] TestLog10Device.Rev (0 ms)
2023-01-11T22:08:36.5519590Z [----------] 1 test from TestLog10Device (0 ms total)
2023-01-11T22:08:36.5519760Z 
2023-01-11T22:08:36.5519951Z [----------] 1 test from TestLog2Device
2023-01-11T22:08:36.5520269Z [ RUN      ] TestLog2Device.Rev
2023-01-11T22:08:36.5520559Z [       OK ] TestLog2Device.Rev (0 ms)
2023-01-11T22:08:36.5520909Z [----------] 1 test from TestLog2Device (0 ms total)
2023-01-11T22:08:36.5521060Z 
2023-01-11T22:08:36.5521219Z [----------] 3 tests from TestLog1pDevice
2023-01-11T22:08:36.5521560Z [ RUN      ] TestLog1pDevice.Normal
2023-01-11T22:08:36.5521842Z [       OK ] TestLog1pDevice.Normal (0 ms)
2023-01-11T22:08:36.5522171Z [ RUN      ] TestLog1pDevice.Small
2023-01-11T22:08:36.5522475Z [       OK ] TestLog1pDevice.Small (0 ms)
2023-01-11T22:08:36.5522749Z [ RUN      ] TestLog1pDevice.Extreme
2023-01-11T22:08:36.5523108Z [       OK ] TestLog1pDevice.Extreme (0 ms)
2023-01-11T22:08:36.5523430Z [----------] 3 tests from TestLog1pDevice (0 ms total)
2023-01-11T22:08:36.5523587Z 
2023-01-11T22:08:36.5523793Z [----------] 1 test from TestPowSqrtDevice
2023-01-11T22:08:36.5524088Z [ RUN      ] TestPowSqrtDevice.Equal
2023-01-11T22:08:36.5524384Z [       OK ] TestPowSqrtDevice.Equal (0 ms)
2023-01-11T22:08:36.5524758Z [----------] 1 test from TestPowSqrtDevice (0 ms total)
2023-01-11T22:08:36.5524917Z 
2023-01-11T22:08:36.5525065Z [----------] 1 test from TestPowDevice
2023-01-11T22:08:36.5525398Z [ RUN      ] TestPowDevice.Square
2023-01-11T22:08:36.5525683Z [       OK ] TestPowDevice.Square (0 ms)
2023-01-11T22:08:36.5526008Z [----------] 1 test from TestPowDevice (0 ms total)
2023-01-11T22:08:36.5526194Z 
2023-01-11T22:08:36.5526375Z [----------] 1 test from TestSinCosSinhCoshDevice
2023-01-11T22:08:36.5526706Z [ RUN      ] TestSinCosSinhCoshDevice.Identity
2023-01-11T22:08:36.5527102Z [       OK ] TestSinCosSinhCoshDevice.Identity (0 ms)
2023-01-11T22:08:36.5527470Z [----------] 1 test from TestSinCosSinhCoshDevice (0 ms total)
2023-01-11T22:08:36.5527709Z 
2023-01-11T22:08:36.5527858Z [----------] 1 test from TestTanDevice
2023-01-11T22:08:36.5528134Z [ RUN      ] TestTanDevice.Identity
2023-01-11T22:08:36.5528468Z [       OK ] TestTanDevice.Identity (0 ms)
2023-01-11T22:08:36.5528784Z [----------] 1 test from TestTanDevice (0 ms total)
2023-01-11T22:08:36.5528935Z 
2023-01-11T22:08:36.5529092Z [----------] 1 test from TestTanhDevice
2023-01-11T22:08:36.5529407Z [ RUN      ] TestTanhDevice.Identity
2023-01-11T22:08:36.5529787Z [       OK ] TestTanhDevice.Identity (0 ms)
2023-01-11T22:08:36.5530165Z [----------] 1 test from TestTanhDevice (0 ms total)
2023-01-11T22:08:36.5530318Z 
2023-01-11T22:08:36.5530500Z [----------] 1 test from TestRevTrigonometricDevice
2023-01-11T22:08:36.5530870Z [ RUN      ] TestRevTrigonometricDevice.Rev
2023-01-11T22:08:36.5531202Z [       OK ] TestRevTrigonometricDevice.Rev (0 ms)
2023-01-11T22:08:36.5531624Z [----------] 1 test from TestRevTrigonometricDevice (0 ms total)
2023-01-11T22:08:36.5531806Z 
2023-01-11T22:08:36.5531971Z [----------] 1 test from TestRevHyperbolicDevice
2023-01-11T22:08:36.5532336Z [ RUN      ] TestRevHyperbolicDevice.Rev
2023-01-11T22:08:36.5532659Z [       OK ] TestRevHyperbolicDevice.Rev (0 ms)
2023-01-11T22:08:36.5533026Z [----------] 1 test from TestRevHyperbolicDevice (0 ms total)
2023-01-11T22:08:36.5533223Z 
2023-01-11T22:08:36.5533440Z [----------] 2 tests from TestExponentialHost
2023-01-11T22:08:36.5533742Z [ RUN      ] TestExponentialHost.IPi
2023-01-11T22:08:36.5534100Z [       OK ] TestExponentialHost.IPi (0 ms)
2023-01-11T22:08:36.5534404Z [ RUN      ] TestExponentialHost.EulerFormula
2023-01-11T22:08:36.5534809Z [       OK ] TestExponentialHost.EulerFormula (0 ms)
2023-01-11T22:08:36.5535169Z [----------] 2 tests from TestExponentialHost (0 ms total)
2023-01-11T22:08:36.5535354Z 
2023-01-11T22:08:36.5535521Z [----------] 1 test from TestLogHost
2023-01-11T22:08:36.5535797Z [ RUN      ] TestLogHost.Definition
2023-01-11T22:08:36.5536105Z [       OK ] TestLogHost.Definition (0 ms)
2023-01-11T22:08:36.5536460Z [----------] 1 test from TestLogHost (0 ms total)
2023-01-11T22:08:36.5536596Z 
2023-01-11T22:08:36.5536745Z [----------] 1 test from TestLog10Host
2023-01-11T22:08:36.5537066Z [ RUN      ] TestLog10Host.Rev
2023-01-11T22:08:36.5537340Z [       OK ] TestLog10Host.Rev (0 ms)
2023-01-11T22:08:36.5537634Z [----------] 1 test from TestLog10Host (0 ms total)
2023-01-11T22:08:36.5537836Z 
2023-01-11T22:08:36.5537988Z [----------] 1 test from TestLog2Host
2023-01-11T22:08:36.5538246Z [ RUN      ] TestLog2Host.Rev
2023-01-11T22:08:36.5538554Z [       OK ] TestLog2Host.Rev (0 ms)
2023-01-11T22:08:36.5538854Z [----------] 1 test from TestLog2Host (0 ms total)
2023-01-11T22:08:36.5539002Z 
2023-01-11T22:08:36.5539152Z [----------] 3 tests from TestLog1pHost
2023-01-11T22:08:36.5539487Z [ RUN      ] TestLog1pHost.Normal
2023-01-11T22:08:36.5539755Z [       OK ] TestLog1pHost.Normal (0 ms)
2023-01-11T22:08:36.5540072Z [ RUN      ] TestLog1pHost.Small
2023-01-11T22:08:36.5540356Z [       OK ] TestLog1pHost.Small (0 ms)
2023-01-11T22:08:36.5540618Z [ RUN      ] TestLog1pHost.Extreme
2023-01-11T22:08:36.5540970Z [       OK ] TestLog1pHost.Extreme (0 ms)
2023-01-11T22:08:36.5541287Z [----------] 3 tests from TestLog1pHost (0 ms total)
2023-01-11T22:08:36.5541438Z 
2023-01-11T22:08:36.5541629Z [----------] 1 test from TestPowSqrtHost
2023-01-11T22:08:36.5541917Z [ RUN      ] TestPowSqrtHost.Equal
2023-01-11T22:08:36.5542205Z [       OK ] TestPowSqrtHost.Equal (0 ms)
2023-01-11T22:08:36.5542724Z [----------] 1 test from TestPowSqrtHost (0 ms total)
2023-01-11T22:08:36.5542882Z 
2023-01-11T22:08:36.5543025Z [----------] 1 test from TestPowHost
2023-01-11T22:08:36.5543342Z [ RUN      ] TestPowHost.Square
2023-01-11T22:08:36.5543620Z [       OK ] TestPowHost.Square (0 ms)
2023-01-11T22:08:36.5543909Z [----------] 1 test from TestPowHost (0 ms total)
2023-01-11T22:08:36.5544109Z 
2023-01-11T22:08:36.5544282Z [----------] 1 test from TestSinCosSinhCoshHost
2023-01-11T22:08:36.5544600Z [ RUN      ] TestSinCosSinhCoshHost.Identity
2023-01-11T22:08:36.5545081Z [       OK ] TestSinCosSinhCoshHost.Identity (0 ms)
2023-01-11T22:08:36.5545439Z [----------] 1 test from TestSinCosSinhCoshHost (0 ms total)
2023-01-11T22:08:36.5545663Z 
2023-01-11T22:08:36.5545808Z [----------] 1 test from TestTanHost
2023-01-11T22:08:36.5546075Z [ RUN      ] TestTanHost.Identity
2023-01-11T22:08:36.5546383Z [       OK ] TestTanHost.Identity (0 ms)
2023-01-11T22:08:36.5546702Z [----------] 1 test from TestTanHost (0 ms total)
2023-01-11T22:08:36.5546847Z 
2023-01-11T22:08:36.5546993Z [----------] 1 test from TestTanhHost
2023-01-11T22:08:36.5547311Z [ RUN      ] TestTanhHost.Identity
2023-01-11T22:08:36.5547604Z [       OK ] TestTanhHost.Identity (0 ms)
2023-01-11T22:08:36.5547951Z [----------] 1 test from TestTanhHost (0 ms total)
2023-01-11T22:08:36.5548111Z 
2023-01-11T22:08:36.5548291Z [----------] 1 test from TestRevTrigonometricHost
2023-01-11T22:08:36.5548648Z [ RUN      ] TestRevTrigonometricHost.Rev
2023-01-11T22:08:36.5549037Z [       OK ] TestRevTrigonometricHost.Rev (0 ms)
2023-01-11T22:08:36.5549396Z [----------] 1 test from TestRevTrigonometricHost (0 ms total)
2023-01-11T22:08:36.5549557Z 
2023-01-11T22:08:36.5549728Z [----------] 1 test from TestRevHyperbolicHost
2023-01-11T22:08:36.5550031Z [ RUN      ] TestRevHyperbolicHost.Rev
2023-01-11T22:08:36.5550402Z [       OK ] TestRevHyperbolicHost.Rev (0 ms)
2023-01-11T22:08:36.5550734Z [----------] 1 test from TestRevHyperbolicHost (0 ms total)
2023-01-11T22:08:36.5550901Z 
2023-01-11T22:08:36.5551068Z [----------] Global test environment tear-down
2023-01-11T22:08:36.5551380Z [==========] 30 tests from 24 test suites ran. (0 ms total)
2023-01-11T22:08:36.5551639Z [  PASSED  ] 30 tests.
2023-01-11T22:08:36.6214251Z + [[ -x ./cuda_cub_test ]]
2023-01-11T22:08:36.6214518Z + ./cuda_cub_test
2023-01-11T22:08:36.9406397Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:36.9407189Z [==========] Running 3 tests from 3 test suites.
2023-01-11T22:08:36.9407733Z [----------] Global test environment set-up.
2023-01-11T22:08:36.9408028Z [----------] 1 test from NumBits
2023-01-11T22:08:36.9408401Z [ RUN      ] NumBits.CubTest
2023-01-11T22:08:36.9408737Z [       OK ] NumBits.CubTest (0 ms)
2023-01-11T22:08:36.9409031Z [----------] 1 test from NumBits (0 ms total)
2023-01-11T22:08:36.9409226Z 
2023-01-11T22:08:36.9409399Z [----------] 1 test from InclusiveScanSplit
2023-01-11T22:08:36.9409687Z [ RUN      ] InclusiveScanSplit.CubTest
2023-01-11T22:08:36.9410003Z [       OK ] InclusiveScanSplit.CubTest (0 ms)
2023-01-11T22:08:36.9410341Z [----------] 1 test from InclusiveScanSplit (0 ms total)
2023-01-11T22:08:36.9410505Z 
2023-01-11T22:08:36.9410664Z [----------] 1 test from ExclusiveScanSplit
2023-01-11T22:08:36.9410963Z [ RUN      ] ExclusiveScanSplit.CubTest
2023-01-11T22:08:36.9411276Z [       OK ] ExclusiveScanSplit.CubTest (0 ms)
2023-01-11T22:08:36.9411610Z [----------] 1 test from ExclusiveScanSplit (0 ms total)
2023-01-11T22:08:36.9411758Z 
2023-01-11T22:08:36.9411925Z [----------] Global test environment tear-down
2023-01-11T22:08:36.9412242Z [==========] 3 tests from 3 test suites ran. (0 ms total)
2023-01-11T22:08:36.9412501Z [  PASSED  ] 3 tests.
2023-01-11T22:08:37.0124103Z + [[ -x ./cuda_atomic_ops_test ]]
2023-01-11T22:08:37.0124358Z + ./cuda_atomic_ops_test
2023-01-11T22:08:37.3370448Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:37.3371187Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:08:37.3371487Z [----------] Global test environment set-up.
2023-01-11T22:08:37.3372084Z [----------] 4 tests from TestAtomicOps
2023-01-11T22:08:37.3372562Z [ RUN      ] TestAtomicOps.TestAtomicAdd
2023-01-11T22:08:37.3373102Z [       OK ] TestAtomicOps.TestAtomicAdd (0 ms)
2023-01-11T22:08:37.3373678Z [ RUN      ] TestAtomicOps.TestAtomicMul
2023-01-11T22:08:37.3374265Z [       OK ] TestAtomicOps.TestAtomicMul (0 ms)
2023-01-11T22:08:37.3374617Z [ RUN      ] TestAtomicOps.TestAtomicMax
2023-01-11T22:08:37.3374922Z [       OK ] TestAtomicOps.TestAtomicMax (0 ms)
2023-01-11T22:08:37.3375229Z [ RUN      ] TestAtomicOps.TestAtomicMin
2023-01-11T22:08:37.3375543Z [       OK ] TestAtomicOps.TestAtomicMin (0 ms)
2023-01-11T22:08:37.3375856Z [----------] 4 tests from TestAtomicOps (0 ms total)
2023-01-11T22:08:37.3376010Z 
2023-01-11T22:08:37.3376177Z [----------] Global test environment tear-down
2023-01-11T22:08:37.3376567Z [==========] 4 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:08:37.3376818Z [  PASSED  ] 4 tests.
2023-01-11T22:08:37.4095288Z + '[' ON == ON ']'
2023-01-11T22:08:37.4095779Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 ./basic '--gtest_filter=-*CUDA'
2023-01-11T22:08:37.4487428Z ==24431== Memcheck, a memory error detector
2023-01-11T22:08:37.4488077Z ==24431== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
2023-01-11T22:08:37.4488465Z ==24431== Using Valgrind-3.16.1 and LibVEX; rerun with -h for copyright info
2023-01-11T22:08:37.4488779Z ==24431== Command: ./basic --gtest_filter=-*CUDA
2023-01-11T22:08:37.4488976Z ==24431== 
2023-01-11T22:08:40.8884812Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8885473Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8886032Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8886530Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8887098Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8887658Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8888176Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8888737Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8889259Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8889764Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8890317Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8890855Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8891343Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8891923Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8892462Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8892974Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8893557Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8894140Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8894654Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8895236Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8895816Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8896347Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8896953Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8897508Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8897985Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8898535Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8899351Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8899826Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8900424Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8900954Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8901459Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8901996Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8902648Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8903155Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8903650Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8904152Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8904567Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8905263Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8905695Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8906135Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8906628Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8907157Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8907677Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8908256Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8908710Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8909139Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8909663Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8910119Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8910543Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8911067Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8911397Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8911737Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8912205Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8912730Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8913220Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8913755Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8914279Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8914760Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8915278Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8915747Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8916172Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8916596Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8916911Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8917218Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8917559Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8917868Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8918171Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8918509Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8918818Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8919193Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8919532Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8919984Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8920275Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8920613Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8920940Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8921230Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8921766Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8922088Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8922374Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8922709Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8923029Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8923331Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8923703Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8924030Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8924326Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8924651Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8924977Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8925277Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8925603Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8925920Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8926220Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8926555Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8926862Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8927172Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8927509Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8927822Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8928128Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8928469Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8928778Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8929080Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8929426Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8929751Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8930042Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8930384Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8930710Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8931003Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8931337Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8931662Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8931951Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8932291Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8932617Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8932922Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8933250Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8933577Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8933882Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8934212Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8934584Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8934887Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8935214Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8935538Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8935839Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8936182Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8936492Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8936793Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8937134Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8937445Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8937747Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8938122Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8938437Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8938738Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8939080Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8939400Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8939688Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8940023Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8940343Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8940633Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8940968Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8941285Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8941581Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8941917Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8942238Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8942691Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8943019Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8943341Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8943647Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8943970Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8944291Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8944594Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8944920Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8945245Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8945543Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8945881Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8946191Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8946492Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8946826Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8947132Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8947431Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8947765Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8948083Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8948371Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8948709Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8949106Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8949396Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8949741Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8950060Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8950348Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8950685Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8951006Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8951308Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8951630Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8951948Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8952249Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8952634Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8952960Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8953264Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8953593Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8953910Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8954211Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8954548Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8954855Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8955159Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8955497Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8955804Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8956109Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8956448Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8956753Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8957057Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8957392Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8957711Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8957999Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8958338Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8958656Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8959005Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8959352Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8959685Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8959974Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8960309Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8960627Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8960928Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8961251Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8961574Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8961877Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8962201Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8962524Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8962824Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8963154Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8963514Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8963814Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8964152Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8964459Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8964758Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8965090Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8965396Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8965696Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8966031Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8966336Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8966682Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8967021Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8967339Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8967625Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8967963Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8968282Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8968574Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8968907Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8969227Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8969516Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8969853Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8970176Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8970485Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8970808Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8971128Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8971431Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8971756Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8972075Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8972376Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8972698Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8973018Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8973318Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8973657Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8973967Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8974269Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8974609Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8974916Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8975218Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8975559Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8975868Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8976179Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8976519Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8976844Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8977135Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8977518Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8977843Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8978134Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8978471Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8978794Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8979088Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8979429Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8979752Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8980053Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8980378Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8980706Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8981048Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8981379Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8981704Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8982009Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8982448Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8982866Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8983175Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8983516Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8983829Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8984132Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8984475Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8984789Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8985094Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8985434Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8985744Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8986047Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8986385Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8986703Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8986991Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8987331Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8987652Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8987941Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8988277Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8988602Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8988888Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8989223Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8989543Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8989842Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8990164Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8990480Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8990777Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8991103Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8991429Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8991730Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8992131Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8992452Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8992753Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8993092Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8993402Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8993706Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8994044Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8994354Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8994655Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8994992Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8995301Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8995651Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8995993Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8996318Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8996609Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8996944Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8997264Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8997558Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8997890Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8998212Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8998501Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8998841Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.8999233Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.8999540Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.8999866Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9000189Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9000496Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9000824Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9001142Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9001443Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9001770Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9002091Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9002393Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9002732Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9003041Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9003340Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9003677Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9003985Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9004284Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9004620Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9004929Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9005230Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9005569Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9005886Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9006174Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9006566Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9006887Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9007179Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9007514Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9007836Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9008130Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9008464Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9008785Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9009088Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9009412Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9009734Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9010073Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9010398Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9010718Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9011023Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9011346Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9011666Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9011966Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9012304Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9012610Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9012912Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9013246Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9013559Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9013862Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9014199Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9014509Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9014806Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9015141Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9015462Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9015750Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9016090Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9016407Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9016695Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9017031Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9017352Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9017643Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9017979Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9018302Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9018602Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9018928Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9019253Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9019552Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9019873Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9020198Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9020500Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9020864Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9021184Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9021491Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9021826Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9022134Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9022544Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9022888Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9023203Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9023505Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9023842Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9024154Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9024516Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9024859Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9025183Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9025473Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9025811Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9026138Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9026430Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9026770Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9027096Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9027389Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9027728Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9028057Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9028362Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9028689Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9029013Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9029316Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9029645Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9029970Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9030271Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9030599Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9030921Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9031232Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9031574Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9031887Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9032198Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9032534Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9032845Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9033147Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9033490Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9033800Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9034105Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9034444Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9034765Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9035055Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9035439Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9035762Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9036051Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9036389Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9036712Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9037000Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9037337Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9037658Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9037958Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9038283Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9038608Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9039007Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9039345Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9039667Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9039968Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9040295Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9040618Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9040922Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9041261Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9041567Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9041868Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9042205Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9042519Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9042823Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9043156Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9043467Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9043765Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9044102Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9044421Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9044706Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9045042Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9045359Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9045649Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9045990Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9046313Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9046599Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9046934Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9047257Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9047557Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9047881Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9048198Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9048501Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9048823Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9049141Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9049446Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9049824Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9050143Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9050443Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9050779Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9051083Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9051383Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9051722Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9052029Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9052330Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9052667Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9053026Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9053323Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9053661Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9053982Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9054274Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9054612Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9054932Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9055221Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9055553Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9055871Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9056173Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9056496Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9056821Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9057119Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9057445Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9057769Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9058072Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9058393Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9058716Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9059014Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9059354Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9059661Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9059962Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9060302Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9060611Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9060910Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9061248Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9061553Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9061855Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9062191Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9062709Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9063004Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9063345Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9063666Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9064030Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9064369Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9064692Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9064984Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9065323Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9065644Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9065945Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9066274Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9066603Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9066904Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9067225Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9067608Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9067912Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9068235Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9068564Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9068866Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9069205Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9069517Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9069818Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9070157Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9070464Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9070764Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9071102Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9071414Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9071713Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9072050Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9072371Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9072660Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9073002Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9073325Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9073617Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9073957Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9074283Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:40.9074574Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:40.9074916Z --24431-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:08:40.9075269Z --24431-- parse_CU_Header: is neither DWARF2 nor DWARF3 nor DWARF4
2023-01-11T22:08:41.0053648Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0054356Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0054726Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0055037Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0055372Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0055680Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0055984Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0056310Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0056618Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0057144Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0057494Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0057797Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0058103Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0058430Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0058752Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0059040Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0059368Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0059686Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0059973Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0060297Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0060681Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0066366Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0066980Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0067532Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0068086Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0068633Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0069185Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0069701Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0070232Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0070770Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0071289Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0071838Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0072385Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0072898Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0073450Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0073976Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0074482Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0075059Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0075563Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0076072Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0076629Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0077187Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0077701Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0078277Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0078834Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0079411Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0079953Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0080435Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0080907Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0081490Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0082066Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0082550Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0083093Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0083666Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0084372Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0085137Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0085717Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0086240Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0086738Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0087282Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0087889Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0088482Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0088973Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0089492Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0090073Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0090624Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0091296Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0091915Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0092478Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0092979Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0093541Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0094068Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0094567Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0095122Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0095646Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0096140Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0096697Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0097230Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0097739Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0098292Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0098816Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0099324Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0099858Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0100381Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0100895Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0101432Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0101956Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0102697Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0103240Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0103786Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0104297Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0104823Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0105351Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0105860Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0106409Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0106920Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0107430Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0107977Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0108494Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0109002Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0109548Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0110203Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0110713Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0111269Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0111811Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0112316Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0112876Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0113412Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0113909Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0114472Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0114992Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0127949Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0128688Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0129263Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0129787Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0130339Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0130886Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0131408Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0131953Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0132495Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0133011Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0133557Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0134098Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0134602Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0135173Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0135698Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0136211Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0136771Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0137295Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0137806Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0138371Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0138894Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0139410Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0139968Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0140488Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0141016Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0141579Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0142107Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0142746Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0143297Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0143717Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0144010Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0144337Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0144652Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0144941Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0145265Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0145579Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0145991Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0146303Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0146617Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0146917Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0147231Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0147547Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0147847Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0148157Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0148656Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0149168Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0149729Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0150325Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0150846Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0151403Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0151914Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0152421Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0152984Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0153505Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0154021Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0154577Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0155109Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0155600Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0155997Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0156313Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0156603Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0156925Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0157241Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0157531Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0157853Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0158163Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0158462Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0158773Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0159165Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0159466Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0159786Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0160097Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0160397Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0160709Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0161020Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0161320Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0161642Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0161993Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0162498Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0163056Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0163421Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0163936Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0164565Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0165078Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0165593Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0166152Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0166666Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0167183Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0167729Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0168047Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0168337Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0168658Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0168970Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0169310Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0169639Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0169954Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0170242Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0170566Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0170877Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0171175Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0171486Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0171939Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0172452Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0172987Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0173520Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0174029Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0174566Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0175095Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0175601Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0176149Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0176663Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0177168Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0177713Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0178214Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0178727Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0179283Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0179811Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0180330Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0180889Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0181421Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0181911Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0182593Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0183128Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0183630Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0184138Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0184589Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0185014Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0185502Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0186136Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0186564Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0187023Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0187478Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0187906Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0188381Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0188761Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0189069Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0189386Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0189701Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0190003Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0190423Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0190728Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0191032Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0191358Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0191660Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0191961Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0192287Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0192589Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0192891Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0193218Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0193519Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0193822Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0194153Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0194467Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0194755Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0195079Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0195395Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0195682Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0196160Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0196666Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0196967Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0197291Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0197603Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0197912Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0198227Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0198540Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0198836Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0199217Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0199532Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0199831Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0200142Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0200454Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0200754Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0201076Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0201440Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0201740Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0202064Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0202365Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0202665Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0203098Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0203612Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0204119Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0204486Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0205013Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0205496Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0206037Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0206629Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0207030Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0207353Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0207668Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0207957Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0208282Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0208592Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0208891Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0209203Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0209517Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0209817Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0210136Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0210450Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0210748Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0211058Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0211376Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0211680Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0211994Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0212304Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0212606Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0212931Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0213231Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0213531Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0213859Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0214160Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0214458Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0214782Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0215082Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0215381Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0215704Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0216018Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0216310Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0216636Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0216947Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.0217283Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.0217610Z --24431-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:08:41.0217990Z --24431-- parse_CU_Header: is neither DWARF2 nor DWARF3 nor DWARF4
2023-01-11T22:08:41.3392881Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3393370Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3393712Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3394040Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3394372Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3394703Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3395010Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3395332Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3395867Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3396175Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3396509Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3396818Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3402896Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3403595Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3404065Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3404360Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3404698Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3405021Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3405313Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3405657Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3405982Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3406271Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3406611Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3406928Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3407228Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3407575Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3408051Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3408352Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3408670Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3409009Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3409326Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3409651Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3409977Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3410274Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3410606Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3410910Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3411209Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3411542Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3411847Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3412148Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3412478Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3412782Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3413087Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3413549Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3413868Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3414157Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3414489Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3414806Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3415182Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3415748Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3416163Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3416542Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3416912Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3417290Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3417678Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3418062Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3418385Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3418743Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3419065Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3419426Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3419734Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3420064Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3420433Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3425255Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3425932Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3426550Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3427128Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3427755Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3428354Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3428718Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3429060Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3429383Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3429675Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3430010Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3430329Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3430618Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3430958Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3431279Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3431581Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3431902Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3432219Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3432518Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3432838Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3433157Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3433456Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3433773Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3434099Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3434399Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3434885Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3435202Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3438197Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3438783Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3439442Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3439948Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3440425Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3441024Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3441494Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3441832Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3442158Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3442583Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3442913Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3443239Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3443539Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3443861Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3444182Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3444480Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3444805Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3445131Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3445433Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3445753Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3446090Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3446391Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3446722Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3447029Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3447327Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3447664Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3447972Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3448271Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3448602Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3448908Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3449209Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3449542Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3449864Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3450159Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3450490Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3450808Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3451101Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3451430Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3451745Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3452032Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3452368Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3452681Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3452980Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3453354Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3453673Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3453975Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3454295Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3454608Z --24431-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:08:41.3454914Z --24431-- WARNING: Serious error when reading debug info
2023-01-11T22:08:41.3455232Z --24431-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:08:41.3455591Z --24431-- parse_CU_Header: is neither DWARF2 nor DWARF3 nor DWARF4
2023-01-11T22:08:42.3280781Z ==24431== Warning: set address range perms: large range [0x59eb4000, 0x6ed02000) (defined)
2023-01-11T22:09:25.0180230Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:09:25.0391922Z Note: Google Test filter = -*CUDA
2023-01-11T22:09:25.0445435Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:09:25.0461712Z [----------] Global test environment set-up.
2023-01-11T22:09:25.0490999Z [----------] 4 tests from BasicTest
2023-01-11T22:09:25.0505230Z [ RUN      ] BasicTest.BasicTestCPU
2023-01-11T22:09:26.1852605Z    336 ms
2023-01-11T22:09:30.9627710Z    4746 ms
2023-01-11T22:09:37.5974530Z    6626 ms
2023-01-11T22:09:38.2523880Z [       OK ] BasicTest.BasicTestCPU (13199 ms)
2023-01-11T22:09:38.2528793Z [ RUN      ] BasicTest.BasicTestHalfCPU
2023-01-11T22:09:38.4477481Z    140 ms
2023-01-11T22:09:43.5096544Z    5053 ms
2023-01-11T22:09:50.5311429Z    7017 ms
2023-01-11T22:09:50.6564259Z [       OK ] BasicTest.BasicTestHalfCPU (12403 ms)
2023-01-11T22:09:50.6565962Z [ RUN      ] BasicTest.FactoryMethodsTest
2023-01-11T22:09:50.6927606Z [       OK ] BasicTest.FactoryMethodsTest (36 ms)
2023-01-11T22:09:50.6928030Z [ RUN      ] BasicTest.BasicStdTestCPU
2023-01-11T22:09:50.7328016Z Simple example: called once
2023-01-11T22:09:50.8609104Z throw: call_once will retry
2023-01-11T22:09:50.9039129Z throw: call_once will retry
2023-01-11T22:09:50.9053492Z Didn't throw, call_once will not attempt again
2023-01-11T22:09:50.9079638Z [       OK ] BasicTest.BasicStdTestCPU (215 ms)
2023-01-11T22:09:50.9098167Z [----------] 4 tests from BasicTest (25858 ms total)
2023-01-11T22:09:50.9098358Z 
2023-01-11T22:09:50.9109303Z [----------] Global test environment tear-down
2023-01-11T22:09:50.9136240Z [==========] 4 tests from 1 test suite ran. (25875 ms total)
2023-01-11T22:09:50.9151085Z [  PASSED  ] 4 tests.
2023-01-11T22:09:52.9868282Z ==24431== 
2023-01-11T22:09:52.9872148Z ==24431== HEAP SUMMARY:
2023-01-11T22:09:52.9872559Z ==24431==     in use at exit: 687,403 bytes in 4,089 blocks
2023-01-11T22:09:52.9873002Z ==24431==   total heap usage: 1,929,172 allocs, 1,925,083 frees, 311,449,931 bytes allocated
2023-01-11T22:09:52.9873369Z ==24431== 
2023-01-11T22:09:53.5283068Z ==24431== LEAK SUMMARY:
2023-01-11T22:09:53.5283512Z ==24431==    definitely lost: 0 bytes in 0 blocks
2023-01-11T22:09:53.5283894Z ==24431==    indirectly lost: 0 bytes in 0 blocks
2023-01-11T22:09:53.5284292Z ==24431==      possibly lost: 1,584 bytes in 3 blocks
2023-01-11T22:09:53.5284690Z ==24431==    still reachable: 685,819 bytes in 4,086 blocks
2023-01-11T22:09:53.5285056Z ==24431==         suppressed: 0 bytes in 0 blocks
2023-01-11T22:09:53.5285742Z ==24431== Rerun with --leak-check=full to see details of leaked memory
2023-01-11T22:09:53.5286122Z ==24431== 
2023-01-11T22:09:53.5286561Z ==24431== For lists of detected and suppressed errors, rerun with: -s
2023-01-11T22:09:53.5287070Z ==24431== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 4 from 4)
2023-01-11T22:09:53.5768809Z + [[ -x ./tensor_interop_test ]]
2023-01-11T22:09:53.5769051Z + popd
2023-01-11T22:09:53.5769224Z ~/workspace
2023-01-11T22:09:53.5770861Z + [[ -n '' ]]
2023-01-11T22:09:53.5771084Z + assert_git_not_dirty
2023-01-11T22:09:53.5771375Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:53.5771700Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:09:53.5774375Z ++ git status --porcelain
2023-01-11T22:09:53.6589039Z + git_status=
2023-01-11T22:09:53.6589458Z + [[ -n '' ]]
2023-01-11T22:09:53.6589635Z + test_vec256
2023-01-11T22:09:53.6589943Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:09:53.6590272Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:53.6590559Z + echo 'Testing vec256 instructions'
2023-01-11T22:09:53.6590760Z Testing vec256 instructions
2023-01-11T22:09:53.6591013Z + mkdir -p test/test-reports/vec256
2023-01-11T22:09:53.6639527Z + pushd build/bin
2023-01-11T22:09:53.6639757Z ~/workspace/build/bin ~/workspace
2023-01-11T22:09:53.6642320Z ++ find . -maxdepth 1 -executable -name 'vec256_test*'
2023-01-11T22:09:53.6694740Z + vec256_tests=
2023-01-11T22:09:53.6695302Z + popd
2023-01-11T22:09:53.6695515Z ~/workspace
2023-01-11T22:09:53.6702935Z + assert_git_not_dirty
2023-01-11T22:09:53.6703495Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:53.6703991Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:09:53.6704254Z ++ git status --porcelain
2023-01-11T22:09:53.7502093Z + git_status=
2023-01-11T22:09:53.7502677Z + [[ -n '' ]]
2023-01-11T22:09:53.7503003Z + test_libtorch
2023-01-11T22:09:53.7503395Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:53.7503650Z + echo 'Testing libtorch'
2023-01-11T22:09:53.7503835Z Testing libtorch
2023-01-11T22:09:53.7504264Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libbackend_with_compiler.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:53.7512327Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libjitbackend_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:53.7521449Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10d_cuda_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:53.7530925Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libshm.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:53.7540220Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda_linalg.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorchbind_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:53.7548639Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libtbb*' /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:53.7557801Z + TEST_REPORTS_DIR=test/test-reports/cpp-unittest/test_libtorch
2023-01-11T22:09:53.7558157Z + mkdir -p test/test-reports/cpp-unittest/test_libtorch
2023-01-11T22:09:53.7558504Z + python tools/download_mnist.py --quiet -d test/cpp/api/mnist
2023-01-11T22:09:53.7592193Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *-tsan* ]]
2023-01-11T22:09:53.7592490Z + python test/cpp/jit/tests_setup.py setup
2023-01-11T22:09:53.7979233Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ...
2023-01-11T22:09:54.1604398Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ...
2023-01-11T22:09:54.2052957Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ...
2023-01-11T22:09:54.3089973Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ...
2023-01-11T22:09:55.1375813Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T22:09:55.1376562Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_jit --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_jit.xml
2023-01-11T22:09:55.5452410Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:09:55.5460529Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:09:55.5460947Z [==========] Running 569 tests from 119 test suites.
2023-01-11T22:09:55.5461266Z [----------] Global test environment set-up.
2023-01-11T22:09:55.5461580Z [----------] 2 tests from AddIfThenElseOpTest
2023-01-11T22:09:55.5461921Z [ RUN      ] AddIfThenElseOpTest.AddIfThenElseOpSimple
2023-01-11T22:09:55.5528704Z [       OK ] AddIfThenElseOpTest.AddIfThenElseOpSimple (6 ms)
2023-01-11T22:09:55.5529457Z [ RUN      ] AddIfThenElseOpTest.NoIfThenElseOpMultipleOutputs
2023-01-11T22:09:55.5530277Z [       OK ] AddIfThenElseOpTest.NoIfThenElseOpMultipleOutputs (0 ms)
2023-01-11T22:09:55.5531268Z [----------] 2 tests from AddIfThenElseOpTest (6 ms total)
2023-01-11T22:09:55.5531585Z 
2023-01-11T22:09:55.5531890Z [----------] 15 tests from TopologicalMoveTest
2023-01-11T22:09:55.5532450Z [ RUN      ] TopologicalMoveTest.SplitsDeps
2023-01-11T22:09:55.5533067Z [       OK ] TopologicalMoveTest.SplitsDeps (0 ms)
2023-01-11T22:09:55.5533707Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardSimple
2023-01-11T22:09:55.5534451Z [       OK ] TopologicalMoveTest.MoveAfterBackwardSimple (0 ms)
2023-01-11T22:09:55.5535204Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardInvalid
2023-01-11T22:09:55.5535955Z [       OK ] TopologicalMoveTest.MoveAfterBackwardInvalid (0 ms)
2023-01-11T22:09:55.5536605Z [ RUN      ] TopologicalMoveTest.MoveAfterNoOp
2023-01-11T22:09:55.5537240Z [       OK ] TopologicalMoveTest.MoveAfterNoOp (0 ms)
2023-01-11T22:09:55.5537960Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardMultipleDeps
2023-01-11T22:09:55.5538782Z [       OK ] TopologicalMoveTest.MoveAfterBackwardMultipleDeps (0 ms)
2023-01-11T22:09:55.5539633Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardNonZeroWorkingSet
2023-01-11T22:09:55.5540515Z [       OK ] TopologicalMoveTest.MoveAfterBackwardNonZeroWorkingSet (0 ms)
2023-01-11T22:09:55.5541300Z [ RUN      ] TopologicalMoveTest.MoveAfterForwardSimple
2023-01-11T22:09:55.5542042Z [       OK ] TopologicalMoveTest.MoveAfterForwardSimple (0 ms)
2023-01-11T22:09:55.5543017Z [ RUN      ] TopologicalMoveTest.MoveAfterForwardNonZeroWorkingSet
2023-01-11T22:09:55.5543884Z [       OK ] TopologicalMoveTest.MoveAfterForwardNonZeroWorkingSet (0 ms)
2023-01-11T22:09:55.5544658Z [ RUN      ] TopologicalMoveTest.MoveBeforeForwardSimple
2023-01-11T22:09:55.5545407Z [       OK ] TopologicalMoveTest.MoveBeforeForwardSimple (0 ms)
2023-01-11T22:09:55.5546169Z [ RUN      ] TopologicalMoveTest.MoveBeforeBackwardSimple
2023-01-11T22:09:55.5546938Z [       OK ] TopologicalMoveTest.MoveBeforeBackwardSimple (0 ms)
2023-01-11T22:09:55.5547630Z [ RUN      ] TopologicalMoveTest.MoveBeforeNoOp
2023-01-11T22:09:55.5548239Z [       OK ] TopologicalMoveTest.MoveBeforeNoOp (0 ms)
2023-01-11T22:09:55.5548859Z [ RUN      ] TopologicalMoveTest.MoveBeforeForwardWithDeps
2023-01-11T22:09:55.5549590Z [       OK ] TopologicalMoveTest.MoveBeforeForwardWithDeps (0 ms)
2023-01-11T22:09:55.5550297Z [ RUN      ] TopologicalMoveTest.MoveBeforeBackwardWithDeps
2023-01-11T22:09:55.5550948Z [       OK ] TopologicalMoveTest.MoveBeforeBackwardWithDeps (0 ms)
2023-01-11T22:09:55.5551548Z [ RUN      ] TopologicalMoveTest.DepsDisallowMove
2023-01-11T22:09:55.5552129Z [       OK ] TopologicalMoveTest.DepsDisallowMove (0 ms)
2023-01-11T22:09:55.5552735Z [ RUN      ] TopologicalMoveTest.MoveAfterBeforeWithDeps
2023-01-11T22:09:55.5553591Z [       OK ] TopologicalMoveTest.MoveAfterBeforeWithDeps (0 ms)
2023-01-11T22:09:55.5554241Z [----------] 15 tests from TopologicalMoveTest (2 ms total)
2023-01-11T22:09:55.5554519Z 
2023-01-11T22:09:55.5554804Z [----------] 6 tests from AliasAnalysisTest
2023-01-11T22:09:55.5555422Z [ RUN      ] AliasAnalysisTest.AliasingMutationBlocksMoves
2023-01-11T22:09:55.5588424Z [       OK ] AliasAnalysisTest.AliasingMutationBlocksMoves (3 ms)
2023-01-11T22:09:55.5589214Z [ RUN      ] AliasAnalysisTest.AliasingMutationBlocksMoves2
2023-01-11T22:09:55.5589925Z [       OK ] AliasAnalysisTest.AliasingMutationBlocksMoves2 (0 ms)
2023-01-11T22:09:55.5590629Z [ RUN      ] AliasAnalysisTest.SideEffectsBlockMoves
2023-01-11T22:09:55.5591268Z [       OK ] AliasAnalysisTest.SideEffectsBlockMoves (0 ms)
2023-01-11T22:09:55.5591897Z [ RUN      ] AliasAnalysisTest.MovingAcrossInnerBlocks
2023-01-11T22:09:55.5592728Z [       OK ] AliasAnalysisTest.MovingAcrossInnerBlocks (0 ms)
2023-01-11T22:09:55.5593277Z [ RUN      ] AliasAnalysisTest.NoneHasNoWriters
2023-01-11T22:09:55.5593638Z [       OK ] AliasAnalysisTest.NoneHasNoWriters (0 ms)
2023-01-11T22:09:55.5594047Z [ RUN      ] AliasAnalysisTest.SafeToChangeAliasingRelationship
2023-01-11T22:09:55.5594503Z [       OK ] AliasAnalysisTest.SafeToChangeAliasingRelationship (0 ms)
2023-01-11T22:09:55.5594904Z [----------] 6 tests from AliasAnalysisTest (4 ms total)
2023-01-11T22:09:55.5595067Z 
2023-01-11T22:09:55.5595230Z [----------] 4 tests from WriteTrackingTest
2023-01-11T22:09:55.5595523Z [ RUN      ] WriteTrackingTest.Basic
2023-01-11T22:09:55.5595810Z [       OK ] WriteTrackingTest.Basic (0 ms)
2023-01-11T22:09:55.5596117Z [ RUN      ] WriteTrackingTest.IsMutable
2023-01-11T22:09:55.5600833Z [       OK ] WriteTrackingTest.IsMutable (0 ms)
2023-01-11T22:09:55.5601416Z [ RUN      ] WriteTrackingTest.IsImmutable
2023-01-11T22:09:55.5602031Z [       OK ] WriteTrackingTest.IsImmutable (0 ms)
2023-01-11T22:09:55.5602575Z [ RUN      ] WriteTrackingTest.HasWriters
2023-01-11T22:09:55.5603174Z [       OK ] WriteTrackingTest.HasWriters (0 ms)
2023-01-11T22:09:55.5603748Z [----------] 4 tests from WriteTrackingTest (0 ms total)
2023-01-11T22:09:55.5604011Z 
2023-01-11T22:09:55.5604281Z [----------] 13 tests from ContainerAliasingTest
2023-01-11T22:09:55.5604652Z [ RUN      ] ContainerAliasingTest.MayContainAlias
2023-01-11T22:09:55.5605114Z [       OK ] ContainerAliasingTest.MayContainAlias (0 ms)
2023-01-11T22:09:55.5605552Z [ RUN      ] ContainerAliasingTest.MayContainAlias_cast
2023-01-11T22:09:55.5606057Z [       OK ] ContainerAliasingTest.MayContainAlias_cast (0 ms)
2023-01-11T22:09:55.5606820Z [ RUN      ] ContainerAliasingTest.PrimitveValuesDontAliasContainers
2023-01-11T22:09:55.5607518Z [       OK ] ContainerAliasingTest.PrimitveValuesDontAliasContainers (0 ms)
2023-01-11T22:09:55.5608016Z [ RUN      ] ContainerAliasingTest.UnionAliasing
2023-01-11T22:09:55.5608358Z [       OK ] ContainerAliasingTest.UnionAliasing (0 ms)
2023-01-11T22:09:55.5608734Z [ RUN      ] ContainerAliasingTest.InputsCanAliasOutputs
2023-01-11T22:09:55.5609154Z [       OK ] ContainerAliasingTest.InputsCanAliasOutputs (0 ms)
2023-01-11T22:09:55.5609538Z [ RUN      ] ContainerAliasingTest.NestedTupleConstruct
2023-01-11T22:09:55.5609934Z [       OK ] ContainerAliasingTest.NestedTupleConstruct (0 ms)
2023-01-11T22:09:55.5610417Z [ RUN      ] ContainerAliasingTest.NestedTypes
2023-01-11T22:09:55.5610770Z [       OK ] ContainerAliasingTest.NestedTypes (0 ms)
2023-01-11T22:09:55.5611084Z [ RUN      ] ContainerAliasingTest.Simple
2023-01-11T22:09:55.5611406Z [       OK ] ContainerAliasingTest.Simple (0 ms)
2023-01-11T22:09:55.5611894Z [ RUN      ] ContainerAliasingTest.Lists
2023-01-11T22:09:55.5612197Z [       OK ] ContainerAliasingTest.Lists (0 ms)
2023-01-11T22:09:55.5612564Z [ RUN      ] ContainerAliasingTest.Lists2
2023-01-11T22:09:55.5612982Z [       OK ] ContainerAliasingTest.Lists2 (0 ms)
2023-01-11T22:09:55.5613299Z [ RUN      ] ContainerAliasingTest.Conservative
2023-01-11T22:09:55.5613672Z [       OK ] ContainerAliasingTest.Conservative (0 ms)
2023-01-11T22:09:55.5614159Z [ RUN      ] ContainerAliasingTest.MovesAcrossContainedWrites
2023-01-11T22:09:55.5614601Z [       OK ] ContainerAliasingTest.MovesAcrossContainedWrites (0 ms)
2023-01-11T22:09:55.5615073Z [ RUN      ] ContainerAliasingTest.MovesAcrossContainedWritesNested
2023-01-11T22:09:55.5616386Z [       OK ] ContainerAliasingTest.MovesAcrossContainedWritesNested (0 ms)
2023-01-11T22:09:55.5616935Z [----------] 13 tests from ContainerAliasingTest (1 ms total)
2023-01-11T22:09:55.5617130Z 
2023-01-11T22:09:55.5617323Z [----------] 3 tests from WildcardsTest
2023-01-11T22:09:55.5617617Z [ RUN      ] WildcardsTest.Basic
2023-01-11T22:09:55.5618151Z [       OK ] WildcardsTest.Basic (0 ms)
2023-01-11T22:09:55.5618570Z [ RUN      ] WildcardsTest.TypeIsolation
2023-01-11T22:09:55.5619885Z [       OK ] WildcardsTest.TypeIsolation (0 ms)
2023-01-11T22:09:55.5620362Z [ RUN      ] WildcardsTest.InvariantContainerAliasing
2023-01-11T22:09:55.5620980Z [       OK ] WildcardsTest.InvariantContainerAliasing (0 ms)
2023-01-11T22:09:55.5621594Z [----------] 3 tests from WildcardsTest (0 ms total)
2023-01-11T22:09:55.5621807Z 
2023-01-11T22:09:55.5622011Z [----------] 18 tests from AliasRegistrationTest
2023-01-11T22:09:55.5622572Z [ RUN      ] AliasRegistrationTest.ConservativeWithInferredSchema
2023-01-11T22:09:55.5623207Z [       OK ] AliasRegistrationTest.ConservativeWithInferredSchema (0 ms)
2023-01-11T22:09:55.5623804Z [ RUN      ] AliasRegistrationTest.ConservativeWithSpecifiedSchema
2023-01-11T22:09:55.5624478Z [       OK ] AliasRegistrationTest.ConservativeWithSpecifiedSchema (0 ms)
2023-01-11T22:09:55.5625013Z [ RUN      ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError
2023-01-11T22:09:55.5728948Z [       OK ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError (10 ms)
2023-01-11T22:09:55.5729535Z [ RUN      ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError2
2023-01-11T22:09:55.5773812Z [       OK ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError2 (4 ms)
2023-01-11T22:09:55.5774375Z [ RUN      ] AliasRegistrationTest.FromSchemaWithInferredSchemaShouldError
2023-01-11T22:09:55.5788272Z [       OK ] AliasRegistrationTest.FromSchemaWithInferredSchemaShouldError (1 ms)
2023-01-11T22:09:55.5788912Z [ RUN      ] AliasRegistrationTest.FromSchemaInferredPure
2023-01-11T22:09:55.5789457Z [       OK ] AliasRegistrationTest.FromSchemaInferredPure (0 ms)
2023-01-11T22:09:55.5789986Z [ RUN      ] AliasRegistrationTest.FromSchemaAliased
2023-01-11T22:09:55.5790698Z [       OK ] AliasRegistrationTest.FromSchemaAliased (0 ms)
2023-01-11T22:09:55.5791162Z [ RUN      ] AliasRegistrationTest.FromSchemaPure
2023-01-11T22:09:55.5792067Z [       OK ] AliasRegistrationTest.FromSchemaPure (0 ms)
2023-01-11T22:09:55.5792469Z [ RUN      ] AliasRegistrationTest.PureNoSchema
2023-01-11T22:09:55.5793220Z [       OK ] AliasRegistrationTest.PureNoSchema (0 ms)
2023-01-11T22:09:55.5793598Z [ RUN      ] AliasRegistrationTest.PureWithSchema
2023-01-11T22:09:55.5794677Z [       OK ] AliasRegistrationTest.PureWithSchema (0 ms)
2023-01-11T22:09:55.5795142Z [ RUN      ] AliasRegistrationTest.PureWithAnnotationsShouldError
2023-01-11T22:09:55.5828938Z [       OK ] AliasRegistrationTest.PureWithAnnotationsShouldError (3 ms)
2023-01-11T22:09:55.5829660Z [ RUN      ] AliasRegistrationTest.AliasMoveAtenListOp
2023-01-11T22:09:55.5830367Z [       OK ] AliasRegistrationTest.AliasMoveAtenListOp (0 ms)
2023-01-11T22:09:55.5831060Z [ RUN      ] AliasRegistrationTest.AliasMoveForTupleConstructWithSingleUseAsGraphOutput
2023-01-11T22:09:55.5832146Z [       OK ] AliasRegistrationTest.AliasMoveForTupleConstructWithSingleUseAsGraphOutput (0 ms)
2023-01-11T22:09:55.5833079Z [ RUN      ] AliasRegistrationTest.RecursiveSubgraphTupleContainment
2023-01-11T22:09:55.5833982Z [       OK ] AliasRegistrationTest.RecursiveSubgraphTupleContainment (0 ms)
2023-01-11T22:09:55.5834732Z [ RUN      ] AliasRegistrationTest.WildcardAliasForTupleConstructWithUses
2023-01-11T22:09:55.5835256Z [       OK ] AliasRegistrationTest.WildcardAliasForTupleConstructWithUses (0 ms)
2023-01-11T22:09:55.5835830Z [ RUN      ] AliasRegistrationTest.ATenSplitIntListAliasCheck
2023-01-11T22:09:55.5836278Z [       OK ] AliasRegistrationTest.ATenSplitIntListAliasCheck (0 ms)
2023-01-11T22:09:55.5836683Z [ RUN      ] AliasRegistrationTest.ATenSplitIntAliasCheck
2023-01-11T22:09:55.5837128Z [       OK ] AliasRegistrationTest.ATenSplitIntAliasCheck (0 ms)
2023-01-11T22:09:55.5837885Z [ RUN      ] AliasRegistrationTest.PureWithAnnotationsShouldError2
2023-01-11T22:09:55.5868305Z [       OK ] AliasRegistrationTest.PureWithAnnotationsShouldError2 (3 ms)
2023-01-11T22:09:55.5869090Z [----------] 18 tests from AliasRegistrationTest (24 ms total)
2023-01-11T22:09:55.5869376Z 
2023-01-11T22:09:55.5869611Z [----------] 2 tests from IRNonDeterminismTest
2023-01-11T22:09:55.5870139Z [ RUN      ] IRNonDeterminismTest.Basic
2023-01-11T22:09:55.5870662Z [       OK ] IRNonDeterminismTest.Basic (0 ms)
2023-01-11T22:09:55.5871025Z [ RUN      ] IRNonDeterminismTest.DropoutSpecialCase
2023-01-11T22:09:55.5871420Z [       OK ] IRNonDeterminismTest.DropoutSpecialCase (0 ms)
2023-01-11T22:09:55.5871798Z [----------] 2 tests from IRNonDeterminismTest (0 ms total)
2023-01-11T22:09:55.5872004Z 
2023-01-11T22:09:55.5872411Z [----------] 1 test from NonDeterminismBackwardsCompatibility
2023-01-11T22:09:55.5873042Z [ RUN      ] NonDeterminismBackwardsCompatibility.BackwardsCompatibility
2023-01-11T22:09:55.5873561Z [       OK ] NonDeterminismBackwardsCompatibility.BackwardsCompatibility (0 ms)
2023-01-11T22:09:55.5874054Z [----------] 1 test from NonDeterminismBackwardsCompatibility (0 ms total)
2023-01-11T22:09:55.5874242Z 
2023-01-11T22:09:55.5874392Z [----------] 3 tests from AutodiffTest
2023-01-11T22:09:55.5874675Z [ RUN      ] AutodiffTest.ADFormulas
2023-01-11T22:09:55.6344218Z [       OK ] AutodiffTest.ADFormulas (47 ms)
2023-01-11T22:09:55.6344589Z [ RUN      ] AutodiffTest.Differentiate
2023-01-11T22:09:55.6350140Z [       OK ] AutodiffTest.Differentiate (0 ms)
2023-01-11T22:09:55.6350599Z [ RUN      ] AutodiffTest.DifferentiateWithRequiresGrad
2023-01-11T22:09:55.6368903Z [       OK ] AutodiffTest.DifferentiateWithRequiresGrad (1 ms)
2023-01-11T22:09:55.6369548Z [----------] 3 tests from AutodiffTest (49 ms total)
2023-01-11T22:09:55.6369712Z 
2023-01-11T22:09:55.6369927Z [----------] 1 test from AutodiffRemoveUnusedGradientsTest
2023-01-11T22:09:55.6370312Z [ RUN      ] AutodiffRemoveUnusedGradientsTest.Linear
2023-01-11T22:09:55.6381886Z [       OK ] AutodiffRemoveUnusedGradientsTest.Linear (1 ms)
2023-01-11T22:09:55.6382750Z [----------] 1 test from AutodiffRemoveUnusedGradientsTest (1 ms total)
2023-01-11T22:09:55.6382955Z 
2023-01-11T22:09:55.6383112Z [----------] 1 test from UpgraderLoad
2023-01-11T22:09:55.6383639Z [ RUN      ] UpgraderLoad.CanPopulateUpgradersGraph
2023-01-11T22:09:55.6425394Z [       OK ] UpgraderLoad.CanPopulateUpgradersGraph (4 ms)
2023-01-11T22:09:55.6426075Z [----------] 1 test from UpgraderLoad (4 ms total)
2023-01-11T22:09:55.6426290Z 
2023-01-11T22:09:55.6426454Z [----------] 4 tests from OpReplacementTest
2023-01-11T22:09:55.6426871Z [ RUN      ] OpReplacementTest.ReplaceDivInSimpleFunction
2023-01-11T22:09:55.6427714Z [       OK ] OpReplacementTest.ReplaceDivInSimpleFunction (0 ms)
2023-01-11T22:09:55.6428172Z [ RUN      ] OpReplacementTest.ReplaceTwoOpsInSimpleFunction
2023-01-11T22:09:55.6430314Z [       OK ] OpReplacementTest.ReplaceTwoOpsInSimpleFunction (0 ms)
2023-01-11T22:09:55.6430833Z [ RUN      ] OpReplacementTest.ReplaceDivInNestedFunction
2023-01-11T22:09:55.6432103Z [       OK ] OpReplacementTest.ReplaceDivInNestedFunction (0 ms)
2023-01-11T22:09:55.6432933Z [ RUN      ] OpReplacementTest.ReplaceTestSubcmulInSimpleFunction
2023-01-11T22:09:55.6434389Z [       OK ] OpReplacementTest.ReplaceTestSubcmulInSimpleFunction (0 ms)
2023-01-11T22:09:55.6434851Z [----------] 4 tests from OpReplacementTest (0 ms total)
2023-01-11T22:09:55.6435031Z 
2023-01-11T22:09:55.6435186Z [----------] 4 tests from UpgraderUtils
2023-01-11T22:09:55.6435739Z [ RUN      ] UpgraderUtils.FindCorrectUpgrader
2023-01-11T22:09:55.6436317Z [       OK ] UpgraderUtils.FindCorrectUpgrader (0 ms)
2023-01-11T22:09:55.6436819Z [ RUN      ] UpgraderUtils.IsVersionMapSorted
2023-01-11T22:09:55.6437172Z [       OK ] UpgraderUtils.IsVersionMapSorted (0 ms)
2023-01-11T22:09:55.6437511Z [ RUN      ] UpgraderUtils.FindIfOpIsCurrent
2023-01-11T22:09:55.6437892Z [       OK ] UpgraderUtils.FindIfOpIsCurrent (0 ms)
2023-01-11T22:09:55.6438528Z [ RUN      ] UpgraderUtils.CanLoadHistoricOp
2023-01-11T22:09:55.6439111Z [       OK ] UpgraderUtils.CanLoadHistoricOp (0 ms)
2023-01-11T22:09:55.6439462Z [----------] 4 tests from UpgraderUtils (0 ms total)
2023-01-11T22:09:55.6439603Z 
2023-01-11T22:09:55.6439751Z [----------] 9 tests from BackendTest
2023-01-11T22:09:55.6440022Z [ RUN      ] BackendTest.ToBackend
2023-01-11T22:09:55.6483798Z [       OK ] BackendTest.ToBackend (4 ms)
2023-01-11T22:09:55.6484178Z [ RUN      ] BackendTest.ToBackendNotAvailable
2023-01-11T22:09:55.6505455Z [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T22:09:55.6523119Z [       OK ] BackendTest.ToBackendNotAvailable (3 ms)
2023-01-11T22:09:55.6523509Z [ RUN      ] BackendTest.TestCompiler
2023-01-11T22:09:55.6580633Z [       OK ] BackendTest.TestCompiler (5 ms)
2023-01-11T22:09:55.6581043Z [ RUN      ] BackendTest.TestCompilerWithStringTable
2023-01-11T22:09:55.6634928Z [       OK ] BackendTest.TestCompilerWithStringTable (5 ms)
2023-01-11T22:09:55.6635357Z [ RUN      ] BackendTest.TestComposite
2023-01-11T22:09:55.6739056Z [       OK ] BackendTest.TestComposite (10 ms)
2023-01-11T22:09:55.6739443Z [ RUN      ] BackendTest.TestPrimDtype
2023-01-11T22:09:55.6745655Z [       OK ] BackendTest.TestPrimDtype (0 ms)
2023-01-11T22:09:55.6746079Z [ RUN      ] BackendTest.TestCompositeWithSetStates
2023-01-11T22:09:55.6848510Z [       OK ] BackendTest.TestCompositeWithSetStates (10 ms)
2023-01-11T22:09:55.6848984Z [ RUN      ] BackendTest.TestConsistencyOfCompositeWithSetStates
2023-01-11T22:09:55.7038059Z [       OK ] BackendTest.TestConsistencyOfCompositeWithSetStates (18 ms)
2023-01-11T22:09:55.7038508Z [ RUN      ] BackendTest.TestCompilerNotSupport
2023-01-11T22:09:55.7058059Z [       OK ] BackendTest.TestCompilerNotSupport (1 ms)
2023-01-11T22:09:55.7058450Z [----------] 9 tests from BackendTest (62 ms total)
2023-01-11T22:09:55.7058607Z 
2023-01-11T22:09:55.7058781Z [----------] 6 tests from BackendTestDebugInfo
2023-01-11T22:09:55.7059094Z [ RUN      ] BackendTestDebugInfo.TestCompiler
2023-01-11T22:09:55.7183693Z [       OK ] BackendTestDebugInfo.TestCompiler (12 ms)
2023-01-11T22:09:55.7184166Z [ RUN      ] BackendTestDebugInfo.TestCompilerWithStringTable
2023-01-11T22:09:55.7321386Z [       OK ] BackendTestDebugInfo.TestCompilerWithStringTable (13 ms)
2023-01-11T22:09:55.7322323Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithModuleHierarchy
2023-01-11T22:09:55.7459279Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithModuleHierarchy (13 ms)
2023-01-11T22:09:55.7460657Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithTwoLevelModuleHierarchy
2023-01-11T22:09:55.7588823Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithTwoLevelModuleHierarchy (12 ms)
2023-01-11T22:09:55.7589966Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithLoweredSubModule
2023-01-11T22:09:55.7730593Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithLoweredSubModule (14 ms)
2023-01-11T22:09:55.7731748Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithSelectiveLoweredSubModule
2023-01-11T22:09:55.7864557Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithSelectiveLoweredSubModule (13 ms)
2023-01-11T22:09:55.7865476Z [----------] 6 tests from BackendTestDebugInfo (80 ms total)
2023-01-11T22:09:55.7865767Z 
2023-01-11T22:09:55.7866192Z [----------] 4 tests from ClassImportTest
2023-01-11T22:09:55.7866681Z [ RUN      ] ClassImportTest.Basic
2023-01-11T22:09:55.7871839Z [       OK ] ClassImportTest.Basic (0 ms)
2023-01-11T22:09:55.7872373Z [ RUN      ] ClassImportTest.ScriptObject
2023-01-11T22:09:55.7898127Z [       OK ] ClassImportTest.ScriptObject (2 ms)
2023-01-11T22:09:55.7898661Z [ RUN      ] ClassImportTest.ClassDerive
2023-01-11T22:09:55.7899206Z [       OK ] ClassImportTest.ClassDerive (0 ms)
2023-01-11T22:09:55.7899714Z [ RUN      ] ClassImportTest.CustomClass
2023-01-11T22:09:55.7900674Z [       OK ] ClassImportTest.CustomClass (0 ms)
2023-01-11T22:09:55.7901281Z [----------] 4 tests from ClassImportTest (3 ms total)
2023-01-11T22:09:55.7901587Z 
2023-01-11T22:09:55.7901842Z [----------] 1 test from ClassParserTest
2023-01-11T22:09:55.7902523Z [ RUN      ] ClassParserTest.Basic
2023-01-11T22:09:55.7903046Z [       OK ] ClassParserTest.Basic (0 ms)
2023-01-11T22:09:55.7903622Z [----------] 1 test from ClassParserTest (0 ms total)
2023-01-11T22:09:55.7903895Z 
2023-01-11T22:09:55.7904161Z [----------] 3 tests from ClassTypeTest
2023-01-11T22:09:55.7904680Z [ RUN      ] ClassTypeTest.AddRemoveAttr
2023-01-11T22:09:55.7905167Z [       OK ] ClassTypeTest.AddRemoveAttr (0 ms)
2023-01-11T22:09:55.7905493Z [ RUN      ] ClassTypeTest.AddRemoveConstant
2023-01-11T22:09:55.7905832Z [       OK ] ClassTypeTest.AddRemoveConstant (0 ms)
2023-01-11T22:09:55.7906193Z [ RUN      ] ClassTypeTest.IdenticalTypesDifferentCus
2023-01-11T22:09:55.7926574Z [       OK ] ClassTypeTest.IdenticalTypesDifferentCus (2 ms)
2023-01-11T22:09:55.7927197Z [----------] 3 tests from ClassTypeTest (2 ms total)
2023-01-11T22:09:55.7927486Z 
2023-01-11T22:09:55.7927765Z [----------] 2 tests from TestCodeTemplate
2023-01-11T22:09:55.7928288Z [ RUN      ] TestCodeTemplate.Copying
2023-01-11T22:09:55.7928816Z [       OK ] TestCodeTemplate.Copying (0 ms)
2023-01-11T22:09:55.7929361Z [ RUN      ] TestCodeTemplate.Formatting
2023-01-11T22:09:55.7930123Z [       OK ] TestCodeTemplate.Formatting (0 ms)
2023-01-11T22:09:55.7930719Z [----------] 2 tests from TestCodeTemplate (0 ms total)
2023-01-11T22:09:55.7931007Z 
2023-01-11T22:09:55.7931288Z [----------] 13 tests from ConcatOptTest
2023-01-11T22:09:55.7931958Z [ RUN      ] ConcatOptTest.SimpleCommonInputsEliminationPrefix
2023-01-11T22:09:55.7987515Z [       OK ] ConcatOptTest.SimpleCommonInputsEliminationPrefix (5 ms)
2023-01-11T22:09:55.7988350Z [ RUN      ] ConcatOptTest.SimpleCommonInputsEliminationSuffix
2023-01-11T22:09:55.8031835Z [       OK ] ConcatOptTest.SimpleCommonInputsEliminationSuffix (4 ms)
2023-01-11T22:09:55.8032768Z [ RUN      ] ConcatOptTest.CommonInputsEliminationWithDifferentOrderInputs
2023-01-11T22:09:55.8066938Z [       OK ] ConcatOptTest.CommonInputsEliminationWithDifferentOrderInputs (3 ms)
2023-01-11T22:09:55.8067716Z [ RUN      ] ConcatOptTest.MoreCommonInputsElimination
2023-01-11T22:09:55.8154953Z [       OK ] ConcatOptTest.MoreCommonInputsElimination (8 ms)
2023-01-11T22:09:55.8155553Z [ RUN      ] ConcatOptTest.ExpandConcat
2023-01-11T22:09:55.8179231Z [       OK ] ConcatOptTest.ExpandConcat (2 ms)
2023-01-11T22:09:55.8179697Z [ RUN      ] ConcatOptTest.ConcatWithoutResultShape
2023-01-11T22:09:55.8202124Z [       OK ] ConcatOptTest.ConcatWithoutResultShape (2 ms)
2023-01-11T22:09:55.8202618Z [ RUN      ] ConcatOptTest.ConcatWithoutInputShape
2023-01-11T22:09:55.8230565Z [       OK ] ConcatOptTest.ConcatWithoutInputShape (2 ms)
2023-01-11T22:09:55.8231003Z [ RUN      ] ConcatOptTest.UseVariadicCat
2023-01-11T22:09:55.8290976Z [       OK ] ConcatOptTest.UseVariadicCat (6 ms)
2023-01-11T22:09:55.8291486Z [ RUN      ] ConcatOptTest.UseVariadicCatWithMultipleListUses
2023-01-11T22:09:55.8311280Z [       OK ] ConcatOptTest.UseVariadicCatWithMultipleListUses (2 ms)
2023-01-11T22:09:55.8311890Z [ RUN      ] ConcatOptTest.UseVariadicCatWithListMutationAfterCat
2023-01-11T22:09:55.8337543Z [       OK ] ConcatOptTest.UseVariadicCatWithListMutationAfterCat (2 ms)
2023-01-11T22:09:55.8338143Z [ RUN      ] ConcatOptTest.UseVariadicCatWithListMutationBeforeCat
2023-01-11T22:09:55.8372590Z [       OK ] ConcatOptTest.UseVariadicCatWithListMutationBeforeCat (3 ms)
2023-01-11T22:09:55.8373212Z [ RUN      ] ConcatOptTest.UseVariadicCatWithMultipleListMutations
2023-01-11T22:09:55.8417140Z [       OK ] ConcatOptTest.UseVariadicCatWithMultipleListMutations (4 ms)
2023-01-11T22:09:55.8417848Z [ RUN      ] ConcatOptTest.RemoveListMutationUseVariadicCatAndCommonInputsElimination
2023-01-11T22:09:55.8460733Z [       OK ] ConcatOptTest.RemoveListMutationUseVariadicCatAndCommonInputsElimination (4 ms)
2023-01-11T22:09:55.8461382Z [----------] 13 tests from ConcatOptTest (53 ms total)
2023-01-11T22:09:55.8461600Z 
2023-01-11T22:09:55.8461816Z [----------] 1 test from OptimizeConcatTest
2023-01-11T22:09:55.8462292Z [ RUN      ] OptimizeConcatTest.UseVariadicCatReplaceMultiple
2023-01-11T22:09:55.8492713Z [       OK ] OptimizeConcatTest.UseVariadicCatReplaceMultiple (3 ms)
2023-01-11T22:09:55.8493247Z [----------] 1 test from OptimizeConcatTest (3 ms total)
2023-01-11T22:09:55.8493464Z 
2023-01-11T22:09:55.8493652Z [----------] 3 tests from ConcatOpt
2023-01-11T22:09:55.8494051Z [ RUN      ] ConcatOpt.CombineConcatsSimpleCase
2023-01-11T22:09:55.8494861Z [       OK ] ConcatOpt.CombineConcatsSimpleCase (0 ms)
2023-01-11T22:09:55.8495305Z [ RUN      ] ConcatOpt.CombineConcatsLongChain
2023-01-11T22:09:55.8497544Z [       OK ] ConcatOpt.CombineConcatsLongChain (0 ms)
2023-01-11T22:09:55.8498025Z [ RUN      ] ConcatOpt.CombineConcatsMutation
2023-01-11T22:09:55.8503082Z [       OK ] ConcatOpt.CombineConcatsMutation (0 ms)
2023-01-11T22:09:55.8504236Z [----------] 3 tests from ConcatOpt (0 ms total)
2023-01-11T22:09:55.8504427Z 
2023-01-11T22:09:55.8505439Z [----------] 4 tests from ConstantPoolingTest
2023-01-11T22:09:55.8505821Z [ RUN      ] ConstantPoolingTest.Int
2023-01-11T22:09:55.8506214Z [       OK ] ConstantPoolingTest.Int (0 ms)
2023-01-11T22:09:55.8507519Z [ RUN      ] ConstantPoolingTest.PoolingAcrossBlocks
2023-01-11T22:09:55.8508021Z [       OK ] ConstantPoolingTest.PoolingAcrossBlocks (0 ms)
2023-01-11T22:09:55.8508525Z [ RUN      ] ConstantPoolingTest.PoolingDifferentDevices
2023-01-11T22:09:55.8509042Z [       OK ] ConstantPoolingTest.PoolingDifferentDevices (0 ms)
2023-01-11T22:09:55.8509540Z [ RUN      ] ConstantPoolingTest.DictConstantPooling
2023-01-11T22:09:55.8510027Z [       OK ] ConstantPoolingTest.DictConstantPooling (0 ms)
2023-01-11T22:09:55.8510607Z [----------] 4 tests from ConstantPoolingTest (0 ms total)
2023-01-11T22:09:55.8510812Z 
2023-01-11T22:09:55.8511012Z [----------] 1 test from CleanupPassTest
2023-01-11T22:09:55.8511379Z [ RUN      ] CleanupPassTest.Basic
2023-01-11T22:09:55.8511753Z [       OK ] CleanupPassTest.Basic (0 ms)
2023-01-11T22:09:55.8512156Z [----------] 1 test from CleanupPassTest (0 ms total)
2023-01-11T22:09:55.8512358Z 
2023-01-11T22:09:55.8512602Z [----------] 1 test from CreateAutodiffSubgraphsTest
2023-01-11T22:09:55.8513040Z [ RUN      ] CreateAutodiffSubgraphsTest.Basic
2023-01-11T22:09:55.8514446Z [       OK ] CreateAutodiffSubgraphsTest.Basic (0 ms)
2023-01-11T22:09:55.8514932Z [----------] 1 test from CreateAutodiffSubgraphsTest (0 ms total)
2023-01-11T22:09:55.8515165Z 
2023-01-11T22:09:55.8515367Z [----------] 4 tests from CustomClassTest
2023-01-11T22:09:55.8515781Z [ RUN      ] CustomClassTest.TorchbindIValueAPI
2023-01-11T22:09:55.8516844Z [       OK ] CustomClassTest.TorchbindIValueAPI (0 ms)
2023-01-11T22:09:55.8517279Z [ RUN      ] CustomClassTest.ScalarTypeClass
2023-01-11T22:09:55.8520483Z [       OK ] CustomClassTest.ScalarTypeClass (0 ms)
2023-01-11T22:09:55.8520917Z [ RUN      ] CustomClassTest.TestDocString
2023-01-11T22:09:55.8521349Z [       OK ] CustomClassTest.TestDocString (0 ms)
2023-01-11T22:09:55.8521757Z [ RUN      ] CustomClassTest.Serialization
2023-01-11T22:09:55.8541694Z [       OK ] CustomClassTest.Serialization (2 ms)
2023-01-11T22:09:55.8542530Z [----------] 4 tests from CustomClassTest (3 ms total)
2023-01-11T22:09:55.8542808Z 
2023-01-11T22:09:55.8542985Z [----------] 5 tests from CustomOperatorTest
2023-01-11T22:09:55.8543309Z [ RUN      ] CustomOperatorTest.InferredSchema
2023-01-11T22:09:55.8545206Z [       OK ] CustomOperatorTest.InferredSchema (0 ms)
2023-01-11T22:09:55.8545851Z [ RUN      ] CustomOperatorTest.ExplicitSchema
2023-01-11T22:09:55.8546959Z [       OK ] CustomOperatorTest.ExplicitSchema (0 ms)
2023-01-11T22:09:55.8547567Z [ RUN      ] CustomOperatorTest.ListParameters
2023-01-11T22:09:55.8548549Z [       OK ] CustomOperatorTest.ListParameters (0 ms)
2023-01-11T22:09:55.8549156Z [ RUN      ] CustomOperatorTest.ListParameters2
2023-01-11T22:09:55.8550322Z [       OK ] CustomOperatorTest.ListParameters2 (0 ms)
2023-01-11T22:09:55.8550901Z [ RUN      ] CustomOperatorTest.Aliasing
2023-01-11T22:09:55.8552340Z [       OK ] CustomOperatorTest.Aliasing (0 ms)
2023-01-11T22:09:55.8552948Z [----------] 5 tests from CustomOperatorTest (1 ms total)
2023-01-11T22:09:55.8553262Z 
2023-01-11T22:09:55.8553533Z [----------] 2 tests from TestCustomOperator
2023-01-11T22:09:55.8554196Z [ RUN      ] TestCustomOperator.OperatorGeneratorUndeclared
2023-01-11T22:09:55.8554814Z [       OK ] TestCustomOperator.OperatorGeneratorUndeclared (0 ms)
2023-01-11T22:09:55.8555266Z [ RUN      ] TestCustomOperator.OperatorGeneratorBasic
2023-01-11T22:09:55.8555665Z [       OK ] TestCustomOperator.OperatorGeneratorBasic (0 ms)
2023-01-11T22:09:55.8556189Z [----------] 2 tests from TestCustomOperator (0 ms total)
2023-01-11T22:09:55.8556415Z 
2023-01-11T22:09:55.8556597Z [----------] 1 test from EliminateDeadCodeTest
2023-01-11T22:09:55.8556909Z [ RUN      ] EliminateDeadCodeTest.Basic
2023-01-11T22:09:55.8557214Z [       OK ] EliminateDeadCodeTest.Basic (0 ms)
2023-01-11T22:09:55.8557557Z [----------] 1 test from EliminateDeadCodeTest (0 ms total)
2023-01-11T22:09:55.8557727Z 
2023-01-11T22:09:55.8557866Z [----------] 2 tests from FuserTest
2023-01-11T22:09:55.8558144Z [ RUN      ] FuserTest.FusionAliasing
2023-01-11T22:09:55.8560410Z [       OK ] FuserTest.FusionAliasing (0 ms)
2023-01-11T22:09:55.8560829Z [ RUN      ] FuserTest.KernelCaching
2023-01-11T22:09:55.8563561Z [       OK ] FuserTest.KernelCaching (0 ms)
2023-01-11T22:09:55.8563877Z [----------] 2 tests from FuserTest (0 ms total)
2023-01-11T22:09:55.8564029Z 
2023-01-11T22:09:55.8564190Z [----------] 1 test from GraphExecutorTest
2023-01-11T22:09:55.8564506Z [ RUN      ] GraphExecutorTest.runAsync_executor
2023-01-11T22:09:55.8628392Z [       OK ] GraphExecutorTest.runAsync_executor (6 ms)
2023-01-11T22:09:55.8629294Z [----------] 1 test from GraphExecutorTest (6 ms total)
2023-01-11T22:09:55.8629741Z 
2023-01-11T22:09:55.8630006Z [----------] 5 tests from GraphIteratorTest
2023-01-11T22:09:55.8630682Z [ RUN      ] GraphIteratorTest.ConstantReturnGraph
2023-01-11T22:09:55.8631129Z [       OK ] GraphIteratorTest.ConstantReturnGraph (0 ms)
2023-01-11T22:09:55.8631629Z [ RUN      ] GraphIteratorTest.GraphWithParameters
2023-01-11T22:09:55.8632116Z [       OK ] GraphIteratorTest.GraphWithParameters (0 ms)
2023-01-11T22:09:55.8632710Z [ RUN      ] GraphIteratorTest.GraphWithIf
2023-01-11T22:09:55.8633297Z [       OK ] GraphIteratorTest.GraphWithIf (0 ms)
2023-01-11T22:09:55.8633924Z [ RUN      ] GraphIteratorTest.GraphWithNestedIf
2023-01-11T22:09:55.8634501Z [       OK ] GraphIteratorTest.GraphWithNestedIf (0 ms)
2023-01-11T22:09:55.8634834Z [ RUN      ] GraphIteratorTest.GraphWithLoop
2023-01-11T22:09:55.8635176Z [       OK ] GraphIteratorTest.GraphWithLoop (0 ms)
2023-01-11T22:09:55.8635525Z [----------] 5 tests from GraphIteratorTest (0 ms total)
2023-01-11T22:09:55.8635687Z 
2023-01-11T22:09:55.8635883Z [----------] 1 test from CSDebugInfoSerializaitionTest
2023-01-11T22:09:55.8636242Z [ RUN      ] CSDebugInfoSerializaitionTest.TwoSubmodules
2023-01-11T22:09:55.8637884Z [       OK ] CSDebugInfoSerializaitionTest.TwoSubmodules (0 ms)
2023-01-11T22:09:55.8638597Z [----------] 1 test from CSDebugInfoSerializaitionTest (0 ms total)
2023-01-11T22:09:55.8638793Z 
2023-01-11T22:09:55.8638944Z [----------] 1 test from InlinerTest
2023-01-11T22:09:55.8639256Z [ RUN      ] InlinerTest.Basic
2023-01-11T22:09:55.8640890Z [       OK ] InlinerTest.Basic (0 ms)
2023-01-11T22:09:55.8641461Z [----------] 1 test from InlinerTest (0 ms total)
2023-01-11T22:09:55.8641721Z 
2023-01-11T22:09:55.8641861Z [----------] 1 test from InterfaceTest
2023-01-11T22:09:55.8642200Z [ RUN      ] InterfaceTest.ModuleInterfaceSerialization
2023-01-11T22:09:55.8654700Z [       OK ] InterfaceTest.ModuleInterfaceSerialization (1 ms)
2023-01-11T22:09:55.8655359Z [----------] 1 test from InterfaceTest (1 ms total)
2023-01-11T22:09:55.8655538Z 
2023-01-11T22:09:55.8655776Z [----------] 4 tests from TypeCheckTest
2023-01-11T22:09:55.8656195Z [ RUN      ] TypeCheckTest.MatchingType
2023-01-11T22:09:55.8656871Z [       OK ] TypeCheckTest.MatchingType (0 ms)
2023-01-11T22:09:55.8657285Z [ RUN      ] TypeCheckTest.SizeMismatch
2023-01-11T22:09:55.8657725Z [       OK ] TypeCheckTest.SizeMismatch (0 ms)
2023-01-11T22:09:55.8658181Z [ RUN      ] TypeCheckTest.GradientMismatch
2023-01-11T22:09:55.8658642Z [       OK ] TypeCheckTest.GradientMismatch (0 ms)
2023-01-11T22:09:55.8658978Z [ RUN      ] TypeCheckTest.ScalarTypeMismatch
2023-01-11T22:09:55.8659380Z [       OK ] TypeCheckTest.ScalarTypeMismatch (0 ms)
2023-01-11T22:09:55.8659727Z [----------] 4 tests from TypeCheckTest (0 ms total)
2023-01-11T22:09:55.8659882Z 
2023-01-11T22:09:55.8660028Z [----------] 3 tests from InterpreterTest
2023-01-11T22:09:55.8660357Z [ RUN      ] InterpreterTest.IgnorableArgsInSchema
2023-01-11T22:09:55.8663936Z [       OK ] InterpreterTest.IgnorableArgsInSchema (0 ms)
2023-01-11T22:09:55.8664513Z [ RUN      ] InterpreterTest.IgnorableArgsInSchemaWithOut
2023-01-11T22:09:55.8664928Z [       OK ] InterpreterTest.IgnorableArgsInSchemaWithOut (0 ms)
2023-01-11T22:09:55.8665301Z [ RUN      ] InterpreterTest.runAsyncBasicTest
2023-01-11T22:09:55.8681458Z [       OK ] InterpreterTest.runAsyncBasicTest (1 ms)
2023-01-11T22:09:55.8682156Z [----------] 3 tests from InterpreterTest (2 ms total)
2023-01-11T22:09:55.8682343Z 
2023-01-11T22:09:55.8682596Z [----------] 1 test from EnableRethrowCaughtExceptionTest
2023-01-11T22:09:55.8683265Z [ RUN      ] EnableRethrowCaughtExceptionTest.EnableRethrowCaughtExceptionTestRethrowsCaughtException
2023-01-11T22:09:55.8894403Z [       OK ] EnableRethrowCaughtExceptionTest.EnableRethrowCaughtExceptionTestRethrowsCaughtException (21 ms)
2023-01-11T22:09:55.8895446Z [----------] 1 test from EnableRethrowCaughtExceptionTest (21 ms total)
2023-01-11T22:09:55.8895716Z 
2023-01-11T22:09:55.8895869Z [----------] 4 tests from IRTest
2023-01-11T22:09:55.8896180Z [ RUN      ] IRTest.Attributes
2023-01-11T22:09:55.8896559Z [       OK ] IRTest.Attributes (0 ms)
2023-01-11T22:09:55.8896848Z [ RUN      ] IRTest.Blocks
2023-01-11T22:09:55.8897197Z [       OK ] IRTest.Blocks (0 ms)
2023-01-11T22:09:55.8897613Z [ RUN      ] IRTest.CommonAncestor
2023-01-11T22:09:55.8897946Z [       OK ] IRTest.CommonAncestor (0 ms)
2023-01-11T22:09:55.8898354Z [ RUN      ] IRTest.OperatorMap
2023-01-11T22:09:55.8898763Z [       OK ] IRTest.OperatorMap (0 ms)
2023-01-11T22:09:55.8899178Z [----------] 4 tests from IRTest (0 ms total)
2023-01-11T22:09:55.8899366Z 
2023-01-11T22:09:55.8899578Z [----------] 21 tests from IRParserTest
2023-01-11T22:09:55.8899969Z [ RUN      ] IRParserTest.Basic
2023-01-11T22:09:55.8900370Z [       OK ] IRParserTest.Basic (0 ms)
2023-01-11T22:09:55.8900878Z [ RUN      ] IRParserTest.NestedBlock
2023-01-11T22:09:55.8901421Z [       OK ] IRParserTest.NestedBlock (0 ms)
2023-01-11T22:09:55.8901915Z [ RUN      ] IRParserTest.If
2023-01-11T22:09:55.8902563Z [       OK ] IRParserTest.If (0 ms)
2023-01-11T22:09:55.8902933Z [ RUN      ] IRParserTest.If2
2023-01-11T22:09:55.8903263Z [       OK ] IRParserTest.If2 (0 ms)
2023-01-11T22:09:55.8903625Z [ RUN      ] IRParserTest.InferredTypeIsTensor
2023-01-11T22:09:55.8904148Z [       OK ] IRParserTest.InferredTypeIsTensor (0 ms)
2023-01-11T22:09:55.8904631Z [ RUN      ] IRParserTest.ValueReuse
2023-01-11T22:09:55.8905093Z [       OK ] IRParserTest.ValueReuse (0 ms)
2023-01-11T22:09:55.8905452Z [ RUN      ] IRParserTest.Attributes
2023-01-11T22:09:55.8905875Z [       OK ] IRParserTest.Attributes (0 ms)
2023-01-11T22:09:55.8906212Z [ RUN      ] IRParserTest.OptionalTypes
2023-01-11T22:09:55.8906930Z [       OK ] IRParserTest.OptionalTypes (0 ms)
2023-01-11T22:09:55.8907321Z [ RUN      ] IRParserTest.StarTensor
2023-01-11T22:09:55.8907732Z [       OK ] IRParserTest.StarTensor (0 ms)
2023-01-11T22:09:55.8908139Z [ RUN      ] IRParserTest.UnshapedTensor
2023-01-11T22:09:55.8908654Z [       OK ] IRParserTest.UnshapedTensor (0 ms)
2023-01-11T22:09:55.8909179Z [ RUN      ] IRParserTest.ShapedTensor
2023-01-11T22:09:55.8909634Z [       OK ] IRParserTest.ShapedTensor (0 ms)
2023-01-11T22:09:55.8910001Z [ RUN      ] IRParserTest.NestedContrainer
2023-01-11T22:09:55.8910450Z [       OK ] IRParserTest.NestedContrainer (0 ms)
2023-01-11T22:09:55.8910809Z [ RUN      ] IRParserTest.MalformedShapeAnnotation
2023-01-11T22:09:55.8911333Z [       OK ] IRParserTest.MalformedShapeAnnotation (0 ms)
2023-01-11T22:09:55.8911685Z [ RUN      ] IRParserTest.FileCheck
2023-01-11T22:09:55.8912158Z [       OK ] IRParserTest.FileCheck (0 ms)
2023-01-11T22:09:55.8912455Z [ RUN      ] IRParserTest.Strides
2023-01-11T22:09:55.8912756Z [       OK ] IRParserTest.Strides (0 ms)
2023-01-11T22:09:55.8913091Z [ RUN      ] IRParserTest.MalformedStrides
2023-01-11T22:09:55.8913420Z [       OK ] IRParserTest.MalformedStrides (0 ms)
2023-01-11T22:09:55.8913780Z [ RUN      ] IRParserTest.TensorShapes
2023-01-11T22:09:55.8914092Z [       OK ] IRParserTest.TensorShapes (0 ms)
2023-01-11T22:09:55.8914508Z [ RUN      ] IRParserTest.DeviceAndRequiresGradTensors
2023-01-11T22:09:55.8914894Z [       OK ] IRParserTest.DeviceAndRequiresGradTensors (0 ms)
2023-01-11T22:09:55.8915300Z [ RUN      ] IRParserTest.ListConstant
2023-01-11T22:09:55.8915613Z [       OK ] IRParserTest.ListConstant (0 ms)
2023-01-11T22:09:55.8915996Z [ RUN      ] IRParserTest.PartialStarTensor
2023-01-11T22:09:55.8916333Z [       OK ] IRParserTest.PartialStarTensor (0 ms)
2023-01-11T22:09:55.8916735Z [ RUN      ] IRParserTest.ComplexTensorAttributes
2023-01-11T22:09:55.8917107Z [       OK ] IRParserTest.ComplexTensorAttributes (0 ms)
2023-01-11T22:09:55.8917504Z [----------] 21 tests from IRParserTest (1 ms total)
2023-01-11T22:09:55.8917661Z 
2023-01-11T22:09:55.8917814Z [----------] 2 tests from JitTypeTest
2023-01-11T22:09:55.8918094Z [ RUN      ] JitTypeTest.IsComplete
2023-01-11T22:09:55.8918439Z [       OK ] JitTypeTest.IsComplete (0 ms)
2023-01-11T22:09:55.8918770Z [ RUN      ] JitTypeTest.UnifyTypes
2023-01-11T22:09:55.8919186Z [       OK ] JitTypeTest.UnifyTypes (0 ms)
2023-01-11T22:09:55.8919545Z [----------] 2 tests from JitTypeTest (0 ms total)
2023-01-11T22:09:55.8919689Z 
2023-01-11T22:09:55.8919923Z [----------] 42 tests from LiteInterpreterTest
2023-01-11T22:09:55.8920269Z [ RUN      ] LiteInterpreterTest.UpsampleNearest2d
2023-01-11T22:09:55.8926780Z [       OK ] LiteInterpreterTest.UpsampleNearest2d (1 ms)
2023-01-11T22:09:55.8927239Z [ RUN      ] LiteInterpreterTest.CheckAttrAccess
2023-01-11T22:09:55.8928382Z [       OK ] LiteInterpreterTest.CheckAttrAccess (0 ms)
2023-01-11T22:09:55.8928782Z [ RUN      ] LiteInterpreterTest.MethodInvocation
2023-01-11T22:09:55.8955079Z [       OK ] LiteInterpreterTest.MethodInvocation (2 ms)
2023-01-11T22:09:55.8955475Z [ RUN      ] LiteInterpreterTest.Conv
2023-01-11T22:09:55.8981228Z [       OK ] LiteInterpreterTest.Conv (2 ms)
2023-01-11T22:09:55.8981618Z [ RUN      ] LiteInterpreterTest.Inline
2023-01-11T22:09:55.8991559Z [       OK ] LiteInterpreterTest.Inline (1 ms)
2023-01-11T22:09:55.8991925Z [ RUN      ] LiteInterpreterTest.Tuple
2023-01-11T22:09:55.8998620Z [       OK ] LiteInterpreterTest.Tuple (0 ms)
2023-01-11T22:09:55.8998994Z [ RUN      ] LiteInterpreterTest.AtenFormat
2023-01-11T22:09:55.9005188Z [       OK ] LiteInterpreterTest.AtenFormat (0 ms)
2023-01-11T22:09:55.9005584Z [ RUN      ] LiteInterpreterTest.PrimDevice
2023-01-11T22:09:55.9009519Z [       OK ] LiteInterpreterTest.PrimDevice (0 ms)
2023-01-11T22:09:55.9009905Z [ RUN      ] LiteInterpreterTest.Dict
2023-01-11T22:09:55.9017089Z [       OK ] LiteInterpreterTest.Dict (0 ms)
2023-01-11T22:09:55.9017649Z [ RUN      ] LiteInterpreterTest.List
2023-01-11T22:09:55.9025882Z [       OK ] LiteInterpreterTest.List (0 ms)
2023-01-11T22:09:55.9026486Z [ RUN      ] LiteInterpreterTest.PrimOverload
2023-01-11T22:09:55.9026893Z [       OK ] LiteInterpreterTest.PrimOverload (0 ms)
2023-01-11T22:09:55.9027341Z [ RUN      ] LiteInterpreterTest.Prim
2023-01-11T22:09:55.9030683Z [       OK ] LiteInterpreterTest.Prim (0 ms)
2023-01-11T22:09:55.9031124Z [ RUN      ] LiteInterpreterTest.PrimScalar
2023-01-11T22:09:55.9035713Z [       OK ] LiteInterpreterTest.PrimScalar (0 ms)
2023-01-11T22:09:55.9036051Z [ RUN      ] LiteInterpreterTest.LoadOrigJit
2023-01-11T22:09:55.9092853Z [       OK ] LiteInterpreterTest.LoadOrigJit (5 ms)
2023-01-11T22:09:55.9093213Z [ RUN      ] LiteInterpreterTest.WrongMethodName
2023-01-11T22:09:55.9113532Z [       OK ] LiteInterpreterTest.WrongMethodName (2 ms)
2023-01-11T22:09:55.9113876Z [ RUN      ] LiteInterpreterTest.SetState
2023-01-11T22:09:55.9139807Z [       OK ] LiteInterpreterTest.SetState (2 ms)
2023-01-11T22:09:55.9140406Z [ RUN      ] LiteInterpreterTest.BuiltinClass
2023-01-11T22:09:55.9148819Z [       OK ] LiteInterpreterTest.BuiltinClass (0 ms)
2023-01-11T22:09:55.9149440Z [ RUN      ] LiteInterpreterTest.BuiltinFunction
2023-01-11T22:09:55.9153151Z [       OK ] LiteInterpreterTest.BuiltinFunction (0 ms)
2023-01-11T22:09:55.9153646Z [ RUN      ] LiteInterpreterTest.GetRuntimeByteCodeVersion
2023-01-11T22:09:55.9154085Z [       OK ] LiteInterpreterTest.GetRuntimeByteCodeVersion (0 ms)
2023-01-11T22:09:55.9154583Z [ RUN      ] LiteInterpreterTest.GetRuntimeOperatorsVersion
2023-01-11T22:09:55.9155031Z [       OK ] LiteInterpreterTest.GetRuntimeOperatorsVersion (0 ms)
2023-01-11T22:09:55.9155458Z [ RUN      ] LiteInterpreterTest.GetByteCodeVersion
2023-01-11T22:09:55.9160281Z [       OK ] LiteInterpreterTest.GetByteCodeVersion (0 ms)
2023-01-11T22:09:55.9160907Z [ RUN      ] LiteInterpreterTest.GetContainTypes
2023-01-11T22:09:55.9163196Z [       OK ] LiteInterpreterTest.GetContainTypes (0 ms)
2023-01-11T22:09:55.9163925Z [ RUN      ] LiteInterpreterTest.BackPortByteCodeModelAllVersions
2023-01-11T22:09:56.0062785Z [       OK ] LiteInterpreterTest.BackPortByteCodeModelAllVersions (89 ms)
2023-01-11T22:09:56.0063495Z [ RUN      ] LiteInterpreterTest.GetRuntimeOpsAndInfo
2023-01-11T22:09:56.0131482Z [       OK ] LiteInterpreterTest.GetRuntimeOpsAndInfo (7 ms)
2023-01-11T22:09:56.0132034Z [ RUN      ] LiteInterpreterTest.isCompatibleSuccess
2023-01-11T22:09:56.0190952Z [       OK ] LiteInterpreterTest.isCompatibleSuccess (5 ms)
2023-01-11T22:09:56.0191685Z [ RUN      ] LiteInterpreterTest.isCompatibleFail
2023-01-11T22:09:56.0300923Z [       OK ] LiteInterpreterTest.isCompatibleFail (10 ms)
2023-01-11T22:09:56.0301298Z [ RUN      ] LiteInterpreterTest.Eval
2023-01-11T22:09:56.0312294Z [       OK ] LiteInterpreterTest.Eval (1 ms)
2023-01-11T22:09:56.0312657Z [ RUN      ] LiteInterpreterTest.FindWrongMethodName
2023-01-11T22:09:56.0316931Z [       OK ] LiteInterpreterTest.FindWrongMethodName (0 ms)
2023-01-11T22:09:56.0317310Z [ RUN      ] LiteInterpreterTest.FindAndRunMethod
2023-01-11T22:09:56.0325060Z [       OK ] LiteInterpreterTest.FindAndRunMethod (0 ms)
2023-01-11T22:09:56.0325698Z [ RUN      ] LiteInterpreterTest.RunMethodVariadic
2023-01-11T22:09:56.0332534Z [       OK ] LiteInterpreterTest.RunMethodVariadic (0 ms)
2023-01-11T22:09:56.0332922Z [ RUN      ] LiteInterpreterTest.DuplicateSetState
2023-01-11T22:09:56.0342954Z [       OK ] LiteInterpreterTest.DuplicateSetState (1 ms)
2023-01-11T22:09:56.0343400Z [ RUN      ] LiteInterpreterTest.ExtraFiles
2023-01-11T22:09:56.0348138Z [       OK ] LiteInterpreterTest.ExtraFiles (0 ms)
2023-01-11T22:09:56.0348529Z [ RUN      ] LiteInterpreterTest.OpNameExportFetchRootOperators
2023-01-11T22:09:56.0357370Z [       OK ] LiteInterpreterTest.OpNameExportFetchRootOperators (0 ms)
2023-01-11T22:09:56.0357801Z [ RUN      ] LiteInterpreterTest.DefaultArgsConv
2023-01-11T22:09:56.0372209Z [       OK ] LiteInterpreterTest.DefaultArgsConv (1 ms)
2023-01-11T22:09:56.0372884Z [ RUN      ] LiteInterpreterTest.DefaultArgsPinv
2023-01-11T22:09:56.0433599Z [       OK ] LiteInterpreterTest.DefaultArgsPinv (6 ms)
2023-01-11T22:09:56.0434049Z [ RUN      ] LiteInterpreterTest.DefaultArgsTensorinvSpecifyDefault
2023-01-11T22:09:56.0470151Z [       OK ] LiteInterpreterTest.DefaultArgsTensorinvSpecifyDefault (3 ms)
2023-01-11T22:09:56.0470592Z [ RUN      ] LiteInterpreterTest.DefaultArgsPinvWithOutArg
2023-01-11T22:09:56.0490362Z [       OK ] LiteInterpreterTest.DefaultArgsPinvWithOutArg (1 ms)
2023-01-11T22:09:56.0490773Z [ RUN      ] LiteInterpreterTest.DefaultArgsWithOutArg
2023-01-11T22:09:56.0498648Z [       OK ] LiteInterpreterTest.DefaultArgsWithOutArg (0 ms)
2023-01-11T22:09:56.0499162Z [ RUN      ] LiteInterpreterTest.TestExceptionStackWithTwoLevelModuleHierarchy
2023-01-11T22:09:56.0598877Z [       OK ] LiteInterpreterTest.TestExceptionStackWithTwoLevelModuleHierarchy (9 ms)
2023-01-11T22:09:56.0599486Z [ RUN      ] LiteInterpreterTest.OperatorCacheDifferentiatesDefaultArgs
2023-01-11T22:09:56.0634762Z [       OK ] LiteInterpreterTest.OperatorCacheDifferentiatesDefaultArgs (3 ms)
2023-01-11T22:09:56.0635238Z [ RUN      ] LiteInterpreterTest.OperatorSize1
2023-01-11T22:09:56.0639151Z [       OK ] LiteInterpreterTest.OperatorSize1 (0 ms)
2023-01-11T22:09:56.0639737Z [ RUN      ] LiteInterpreterTest.OperatorTest2
2023-01-11T22:09:56.0655708Z [       OK ] LiteInterpreterTest.OperatorTest2 (1 ms)
2023-01-11T22:09:56.0656346Z [----------] 42 tests from LiteInterpreterTest (174 ms total)
2023-01-11T22:09:56.0656523Z 
2023-01-11T22:09:56.0656678Z [----------] 3 tests from RunTimeTest
2023-01-11T22:09:56.0656958Z [ RUN      ] RunTimeTest.ParseBytecode
2023-01-11T22:09:56.0657274Z [       OK ] RunTimeTest.ParseBytecode (0 ms)
2023-01-11T22:09:56.0657581Z [ RUN      ] RunTimeTest.ParseOperator
2023-01-11T22:09:56.0657894Z [       OK ] RunTimeTest.ParseOperator (0 ms)
2023-01-11T22:09:56.0658188Z [ RUN      ] RunTimeTest.RuntimeCall
2023-01-11T22:09:56.0658485Z [       OK ] RunTimeTest.RuntimeCall (0 ms)
2023-01-11T22:09:56.0658787Z [----------] 3 tests from RunTimeTest (0 ms total)
2023-01-11T22:09:56.0658939Z 
2023-01-11T22:09:56.0659132Z [----------] 11 tests from LiteInterpreterUpgraderTest
2023-01-11T22:09:56.0659491Z [ RUN      ] LiteInterpreterUpgraderTest.DivTensorV2
2023-01-11T22:09:56.0664628Z [       OK ] LiteInterpreterUpgraderTest.DivTensorV2 (0 ms)
2023-01-11T22:09:56.0665012Z [ RUN      ] LiteInterpreterUpgraderTest.DivTensorOutV2
2023-01-11T22:09:56.0672176Z [       OK ] LiteInterpreterUpgraderTest.DivTensorOutV2 (0 ms)
2023-01-11T22:09:56.0672610Z [ RUN      ] LiteInterpreterUpgraderTest.DivTensorInplaceV2
2023-01-11T22:09:56.0678327Z [       OK ] LiteInterpreterUpgraderTest.DivTensorInplaceV2 (0 ms)
2023-01-11T22:09:56.0678947Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarFloatV2
2023-01-11T22:09:56.0689283Z [       OK ] LiteInterpreterUpgraderTest.DivScalarFloatV2 (1 ms)
2023-01-11T22:09:56.0689820Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarReciprocalFloatV2
2023-01-11T22:09:56.0694547Z expect output:  0.5000
2023-01-11T22:09:56.0694883Z [ CPUFloatType{1} ]actual output:  0.5000
2023-01-11T22:09:56.0695679Z [ CPUFloatType{1} ][       OK ] LiteInterpreterUpgraderTest.DivScalarReciprocalFloatV2 (0 ms)
2023-01-11T22:09:56.0696174Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarReciprocalIntV2
2023-01-11T22:09:56.0700579Z [       OK ] LiteInterpreterUpgraderTest.DivScalarReciprocalIntV2 (0 ms)
2023-01-11T22:09:56.0701212Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarScalarV2
2023-01-11T22:09:56.0707280Z [       OK ] LiteInterpreterUpgraderTest.DivScalarScalarV2 (0 ms)
2023-01-11T22:09:56.0708011Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarIntV2
2023-01-11T22:09:56.0712480Z [       OK ] LiteInterpreterUpgraderTest.DivScalarIntV2 (0 ms)
2023-01-11T22:09:56.0713144Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarInplaceFloatV2
2023-01-11T22:09:56.0720231Z [       OK ] LiteInterpreterUpgraderTest.DivScalarInplaceFloatV2 (0 ms)
2023-01-11T22:09:56.0720887Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarInplaceIntV2
2023-01-11T22:09:56.0728328Z [       OK ] LiteInterpreterUpgraderTest.DivScalarInplaceIntV2 (0 ms)
2023-01-11T22:09:56.0728964Z [ RUN      ] LiteInterpreterUpgraderTest.Upgrader
2023-01-11T22:09:56.0729350Z [       OK ] LiteInterpreterUpgraderTest.Upgrader (0 ms)
2023-01-11T22:09:56.0729743Z [----------] 11 tests from LiteInterpreterUpgraderTest (7 ms total)
2023-01-11T22:09:56.0729930Z 
2023-01-11T22:09:56.0730120Z [----------] 29 tests from LiteInterpreterDirectTest
2023-01-11T22:09:56.0730504Z [ RUN      ] LiteInterpreterDirectTest.UpsampleNearest2d
2023-01-11T22:09:56.0737806Z [       OK ] LiteInterpreterDirectTest.UpsampleNearest2d (0 ms)
2023-01-11T22:09:56.0738252Z [ RUN      ] LiteInterpreterDirectTest.CheckAttrAccess
2023-01-11T22:09:56.0738658Z [       OK ] LiteInterpreterDirectTest.CheckAttrAccess (0 ms)
2023-01-11T22:09:56.0739057Z [ RUN      ] LiteInterpreterDirectTest.MethodInvocation
2023-01-11T22:09:56.0743334Z hello 
2023-01-11T22:09:56.0743588Z hello 3
2023-01-11T22:09:56.0750666Z hello 
2023-01-11T22:09:56.0751471Z hello 3
2023-01-11T22:09:56.0756279Z hello 
2023-01-11T22:09:56.0756874Z hello 3
2023-01-11T22:09:56.0757516Z [       OK ] LiteInterpreterDirectTest.MethodInvocation (1 ms)
2023-01-11T22:09:56.0758137Z [ RUN      ] LiteInterpreterDirectTest.Conv
2023-01-11T22:09:56.0773148Z [       OK ] LiteInterpreterDirectTest.Conv (1 ms)
2023-01-11T22:09:56.0773930Z [ RUN      ] LiteInterpreterDirectTest.Inline
2023-01-11T22:09:56.0780472Z [       OK ] LiteInterpreterDirectTest.Inline (0 ms)
2023-01-11T22:09:56.0781083Z [ RUN      ] LiteInterpreterDirectTest.Tuple
2023-01-11T22:09:56.0785484Z [       OK ] LiteInterpreterDirectTest.Tuple (0 ms)
2023-01-11T22:09:56.0786076Z [ RUN      ] LiteInterpreterDirectTest.Dict
2023-01-11T22:09:56.0789978Z [       OK ] LiteInterpreterDirectTest.Dict (0 ms)
2023-01-11T22:09:56.0790572Z [ RUN      ] LiteInterpreterDirectTest.Prim
2023-01-11T22:09:56.0793602Z [       OK ] LiteInterpreterDirectTest.Prim (0 ms)
2023-01-11T22:09:56.0794237Z [ RUN      ] LiteInterpreterDirectTest.PrimScalar
2023-01-11T22:09:56.0797675Z [       OK ] LiteInterpreterDirectTest.PrimScalar (0 ms)
2023-01-11T22:09:56.0798341Z [ RUN      ] LiteInterpreterDirectTest.WrongMethodName
2023-01-11T22:09:56.0817104Z [       OK ] LiteInterpreterDirectTest.WrongMethodName (1 ms)
2023-01-11T22:09:56.0817995Z [ RUN      ] LiteInterpreterDirectTest.SetState
2023-01-11T22:09:56.0835435Z [       OK ] LiteInterpreterDirectTest.SetState (1 ms)
2023-01-11T22:09:56.0836075Z [ RUN      ] LiteInterpreterDirectTest.BuiltinFunction
2023-01-11T22:09:56.0837554Z [       OK ] LiteInterpreterDirectTest.BuiltinFunction (0 ms)
2023-01-11T22:09:56.0838139Z [ RUN      ] LiteInterpreterDirectTest.GetRuntimeByteCodeVersion
2023-01-11T22:09:56.0838738Z [       OK ] LiteInterpreterDirectTest.GetRuntimeByteCodeVersion (0 ms)
2023-01-11T22:09:56.0839413Z [ RUN      ] LiteInterpreterDirectTest.GetRuntimeOperatorsVersion
2023-01-11T22:09:56.0839987Z [       OK ] LiteInterpreterDirectTest.GetRuntimeOperatorsVersion (0 ms)
2023-01-11T22:09:56.0840552Z [ RUN      ] LiteInterpreterDirectTest.GetByteCodeVersion
2023-01-11T22:09:56.0841074Z [       OK ] LiteInterpreterDirectTest.GetByteCodeVersion (0 ms)
2023-01-11T22:09:56.0841757Z [ RUN      ] LiteInterpreterDirectTest.GetRuntimeOpsAndInfo
2023-01-11T22:09:56.0903795Z [       OK ] LiteInterpreterDirectTest.GetRuntimeOpsAndInfo (6 ms)
2023-01-11T22:09:56.0904442Z [ RUN      ] LiteInterpreterDirectTest.Eval
2023-01-11T22:09:56.0910556Z [       OK ] LiteInterpreterDirectTest.Eval (0 ms)
2023-01-11T22:09:56.0911216Z [ RUN      ] LiteInterpreterDirectTest.FindWrongMethodName
2023-01-11T22:09:56.0913088Z [       OK ] LiteInterpreterDirectTest.FindWrongMethodName (0 ms)
2023-01-11T22:09:56.0913770Z [ RUN      ] LiteInterpreterDirectTest.FindAndRunMethod
2023-01-11T22:09:56.0919427Z [       OK ] LiteInterpreterDirectTest.FindAndRunMethod (0 ms)
2023-01-11T22:09:56.0920148Z [ RUN      ] LiteInterpreterDirectTest.RunMethodVariadic
2023-01-11T22:09:56.0924978Z [       OK ] LiteInterpreterDirectTest.RunMethodVariadic (0 ms)
2023-01-11T22:09:56.0925712Z [ RUN      ] LiteInterpreterDirectTest.DuplicateSetState
2023-01-11T22:09:56.0931023Z [       OK ] LiteInterpreterDirectTest.DuplicateSetState (0 ms)
2023-01-11T22:09:56.0931872Z [ RUN      ] LiteInterpreterDirectTest.OpNameExportFetchRootOperators
2023-01-11T22:09:56.0937040Z [       OK ] LiteInterpreterDirectTest.OpNameExportFetchRootOperators (0 ms)
2023-01-11T22:09:56.0937815Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsConv
2023-01-11T22:09:56.0948241Z [       OK ] LiteInterpreterDirectTest.DefaultArgsConv (1 ms)
2023-01-11T22:09:56.0949184Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsPinv
2023-01-11T22:09:56.0985850Z [       OK ] LiteInterpreterDirectTest.DefaultArgsPinv (3 ms)
2023-01-11T22:09:56.0986948Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsTensorinvSpecifyDefault
2023-01-11T22:09:56.0994762Z [       OK ] LiteInterpreterDirectTest.DefaultArgsTensorinvSpecifyDefault (0 ms)
2023-01-11T22:09:56.0995771Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsPinvWithOutArg
2023-01-11T22:09:56.1015639Z [       OK ] LiteInterpreterDirectTest.DefaultArgsPinvWithOutArg (2 ms)
2023-01-11T22:09:56.1016687Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsWithOutArg
2023-01-11T22:09:56.1022283Z [       OK ] LiteInterpreterDirectTest.DefaultArgsWithOutArg (0 ms)
2023-01-11T22:09:56.1023395Z [ RUN      ] LiteInterpreterDirectTest.TestExceptionStackWithTwoLevelModuleHierarchy
2023-01-11T22:09:56.1124170Z [       OK ] LiteInterpreterDirectTest.TestExceptionStackWithTwoLevelModuleHierarchy (10 ms)
2023-01-11T22:09:56.1125198Z [ RUN      ] LiteInterpreterDirectTest.OperatorCacheDifferentiatesDefaultArgs
2023-01-11T22:09:56.1144787Z [       OK ] LiteInterpreterDirectTest.OperatorCacheDifferentiatesDefaultArgs (2 ms)
2023-01-11T22:09:56.1145556Z [----------] 29 tests from LiteInterpreterDirectTest (41 ms total)
2023-01-11T22:09:56.1145843Z 
2023-01-11T22:09:56.1146338Z [----------] 7 tests from LiteTrainerTest
2023-01-11T22:09:56.1146784Z [ RUN      ] LiteTrainerTest.Params
2023-01-11T22:09:56.1228516Z [       OK ] LiteTrainerTest.Params (8 ms)
2023-01-11T22:09:56.1229009Z [ RUN      ] LiteTrainerTest.SGD
2023-01-11T22:09:56.1300465Z [       OK ] LiteTrainerTest.SGD (7 ms)
2023-01-11T22:09:56.1301009Z [ RUN      ] LiteTrainerTest.SequentialSampler
2023-01-11T22:09:56.1301592Z [       OK ] LiteTrainerTest.SequentialSampler (0 ms)
2023-01-11T22:09:56.1302291Z [ RUN      ] LiteTrainerTest.RandomSamplerReturnsIndicesInCorrectRange
2023-01-11T22:09:56.1303471Z [       OK ] LiteTrainerTest.RandomSamplerReturnsIndicesInCorrectRange (0 ms)
2023-01-11T22:09:56.1304350Z [ RUN      ] LiteTrainerTest.RandomSamplerReturnsLessValuesForLastBatch
2023-01-11T22:09:56.1305181Z [       OK ] LiteTrainerTest.RandomSamplerReturnsLessValuesForLastBatch (0 ms)
2023-01-11T22:09:56.1306180Z [ RUN      ] LiteTrainerTest.RandomSamplerResetsWell
2023-01-11T22:09:56.1306840Z [       OK ] LiteTrainerTest.RandomSamplerResetsWell (0 ms)
2023-01-11T22:09:56.1307495Z [ RUN      ] LiteTrainerTest.RandomSamplerResetsWithNewSizeWell
2023-01-11T22:09:56.1308252Z [       OK ] LiteTrainerTest.RandomSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:09:56.1308914Z [----------] 7 tests from LiteTrainerTest (15 ms total)
2023-01-11T22:09:56.1309150Z 
2023-01-11T22:09:56.1309378Z [----------] 6 tests from MobileTest
2023-01-11T22:09:56.1309910Z [ RUN      ] MobileTest.SaveLoadParametersEmpty
2023-01-11T22:09:56.1310492Z [       OK ] MobileTest.SaveLoadParametersEmpty (0 ms)
2023-01-11T22:09:56.1311110Z [ RUN      ] MobileTest.SaveParametersDefaultsToZip
2023-01-11T22:09:56.1311771Z [       OK ] MobileTest.SaveParametersDefaultsToZip (0 ms)
2023-01-11T22:09:56.1312454Z [ RUN      ] MobileTest.SaveParametersCanUseFlatbuffer
2023-01-11T22:09:56.1313157Z [       OK ] MobileTest.SaveParametersCanUseFlatbuffer (0 ms)
2023-01-11T22:09:56.1313861Z [ RUN      ] MobileTest.SaveLoadParametersUsingFlatbuffers
2023-01-11T22:09:56.1314621Z [       OK ] MobileTest.SaveLoadParametersUsingFlatbuffers (0 ms)
2023-01-11T22:09:56.1315407Z [ RUN      ] MobileTest.LoadParametersUnexpectedFormatShouldThrow
2023-01-11T22:09:56.1335710Z [       OK ] MobileTest.LoadParametersUnexpectedFormatShouldThrow (2 ms)
2023-01-11T22:09:56.1336524Z [ RUN      ] MobileTest.LoadParametersEmptyDataShouldThrow
2023-01-11T22:09:56.1357782Z [       OK ] MobileTest.LoadParametersEmptyDataShouldThrow (2 ms)
2023-01-11T22:09:56.1358188Z [----------] 6 tests from MobileTest (5 ms total)
2023-01-11T22:09:56.1358343Z 
2023-01-11T22:09:56.1358498Z [----------] 1 test from MemoryDAGTest
2023-01-11T22:09:56.1358757Z [ RUN      ] MemoryDAGTest.Basic
2023-01-11T22:09:56.1359051Z [       OK ] MemoryDAGTest.Basic (0 ms)
2023-01-11T22:09:56.1359424Z [----------] 1 test from MemoryDAGTest (0 ms total)
2023-01-11T22:09:56.1359581Z 
2023-01-11T22:09:56.1359735Z [----------] 1 test from InternedStringsTest
2023-01-11T22:09:56.1360037Z [ RUN      ] InternedStringsTest.Basic
2023-01-11T22:09:56.1370867Z [       OK ] InternedStringsTest.Basic (1 ms)
2023-01-11T22:09:56.1371316Z [----------] 1 test from InternedStringsTest (1 ms total)
2023-01-11T22:09:56.1371473Z 
2023-01-11T22:09:56.1371636Z [----------] 1 test from FromQualStringTest
2023-01-11T22:09:56.1371929Z [ RUN      ] FromQualStringTest.Basic
2023-01-11T22:09:56.1380333Z [       OK ] FromQualStringTest.Basic (0 ms)
2023-01-11T22:09:56.1380857Z [----------] 1 test from FromQualStringTest (0 ms total)
2023-01-11T22:09:56.1381101Z 
2023-01-11T22:09:56.1381327Z [----------] 1 test from THNNConvTest
2023-01-11T22:09:56.1382117Z [ RUN      ] THNNConvTest.Basic
2023-01-11T22:09:56.1395371Z [       OK ] THNNConvTest.Basic (1 ms)
2023-01-11T22:09:56.1395981Z [----------] 1 test from THNNConvTest (1 ms total)
2023-01-11T22:09:56.1396306Z 
2023-01-11T22:09:56.1396712Z [----------] 1 test from ATenNativeBatchNormTest
2023-01-11T22:09:56.1397374Z [ RUN      ] ATenNativeBatchNormTest.Basic
2023-01-11T22:09:56.1407460Z [       OK ] ATenNativeBatchNormTest.Basic (1 ms)
2023-01-11T22:09:56.1408112Z [----------] 1 test from ATenNativeBatchNormTest (1 ms total)
2023-01-11T22:09:56.1408408Z 
2023-01-11T22:09:56.1408686Z [----------] 2 tests from CustomFusionTest
2023-01-11T22:09:56.1409196Z [ RUN      ] CustomFusionTest.Basic
2023-01-11T22:09:56.1409677Z [       OK ] CustomFusionTest.Basic (0 ms)
2023-01-11T22:09:56.1410215Z [ RUN      ] CustomFusionTest.NestedBlocks
2023-01-11T22:09:56.1411161Z [       OK ] CustomFusionTest.NestedBlocks (0 ms)
2023-01-11T22:09:56.1411558Z [----------] 2 tests from CustomFusionTest (0 ms total)
2023-01-11T22:09:56.1411728Z 
2023-01-11T22:09:56.1411887Z [----------] 1 test from ControlFlowTest
2023-01-11T22:09:56.1412161Z [ RUN      ] ControlFlowTest.Basic
2023-01-11T22:09:56.1418841Z [       OK ] ControlFlowTest.Basic (0 ms)
2023-01-11T22:09:56.1419701Z [----------] 1 test from ControlFlowTest (0 ms total)
2023-01-11T22:09:56.1419997Z 
2023-01-11T22:09:56.1420186Z [----------] 1 test from ProtoTest
2023-01-11T22:09:56.1420434Z [ RUN      ] ProtoTest.Basic
2023-01-11T22:09:56.1420759Z [       OK ] ProtoTest.Basic (0 ms)
2023-01-11T22:09:56.1421230Z [----------] 1 test from ProtoTest (0 ms total)
2023-01-11T22:09:56.1421496Z 
2023-01-11T22:09:56.1421773Z [----------] 9 tests from SchemaParserTest
2023-01-11T22:09:56.1422491Z [ RUN      ] SchemaParserTest.NestedArrays
2023-01-11T22:09:56.1422917Z [       OK ] SchemaParserTest.NestedArrays (0 ms)
2023-01-11T22:09:56.1423237Z [ RUN      ] SchemaParserTest.OutVariant
2023-01-11T22:09:56.1423545Z [       OK ] SchemaParserTest.OutVariant (0 ms)
2023-01-11T22:09:56.1423866Z [ RUN      ] SchemaParserTest.NamedReturns
2023-01-11T22:09:56.1424269Z [       OK ] SchemaParserTest.NamedReturns (0 ms)
2023-01-11T22:09:56.1424569Z [ RUN      ] SchemaParserTest.Futures
2023-01-11T22:09:56.1424873Z [       OK ] SchemaParserTest.Futures (0 ms)
2023-01-11T22:09:56.1425336Z [ RUN      ] SchemaParserTest.AnnotatedAliasSets
2023-01-11T22:09:56.1425687Z [       OK ] SchemaParserTest.AnnotatedAliasSets (0 ms)
2023-01-11T22:09:56.1437623Z [ RUN      ] SchemaParserTest.TensorListAnnotatedAliasSets
2023-01-11T22:09:56.1438241Z [       OK ] SchemaParserTest.TensorListAnnotatedAliasSets (0 ms)
2023-01-11T22:09:56.1438686Z [ RUN      ] SchemaParserTest.AnnotatedAliasWithoutBeforeSet
2023-01-11T22:09:56.1439215Z [       OK ] SchemaParserTest.AnnotatedAliasWithoutBeforeSet (0 ms)
2023-01-11T22:09:56.1439609Z [ RUN      ] SchemaParserTest.BeforeAfterSets
2023-01-11T22:09:56.1439944Z [       OK ] SchemaParserTest.BeforeAfterSets (0 ms)
2023-01-11T22:09:56.1440288Z [ RUN      ] SchemaParserTest.BeforeAfterSets2
2023-01-11T22:09:56.1440638Z [       OK ] SchemaParserTest.BeforeAfterSets2 (0 ms)
2023-01-11T22:09:56.1440983Z [----------] 9 tests from SchemaParserTest (0 ms total)
2023-01-11T22:09:56.1441145Z 
2023-01-11T22:09:56.1441318Z [----------] 2 tests from TopologicalIndexTest
2023-01-11T22:09:56.1441622Z [ RUN      ] TopologicalIndexTest.Basic
2023-01-11T22:09:56.1441936Z [       OK ] TopologicalIndexTest.Basic (0 ms)
2023-01-11T22:09:56.1442235Z [ RUN      ] TopologicalIndexTest.Reindex
2023-01-11T22:09:56.1442557Z [       OK ] TopologicalIndexTest.Reindex (0 ms)
2023-01-11T22:09:56.1443031Z [----------] 2 tests from TopologicalIndexTest (0 ms total)
2023-01-11T22:09:56.1443203Z 
2023-01-11T22:09:56.1443355Z [----------] 7 tests from RecordFunctionTest
2023-01-11T22:09:56.1443705Z [ RUN      ] RecordFunctionTest.TracedTestInputsOutputs
2023-01-11T22:09:56.1444108Z [       OK ] RecordFunctionTest.TracedTestInputsOutputs (0 ms)
2023-01-11T22:09:56.1444470Z [ RUN      ] RecordFunctionTest.SampledCallbacks
2023-01-11T22:09:56.1489635Z [       OK ] RecordFunctionTest.SampledCallbacks (5 ms)
2023-01-11T22:09:56.1490221Z [ RUN      ] RecordFunctionTest.RecordFunctionGuard
2023-01-11T22:09:56.1490617Z [       OK ] RecordFunctionTest.RecordFunctionGuard (0 ms)
2023-01-11T22:09:56.1490947Z [ RUN      ] RecordFunctionTest.Callbacks
2023-01-11T22:09:56.1492362Z [       OK ] RecordFunctionTest.Callbacks (0 ms)
2023-01-11T22:09:56.1492989Z [ RUN      ] RecordFunctionTest.ShouldRun
2023-01-11T22:09:56.1493413Z [       OK ] RecordFunctionTest.ShouldRun (0 ms)
2023-01-11T22:09:56.1493743Z [ RUN      ] RecordFunctionTest.Basic
2023-01-11T22:09:56.1494723Z [       OK ] RecordFunctionTest.Basic (0 ms)
2023-01-11T22:09:56.1495235Z [ RUN      ] RecordFunctionTest.OperatorNameOverload
2023-01-11T22:09:56.1495906Z [       OK ] RecordFunctionTest.OperatorNameOverload (0 ms)
2023-01-11T22:09:56.1496356Z [----------] 7 tests from RecordFunctionTest (6 ms total)
2023-01-11T22:09:56.1496524Z 
2023-01-11T22:09:56.1496711Z [----------] 1 test from ThreadLocalDebugInfoTest
2023-01-11T22:09:56.1497210Z [ RUN      ] ThreadLocalDebugInfoTest.Basic
2023-01-11T22:09:56.1497789Z [       OK ] ThreadLocalDebugInfoTest.Basic (0 ms)
2023-01-11T22:09:56.1498283Z [----------] 1 test from ThreadLocalDebugInfoTest (0 ms total)
2023-01-11T22:09:56.1498488Z 
2023-01-11T22:09:56.1498786Z [----------] 1 test from TestSymIntArrayRef
2023-01-11T22:09:56.1499352Z [ RUN      ] TestSymIntArrayRef.BasicConversion
2023-01-11T22:09:56.1499718Z [       OK ] TestSymIntArrayRef.BasicConversion (0 ms)
2023-01-11T22:09:56.1500251Z [----------] 1 test from TestSymIntArrayRef (0 ms total)
2023-01-11T22:09:56.1500551Z 
2023-01-11T22:09:56.1500778Z [----------] 4 tests from TestSymInt
2023-01-11T22:09:56.1501348Z [ RUN      ] TestSymInt.NarrowCopyWithSymbolicInt
2023-01-11T22:09:56.1501764Z [       OK ] TestSymInt.NarrowCopyWithSymbolicInt (0 ms)
2023-01-11T22:09:56.1502079Z [ RUN      ] TestSymInt.NarrowCopy
2023-01-11T22:09:56.1502525Z [       OK ] TestSymInt.NarrowCopy (0 ms)
2023-01-11T22:09:56.1502828Z [ RUN      ] TestSymInt.AddSymbolicInt
2023-01-11T22:09:56.1503135Z [       OK ] TestSymInt.AddSymbolicInt (0 ms)
2023-01-11T22:09:56.1503473Z [ RUN      ] TestSymInt.TestSymIntToSymNodeDispatch
2023-01-11T22:09:56.1503844Z [       OK ] TestSymInt.TestSymIntToSymNodeDispatch (0 ms)
2023-01-11T22:09:56.1504195Z [----------] 4 tests from TestSymInt (0 ms total)
2023-01-11T22:09:56.1504345Z 
2023-01-11T22:09:56.1504506Z [----------] 1 test from FallbackGraphsTest
2023-01-11T22:09:56.1504789Z [ RUN      ] FallbackGraphsTest.Basic
2023-01-11T22:09:56.1507696Z [       OK ] FallbackGraphsTest.Basic (0 ms)
2023-01-11T22:09:56.1508233Z [----------] 1 test from FallbackGraphsTest (0 ms total)
2023-01-11T22:09:56.1508480Z 
2023-01-11T22:09:56.1508739Z [----------] 1 test from NoneSchemaMatchTest
2023-01-11T22:09:56.1509208Z [ RUN      ] NoneSchemaMatchTest.Basic
2023-01-11T22:09:56.1509769Z [       OK ] NoneSchemaMatchTest.Basic (0 ms)
2023-01-11T22:09:56.1510304Z [----------] 1 test from NoneSchemaMatchTest (0 ms total)
2023-01-11T22:09:56.1510471Z 
2023-01-11T22:09:56.1510628Z [----------] 1 test from PassManagementTest
2023-01-11T22:09:56.1511040Z [ RUN      ] PassManagementTest.Basic
2023-01-11T22:09:56.1511344Z [       OK ] PassManagementTest.Basic (0 ms)
2023-01-11T22:09:56.1511678Z [----------] 1 test from PassManagementTest (0 ms total)
2023-01-11T22:09:56.1511826Z 
2023-01-11T22:09:56.1511981Z [----------] 5 tests from LoopPeelerTest
2023-01-11T22:09:56.1512311Z [ RUN      ] LoopPeelerTest.NoInductionVariableUse
2023-01-11T22:09:56.1513552Z [       OK ] LoopPeelerTest.NoInductionVariableUse (0 ms)
2023-01-11T22:09:56.1514146Z [ RUN      ] LoopPeelerTest.YesInductionVariableUse
2023-01-11T22:09:56.1516718Z [       OK ] LoopPeelerTest.YesInductionVariableUse (0 ms)
2023-01-11T22:09:56.1517378Z [ RUN      ] LoopPeelerTest.LoopWithTerminationCondition
2023-01-11T22:09:56.1521280Z [       OK ] LoopPeelerTest.LoopWithTerminationCondition (0 ms)
2023-01-11T22:09:56.1521938Z [ RUN      ] LoopPeelerTest.SimpleNestedLoops
2023-01-11T22:09:56.1527165Z [       OK ] LoopPeelerTest.SimpleNestedLoops (0 ms)
2023-01-11T22:09:56.1527729Z [ RUN      ] LoopPeelerTest.SimpleNestedLoops2
2023-01-11T22:09:56.1535076Z [       OK ] LoopPeelerTest.SimpleNestedLoops2 (0 ms)
2023-01-11T22:09:56.1535631Z [----------] 5 tests from LoopPeelerTest (2 ms total)
2023-01-11T22:09:56.1535791Z 
2023-01-11T22:09:56.1535938Z [----------] 1 test from JitTracing
2023-01-11T22:09:56.1536196Z [ RUN      ] JitTracing.Basic
2023-01-11T22:09:56.1805801Z [       OK ] JitTracing.Basic (26 ms)
2023-01-11T22:09:56.1806386Z [----------] 1 test from JitTracing (26 ms total)
2023-01-11T22:09:56.1806545Z 
2023-01-11T22:09:56.1806771Z [----------] 1 test from InsertAndEliminateRedundantGuardsTest
2023-01-11T22:09:56.1807176Z [ RUN      ] InsertAndEliminateRedundantGuardsTest.Basic
2023-01-11T22:09:56.1811372Z [       OK ] InsertAndEliminateRedundantGuardsTest.Basic (0 ms)
2023-01-11T22:09:56.1812101Z [----------] 1 test from InsertAndEliminateRedundantGuardsTest (0 ms total)
2023-01-11T22:09:56.1812315Z 
2023-01-11T22:09:56.1812479Z [----------] 1 test from InsertBailOutsTest
2023-01-11T22:09:56.1812774Z [ RUN      ] InsertBailOutsTest.Basic
2023-01-11T22:09:56.1821054Z [       OK ] InsertBailOutsTest.Basic (0 ms)
2023-01-11T22:09:56.1821414Z [----------] 1 test from InsertBailOutsTest (0 ms total)
2023-01-11T22:09:56.1821583Z 
2023-01-11T22:09:56.1821740Z [----------] 2 tests from ProfilerTest
2023-01-11T22:09:56.1822004Z [ RUN      ] ProfilerTest.Basic
2023-01-11T22:09:56.2065205Z [       OK ] ProfilerTest.Basic (24 ms)
2023-01-11T22:09:56.2065705Z [ RUN      ] ProfilerTest.OptionalProfiling
2023-01-11T22:09:56.2066165Z [       OK ] ProfilerTest.OptionalProfiling (0 ms)
2023-01-11T22:09:56.2066508Z [----------] 2 tests from ProfilerTest (24 ms total)
2023-01-11T22:09:56.2066679Z 
2023-01-11T22:09:56.2066886Z [----------] 2 tests from CallStackTest
2023-01-11T22:09:56.2067158Z [ RUN      ] CallStackTest.Basic
2023-01-11T22:09:56.2071302Z [       OK ] CallStackTest.Basic (0 ms)
2023-01-11T22:09:56.2071665Z [ RUN      ] CallStackTest.Caching
2023-01-11T22:09:56.2075077Z [       OK ] CallStackTest.Caching (0 ms)
2023-01-11T22:09:56.2075510Z [----------] 2 tests from CallStackTest (0 ms total)
2023-01-11T22:09:56.2075669Z 
2023-01-11T22:09:56.2075829Z [----------] 2 tests from InlinedCallStackTest
2023-01-11T22:09:56.2076160Z [ RUN      ] InlinedCallStackTest.BlockAnnotation
2023-01-11T22:09:56.2082221Z [       OK ] InlinedCallStackTest.BlockAnnotation (0 ms)
2023-01-11T22:09:56.2082727Z [ RUN      ] InlinedCallStackTest.SelfCallMethods
2023-01-11T22:09:56.2090424Z [       OK ] InlinedCallStackTest.SelfCallMethods (0 ms)
2023-01-11T22:09:56.2091310Z [----------] 2 tests from InlinedCallStackTest (1 ms total)
2023-01-11T22:09:56.2091640Z 
2023-01-11T22:09:56.2091943Z [----------] 1 test from AutogradSymbolsTest
2023-01-11T22:09:56.2092458Z [ RUN      ] AutogradSymbolsTest.Basic
2023-01-11T22:09:56.2092790Z [       OK ] AutogradSymbolsTest.Basic (0 ms)
2023-01-11T22:09:56.2093368Z [----------] 1 test from AutogradSymbolsTest (0 ms total)
2023-01-11T22:09:56.2093672Z 
2023-01-11T22:09:56.2093947Z [----------] 1 test from DefaultArgTypeHintingTest
2023-01-11T22:09:56.2094267Z [ RUN      ] DefaultArgTypeHintingTest.Basic
2023-01-11T22:09:56.2094612Z [       OK ] DefaultArgTypeHintingTest.Basic (0 ms)
2023-01-11T22:09:56.2094978Z [----------] 1 test from DefaultArgTypeHintingTest (0 ms total)
2023-01-11T22:09:56.2095157Z 
2023-01-11T22:09:56.2095290Z [----------] 5 tests from FuturesTest
2023-01-11T22:09:56.2095623Z [ RUN      ] FuturesTest.Basic
2023-01-11T22:09:56.2095900Z [       OK ] FuturesTest.Basic (0 ms)
2023-01-11T22:09:56.2096165Z [ RUN      ] FuturesTest.Error
2023-01-11T22:09:56.2106131Z [       OK ] FuturesTest.Error (1 ms)
2023-01-11T22:09:56.2106614Z [ RUN      ] FuturesTest.Then
2023-01-11T22:09:56.2107112Z [       OK ] FuturesTest.Then (0 ms)
2023-01-11T22:09:56.2107433Z [ RUN      ] FuturesTest.CollectAll
2023-01-11T22:09:56.2107726Z [       OK ] FuturesTest.CollectAll (0 ms)
2023-01-11T22:09:56.2108082Z [ RUN      ] FuturesTest.CollectAny
2023-01-11T22:09:56.2108493Z [       OK ] FuturesTest.CollectAny (0 ms)
2023-01-11T22:09:56.2108973Z [----------] 5 tests from FuturesTest (1 ms total)
2023-01-11T22:09:56.2109218Z 
2023-01-11T22:09:56.2109499Z [----------] 1 test from TLSFutureCallbacksTest
2023-01-11T22:09:56.2109822Z [ RUN      ] TLSFutureCallbacksTest.Basic
2023-01-11T22:09:56.2110145Z [       OK ] TLSFutureCallbacksTest.Basic (0 ms)
2023-01-11T22:09:56.2110542Z [----------] 1 test from TLSFutureCallbacksTest (0 ms total)
2023-01-11T22:09:56.2110776Z 
2023-01-11T22:09:56.2110978Z [----------] 1 test from ProfilerDisableInCallbackTest
2023-01-11T22:09:56.2111391Z [ RUN      ] ProfilerDisableInCallbackTest.Basic
2023-01-11T22:09:56.2112014Z [       OK ] ProfilerDisableInCallbackTest.Basic (0 ms)
2023-01-11T22:09:56.2112525Z [----------] 1 test from ProfilerDisableInCallbackTest (0 ms total)
2023-01-11T22:09:56.2112749Z 
2023-01-11T22:09:56.2112979Z [----------] 2 tests from RecordDebugHandles
2023-01-11T22:09:56.2113262Z [ RUN      ] RecordDebugHandles.Basic
2023-01-11T22:09:56.2113785Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:56.2115637Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:56.2116646Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:56.2117308Z [       OK ] RecordDebugHandles.Basic (0 ms)
2023-01-11T22:09:56.2117882Z [ RUN      ] RecordDebugHandles.ScopedCallbacks
2023-01-11T22:09:56.2118602Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:56.2123348Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:56.2123935Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:56.2124653Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:56.2127986Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:56.2128581Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:56.2129158Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:56.2134622Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:56.2135085Z STAGE:2023-01-11 22:09:56 24563:24563 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:56.2135490Z [       OK ] RecordDebugHandles.ScopedCallbacks (1 ms)
2023-01-11T22:09:56.2135853Z [----------] 2 tests from RecordDebugHandles (2 ms total)
2023-01-11T22:09:56.2136006Z 
2023-01-11T22:09:56.2136163Z [----------] 1 test from IValueKWargsTest
2023-01-11T22:09:56.2136446Z [ RUN      ] IValueKWargsTest.Basic
2023-01-11T22:09:56.2140048Z [       OK ] IValueKWargsTest.Basic (0 ms)
2023-01-11T22:09:56.2140577Z [----------] 1 test from IValueKWargsTest (0 ms total)
2023-01-11T22:09:56.2140972Z 
2023-01-11T22:09:56.2141259Z [----------] 1 test from ComputeFlopsTest
2023-01-11T22:09:56.2141787Z [ RUN      ] ComputeFlopsTest.Basic
2023-01-11T22:09:56.2142305Z [W util.cpp:501] Warning: Failed to compute flops for op aten::conv2d because both input and weight must be size 4. (function computeFlops)
2023-01-11T22:09:56.2143094Z [W util.cpp:516] Warning: Failed to compute flops for op aten::conv2d because stride must be size 2 and cannot be 0. (function computeFlops)
2023-01-11T22:09:56.2143817Z [W util.cpp:472] Warning: Calculating flops for aten::conv2d requires groups, padding, stride, dilation, input_size, and weight_size in saved arguments. (function computeFlops)
2023-01-11T22:09:56.2144672Z [W util.cpp:545] Warning: Calculating flops for aten::mm requires mat1_size and mat2_size in saved arguments. (function computeFlops)
2023-01-11T22:09:56.2145235Z [       OK ] ComputeFlopsTest.Basic (0 ms)
2023-01-11T22:09:56.2145559Z [----------] 1 test from ComputeFlopsTest (0 ms total)
2023-01-11T22:09:56.2145720Z 
2023-01-11T22:09:56.2145867Z [----------] 1 test from TestConstant
2023-01-11T22:09:56.2146147Z [ RUN      ] TestConstant.TensorGrad
2023-01-11T22:09:56.2146458Z [       OK ] TestConstant.TensorGrad (0 ms)
2023-01-11T22:09:56.2146760Z [----------] 1 test from TestConstant (0 ms total)
2023-01-11T22:09:56.2146907Z 
2023-01-11T22:09:56.2147051Z [----------] 1 test from TestMutation
2023-01-11T22:09:56.2147312Z [ RUN      ] TestMutation.Basic
2023-01-11T22:09:56.2147573Z [       OK ] TestMutation.Basic (0 ms)
2023-01-11T22:09:56.2147874Z [----------] 1 test from TestMutation (0 ms total)
2023-01-11T22:09:56.2148022Z 
2023-01-11T22:09:56.2148225Z [----------] 1 test from TestInplaceToFunctionalActivation
2023-01-11T22:09:56.2148599Z [ RUN      ] TestInplaceToFunctionalActivation.Basic
2023-01-11T22:09:56.2148971Z [       OK ] TestInplaceToFunctionalActivation.Basic (0 ms)
2023-01-11T22:09:56.2149381Z [----------] 1 test from TestInplaceToFunctionalActivation (0 ms total)
2023-01-11T22:09:56.2149575Z 
2023-01-11T22:09:56.2149740Z [----------] 1 test from TestRegisterShapeOp
2023-01-11T22:09:56.2150029Z [ RUN      ] TestRegisterShapeOp.Basic
2023-01-11T22:09:56.3342773Z [       OK ] TestRegisterShapeOp.Basic (119 ms)
2023-01-11T22:09:56.3343267Z [----------] 1 test from TestRegisterShapeOp (119 ms total)
2023-01-11T22:09:56.3343442Z 
2023-01-11T22:09:56.3343663Z [----------] 1 test from TestFunctionalToInplaceActivation
2023-01-11T22:09:56.3344026Z [ RUN      ] TestFunctionalToInplaceActivation.Basic
2023-01-11T22:09:56.3344410Z [       OK ] TestFunctionalToInplaceActivation.Basic (0 ms)
2023-01-11T22:09:56.3344820Z [----------] 1 test from TestFunctionalToInplaceActivation (0 ms total)
2023-01-11T22:09:56.3345012Z 
2023-01-11T22:09:56.3345429Z [----------] 2 tests from TestFunctionExecutor
2023-01-11T22:09:56.3345778Z [ RUN      ] TestFunctionExecutor.SimpleExecutorTest
2023-01-11T22:09:56.3346600Z [       OK ] TestFunctionExecutor.SimpleExecutorTest (0 ms)
2023-01-11T22:09:56.3347013Z [ RUN      ] TestFunctionExecutor.RunDecompositionTest
2023-01-11T22:09:56.3363288Z [       OK ] TestFunctionExecutor.RunDecompositionTest (1 ms)
2023-01-11T22:09:56.3363708Z [----------] 2 tests from TestFunctionExecutor (2 ms total)
2023-01-11T22:09:56.3363879Z 
2023-01-11T22:09:56.3364079Z [----------] 1 test from TestShapeGraphLinting
2023-01-11T22:09:56.3364416Z [ RUN      ] TestShapeGraphLinting.Basic
2023-01-11T22:09:56.3369090Z [       OK ] TestShapeGraphLinting.Basic (0 ms)
2023-01-11T22:09:56.3369507Z [----------] 1 test from TestShapeGraphLinting (0 ms total)
2023-01-11T22:09:56.3369687Z 
2023-01-11T22:09:56.3369829Z [----------] 1 test from Composed
2023-01-11T22:09:56.3370210Z [ RUN      ] Composed.ComposedOp
2023-01-11T22:09:56.6363360Z [       OK ] Composed.ComposedOp (299 ms)
2023-01-11T22:09:56.6363876Z [----------] 1 test from Composed (299 ms total)
2023-01-11T22:09:56.6364040Z 
2023-01-11T22:09:56.6364210Z [----------] 1 test from ConstantPropagation
2023-01-11T22:09:56.6364573Z [ RUN      ] ConstantPropagation.CustomClassesCanBePropagated
2023-01-11T22:09:56.6550370Z [       OK ] ConstantPropagation.CustomClassesCanBePropagated (18 ms)
2023-01-11T22:09:56.6551144Z [----------] 1 test from ConstantPropagation (18 ms total)
2023-01-11T22:09:56.6551443Z 
2023-01-11T22:09:56.6551734Z [----------] 19 tests from MobileTypeParserTest
2023-01-11T22:09:56.6552268Z [ RUN      ] MobileTypeParserTest.Int
2023-01-11T22:09:56.6552803Z [       OK ] MobileTypeParserTest.Int (0 ms)
2023-01-11T22:09:56.6553476Z [ RUN      ] MobileTypeParserTest.NestedContainersAnnotationStr
2023-01-11T22:09:56.6554337Z [       OK ] MobileTypeParserTest.NestedContainersAnnotationStr (0 ms)
2023-01-11T22:09:56.6555046Z [ RUN      ] MobileTypeParserTest.TorchBindClass
2023-01-11T22:09:56.6555684Z [       OK ] MobileTypeParserTest.TorchBindClass (0 ms)
2023-01-11T22:09:56.6556338Z [ RUN      ] MobileTypeParserTest.ListOfTorchBindClass
2023-01-11T22:09:56.6557042Z [       OK ] MobileTypeParserTest.ListOfTorchBindClass (0 ms)
2023-01-11T22:09:56.6557870Z [ RUN      ] MobileTypeParserTest.NestedContainersAnnotationStrWithSpaces
2023-01-11T22:09:56.6558821Z [       OK ] MobileTypeParserTest.NestedContainersAnnotationStrWithSpaces (0 ms)
2023-01-11T22:09:56.6559640Z [ RUN      ] MobileTypeParserTest.NamedTuple
2023-01-11T22:09:56.6560244Z [       OK ] MobileTypeParserTest.NamedTuple (0 ms)
2023-01-11T22:09:56.6560938Z [ RUN      ] MobileTypeParserTest.DictNestedNamedTupleTypeList
2023-01-11T22:09:56.6561748Z [       OK ] MobileTypeParserTest.DictNestedNamedTupleTypeList (0 ms)
2023-01-11T22:09:56.6562597Z [ RUN      ] MobileTypeParserTest.NamedTupleNestedNamedTupleTypeList
2023-01-11T22:09:56.6563489Z [       OK ] MobileTypeParserTest.NamedTupleNestedNamedTupleTypeList (0 ms)
2023-01-11T22:09:56.6564315Z [ RUN      ] MobileTypeParserTest.NamedTupleNestedNamedTuple
2023-01-11T22:09:56.6565089Z [       OK ] MobileTypeParserTest.NamedTupleNestedNamedTuple (0 ms)
2023-01-11T22:09:56.6565737Z [ RUN      ] MobileTypeParserTest.Empty
2023-01-11T22:09:56.6588283Z [       OK ] MobileTypeParserTest.Empty (3 ms)
2023-01-11T22:09:56.6588874Z [ RUN      ] MobileTypeParserTest.TypoRaises
2023-01-11T22:09:56.6626437Z [       OK ] MobileTypeParserTest.TypoRaises (3 ms)
2023-01-11T22:09:56.6627128Z [ RUN      ] MobileTypeParserTest.MismatchBracketRaises
2023-01-11T22:09:56.6673160Z [       OK ] MobileTypeParserTest.MismatchBracketRaises (4 ms)
2023-01-11T22:09:56.6673753Z [ RUN      ] MobileTypeParserTest.MismatchBracketRaises2
2023-01-11T22:09:56.6710832Z [       OK ] MobileTypeParserTest.MismatchBracketRaises2 (3 ms)
2023-01-11T22:09:56.6711255Z [ RUN      ] MobileTypeParserTest.DictWithoutValueRaises
2023-01-11T22:09:56.6743028Z [       OK ] MobileTypeParserTest.DictWithoutValueRaises (3 ms)
2023-01-11T22:09:56.6743438Z [ RUN      ] MobileTypeParserTest.ListArgCountMismatchRaises
2023-01-11T22:09:56.6780444Z [       OK ] MobileTypeParserTest.ListArgCountMismatchRaises (3 ms)
2023-01-11T22:09:56.6780896Z [ RUN      ] MobileTypeParserTest.DictArgCountMismatchRaises
2023-01-11T22:09:56.6812797Z [       OK ] MobileTypeParserTest.DictArgCountMismatchRaises (3 ms)
2023-01-11T22:09:56.6813252Z [ RUN      ] MobileTypeParserTest.ValidTypeWithExtraStuffRaises
2023-01-11T22:09:56.6834179Z [       OK ] MobileTypeParserTest.ValidTypeWithExtraStuffRaises (2 ms)
2023-01-11T22:09:56.6834662Z [ RUN      ] MobileTypeParserTest.NonIdentifierRaises
2023-01-11T22:09:56.6855202Z [       OK ] MobileTypeParserTest.NonIdentifierRaises (2 ms)
2023-01-11T22:09:56.6855674Z [ RUN      ] MobileTypeParserTest.DictNestedNamedTupleTypeListRaises
2023-01-11T22:09:56.6898442Z [       OK ] MobileTypeParserTest.DictNestedNamedTupleTypeListRaises (4 ms)
2023-01-11T22:09:56.6898897Z [----------] 19 tests from MobileTypeParserTest (34 ms total)
2023-01-11T22:09:56.6899075Z 
2023-01-11T22:09:56.6899217Z [----------] 13 tests from ModuleAPITest
2023-01-11T22:09:56.6899575Z [ RUN      ] ModuleAPITest.MethodRunAsync
2023-01-11T22:09:56.6925380Z [       OK ] ModuleAPITest.MethodRunAsync (2 ms)
2023-01-11T22:09:56.6926019Z [ RUN      ] ModuleAPITest.Clone
2023-01-11T22:09:56.6926622Z [       OK ] ModuleAPITest.Clone (0 ms)
2023-01-11T22:09:56.6927460Z [ RUN      ] ModuleAPITest.CloneWithModuleInterface
2023-01-11T22:09:56.6935272Z [       OK ] ModuleAPITest.CloneWithModuleInterface (0 ms)
2023-01-11T22:09:56.6935795Z [ RUN      ] ModuleAPITest.Copy
2023-01-11T22:09:56.6936298Z [       OK ] ModuleAPITest.Copy (0 ms)
2023-01-11T22:09:56.6936741Z [ RUN      ] ModuleAPITest.DeepCopy
2023-01-11T22:09:56.6937223Z [       OK ] ModuleAPITest.DeepCopy (0 ms)
2023-01-11T22:09:56.6937682Z [ RUN      ] ModuleAPITest.DeepCopyString
2023-01-11T22:09:56.6938187Z [       OK ] ModuleAPITest.DeepCopyString (0 ms)
2023-01-11T22:09:56.6938678Z [ RUN      ] ModuleAPITest.DeepCopyEnum
2023-01-11T22:09:56.6939189Z [       OK ] ModuleAPITest.DeepCopyEnum (0 ms)
2023-01-11T22:09:56.6939755Z [ RUN      ] ModuleAPITest.DeepCopyPreservesAliasing
2023-01-11T22:09:56.6940420Z [       OK ] ModuleAPITest.DeepCopyPreservesAliasing (0 ms)
2023-01-11T22:09:56.6940983Z [ RUN      ] ModuleAPITest.Constants
2023-01-11T22:09:56.6941499Z [       OK ] ModuleAPITest.Constants (0 ms)
2023-01-11T22:09:56.6942034Z [ RUN      ] ModuleAPITest.Parameters
2023-01-11T22:09:56.6942778Z [       OK ] ModuleAPITest.Parameters (0 ms)
2023-01-11T22:09:56.6943225Z [ RUN      ] ModuleAPITest.Define
2023-01-11T22:09:56.6945499Z [       OK ] ModuleAPITest.Define (0 ms)
2023-01-11T22:09:56.6945819Z [ RUN      ] ModuleAPITest.Freezing
2023-01-11T22:09:56.6974435Z [       OK ] ModuleAPITest.Freezing (2 ms)
2023-01-11T22:09:56.6975033Z [ RUN      ] ModuleAPITest.OfiFreezesTraining
2023-01-11T22:09:56.7002627Z [       OK ] ModuleAPITest.OfiFreezesTraining (2 ms)
2023-01-11T22:09:56.7003233Z [----------] 13 tests from ModuleAPITest (10 ms total)
2023-01-11T22:09:56.7003503Z 
2023-01-11T22:09:56.7003727Z [----------] 6 tests from PeepholeOptimizeTest
2023-01-11T22:09:56.7004259Z [ RUN      ] PeepholeOptimizeTest.IsAndIsNot
2023-01-11T22:09:56.7004970Z [       OK ] PeepholeOptimizeTest.IsAndIsNot (0 ms)
2023-01-11T22:09:56.7005491Z [ RUN      ] PeepholeOptimizeTest.IsAndIsNot2
2023-01-11T22:09:56.7005985Z [       OK ] PeepholeOptimizeTest.IsAndIsNot2 (0 ms)
2023-01-11T22:09:56.7006615Z [ RUN      ] PeepholeOptimizeTest.IsAndIsNot3
2023-01-11T22:09:56.7007172Z [       OK ] PeepholeOptimizeTest.IsAndIsNot3 (0 ms)
2023-01-11T22:09:56.7007810Z [ RUN      ] PeepholeOptimizeTest.UnwrapOptional
2023-01-11T22:09:56.7008274Z [       OK ] PeepholeOptimizeTest.UnwrapOptional (0 ms)
2023-01-11T22:09:56.7008638Z [ RUN      ] PeepholeOptimizeTest.UnwrapOptional2
2023-01-11T22:09:56.7008994Z [       OK ] PeepholeOptimizeTest.UnwrapOptional2 (0 ms)
2023-01-11T22:09:56.7009341Z [ RUN      ] PeepholeOptimizeTest.AddMMFusion
2023-01-11T22:09:56.7009682Z [       OK ] PeepholeOptimizeTest.AddMMFusion (0 ms)
2023-01-11T22:09:56.7010138Z [----------] 6 tests from PeepholeOptimizeTest (0 ms total)
2023-01-11T22:09:56.7010310Z 
2023-01-11T22:09:56.7010473Z [----------] 5 tests from QualifiedNameTest
2023-01-11T22:09:56.7010802Z [ RUN      ] QualifiedNameTest.PrefixConstruction
2023-01-11T22:09:56.7011166Z [       OK ] QualifiedNameTest.PrefixConstruction (0 ms)
2023-01-11T22:09:56.7011508Z [ RUN      ] QualifiedNameTest.DottedConstruction
2023-01-11T22:09:56.7011866Z [       OK ] QualifiedNameTest.DottedConstruction (0 ms)
2023-01-11T22:09:56.7012208Z [ RUN      ] QualifiedNameTest.BadInputRaises
2023-01-11T22:09:56.7054669Z [       OK ] QualifiedNameTest.BadInputRaises (4 ms)
2023-01-11T22:09:56.7055123Z [ RUN      ] QualifiedNameTest.Equality
2023-01-11T22:09:56.7055443Z [       OK ] QualifiedNameTest.Equality (0 ms)
2023-01-11T22:09:56.7055753Z [ RUN      ] QualifiedNameTest.IsPrefixOf
2023-01-11T22:09:56.7056070Z [       OK ] QualifiedNameTest.IsPrefixOf (0 ms)
2023-01-11T22:09:56.7056544Z [----------] 5 tests from QualifiedNameTest (4 ms total)
2023-01-11T22:09:56.7056708Z 
2023-01-11T22:09:56.7056900Z [----------] 6 tests from SerializationTest
2023-01-11T22:09:56.7057336Z [ RUN      ] SerializationTest.ExtraFilesHookPreference
2023-01-11T22:09:56.7057816Z [W export_module.cpp:587] Warning: An extra files hook attempted to write metadata.json but this is already written in extra files and so will be skipped. This warning will only appear once per process. (function operator())
2023-01-11T22:09:56.7062566Z [       OK ] SerializationTest.ExtraFilesHookPreference (0 ms)
2023-01-11T22:09:56.7062994Z [ RUN      ] SerializationTest.ExtraFileHooksNoSecret
2023-01-11T22:09:56.7063936Z [       OK ] SerializationTest.ExtraFileHooksNoSecret (0 ms)
2023-01-11T22:09:56.7064436Z [ RUN      ] SerializationTest.ExtraFileHooksWithSecret
2023-01-11T22:09:56.7065039Z [       OK ] SerializationTest.ExtraFileHooksWithSecret (0 ms)
2023-01-11T22:09:56.7065387Z [ RUN      ] SerializationTest.TypeTags
2023-01-11T22:09:56.7067536Z [       OK ] SerializationTest.TypeTags (0 ms)
2023-01-11T22:09:56.7067876Z [ RUN      ] SerializationTest.ParentDirNotExist
2023-01-11T22:09:56.7113480Z [       OK ] SerializationTest.ParentDirNotExist (4 ms)
2023-01-11T22:09:56.7114028Z [ RUN      ] SerializationTest.CalculateNecessaryArgsTest
2023-01-11T22:09:56.7114462Z [       OK ] SerializationTest.CalculateNecessaryArgsTest (0 ms)
2023-01-11T22:09:56.7114849Z [----------] 6 tests from SerializationTest (5 ms total)
2023-01-11T22:09:56.7115012Z 
2023-01-11T22:09:56.7115181Z [----------] 3 tests from TestSourceRoundTrip
2023-01-11T22:09:56.7115508Z [ RUN      ] TestSourceRoundTrip.UpsampleNearest2d
2023-01-11T22:09:56.7128579Z [       OK ] TestSourceRoundTrip.UpsampleNearest2d (1 ms)
2023-01-11T22:09:56.7129139Z [ RUN      ] TestSourceRoundTrip.CheckAttrAccess
2023-01-11T22:09:56.7129662Z [       OK ] TestSourceRoundTrip.CheckAttrAccess (0 ms)
2023-01-11T22:09:56.7130239Z [ RUN      ] TestSourceRoundTrip.MethodInvocation
2023-01-11T22:09:56.7178376Z [       OK ] TestSourceRoundTrip.MethodInvocation (4 ms)
2023-01-11T22:09:56.7179064Z [----------] 3 tests from TestSourceRoundTrip (6 ms total)
2023-01-11T22:09:56.7179353Z 
2023-01-11T22:09:56.7179492Z [----------] 1 test from TestSaveLoad
2023-01-11T22:09:56.7179801Z [ RUN      ] TestSaveLoad.LoadWithoutDebugInfo
2023-01-11T22:09:56.7199588Z [       OK ] TestSaveLoad.LoadWithoutDebugInfo (2 ms)
2023-01-11T22:09:56.7200220Z [----------] 1 test from TestSaveLoad (2 ms total)
2023-01-11T22:09:56.7200443Z 
2023-01-11T22:09:56.7200625Z [----------] 2 tests from FunctionSchemaIsAliasingTest
2023-01-11T22:09:56.7201072Z [ RUN      ] FunctionSchemaIsAliasingTest.Basic
2023-01-11T22:09:56.7201427Z [       OK ] FunctionSchemaIsAliasingTest.Basic (0 ms)
2023-01-11T22:09:56.7201795Z [ RUN      ] FunctionSchemaIsAliasingTest.InvalidArgument
2023-01-11T22:09:56.7222660Z [       OK ] FunctionSchemaIsAliasingTest.InvalidArgument (2 ms)
2023-01-11T22:09:56.7223314Z [----------] 2 tests from FunctionSchemaIsAliasingTest (2 ms total)
2023-01-11T22:09:56.7223511Z 
2023-01-11T22:09:56.7223704Z [----------] 2 tests from FunctionSchemaIsMutableTest
2023-01-11T22:09:56.7224033Z [ RUN      ] FunctionSchemaIsMutableTest.Basic
2023-01-11T22:09:56.7224380Z [       OK ] FunctionSchemaIsMutableTest.Basic (0 ms)
2023-01-11T22:09:56.7224753Z [ RUN      ] FunctionSchemaIsMutableTest.InvalidArgument
2023-01-11T22:09:56.7254970Z [       OK ] FunctionSchemaIsMutableTest.InvalidArgument (3 ms)
2023-01-11T22:09:56.7255679Z [----------] 2 tests from FunctionSchemaIsMutableTest (3 ms total)
2023-01-11T22:09:56.7255870Z 
2023-01-11T22:09:56.7256053Z [----------] 5 tests from SchemaInfoIsMutableTest
2023-01-11T22:09:56.7256477Z [ RUN      ] SchemaInfoIsMutableTest.Basic
2023-01-11T22:09:56.7257409Z [       OK ] SchemaInfoIsMutableTest.Basic (0 ms)
2023-01-11T22:09:56.7258058Z [ RUN      ] SchemaInfoIsMutableTest.InvalidArgument
2023-01-11T22:09:56.7289673Z [       OK ] SchemaInfoIsMutableTest.InvalidArgument (3 ms)
2023-01-11T22:09:56.7290533Z [ RUN      ] SchemaInfoIsMutableTest.AliasingInputs
2023-01-11T22:09:56.7291168Z [       OK ] SchemaInfoIsMutableTest.AliasingInputs (0 ms)
2023-01-11T22:09:56.7291840Z [ RUN      ] SchemaInfoIsMutableTest.InstanceNorm
2023-01-11T22:09:56.7292464Z [       OK ] SchemaInfoIsMutableTest.InstanceNorm (0 ms)
2023-01-11T22:09:56.7293099Z [ RUN      ] SchemaInfoIsMutableTest.BatchNorm
2023-01-11T22:09:56.7293723Z [       OK ] SchemaInfoIsMutableTest.BatchNorm (0 ms)
2023-01-11T22:09:56.7294344Z [----------] 5 tests from SchemaInfoIsMutableTest (3 ms total)
2023-01-11T22:09:56.7294529Z 
2023-01-11T22:09:56.7294738Z [----------] 2 tests from SchemaInfoIsNonDeterministicTest
2023-01-11T22:09:56.7295092Z [ RUN      ] SchemaInfoIsNonDeterministicTest.Basic
2023-01-11T22:09:56.7295467Z [       OK ] SchemaInfoIsNonDeterministicTest.Basic (0 ms)
2023-01-11T22:09:56.7295845Z [ RUN      ] SchemaInfoIsNonDeterministicTest.Dropout
2023-01-11T22:09:56.7296234Z [       OK ] SchemaInfoIsNonDeterministicTest.Dropout (0 ms)
2023-01-11T22:09:56.7296631Z [----------] 2 tests from SchemaInfoIsNonDeterministicTest (0 ms total)
2023-01-11T22:09:56.7296822Z 
2023-01-11T22:09:56.7297011Z [----------] 3 tests from FunctionSchemaMayAliasTest
2023-01-11T22:09:56.7297342Z [ RUN      ] FunctionSchemaMayAliasTest.Basic
2023-01-11T22:09:56.7297675Z [       OK ] FunctionSchemaMayAliasTest.Basic (0 ms)
2023-01-11T22:09:56.7298153Z [ RUN      ] FunctionSchemaMayAliasTest.InvalidArgument
2023-01-11T22:09:56.7320961Z [       OK ] FunctionSchemaMayAliasTest.InvalidArgument (2 ms)
2023-01-11T22:09:56.7321623Z [ RUN      ] FunctionSchemaMayAliasTest.Wildcard
2023-01-11T22:09:56.7322155Z [       OK ] FunctionSchemaMayAliasTest.Wildcard (0 ms)
2023-01-11T22:09:56.7322616Z [----------] 3 tests from FunctionSchemaMayAliasTest (2 ms total)
2023-01-11T22:09:56.7322870Z 
2023-01-11T22:09:56.7323049Z [----------] 7 tests from SchemaInfoMayAliasTest
2023-01-11T22:09:56.7323555Z [ RUN      ] SchemaInfoMayAliasTest.AliasingInputs
2023-01-11T22:09:56.7324162Z [       OK ] SchemaInfoMayAliasTest.AliasingInputs (0 ms)
2023-01-11T22:09:56.7324774Z [ RUN      ] SchemaInfoMayAliasTest.AliasingOutputs
2023-01-11T22:09:56.7325403Z [       OK ] SchemaInfoMayAliasTest.AliasingOutputs (0 ms)
2023-01-11T22:09:56.7326118Z [ RUN      ] SchemaInfoMayAliasTest.AliasingInputOutput
2023-01-11T22:09:56.7326742Z [       OK ] SchemaInfoMayAliasTest.AliasingInputOutput (0 ms)
2023-01-11T22:09:56.7327376Z [ RUN      ] SchemaInfoMayAliasTest.MultipleWildcardInputs
2023-01-11T22:09:56.7328066Z [       OK ] SchemaInfoMayAliasTest.MultipleWildcardInputs (0 ms)
2023-01-11T22:09:56.7328686Z [ RUN      ] SchemaInfoMayAliasTest.MultipleNonWildcardInputs
2023-01-11T22:09:56.7329360Z [W schema_info.cpp:333] Warning: alias::a appears twice in same argument list which will make aliasing checks more conservative. (function operator())
2023-01-11T22:09:56.7330107Z [       OK ] SchemaInfoMayAliasTest.MultipleNonWildcardInputs (0 ms)
2023-01-11T22:09:56.7330675Z [ RUN      ] SchemaInfoMayAliasTest.MultipleNonWildcardOutputs
2023-01-11T22:09:56.7331171Z [W schema_info.cpp:333] Warning: alias::a appears twice in same argument list which will make aliasing checks more conservative. (function operator())
2023-01-11T22:09:56.7331665Z [       OK ] SchemaInfoMayAliasTest.MultipleNonWildcardOutputs (0 ms)
2023-01-11T22:09:56.7332073Z [ RUN      ] SchemaInfoMayAliasTest.MismatchingTypes
2023-01-11T22:09:56.7332444Z [       OK ] SchemaInfoMayAliasTest.MismatchingTypes (0 ms)
2023-01-11T22:09:56.7332832Z [----------] 7 tests from SchemaInfoMayAliasTest (0 ms total)
2023-01-11T22:09:56.7333006Z 
2023-01-11T22:09:56.7333213Z [----------] 3 tests from FunctionSchemaMayContainAliasTest
2023-01-11T22:09:56.7333569Z [ RUN      ] FunctionSchemaMayContainAliasTest.Basic
2023-01-11T22:09:56.7333972Z [       OK ] FunctionSchemaMayContainAliasTest.Basic (0 ms)
2023-01-11T22:09:56.7334507Z [ RUN      ] FunctionSchemaMayContainAliasTest.Wildcard
2023-01-11T22:09:56.7335041Z [       OK ] FunctionSchemaMayContainAliasTest.Wildcard (0 ms)
2023-01-11T22:09:56.7335608Z [ RUN      ] FunctionSchemaMayContainAliasTest.InputAndOutputContainers
2023-01-11T22:09:56.7336143Z [       OK ] FunctionSchemaMayContainAliasTest.InputAndOutputContainers (0 ms)
2023-01-11T22:09:56.7336616Z [----------] 3 tests from FunctionSchemaMayContainAliasTest (0 ms total)
2023-01-11T22:09:56.7336814Z 
2023-01-11T22:09:56.7337010Z [----------] 6 tests from SchemaInfoMayContainAliasTest
2023-01-11T22:09:56.7337407Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasInputsEqual
2023-01-11T22:09:56.7337885Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasInputsEqual (0 ms)
2023-01-11T22:09:56.7338485Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasInputsContained
2023-01-11T22:09:56.7339080Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasInputsContained (0 ms)
2023-01-11T22:09:56.7339788Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasOutputs
2023-01-11T22:09:56.7340425Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasOutputs (0 ms)
2023-01-11T22:09:56.7340984Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasInputOutput
2023-01-11T22:09:56.7341445Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasInputOutput (0 ms)
2023-01-11T22:09:56.7341916Z [ RUN      ] SchemaInfoMayContainAliasTest.InputAndOutputContainers
2023-01-11T22:09:56.7342566Z [       OK ] SchemaInfoMayContainAliasTest.InputAndOutputContainers (0 ms)
2023-01-11T22:09:56.7343006Z [ RUN      ] SchemaInfoMayContainAliasTest.Wildcard
2023-01-11T22:09:56.7343381Z [       OK ] SchemaInfoMayContainAliasTest.Wildcard (0 ms)
2023-01-11T22:09:56.7343783Z [----------] 6 tests from SchemaInfoMayContainAliasTest (0 ms total)
2023-01-11T22:09:56.7343971Z 
2023-01-11T22:09:56.7344133Z [----------] 2 tests from SchemaMatchingTest
2023-01-11T22:09:56.7344421Z [ RUN      ] SchemaMatchingTest.VarType
2023-01-11T22:09:56.7344808Z [       OK ] SchemaMatchingTest.VarType (0 ms)
2023-01-11T22:09:56.7345126Z [ RUN      ] SchemaMatchingTest.VarType2
2023-01-11T22:09:56.7345430Z [       OK ] SchemaMatchingTest.VarType2 (0 ms)
2023-01-11T22:09:56.7345770Z [----------] 2 tests from SchemaMatchingTest (0 ms total)
2023-01-11T22:09:56.7345936Z 
2023-01-11T22:09:56.7346086Z [----------] 6 tests from StackOptTest
2023-01-11T22:09:56.7346370Z [ RUN      ] StackOptTest.UseVariadicStack
2023-01-11T22:09:56.7449957Z [       OK ] StackOptTest.UseVariadicStack (10 ms)
2023-01-11T22:09:56.7450343Z [ RUN      ] StackOptTest.UseVariadicStackReplaceMultiple
2023-01-11T22:09:56.7504843Z [       OK ] StackOptTest.UseVariadicStackReplaceMultiple (5 ms)
2023-01-11T22:09:56.7505273Z [ RUN      ] StackOptTest.UseVariadicStackWithMultipleListUses
2023-01-11T22:09:56.7531038Z [       OK ] StackOptTest.UseVariadicStackWithMultipleListUses (2 ms)
2023-01-11T22:09:56.7531516Z [ RUN      ] StackOptTest.UseVariadicStackWithListMutationAfterCat
2023-01-11T22:09:56.7566834Z [       OK ] StackOptTest.UseVariadicStackWithListMutationAfterCat (3 ms)
2023-01-11T22:09:56.7567307Z [ RUN      ] StackOptTest.UseVariadicStackWithListMutationBeforeCat
2023-01-11T22:09:56.7619344Z [       OK ] StackOptTest.UseVariadicStackWithListMutationBeforeCat (5 ms)
2023-01-11T22:09:56.7619831Z [ RUN      ] StackOptTest.UseVariadicStackWithMultipleListMutations
2023-01-11T22:09:56.7695469Z [       OK ] StackOptTest.UseVariadicStackWithMultipleListMutations (7 ms)
2023-01-11T22:09:56.7696128Z [----------] 6 tests from StackOptTest (35 ms total)
2023-01-11T22:09:56.7696299Z 
2023-01-11T22:09:56.7696514Z [----------] 16 tests from SubgraphMatcherTest
2023-01-11T22:09:56.7696845Z [ RUN      ] SubgraphMatcherTest.Trivial1
2023-01-11T22:09:56.7697344Z [       OK ] SubgraphMatcherTest.Trivial1 (0 ms)
2023-01-11T22:09:56.7697822Z [ RUN      ] SubgraphMatcherTest.Trivial2
2023-01-11T22:09:56.7698216Z [       OK ] SubgraphMatcherTest.Trivial2 (0 ms)
2023-01-11T22:09:56.7698722Z [ RUN      ] SubgraphMatcherTest.Trivial3
2023-01-11T22:09:56.7699181Z [       OK ] SubgraphMatcherTest.Trivial3 (0 ms)
2023-01-11T22:09:56.7699575Z [ RUN      ] SubgraphMatcherTest.Trivial4
2023-01-11T22:09:56.7700026Z [       OK ] SubgraphMatcherTest.Trivial4 (0 ms)
2023-01-11T22:09:56.7700404Z [ RUN      ] SubgraphMatcherTest.Linear1
2023-01-11T22:09:56.7700883Z [       OK ] SubgraphMatcherTest.Linear1 (0 ms)
2023-01-11T22:09:56.7701317Z [ RUN      ] SubgraphMatcherTest.Linear2
2023-01-11T22:09:56.7701740Z [       OK ] SubgraphMatcherTest.Linear2 (0 ms)
2023-01-11T22:09:56.7702056Z [ RUN      ] SubgraphMatcherTest.Diamond1
2023-01-11T22:09:56.7702535Z [       OK ] SubgraphMatcherTest.Diamond1 (0 ms)
2023-01-11T22:09:56.7703187Z [ RUN      ] SubgraphMatcherTest.Diamond2
2023-01-11T22:09:56.7703561Z [       OK ] SubgraphMatcherTest.Diamond2 (0 ms)
2023-01-11T22:09:56.7703874Z [ RUN      ] SubgraphMatcherTest.XPattern
2023-01-11T22:09:56.7704251Z [       OK ] SubgraphMatcherTest.XPattern (0 ms)
2023-01-11T22:09:56.7704576Z [ RUN      ] SubgraphMatcherTest.MultipleMatches
2023-01-11T22:09:56.7704933Z [       OK ] SubgraphMatcherTest.MultipleMatches (0 ms)
2023-01-11T22:09:56.7705301Z [ RUN      ] SubgraphMatcherTest.OverlappingMatches
2023-01-11T22:09:56.7705672Z [       OK ] SubgraphMatcherTest.OverlappingMatches (0 ms)
2023-01-11T22:09:56.7706050Z [ RUN      ] SubgraphMatcherTest.MatchInBasicBlocks1
2023-01-11T22:09:56.7706442Z [       OK ] SubgraphMatcherTest.MatchInBasicBlocks1 (0 ms)
2023-01-11T22:09:56.7706823Z [ RUN      ] SubgraphMatcherTest.MatchInBasicBlocks2
2023-01-11T22:09:56.7707280Z [       OK ] SubgraphMatcherTest.MatchInBasicBlocks2 (0 ms)
2023-01-11T22:09:56.7707659Z [ RUN      ] SubgraphMatcherTest.MatchesAttributes
2023-01-11T22:09:56.7708029Z [       OK ] SubgraphMatcherTest.MatchesAttributes (0 ms)
2023-01-11T22:09:56.7708365Z [ RUN      ] SubgraphMatcherTest.BadPattern
2023-01-11T22:09:56.7745542Z [       OK ] SubgraphMatcherTest.BadPattern (4 ms)
2023-01-11T22:09:56.7745979Z [ RUN      ] SubgraphMatcherTest.MultiOutput
2023-01-11T22:09:56.7746617Z [       OK ] SubgraphMatcherTest.MultiOutput (0 ms)
2023-01-11T22:09:56.7747053Z [----------] 16 tests from SubgraphMatcherTest (5 ms total)
2023-01-11T22:09:56.7747230Z 
2023-01-11T22:09:56.7747404Z [----------] 4 tests from SubgraphRewriterTest
2023-01-11T22:09:56.7747724Z [ RUN      ] SubgraphRewriterTest.FilterMatch
2023-01-11T22:09:56.7749164Z [       OK ] SubgraphRewriterTest.FilterMatch (0 ms)
2023-01-11T22:09:56.7749809Z [ RUN      ] SubgraphRewriterTest.FilterNoMatch
2023-01-11T22:09:56.7750223Z [       OK ] SubgraphRewriterTest.FilterNoMatch (0 ms)
2023-01-11T22:09:56.7750569Z [ RUN      ] SubgraphRewriterTest.MultiOutput
2023-01-11T22:09:56.7753564Z [       OK ] SubgraphRewriterTest.MultiOutput (0 ms)
2023-01-11T22:09:56.7754167Z [ RUN      ] SubgraphRewriterTest.OutputType
2023-01-11T22:09:56.7754897Z [       OK ] SubgraphRewriterTest.OutputType (0 ms)
2023-01-11T22:09:56.7755568Z [----------] 4 tests from SubgraphRewriterTest (0 ms total)
2023-01-11T22:09:56.7755741Z 
2023-01-11T22:09:56.7755904Z [----------] 3 tests from SubgraphUtilsTest
2023-01-11T22:09:56.7756194Z [ RUN      ] SubgraphUtilsTest.Basic
2023-01-11T22:09:56.7758623Z [       OK ] SubgraphUtilsTest.Basic (0 ms)
2023-01-11T22:09:56.7759256Z [ RUN      ] SubgraphUtilsTest.MergeSubgraphs
2023-01-11T22:09:56.7761340Z [       OK ] SubgraphUtilsTest.MergeSubgraphs (0 ms)
2023-01-11T22:09:56.7761905Z [ RUN      ] SubgraphUtilsTest.GraphName
2023-01-11T22:09:56.7762494Z [       OK ] SubgraphUtilsTest.GraphName (0 ms)
2023-01-11T22:09:56.7763078Z [----------] 3 tests from SubgraphUtilsTest (0 ms total)
2023-01-11T22:09:56.7763241Z 
2023-01-11T22:09:56.7763436Z [----------] 8 tests from UnionTypeTest
2023-01-11T22:09:56.7764017Z [ RUN      ] UnionTypeTest.UnionOperatorEquals
2023-01-11T22:09:56.7764491Z [       OK ] UnionTypeTest.UnionOperatorEquals (0 ms)
2023-01-11T22:09:56.7764942Z [ RUN      ] UnionTypeTest.UnionCreate_OptionalT1AndOptionalT2
2023-01-11T22:09:56.7765340Z [       OK ] UnionTypeTest.UnionCreate_OptionalT1AndOptionalT2 (0 ms)
2023-01-11T22:09:56.7765707Z [ RUN      ] UnionTypeTest.UnionCreate_OptionalTAndT
2023-01-11T22:09:56.7766057Z [       OK ] UnionTypeTest.UnionCreate_OptionalTAndT (0 ms)
2023-01-11T22:09:56.7766450Z [ RUN      ] UnionTypeTest.UnionCreate_TupleWithSubtypingRelationship
2023-01-11T22:09:56.7766999Z [       OK ] UnionTypeTest.UnionCreate_TupleWithSubtypingRelationship (0 ms)
2023-01-11T22:09:56.7767371Z [ RUN      ] UnionTypeTest.UnionCreate_ContainerTAndT
2023-01-11T22:09:56.7767725Z [       OK ] UnionTypeTest.UnionCreate_ContainerTAndT (0 ms)
2023-01-11T22:09:56.7768129Z [ RUN      ] UnionTypeTest.UnionCreate_OptionalContainerTAndContainerTAndT
2023-01-11T22:09:56.7768582Z [       OK ] UnionTypeTest.UnionCreate_OptionalContainerTAndContainerTAndT (0 ms)
2023-01-11T22:09:56.7768951Z [ RUN      ] UnionTypeTest.Subtyping_NumberType
2023-01-11T22:09:56.7769285Z [       OK ] UnionTypeTest.Subtyping_NumberType (0 ms)
2023-01-11T22:09:56.7769621Z [ RUN      ] UnionTypeTest.Subtyping_OptionalType
2023-01-11T22:09:56.7769945Z [       OK ] UnionTypeTest.Subtyping_OptionalType (0 ms)
2023-01-11T22:09:56.7770280Z [----------] 8 tests from UnionTypeTest (0 ms total)
2023-01-11T22:09:56.7770488Z 
2023-01-11T22:09:56.7770651Z [----------] 2 tests from ScriptProfileTest
2023-01-11T22:09:56.7770947Z [ RUN      ] ScriptProfileTest.Basic
2023-01-11T22:09:56.7771236Z [       OK ] ScriptProfileTest.Basic (0 ms)
2023-01-11T22:09:56.7771546Z [ RUN      ] ScriptProfileTest.CallingOrder
2023-01-11T22:09:56.7797075Z [       OK ] ScriptProfileTest.CallingOrder (3 ms)
2023-01-11T22:09:56.7797420Z [----------] 2 tests from ScriptProfileTest (3 ms total)
2023-01-11T22:09:56.7797584Z 
2023-01-11T22:09:56.7797753Z [----------] 7 tests from ShapeAnalysisTest
2023-01-11T22:09:56.7798085Z [ RUN      ] ShapeAnalysisTest.DynamicShapesFusion
2023-01-11T22:09:56.7870719Z [       OK ] ShapeAnalysisTest.DynamicShapesFusion (7 ms)
2023-01-11T22:09:56.7871131Z [ RUN      ] ShapeAnalysisTest.MovingConstantOutOfFusionGroups
2023-01-11T22:09:56.7887823Z [       OK ] ShapeAnalysisTest.MovingConstantOutOfFusionGroups (1 ms)
2023-01-11T22:09:56.7888249Z [ RUN      ] ShapeAnalysisTest.SymbolicShapeAPI
2023-01-11T22:09:56.7960796Z [       OK ] ShapeAnalysisTest.SymbolicShapeAPI (7 ms)
2023-01-11T22:09:56.7961175Z [ RUN      ] ShapeAnalysisTest.BoundedSymbolicShapes
2023-01-11T22:09:56.7967117Z [       OK ] ShapeAnalysisTest.BoundedSymbolicShapes (0 ms)
2023-01-11T22:09:56.7967568Z [ RUN      ] ShapeAnalysisTest.SymbolicShapeCaching
2023-01-11T22:09:56.7973654Z [       OK ] ShapeAnalysisTest.SymbolicShapeCaching (0 ms)
2023-01-11T22:09:56.7974106Z [ RUN      ] ShapeAnalysisTest.ShapeCacheMultipleFns
2023-01-11T22:09:56.8002498Z [       OK ] ShapeAnalysisTest.ShapeCacheMultipleFns (2 ms)
2023-01-11T22:09:56.8002966Z [ RUN      ] ShapeAnalysisTest.TestShapeMultipleReturns
2023-01-11T22:09:56.8015271Z [       OK ] ShapeAnalysisTest.TestShapeMultipleReturns (1 ms)
2023-01-11T22:09:56.8015900Z [----------] 7 tests from ShapeAnalysisTest (21 ms total)
2023-01-11T22:09:56.8016160Z 
2023-01-11T22:09:56.8016399Z [----------] 5 tests from JitLoggingTest
2023-01-11T22:09:56.8016948Z [ RUN      ] JitLoggingTest.CheckSetLoggingLevel
2023-01-11T22:09:56.8017564Z [       OK ] JitLoggingTest.CheckSetLoggingLevel (0 ms)
2023-01-11T22:09:56.8017944Z [ RUN      ] JitLoggingTest.CheckSetMultipleLogLevels
2023-01-11T22:09:56.8018334Z [       OK ] JitLoggingTest.CheckSetMultipleLogLevels (0 ms)
2023-01-11T22:09:56.8018707Z [ RUN      ] JitLoggingTest.CheckLoggingLevelAfterUnset
2023-01-11T22:09:56.8019103Z [       OK ] JitLoggingTest.CheckLoggingLevelAfterUnset (0 ms)
2023-01-11T22:09:56.8019486Z [ RUN      ] JitLoggingTest.CheckAfterChangingLevel
2023-01-11T22:09:56.8019850Z [       OK ] JitLoggingTest.CheckAfterChangingLevel (0 ms)
2023-01-11T22:09:56.8020222Z [ RUN      ] JitLoggingTest.CheckOutputStreamSetting
2023-01-11T22:09:56.8020770Z [       OK ] JitLoggingTest.CheckOutputStreamSetting (0 ms)
2023-01-11T22:09:56.8021130Z [----------] 5 tests from JitLoggingTest (0 ms total)
2023-01-11T22:09:56.8021285Z 
2023-01-11T22:09:56.8021426Z [----------] 9 tests from FileFormatTest
2023-01-11T22:09:56.8021764Z [ RUN      ] FileFormatTest.IdentifiesFlatbufferStream
2023-01-11T22:09:56.8022156Z [       OK ] FileFormatTest.IdentifiesFlatbufferStream (0 ms)
2023-01-11T22:09:56.8022812Z [ RUN      ] FileFormatTest.IdentifiesZipStream
2023-01-11T22:09:56.8023171Z [       OK ] FileFormatTest.IdentifiesZipStream (0 ms)
2023-01-11T22:09:56.8023540Z [ RUN      ] FileFormatTest.FlatbufferTakesPrecedence
2023-01-11T22:09:56.8023987Z [       OK ] FileFormatTest.FlatbufferTakesPrecedence (0 ms)
2023-01-11T22:09:56.8024389Z [ RUN      ] FileFormatTest.HandlesUnknownStream
2023-01-11T22:09:56.8024838Z [       OK ] FileFormatTest.HandlesUnknownStream (0 ms)
2023-01-11T22:09:56.8025201Z [ RUN      ] FileFormatTest.ShortStreamIsUnknown
2023-01-11T22:09:56.8025545Z [       OK ] FileFormatTest.ShortStreamIsUnknown (0 ms)
2023-01-11T22:09:56.8025894Z [ RUN      ] FileFormatTest.EmptyStreamIsUnknown
2023-01-11T22:09:56.8026250Z [       OK ] FileFormatTest.EmptyStreamIsUnknown (0 ms)
2023-01-11T22:09:56.8026596Z [ RUN      ] FileFormatTest.BadStreamIsUnknown
2023-01-11T22:09:56.8026931Z [       OK ] FileFormatTest.BadStreamIsUnknown (0 ms)
2023-01-11T22:09:56.8027320Z [ RUN      ] FileFormatTest.StreamOffsetIsObservedAndRestored
2023-01-11T22:09:56.8027757Z [       OK ] FileFormatTest.StreamOffsetIsObservedAndRestored (0 ms)
2023-01-11T22:09:56.8028125Z [ RUN      ] FileFormatTest.HandlesMissingFile
2023-01-11T22:09:56.8028471Z [       OK ] FileFormatTest.HandlesMissingFile (0 ms)
2023-01-11T22:09:56.8028816Z [----------] 9 tests from FileFormatTest (0 ms total)
2023-01-11T22:09:56.8028973Z 
2023-01-11T22:09:56.8029114Z [----------] 35 tests from FlatbufferTest
2023-01-11T22:09:56.8029478Z [ RUN      ] FlatbufferTest.UpsampleNearest2d
2023-01-11T22:09:56.8033054Z [       OK ] FlatbufferTest.UpsampleNearest2d (1 ms)
2023-01-11T22:09:56.8033463Z [ RUN      ] FlatbufferTest.UpsampleNearest2dWithCopyTensorMemory
2023-01-11T22:09:56.8044154Z [       OK ] FlatbufferTest.UpsampleNearest2dWithCopyTensorMemory (1 ms)
2023-01-11T22:09:56.8044593Z [ RUN      ] FlatbufferTest.CheckAttrAccess
2023-01-11T22:09:56.8044941Z [       OK ] FlatbufferTest.CheckAttrAccess (0 ms)
2023-01-11T22:09:56.8045262Z [ RUN      ] FlatbufferTest.MethodInvocation
2023-01-11T22:09:56.8064957Z [       OK ] FlatbufferTest.MethodInvocation (2 ms)
2023-01-11T22:09:56.8065319Z [ RUN      ] FlatbufferTest.FlatbufferBackPortTest
2023-01-11T22:09:56.8094077Z [       OK ] FlatbufferTest.FlatbufferBackPortTest (2 ms)
2023-01-11T22:09:56.8094432Z [ RUN      ] FlatbufferTest.ExtraFiles
2023-01-11T22:09:56.8097457Z [       OK ] FlatbufferTest.ExtraFiles (0 ms)
2023-01-11T22:09:56.8097750Z [ RUN      ] FlatbufferTest.Conv
2023-01-11T22:09:56.8116693Z [       OK ] FlatbufferTest.Conv (1 ms)
2023-01-11T22:09:56.8117149Z [ RUN      ] FlatbufferTest.ConvWithCopyTensorMemory
2023-01-11T22:09:56.8136819Z [       OK ] FlatbufferTest.ConvWithCopyTensorMemory (1 ms)
2023-01-11T22:09:56.8137261Z [ RUN      ] FlatbufferTest.Inline
2023-01-11T22:09:56.8144450Z [       OK ] FlatbufferTest.Inline (0 ms)
2023-01-11T22:09:56.8144820Z [ RUN      ] FlatbufferTest.InlineWithCopyTensorMemory
2023-01-11T22:09:56.8150734Z [       OK ] FlatbufferTest.InlineWithCopyTensorMemory (0 ms)
2023-01-11T22:09:56.8151070Z [ RUN      ] FlatbufferTest.Tuple
2023-01-11T22:09:56.8155585Z [       OK ] FlatbufferTest.Tuple (0 ms)
2023-01-11T22:09:56.8156016Z [ RUN      ] FlatbufferTest.Dict
2023-01-11T22:09:56.8160637Z [       OK ] FlatbufferTest.Dict (0 ms)
2023-01-11T22:09:56.8160909Z [ RUN      ] FlatbufferTest.Prim
2023-01-11T22:09:56.8164090Z [       OK ] FlatbufferTest.Prim (0 ms)
2023-01-11T22:09:56.8164384Z [ RUN      ] FlatbufferTest.PrimScalar
2023-01-11T22:09:56.8168261Z [       OK ] FlatbufferTest.PrimScalar (0 ms)
2023-01-11T22:09:56.8168591Z [ RUN      ] FlatbufferTest.WrongMethodName
2023-01-11T22:09:56.8203537Z [       OK ] FlatbufferTest.WrongMethodName (3 ms)
2023-01-11T22:09:56.8203874Z [ RUN      ] FlatbufferTest.SetState
2023-01-11T22:09:56.8223830Z [       OK ] FlatbufferTest.SetState (1 ms)
2023-01-11T22:09:56.8224238Z [ RUN      ] FlatbufferTest.BuiltinClass
2023-01-11T22:09:56.8229571Z [       OK ] FlatbufferTest.BuiltinClass (0 ms)
2023-01-11T22:09:56.8230067Z [ RUN      ] FlatbufferTest.BuiltinFunction
2023-01-11T22:09:56.8232223Z [       OK ] FlatbufferTest.BuiltinFunction (0 ms)
2023-01-11T22:09:56.8232547Z [ RUN      ] FlatbufferTest.Eval
2023-01-11T22:09:56.8238239Z [       OK ] FlatbufferTest.Eval (0 ms)
2023-01-11T22:09:56.8238552Z [ RUN      ] FlatbufferTest.FindWrongMethodName
2023-01-11T22:09:56.8241118Z [       OK ] FlatbufferTest.FindWrongMethodName (0 ms)
2023-01-11T22:09:56.8241517Z [ RUN      ] FlatbufferTest.FindAndRunMethod
2023-01-11T22:09:56.8247197Z [       OK ] FlatbufferTest.FindAndRunMethod (0 ms)
2023-01-11T22:09:56.8247609Z [ RUN      ] FlatbufferTest.RunMethodVariadic
2023-01-11T22:09:56.8252809Z [       OK ] FlatbufferTest.RunMethodVariadic (0 ms)
2023-01-11T22:09:56.8253216Z [ RUN      ] FlatbufferTest.DuplicateSetState
2023-01-11T22:09:56.8261466Z [       OK ] FlatbufferTest.DuplicateSetState (0 ms)
2023-01-11T22:09:56.8261936Z [ RUN      ] FlatbufferTest.OpNameExportFetchRootOperators
2023-01-11T22:09:56.8268268Z [       OK ] FlatbufferTest.OpNameExportFetchRootOperators (0 ms)
2023-01-11T22:09:56.8268712Z [ RUN      ] FlatbufferTest.DefaultArgsConv
2023-01-11T22:09:56.8281389Z [       OK ] FlatbufferTest.DefaultArgsConv (1 ms)
2023-01-11T22:09:56.8281891Z [ RUN      ] FlatbufferTest.DefaultArgsPinv
2023-01-11T22:09:56.8336829Z [       OK ] FlatbufferTest.DefaultArgsPinv (5 ms)
2023-01-11T22:09:56.8337351Z [ RUN      ] FlatbufferTest.DefaultArgsTensorinvSpecifyDefault
2023-01-11T22:09:56.8347321Z [       OK ] FlatbufferTest.DefaultArgsTensorinvSpecifyDefault (1 ms)
2023-01-11T22:09:56.8347804Z [ RUN      ] FlatbufferTest.DefaultArgsPinvWithOutArg
2023-01-11T22:09:56.8367791Z [       OK ] FlatbufferTest.DefaultArgsPinvWithOutArg (2 ms)
2023-01-11T22:09:56.8368290Z [ RUN      ] FlatbufferTest.DefaultArgsWithOutArg
2023-01-11T22:09:56.8377539Z [       OK ] FlatbufferTest.DefaultArgsWithOutArg (0 ms)
2023-01-11T22:09:56.8378032Z [ RUN      ] FlatbufferTest.OperatorCacheDifferentiatesDefaultArgs
2023-01-11T22:09:56.8401157Z [       OK ] FlatbufferTest.OperatorCacheDifferentiatesDefaultArgs (2 ms)
2023-01-11T22:09:56.8401606Z [ RUN      ] FlatbufferTest.OperatorSize1
2023-01-11T22:09:56.8404374Z [       OK ] FlatbufferTest.OperatorSize1 (0 ms)
2023-01-11T22:09:56.8404884Z [ RUN      ] FlatbufferTest.BoolAndDoubleList
2023-01-11T22:09:56.8405283Z [       OK ] FlatbufferTest.BoolAndDoubleList (0 ms)
2023-01-11T22:09:56.8405609Z [ RUN      ] FlatbufferTest.OperatorTest2
2023-01-11T22:09:56.8416087Z [       OK ] FlatbufferTest.OperatorTest2 (1 ms)
2023-01-11T22:09:56.8416532Z [ RUN      ] FlatbufferTest.DetachedBufferSmoke
2023-01-11T22:09:56.8416886Z [       OK ] FlatbufferTest.DetachedBufferSmoke (0 ms)
2023-01-11T22:09:56.8417382Z [ RUN      ] FlatbufferTest.DetachedBufferNullOwner
2023-01-11T22:09:56.8417757Z [       OK ] FlatbufferTest.DetachedBufferNullOwner (0 ms)
2023-01-11T22:09:56.8418111Z [----------] 35 tests from FlatbufferTest (39 ms total)
2023-01-11T22:09:56.8418269Z 
2023-01-11T22:09:56.8418440Z [----------] 3 tests from TestSourceFlatbuffer
2023-01-11T22:09:56.8418782Z [ RUN      ] TestSourceFlatbuffer.UpsampleNearest2d
2023-01-11T22:09:56.8432585Z [       OK ] TestSourceFlatbuffer.UpsampleNearest2d (1 ms)
2023-01-11T22:09:56.8433016Z [ RUN      ] TestSourceFlatbuffer.CheckAttrAccess
2023-01-11T22:09:56.8433831Z [       OK ] TestSourceFlatbuffer.CheckAttrAccess (0 ms)
2023-01-11T22:09:56.8434244Z [ RUN      ] TestSourceFlatbuffer.MethodInvocation
2023-01-11T22:09:56.8486734Z [       OK ] TestSourceFlatbuffer.MethodInvocation (5 ms)
2023-01-11T22:09:56.8487312Z [----------] 3 tests from TestSourceFlatbuffer (7 ms total)
2023-01-11T22:09:56.8487499Z 
2023-01-11T22:09:56.8487682Z [----------] 10 tests from FlatbufferUpgraderTest
2023-01-11T22:09:56.8488015Z [ RUN      ] FlatbufferUpgraderTest.DivTensorV2
2023-01-11T22:09:56.8494031Z [       OK ] FlatbufferUpgraderTest.DivTensorV2 (0 ms)
2023-01-11T22:09:56.8494460Z [ RUN      ] FlatbufferUpgraderTest.DivTensorOutV2
2023-01-11T22:09:56.8499458Z [       OK ] FlatbufferUpgraderTest.DivTensorOutV2 (0 ms)
2023-01-11T22:09:56.8499903Z [ RUN      ] FlatbufferUpgraderTest.DivTensorInplaceV2
2023-01-11T22:09:56.8504554Z [       OK ] FlatbufferUpgraderTest.DivTensorInplaceV2 (0 ms)
2023-01-11T22:09:56.8505004Z [ RUN      ] FlatbufferUpgraderTest.DivScalarFloatV2
2023-01-11T22:09:56.8509637Z [       OK ] FlatbufferUpgraderTest.DivScalarFloatV2 (0 ms)
2023-01-11T22:09:56.8510105Z [ RUN      ] FlatbufferUpgraderTest.DivScalarReciprocalFloatV2
2023-01-11T22:09:56.8515807Z [       OK ] FlatbufferUpgraderTest.DivScalarReciprocalFloatV2 (0 ms)
2023-01-11T22:09:56.8516307Z [ RUN      ] FlatbufferUpgraderTest.DivScalarReciprocalIntV2
2023-01-11T22:09:56.8521372Z [       OK ] FlatbufferUpgraderTest.DivScalarReciprocalIntV2 (0 ms)
2023-01-11T22:09:56.8521832Z [ RUN      ] FlatbufferUpgraderTest.DivScalarScalarV2
2023-01-11T22:09:56.8527296Z [       OK ] FlatbufferUpgraderTest.DivScalarScalarV2 (0 ms)
2023-01-11T22:09:56.8527735Z [ RUN      ] FlatbufferUpgraderTest.DivScalarIntV2
2023-01-11T22:09:56.8533019Z [       OK ] FlatbufferUpgraderTest.DivScalarIntV2 (0 ms)
2023-01-11T22:09:56.8533467Z [ RUN      ] FlatbufferUpgraderTest.DivScalarInplaceFloatV2
2023-01-11T22:09:56.8537809Z [       OK ] FlatbufferUpgraderTest.DivScalarInplaceFloatV2 (0 ms)
2023-01-11T22:09:56.8538288Z [ RUN      ] FlatbufferUpgraderTest.DivScalarInplaceIntV2
2023-01-11T22:09:56.8543439Z [       OK ] FlatbufferUpgraderTest.DivScalarInplaceIntV2 (0 ms)
2023-01-11T22:09:56.8544070Z [----------] 10 tests from FlatbufferUpgraderTest (5 ms total)
2023-01-11T22:09:56.8544352Z 
2023-01-11T22:09:56.8544803Z [----------] 12 tests from AliasAnalysisTest/BatchAndInstanceNormFixture
2023-01-11T22:09:56.8545611Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/0
2023-01-11T22:09:56.8546513Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/0 (0 ms)
2023-01-11T22:09:56.8547413Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/1
2023-01-11T22:09:56.8548228Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/1 (0 ms)
2023-01-11T22:09:56.8548854Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/2
2023-01-11T22:09:56.8549583Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/2 (0 ms)
2023-01-11T22:09:56.8550558Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/3
2023-01-11T22:09:56.8551294Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/3 (0 ms)
2023-01-11T22:09:56.8552034Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/0
2023-01-11T22:09:56.8552958Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/0 (0 ms)
2023-01-11T22:09:56.8553935Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/1
2023-01-11T22:09:56.8554718Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/1 (0 ms)
2023-01-11T22:09:56.8555303Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/2
2023-01-11T22:09:56.8556000Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/2 (0 ms)
2023-01-11T22:09:56.8556604Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/3
2023-01-11T22:09:56.8557208Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/3 (0 ms)
2023-01-11T22:09:56.8557778Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/0
2023-01-11T22:09:56.8558363Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/0 (0 ms)
2023-01-11T22:09:56.8558938Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/1
2023-01-11T22:09:56.8559601Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/1 (0 ms)
2023-01-11T22:09:56.8560159Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/2
2023-01-11T22:09:56.8560751Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/2 (0 ms)
2023-01-11T22:09:56.8561320Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/3
2023-01-11T22:09:56.8561897Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/3 (0 ms)
2023-01-11T22:09:56.8562392Z [----------] 12 tests from AliasAnalysisTest/BatchAndInstanceNormFixture (1 ms total)
2023-01-11T22:09:56.8562596Z 
2023-01-11T22:09:56.8562842Z [----------] 10 tests from PyTorch/LiteInterpreterDynamicTypeTestFixture
2023-01-11T22:09:56.8563294Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/0
2023-01-11T22:09:57.6716901Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/0 (816 ms)
2023-01-11T22:09:57.6717434Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/1
2023-01-11T22:09:58.8269077Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/1 (1155 ms)
2023-01-11T22:09:58.8269597Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/2
2023-01-11T22:10:00.2151340Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/2 (1388 ms)
2023-01-11T22:10:00.2151916Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/3
2023-01-11T22:10:01.5619267Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/3 (1346 ms)
2023-01-11T22:10:01.5619804Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/4
2023-01-11T22:10:02.9045986Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/4 (1342 ms)
2023-01-11T22:10:02.9046516Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/5
2023-01-11T22:10:04.2537296Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/5 (1349 ms)
2023-01-11T22:10:04.2537993Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/6
2023-01-11T22:10:05.5960305Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/6 (1342 ms)
2023-01-11T22:10:05.5960832Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/7
2023-01-11T22:10:06.9100898Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/7 (1314 ms)
2023-01-11T22:10:06.9101444Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/8
2023-01-11T22:10:08.0995867Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/8 (1189 ms)
2023-01-11T22:10:08.0996378Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/9
2023-01-11T22:10:09.2817263Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/9 (1182 ms)
2023-01-11T22:10:09.2818204Z [----------] 10 tests from PyTorch/LiteInterpreterDynamicTypeTestFixture (12426 ms total)
2023-01-11T22:10:09.2818430Z 
2023-01-11T22:10:09.2818613Z [----------] Global test environment tear-down
2023-01-11T22:10:09.2887793Z [==========] 569 tests from 119 test suites ran. (13735 ms total)
2023-01-11T22:10:09.2888279Z [  PASSED  ] 569 tests.
2023-01-11T22:10:09.3738390Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T22:10:09.3738705Z + [[ nogpu_NO_AVX2 != *nogpu* ]]
2023-01-11T22:10:09.3739157Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_lazy --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_lazy.xml
2023-01-11T22:10:09.7354724Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:10:09.7357834Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:10:09.7358579Z [==========] Running 611 tests from 10 test suites.
2023-01-11T22:10:09.7359161Z [----------] Global test environment set-up.
2023-01-11T22:10:09.7359570Z [----------] 11 tests from BackendDeviceTest
2023-01-11T22:10:09.7359926Z [ RUN      ] BackendDeviceTest.BackendDeviceType
2023-01-11T22:10:09.7360321Z [       OK ] BackendDeviceTest.BackendDeviceType (0 ms)
2023-01-11T22:10:09.7360632Z [ RUN      ] BackendDeviceTest.Basic1
2023-01-11T22:10:09.7360993Z [       OK ] BackendDeviceTest.Basic1 (0 ms)
2023-01-11T22:10:09.7361294Z [ RUN      ] BackendDeviceTest.Basic2
2023-01-11T22:10:09.7361634Z [       OK ] BackendDeviceTest.Basic2 (0 ms)
2023-01-11T22:10:09.7361926Z [ RUN      ] BackendDeviceTest.Basic3
2023-01-11T22:10:09.7362235Z [       OK ] BackendDeviceTest.Basic3 (0 ms)
2023-01-11T22:10:09.7362561Z [ RUN      ] BackendDeviceTest.Basic4
2023-01-11T22:10:09.7362855Z [       OK ] BackendDeviceTest.Basic4 (0 ms)
2023-01-11T22:10:09.7363208Z [ RUN      ] BackendDeviceTest.Compare
2023-01-11T22:10:09.7363525Z [       OK ] BackendDeviceTest.Compare (0 ms)
2023-01-11T22:10:09.7363843Z [ RUN      ] BackendDeviceTest.Ostream
2023-01-11T22:10:09.7364178Z [       OK ] BackendDeviceTest.Ostream (0 ms)
2023-01-11T22:10:09.7364478Z [ RUN      ] BackendDeviceTest.FromAten
2023-01-11T22:10:09.7422751Z [       OK ] BackendDeviceTest.FromAten (6 ms)
2023-01-11T22:10:09.7423325Z [ RUN      ] BackendDeviceTest.ToAten
2023-01-11T22:10:09.7423671Z [       OK ] BackendDeviceTest.ToAten (0 ms)
2023-01-11T22:10:09.7424105Z [ RUN      ] BackendDeviceTest.GetBackendDevice1
2023-01-11T22:10:09.7425038Z [       OK ] BackendDeviceTest.GetBackendDevice1 (0 ms)
2023-01-11T22:10:09.7425589Z [ RUN      ] BackendDeviceTest.GetBackendDevice2
2023-01-11T22:10:09.7426247Z [       OK ] BackendDeviceTest.GetBackendDevice2 (0 ms)
2023-01-11T22:10:09.7426841Z [----------] 11 tests from BackendDeviceTest (6 ms total)
2023-01-11T22:10:09.7427408Z 
2023-01-11T22:10:09.7427677Z [----------] 2 tests from CacheTest
2023-01-11T22:10:09.7428150Z [ RUN      ] CacheTest.BasicTest
2023-01-11T22:10:09.7428595Z [       OK ] CacheTest.BasicTest (0 ms)
2023-01-11T22:10:09.7428922Z [ RUN      ] CacheTest.ShapeCacheTestForDynamicShape
2023-01-11T22:10:09.7429307Z [       OK ] CacheTest.ShapeCacheTestForDynamicShape (0 ms)
2023-01-11T22:10:09.7429657Z [----------] 2 tests from CacheTest (0 ms total)
2023-01-11T22:10:09.7429802Z 
2023-01-11T22:10:09.7429925Z [----------] 5 tests from IrTest
2023-01-11T22:10:09.7430180Z [ RUN      ] IrTest.BasicTest
2023-01-11T22:10:09.7430448Z [       OK ] IrTest.BasicTest (0 ms)
2023-01-11T22:10:09.7430702Z [ RUN      ] IrTest.MetaDataTest
2023-01-11T22:10:09.7430978Z [       OK ] IrTest.MetaDataTest (0 ms)
2023-01-11T22:10:09.7431245Z [ RUN      ] IrTest.TsNodeTest
2023-01-11T22:10:09.7431582Z [       OK ] IrTest.TsNodeTest (0 ms)
2023-01-11T22:10:09.7431862Z [ RUN      ] IrTest.DimensionNodeTest
2023-01-11T22:10:09.7432161Z [       OK ] IrTest.DimensionNodeTest (0 ms)
2023-01-11T22:10:09.7432478Z [ RUN      ] IrTest.DimensionIsDynamicTest
2023-01-11T22:10:09.7432792Z [       OK ] IrTest.DimensionIsDynamicTest (0 ms)
2023-01-11T22:10:09.7433103Z [----------] 5 tests from IrTest (0 ms total)
2023-01-11T22:10:09.7433246Z 
2023-01-11T22:10:09.7433391Z [----------] 2 tests from IrUtilTest
2023-01-11T22:10:09.7433647Z [ RUN      ] IrUtilTest.BasicTest
2023-01-11T22:10:09.7433925Z [       OK ] IrUtilTest.BasicTest (0 ms)
2023-01-11T22:10:09.7434203Z [ RUN      ] IrUtilTest.TestCircle
2023-01-11T22:10:09.7449982Z [       OK ] IrUtilTest.TestCircle (2 ms)
2023-01-11T22:10:09.7450574Z [----------] 2 tests from IrUtilTest (2 ms total)
2023-01-11T22:10:09.7450883Z 
2023-01-11T22:10:09.7451155Z [----------] 2 tests from HashTest
2023-01-11T22:10:09.7451426Z [ RUN      ] HashTest.Scalar
2023-01-11T22:10:09.7451678Z [       OK ] HashTest.Scalar (0 ms)
2023-01-11T22:10:09.7451936Z [ RUN      ] HashTest.Sanity
2023-01-11T22:10:09.7452198Z [       OK ] HashTest.Sanity (0 ms)
2023-01-11T22:10:09.7452477Z [----------] 2 tests from HashTest (0 ms total)
2023-01-11T22:10:09.7452622Z 
2023-01-11T22:10:09.7452794Z [----------] 3 tests from PermutationUtilTest
2023-01-11T22:10:09.7453145Z [ RUN      ] PermutationUtilTest.TestInversePermutation
2023-01-11T22:10:09.7483871Z [       OK ] PermutationUtilTest.TestInversePermutation (3 ms)
2023-01-11T22:10:09.7484460Z [ RUN      ] PermutationUtilTest.TestIsPermutation
2023-01-11T22:10:09.7484839Z [       OK ] PermutationUtilTest.TestIsPermutation (0 ms)
2023-01-11T22:10:09.7485188Z [ RUN      ] PermutationUtilTest.TestPermute
2023-01-11T22:10:09.7504253Z [       OK ] PermutationUtilTest.TestPermute (2 ms)
2023-01-11T22:10:09.7504823Z [----------] 3 tests from PermutationUtilTest (5 ms total)
2023-01-11T22:10:09.7505092Z 
2023-01-11T22:10:09.7505360Z [----------] 7 tests from ShapeTest
2023-01-11T22:10:09.7505802Z [ RUN      ] ShapeTest.Basic1
2023-01-11T22:10:09.7506282Z [       OK ] ShapeTest.Basic1 (0 ms)
2023-01-11T22:10:09.7506844Z [ RUN      ] ShapeTest.Basic2
2023-01-11T22:10:09.7507282Z [       OK ] ShapeTest.Basic2 (0 ms)
2023-01-11T22:10:09.7507695Z [ RUN      ] ShapeTest.Basic3
2023-01-11T22:10:09.7508200Z [       OK ] ShapeTest.Basic3 (0 ms)
2023-01-11T22:10:09.7508695Z [ RUN      ] ShapeTest.SetScalarType
2023-01-11T22:10:09.7509235Z [       OK ] ShapeTest.SetScalarType (0 ms)
2023-01-11T22:10:09.7509701Z [ RUN      ] ShapeTest.SetSize
2023-01-11T22:10:09.7510112Z [       OK ] ShapeTest.SetSize (0 ms)
2023-01-11T22:10:09.7510765Z [ RUN      ] ShapeTest.Equal
2023-01-11T22:10:09.7511135Z [       OK ] ShapeTest.Equal (0 ms)
2023-01-11T22:10:09.7511397Z [ RUN      ] ShapeTest.Ostream
2023-01-11T22:10:09.7511666Z [       OK ] ShapeTest.Ostream (0 ms)
2023-01-11T22:10:09.7511952Z [----------] 7 tests from ShapeTest (0 ms total)
2023-01-11T22:10:09.7512101Z 
2023-01-11T22:10:09.7512252Z [----------] 2 tests from TrieCacheTest
2023-01-11T22:10:09.7512543Z [ RUN      ] TrieCacheTest.TestSinglePath
2023-01-11T22:10:09.7512865Z [       OK ] TrieCacheTest.TestSinglePath (0 ms)
2023-01-11T22:10:09.7513160Z [ RUN      ] TrieCacheTest.TestTwoPaths
2023-01-11T22:10:09.7513471Z [       OK ] TrieCacheTest.TestTwoPaths (0 ms)
2023-01-11T22:10:09.7513796Z [----------] 2 tests from TrieCacheTest (0 ms total)
2023-01-11T22:10:09.7513949Z 
2023-01-11T22:10:09.7514075Z [----------] 3 tests from UtilTest
2023-01-11T22:10:09.7514425Z [ RUN      ] UtilTest.ExceptionCleanup
2023-01-11T22:10:09.7514739Z [       OK ] UtilTest.ExceptionCleanup (0 ms)
2023-01-11T22:10:09.7515005Z [ RUN      ] UtilTest.MaybeRef
2023-01-11T22:10:09.7515279Z [       OK ] UtilTest.MaybeRef (0 ms)
2023-01-11T22:10:09.7515536Z [ RUN      ] UtilTest.Iota
2023-01-11T22:10:09.7515795Z [       OK ] UtilTest.Iota (0 ms)
2023-01-11T22:10:09.7516071Z [----------] 3 tests from UtilTest (0 ms total)
2023-01-11T22:10:09.7516216Z 
2023-01-11T22:10:09.7516366Z [----------] 574 tests from LazyOpsTest
2023-01-11T22:10:09.7519215Z [ RUN      ] LazyOpsTest.TestScalarTensor
2023-01-11T22:10:09.7652833Z [       OK ] LazyOpsTest.TestScalarTensor (13 ms)
2023-01-11T22:10:09.7653369Z [ RUN      ] LazyOpsTest.TestClone
2023-01-11T22:10:09.7679790Z [       OK ] LazyOpsTest.TestClone (2 ms)
2023-01-11T22:10:09.7680219Z [ RUN      ] LazyOpsTest.TestTo
2023-01-11T22:10:09.7680822Z [       OK ] LazyOpsTest.TestTo (0 ms)
2023-01-11T22:10:09.7681379Z [ RUN      ] LazyOpsTest.TestIsFloatingPoint
2023-01-11T22:10:09.7681835Z [       OK ] LazyOpsTest.TestIsFloatingPoint (0 ms)
2023-01-11T22:10:09.7682138Z [ RUN      ] LazyOpsTest.TestIsSigned
2023-01-11T22:10:09.7682445Z [       OK ] LazyOpsTest.TestIsSigned (0 ms)
2023-01-11T22:10:09.7682746Z [ RUN      ] LazyOpsTest.TestCastByte
2023-01-11T22:10:09.8056981Z [       OK ] LazyOpsTest.TestCastByte (37 ms)
2023-01-11T22:10:09.8057416Z [ RUN      ] LazyOpsTest.TestCastChar
2023-01-11T22:10:09.8059925Z [       OK ] LazyOpsTest.TestCastChar (0 ms)
2023-01-11T22:10:09.8060485Z [ RUN      ] LazyOpsTest.TestCastShort
2023-01-11T22:10:09.8063166Z [       OK ] LazyOpsTest.TestCastShort (0 ms)
2023-01-11T22:10:09.8063717Z [ RUN      ] LazyOpsTest.TestCastInt
2023-01-11T22:10:09.8066090Z [       OK ] LazyOpsTest.TestCastInt (0 ms)
2023-01-11T22:10:09.8066627Z [ RUN      ] LazyOpsTest.TestCastLong
2023-01-11T22:10:09.8068776Z [       OK ] LazyOpsTest.TestCastLong (0 ms)
2023-01-11T22:10:09.8069171Z [ RUN      ] LazyOpsTest.TestCastFloat
2023-01-11T22:10:09.8069487Z [       OK ] LazyOpsTest.TestCastFloat (0 ms)
2023-01-11T22:10:09.8069848Z [ RUN      ] LazyOpsTest.TestRetainType
2023-01-11T22:10:09.8070892Z [       OK ] LazyOpsTest.TestRetainType (0 ms)
2023-01-11T22:10:09.8071301Z [ RUN      ] LazyOpsTest.TestLogicalTypeWithInterop
2023-01-11T22:10:09.8114240Z [       OK ] LazyOpsTest.TestLogicalTypeWithInterop (4 ms)
2023-01-11T22:10:09.8114574Z [ RUN      ] LazyOpsTest.TestAdd
2023-01-11T22:10:09.8117185Z [       OK ] LazyOpsTest.TestAdd (0 ms)
2023-01-11T22:10:09.8117521Z [ RUN      ] LazyOpsTest.TestAddHalf
2023-01-11T22:10:09.8120270Z [       OK ] LazyOpsTest.TestAddHalf (0 ms)
2023-01-11T22:10:09.8120779Z [ RUN      ] LazyOpsTest.TestAddMixedPrecision
2023-01-11T22:10:09.8125397Z [       OK ] LazyOpsTest.TestAddMixedPrecision (0 ms)
2023-01-11T22:10:09.8125804Z [ RUN      ] LazyOpsTest.TestAddInPlace
2023-01-11T22:10:09.8130065Z [       OK ] LazyOpsTest.TestAddInPlace (0 ms)
2023-01-11T22:10:09.8130392Z [ RUN      ] LazyOpsTest.TestAddScalar
2023-01-11T22:10:09.8133129Z [       OK ] LazyOpsTest.TestAddScalar (0 ms)
2023-01-11T22:10:09.8133498Z [ RUN      ] LazyOpsTest.TestAddScalarInPlace
2023-01-11T22:10:09.8137384Z [       OK ] LazyOpsTest.TestAddScalarInPlace (0 ms)
2023-01-11T22:10:09.8137742Z [ RUN      ] LazyOpsTest.TestAddZeroSizeDim
2023-01-11T22:10:09.8140345Z [       OK ] LazyOpsTest.TestAddZeroSizeDim (0 ms)
2023-01-11T22:10:09.8140696Z [ RUN      ] LazyOpsTest.TestSub
2023-01-11T22:10:09.8143936Z [       OK ] LazyOpsTest.TestSub (0 ms)
2023-01-11T22:10:09.8144377Z [ RUN      ] LazyOpsTest.TestSubInPlace
2023-01-11T22:10:09.8172712Z [       OK ] LazyOpsTest.TestSubInPlace (2 ms)
2023-01-11T22:10:09.8173040Z [ RUN      ] LazyOpsTest.TestSubScalar
2023-01-11T22:10:09.8175853Z [       OK ] LazyOpsTest.TestSubScalar (0 ms)
2023-01-11T22:10:09.8176201Z [ RUN      ] LazyOpsTest.TestSubScalarInPlace
2023-01-11T22:10:09.8179871Z [       OK ] LazyOpsTest.TestSubScalarInPlace (0 ms)
2023-01-11T22:10:09.8180220Z [ RUN      ] LazyOpsTest.TestMul
2023-01-11T22:10:09.8189832Z [       OK ] LazyOpsTest.TestMul (0 ms)
2023-01-11T22:10:09.8190149Z [ RUN      ] LazyOpsTest.TestMulInPlace
2023-01-11T22:10:09.8193800Z [       OK ] LazyOpsTest.TestMulInPlace (0 ms)
2023-01-11T22:10:09.8194142Z [ RUN      ] LazyOpsTest.TestMulScalar
2023-01-11T22:10:09.8196660Z [       OK ] LazyOpsTest.TestMulScalar (0 ms)
2023-01-11T22:10:09.8197032Z [ RUN      ] LazyOpsTest.TestMulScalarInPlace
2023-01-11T22:10:09.8200973Z [       OK ] LazyOpsTest.TestMulScalarInPlace (0 ms)
2023-01-11T22:10:09.8201281Z [ RUN      ] LazyOpsTest.TestDiv
2023-01-11T22:10:09.8286505Z [       OK ] LazyOpsTest.TestDiv (8 ms)
2023-01-11T22:10:09.8286845Z [ RUN      ] LazyOpsTest.TestDivWithRoundingMode
2023-01-11T22:10:09.8590981Z [       OK ] LazyOpsTest.TestDivWithRoundingMode (30 ms)
2023-01-11T22:10:09.8591317Z [ RUN      ] LazyOpsTest.TestDivInPlace
2023-01-11T22:10:09.8594352Z [       OK ] LazyOpsTest.TestDivInPlace (0 ms)
2023-01-11T22:10:09.8594828Z [ RUN      ] LazyOpsTest.TestDivInPlaceWithRoundingMode
2023-01-11T22:10:09.8604595Z [       OK ] LazyOpsTest.TestDivInPlaceWithRoundingMode (1 ms)
2023-01-11T22:10:09.8604959Z [ RUN      ] LazyOpsTest.TestDivScalar
2023-01-11T22:10:09.8633466Z [       OK ] LazyOpsTest.TestDivScalar (2 ms)
2023-01-11T22:10:09.8633829Z [ RUN      ] LazyOpsTest.TestDivScalarInPlace
2023-01-11T22:10:09.8640250Z [       OK ] LazyOpsTest.TestDivScalarInPlace (0 ms)
2023-01-11T22:10:09.8640569Z [ RUN      ] LazyOpsTest.TestDivOut
2023-01-11T22:10:09.8644507Z [       OK ] LazyOpsTest.TestDivOut (0 ms)
2023-01-11T22:10:09.8644813Z [ RUN      ] LazyOpsTest.TestRsubScalar
2023-01-11T22:10:09.8648091Z [       OK ] LazyOpsTest.TestRsubScalar (0 ms)
2023-01-11T22:10:09.8648380Z [ RUN      ] LazyOpsTest.TestNe
2023-01-11T22:10:09.8651076Z [       OK ] LazyOpsTest.TestNe (0 ms)
2023-01-11T22:10:09.8651381Z [ RUN      ] LazyOpsTest.TestNeInplace
2023-01-11T22:10:09.8656181Z [       OK ] LazyOpsTest.TestNeInplace (0 ms)
2023-01-11T22:10:09.8656470Z [ RUN      ] LazyOpsTest.TestEq
2023-01-11T22:10:09.8659136Z [       OK ] LazyOpsTest.TestEq (0 ms)
2023-01-11T22:10:09.8659450Z [ RUN      ] LazyOpsTest.TestEqInplace
2023-01-11T22:10:09.8664241Z [       OK ] LazyOpsTest.TestEqInplace (0 ms)
2023-01-11T22:10:09.8664535Z [ RUN      ] LazyOpsTest.TestGe
2023-01-11T22:10:09.8667240Z [       OK ] LazyOpsTest.TestGe (0 ms)
2023-01-11T22:10:09.8667542Z [ RUN      ] LazyOpsTest.TestGeInplace
2023-01-11T22:10:09.8682091Z [       OK ] LazyOpsTest.TestGeInplace (1 ms)
2023-01-11T22:10:09.8682414Z [ RUN      ] LazyOpsTest.TestLe
2023-01-11T22:10:09.8685048Z [       OK ] LazyOpsTest.TestLe (0 ms)
2023-01-11T22:10:09.8685355Z [ RUN      ] LazyOpsTest.TestLeInplace
2023-01-11T22:10:09.8690014Z [       OK ] LazyOpsTest.TestLeInplace (0 ms)
2023-01-11T22:10:09.8690324Z [ RUN      ] LazyOpsTest.TestGt
2023-01-11T22:10:09.8693246Z [       OK ] LazyOpsTest.TestGt (0 ms)
2023-01-11T22:10:09.8693578Z [ RUN      ] LazyOpsTest.TestGtInplace
2023-01-11T22:10:09.8698197Z [       OK ] LazyOpsTest.TestGtInplace (0 ms)
2023-01-11T22:10:09.8698604Z [ RUN      ] LazyOpsTest.TestLt
2023-01-11T22:10:09.8701219Z [       OK ] LazyOpsTest.TestLt (0 ms)
2023-01-11T22:10:09.8701534Z [ RUN      ] LazyOpsTest.TestLtInplace
2023-01-11T22:10:09.8721960Z [       OK ] LazyOpsTest.TestLtInplace (2 ms)
2023-01-11T22:10:09.8722273Z [ RUN      ] LazyOpsTest.TestNeScalar
2023-01-11T22:10:09.8724865Z [       OK ] LazyOpsTest.TestNeScalar (0 ms)
2023-01-11T22:10:09.8725175Z [ RUN      ] LazyOpsTest.TestEqScalar
2023-01-11T22:10:09.8727737Z [       OK ] LazyOpsTest.TestEqScalar (0 ms)
2023-01-11T22:10:09.8728052Z [ RUN      ] LazyOpsTest.TestGeScalar
2023-01-11T22:10:09.8730597Z [       OK ] LazyOpsTest.TestGeScalar (0 ms)
2023-01-11T22:10:09.8730920Z [ RUN      ] LazyOpsTest.TestGeScalarInplace
2023-01-11T22:10:09.8746707Z [       OK ] LazyOpsTest.TestGeScalarInplace (1 ms)
2023-01-11T22:10:09.8747072Z [ RUN      ] LazyOpsTest.TestLeScalar
2023-01-11T22:10:09.8749446Z [       OK ] LazyOpsTest.TestLeScalar (0 ms)
2023-01-11T22:10:09.8749795Z [ RUN      ] LazyOpsTest.TestLeScalarInplace
2023-01-11T22:10:09.8753919Z [       OK ] LazyOpsTest.TestLeScalarInplace (0 ms)
2023-01-11T22:10:09.8754249Z [ RUN      ] LazyOpsTest.TestGtScalar
2023-01-11T22:10:09.8756712Z [       OK ] LazyOpsTest.TestGtScalar (0 ms)
2023-01-11T22:10:09.8757059Z [ RUN      ] LazyOpsTest.TestGtScalarInplace
2023-01-11T22:10:09.8761390Z [       OK ] LazyOpsTest.TestGtScalarInplace (0 ms)
2023-01-11T22:10:09.8761709Z [ RUN      ] LazyOpsTest.TestLtScalar
2023-01-11T22:10:09.8764112Z [       OK ] LazyOpsTest.TestLtScalar (0 ms)
2023-01-11T22:10:09.8764457Z [ RUN      ] LazyOpsTest.TestLtScalarInplace
2023-01-11T22:10:09.8768645Z [       OK ] LazyOpsTest.TestLtScalarInplace (0 ms)
2023-01-11T22:10:09.8768972Z [ RUN      ] LazyOpsTest.TestIntegerAdd
2023-01-11T22:10:09.8780183Z [       OK ] LazyOpsTest.TestIntegerAdd (1 ms)
2023-01-11T22:10:09.8780501Z [ RUN      ] LazyOpsTest.TestSVD
2023-01-11T22:10:09.8845163Z [       OK ] LazyOpsTest.TestSVD (6 ms)
2023-01-11T22:10:09.8845696Z [ RUN      ] LazyOpsTest.TestQR
2023-01-11T22:10:09.8846364Z [W BatchLinearAlgebra.cpp:2459] Warning: torch.qr is deprecated in favor of torch.linalg.qr and will be removed in a future PyTorch release.
2023-01-11T22:10:09.8847191Z The boolean parameter 'some' has been replaced with a string parameter 'mode'.
2023-01-11T22:10:09.8847576Z Q, R = torch.qr(A, some)
2023-01-11T22:10:09.8847769Z should be replaced with
2023-01-11T22:10:09.8848098Z Q, R = torch.linalg.qr(A, 'reduced' if some else 'complete') (function operator())
2023-01-11T22:10:09.8868883Z [       OK ] LazyOpsTest.TestQR (2 ms)
2023-01-11T22:10:09.8869288Z [ RUN      ] LazyOpsTest.TestSymEig
2023-01-11T22:10:09.8871167Z [W BatchLinearAlgebra.cpp:2910] Warning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T22:10:09.8872037Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T22:10:09.8872520Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T22:10:09.8872792Z should be replaced with
2023-01-11T22:10:09.8873188Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T22:10:09.8873459Z and
2023-01-11T22:10:09.8873699Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T22:10:09.8873951Z should be replaced with
2023-01-11T22:10:09.8874368Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (function operator())
2023-01-11T22:10:09.8889907Z [       OK ] LazyOpsTest.TestSymEig (2 ms)
2023-01-11T22:10:09.8890528Z [ RUN      ] LazyOpsTest.TestCholesky
2023-01-11T22:10:09.8891495Z [W BatchLinearAlgebra.cpp:1730] Warning: torch.cholesky is deprecated in favor of torch.linalg.cholesky and will be removed in a future PyTorch release.
2023-01-11T22:10:09.8892148Z L = torch.cholesky(A)
2023-01-11T22:10:09.8892480Z should be replaced with
2023-01-11T22:10:09.8892827Z L = torch.linalg.cholesky(A)
2023-01-11T22:10:09.8893122Z and
2023-01-11T22:10:09.8893310Z U = torch.cholesky(A, upper=True)
2023-01-11T22:10:09.8893499Z should be replaced with
2023-01-11T22:10:09.8893708Z U = torch.linalg.cholesky(A).mH().
2023-01-11T22:10:09.8894034Z This transform will produce equivalent results for all valid (symmetric positive definite) inputs. (function operator())
2023-01-11T22:10:09.8899936Z [       OK ] LazyOpsTest.TestCholesky (1 ms)
2023-01-11T22:10:09.8900378Z [ RUN      ] LazyOpsTest.TestLogDet
2023-01-11T22:10:09.8922408Z [       OK ] LazyOpsTest.TestLogDet (2 ms)
2023-01-11T22:10:09.8922954Z [ RUN      ] LazyOpsTest.TestTriangularSolve
2023-01-11T22:10:09.8923591Z [W BatchLinearAlgebra.cpp:2225] Warning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.
2023-01-11T22:10:09.8924178Z torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.
2023-01-11T22:10:09.8924569Z X = torch.triangular_solve(B, A).solution
2023-01-11T22:10:09.8924824Z should be replaced with
2023-01-11T22:10:09.8925141Z X = torch.linalg.solve_triangular(A, B). (function operator())
2023-01-11T22:10:09.9131863Z [       OK ] LazyOpsTest.TestTriangularSolve (20 ms)
2023-01-11T22:10:09.9132213Z [ RUN      ] LazyOpsTest.TestKthValue
2023-01-11T22:10:09.9163627Z [       OK ] LazyOpsTest.TestKthValue (3 ms)
2023-01-11T22:10:09.9163992Z [ RUN      ] LazyOpsTest.TestTopK
2023-01-11T22:10:09.9343559Z [       OK ] LazyOpsTest.TestTopK (17 ms)
2023-01-11T22:10:09.9343947Z [ RUN      ] LazyOpsTest.TestSort
2023-01-11T22:10:09.9411069Z [       OK ] LazyOpsTest.TestSort (6 ms)
2023-01-11T22:10:09.9411594Z [ RUN      ] LazyOpsTest.TestSortDescWithMinValue
2023-01-11T22:10:09.9416332Z [       OK ] LazyOpsTest.TestSortDescWithMinValue (0 ms)
2023-01-11T22:10:09.9416910Z [ RUN      ] LazyOpsTest.TestArgSort
2023-01-11T22:10:09.9428455Z [       OK ] LazyOpsTest.TestArgSort (1 ms)
2023-01-11T22:10:09.9428748Z [ RUN      ] LazyOpsTest.TestMin
2023-01-11T22:10:09.9431465Z [       OK ] LazyOpsTest.TestMin (0 ms)
2023-01-11T22:10:09.9431761Z [ RUN      ] LazyOpsTest.TestMax
2023-01-11T22:10:09.9434546Z [       OK ] LazyOpsTest.TestMax (0 ms)
2023-01-11T22:10:09.9434881Z [ RUN      ] LazyOpsTest.TestUnaryMin
2023-01-11T22:10:09.9437382Z [       OK ] LazyOpsTest.TestUnaryMin (0 ms)
2023-01-11T22:10:09.9437686Z [ RUN      ] LazyOpsTest.TestUnaryMax
2023-01-11T22:10:09.9440050Z [       OK ] LazyOpsTest.TestUnaryMax (0 ms)
2023-01-11T22:10:09.9440336Z [ RUN      ] LazyOpsTest.TestAll
2023-01-11T22:10:09.9450926Z [       OK ] LazyOpsTest.TestAll (1 ms)
2023-01-11T22:10:09.9451446Z [ RUN      ] LazyOpsTest.TestAllDim
2023-01-11T22:10:09.9453857Z [       OK ] LazyOpsTest.TestAllDim (0 ms)
2023-01-11T22:10:09.9454180Z [ RUN      ] LazyOpsTest.TestAllDimKeep
2023-01-11T22:10:09.9456516Z [       OK ] LazyOpsTest.TestAllDimKeep (0 ms)
2023-01-11T22:10:09.9456835Z [ RUN      ] LazyOpsTest.TestAmax
2023-01-11T22:10:09.9492611Z [       OK ] LazyOpsTest.TestAmax (3 ms)
2023-01-11T22:10:09.9492920Z [ RUN      ] LazyOpsTest.TestAmin
2023-01-11T22:10:09.9528439Z [       OK ] LazyOpsTest.TestAmin (3 ms)
2023-01-11T22:10:09.9528770Z [ RUN      ] LazyOpsTest.TestAny
2023-01-11T22:10:09.9539769Z [       OK ] LazyOpsTest.TestAny (1 ms)
2023-01-11T22:10:09.9540089Z [ RUN      ] LazyOpsTest.TestAnyDim
2023-01-11T22:10:09.9542545Z [       OK ] LazyOpsTest.TestAnyDim (0 ms)
2023-01-11T22:10:09.9543018Z [ RUN      ] LazyOpsTest.TestAnyDimKeep
2023-01-11T22:10:09.9545080Z [       OK ] LazyOpsTest.TestAnyDimKeep (0 ms)
2023-01-11T22:10:09.9545432Z [ RUN      ] LazyOpsTest.TestMean
2023-01-11T22:10:09.9548644Z [       OK ] LazyOpsTest.TestMean (0 ms)
2023-01-11T22:10:09.9548998Z [ RUN      ] LazyOpsTest.TestMeanCast
2023-01-11T22:10:09.9551898Z [       OK ] LazyOpsTest.TestMeanCast (0 ms)
2023-01-11T22:10:09.9552279Z [ RUN      ] LazyOpsTest.TestMeanInDim
2023-01-11T22:10:09.9571681Z [       OK ] LazyOpsTest.TestMeanInDim (1 ms)
2023-01-11T22:10:09.9572267Z [ RUN      ] LazyOpsTest.TestMeanInDims
2023-01-11T22:10:09.9578953Z [       OK ] LazyOpsTest.TestMeanInDims (0 ms)
2023-01-11T22:10:09.9579545Z [ RUN      ] LazyOpsTest.TestMeanInDimsKeepCast
2023-01-11T22:10:09.9586287Z [       OK ] LazyOpsTest.TestMeanInDimsKeepCast (0 ms)
2023-01-11T22:10:09.9586653Z [ RUN      ] LazyOpsTest.TestMeanInDimOut
2023-01-11T22:10:09.9614825Z [       OK ] LazyOpsTest.TestMeanInDimOut (2 ms)
2023-01-11T22:10:09.9615362Z [ RUN      ] LazyOpsTest.TestStd
2023-01-11T22:10:09.9622288Z [       OK ] LazyOpsTest.TestStd (0 ms)
2023-01-11T22:10:09.9622994Z [ RUN      ] LazyOpsTest.TestStdInDim
2023-01-11T22:10:09.9693974Z [       OK ] LazyOpsTest.TestStdInDim (7 ms)
2023-01-11T22:10:09.9694551Z [ RUN      ] LazyOpsTest.TestStdWithCorrection
2023-01-11T22:10:09.9729890Z [       OK ] LazyOpsTest.TestStdWithCorrection (3 ms)
2023-01-11T22:10:09.9730523Z [ RUN      ] LazyOpsTest.TestStdMeanWithCorrection
2023-01-11T22:10:09.9743910Z [       OK ] LazyOpsTest.TestStdMeanWithCorrection (1 ms)
2023-01-11T22:10:09.9744476Z [ RUN      ] LazyOpsTest.TestSum
2023-01-11T22:10:09.9747578Z [       OK ] LazyOpsTest.TestSum (0 ms)
2023-01-11T22:10:09.9748095Z [ RUN      ] LazyOpsTest.TestSumCast
2023-01-11T22:10:09.9751210Z [       OK ] LazyOpsTest.TestSumCast (0 ms)
2023-01-11T22:10:09.9751720Z [ RUN      ] LazyOpsTest.TestSumU8
2023-01-11T22:10:09.9754397Z [       OK ] LazyOpsTest.TestSumU8 (0 ms)
2023-01-11T22:10:09.9754904Z [ RUN      ] LazyOpsTest.TestSumInDim
2023-01-11T22:10:09.9772429Z [       OK ] LazyOpsTest.TestSumInDim (1 ms)
2023-01-11T22:10:09.9772989Z [ RUN      ] LazyOpsTest.TestSumInDims
2023-01-11T22:10:09.9779479Z [       OK ] LazyOpsTest.TestSumInDims (0 ms)
2023-01-11T22:10:09.9780052Z [ RUN      ] LazyOpsTest.TestSumInDimsKeep
2023-01-11T22:10:09.9786752Z [       OK ] LazyOpsTest.TestSumInDimsKeep (0 ms)
2023-01-11T22:10:09.9787370Z [ RUN      ] LazyOpsTest.TestSumInDimsKeepCast
2023-01-11T22:10:09.9793682Z [       OK ] LazyOpsTest.TestSumInDimsKeepCast (0 ms)
2023-01-11T22:10:09.9794217Z [ RUN      ] LazyOpsTest.TestVar
2023-01-11T22:10:09.9796578Z [       OK ] LazyOpsTest.TestVar (0 ms)
2023-01-11T22:10:09.9797314Z [ RUN      ] LazyOpsTest.TestVarWithDim
2023-01-11T22:10:09.9803463Z [       OK ] LazyOpsTest.TestVarWithDim (0 ms)
2023-01-11T22:10:09.9804023Z [ RUN      ] LazyOpsTest.TestVarWithCorrection
2023-01-11T22:10:09.9812088Z [       OK ] LazyOpsTest.TestVarWithCorrection (0 ms)
2023-01-11T22:10:09.9812715Z [ RUN      ] LazyOpsTest.TestVarMeanWithCorrection
2023-01-11T22:10:09.9825797Z [       OK ] LazyOpsTest.TestVarMeanWithCorrection (1 ms)
2023-01-11T22:10:09.9826354Z [ RUN      ] LazyOpsTest.TestMaxInDim
2023-01-11T22:10:09.9886894Z [       OK ] LazyOpsTest.TestMaxInDim (6 ms)
2023-01-11T22:10:09.9887414Z [ RUN      ] LazyOpsTest.TestMinInDim
2023-01-11T22:10:09.9898501Z [       OK ] LazyOpsTest.TestMinInDim (1 ms)
2023-01-11T22:10:09.9898995Z [ RUN      ] LazyOpsTest.TestNorm
2023-01-11T22:10:09.9902892Z [       OK ] LazyOpsTest.TestNorm (0 ms)
2023-01-11T22:10:09.9903595Z [ RUN      ] LazyOpsTest.TestNormInDim
2023-01-11T22:10:09.9910590Z [       OK ] LazyOpsTest.TestNormInDim (0 ms)
2023-01-11T22:10:09.9911162Z [ RUN      ] LazyOpsTest.TestNormInDims
2023-01-11T22:10:09.9917960Z [       OK ] LazyOpsTest.TestNormInDims (0 ms)
2023-01-11T22:10:09.9918545Z [ RUN      ] LazyOpsTest.TestNormInDimsKeep
2023-01-11T22:10:09.9925092Z [       OK ] LazyOpsTest.TestNormInDimsKeep (0 ms)
2023-01-11T22:10:09.9925758Z [ RUN      ] LazyOpsTest.TestNormalTwoTensor
2023-01-11T22:10:09.9931915Z [       OK ] LazyOpsTest.TestNormalTwoTensor (0 ms)
2023-01-11T22:10:09.9932490Z [ RUN      ] LazyOpsTest.TestNormalDoubleMean
2023-01-11T22:10:09.9938850Z [       OK ] LazyOpsTest.TestNormalDoubleMean (0 ms)
2023-01-11T22:10:09.9939443Z [ RUN      ] LazyOpsTest.TestNormalDoubleStd
2023-01-11T22:10:09.9941346Z [       OK ] LazyOpsTest.TestNormalDoubleStd (0 ms)
2023-01-11T22:10:09.9941894Z [ RUN      ] LazyOpsTest.TestNormalInPlace
2023-01-11T22:10:09.9945501Z [       OK ] LazyOpsTest.TestNormalInPlace (0 ms)
2023-01-11T22:10:09.9946038Z [ RUN      ] LazyOpsTest.TestUniformInPlace
2023-01-11T22:10:09.9948116Z [       OK ] LazyOpsTest.TestUniformInPlace (0 ms)
2023-01-11T22:10:09.9948674Z [ RUN      ] LazyOpsTest.TestRandomInPlace
2023-01-11T22:10:09.9999758Z [       OK ] LazyOpsTest.TestRandomInPlace (5 ms)
2023-01-11T22:10:10.0000409Z [ RUN      ] LazyOpsTest.TestRandomInPlaceDefaultFrom
2023-01-11T22:10:10.0049537Z [       OK ] LazyOpsTest.TestRandomInPlaceDefaultFrom (4 ms)
2023-01-11T22:10:10.0050181Z [ RUN      ] LazyOpsTest.TestRandomInPlaceDefault
2023-01-11T22:10:10.0063261Z [       OK ] LazyOpsTest.TestRandomInPlaceDefault (1 ms)
2023-01-11T22:10:10.0063879Z [ RUN      ] LazyOpsTest.TestNormGeneral
2023-01-11T22:10:10.0067274Z [       OK ] LazyOpsTest.TestNormGeneral (0 ms)
2023-01-11T22:10:10.0067878Z [ RUN      ] LazyOpsTest.TestNormNuclear
2023-01-11T22:10:10.0071756Z [       OK ] LazyOpsTest.TestNormNuclear (0 ms)
2023-01-11T22:10:10.0072342Z [ RUN      ] LazyOpsTest.TestFrobeniusNormInDim
2023-01-11T22:10:10.0073193Z [W LinearAlgebra.cpp:2783] Warning: at::frobenius_norm is deprecated and it is just left for JIT compatibility. It will be removed in a future PyTorch release. Please use `linalg.vector_norm(A, 2., dim, keepdim)` instead (function operator())
2023-01-11T22:10:10.0079574Z [       OK ] LazyOpsTest.TestFrobeniusNormInDim (0 ms)
2023-01-11T22:10:10.0080227Z [ RUN      ] LazyOpsTest.TestFrobeniusNormInDims
2023-01-11T22:10:10.0086561Z [       OK ] LazyOpsTest.TestFrobeniusNormInDims (0 ms)
2023-01-11T22:10:10.0087099Z [ RUN      ] LazyOpsTest.TestGroupNorm
2023-01-11T22:10:10.0123792Z [       OK ] LazyOpsTest.TestGroupNorm (3 ms)
2023-01-11T22:10:10.0124631Z [ RUN      ] LazyOpsTest.TestGroupNormBackward
2023-01-11T22:10:10.0574031Z [       OK ] LazyOpsTest.TestGroupNormBackward (44 ms)
2023-01-11T22:10:10.0574501Z [ RUN      ] LazyOpsTest.TestInstanceNorm
2023-01-11T22:10:10.0601250Z [       OK ] LazyOpsTest.TestInstanceNorm (2 ms)
2023-01-11T22:10:10.0601701Z [ RUN      ] LazyOpsTest.TestLayerNorm
2023-01-11T22:10:10.0637541Z [       OK ] LazyOpsTest.TestLayerNorm (3 ms)
2023-01-11T22:10:10.0638021Z [ RUN      ] LazyOpsTest.TestLayerNormBackward
2023-01-11T22:10:10.0884852Z [       OK ] LazyOpsTest.TestLayerNormBackward (24 ms)
2023-01-11T22:10:10.0885547Z [ RUN      ] LazyOpsTest.TestNuclearNorm
2023-01-11T22:10:10.0886822Z [W LinearAlgebra.cpp:2837] Warning: at::nuclear_norm is deprecated and it is just left for JIT compatibility. It will be removed in a future PyTorch release. Please use `linalg.matrix_norm(A, 'nuc', dim, keepdim)` instead (function operator())
2023-01-11T22:10:10.0894524Z [       OK ] LazyOpsTest.TestNuclearNorm (1 ms)
2023-01-11T22:10:10.0895328Z [ RUN      ] LazyOpsTest.TestPairwiseDistance
2023-01-11T22:10:10.1566296Z [       OK ] LazyOpsTest.TestPairwiseDistance (67 ms)
2023-01-11T22:10:10.1566699Z [ RUN      ] LazyOpsTest.TestCosineSimilarity
2023-01-11T22:10:10.1611543Z [       OK ] LazyOpsTest.TestCosineSimilarity (4 ms)
2023-01-11T22:10:10.1612088Z [ RUN      ] LazyOpsTest.TestCosineEmbeddingLoss
2023-01-11T22:10:10.2233941Z [       OK ] LazyOpsTest.TestCosineEmbeddingLoss (62 ms)
2023-01-11T22:10:10.2234293Z [ RUN      ] LazyOpsTest.TestHingeEmbeddingLoss
2023-01-11T22:10:10.2271288Z [       OK ] LazyOpsTest.TestHingeEmbeddingLoss (3 ms)
2023-01-11T22:10:10.2271644Z [ RUN      ] LazyOpsTest.TestTripletMarginLoss
2023-01-11T22:10:10.5070552Z [       OK ] LazyOpsTest.TestTripletMarginLoss (279 ms)
2023-01-11T22:10:10.5071334Z [ RUN      ] LazyOpsTest.TestBinaryCrossEntropy
2023-01-11T22:10:10.5090759Z [       OK ] LazyOpsTest.TestBinaryCrossEntropy (2 ms)
2023-01-11T22:10:10.5091348Z [ RUN      ] LazyOpsTest.TestMarginRankingLoss
2023-01-11T22:10:10.5791283Z [       OK ] LazyOpsTest.TestMarginRankingLoss (69 ms)
2023-01-11T22:10:10.5791975Z [ RUN      ] LazyOpsTest.TestBCEWithLogits
2023-01-11T22:10:10.5806372Z [       OK ] LazyOpsTest.TestBCEWithLogits (1 ms)
2023-01-11T22:10:10.5806998Z [ RUN      ] LazyOpsTest.TestKlDiv
2023-01-11T22:10:10.5827920Z [       OK ] LazyOpsTest.TestKlDiv (2 ms)
2023-01-11T22:10:10.5828433Z [ RUN      ] LazyOpsTest.TestProd
2023-01-11T22:10:10.5828899Z [       OK ] LazyOpsTest.TestProd (0 ms)
2023-01-11T22:10:10.5829365Z [ RUN      ] LazyOpsTest.TestProdCast
2023-01-11T22:10:10.5830115Z [       OK ] LazyOpsTest.TestProdCast (0 ms)
2023-01-11T22:10:10.5830441Z [ RUN      ] LazyOpsTest.TestProdInDim
2023-01-11T22:10:10.5834671Z [       OK ] LazyOpsTest.TestProdInDim (0 ms)
2023-01-11T22:10:10.5835024Z [ RUN      ] LazyOpsTest.TestProdInDimKeepCast
2023-01-11T22:10:10.5838828Z [       OK ] LazyOpsTest.TestProdInDimKeepCast (0 ms)
2023-01-11T22:10:10.5839158Z [ RUN      ] LazyOpsTest.TestProdInDimKeep
2023-01-11T22:10:10.5843274Z [       OK ] LazyOpsTest.TestProdInDimKeep (0 ms)
2023-01-11T22:10:10.5843589Z [ RUN      ] LazyOpsTest.TestCumSum
2023-01-11T22:10:10.5857623Z [       OK ] LazyOpsTest.TestCumSum (1 ms)
2023-01-11T22:10:10.5858002Z [ RUN      ] LazyOpsTest.TestCumSumCast
2023-01-11T22:10:10.5873515Z [       OK ] LazyOpsTest.TestCumSumCast (1 ms)
2023-01-11T22:10:10.5873864Z [ RUN      ] LazyOpsTest.TestCumSumLong
2023-01-11T22:10:10.5886257Z [       OK ] LazyOpsTest.TestCumSumLong (1 ms)
2023-01-11T22:10:10.5886594Z [ RUN      ] LazyOpsTest.TestCumSumCastLong
2023-01-11T22:10:10.5898979Z [       OK ] LazyOpsTest.TestCumSumCastLong (1 ms)
2023-01-11T22:10:10.5899293Z [ RUN      ] LazyOpsTest.TestCumProd
2023-01-11T22:10:10.5903741Z [       OK ] LazyOpsTest.TestCumProd (0 ms)
2023-01-11T22:10:10.5904067Z [ RUN      ] LazyOpsTest.TestCumProdCast
2023-01-11T22:10:10.5908008Z [       OK ] LazyOpsTest.TestCumProdCast (0 ms)
2023-01-11T22:10:10.5908319Z [ RUN      ] LazyOpsTest.TestCumProdLong
2023-01-11T22:10:10.5916799Z [       OK ] LazyOpsTest.TestCumProdLong (0 ms)
2023-01-11T22:10:10.5917148Z [ RUN      ] LazyOpsTest.TestCumProdCastLong
2023-01-11T22:10:10.5925835Z [       OK ] LazyOpsTest.TestCumProdCastLong (0 ms)
2023-01-11T22:10:10.5926325Z [ RUN      ] LazyOpsTest.TestArgMin
2023-01-11T22:10:10.5927045Z [       OK ] LazyOpsTest.TestArgMin (0 ms)
2023-01-11T22:10:10.5927354Z [ RUN      ] LazyOpsTest.TestArgMinDim
2023-01-11T22:10:10.5928982Z [       OK ] LazyOpsTest.TestArgMinDim (0 ms)
2023-01-11T22:10:10.5929505Z [ RUN      ] LazyOpsTest.TestArgMinDimKeep
2023-01-11T22:10:10.5929990Z [       OK ] LazyOpsTest.TestArgMinDimKeep (0 ms)
2023-01-11T22:10:10.5930307Z [ RUN      ] LazyOpsTest.TestArgMinSameValue
2023-01-11T22:10:10.5930651Z [       OK ] LazyOpsTest.TestArgMinSameValue (0 ms)
2023-01-11T22:10:10.5930979Z [ RUN      ] LazyOpsTest.TestArgMinWrapper
2023-01-11T22:10:10.5931707Z [       OK ] LazyOpsTest.TestArgMinWrapper (0 ms)
2023-01-11T22:10:10.5932088Z [ RUN      ] LazyOpsTest.TestArgMax
2023-01-11T22:10:10.5932390Z [       OK ] LazyOpsTest.TestArgMax (0 ms)
2023-01-11T22:10:10.5932690Z [ RUN      ] LazyOpsTest.TestArgMaxDim
2023-01-11T22:10:10.5933556Z [       OK ] LazyOpsTest.TestArgMaxDim (0 ms)
2023-01-11T22:10:10.5933961Z [ RUN      ] LazyOpsTest.TestArgMaxDimKeep
2023-01-11T22:10:10.5934905Z [       OK ] LazyOpsTest.TestArgMaxDimKeep (0 ms)
2023-01-11T22:10:10.5935468Z [ RUN      ] LazyOpsTest.TestArgMaxSameValue
2023-01-11T22:10:10.5936063Z [       OK ] LazyOpsTest.TestArgMaxSameValue (0 ms)
2023-01-11T22:10:10.5936440Z [ RUN      ] LazyOpsTest.TestArgMaxWrapper
2023-01-11T22:10:10.5937102Z [       OK ] LazyOpsTest.TestArgMaxWrapper (0 ms)
2023-01-11T22:10:10.5937618Z [ RUN      ] LazyOpsTest.TestAsin
2023-01-11T22:10:10.5938667Z [       OK ] LazyOpsTest.TestAsin (0 ms)
2023-01-11T22:10:10.5939193Z [ RUN      ] LazyOpsTest.TestAsinh
2023-01-11T22:10:10.5940338Z [       OK ] LazyOpsTest.TestAsinh (0 ms)
2023-01-11T22:10:10.5940889Z [ RUN      ] LazyOpsTest.TestAsinhInPlace
2023-01-11T22:10:10.5942044Z [       OK ] LazyOpsTest.TestAsinhInPlace (0 ms)
2023-01-11T22:10:10.5942739Z [ RUN      ] LazyOpsTest.TestSin
2023-01-11T22:10:10.5943720Z [       OK ] LazyOpsTest.TestSin (0 ms)
2023-01-11T22:10:10.5944254Z [ RUN      ] LazyOpsTest.TestSinh
2023-01-11T22:10:10.5945037Z [       OK ] LazyOpsTest.TestSinh (0 ms)
2023-01-11T22:10:10.5945533Z [ RUN      ] LazyOpsTest.TestAcos
2023-01-11T22:10:10.5946431Z [       OK ] LazyOpsTest.TestAcos (0 ms)
2023-01-11T22:10:10.5946957Z [ RUN      ] LazyOpsTest.TestAcosh
2023-01-11T22:10:10.5947685Z [       OK ] LazyOpsTest.TestAcosh (0 ms)
2023-01-11T22:10:10.5948228Z [ RUN      ] LazyOpsTest.TestAcoshInPlace
2023-01-11T22:10:10.5949294Z [       OK ] LazyOpsTest.TestAcoshInPlace (0 ms)
2023-01-11T22:10:10.5949827Z [ RUN      ] LazyOpsTest.TestCos
2023-01-11T22:10:10.5952511Z [       OK ] LazyOpsTest.TestCos (0 ms)
2023-01-11T22:10:10.5953020Z [ RUN      ] LazyOpsTest.TestCosh
2023-01-11T22:10:10.5953601Z [       OK ] LazyOpsTest.TestCosh (0 ms)
2023-01-11T22:10:10.5954125Z [ RUN      ] LazyOpsTest.TestAtan
2023-01-11T22:10:10.5954982Z [       OK ] LazyOpsTest.TestAtan (0 ms)
2023-01-11T22:10:10.5955633Z [ RUN      ] LazyOpsTest.TestAtanh
2023-01-11T22:10:10.5956433Z [       OK ] LazyOpsTest.TestAtanh (0 ms)
2023-01-11T22:10:10.5956982Z [ RUN      ] LazyOpsTest.TestAtanhInPlace
2023-01-11T22:10:10.5958106Z [       OK ] LazyOpsTest.TestAtanhInPlace (0 ms)
2023-01-11T22:10:10.5958648Z [ RUN      ] LazyOpsTest.TestAtan2
2023-01-11T22:10:10.5959644Z [       OK ] LazyOpsTest.TestAtan2 (0 ms)
2023-01-11T22:10:10.5960137Z [ RUN      ] LazyOpsTest.TestTan
2023-01-11T22:10:10.5960943Z [       OK ] LazyOpsTest.TestTan (0 ms)
2023-01-11T22:10:10.5961456Z [ RUN      ] LazyOpsTest.TestTanh
2023-01-11T22:10:10.5964366Z [       OK ] LazyOpsTest.TestTanh (0 ms)
2023-01-11T22:10:10.5964933Z [ RUN      ] LazyOpsTest.TestClampMinMax
2023-01-11T22:10:10.5967864Z [       OK ] LazyOpsTest.TestClampMinMax (0 ms)
2023-01-11T22:10:10.5968513Z [ RUN      ] LazyOpsTest.TestClampMin
2023-01-11T22:10:10.5971177Z [       OK ] LazyOpsTest.TestClampMin (0 ms)
2023-01-11T22:10:10.5971730Z [ RUN      ] LazyOpsTest.TestClampMax
2023-01-11T22:10:10.5974221Z [       OK ] LazyOpsTest.TestClampMax (0 ms)
2023-01-11T22:10:10.5974795Z [ RUN      ] LazyOpsTest.TestClampMinExplicit
2023-01-11T22:10:10.5977084Z [       OK ] LazyOpsTest.TestClampMinExplicit (0 ms)
2023-01-11T22:10:10.5977681Z [ RUN      ] LazyOpsTest.TestClampMaxExplicit
2023-01-11T22:10:10.5978356Z [       OK ] LazyOpsTest.TestClampMaxExplicit (0 ms)
2023-01-11T22:10:10.5978954Z [ RUN      ] LazyOpsTest.TestClampMinExplicitInPlace
2023-01-11T22:10:10.5981859Z [       OK ] LazyOpsTest.TestClampMinExplicitInPlace (0 ms)
2023-01-11T22:10:10.5982605Z [ RUN      ] LazyOpsTest.TestClampMaxExplicitInPlace
2023-01-11T22:10:10.5983537Z [       OK ] LazyOpsTest.TestClampMaxExplicitInPlace (0 ms)
2023-01-11T22:10:10.5984102Z [ RUN      ] LazyOpsTest.TestCeil
2023-01-11T22:10:10.5984808Z [       OK ] LazyOpsTest.TestCeil (0 ms)
2023-01-11T22:10:10.5985332Z [ RUN      ] LazyOpsTest.TestFloor
2023-01-11T22:10:10.5987659Z [       OK ] LazyOpsTest.TestFloor (0 ms)
2023-01-11T22:10:10.5987977Z [ RUN      ] LazyOpsTest.TestRound
2023-01-11T22:10:10.5989402Z [       OK ] LazyOpsTest.TestRound (0 ms)
2023-01-11T22:10:10.5989913Z [ RUN      ] LazyOpsTest.TestTrunc
2023-01-11T22:10:10.5992815Z [       OK ] LazyOpsTest.TestTrunc (0 ms)
2023-01-11T22:10:10.5993351Z [ RUN      ] LazyOpsTest.TestFrac
2023-01-11T22:10:10.5995960Z [       OK ] LazyOpsTest.TestFrac (0 ms)
2023-01-11T22:10:10.5996463Z [ RUN      ] LazyOpsTest.TestNeg
2023-01-11T22:10:10.5999029Z [       OK ] LazyOpsTest.TestNeg (0 ms)
2023-01-11T22:10:10.5999634Z [ RUN      ] LazyOpsTest.TestBitwiseNot
2023-01-11T22:10:10.6001437Z [       OK ] LazyOpsTest.TestBitwiseNot (0 ms)
2023-01-11T22:10:10.6002029Z [ RUN      ] LazyOpsTest.TestBitwiseNotInPlace
2023-01-11T22:10:10.6003512Z [       OK ] LazyOpsTest.TestBitwiseNotInPlace (0 ms)
2023-01-11T22:10:10.6004058Z [ RUN      ] LazyOpsTest.TestSign
2023-01-11T22:10:10.6004798Z [       OK ] LazyOpsTest.TestSign (0 ms)
2023-01-11T22:10:10.6005332Z [ RUN      ] LazyOpsTest.TestSignByte
2023-01-11T22:10:10.6005824Z [       OK ] LazyOpsTest.TestSignByte (0 ms)
2023-01-11T22:10:10.6006096Z [ RUN      ] LazyOpsTest.TestAbs
2023-01-11T22:10:10.6008278Z [       OK ] LazyOpsTest.TestAbs (0 ms)
2023-01-11T22:10:10.6008829Z [ RUN      ] LazyOpsTest.TestAbsByte
2023-01-11T22:10:10.6010571Z [       OK ] LazyOpsTest.TestAbsByte (0 ms)
2023-01-11T22:10:10.6011126Z [ RUN      ] LazyOpsTest.TestEmptyLike
2023-01-11T22:10:10.6011687Z [       OK ] LazyOpsTest.TestEmptyLike (0 ms)
2023-01-11T22:10:10.6012397Z [ RUN      ] LazyOpsTest.TestEmptyLikeOptions
2023-01-11T22:10:10.6012761Z [       OK ] LazyOpsTest.TestEmptyLikeOptions (0 ms)
2023-01-11T22:10:10.6013073Z [ RUN      ] LazyOpsTest.TestEmpty
2023-01-11T22:10:10.6013368Z [       OK ] LazyOpsTest.TestEmpty (0 ms)
2023-01-11T22:10:10.6013843Z [ RUN      ] LazyOpsTest.TestZeroInPlace
2023-01-11T22:10:10.6014176Z [       OK ] LazyOpsTest.TestZeroInPlace (0 ms)
2023-01-11T22:10:10.6014537Z [ RUN      ] LazyOpsTest.TestZerosLike
2023-01-11T22:10:10.6015074Z [       OK ] LazyOpsTest.TestZerosLike (0 ms)
2023-01-11T22:10:10.6015476Z [ RUN      ] LazyOpsTest.TestZerosLikeOptions
2023-01-11T22:10:10.6016096Z [       OK ] LazyOpsTest.TestZerosLikeOptions (0 ms)
2023-01-11T22:10:10.6016409Z [ RUN      ] LazyOpsTest.TestZeros
2023-01-11T22:10:10.6019237Z [       OK ] LazyOpsTest.TestZeros (0 ms)
2023-01-11T22:10:10.6019967Z [ RUN      ] LazyOpsTest.TestOnes
2023-01-11T22:10:10.6023006Z [       OK ] LazyOpsTest.TestOnes (0 ms)
2023-01-11T22:10:10.6023542Z [ RUN      ] LazyOpsTest.TestOnesLike
2023-01-11T22:10:10.6024216Z [       OK ] LazyOpsTest.TestOnesLike (0 ms)
2023-01-11T22:10:10.6024583Z [ RUN      ] LazyOpsTest.TestOnesLikeOptions
2023-01-11T22:10:10.6024932Z [       OK ] LazyOpsTest.TestOnesLikeOptions (0 ms)
2023-01-11T22:10:10.6025223Z [ RUN      ] LazyOpsTest.TestFull
2023-01-11T22:10:10.6029384Z [       OK ] LazyOpsTest.TestFull (0 ms)
2023-01-11T22:10:10.6029768Z [ RUN      ] LazyOpsTest.TestFullLike
2023-01-11T22:10:10.6030578Z [       OK ] LazyOpsTest.TestFullLike (0 ms)
2023-01-11T22:10:10.6030976Z [ RUN      ] LazyOpsTest.TestFullLikeOptions
2023-01-11T22:10:10.6040326Z [       OK ] LazyOpsTest.TestFullLikeOptions (0 ms)
2023-01-11T22:10:10.6040810Z [ RUN      ] LazyOpsTest.TestARange
2023-01-11T22:10:10.6041287Z [       OK ] LazyOpsTest.TestARange (0 ms)
2023-01-11T22:10:10.6041601Z [ RUN      ] LazyOpsTest.TestARangeOut
2023-01-11T22:10:10.6042086Z [W RangeFactories.cpp:216] Warning: The number of elements in the out tensor of shape [4] is 4 which does not match the computed number of elements 200. Note that this may occur as a result of rounding error. The out tensor will be resized to a tensor of shape (200,). (function operator())
2023-01-11T22:10:10.6042601Z [       OK ] LazyOpsTest.TestARangeOut (0 ms)
2023-01-11T22:10:10.6042906Z [ RUN      ] LazyOpsTest.TestDimARange
2023-01-11T22:10:10.6045079Z [       OK ] LazyOpsTest.TestDimARange (0 ms)
2023-01-11T22:10:10.6045401Z [ RUN      ] LazyOpsTest.TestBartlettWindow
2023-01-11T22:10:10.6072191Z [       OK ] LazyOpsTest.TestBartlettWindow (2 ms)
2023-01-11T22:10:10.6072521Z [ RUN      ] LazyOpsTest.TestBlackmanWindow
2023-01-11T22:10:10.6094493Z [       OK ] LazyOpsTest.TestBlackmanWindow (2 ms)
2023-01-11T22:10:10.6094861Z [ RUN      ] LazyOpsTest.TestHammingWindow
2023-01-11T22:10:10.6110672Z [       OK ] LazyOpsTest.TestHammingWindow (1 ms)
2023-01-11T22:10:10.6111093Z [ RUN      ] LazyOpsTest.TestHannWindow
2023-01-11T22:10:10.6690307Z [       OK ] LazyOpsTest.TestHannWindow (57 ms)
2023-01-11T22:10:10.6690723Z [ RUN      ] LazyOpsTest.TestLogSigmoid
2023-01-11T22:10:10.6693499Z [       OK ] LazyOpsTest.TestLogSigmoid (0 ms)
2023-01-11T22:10:10.6693929Z [ RUN      ] LazyOpsTest.TestLogSigmoidForward
2023-01-11T22:10:10.6699937Z [       OK ] LazyOpsTest.TestLogSigmoidForward (0 ms)
2023-01-11T22:10:10.6700385Z [ RUN      ] LazyOpsTest.TestLogsumexp
2023-01-11T22:10:10.6738969Z [       OK ] LazyOpsTest.TestLogsumexp (3 ms)
2023-01-11T22:10:10.6739330Z [ RUN      ] LazyOpsTest.TestSiLU
2023-01-11T22:10:10.6742866Z [       OK ] LazyOpsTest.TestSiLU (0 ms)
2023-01-11T22:10:10.6743408Z [ RUN      ] LazyOpsTest.TestSigmoid
2023-01-11T22:10:10.6745934Z [       OK ] LazyOpsTest.TestSigmoid (0 ms)
2023-01-11T22:10:10.6746235Z [ RUN      ] LazyOpsTest.TestMatmul_1x1
2023-01-11T22:10:10.6747828Z [       OK ] LazyOpsTest.TestMatmul_1x1 (0 ms)
2023-01-11T22:10:10.6748134Z [ RUN      ] LazyOpsTest.TestMatmul_2x1
2023-01-11T22:10:10.6751738Z [       OK ] LazyOpsTest.TestMatmul_2x1 (0 ms)
2023-01-11T22:10:10.6752059Z [ RUN      ] LazyOpsTest.TestMatmul_1x2
2023-01-11T22:10:10.6757327Z [       OK ] LazyOpsTest.TestMatmul_1x2 (0 ms)
2023-01-11T22:10:10.6757641Z [ RUN      ] LazyOpsTest.TestMatmul_2x2
2023-01-11T22:10:10.6769589Z [       OK ] LazyOpsTest.TestMatmul_2x2 (0 ms)
2023-01-11T22:10:10.6769987Z [ RUN      ] LazyOpsTest.TestMatmulBcast
2023-01-11T22:10:10.6770396Z [       OK ] LazyOpsTest.TestMatmulBcast (0 ms)
2023-01-11T22:10:10.6770880Z [ RUN      ] LazyOpsTest.TestDot
2023-01-11T22:10:10.6771686Z [       OK ] LazyOpsTest.TestDot (0 ms)
2023-01-11T22:10:10.6772056Z [ RUN      ] LazyOpsTest.TestTensorDot
2023-01-11T22:10:10.6823058Z [       OK ] LazyOpsTest.TestTensorDot (0 ms)
2023-01-11T22:10:10.6823453Z [ RUN      ] LazyOpsTest.TestGer
2023-01-11T22:10:10.6823814Z [       OK ] LazyOpsTest.TestGer (1 ms)
2023-01-11T22:10:10.6824144Z [ RUN      ] LazyOpsTest.TestMv
2023-01-11T22:10:10.6824502Z [       OK ] LazyOpsTest.TestMv (0 ms)
2023-01-11T22:10:10.6824860Z [ RUN      ] LazyOpsTest.TestMvOut
2023-01-11T22:10:10.6825229Z [       OK ] LazyOpsTest.TestMvOut (1 ms)
2023-01-11T22:10:10.6825628Z [ RUN      ] LazyOpsTest.TestBatchAddBatchMatMul
2023-01-11T22:10:10.6826085Z [       OK ] LazyOpsTest.TestBatchAddBatchMatMul (0 ms)
2023-01-11T22:10:10.6826567Z [ RUN      ] LazyOpsTest.TestBatchAddBatchMatMulInPlace
2023-01-11T22:10:10.6827073Z [       OK ] LazyOpsTest.TestBatchAddBatchMatMulInPlace (0 ms)
2023-01-11T22:10:10.6827519Z [ RUN      ] LazyOpsTest.TestBatchMatMul
2023-01-11T22:10:10.6827923Z [       OK ] LazyOpsTest.TestBatchMatMul (0 ms)
2023-01-11T22:10:10.6828306Z [ RUN      ] LazyOpsTest.TestChainMatMul
2023-01-11T22:10:10.6828926Z [W LinearAlgebra.cpp:1077] Warning: torch.chain_matmul is deprecated and will be removed in a future PyTorch release. Use torch.linalg.multi_dot instead, which accepts a list of two or more tensors rather than multiple parameters. (function operator())
2023-01-11T22:10:10.6862906Z [       OK ] LazyOpsTest.TestChainMatMul (0 ms)
2023-01-11T22:10:10.6863301Z [ RUN      ] LazyOpsTest.TestLinear
2023-01-11T22:10:10.6863707Z [       OK ] LazyOpsTest.TestLinear (0 ms)
2023-01-11T22:10:10.6864100Z [ RUN      ] LazyOpsTest.TestPinverse
2023-01-11T22:10:10.6864511Z [       OK ] LazyOpsTest.TestPinverse (1 ms)
2023-01-11T22:10:10.6864821Z [ RUN      ] LazyOpsTest.TestEinsumOuter
2023-01-11T22:10:10.6865126Z [       OK ] LazyOpsTest.TestEinsumOuter (0 ms)
2023-01-11T22:10:10.6865459Z [ RUN      ] LazyOpsTest.TestEinsumOuterBackward
2023-01-11T22:10:10.6888037Z [       OK ] LazyOpsTest.TestEinsumOuterBackward (2 ms)
2023-01-11T22:10:10.6888546Z [ RUN      ] LazyOpsTest.TestEinsumBatchMatMul
2023-01-11T22:10:10.6898763Z [       OK ] LazyOpsTest.TestEinsumBatchMatMul (1 ms)
2023-01-11T22:10:10.6899343Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerBilinear
2023-01-11T22:10:10.6943163Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerBilinear (1 ms)
2023-01-11T22:10:10.6944674Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerDiagonal
2023-01-11T22:10:10.6945145Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerDiagonal (0 ms)
2023-01-11T22:10:10.6945617Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerBatchDiagonal
2023-01-11T22:10:10.6946203Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerBatchDiagonal (0 ms)
2023-01-11T22:10:10.6946695Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerBatchPermute
2023-01-11T22:10:10.6947139Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerBatchPermute (0 ms)
2023-01-11T22:10:10.6947593Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerRepeatedAxis
2023-01-11T22:10:10.6948077Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerRepeatedAxis (0 ms)
2023-01-11T22:10:10.6948431Z [ RUN      ] LazyOpsTest.TestBilinear
2023-01-11T22:10:10.7073359Z [       OK ] LazyOpsTest.TestBilinear (13 ms)
2023-01-11T22:10:10.7073940Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2D
2023-01-11T22:10:10.7078258Z [       OK ] LazyOpsTest.TestUpsampleNearest2D (0 ms)
2023-01-11T22:10:10.7078664Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2DBackward
2023-01-11T22:10:10.7088859Z [       OK ] LazyOpsTest.TestUpsampleNearest2DBackward (1 ms)
2023-01-11T22:10:10.7089330Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2DWithScale
2023-01-11T22:10:10.7093125Z [       OK ] LazyOpsTest.TestUpsampleNearest2DWithScale (0 ms)
2023-01-11T22:10:10.7093687Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2DBackwardWithScale
2023-01-11T22:10:10.7103106Z [       OK ] LazyOpsTest.TestUpsampleNearest2DBackwardWithScale (0 ms)
2023-01-11T22:10:10.7103619Z [ RUN      ] LazyOpsTest.TestUpsampleBilinear2D
2023-01-11T22:10:10.7111280Z [       OK ] LazyOpsTest.TestUpsampleBilinear2D (0 ms)
2023-01-11T22:10:10.7111725Z [ RUN      ] LazyOpsTest.TestUpsampleBilinear2DBackward
2023-01-11T22:10:10.7129236Z [       OK ] LazyOpsTest.TestUpsampleBilinear2DBackward (1 ms)
2023-01-11T22:10:10.7129645Z [ RUN      ] LazyOpsTest.TestAddCMul
2023-01-11T22:10:10.7132666Z [       OK ] LazyOpsTest.TestAddCMul (0 ms)
2023-01-11T22:10:10.7133218Z [ RUN      ] LazyOpsTest.TestAddCDiv
2023-01-11T22:10:10.7136238Z [       OK ] LazyOpsTest.TestAddCDiv (0 ms)
2023-01-11T22:10:10.7136815Z [ RUN      ] LazyOpsTest.TestAddCDivWithBroadcast
2023-01-11T22:10:10.7139747Z [       OK ] LazyOpsTest.TestAddCDivWithBroadcast (0 ms)
2023-01-11T22:10:10.7140103Z [ RUN      ] LazyOpsTest.TestSize
2023-01-11T22:10:10.7140440Z [       OK ] LazyOpsTest.TestSize (0 ms)
2023-01-11T22:10:10.7140714Z [ RUN      ] LazyOpsTest.TestSelect
2023-01-11T22:10:10.7222188Z [       OK ] LazyOpsTest.TestSelect (8 ms)
2023-01-11T22:10:10.7222663Z [ RUN      ] LazyOpsTest.TestBernoulliScalarProb
2023-01-11T22:10:10.7227647Z [       OK ] LazyOpsTest.TestBernoulliScalarProb (0 ms)
2023-01-11T22:10:10.7228049Z [ RUN      ] LazyOpsTest.TestBernoulliTensorProb
2023-01-11T22:10:10.7231694Z [       OK ] LazyOpsTest.TestBernoulliTensorProb (0 ms)
2023-01-11T22:10:10.7232208Z [ RUN      ] LazyOpsTest.TestBernoulliScalarProbInPlace
2023-01-11T22:10:10.7236002Z [       OK ] LazyOpsTest.TestBernoulliScalarProbInPlace (0 ms)
2023-01-11T22:10:10.7236508Z [ RUN      ] LazyOpsTest.TestBernoulliTensorProbInPlace
2023-01-11T22:10:10.7240465Z [       OK ] LazyOpsTest.TestBernoulliTensorProbInPlace (0 ms)
2023-01-11T22:10:10.7240887Z [ RUN      ] LazyOpsTest.TestDropout
2023-01-11T22:10:10.7244529Z [       OK ] LazyOpsTest.TestDropout (0 ms)
2023-01-11T22:10:10.7244908Z [ RUN      ] LazyOpsTest.TestDropoutInPlace
2023-01-11T22:10:10.7249935Z [       OK ] LazyOpsTest.TestDropoutInPlace (0 ms)
2023-01-11T22:10:10.7250301Z [ RUN      ] LazyOpsTest.TestRandperm
2023-01-11T22:10:10.7252754Z [       OK ] LazyOpsTest.TestRandperm (0 ms)
2023-01-11T22:10:10.7253290Z [ RUN      ] LazyOpsTest.TestSlice
2023-01-11T22:10:10.7262180Z [       OK ] LazyOpsTest.TestSlice (0 ms)
2023-01-11T22:10:10.7263461Z [ RUN      ] LazyOpsTest.TestTake
2023-01-11T22:10:10.7263837Z [       OK ] LazyOpsTest.TestTake (0 ms)
2023-01-11T22:10:10.7264311Z [ RUN      ] LazyOpsTest.TestTakeBackward
2023-01-11T22:10:10.7274682Z [       OK ] LazyOpsTest.TestTakeBackward (1 ms)
2023-01-11T22:10:10.7275092Z [ RUN      ] LazyOpsTest.TestStack
2023-01-11T22:10:10.7299164Z [       OK ] LazyOpsTest.TestStack (2 ms)
2023-01-11T22:10:10.7299447Z [ RUN      ] LazyOpsTest.TestCat
2023-01-11T22:10:10.7306664Z [       OK ] LazyOpsTest.TestCat (0 ms)
2023-01-11T22:10:10.7306948Z [ RUN      ] LazyOpsTest.TestUnbind
2023-01-11T22:10:10.7318818Z [       OK ] LazyOpsTest.TestUnbind (1 ms)
2023-01-11T22:10:10.7319111Z [ RUN      ] LazyOpsTest.TestRepeat
2023-01-11T22:10:10.7330201Z [       OK ] LazyOpsTest.TestRepeat (1 ms)
2023-01-11T22:10:10.7330494Z [ RUN      ] LazyOpsTest.TestGather
2023-01-11T22:10:10.7337211Z [       OK ] LazyOpsTest.TestGather (0 ms)
2023-01-11T22:10:10.7337498Z [ RUN      ] LazyOpsTest.TestScatter
2023-01-11T22:10:10.7340645Z [       OK ] LazyOpsTest.TestScatter (0 ms)
2023-01-11T22:10:10.7340997Z [ RUN      ] LazyOpsTest.TestScatterR1
2023-01-11T22:10:10.7342553Z [       OK ] LazyOpsTest.TestScatterR1 (0 ms)
2023-01-11T22:10:10.7342876Z [ RUN      ] LazyOpsTest.TestScatterR3
2023-01-11T22:10:10.7345114Z [       OK ] LazyOpsTest.TestScatterR3 (0 ms)
2023-01-11T22:10:10.7345450Z [ RUN      ] LazyOpsTest.TestScatterBiggerSource
2023-01-11T22:10:10.7348423Z [       OK ] LazyOpsTest.TestScatterBiggerSource (0 ms)
2023-01-11T22:10:10.7348770Z [ RUN      ] LazyOpsTest.TestScatterScalar
2023-01-11T22:10:10.7351069Z [       OK ] LazyOpsTest.TestScatterScalar (0 ms)
2023-01-11T22:10:10.7351603Z [ RUN      ] LazyOpsTest.TestScatterReduceAdd
2023-01-11T22:10:10.7353992Z [       OK ] LazyOpsTest.TestScatterReduceAdd (0 ms)
2023-01-11T22:10:10.7354385Z [ RUN      ] LazyOpsTest.TestScatterAdd
2023-01-11T22:10:10.7360581Z [       OK ] LazyOpsTest.TestScatterAdd (0 ms)
2023-01-11T22:10:10.7360912Z [ RUN      ] LazyOpsTest.TestScatterAddInPlace
2023-01-11T22:10:10.7368112Z [       OK ] LazyOpsTest.TestScatterAddInPlace (0 ms)
2023-01-11T22:10:10.7368466Z [ RUN      ] LazyOpsTest.TestIndexSelect
2023-01-11T22:10:10.7458407Z [       OK ] LazyOpsTest.TestIndexSelect (8 ms)
2023-01-11T22:10:10.7458763Z [ RUN      ] LazyOpsTest.TestIndexSelectRank0
2023-01-11T22:10:10.7482179Z [       OK ] LazyOpsTest.TestIndexSelectRank0 (2 ms)
2023-01-11T22:10:10.7482877Z [ RUN      ] LazyOpsTest.TestInverse
2023-01-11T22:10:10.7488346Z [       OK ] LazyOpsTest.TestInverse (0 ms)
2023-01-11T22:10:10.7488872Z [ RUN      ] LazyOpsTest.TestIsnan
2023-01-11T22:10:10.7489388Z [       OK ] LazyOpsTest.TestIsnan (0 ms)
2023-01-11T22:10:10.7489858Z [ RUN      ] LazyOpsTest.TestExpand
2023-01-11T22:10:10.7492782Z [       OK ] LazyOpsTest.TestExpand (0 ms)
2023-01-11T22:10:10.7493308Z [ RUN      ] LazyOpsTest.TestExpandBack
2023-01-11T22:10:10.7497088Z [       OK ] LazyOpsTest.TestExpandBack (0 ms)
2023-01-11T22:10:10.7497639Z [ RUN      ] LazyOpsTest.TestExpandAs
2023-01-11T22:10:10.7501742Z [       OK ] LazyOpsTest.TestExpandAs (0 ms)
2023-01-11T22:10:10.7502247Z [ RUN      ] LazyOpsTest.TestEye
2023-01-11T22:10:10.7502888Z [       OK ] LazyOpsTest.TestEye (0 ms)
2023-01-11T22:10:10.7503338Z [ RUN      ] LazyOpsTest.TestEyeWide
2023-01-11T22:10:10.7504067Z [       OK ] LazyOpsTest.TestEyeWide (0 ms)
2023-01-11T22:10:10.7504534Z [ RUN      ] LazyOpsTest.TestEyeNarrow
2023-01-11T22:10:10.7505097Z [       OK ] LazyOpsTest.TestEyeNarrow (0 ms)
2023-01-11T22:10:10.7505824Z [ RUN      ] LazyOpsTest.TestBroadcastTensors
2023-01-11T22:10:10.7511807Z [       OK ] LazyOpsTest.TestBroadcastTensors (0 ms)
2023-01-11T22:10:10.7512239Z [ RUN      ] LazyOpsTest.TestOneIndex
2023-01-11T22:10:10.7582896Z [       OK ] LazyOpsTest.TestOneIndex (1 ms)
2023-01-11T22:10:10.7583390Z [ RUN      ] LazyOpsTest.TestOneIndexTransfer
2023-01-11T22:10:10.7584003Z [       OK ] LazyOpsTest.TestOneIndexTransfer (1 ms)
2023-01-11T22:10:10.7584520Z [ RUN      ] LazyOpsTest.TestNonzero
2023-01-11T22:10:10.7585015Z [       OK ] LazyOpsTest.TestNonzero (0 ms)
2023-01-11T22:10:10.7585527Z [ RUN      ] LazyOpsTest.TestMaskedSelect
2023-01-11T22:10:10.7586074Z [       OK ] LazyOpsTest.TestMaskedSelect (0 ms)
2023-01-11T22:10:10.7586551Z [ RUN      ] LazyOpsTest.TestMaskedScatter
2023-01-11T22:10:10.7586956Z [       OK ] LazyOpsTest.TestMaskedScatter (0 ms)
2023-01-11T22:10:10.7587419Z [ RUN      ] LazyOpsTest.TestMultiIndexHeadNull
2023-01-11T22:10:10.7587766Z [       OK ] LazyOpsTest.TestMultiIndexHeadNull (0 ms)
2023-01-11T22:10:10.7588120Z [ RUN      ] LazyOpsTest.TestMultiIndexMiddleNull
2023-01-11T22:10:10.7588480Z [       OK ] LazyOpsTest.TestMultiIndexMiddleNull (0 ms)
2023-01-11T22:10:10.7588831Z [ RUN      ] LazyOpsTest.TestMultiIndexTailNull
2023-01-11T22:10:10.7589168Z [       OK ] LazyOpsTest.TestMultiIndexTailNull (0 ms)
2023-01-11T22:10:10.7589535Z [ RUN      ] LazyOpsTest.TestMultiIndexMiddleBroadcast
2023-01-11T22:10:10.7589926Z [       OK ] LazyOpsTest.TestMultiIndexMiddleBroadcast (0 ms)
2023-01-11T22:10:10.7590373Z [ RUN      ] LazyOpsTest.TestMultiIndexTailBroadcast
2023-01-11T22:10:10.7590952Z [       OK ] LazyOpsTest.TestMultiIndexTailBroadcast (0 ms)
2023-01-11T22:10:10.7591349Z [ RUN      ] LazyOpsTest.TestMaskIndex
2023-01-11T22:10:10.7591667Z [       OK ] LazyOpsTest.TestMaskIndex (0 ms)
2023-01-11T22:10:10.7591966Z [ RUN      ] LazyOpsTest.TestOneIndexPut
2023-01-11T22:10:10.7622171Z [       OK ] LazyOpsTest.TestOneIndexPut (3 ms)
2023-01-11T22:10:10.7622752Z [ RUN      ] LazyOpsTest.TestOneIndexPutInPlace
2023-01-11T22:10:10.7645081Z [       OK ] LazyOpsTest.TestOneIndexPutInPlace (2 ms)
2023-01-11T22:10:10.7645589Z [ RUN      ] LazyOpsTest.TestOneIndexPutTransfer
2023-01-11T22:10:10.7661730Z [       OK ] LazyOpsTest.TestOneIndexPutTransfer (1 ms)
2023-01-11T22:10:10.7662164Z [ RUN      ] LazyOpsTest.TestMultiIndexPut
2023-01-11T22:10:10.7680285Z [       OK ] LazyOpsTest.TestMultiIndexPut (1 ms)
2023-01-11T22:10:10.7680734Z [ RUN      ] LazyOpsTest.TestMultiIndexPutHeadNull
2023-01-11T22:10:10.7708335Z [       OK ] LazyOpsTest.TestMultiIndexPutHeadNull (2 ms)
2023-01-11T22:10:10.7708977Z [ RUN      ] LazyOpsTest.TestMultiIndexPutMiddleNull
2023-01-11T22:10:10.7727418Z [       OK ] LazyOpsTest.TestMultiIndexPutMiddleNull (1 ms)
2023-01-11T22:10:10.7728080Z [ RUN      ] LazyOpsTest.TestMultiIndexPutTailNull
2023-01-11T22:10:10.7746898Z [       OK ] LazyOpsTest.TestMultiIndexPutTailNull (1 ms)
2023-01-11T22:10:10.7747568Z [ RUN      ] LazyOpsTest.TestMultiIndexPutMiddleBroadcast
2023-01-11T22:10:10.7767238Z [       OK ] LazyOpsTest.TestMultiIndexPutMiddleBroadcast (2 ms)
2023-01-11T22:10:10.7767788Z [ RUN      ] LazyOpsTest.TestMultiIndexPutTailBroadcast
2023-01-11T22:10:10.7786227Z [       OK ] LazyOpsTest.TestMultiIndexPutTailBroadcast (1 ms)
2023-01-11T22:10:10.7786672Z [ RUN      ] LazyOpsTest.TestMaskIndexPut
2023-01-11T22:10:10.7807314Z [       OK ] LazyOpsTest.TestMaskIndexPut (2 ms)
2023-01-11T22:10:10.7807731Z [ RUN      ] LazyOpsTest.TestIndexPutImpl
2023-01-11T22:10:10.7831816Z [       OK ] LazyOpsTest.TestIndexPutImpl (2 ms)
2023-01-11T22:10:10.7832467Z [ RUN      ] LazyOpsTest.TestIndexFillWithScalar
2023-01-11T22:10:10.7864939Z [       OK ] LazyOpsTest.TestIndexFillWithScalar (3 ms)
2023-01-11T22:10:10.7865428Z [ RUN      ] LazyOpsTest.TestIndexFillWithScalarInPlace
2023-01-11T22:10:10.7894791Z [       OK ] LazyOpsTest.TestIndexFillWithScalarInPlace (2 ms)
2023-01-11T22:10:10.7895186Z [ RUN      ] LazyOpsTest.TestIndexFillWithTensor
2023-01-11T22:10:10.7923087Z [       OK ] LazyOpsTest.TestIndexFillWithTensor (2 ms)
2023-01-11T22:10:10.7923470Z [ RUN      ] LazyOpsTest.TestIndexFillWithTensorInPlace
2023-01-11T22:10:10.7956560Z [       OK ] LazyOpsTest.TestIndexFillWithTensorInPlace (3 ms)
2023-01-11T22:10:10.7956931Z [ RUN      ] LazyOpsTest.TestIndexFillRank0
2023-01-11T22:10:10.7984255Z [       OK ] LazyOpsTest.TestIndexFillRank0 (2 ms)
2023-01-11T22:10:10.7984581Z [ RUN      ] LazyOpsTest.TestIndexAdd
2023-01-11T22:10:10.8057011Z [       OK ] LazyOpsTest.TestIndexAdd (7 ms)
2023-01-11T22:10:10.8057367Z [ RUN      ] LazyOpsTest.TestIndexAddInPlace
2023-01-11T22:10:10.8109912Z [       OK ] LazyOpsTest.TestIndexAddInPlace (5 ms)
2023-01-11T22:10:10.8110277Z [ RUN      ] LazyOpsTest.TestIndexAddRank0
2023-01-11T22:10:10.8131461Z [       OK ] LazyOpsTest.TestIndexAddRank0 (2 ms)
2023-01-11T22:10:10.8131862Z [ RUN      ] LazyOpsTest.TestIndexCopy
2023-01-11T22:10:10.8153892Z [       OK ] LazyOpsTest.TestIndexCopy (2 ms)
2023-01-11T22:10:10.8154293Z [ RUN      ] LazyOpsTest.TestIndexCopyInPlace
2023-01-11T22:10:10.8182195Z [       OK ] LazyOpsTest.TestIndexCopyInPlace (2 ms)
2023-01-11T22:10:10.8182712Z [ RUN      ] LazyOpsTest.TestIndexCopyRank0
2023-01-11T22:10:10.8203162Z [       OK ] LazyOpsTest.TestIndexCopyRank0 (2 ms)
2023-01-11T22:10:10.8203543Z [ RUN      ] LazyOpsTest.TestRelu
2023-01-11T22:10:10.8206674Z [       OK ] LazyOpsTest.TestRelu (0 ms)
2023-01-11T22:10:10.8207030Z [ RUN      ] LazyOpsTest.TestReluInPlace
2023-01-11T22:10:10.8210420Z [       OK ] LazyOpsTest.TestReluInPlace (0 ms)
2023-01-11T22:10:10.8210862Z [ RUN      ] LazyOpsTest.TestHardshrink
2023-01-11T22:10:10.8211995Z [       OK ] LazyOpsTest.TestHardshrink (0 ms)
2023-01-11T22:10:10.8212293Z [ RUN      ] LazyOpsTest.TestHardSigmoid
2023-01-11T22:10:10.8214844Z [       OK ] LazyOpsTest.TestHardSigmoid (0 ms)
2023-01-11T22:10:10.8215182Z [ RUN      ] LazyOpsTest.TestHardSigmoidInPlace
2023-01-11T22:10:10.8218603Z [       OK ] LazyOpsTest.TestHardSigmoidInPlace (0 ms)
2023-01-11T22:10:10.8218965Z [ RUN      ] LazyOpsTest.TestHardSigmoidBackward
2023-01-11T22:10:10.8228720Z [       OK ] LazyOpsTest.TestHardSigmoidBackward (0 ms)
2023-01-11T22:10:10.8229127Z [ RUN      ] LazyOpsTest.TestSoftshrink
2023-01-11T22:10:10.8229979Z [       OK ] LazyOpsTest.TestSoftshrink (0 ms)
2023-01-11T22:10:10.8230294Z [ RUN      ] LazyOpsTest.TestHardtanh
2023-01-11T22:10:10.8231278Z [       OK ] LazyOpsTest.TestHardtanh (0 ms)
2023-01-11T22:10:10.8231666Z [ RUN      ] LazyOpsTest.TestHardtanhInPlace
2023-01-11T22:10:10.8233006Z [       OK ] LazyOpsTest.TestHardtanhInPlace (0 ms)
2023-01-11T22:10:10.8233338Z [ RUN      ] LazyOpsTest.TestLeakyRelu
2023-01-11T22:10:10.8235821Z [       OK ] LazyOpsTest.TestLeakyRelu (0 ms)
2023-01-11T22:10:10.8236192Z [ RUN      ] LazyOpsTest.TestLeakyReluInPlace
2023-01-11T22:10:10.8239749Z [       OK ] LazyOpsTest.TestLeakyReluInPlace (0 ms)
2023-01-11T22:10:10.8240055Z [ RUN      ] LazyOpsTest.TestExp
2023-01-11T22:10:10.8242754Z [       OK ] LazyOpsTest.TestExp (0 ms)
2023-01-11T22:10:10.8243091Z [ RUN      ] LazyOpsTest.TestExpm1
2023-01-11T22:10:10.8244271Z [       OK ] LazyOpsTest.TestExpm1 (0 ms)
2023-01-11T22:10:10.8244763Z [ RUN      ] LazyOpsTest.TestLog
2023-01-11T22:10:10.8247309Z [       OK ] LazyOpsTest.TestLog (0 ms)
2023-01-11T22:10:10.8247656Z [ RUN      ] LazyOpsTest.TestLog2
2023-01-11T22:10:10.8250747Z [       OK ] LazyOpsTest.TestLog2 (0 ms)
2023-01-11T22:10:10.8251087Z [ RUN      ] LazyOpsTest.TestLog10
2023-01-11T22:10:10.8252267Z [       OK ] LazyOpsTest.TestLog10 (0 ms)
2023-01-11T22:10:10.8252574Z [ RUN      ] LazyOpsTest.TestLog1p
2023-01-11T22:10:10.8253615Z [       OK ] LazyOpsTest.TestLog1p (0 ms)
2023-01-11T22:10:10.8253883Z [ RUN      ] LazyOpsTest.TestErf
2023-01-11T22:10:10.8255076Z [       OK ] LazyOpsTest.TestErf (0 ms)
2023-01-11T22:10:10.8255388Z [ RUN      ] LazyOpsTest.TestErfc
2023-01-11T22:10:10.8256499Z [       OK ] LazyOpsTest.TestErfc (0 ms)
2023-01-11T22:10:10.8256830Z [ RUN      ] LazyOpsTest.TestErfinv
2023-01-11T22:10:10.8257884Z [       OK ] LazyOpsTest.TestErfinv (0 ms)
2023-01-11T22:10:10.8258175Z [ RUN      ] LazyOpsTest.TestSqrt
2023-01-11T22:10:10.8261395Z [       OK ] LazyOpsTest.TestSqrt (0 ms)
2023-01-11T22:10:10.8261683Z [ RUN      ] LazyOpsTest.TestRsqrt
2023-01-11T22:10:10.8264688Z [       OK ] LazyOpsTest.TestRsqrt (0 ms)
2023-01-11T22:10:10.8264980Z [ RUN      ] LazyOpsTest.TestReciprocal
2023-01-11T22:10:10.8267732Z [       OK ] LazyOpsTest.TestReciprocal (0 ms)
2023-01-11T22:10:10.8268062Z [ RUN      ] LazyOpsTest.TestPowTensorScalar
2023-01-11T22:10:10.8270903Z [       OK ] LazyOpsTest.TestPowTensorScalar (0 ms)
2023-01-11T22:10:10.8271253Z [ RUN      ] LazyOpsTest.TestPowTensorScalarInPlace
2023-01-11T22:10:10.8274747Z [       OK ] LazyOpsTest.TestPowTensorScalarInPlace (0 ms)
2023-01-11T22:10:10.8275104Z [ RUN      ] LazyOpsTest.TestPowTensorTensor
2023-01-11T22:10:10.8278024Z [       OK ] LazyOpsTest.TestPowTensorTensor (0 ms)
2023-01-11T22:10:10.8278389Z [ RUN      ] LazyOpsTest.TestPowTensorTensorInPlace
2023-01-11T22:10:10.8292098Z [       OK ] LazyOpsTest.TestPowTensorTensorInPlace (1 ms)
2023-01-11T22:10:10.8292497Z [ RUN      ] LazyOpsTest.TestPowTensorTensorBroadcast
2023-01-11T22:10:10.8295407Z [       OK ] LazyOpsTest.TestPowTensorTensorBroadcast (0 ms)
2023-01-11T22:10:10.8295801Z [ RUN      ] LazyOpsTest.TestPowScalarTensor
2023-01-11T22:10:10.8296952Z [       OK ] LazyOpsTest.TestPowScalarTensor (0 ms)
2023-01-11T22:10:10.8297276Z [ RUN      ] LazyOpsTest.TestPowIntExponent
2023-01-11T22:10:10.8299841Z [       OK ] LazyOpsTest.TestPowIntExponent (0 ms)
2023-01-11T22:10:10.8300163Z [ RUN      ] LazyOpsTest.TestFmodScalar
2023-01-11T22:10:10.8301281Z [       OK ] LazyOpsTest.TestFmodScalar (0 ms)
2023-01-11T22:10:10.8301620Z [ RUN      ] LazyOpsTest.TestFmodScalarInPlace
2023-01-11T22:10:10.8303672Z [       OK ] LazyOpsTest.TestFmodScalarInPlace (0 ms)
2023-01-11T22:10:10.8304074Z [ RUN      ] LazyOpsTest.TestFmodTensor
2023-01-11T22:10:10.8304644Z [       OK ] LazyOpsTest.TestFmodTensor (0 ms)
2023-01-11T22:10:10.8304992Z [ RUN      ] LazyOpsTest.TestFmodTensorInPlace
2023-01-11T22:10:10.8316267Z [       OK ] LazyOpsTest.TestFmodTensorInPlace (1 ms)
2023-01-11T22:10:10.8316684Z [ RUN      ] LazyOpsTest.TestRemainderScalar
2023-01-11T22:10:10.8319769Z [       OK ] LazyOpsTest.TestRemainderScalar (0 ms)
2023-01-11T22:10:10.8320170Z [ RUN      ] LazyOpsTest.TestRemainderScalarInPlace
2023-01-11T22:10:10.8324353Z [       OK ] LazyOpsTest.TestRemainderScalarInPlace (0 ms)
2023-01-11T22:10:10.8324702Z [ RUN      ] LazyOpsTest.TestRemainderTensor
2023-01-11T22:10:10.8327493Z [       OK ] LazyOpsTest.TestRemainderTensor (0 ms)
2023-01-11T22:10:10.8327853Z [ RUN      ] LazyOpsTest.TestRemainderTensorInPlace
2023-01-11T22:10:10.8332212Z [       OK ] LazyOpsTest.TestRemainderTensorInPlace (0 ms)
2023-01-11T22:10:10.8332536Z [ RUN      ] LazyOpsTest.TestWhere
2023-01-11T22:10:10.8333117Z [W TensorCompare.cpp:493] Warning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (function operator())
2023-01-11T22:10:10.8334020Z [       OK ] LazyOpsTest.TestWhere (0 ms)
2023-01-11T22:10:10.8334396Z [ RUN      ] LazyOpsTest.TestWhereBroadcast
2023-01-11T22:10:10.8335726Z [       OK ] LazyOpsTest.TestWhereBroadcast (0 ms)
2023-01-11T22:10:10.8336112Z [ RUN      ] LazyOpsTest.TestThreshold
2023-01-11T22:10:10.8339028Z [       OK ] LazyOpsTest.TestThreshold (0 ms)
2023-01-11T22:10:10.8339391Z [ RUN      ] LazyOpsTest.TestThresholdBackward
2023-01-11T22:10:10.8348212Z [       OK ] LazyOpsTest.TestThresholdBackward (0 ms)
2023-01-11T22:10:10.8348574Z [ RUN      ] LazyOpsTest.TestThresholdInPlace
2023-01-11T22:10:10.8351620Z [       OK ] LazyOpsTest.TestThresholdInPlace (0 ms)
2023-01-11T22:10:10.8351915Z [ RUN      ] LazyOpsTest.TestElu
2023-01-11T22:10:10.8354639Z [       OK ] LazyOpsTest.TestElu (0 ms)
2023-01-11T22:10:10.8354935Z [ RUN      ] LazyOpsTest.TestEluInPlace
2023-01-11T22:10:10.8358463Z [       OK ] LazyOpsTest.TestEluInPlace (0 ms)
2023-01-11T22:10:10.8358747Z [ RUN      ] LazyOpsTest.TestSelu
2023-01-11T22:10:10.8361564Z [       OK ] LazyOpsTest.TestSelu (0 ms)
2023-01-11T22:10:10.8361868Z [ RUN      ] LazyOpsTest.TestSeluInPlace
2023-01-11T22:10:10.8365177Z [       OK ] LazyOpsTest.TestSeluInPlace (0 ms)
2023-01-11T22:10:10.8365483Z [ RUN      ] LazyOpsTest.TestCelu
2023-01-11T22:10:10.8366964Z [       OK ] LazyOpsTest.TestCelu (0 ms)
2023-01-11T22:10:10.8367261Z [ RUN      ] LazyOpsTest.TestCeluInPlace
2023-01-11T22:10:10.8369022Z [       OK ] LazyOpsTest.TestCeluInPlace (0 ms)
2023-01-11T22:10:10.8369382Z [ RUN      ] LazyOpsTest.TestGelu
2023-01-11T22:10:10.8376966Z [       OK ] LazyOpsTest.TestGelu (0 ms)
2023-01-11T22:10:10.8377347Z [ RUN      ] LazyOpsTest.TestAddMatMul
2023-01-11T22:10:10.8423301Z [       OK ] LazyOpsTest.TestAddMatMul (1 ms)
2023-01-11T22:10:10.8423733Z [ RUN      ] LazyOpsTest.TestEmbedding
2023-01-11T22:10:10.8424060Z [       OK ] LazyOpsTest.TestEmbedding (0 ms)
2023-01-11T22:10:10.8424373Z [ RUN      ] LazyOpsTest.TestOneHot
2023-01-11T22:10:10.8424714Z [       OK ] LazyOpsTest.TestOneHot (0 ms)
2023-01-11T22:10:10.8425090Z [ RUN      ] LazyOpsTest.TestTranspose
2023-01-11T22:10:10.8425544Z [       OK ] LazyOpsTest.TestTranspose (0 ms)
2023-01-11T22:10:10.8425929Z [ RUN      ] LazyOpsTest.TestTransposeInPlace
2023-01-11T22:10:10.8426336Z [       OK ] LazyOpsTest.TestTransposeInPlace (0 ms)
2023-01-11T22:10:10.8426655Z [ RUN      ] LazyOpsTest.TestReshape
2023-01-11T22:10:10.8426943Z [       OK ] LazyOpsTest.TestReshape (0 ms)
2023-01-11T22:10:10.8427231Z [ RUN      ] LazyOpsTest.TestResize
2023-01-11T22:10:10.8427525Z [       OK ] LazyOpsTest.TestResize (0 ms)
2023-01-11T22:10:10.8427809Z [ RUN      ] LazyOpsTest.TestViewResize
2023-01-11T22:10:10.8432537Z [       OK ] LazyOpsTest.TestViewResize (0 ms)
2023-01-11T22:10:10.8432917Z [ RUN      ] LazyOpsTest.TestView
2023-01-11T22:10:10.8439156Z [       OK ] LazyOpsTest.TestView (0 ms)
2023-01-11T22:10:10.8439560Z [ RUN      ] LazyOpsTest.TestViewMod
2023-01-11T22:10:10.8456326Z [       OK ] LazyOpsTest.TestViewMod (1 ms)
2023-01-11T22:10:10.8456666Z [ RUN      ] LazyOpsTest.TestViewModComplex
2023-01-11T22:10:10.8474273Z [       OK ] LazyOpsTest.TestViewModComplex (1 ms)
2023-01-11T22:10:10.8474803Z [ RUN      ] LazyOpsTest.TestViewOfViewMod
2023-01-11T22:10:10.8495686Z [       OK ] LazyOpsTest.TestViewOfViewMod (2 ms)
2023-01-11T22:10:10.8496107Z [ RUN      ] LazyOpsTest.TestViewSqueezeAddInPlace
2023-01-11T22:10:10.8506556Z [       OK ] LazyOpsTest.TestViewSqueezeAddInPlace (1 ms)
2023-01-11T22:10:10.8507173Z [ RUN      ] LazyOpsTest.TestUnsafeView
2023-01-11T22:10:10.8513379Z [       OK ] LazyOpsTest.TestUnsafeView (0 ms)
2023-01-11T22:10:10.8513992Z [ RUN      ] LazyOpsTest.TestNarrow
2023-01-11T22:10:10.8528852Z [       OK ] LazyOpsTest.TestNarrow (1 ms)
2023-01-11T22:10:10.8529410Z [ RUN      ] LazyOpsTest.TestNarrowUpdate
2023-01-11T22:10:10.8555836Z [       OK ] LazyOpsTest.TestNarrowUpdate (2 ms)
2023-01-11T22:10:10.8556428Z [ RUN      ] LazyOpsTest.TestNarrowUpdateBaseCheck
2023-01-11T22:10:10.8579292Z [       OK ] LazyOpsTest.TestNarrowUpdateBaseCheck (2 ms)
2023-01-11T22:10:10.8579810Z [ RUN      ] LazyOpsTest.TestNarrowUpdateTwoSlices
2023-01-11T22:10:10.8697572Z [       OK ] LazyOpsTest.TestNarrowUpdateTwoSlices (11 ms)
2023-01-11T22:10:10.8698151Z [ RUN      ] LazyOpsTest.TestNarrowUpdateView
2023-01-11T22:10:10.8732413Z [       OK ] LazyOpsTest.TestNarrowUpdateView (3 ms)
2023-01-11T22:10:10.8732957Z [ RUN      ] LazyOpsTest.TestNarrowInNarrowUpdate
2023-01-11T22:10:10.8775404Z [       OK ] LazyOpsTest.TestNarrowInNarrowUpdate (4 ms)
2023-01-11T22:10:10.8775990Z [ RUN      ] LazyOpsTest.TestNarrowCopy
2023-01-11T22:10:10.8783754Z [       OK ] LazyOpsTest.TestNarrowCopy (0 ms)
2023-01-11T22:10:10.8784290Z [ RUN      ] LazyOpsTest.TestViewAs
2023-01-11T22:10:10.8792532Z [       OK ] LazyOpsTest.TestViewAs (0 ms)
2023-01-11T22:10:10.8793095Z [ RUN      ] LazyOpsTest.TestLogSoftmax
2023-01-11T22:10:10.8813229Z [       OK ] LazyOpsTest.TestLogSoftmax (2 ms)
2023-01-11T22:10:10.8813769Z [ RUN      ] LazyOpsTest.TestLogSoftmaxCast
2023-01-11T22:10:10.8842116Z [       OK ] LazyOpsTest.TestLogSoftmaxCast (2 ms)
2023-01-11T22:10:10.8842587Z [ RUN      ] LazyOpsTest.TestLogSoftmaxWrapper
2023-01-11T22:10:10.8862167Z [       OK ] LazyOpsTest.TestLogSoftmaxWrapper (2 ms)
2023-01-11T22:10:10.8862711Z [ RUN      ] LazyOpsTest.TestSoftmax
2023-01-11T22:10:10.8883150Z [       OK ] LazyOpsTest.TestSoftmax (2 ms)
2023-01-11T22:10:10.8883547Z [ RUN      ] LazyOpsTest.TestSoftmaxCast
2023-01-11T22:10:10.8913492Z [       OK ] LazyOpsTest.TestSoftmaxCast (3 ms)
2023-01-11T22:10:10.8913859Z [ RUN      ] LazyOpsTest.TestSoftmaxWrapper
2023-01-11T22:10:10.8935855Z [       OK ] LazyOpsTest.TestSoftmaxWrapper (2 ms)
2023-01-11T22:10:10.8936239Z [ RUN      ] LazyOpsTest.TestSoftplus
2023-01-11T22:10:10.8939558Z [       OK ] LazyOpsTest.TestSoftplus (0 ms)
2023-01-11T22:10:10.8939991Z [ RUN      ] LazyOpsTest.TestMaxPool1D
2023-01-11T22:10:10.9027825Z [       OK ] LazyOpsTest.TestMaxPool1D (8 ms)
2023-01-11T22:10:10.9028378Z [ RUN      ] LazyOpsTest.TestMaxPool2D
2023-01-11T22:10:10.9088430Z [       OK ] LazyOpsTest.TestMaxPool2D (6 ms)
2023-01-11T22:10:10.9089073Z [ RUN      ] LazyOpsTest.TestMaxPool2DWithIndices
2023-01-11T22:10:10.9211542Z [       OK ] LazyOpsTest.TestMaxPool2DWithIndices (12 ms)
2023-01-11T22:10:10.9212023Z [ RUN      ] LazyOpsTest.TestMaxPool2DNonSquare
2023-01-11T22:10:10.9272206Z [       OK ] LazyOpsTest.TestMaxPool2DNonSquare (6 ms)
2023-01-11T22:10:10.9272656Z [ RUN      ] LazyOpsTest.TestMaxPool3D
2023-01-11T22:10:10.9290867Z [       OK ] LazyOpsTest.TestMaxPool3D (1 ms)
2023-01-11T22:10:10.9291493Z [ RUN      ] LazyOpsTest.TestMaxPool3DWithIndices
2023-01-11T22:10:10.9314203Z [       OK ] LazyOpsTest.TestMaxPool3DWithIndices (2 ms)
2023-01-11T22:10:10.9314903Z [ RUN      ] LazyOpsTest.TestMaxPool3DIncompleteAttributes
2023-01-11T22:10:10.9327882Z [       OK ] LazyOpsTest.TestMaxPool3DIncompleteAttributes (1 ms)
2023-01-11T22:10:10.9328572Z [ RUN      ] LazyOpsTest.TestMaxPool3DNonSquare
2023-01-11T22:10:10.9343805Z [       OK ] LazyOpsTest.TestMaxPool3DNonSquare (1 ms)
2023-01-11T22:10:10.9344423Z [ RUN      ] LazyOpsTest.TestMaxPool2DNoBatch
2023-01-11T22:10:10.9404396Z [       OK ] LazyOpsTest.TestMaxPool2DNoBatch (6 ms)
2023-01-11T22:10:10.9405003Z [ RUN      ] LazyOpsTest.TestMaxPool3DNoBatch
2023-01-11T22:10:10.9422490Z [       OK ] LazyOpsTest.TestMaxPool3DNoBatch (1 ms)
2023-01-11T22:10:10.9423060Z [ RUN      ] LazyOpsTest.TestAvgPool1D
2023-01-11T22:10:10.9504518Z [       OK ] LazyOpsTest.TestAvgPool1D (8 ms)
2023-01-11T22:10:10.9504933Z [ RUN      ] LazyOpsTest.TestAvgPool2D
2023-01-11T22:10:10.9596574Z [       OK ] LazyOpsTest.TestAvgPool2D (9 ms)
2023-01-11T22:10:10.9597015Z [ RUN      ] LazyOpsTest.TestAvgPool2DNonSquare
2023-01-11T22:10:10.9661495Z [       OK ] LazyOpsTest.TestAvgPool2DNonSquare (6 ms)
2023-01-11T22:10:10.9661942Z [ RUN      ] LazyOpsTest.TestAvgPool3D
2023-01-11T22:10:10.9675476Z [       OK ] LazyOpsTest.TestAvgPool3D (1 ms)
2023-01-11T22:10:10.9675954Z [ RUN      ] LazyOpsTest.TestAvgPool3DIncompleteAttributes
2023-01-11T22:10:10.9686299Z [       OK ] LazyOpsTest.TestAvgPool3DIncompleteAttributes (1 ms)
2023-01-11T22:10:10.9686787Z [ RUN      ] LazyOpsTest.TestAvgPool3DNonSquare
2023-01-11T22:10:10.9697908Z [       OK ] LazyOpsTest.TestAvgPool3DNonSquare (1 ms)
2023-01-11T22:10:10.9698348Z [ RUN      ] LazyOpsTest.TestAvgPool2DNoBatch
2023-01-11T22:10:10.9751765Z [       OK ] LazyOpsTest.TestAvgPool2DNoBatch (5 ms)
2023-01-11T22:10:10.9752206Z [ RUN      ] LazyOpsTest.TestAvgPool3DNoBatch
2023-01-11T22:10:10.9764179Z [       OK ] LazyOpsTest.TestAvgPool3DNoBatch (1 ms)
2023-01-11T22:10:10.9764621Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2D
2023-01-11T22:10:10.9771004Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2D (0 ms)
2023-01-11T22:10:10.9771432Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3D
2023-01-11T22:10:10.9982729Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3D (20 ms)
2023-01-11T22:10:10.9983251Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatch
2023-01-11T22:10:11.0004478Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatch (2 ms)
2023-01-11T22:10:11.0004983Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatch
2023-01-11T22:10:11.0011696Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatch (0 ms)
2023-01-11T22:10:11.0012146Z [ RUN      ] LazyOpsTest.TestMaxUnpool2D
2023-01-11T22:10:11.0027642Z [       OK ] LazyOpsTest.TestMaxUnpool2D (1 ms)
2023-01-11T22:10:11.0028073Z [ RUN      ] LazyOpsTest.TestMaxUnpool3D
2023-01-11T22:10:11.0041912Z [       OK ] LazyOpsTest.TestMaxUnpool3D (1 ms)
2023-01-11T22:10:11.0042310Z [ RUN      ] LazyOpsTest.TestNllLoss
2023-01-11T22:10:11.0042654Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:8173: Skipped
2023-01-11T22:10:11.0042879Z 
2023-01-11T22:10:11.0043101Z [  SKIPPED ] LazyOpsTest.TestNllLoss (0 ms)
2023-01-11T22:10:11.0043492Z [ RUN      ] LazyOpsTest.TestNllLoss2d
2023-01-11T22:10:11.0119590Z [       OK ] LazyOpsTest.TestNllLoss2d (7 ms)
2023-01-11T22:10:11.0120610Z [ RUN      ] LazyOpsTest.TestSmoothL1Loss
2023-01-11T22:10:11.0138208Z [       OK ] LazyOpsTest.TestSmoothL1Loss (1 ms)
2023-01-11T22:10:11.0138693Z [ RUN      ] LazyOpsTest.TestL1Loss
2023-01-11T22:10:11.0150620Z [       OK ] LazyOpsTest.TestL1Loss (1 ms)
2023-01-11T22:10:11.0151312Z [ RUN      ] LazyOpsTest.TestL1LossBackward
2023-01-11T22:10:11.0190202Z [       OK ] LazyOpsTest.TestL1LossBackward (3 ms)
2023-01-11T22:10:11.0190773Z [ RUN      ] LazyOpsTest.TestMseLoss
2023-01-11T22:10:11.0193339Z [       OK ] LazyOpsTest.TestMseLoss (0 ms)
2023-01-11T22:10:11.0193907Z [ RUN      ] LazyOpsTest.TestMseLossBackward
2023-01-11T22:10:11.0216517Z [       OK ] LazyOpsTest.TestMseLossBackward (2 ms)
2023-01-11T22:10:11.0216933Z [ RUN      ] LazyOpsTest.TestBatchNorm1D
2023-01-11T22:10:11.0237276Z [       OK ] LazyOpsTest.TestBatchNorm1D (2 ms)
2023-01-11T22:10:11.0237688Z [ RUN      ] LazyOpsTest.TestBatchNorm2D
2023-01-11T22:10:11.0257465Z [       OK ] LazyOpsTest.TestBatchNorm2D (1 ms)
2023-01-11T22:10:11.0257851Z [ RUN      ] LazyOpsTest.TestDim
2023-01-11T22:10:11.0258201Z [       OK ] LazyOpsTest.TestDim (0 ms)
2023-01-11T22:10:11.0258571Z [ RUN      ] LazyOpsTest.TestContiguous
2023-01-11T22:10:11.0259349Z [       OK ] LazyOpsTest.TestContiguous (0 ms)
2023-01-11T22:10:11.0259685Z [ RUN      ] LazyOpsTest.TestSqueezeAll
2023-01-11T22:10:11.0261617Z [       OK ] LazyOpsTest.TestSqueezeAll (0 ms)
2023-01-11T22:10:11.0262301Z [ RUN      ] LazyOpsTest.TestSqueezeAllInPlace
2023-01-11T22:10:11.0265384Z [       OK ] LazyOpsTest.TestSqueezeAllInPlace (0 ms)
2023-01-11T22:10:11.0265909Z [ RUN      ] LazyOpsTest.TestSqueezeOne
2023-01-11T22:10:11.0287326Z [       OK ] LazyOpsTest.TestSqueezeOne (2 ms)
2023-01-11T22:10:11.0287973Z [ RUN      ] LazyOpsTest.TestSqueezeOneInPlace
2023-01-11T22:10:11.0312625Z [       OK ] LazyOpsTest.TestSqueezeOneInPlace (2 ms)
2023-01-11T22:10:11.0313041Z [ RUN      ] LazyOpsTest.TestUnsqueeze
2023-01-11T22:10:11.0327121Z [       OK ] LazyOpsTest.TestUnsqueeze (1 ms)
2023-01-11T22:10:11.0327449Z [ RUN      ] LazyOpsTest.TestUnsqueezeInPlace
2023-01-11T22:10:11.0344416Z [       OK ] LazyOpsTest.TestUnsqueezeInPlace (1 ms)
2023-01-11T22:10:11.0344791Z [ RUN      ] LazyOpsTest.TestMaskedFill
2023-01-11T22:10:11.0347426Z [       OK ] LazyOpsTest.TestMaskedFill (0 ms)
2023-01-11T22:10:11.0347831Z [ RUN      ] LazyOpsTest.TestMaskedFillInPlace
2023-01-11T22:10:11.0350805Z [       OK ] LazyOpsTest.TestMaskedFillInPlace (0 ms)
2023-01-11T22:10:11.0351214Z [ RUN      ] LazyOpsTest.TestMaskedFillBroadcast
2023-01-11T22:10:11.0353793Z [       OK ] LazyOpsTest.TestMaskedFillBroadcast (0 ms)
2023-01-11T22:10:11.0354173Z [ RUN      ] LazyOpsTest.TestFill
2023-01-11T22:10:11.0357708Z [       OK ] LazyOpsTest.TestFill (0 ms)
2023-01-11T22:10:11.0358041Z [ RUN      ] LazyOpsTest.TestFillWithRank0
2023-01-11T22:10:11.0359872Z [       OK ] LazyOpsTest.TestFillWithRank0 (0 ms)
2023-01-11T22:10:11.0360263Z [ RUN      ] LazyOpsTest.TestPermute
2023-01-11T22:10:11.0391623Z [       OK ] LazyOpsTest.TestPermute (3 ms)
2023-01-11T22:10:11.0392182Z [ RUN      ] LazyOpsTest.TestPermuteMod
2023-01-11T22:10:11.0504373Z [       OK ] LazyOpsTest.TestPermuteMod (11 ms)
2023-01-11T22:10:11.0504890Z [ RUN      ] LazyOpsTest.TestFlip
2023-01-11T22:10:11.0537129Z [       OK ] LazyOpsTest.TestFlip (3 ms)
2023-01-11T22:10:11.0537700Z [ RUN      ] LazyOpsTest.TestPixelShuffle
2023-01-11T22:10:11.0544415Z [       OK ] LazyOpsTest.TestPixelShuffle (0 ms)
2023-01-11T22:10:11.0544969Z [ RUN      ] LazyOpsTest.TestSumToSize
2023-01-11T22:10:11.0548440Z [       OK ] LazyOpsTest.TestSumToSize (0 ms)
2023-01-11T22:10:11.0548762Z [ RUN      ] LazyOpsTest.TestTransposeDims
2023-01-11T22:10:11.0551982Z [       OK ] LazyOpsTest.TestTransposeDims (0 ms)
2023-01-11T22:10:11.0552332Z [ RUN      ] LazyOpsTest.TestTransposeDimsMod
2023-01-11T22:10:11.0563422Z [       OK ] LazyOpsTest.TestTransposeDimsMod (1 ms)
2023-01-11T22:10:11.0563910Z [ RUN      ] LazyOpsTest.TestTransposeDimsInPlace
2023-01-11T22:10:11.0567403Z [       OK ] LazyOpsTest.TestTransposeDimsInPlace (0 ms)
2023-01-11T22:10:11.0567805Z [ RUN      ] LazyOpsTest.TestSplit
2023-01-11T22:10:11.0590965Z [       OK ] LazyOpsTest.TestSplit (2 ms)
2023-01-11T22:10:11.0591418Z [ RUN      ] LazyOpsTest.TestSplitEmpty
2023-01-11T22:10:11.0591744Z [       OK ] LazyOpsTest.TestSplitEmpty (0 ms)
2023-01-11T22:10:11.0592055Z [ RUN      ] LazyOpsTest.TestSplitWithSizes
2023-01-11T22:10:11.0603697Z [       OK ] LazyOpsTest.TestSplitWithSizes (1 ms)
2023-01-11T22:10:11.0604033Z [ RUN      ] LazyOpsTest.TestCrossImplicitDim
2023-01-11T22:10:11.0606674Z [       OK ] LazyOpsTest.TestCrossImplicitDim (0 ms)
2023-01-11T22:10:11.0607048Z [ RUN      ] LazyOpsTest.TestCrossExplicitDim
2023-01-11T22:10:11.0610428Z [       OK ] LazyOpsTest.TestCrossExplicitDim (0 ms)
2023-01-11T22:10:11.0610866Z [ RUN      ] LazyOpsTest.TestCrossZeroDim
2023-01-11T22:10:11.0611264Z [       OK ] LazyOpsTest.TestCrossZeroDim (0 ms)
2023-01-11T22:10:11.0611642Z [ RUN      ] LazyOpsTest.TestTriu
2023-01-11T22:10:11.0639635Z [       OK ] LazyOpsTest.TestTriu (2 ms)
2023-01-11T22:10:11.0639964Z [ RUN      ] LazyOpsTest.TestTriuNonSquare
2023-01-11T22:10:11.0669656Z [       OK ] LazyOpsTest.TestTriuNonSquare (2 ms)
2023-01-11T22:10:11.0670069Z [ RUN      ] LazyOpsTest.TestTriuBatch
2023-01-11T22:10:11.0700740Z [       OK ] LazyOpsTest.TestTriuBatch (3 ms)
2023-01-11T22:10:11.0701244Z [ RUN      ] LazyOpsTest.TestTril
2023-01-11T22:10:11.0729983Z [       OK ] LazyOpsTest.TestTril (2 ms)
2023-01-11T22:10:11.0730359Z [ RUN      ] LazyOpsTest.TestTrilNonSquare
2023-01-11T22:10:11.0758914Z [       OK ] LazyOpsTest.TestTrilNonSquare (2 ms)
2023-01-11T22:10:11.0759339Z [ RUN      ] LazyOpsTest.TestTrilBatch
2023-01-11T22:10:11.0788062Z [       OK ] LazyOpsTest.TestTrilBatch (2 ms)
2023-01-11T22:10:11.0788765Z [ RUN      ] LazyOpsTest.TestTriuInPlace
2023-01-11T22:10:11.0824743Z [       OK ] LazyOpsTest.TestTriuInPlace (3 ms)
2023-01-11T22:10:11.0825301Z [ RUN      ] LazyOpsTest.TestTrilInPlace
2023-01-11T22:10:11.0860363Z [       OK ] LazyOpsTest.TestTrilInPlace (3 ms)
2023-01-11T22:10:11.0860902Z [ RUN      ] LazyOpsTest.TestTrace
2023-01-11T22:10:11.0863393Z [       OK ] LazyOpsTest.TestTrace (0 ms)
2023-01-11T22:10:11.0863938Z [ RUN      ] LazyOpsTest.TestTraceWide
2023-01-11T22:10:11.0866036Z [       OK ] LazyOpsTest.TestTraceWide (0 ms)
2023-01-11T22:10:11.0866403Z [ RUN      ] LazyOpsTest.TestTraceNarrow
2023-01-11T22:10:11.0868542Z [       OK ] LazyOpsTest.TestTraceNarrow (0 ms)
2023-01-11T22:10:11.0868857Z [ RUN      ] LazyOpsTest.TestDiagRank1
2023-01-11T22:10:11.1046344Z [       OK ] LazyOpsTest.TestDiagRank1 (17 ms)
2023-01-11T22:10:11.1046682Z [ RUN      ] LazyOpsTest.TestDiagRank2
2023-01-11T22:10:11.1083992Z [       OK ] LazyOpsTest.TestDiagRank2 (3 ms)
2023-01-11T22:10:11.1084691Z [ RUN      ] LazyOpsTest.TestDiagFlat
2023-01-11T22:10:11.1603319Z [       OK ] LazyOpsTest.TestDiagFlat (51 ms)
2023-01-11T22:10:11.1603752Z [ RUN      ] LazyOpsTest.TestDiagonal
2023-01-11T22:10:11.1631776Z [       OK ] LazyOpsTest.TestDiagonal (3 ms)
2023-01-11T22:10:11.1632189Z [ RUN      ] LazyOpsTest.TestDiagonalUpdate
2023-01-11T22:10:11.1725998Z [       OK ] LazyOpsTest.TestDiagonalUpdate (9 ms)
2023-01-11T22:10:11.1726366Z [ RUN      ] LazyOpsTest.TestDiagonalNonSquare
2023-01-11T22:10:11.1754253Z [       OK ] LazyOpsTest.TestDiagonalNonSquare (2 ms)
2023-01-11T22:10:11.1754850Z [ RUN      ] LazyOpsTest.TestDiagonalBatch
2023-01-11T22:10:11.1782781Z [       OK ] LazyOpsTest.TestDiagonalBatch (2 ms)
2023-01-11T22:10:11.1783080Z [ RUN      ] LazyOpsTest.TestFlatten
2023-01-11T22:10:11.1842793Z [       OK ] LazyOpsTest.TestFlatten (6 ms)
2023-01-11T22:10:11.1843162Z [ RUN      ] LazyOpsTest.TestLogicalAnd
2023-01-11T22:10:11.1861121Z [       OK ] LazyOpsTest.TestLogicalAnd (1 ms)
2023-01-11T22:10:11.1861490Z [ RUN      ] LazyOpsTest.TestBitwiseAnd
2023-01-11T22:10:11.1864374Z [       OK ] LazyOpsTest.TestBitwiseAnd (0 ms)
2023-01-11T22:10:11.1864767Z [ RUN      ] LazyOpsTest.TestBitwiseAndInPlace
2023-01-11T22:10:11.1867626Z [       OK ] LazyOpsTest.TestBitwiseAndInPlace (0 ms)
2023-01-11T22:10:11.1868046Z [ RUN      ] LazyOpsTest.TestBitwiseAndScalar
2023-01-11T22:10:11.1870765Z [       OK ] LazyOpsTest.TestBitwiseAndScalar (0 ms)
2023-01-11T22:10:11.1871310Z [ RUN      ] LazyOpsTest.TestBitwiseAndScalarInPlace
2023-01-11T22:10:11.1874499Z [       OK ] LazyOpsTest.TestBitwiseAndScalarInPlace (0 ms)
2023-01-11T22:10:11.1874899Z [ RUN      ] LazyOpsTest.TestBitwiseAndPromotion
2023-01-11T22:10:11.1879341Z [       OK ] LazyOpsTest.TestBitwiseAndPromotion (0 ms)
2023-01-11T22:10:11.1879758Z [ RUN      ] LazyOpsTest.TestBitwiseOr
2023-01-11T22:10:11.1882466Z [       OK ] LazyOpsTest.TestBitwiseOr (0 ms)
2023-01-11T22:10:11.1882876Z [ RUN      ] LazyOpsTest.TestBitwiseOrInPlace
2023-01-11T22:10:11.1885715Z [       OK ] LazyOpsTest.TestBitwiseOrInPlace (0 ms)
2023-01-11T22:10:11.1886101Z [ RUN      ] LazyOpsTest.TestBitwiseOrScalar
2023-01-11T22:10:11.1888669Z [       OK ] LazyOpsTest.TestBitwiseOrScalar (0 ms)
2023-01-11T22:10:11.1889092Z [ RUN      ] LazyOpsTest.TestBitwiseOrScalarInPlace
2023-01-11T22:10:11.1891839Z [       OK ] LazyOpsTest.TestBitwiseOrScalarInPlace (0 ms)
2023-01-11T22:10:11.1892336Z [ RUN      ] LazyOpsTest.TestBitwiseXor
2023-01-11T22:10:11.1892784Z [       OK ] LazyOpsTest.TestBitwiseXor (0 ms)
2023-01-11T22:10:11.1893207Z [ RUN      ] LazyOpsTest.TestBitwiseXorInPlace
2023-01-11T22:10:11.1894666Z [       OK ] LazyOpsTest.TestBitwiseXorInPlace (0 ms)
2023-01-11T22:10:11.1895160Z [ RUN      ] LazyOpsTest.TestBitwiseXorScalar
2023-01-11T22:10:11.1895618Z [       OK ] LazyOpsTest.TestBitwiseXorScalar (0 ms)
2023-01-11T22:10:11.1896116Z [ RUN      ] LazyOpsTest.TestBitwiseXorScalarInPlace
2023-01-11T22:10:11.1896502Z [       OK ] LazyOpsTest.TestBitwiseXorScalarInPlace (0 ms)
2023-01-11T22:10:11.1896829Z [ RUN      ] LazyOpsTest.TestLshift
2023-01-11T22:10:11.1897817Z [       OK ] LazyOpsTest.TestLshift (0 ms)
2023-01-11T22:10:11.1898113Z [ RUN      ] LazyOpsTest.TestLshiftInPlace
2023-01-11T22:10:11.1899877Z [       OK ] LazyOpsTest.TestLshiftInPlace (0 ms)
2023-01-11T22:10:11.1900312Z [ RUN      ] LazyOpsTest.TestLshiftScalar
2023-01-11T22:10:11.1900832Z [       OK ] LazyOpsTest.TestLshiftScalar (0 ms)
2023-01-11T22:10:11.1901171Z [ RUN      ] LazyOpsTest.TestLshiftScalarInPlace
2023-01-11T22:10:11.1903017Z [       OK ] LazyOpsTest.TestLshiftScalarInPlace (0 ms)
2023-01-11T22:10:11.1903592Z [ RUN      ] LazyOpsTest.TestRshift
2023-01-11T22:10:11.1904099Z [       OK ] LazyOpsTest.TestRshift (0 ms)
2023-01-11T22:10:11.1904606Z [ RUN      ] LazyOpsTest.TestRshiftInPlace
2023-01-11T22:10:11.1905678Z [       OK ] LazyOpsTest.TestRshiftInPlace (0 ms)
2023-01-11T22:10:11.1906247Z [ RUN      ] LazyOpsTest.TestRshiftScalar
2023-01-11T22:10:11.1906939Z [       OK ] LazyOpsTest.TestRshiftScalar (0 ms)
2023-01-11T22:10:11.1907554Z [ RUN      ] LazyOpsTest.TestRshiftScalarInPlace
2023-01-11T22:10:11.1908786Z [       OK ] LazyOpsTest.TestRshiftScalarInPlace (0 ms)
2023-01-11T22:10:11.1909488Z [ RUN      ] LazyOpsTest.TestMeshgrid
2023-01-11T22:10:11.1909855Z [W TensorShape.cpp:3452] Warning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (function operator())
2023-01-11T22:10:11.1919799Z [       OK ] LazyOpsTest.TestMeshgrid (1 ms)
2023-01-11T22:10:11.1920114Z [ RUN      ] LazyOpsTest.TestConstantPad
2023-01-11T22:10:11.1924100Z [       OK ] LazyOpsTest.TestConstantPad (0 ms)
2023-01-11T22:10:11.1924451Z [ RUN      ] LazyOpsTest.TestConstantPadIncomplete
2023-01-11T22:10:11.1927538Z [       OK ] LazyOpsTest.TestConstantPadIncomplete (0 ms)
2023-01-11T22:10:11.1927995Z [ RUN      ] LazyOpsTest.TestReflectionPad2dRank3
2023-01-11T22:10:11.1929912Z [       OK ] LazyOpsTest.TestReflectionPad2dRank3 (0 ms)
2023-01-11T22:10:11.1930571Z [ RUN      ] LazyOpsTest.TestReflectionPad2dRank4
2023-01-11T22:10:11.1931389Z [       OK ] LazyOpsTest.TestReflectionPad2dRank4 (0 ms)
2023-01-11T22:10:11.1931976Z [ RUN      ] LazyOpsTest.TestReflectionPad2dBackward
2023-01-11T22:10:11.1940148Z [       OK ] LazyOpsTest.TestReflectionPad2dBackward (0 ms)
2023-01-11T22:10:11.1940793Z [ RUN      ] LazyOpsTest.TestReplicationPad1d
2023-01-11T22:10:11.1941465Z [       OK ] LazyOpsTest.TestReplicationPad1d (0 ms)
2023-01-11T22:10:11.1942197Z [ RUN      ] LazyOpsTest.TestReplicationPad1dZeroPad
2023-01-11T22:10:11.1944281Z [       OK ] LazyOpsTest.TestReplicationPad1dZeroPad (0 ms)
2023-01-11T22:10:11.1944663Z [ RUN      ] LazyOpsTest.TestReplicationPad1dBackward
2023-01-11T22:10:11.1950612Z [       OK ] LazyOpsTest.TestReplicationPad1dBackward (0 ms)
2023-01-11T22:10:11.1951012Z [ RUN      ] LazyOpsTest.TestReplicationPad2d
2023-01-11T22:10:11.1951692Z [       OK ] LazyOpsTest.TestReplicationPad2d (0 ms)
2023-01-11T22:10:11.1953393Z [ RUN      ] LazyOpsTest.TestReplicationPad2dZeroPad
2023-01-11T22:10:11.1953995Z [       OK ] LazyOpsTest.TestReplicationPad2dZeroPad (0 ms)
2023-01-11T22:10:11.1954380Z [ RUN      ] LazyOpsTest.TestReplicationPad2dBackward
2023-01-11T22:10:11.1961512Z [       OK ] LazyOpsTest.TestReplicationPad2dBackward (0 ms)
2023-01-11T22:10:11.1962038Z [ RUN      ] LazyOpsTest.TestAsStrided
2023-01-11T22:10:11.1973027Z [       OK ] LazyOpsTest.TestAsStrided (1 ms)
2023-01-11T22:10:11.1973600Z [ RUN      ] LazyOpsTest.TestAsStridedInPlace
2023-01-11T22:10:11.1988904Z [       OK ] LazyOpsTest.TestAsStridedInPlace (1 ms)
2023-01-11T22:10:11.1989285Z [ RUN      ] LazyOpsTest.TestAsStridedWithOffset
2023-01-11T22:10:11.1993710Z [       OK ] LazyOpsTest.TestAsStridedWithOffset (0 ms)
2023-01-11T22:10:11.1994102Z [ RUN      ] LazyOpsTest.TestAsStridedWithInplaceCopy
2023-01-11T22:10:11.2001551Z [       OK ] LazyOpsTest.TestAsStridedWithInplaceCopy (0 ms)
2023-01-11T22:10:11.2001916Z [ RUN      ] LazyOpsTest.TestEmptyStrided
2023-01-11T22:10:11.2002313Z [       OK ] LazyOpsTest.TestEmptyStrided (0 ms)
2023-01-11T22:10:11.2002632Z [ RUN      ] LazyOpsTest.TestAvgPool2DBackward
2023-01-11T22:10:11.2162928Z [       OK ] LazyOpsTest.TestAvgPool2DBackward (16 ms)
2023-01-11T22:10:11.2163531Z [ RUN      ] LazyOpsTest.TestAvgPool3DBackward
2023-01-11T22:10:11.2255370Z [       OK ] LazyOpsTest.TestAvgPool3DBackward (9 ms)
2023-01-11T22:10:11.2255853Z [ RUN      ] LazyOpsTest.TestAvgPool2DNoBatchBackward
2023-01-11T22:10:11.2444678Z [       OK ] LazyOpsTest.TestAvgPool2DNoBatchBackward (18 ms)
2023-01-11T22:10:11.2445198Z [ RUN      ] LazyOpsTest.TestAvgPool3DNoBatchBackward
2023-01-11T22:10:11.2540770Z [       OK ] LazyOpsTest.TestAvgPool3DNoBatchBackward (9 ms)
2023-01-11T22:10:11.2541219Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatchBackward
2023-01-11T22:10:11.2577203Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatchBackward (3 ms)
2023-01-11T22:10:11.2577738Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3DBackward
2023-01-11T22:10:11.2636460Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3DBackward (5 ms)
2023-01-11T22:10:11.2637011Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2DBackward
2023-01-11T22:10:11.2660874Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2DBackward (2 ms)
2023-01-11T22:10:11.2661417Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatchBackward
2023-01-11T22:10:11.2684104Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatchBackward (2 ms)
2023-01-11T22:10:11.2684569Z [ RUN      ] LazyOpsTest.TestConv2D
2023-01-11T22:10:11.3791288Z [       OK ] LazyOpsTest.TestConv2D (110 ms)
2023-01-11T22:10:11.3791707Z [ RUN      ] LazyOpsTest.TestConv2DBackward
2023-01-11T22:10:11.7894318Z [       OK ] LazyOpsTest.TestConv2DBackward (410 ms)
2023-01-11T22:10:11.7895242Z [ RUN      ] LazyOpsTest.TestTransposedConv2DBackward
2023-01-11T22:10:12.0983864Z [       OK ] LazyOpsTest.TestTransposedConv2DBackward (308 ms)
2023-01-11T22:10:12.0984821Z [ RUN      ] LazyOpsTest.TestConv3DBackward
2023-01-11T22:10:12.3835600Z [       OK ] LazyOpsTest.TestConv3DBackward (285 ms)
2023-01-11T22:10:12.3836518Z [ RUN      ] LazyOpsTest.TestTransposedConv3DBackward
2023-01-11T22:10:13.0395868Z [       OK ] LazyOpsTest.TestTransposedConv3DBackward (655 ms)
2023-01-11T22:10:13.0396868Z [ RUN      ] LazyOpsTest.TestMaxPool2DBackward
2023-01-11T22:10:13.0495671Z [       OK ] LazyOpsTest.TestMaxPool2DBackward (10 ms)
2023-01-11T22:10:13.0496321Z [ RUN      ] LazyOpsTest.TestMaxPool3DBackward
2023-01-11T22:10:13.0550577Z [       OK ] LazyOpsTest.TestMaxPool3DBackward (5 ms)
2023-01-11T22:10:13.0551101Z [ RUN      ] LazyOpsTest.TestMaxPool2DNoBatchBackward
2023-01-11T22:10:13.0648163Z [       OK ] LazyOpsTest.TestMaxPool2DNoBatchBackward (9 ms)
2023-01-11T22:10:13.0648674Z [ RUN      ] LazyOpsTest.TestMaxPool3DNoBatchBackward
2023-01-11T22:10:13.0700728Z [       OK ] LazyOpsTest.TestMaxPool3DNoBatchBackward (5 ms)
2023-01-11T22:10:13.0701204Z [ RUN      ] LazyOpsTest.TestMaxUnpool2DBackward
2023-01-11T22:10:13.0879083Z [       OK ] LazyOpsTest.TestMaxUnpool2DBackward (17 ms)
2023-01-11T22:10:13.0879584Z [ RUN      ] LazyOpsTest.TestMaxUnpool3DBackward
2023-01-11T22:10:13.1025854Z [       OK ] LazyOpsTest.TestMaxUnpool3DBackward (14 ms)
2023-01-11T22:10:13.1026293Z [ RUN      ] LazyOpsTest.TestTanhBackward
2023-01-11T22:10:13.1037107Z [       OK ] LazyOpsTest.TestTanhBackward (1 ms)
2023-01-11T22:10:13.1037563Z [ RUN      ] LazyOpsTest.TestSigmoidBackward
2023-01-11T22:10:13.1047147Z [       OK ] LazyOpsTest.TestSigmoidBackward (1 ms)
2023-01-11T22:10:13.1047609Z [ RUN      ] LazyOpsTest.TestLogSigmoidBackward
2023-01-11T22:10:13.1057565Z [       OK ] LazyOpsTest.TestLogSigmoidBackward (1 ms)
2023-01-11T22:10:13.1058011Z [ RUN      ] LazyOpsTest.TestLogSoftmaxBackward
2023-01-11T22:10:13.1138360Z [       OK ] LazyOpsTest.TestLogSoftmaxBackward (8 ms)
2023-01-11T22:10:13.1138804Z [ RUN      ] LazyOpsTest.TestSoftmaxBackward
2023-01-11T22:10:13.1217069Z [       OK ] LazyOpsTest.TestSoftmaxBackward (7 ms)
2023-01-11T22:10:13.1217499Z [ RUN      ] LazyOpsTest.TestSoftplusBackward
2023-01-11T22:10:13.1227080Z [       OK ] LazyOpsTest.TestSoftplusBackward (0 ms)
2023-01-11T22:10:13.1227500Z [ RUN      ] LazyOpsTest.TestReluBackward
2023-01-11T22:10:13.1236888Z [       OK ] LazyOpsTest.TestReluBackward (0 ms)
2023-01-11T22:10:13.1237294Z [ RUN      ] LazyOpsTest.TestRreluBackward
2023-01-11T22:10:13.1247885Z [       OK ] LazyOpsTest.TestRreluBackward (1 ms)
2023-01-11T22:10:13.1248309Z [ RUN      ] LazyOpsTest.TestHardshrinkBackward
2023-01-11T22:10:13.1256722Z [       OK ] LazyOpsTest.TestHardshrinkBackward (0 ms)
2023-01-11T22:10:13.1257102Z [ RUN      ] LazyOpsTest.TestSoftshrinkBackward
2023-01-11T22:10:13.1265252Z [       OK ] LazyOpsTest.TestSoftshrinkBackward (0 ms)
2023-01-11T22:10:13.1265598Z [ RUN      ] LazyOpsTest.TestHardtanhBackward
2023-01-11T22:10:13.1270007Z [       OK ] LazyOpsTest.TestHardtanhBackward (0 ms)
2023-01-11T22:10:13.1270395Z [ RUN      ] LazyOpsTest.TestEluBackward
2023-01-11T22:10:13.1280231Z [       OK ] LazyOpsTest.TestEluBackward (0 ms)
2023-01-11T22:10:13.1280539Z [ RUN      ] LazyOpsTest.TestGeluBackward
2023-01-11T22:10:13.1297157Z [       OK ] LazyOpsTest.TestGeluBackward (1 ms)
2023-01-11T22:10:13.1297584Z [ RUN      ] LazyOpsTest.TestLeakyReluBackward
2023-01-11T22:10:13.1308287Z [       OK ] LazyOpsTest.TestLeakyReluBackward (1 ms)
2023-01-11T22:10:13.1308721Z [ RUN      ] LazyOpsTest.TestTransposeBackward
2023-01-11T22:10:13.1316880Z [       OK ] LazyOpsTest.TestTransposeBackward (0 ms)
2023-01-11T22:10:13.1317318Z [ RUN      ] LazyOpsTest.TestAddMatMulBackward
2023-01-11T22:10:13.1388389Z [       OK ] LazyOpsTest.TestAddMatMulBackward (7 ms)
2023-01-11T22:10:13.1388978Z [ RUN      ] LazyOpsTest.TestBinaryCrossEntropyBackward
2023-01-11T22:10:13.1452456Z [       OK ] LazyOpsTest.TestBinaryCrossEntropyBackward (6 ms)
2023-01-11T22:10:13.1453133Z [ RUN      ] LazyOpsTest.TestNllLossBackward
2023-01-11T22:10:13.1453441Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:10954: Skipped
2023-01-11T22:10:13.1453611Z 
2023-01-11T22:10:13.1453807Z [  SKIPPED ] LazyOpsTest.TestNllLossBackward (0 ms)
2023-01-11T22:10:13.1454145Z [ RUN      ] LazyOpsTest.TestNllLoss2dBackward
2023-01-11T22:10:13.1737970Z [       OK ] LazyOpsTest.TestNllLoss2dBackward (28 ms)
2023-01-11T22:10:13.1738432Z [ RUN      ] LazyOpsTest.TestSmoothL1LossBackward
2023-01-11T22:10:13.1799167Z [       OK ] LazyOpsTest.TestSmoothL1LossBackward (6 ms)
2023-01-11T22:10:13.1799625Z [ RUN      ] LazyOpsTest.TestViewBackward
2023-01-11T22:10:13.1819444Z [       OK ] LazyOpsTest.TestViewBackward (2 ms)
2023-01-11T22:10:13.1819833Z [ RUN      ] LazyOpsTest.TestBatchNorm2DBackward
2023-01-11T22:10:13.1870276Z [       OK ] LazyOpsTest.TestBatchNorm2DBackward (5 ms)
2023-01-11T22:10:13.1870734Z [ RUN      ] LazyOpsTest.TestBatchNorm3DBackward
2023-01-11T22:10:13.1920579Z [       OK ] LazyOpsTest.TestBatchNorm3DBackward (5 ms)
2023-01-11T22:10:13.1921038Z [ RUN      ] LazyOpsTest.TestBCEWithLogitsBackward
2023-01-11T22:10:13.2275500Z [       OK ] LazyOpsTest.TestBCEWithLogitsBackward (35 ms)
2023-01-11T22:10:13.2275986Z [ RUN      ] LazyOpsTest.TestKlDivBackward
2023-01-11T22:10:13.2357442Z [       OK ] LazyOpsTest.TestKlDivBackward (8 ms)
2023-01-11T22:10:13.2357774Z [ RUN      ] LazyOpsTest.TestEmbeddingBackward
2023-01-11T22:10:13.3071685Z [       OK ] LazyOpsTest.TestEmbeddingBackward (71 ms)
2023-01-11T22:10:13.3072428Z [ RUN      ] LazyOpsTest.TestAmpForeachNonFiniteCheckAndUnscale
2023-01-11T22:10:13.3072932Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:11351: Skipped
2023-01-11T22:10:13.3073093Z 
2023-01-11T22:10:13.3073404Z [  SKIPPED ] LazyOpsTest.TestAmpForeachNonFiniteCheckAndUnscale (0 ms)
2023-01-11T22:10:13.3073837Z [ RUN      ] LazyOpsTest.TestAmpUpdateScale
2023-01-11T22:10:13.3074219Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:11400: Skipped
2023-01-11T22:10:13.3074478Z 
2023-01-11T22:10:13.3074670Z [  SKIPPED ] LazyOpsTest.TestAmpUpdateScale (0 ms)
2023-01-11T22:10:13.3075299Z [ RUN      ] LazyOpsTest.TestEarlySyncLiveTensors
2023-01-11T22:10:13.3075759Z [       OK ] LazyOpsTest.TestEarlySyncLiveTensors (0 ms)
2023-01-11T22:10:13.3076078Z [ RUN      ] LazyOpsTest.TestLerp
2023-01-11T22:10:13.3076468Z [       OK ] LazyOpsTest.TestLerp (0 ms)
2023-01-11T22:10:13.3076772Z [ RUN      ] LazyOpsTest.TestLerpScalar
2023-01-11T22:10:13.3077155Z [       OK ] LazyOpsTest.TestLerpScalar (0 ms)
2023-01-11T22:10:13.3077497Z [ RUN      ] LazyOpsTest.TestLerpInplace
2023-01-11T22:10:13.3077970Z [       OK ] LazyOpsTest.TestLerpInplace (0 ms)
2023-01-11T22:10:13.3078388Z [ RUN      ] LazyOpsTest.TestLerpScalarInplace
2023-01-11T22:10:13.3079215Z [       OK ] LazyOpsTest.TestLerpScalarInplace (0 ms)
2023-01-11T22:10:13.3079707Z [ RUN      ] LazyOpsTest.TestLerpOut
2023-01-11T22:10:13.3080476Z [       OK ] LazyOpsTest.TestLerpOut (0 ms)
2023-01-11T22:10:13.3081120Z [ RUN      ] LazyOpsTest.TestLerpScalarOut
2023-01-11T22:10:13.3081842Z [       OK ] LazyOpsTest.TestLerpScalarOut (0 ms)
2023-01-11T22:10:13.3082200Z [ RUN      ] LazyOpsTest.IsAliasOf
2023-01-11T22:10:13.3082496Z [       OK ] LazyOpsTest.IsAliasOf (0 ms)
2023-01-11T22:10:13.3082866Z [----------] 574 tests from LazyOpsTest (3556 ms total)
2023-01-11T22:10:13.3083026Z 
2023-01-11T22:10:13.3083196Z [----------] Global test environment tear-down
2023-01-11T22:10:13.3141432Z [==========] 611 tests from 10 test suites ran. (3572 ms total)
2023-01-11T22:10:13.3141764Z [  PASSED  ] 607 tests.
2023-01-11T22:10:13.3142020Z [  SKIPPED ] 4 tests, listed below:
2023-01-11T22:10:13.3142301Z [  SKIPPED ] LazyOpsTest.TestNllLoss
2023-01-11T22:10:13.3142762Z [  SKIPPED ] LazyOpsTest.TestNllLossBackward
2023-01-11T22:10:13.3143147Z [  SKIPPED ] LazyOpsTest.TestAmpForeachNonFiniteCheckAndUnscale
2023-01-11T22:10:13.3143541Z [  SKIPPED ] LazyOpsTest.TestAmpUpdateScale
2023-01-11T22:10:13.4044042Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *-tsan* ]]
2023-01-11T22:10:13.4044337Z + python test/cpp/jit/tests_setup.py shutdown
2023-01-11T22:10:14.7532911Z + wait
2023-01-11T22:10:14.7533309Z + OMP_NUM_THREADS=2
2023-01-11T22:10:14.7533671Z + TORCH_CPP_TEST_MNIST_PATH=test/cpp/api/mnist
2023-01-11T22:10:14.7534307Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_api '--gtest_filter=-IMethodTest.*' --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_api.xml
2023-01-11T22:10:15.1417967Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:10:15.1425488Z Note: Google Test filter = -IMethodTest.*:*_CUDA:*_MultiCUDA
2023-01-11T22:10:15.1426094Z [==========] Running 992 tests from 48 test suites.
2023-01-11T22:10:15.1426534Z [----------] Global test environment set-up.
2023-01-11T22:10:15.1426827Z [----------] 9 tests from AutogradAPITests
2023-01-11T22:10:15.1427169Z [ RUN      ] AutogradAPITests.BackwardSimpleTest
2023-01-11T22:10:15.1437041Z [       OK ] AutogradAPITests.BackwardSimpleTest (1 ms)
2023-01-11T22:10:15.1437393Z [ RUN      ] AutogradAPITests.BackwardTest
2023-01-11T22:10:15.1438007Z [W engine.cpp:1134] Warning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (function operator())
2023-01-11T22:10:15.1440081Z [       OK ] AutogradAPITests.BackwardTest (0 ms)
2023-01-11T22:10:15.1440517Z [ RUN      ] AutogradAPITests.GradSimpleTest
2023-01-11T22:10:15.1441773Z [       OK ] AutogradAPITests.GradSimpleTest (0 ms)
2023-01-11T22:10:15.1442155Z [ RUN      ] AutogradAPITests.GradTest
2023-01-11T22:10:15.1454043Z [       OK ] AutogradAPITests.GradTest (1 ms)
2023-01-11T22:10:15.1454640Z [ RUN      ] AutogradAPITests.GradNonLeafTest
2023-01-11T22:10:15.1457041Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:10:15.1459176Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:10:15.1461300Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:10:15.1463803Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:10:15.1464533Z [       OK ] AutogradAPITests.GradNonLeafTest (0 ms)
2023-01-11T22:10:15.1464875Z [ RUN      ] AutogradAPITests.GradUnreachableTest
2023-01-11T22:10:15.1519168Z [       OK ] AutogradAPITests.GradUnreachableTest (5 ms)
2023-01-11T22:10:15.1519728Z [ RUN      ] AutogradAPITests.EmptyInput
2023-01-11T22:10:15.1546165Z [       OK ] AutogradAPITests.EmptyInput (2 ms)
2023-01-11T22:10:15.1546731Z [ RUN      ] AutogradAPITests.RetainGrad
2023-01-11T22:10:15.1549955Z [       OK ] AutogradAPITests.RetainGrad (0 ms)
2023-01-11T22:10:15.1550540Z [ RUN      ] AutogradAPITests.AnomalyMode
2023-01-11T22:10:15.1550931Z [W anomaly_mode.cpp:27] Warning: This mode should be enabled only for debugging as the different tests will slow down your program execution. (function operator())
2023-01-11T22:10:15.3167970Z [       OK ] AutogradAPITests.AnomalyMode (161 ms)
2023-01-11T22:10:15.3168507Z [----------] 9 tests from AutogradAPITests (174 ms total)
2023-01-11T22:10:15.3168674Z 
2023-01-11T22:10:15.3168850Z [----------] 33 tests from CustomAutogradTest
2023-01-11T22:10:15.3169207Z [ RUN      ] CustomAutogradTest.GradUnreachableDiscoveryTest
2023-01-11T22:10:15.3177348Z [       OK ] CustomAutogradTest.GradUnreachableDiscoveryTest (1 ms)
2023-01-11T22:10:15.3177831Z [ RUN      ] CustomAutogradTest.CustomFunction
2023-01-11T22:10:15.3179931Z [       OK ] CustomAutogradTest.CustomFunction (0 ms)
2023-01-11T22:10:15.3180352Z [ RUN      ] CustomAutogradTest.CustomFunctionWithTensorList
2023-01-11T22:10:15.3181768Z [       OK ] CustomAutogradTest.CustomFunctionWithTensorList (0 ms)
2023-01-11T22:10:15.3182169Z [ RUN      ] CustomAutogradTest.GraphTaskTrimEdges
2023-01-11T22:10:15.3185326Z [       OK ] CustomAutogradTest.GraphTaskTrimEdges (0 ms)
2023-01-11T22:10:15.3185952Z [ RUN      ] CustomAutogradTest.FunctionReturnsInput
2023-01-11T22:10:15.3186590Z [       OK ] CustomAutogradTest.FunctionReturnsInput (0 ms)
2023-01-11T22:10:15.3186988Z [ RUN      ] CustomAutogradTest.FunctionReturnsUndefined
2023-01-11T22:10:15.3188075Z [       OK ] CustomAutogradTest.FunctionReturnsUndefined (0 ms)
2023-01-11T22:10:15.3188477Z [ RUN      ] CustomAutogradTest.MaterializeGrads
2023-01-11T22:10:15.3190357Z [       OK ] CustomAutogradTest.MaterializeGrads (0 ms)
2023-01-11T22:10:15.3191008Z [ RUN      ] CustomAutogradTest.DontMaterializeGrads
2023-01-11T22:10:15.3191665Z [       OK ] CustomAutogradTest.DontMaterializeGrads (0 ms)
2023-01-11T22:10:15.3192041Z [ RUN      ] CustomAutogradTest.NoGradCustomFunction
2023-01-11T22:10:15.3192416Z [       OK ] CustomAutogradTest.NoGradCustomFunction (0 ms)
2023-01-11T22:10:15.3192870Z [ RUN      ] CustomAutogradTest.MarkDirty
2023-01-11T22:10:15.3198223Z [       OK ] CustomAutogradTest.MarkDirty (0 ms)
2023-01-11T22:10:15.3198679Z [ RUN      ] CustomAutogradTest.MarkNonDifferentiable
2023-01-11T22:10:15.3199756Z [       OK ] CustomAutogradTest.MarkNonDifferentiable (0 ms)
2023-01-11T22:10:15.3200145Z [ RUN      ] CustomAutogradTest.MarkNonDifferentiableMixed
2023-01-11T22:10:15.3202135Z [       OK ] CustomAutogradTest.MarkNonDifferentiableMixed (0 ms)
2023-01-11T22:10:15.3202652Z [ RUN      ] CustomAutogradTest.MarkNonDifferentiableNone
2023-01-11T22:10:15.3203664Z [       OK ] CustomAutogradTest.MarkNonDifferentiableNone (0 ms)
2023-01-11T22:10:15.3204057Z [ RUN      ] CustomAutogradTest.ReturnLeafInplace
2023-01-11T22:10:15.3204488Z [       OK ] CustomAutogradTest.ReturnLeafInplace (0 ms)
2023-01-11T22:10:15.3204933Z [ RUN      ] CustomAutogradTest.ReturnDuplicateInplace
2023-01-11T22:10:15.3236230Z [       OK ] CustomAutogradTest.ReturnDuplicateInplace (3 ms)
2023-01-11T22:10:15.3236911Z [ RUN      ] CustomAutogradTest.ReturnDuplicate
2023-01-11T22:10:15.3237482Z [       OK ] CustomAutogradTest.ReturnDuplicate (0 ms)
2023-01-11T22:10:15.3238038Z [ RUN      ] CustomAutogradTest.SaveEmptyForBackward
2023-01-11T22:10:15.3238661Z [       OK ] CustomAutogradTest.SaveEmptyForBackward (0 ms)
2023-01-11T22:10:15.3239027Z [ RUN      ] CustomAutogradTest.InvalidGradients
2023-01-11T22:10:15.3297421Z [       OK ] CustomAutogradTest.InvalidGradients (5 ms)
2023-01-11T22:10:15.3298034Z [ RUN      ] CustomAutogradTest.NoGradInput
2023-01-11T22:10:15.3298563Z [       OK ] CustomAutogradTest.NoGradInput (0 ms)
2023-01-11T22:10:15.3298992Z [ RUN      ] CustomAutogradTest.TooManyGrads
2023-01-11T22:10:15.3299329Z [       OK ] CustomAutogradTest.TooManyGrads (0 ms)
2023-01-11T22:10:15.3299684Z [ RUN      ] CustomAutogradTest.DepNoGrad
2023-01-11T22:10:15.3300082Z [       OK ] CustomAutogradTest.DepNoGrad (0 ms)
2023-01-11T22:10:15.3300399Z [ RUN      ] CustomAutogradTest.Reentrant
2023-01-11T22:10:15.3300780Z [       OK ] CustomAutogradTest.Reentrant (0 ms)
2023-01-11T22:10:15.3301105Z [ RUN      ] CustomAutogradTest.DeepReentrant
2023-01-11T22:10:15.7688600Z [       OK ] CustomAutogradTest.DeepReentrant (438 ms)
2023-01-11T22:10:15.7689006Z [ RUN      ] CustomAutogradTest.ReentrantPriority
2023-01-11T22:10:15.7693030Z [       OK ] CustomAutogradTest.ReentrantPriority (0 ms)
2023-01-11T22:10:15.7693431Z [ RUN      ] CustomAutogradTest.Hooks
2023-01-11T22:10:15.7729111Z [       OK ] CustomAutogradTest.Hooks (3 ms)
2023-01-11T22:10:15.7729428Z [ RUN      ] CustomAutogradTest.HooksInplace
2023-01-11T22:10:15.7731360Z [       OK ] CustomAutogradTest.HooksInplace (0 ms)
2023-01-11T22:10:15.7731991Z [ RUN      ] CustomAutogradTest.HooksInplaceWithRetainsGrad
2023-01-11T22:10:15.7733758Z [       OK ] CustomAutogradTest.HooksInplaceWithRetainsGrad (0 ms)
2023-01-11T22:10:15.7734187Z [ RUN      ] CustomAutogradTest.HooksInplaceTwiceWithRetainsGrad
2023-01-11T22:10:15.7736320Z [       OK ] CustomAutogradTest.HooksInplaceTwiceWithRetainsGrad (0 ms)
2023-01-11T22:10:15.7736803Z [ RUN      ] CustomAutogradTest.HookNone
2023-01-11T22:10:15.7737318Z [       OK ] CustomAutogradTest.HookNone (0 ms)
2023-01-11T22:10:15.7737708Z [ RUN      ] CustomAutogradTest.BackwardWithInputs
2023-01-11T22:10:15.7739096Z [       OK ] CustomAutogradTest.BackwardWithInputs (0 ms)
2023-01-11T22:10:15.7739573Z [ RUN      ] CustomAutogradTest.BackwardWithEmptyInputs
2023-01-11T22:10:15.7751344Z [       OK ] CustomAutogradTest.BackwardWithEmptyInputs (1 ms)
2023-01-11T22:10:15.7751901Z [ RUN      ] CustomAutogradTest.BackwardWithNonLeafInputs
2023-01-11T22:10:15.7753762Z [       OK ] CustomAutogradTest.BackwardWithNonLeafInputs (0 ms)
2023-01-11T22:10:15.7754345Z [ RUN      ] CustomAutogradTest.BackwardWithCreateGraphWarns
2023-01-11T22:10:15.7754958Z [       OK ] CustomAutogradTest.BackwardWithCreateGraphWarns (0 ms)
2023-01-11T22:10:15.7755376Z [----------] 33 tests from CustomAutogradTest (458 ms total)
2023-01-11T22:10:15.7755545Z 
2023-01-11T22:10:15.7755764Z [----------] 13 tests from TestAutogradNotImplementedFallback
2023-01-11T22:10:15.7756195Z [ RUN      ] TestAutogradNotImplementedFallback.RetSingleNonTensor
2023-01-11T22:10:15.7758713Z [       OK ] TestAutogradNotImplementedFallback.RetSingleNonTensor (0 ms)
2023-01-11T22:10:15.7759163Z [ RUN      ] TestAutogradNotImplementedFallback.InplaceOp
2023-01-11T22:10:15.7804085Z [       OK ] TestAutogradNotImplementedFallback.InplaceOp (4 ms)
2023-01-11T22:10:15.7804520Z [ RUN      ] TestAutogradNotImplementedFallback.DoubleInplaceOp
2023-01-11T22:10:15.7841809Z [       OK ] TestAutogradNotImplementedFallback.DoubleInplaceOp (3 ms)
2023-01-11T22:10:15.7842245Z [ RUN      ] TestAutogradNotImplementedFallback.OptOp
2023-01-11T22:10:15.7844783Z [       OK ] TestAutogradNotImplementedFallback.OptOp (0 ms)
2023-01-11T22:10:15.7845209Z [ RUN      ] TestAutogradNotImplementedFallback.OutOfPlaceAddition
2023-01-11T22:10:15.7878635Z [       OK ] TestAutogradNotImplementedFallback.OutOfPlaceAddition (3 ms)
2023-01-11T22:10:15.7879111Z [ RUN      ] TestAutogradNotImplementedFallback.RetTupleNonTensor
2023-01-11T22:10:15.7912313Z [       OK ] TestAutogradNotImplementedFallback.RetTupleNonTensor (3 ms)
2023-01-11T22:10:15.7912735Z [ RUN      ] TestAutogradNotImplementedFallback.ViewOp
2023-01-11T22:10:15.7977275Z [       OK ] TestAutogradNotImplementedFallback.ViewOp (6 ms)
2023-01-11T22:10:15.7977805Z [ RUN      ] TestAutogradNotImplementedFallback.ViewOpWithExtraArg
2023-01-11T22:10:15.8010922Z [       OK ] TestAutogradNotImplementedFallback.ViewOpWithExtraArg (3 ms)
2023-01-11T22:10:15.8011396Z [ RUN      ] TestAutogradNotImplementedFallback.RetTensorVectorView
2023-01-11T22:10:15.8012937Z [       OK ] TestAutogradNotImplementedFallback.RetTensorVectorView (0 ms)
2023-01-11T22:10:15.8013397Z [ RUN      ] TestAutogradNotImplementedFallback.DoubleViewOP
2023-01-11T22:10:15.8032457Z [       OK ] TestAutogradNotImplementedFallback.DoubleViewOP (1 ms)
2023-01-11T22:10:15.8032929Z [ RUN      ] TestAutogradNotImplementedFallback.NonFirstViewOP
2023-01-11T22:10:15.8063046Z [       OK ] TestAutogradNotImplementedFallback.NonFirstViewOP (3 ms)
2023-01-11T22:10:15.8063535Z [ RUN      ] TestAutogradNotImplementedFallback.RetTensorVector
2023-01-11T22:10:15.8096606Z [       OK ] TestAutogradNotImplementedFallback.RetTensorVector (3 ms)
2023-01-11T22:10:15.8097279Z [ RUN      ] TestAutogradNotImplementedFallback.TensorlistOp
2023-01-11T22:10:15.8122208Z [       OK ] TestAutogradNotImplementedFallback.TensorlistOp (2 ms)
2023-01-11T22:10:15.8122914Z [----------] 13 tests from TestAutogradNotImplementedFallback (36 ms total)
2023-01-11T22:10:15.8123297Z 
2023-01-11T22:10:15.8123549Z [----------] 18 tests from AnyModuleTest
2023-01-11T22:10:15.8123865Z [ RUN      ] AnyModuleTest.SimpleReturnType
2023-01-11T22:10:15.8124205Z [       OK ] AnyModuleTest.SimpleReturnType (0 ms)
2023-01-11T22:10:15.8124602Z [ RUN      ] AnyModuleTest.SimpleReturnTypeAndSingleArgument
2023-01-11T22:10:15.8125022Z [       OK ] AnyModuleTest.SimpleReturnTypeAndSingleArgument (0 ms)
2023-01-11T22:10:15.8125452Z [ RUN      ] AnyModuleTest.StringLiteralReturnTypeAndArgument
2023-01-11T22:10:15.8125891Z [       OK ] AnyModuleTest.StringLiteralReturnTypeAndArgument (0 ms)
2023-01-11T22:10:15.8126514Z [ RUN      ] AnyModuleTest.StringReturnTypeWithConstArgument
2023-01-11T22:10:15.8126953Z [       OK ] AnyModuleTest.StringReturnTypeWithConstArgument (0 ms)
2023-01-11T22:10:15.8127473Z [ RUN      ] AnyModuleTest.TensorReturnTypeAndStringArgumentsWithFunkyQualifications
2023-01-11T22:10:15.8128079Z [       OK ] AnyModuleTest.TensorReturnTypeAndStringArgumentsWithFunkyQualifications (0 ms)
2023-01-11T22:10:15.8128529Z [ RUN      ] AnyModuleTest.WrongArgumentType
2023-01-11T22:10:15.8136225Z [       OK ] AnyModuleTest.WrongArgumentType (1 ms)
2023-01-11T22:10:15.8136591Z [ RUN      ] AnyModuleTest.WrongNumberOfArguments
2023-01-11T22:10:15.8173645Z [       OK ] AnyModuleTest.WrongNumberOfArguments (3 ms)
2023-01-11T22:10:15.8174183Z [ RUN      ] AnyModuleTest.PassingArgumentsToModuleWithDefaultArgumentsInForwardMethod
2023-01-11T22:10:15.8257644Z [       OK ] AnyModuleTest.PassingArgumentsToModuleWithDefaultArgumentsInForwardMethod (8 ms)
2023-01-11T22:10:15.8258179Z [ RUN      ] AnyModuleTest.GetWithCorrectTypeSucceeds
2023-01-11T22:10:15.8258564Z [       OK ] AnyModuleTest.GetWithCorrectTypeSucceeds (0 ms)
2023-01-11T22:10:15.8258954Z [ RUN      ] AnyModuleTest.GetWithIncorrectTypeThrows
2023-01-11T22:10:15.8267801Z [       OK ] AnyModuleTest.GetWithIncorrectTypeThrows (1 ms)
2023-01-11T22:10:15.8268484Z [ RUN      ] AnyModuleTest.PtrWithBaseClassSucceeds
2023-01-11T22:10:15.8268861Z [       OK ] AnyModuleTest.PtrWithBaseClassSucceeds (0 ms)
2023-01-11T22:10:15.8269253Z [ RUN      ] AnyModuleTest.PtrWithGoodDowncastSuccceeds
2023-01-11T22:10:15.8269654Z [       OK ] AnyModuleTest.PtrWithGoodDowncastSuccceeds (0 ms)
2023-01-11T22:10:15.8270028Z [ RUN      ] AnyModuleTest.PtrWithBadDowncastThrows
2023-01-11T22:10:15.8284063Z [       OK ] AnyModuleTest.PtrWithBadDowncastThrows (1 ms)
2023-01-11T22:10:15.8284734Z [ RUN      ] AnyModuleTest.DefaultStateIsEmpty
2023-01-11T22:10:15.8285089Z [       OK ] AnyModuleTest.DefaultStateIsEmpty (0 ms)
2023-01-11T22:10:15.8285462Z [ RUN      ] AnyModuleTest.AllMethodsThrowForEmptyAnyModule
2023-01-11T22:10:15.8336719Z [       OK ] AnyModuleTest.AllMethodsThrowForEmptyAnyModule (5 ms)
2023-01-11T22:10:15.8337393Z [ RUN      ] AnyModuleTest.CanMoveAssignDifferentModules
2023-01-11T22:10:15.8338083Z [       OK ] AnyModuleTest.CanMoveAssignDifferentModules (0 ms)
2023-01-11T22:10:15.8338795Z [ RUN      ] AnyModuleTest.ConstructsFromModuleHolder
2023-01-11T22:10:15.8339506Z [       OK ] AnyModuleTest.ConstructsFromModuleHolder (0 ms)
2023-01-11T22:10:15.8340045Z [ RUN      ] AnyModuleTest.ConvertsVariableToTensorCorrectly
2023-01-11T22:10:15.8340463Z [       OK ] AnyModuleTest.ConvertsVariableToTensorCorrectly (0 ms)
2023-01-11T22:10:15.8341059Z [----------] 18 tests from AnyModuleTest (21 ms total)
2023-01-11T22:10:15.8341217Z 
2023-01-11T22:10:15.8341369Z [----------] 12 tests from AnyValueTest
2023-01-11T22:10:15.8341734Z [ RUN      ] AnyValueTest.CorrectlyAccessesIntWhenCorrectType
2023-01-11T22:10:15.8342163Z [       OK ] AnyValueTest.CorrectlyAccessesIntWhenCorrectType (0 ms)
2023-01-11T22:10:15.8342819Z [ RUN      ] AnyValueTest.CorrectlyAccessesStringLiteralWhenCorrectType
2023-01-11T22:10:15.8343326Z [       OK ] AnyValueTest.CorrectlyAccessesStringLiteralWhenCorrectType (0 ms)
2023-01-11T22:10:15.8343787Z [ RUN      ] AnyValueTest.CorrectlyAccessesStringWhenCorrectType
2023-01-11T22:10:15.8344243Z [       OK ] AnyValueTest.CorrectlyAccessesStringWhenCorrectType (0 ms)
2023-01-11T22:10:15.8344705Z [ RUN      ] AnyValueTest.CorrectlyAccessesPointersWhenCorrectType
2023-01-11T22:10:15.8345187Z [       OK ] AnyValueTest.CorrectlyAccessesPointersWhenCorrectType (0 ms)
2023-01-11T22:10:15.8345718Z [ RUN      ] AnyValueTest.CorrectlyAccessesReferencesWhenCorrectType
2023-01-11T22:10:15.8346210Z [       OK ] AnyValueTest.CorrectlyAccessesReferencesWhenCorrectType (0 ms)
2023-01-11T22:10:15.8346663Z [ RUN      ] AnyValueTest.TryGetReturnsNullptrForTheWrongType
2023-01-11T22:10:15.8347088Z [       OK ] AnyValueTest.TryGetReturnsNullptrForTheWrongType (0 ms)
2023-01-11T22:10:15.8347485Z [ RUN      ] AnyValueTest.GetThrowsForTheWrongType
2023-01-11T22:10:15.8361348Z [       OK ] AnyValueTest.GetThrowsForTheWrongType (2 ms)
2023-01-11T22:10:15.8362050Z [ RUN      ] AnyValueTest.MoveConstructionIsAllowed
2023-01-11T22:10:15.8362440Z [       OK ] AnyValueTest.MoveConstructionIsAllowed (0 ms)
2023-01-11T22:10:15.8362800Z [ RUN      ] AnyValueTest.MoveAssignmentIsAllowed
2023-01-11T22:10:15.8363163Z [       OK ] AnyValueTest.MoveAssignmentIsAllowed (0 ms)
2023-01-11T22:10:15.8363518Z [ RUN      ] AnyValueTest.TypeInfoIsCorrectForInt
2023-01-11T22:10:15.8363879Z [       OK ] AnyValueTest.TypeInfoIsCorrectForInt (0 ms)
2023-01-11T22:10:15.8364271Z [ RUN      ] AnyValueTest.TypeInfoIsCorrectForStringLiteral
2023-01-11T22:10:15.8364699Z [       OK ] AnyValueTest.TypeInfoIsCorrectForStringLiteral (0 ms)
2023-01-11T22:10:15.8365083Z [ RUN      ] AnyValueTest.TypeInfoIsCorrectForString
2023-01-11T22:10:15.8365462Z [       OK ] AnyValueTest.TypeInfoIsCorrectForString (0 ms)
2023-01-11T22:10:15.8365822Z [----------] 12 tests from AnyValueTest (2 ms total)
2023-01-11T22:10:15.8365977Z 
2023-01-11T22:10:15.8366106Z [----------] 50 tests from DataTest
2023-01-11T22:10:15.8366412Z [ RUN      ] DataTest.DatasetCallsGetCorrectly
2023-01-11T22:10:15.8374206Z [       OK ] DataTest.DatasetCallsGetCorrectly (1 ms)
2023-01-11T22:10:15.8374683Z [ RUN      ] DataTest.TransformCallsGetApplyCorrectly
2023-01-11T22:10:15.8375072Z [       OK ] DataTest.TransformCallsGetApplyCorrectly (0 ms)
2023-01-11T22:10:15.8375479Z [ RUN      ] DataTest.ChunkDataSetWithInvalidInitParameter
2023-01-11T22:10:15.8440381Z [       OK ] DataTest.ChunkDataSetWithInvalidInitParameter (6 ms)
2023-01-11T22:10:15.8440746Z [ RUN      ] DataTest.InfiniteStreamDataset
2023-01-11T22:10:15.8441083Z [       OK ] DataTest.InfiniteStreamDataset (0 ms)
2023-01-11T22:10:15.8441407Z [ RUN      ] DataTest.NoSequencerIsIdentity
2023-01-11T22:10:15.8451201Z [       OK ] DataTest.NoSequencerIsIdentity (1 ms)
2023-01-11T22:10:15.8451715Z [ RUN      ] DataTest.OrderedSequencerIsSetUpWell
2023-01-11T22:10:15.8452171Z [       OK ] DataTest.OrderedSequencerIsSetUpWell (0 ms)
2023-01-11T22:10:15.8452707Z [ RUN      ] DataTest.OrderedSequencerReOrdersValues
2023-01-11T22:10:15.8453357Z [       OK ] DataTest.OrderedSequencerReOrdersValues (0 ms)
2023-01-11T22:10:15.8453890Z [ RUN      ] DataTest.BatchLambdaAppliesFunctionToBatch
2023-01-11T22:10:15.8454298Z [       OK ] DataTest.BatchLambdaAppliesFunctionToBatch (0 ms)
2023-01-11T22:10:15.8454686Z [ RUN      ] DataTest.LambdaAppliesFunctionToExample
2023-01-11T22:10:15.8455070Z [       OK ] DataTest.LambdaAppliesFunctionToExample (0 ms)
2023-01-11T22:10:15.8455411Z [ RUN      ] DataTest.CollateReducesBatch
2023-01-11T22:10:15.8455734Z [       OK ] DataTest.CollateReducesBatch (0 ms)
2023-01-11T22:10:15.8456044Z [ RUN      ] DataTest.CollationReducesBatch
2023-01-11T22:10:15.8456377Z [       OK ] DataTest.CollationReducesBatch (0 ms)
2023-01-11T22:10:15.8456758Z [ RUN      ] DataTest.SequentialSamplerReturnsIndicesInOrder
2023-01-11T22:10:15.8457172Z [       OK ] DataTest.SequentialSamplerReturnsIndicesInOrder (0 ms)
2023-01-11T22:10:15.8457703Z [ RUN      ] DataTest.SequentialSamplerReturnsLessValuesForLastBatch
2023-01-11T22:10:15.8458195Z [       OK ] DataTest.SequentialSamplerReturnsLessValuesForLastBatch (0 ms)
2023-01-11T22:10:15.8458612Z [ RUN      ] DataTest.SequentialSamplerResetsWell
2023-01-11T22:10:15.8458960Z [       OK ] DataTest.SequentialSamplerResetsWell (0 ms)
2023-01-11T22:10:15.8459354Z [ RUN      ] DataTest.SequentialSamplerResetsWithNewSizeWell
2023-01-11T22:10:15.8459783Z [       OK ] DataTest.SequentialSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:10:15.8460172Z [ RUN      ] DataTest.CanSaveAndLoadSequentialSampler
2023-01-11T22:10:15.8595965Z [       OK ] DataTest.CanSaveAndLoadSequentialSampler (14 ms)
2023-01-11T22:10:15.8596678Z [ RUN      ] DataTest.RandomSamplerReturnsIndicesInCorrectRange
2023-01-11T22:10:15.8597526Z [       OK ] DataTest.RandomSamplerReturnsIndicesInCorrectRange (0 ms)
2023-01-11T22:10:15.8597980Z [ RUN      ] DataTest.RandomSamplerReturnsLessValuesForLastBatch
2023-01-11T22:10:15.8598437Z [       OK ] DataTest.RandomSamplerReturnsLessValuesForLastBatch (0 ms)
2023-01-11T22:10:15.8598825Z [ RUN      ] DataTest.RandomSamplerResetsWell
2023-01-11T22:10:15.8599151Z [       OK ] DataTest.RandomSamplerResetsWell (0 ms)
2023-01-11T22:10:15.8599562Z [ RUN      ] DataTest.RandomSamplerResetsWithNewSizeWell
2023-01-11T22:10:15.8599969Z [       OK ] DataTest.RandomSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:10:15.8600532Z [ RUN      ] DataTest.SavingAndLoadingRandomSamplerYieldsSameSequence
2023-01-11T22:10:15.8601689Z [       OK ] DataTest.SavingAndLoadingRandomSamplerYieldsSameSequence (0 ms)
2023-01-11T22:10:15.8602442Z [ RUN      ] DataTest.StreamSamplerReturnsTheBatchSizeAndThenRemainder
2023-01-11T22:10:15.8603013Z [       OK ] DataTest.StreamSamplerReturnsTheBatchSizeAndThenRemainder (0 ms)
2023-01-11T22:10:15.8603526Z [ RUN      ] DataTest.StreamSamplerResetsWell
2023-01-11T22:10:15.8603982Z [       OK ] DataTest.StreamSamplerResetsWell (0 ms)
2023-01-11T22:10:15.8604410Z [ RUN      ] DataTest.StreamSamplerResetsWithNewSizeWell
2023-01-11T22:10:15.8604877Z [       OK ] DataTest.StreamSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:10:15.8605280Z [ RUN      ] DataTest.TensorDatasetConstructsFromSingleTensor
2023-01-11T22:10:15.8605742Z [       OK ] DataTest.TensorDatasetConstructsFromSingleTensor (0 ms)
2023-01-11T22:10:15.8606316Z [ RUN      ] DataTest.TensorDatasetConstructsFromInitializerListOfTensors
2023-01-11T22:10:15.8606906Z [       OK ] DataTest.TensorDatasetConstructsFromInitializerListOfTensors (0 ms)
2023-01-11T22:10:15.8607373Z [ RUN      ] DataTest.StackTransformWorksForExample
2023-01-11T22:10:15.8607907Z [       OK ] DataTest.StackTransformWorksForExample (0 ms)
2023-01-11T22:10:15.8608306Z [ RUN      ] DataTest.StackTransformWorksForTensorExample
2023-01-11T22:10:15.8609036Z [       OK ] DataTest.StackTransformWorksForTensorExample (0 ms)
2023-01-11T22:10:15.8609775Z [ RUN      ] DataTest.TensorTransformWorksForAnyTargetType
2023-01-11T22:10:15.8610547Z [       OK ] DataTest.TensorTransformWorksForAnyTargetType (0 ms)
2023-01-11T22:10:15.8610989Z [ RUN      ] DataTest.TensorLambdaWorksforAnyTargetType
2023-01-11T22:10:15.8611375Z [       OK ] DataTest.TensorLambdaWorksforAnyTargetType (0 ms)
2023-01-11T22:10:15.8611719Z [ RUN      ] DataTest.NormalizeTransform
2023-01-11T22:10:15.8614956Z [       OK ] DataTest.NormalizeTransform (0 ms)
2023-01-11T22:10:15.8615496Z [ RUN      ] DataTest.MapDoesNotCopy
2023-01-11T22:10:15.8615829Z [       OK ] DataTest.MapDoesNotCopy (0 ms)
2023-01-11T22:10:15.8616168Z [ RUN      ] DataTest.QueuePushAndPopFromSameThread
2023-01-11T22:10:15.8616682Z [       OK ] DataTest.QueuePushAndPopFromSameThread (0 ms)
2023-01-11T22:10:15.8617077Z [ RUN      ] DataTest.QueuePopWithTimeoutThrowsUponTimeout
2023-01-11T22:10:15.8727700Z [       OK ] DataTest.QueuePopWithTimeoutThrowsUponTimeout (11 ms)
2023-01-11T22:10:15.8728344Z [ RUN      ] DataTest.QueuePushAndPopFromDifferentThreads
2023-01-11T22:10:15.8934305Z [       OK ] DataTest.QueuePushAndPopFromDifferentThreads (20 ms)
2023-01-11T22:10:15.8934885Z [ RUN      ] DataTest.QueueClearEmptiesTheQueue
2023-01-11T22:10:15.8955957Z [       OK ] DataTest.QueueClearEmptiesTheQueue (2 ms)
2023-01-11T22:10:15.8956732Z [ RUN      ] DataTest.DataShuttleCanPushAndPopJob
2023-01-11T22:10:15.8957120Z [       OK ] DataTest.DataShuttleCanPushAndPopJob (0 ms)
2023-01-11T22:10:15.8957493Z [ RUN      ] DataTest.DataShuttleCanPushAndPopResult
2023-01-11T22:10:15.8957873Z [       OK ] DataTest.DataShuttleCanPushAndPopResult (0 ms)
2023-01-11T22:10:15.8958327Z [ RUN      ] DataTest.DataShuttlePopResultReturnsNulloptWhenNoJobsInFlight
2023-01-11T22:10:15.8958867Z [       OK ] DataTest.DataShuttlePopResultReturnsNulloptWhenNoJobsInFlight (0 ms)
2023-01-11T22:10:15.8959360Z [ RUN      ] DataTest.DataShuttleDrainMeansPopResultReturnsNullopt
2023-01-11T22:10:15.8959934Z [       OK ] DataTest.DataShuttleDrainMeansPopResultReturnsNullopt (0 ms)
2023-01-11T22:10:15.8960338Z [ RUN      ] DataTest.DataShuttlePopResultTimesOut
2023-01-11T22:10:15.9068496Z [       OK ] DataTest.DataShuttlePopResultTimesOut (11 ms)
2023-01-11T22:10:15.9068890Z [ RUN      ] DataTest.SharedBatchDatasetReallyIsShared
2023-01-11T22:10:15.9083707Z [       OK ] DataTest.SharedBatchDatasetReallyIsShared (1 ms)
2023-01-11T22:10:15.9084441Z [ RUN      ] DataTest.SharedBatchDatasetDoesNotIncurCopyWhenPassedDatasetObject
2023-01-11T22:10:15.9085028Z [       OK ] DataTest.SharedBatchDatasetDoesNotIncurCopyWhenPassedDatasetObject (0 ms)
2023-01-11T22:10:15.9085504Z [ RUN      ] DataTest.CanUseCustomTypeAsIndexType
2023-01-11T22:10:15.9085858Z [       OK ] DataTest.CanUseCustomTypeAsIndexType (0 ms)
2023-01-11T22:10:15.9086332Z [ RUN      ] DataTest.DistributedRandomSamplerSingleReplicaProduceCorrectSamples
2023-01-11T22:10:15.9092410Z [       OK ] DataTest.DistributedRandomSamplerSingleReplicaProduceCorrectSamples (0 ms)
2023-01-11T22:10:15.9093107Z [ RUN      ] DataTest.DistributedRandomSamplerMultiReplicaProduceCorrectSamples
2023-01-11T22:10:15.9094004Z [       OK ] DataTest.DistributedRandomSamplerMultiReplicaProduceCorrectSamples (0 ms)
2023-01-11T22:10:15.9094494Z [ RUN      ] DataTest.CanSaveAndLoadDistributedRandomSampler
2023-01-11T22:10:15.9098932Z [       OK ] DataTest.CanSaveAndLoadDistributedRandomSampler (0 ms)
2023-01-11T22:10:15.9099781Z [ RUN      ] DataTest.DistributedSequentialSamplerSingleReplicaProduceCorrectSamples
2023-01-11T22:10:15.9100532Z [       OK ] DataTest.DistributedSequentialSamplerSingleReplicaProduceCorrectSamples (0 ms)
2023-01-11T22:10:15.9101408Z [ RUN      ] DataTest.DistributedSequentialSamplerMultiReplicaProduceCorrectSamples
2023-01-11T22:10:15.9102253Z [       OK ] DataTest.DistributedSequentialSamplerMultiReplicaProduceCorrectSamples (0 ms)
2023-01-11T22:10:15.9102924Z [ RUN      ] DataTest.CanSaveAndLoadDistributedSequentialSampler
2023-01-11T22:10:15.9103401Z [       OK ] DataTest.CanSaveAndLoadDistributedSequentialSampler (0 ms)
2023-01-11T22:10:15.9103797Z [----------] 50 tests from DataTest (74 ms total)
2023-01-11T22:10:15.9103946Z 
2023-01-11T22:10:15.9104100Z [----------] 37 tests from DataLoaderTest
2023-01-11T22:10:15.9104456Z [ RUN      ] DataLoaderTest.DataLoaderOptionsDefaultAsExpected
2023-01-11T22:10:15.9104900Z [       OK ] DataLoaderTest.DataLoaderOptionsDefaultAsExpected (0 ms)
2023-01-11T22:10:15.9105450Z [ RUN      ] DataLoaderTest.DataLoaderOptionsCoalesceOptionalValues
2023-01-11T22:10:15.9105937Z [       OK ] DataLoaderTest.DataLoaderOptionsCoalesceOptionalValues (0 ms)
2023-01-11T22:10:15.9106377Z [ RUN      ] DataLoaderTest.MakeDataLoaderDefaultsAsExpected
2023-01-11T22:10:15.9106810Z [       OK ] DataLoaderTest.MakeDataLoaderDefaultsAsExpected (0 ms)
2023-01-11T22:10:15.9107355Z [ RUN      ] DataLoaderTest.MakeDataLoaderThrowsWhenConstructingSamplerWithUnsizedDataset
2023-01-11T22:10:15.9114167Z [       OK ] DataLoaderTest.MakeDataLoaderThrowsWhenConstructingSamplerWithUnsizedDataset (1 ms)
2023-01-11T22:10:15.9114999Z [ RUN      ] DataLoaderTest.IteratorsCompareEqualToThemselves
2023-01-11T22:10:15.9115794Z [       OK ] DataLoaderTest.IteratorsCompareEqualToThemselves (0 ms)
2023-01-11T22:10:15.9116337Z [ RUN      ] DataLoaderTest.ValidIteratorsCompareUnequalToEachOther
2023-01-11T22:10:15.9116818Z [       OK ] DataLoaderTest.ValidIteratorsCompareUnequalToEachOther (0 ms)
2023-01-11T22:10:15.9117299Z [ RUN      ] DataLoaderTest.SentinelIteratorsCompareEqualToEachOther
2023-01-11T22:10:15.9117786Z [       OK ] DataLoaderTest.SentinelIteratorsCompareEqualToEachOther (0 ms)
2023-01-11T22:10:15.9118276Z [ RUN      ] DataLoaderTest.IteratorsCompareEqualToSentinelWhenExhausted
2023-01-11T22:10:15.9118795Z [       OK ] DataLoaderTest.IteratorsCompareEqualToSentinelWhenExhausted (0 ms)
2023-01-11T22:10:15.9119213Z [ RUN      ] DataLoaderTest.IteratorsShareState
2023-01-11T22:10:15.9119624Z [       OK ] DataLoaderTest.IteratorsShareState (0 ms)
2023-01-11T22:10:15.9120017Z [ RUN      ] DataLoaderTest.CanDereferenceIteratorMultipleTimes
2023-01-11T22:10:15.9120469Z [       OK ] DataLoaderTest.CanDereferenceIteratorMultipleTimes (0 ms)
2023-01-11T22:10:15.9120883Z [ RUN      ] DataLoaderTest.CanUseIteratorAlgorithms
2023-01-11T22:10:15.9121257Z [       OK ] DataLoaderTest.CanUseIteratorAlgorithms (0 ms)
2023-01-11T22:10:15.9121719Z [ RUN      ] DataLoaderTest.CallingBeginWhileOtherIteratorIsInFlightThrows
2023-01-11T22:10:15.9131685Z [       OK ] DataLoaderTest.CallingBeginWhileOtherIteratorIsInFlightThrows (1 ms)
2023-01-11T22:10:15.9132360Z [ RUN      ] DataLoaderTest.IncrementingExhaustedValidIteratorThrows
2023-01-11T22:10:15.9142991Z [       OK ] DataLoaderTest.IncrementingExhaustedValidIteratorThrows (1 ms)
2023-01-11T22:10:15.9143636Z [ RUN      ] DataLoaderTest.DereferencingExhaustedValidIteratorThrows
2023-01-11T22:10:15.9154391Z [       OK ] DataLoaderTest.DereferencingExhaustedValidIteratorThrows (1 ms)
2023-01-11T22:10:15.9155003Z [ RUN      ] DataLoaderTest.IncrementingSentinelIteratorThrows
2023-01-11T22:10:15.9165691Z [       OK ] DataLoaderTest.IncrementingSentinelIteratorThrows (1 ms)
2023-01-11T22:10:15.9166418Z [ RUN      ] DataLoaderTest.DereferencingSentinelIteratorThrows
2023-01-11T22:10:15.9177330Z [       OK ] DataLoaderTest.DereferencingSentinelIteratorThrows (1 ms)
2023-01-11T22:10:15.9177804Z [ RUN      ] DataLoaderTest.YieldsCorrectBatchSize
2023-01-11T22:10:15.9178180Z [       OK ] DataLoaderTest.YieldsCorrectBatchSize (0 ms)
2023-01-11T22:10:15.9178694Z [ RUN      ] DataLoaderTest.ReturnsLastBatchWhenSmallerThanBatchSizeWhenDropLastIsFalse
2023-01-11T22:10:15.9179333Z [       OK ] DataLoaderTest.ReturnsLastBatchWhenSmallerThanBatchSizeWhenDropLastIsFalse (0 ms)
2023-01-11T22:10:15.9179976Z [ RUN      ] DataLoaderTest.DoesNotReturnLastBatchWhenSmallerThanBatchSizeWhenDropLastIsTrue
2023-01-11T22:10:15.9180656Z [       OK ] DataLoaderTest.DoesNotReturnLastBatchWhenSmallerThanBatchSizeWhenDropLastIsTrue (0 ms)
2023-01-11T22:10:15.9181148Z [ RUN      ] DataLoaderTest.RespectsTimeout
2023-01-11T22:10:15.9294521Z [       OK ] DataLoaderTest.RespectsTimeout (11 ms)
2023-01-11T22:10:15.9295032Z [ RUN      ] DataLoaderTest.EnforcesOrderingAmongThreadsWhenConfigured
2023-01-11T22:10:15.9319340Z [       OK ] DataLoaderTest.EnforcesOrderingAmongThreadsWhenConfigured (2 ms)
2023-01-11T22:10:15.9319826Z [ RUN      ] DataLoaderTest.Reset
2023-01-11T22:10:15.9320123Z [       OK ] DataLoaderTest.Reset (0 ms)
2023-01-11T22:10:15.9320502Z [ RUN      ] DataLoaderTest.TestExceptionsArePropagatedFromWorkers
2023-01-11T22:10:15.9324665Z [       OK ] DataLoaderTest.TestExceptionsArePropagatedFromWorkers (0 ms)
2023-01-11T22:10:15.9325410Z [ RUN      ] DataLoaderTest.StatefulDatasetWithNoWorkers
2023-01-11T22:10:15.9325814Z [       OK ] DataLoaderTest.StatefulDatasetWithNoWorkers (0 ms)
2023-01-11T22:10:15.9326226Z [ RUN      ] DataLoaderTest.StatefulDatasetWithManyWorkers
2023-01-11T22:10:15.9343869Z [       OK ] DataLoaderTest.StatefulDatasetWithManyWorkers (1 ms)
2023-01-11T22:10:15.9344282Z [ RUN      ] DataLoaderTest.StatefulDatasetWithMap
2023-01-11T22:10:15.9344704Z [       OK ] DataLoaderTest.StatefulDatasetWithMap (0 ms)
2023-01-11T22:10:15.9345101Z [ RUN      ] DataLoaderTest.StatefulDatasetWithCollate
2023-01-11T22:10:15.9346562Z [       OK ] DataLoaderTest.StatefulDatasetWithCollate (0 ms)
2023-01-11T22:10:15.9346925Z [ RUN      ] DataLoaderTest.ChunkDataSetGetBatch
2023-01-11T22:10:15.9448519Z [       OK ] DataLoaderTest.ChunkDataSetGetBatch (10 ms)
2023-01-11T22:10:15.9448951Z [ RUN      ] DataLoaderTest.ChunkDataSetWithBatchSizeMismatch
2023-01-11T22:10:15.9462908Z [       OK ] DataLoaderTest.ChunkDataSetWithBatchSizeMismatch (1 ms)
2023-01-11T22:10:15.9463445Z [ RUN      ] DataLoaderTest.ChunkDataSetWithEmptyBatch
2023-01-11T22:10:15.9463987Z [       OK ] DataLoaderTest.ChunkDataSetWithEmptyBatch (0 ms)
2023-01-11T22:10:15.9464482Z [ RUN      ] DataLoaderTest.ChunkDataSetGetBatchWithUnevenBatchSize
2023-01-11T22:10:15.9465333Z [       OK ] DataLoaderTest.ChunkDataSetGetBatchWithUnevenBatchSize (0 ms)
2023-01-11T22:10:15.9465975Z [ RUN      ] DataLoaderTest.CanAccessChunkSamplerWithChunkDataSet
2023-01-11T22:10:15.9466834Z [       OK ] DataLoaderTest.CanAccessChunkSamplerWithChunkDataSet (0 ms)
2023-01-11T22:10:15.9467262Z [ RUN      ] DataLoaderTest.ChunkDatasetDoesNotHang
2023-01-11T22:10:15.9467627Z [       OK ] DataLoaderTest.ChunkDatasetDoesNotHang (0 ms)
2023-01-11T22:10:15.9467979Z [ RUN      ] DataLoaderTest.ChunkDatasetSave
2023-01-11T22:10:15.9626477Z [       OK ] DataLoaderTest.ChunkDatasetSave (15 ms)
2023-01-11T22:10:15.9626868Z [ RUN      ] DataLoaderTest.ChunkDatasetLoad
2023-01-11T22:10:15.9631635Z [       OK ] DataLoaderTest.ChunkDatasetLoad (0 ms)
2023-01-11T22:10:15.9632149Z [ RUN      ] DataLoaderTest.ChunkDatasetCrossChunkShuffle
2023-01-11T22:10:15.9636023Z [       OK ] DataLoaderTest.ChunkDatasetCrossChunkShuffle (0 ms)
2023-01-11T22:10:15.9636465Z [ RUN      ] DataLoaderTest.CustomPreprocessPolicy
2023-01-11T22:10:15.9638719Z [       OK ] DataLoaderTest.CustomPreprocessPolicy (0 ms)
2023-01-11T22:10:15.9639386Z [----------] 37 tests from DataLoaderTest (53 ms total)
2023-01-11T22:10:15.9639739Z 
2023-01-11T22:10:15.9639883Z [----------] 1 test from EnumTest
2023-01-11T22:10:15.9640129Z [ RUN      ] EnumTest.AllEnums
2023-01-11T22:10:15.9640405Z [       OK ] EnumTest.AllEnums (0 ms)
2023-01-11T22:10:15.9640700Z [----------] 1 test from EnumTest (0 ms total)
2023-01-11T22:10:15.9640847Z 
2023-01-11T22:10:15.9641012Z [----------] 6 tests from ExpandingArrayTest
2023-01-11T22:10:15.9641379Z [ RUN      ] ExpandingArrayTest.CanConstructFromInitializerList
2023-01-11T22:10:15.9641964Z [       OK ] ExpandingArrayTest.CanConstructFromInitializerList (0 ms)
2023-01-11T22:10:15.9642396Z [ RUN      ] ExpandingArrayTest.CanConstructFromVector
2023-01-11T22:10:15.9642778Z [       OK ] ExpandingArrayTest.CanConstructFromVector (0 ms)
2023-01-11T22:10:15.9643165Z [ RUN      ] ExpandingArrayTest.CanConstructFromArray
2023-01-11T22:10:15.9643552Z [       OK ] ExpandingArrayTest.CanConstructFromArray (0 ms)
2023-01-11T22:10:15.9643962Z [ RUN      ] ExpandingArrayTest.CanConstructFromSingleValue
2023-01-11T22:10:15.9644377Z [       OK ] ExpandingArrayTest.CanConstructFromSingleValue (0 ms)
2023-01-11T22:10:15.9644978Z [ RUN      ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInInitializerList
2023-01-11T22:10:15.9651756Z [       OK ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInInitializerList (1 ms)
2023-01-11T22:10:15.9652462Z [ RUN      ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInVector
2023-01-11T22:10:15.9662596Z [       OK ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInVector (1 ms)
2023-01-11T22:10:15.9663503Z [----------] 6 tests from ExpandingArrayTest (2 ms total)
2023-01-11T22:10:15.9663679Z 
2023-01-11T22:10:15.9663820Z [----------] 10 tests from FFTTest
2023-01-11T22:10:15.9664054Z [ RUN      ] FFTTest.fft
2023-01-11T22:10:15.9789158Z [       OK ] FFTTest.fft (12 ms)
2023-01-11T22:10:15.9789426Z [ RUN      ] FFTTest.fft_real
2023-01-11T22:10:15.9790745Z [       OK ] FFTTest.fft_real (0 ms)
2023-01-11T22:10:15.9791209Z [ RUN      ] FFTTest.fft_pad
2023-01-11T22:10:15.9804967Z [       OK ] FFTTest.fft_pad (1 ms)
2023-01-11T22:10:15.9805438Z [ RUN      ] FFTTest.fft_norm
2023-01-11T22:10:15.9807922Z [       OK ] FFTTest.fft_norm (0 ms)
2023-01-11T22:10:15.9808322Z [ RUN      ] FFTTest.ifft
2023-01-11T22:10:15.9811172Z [       OK ] FFTTest.ifft (0 ms)
2023-01-11T22:10:15.9811632Z [ RUN      ] FFTTest.fft_ifft
2023-01-11T22:10:15.9960842Z [       OK ] FFTTest.fft_ifft (14 ms)
2023-01-11T22:10:15.9961318Z [ RUN      ] FFTTest.rfft
2023-01-11T22:10:15.9986776Z [       OK ] FFTTest.rfft (2 ms)
2023-01-11T22:10:15.9987252Z [ RUN      ] FFTTest.rfft_irfft
2023-01-11T22:10:15.9988192Z [       OK ] FFTTest.rfft_irfft (0 ms)
2023-01-11T22:10:15.9988666Z [ RUN      ] FFTTest.ihfft
2023-01-11T22:10:15.9992596Z [       OK ] FFTTest.ihfft (0 ms)
2023-01-11T22:10:15.9993045Z [ RUN      ] FFTTest.hfft_ihfft
2023-01-11T22:10:16.0033339Z [       OK ] FFTTest.hfft_ihfft (4 ms)
2023-01-11T22:10:16.0033917Z [----------] 10 tests from FFTTest (37 ms total)
2023-01-11T22:10:16.0034169Z 
2023-01-11T22:10:16.0034316Z [----------] 132 tests from FunctionalTest
2023-01-11T22:10:16.0034605Z [ RUN      ] FunctionalTest.Conv1d
2023-01-11T22:10:16.0053590Z [       OK ] FunctionalTest.Conv1d (1 ms)
2023-01-11T22:10:16.0053909Z [ RUN      ] FunctionalTest.Conv2dEven
2023-01-11T22:10:16.0059624Z [       OK ] FunctionalTest.Conv2dEven (0 ms)
2023-01-11T22:10:16.0059940Z [ RUN      ] FunctionalTest.Conv2dUneven
2023-01-11T22:10:16.0061927Z [       OK ] FunctionalTest.Conv2dUneven (0 ms)
2023-01-11T22:10:16.0062222Z [ RUN      ] FunctionalTest.Conv3d
2023-01-11T22:10:16.0066351Z [       OK ] FunctionalTest.Conv3d (0 ms)
2023-01-11T22:10:16.0066656Z [ RUN      ] FunctionalTest.MaxPool1d
2023-01-11T22:10:16.0088541Z [       OK ] FunctionalTest.MaxPool1d (2 ms)
2023-01-11T22:10:16.0088922Z [ RUN      ] FunctionalTest.MaxPool2d
2023-01-11T22:10:16.0089306Z [       OK ] FunctionalTest.MaxPool2d (0 ms)
2023-01-11T22:10:16.0089631Z [ RUN      ] FunctionalTest.MaxPool2dBackward
2023-01-11T22:10:16.0091508Z [       OK ] FunctionalTest.MaxPool2dBackward (0 ms)
2023-01-11T22:10:16.0091911Z [ RUN      ] FunctionalTest.MaxPool3d
2023-01-11T22:10:16.0092372Z [       OK ] FunctionalTest.MaxPool3d (0 ms)
2023-01-11T22:10:16.0092698Z [ RUN      ] FunctionalTest.AvgPool1d
2023-01-11T22:10:16.0093207Z [       OK ] FunctionalTest.AvgPool1d (0 ms)
2023-01-11T22:10:16.0093725Z [ RUN      ] FunctionalTest.AvgPool2d
2023-01-11T22:10:16.0094284Z [       OK ] FunctionalTest.AvgPool2d (0 ms)
2023-01-11T22:10:16.0094747Z [ RUN      ] FunctionalTest.AvgPool3d
2023-01-11T22:10:16.0095317Z [       OK ] FunctionalTest.AvgPool3d (0 ms)
2023-01-11T22:10:16.0095735Z [ RUN      ] FunctionalTest.FractionalMaxPool2d
2023-01-11T22:10:16.0098199Z [       OK ] FunctionalTest.FractionalMaxPool2d (0 ms)
2023-01-11T22:10:16.0098877Z [ RUN      ] FunctionalTest.FractionalMaxPool3d
2023-01-11T22:10:16.0099863Z [       OK ] FunctionalTest.FractionalMaxPool3d (0 ms)
2023-01-11T22:10:16.0100460Z [ RUN      ] FunctionalTest.LPPool1d
2023-01-11T22:10:16.0102577Z [       OK ] FunctionalTest.LPPool1d (0 ms)
2023-01-11T22:10:16.0103127Z [ RUN      ] FunctionalTest.LPPool2d
2023-01-11T22:10:16.0103678Z [       OK ] FunctionalTest.LPPool2d (0 ms)
2023-01-11T22:10:16.0104032Z [ RUN      ] FunctionalTest.CosineSimilarity
2023-01-11T22:10:16.0106259Z [       OK ] FunctionalTest.CosineSimilarity (0 ms)
2023-01-11T22:10:16.0106720Z [ RUN      ] FunctionalTest.SmoothL1LossDefaultOptions
2023-01-11T22:10:16.0108855Z [       OK ] FunctionalTest.SmoothL1LossDefaultOptions (0 ms)
2023-01-11T22:10:16.0109559Z [ RUN      ] FunctionalTest.SmoothL1LossBeta
2023-01-11T22:10:16.0110430Z [       OK ] FunctionalTest.SmoothL1LossBeta (0 ms)
2023-01-11T22:10:16.0111101Z [ RUN      ] FunctionalTest.SmoothL1LossNoReduction
2023-01-11T22:10:16.0112376Z [       OK ] FunctionalTest.SmoothL1LossNoReduction (0 ms)
2023-01-11T22:10:16.0113072Z [ RUN      ] FunctionalTest.HuberLossDefaultOptions
2023-01-11T22:10:16.0114571Z [       OK ] FunctionalTest.HuberLossDefaultOptions (0 ms)
2023-01-11T22:10:16.0115206Z [ RUN      ] FunctionalTest.HuberLossDelta
2023-01-11T22:10:16.0116346Z [       OK ] FunctionalTest.HuberLossDelta (0 ms)
2023-01-11T22:10:16.0116990Z [ RUN      ] FunctionalTest.HuberLossNoReduction
2023-01-11T22:10:16.0118093Z [       OK ] FunctionalTest.HuberLossNoReduction (0 ms)
2023-01-11T22:10:16.0118779Z [ RUN      ] FunctionalTest.SoftMarginLossDefaultOptions
2023-01-11T22:10:16.0121620Z [       OK ] FunctionalTest.SoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:10:16.0122431Z [ RUN      ] FunctionalTest.MultiLabelSoftMarginLossDefaultOptions
2023-01-11T22:10:16.0126775Z [       OK ] FunctionalTest.MultiLabelSoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:10:16.0127835Z [ RUN      ] FunctionalTest.SoftMarginLossNoReduction
2023-01-11T22:10:16.0128540Z [       OK ] FunctionalTest.SoftMarginLossNoReduction (0 ms)
2023-01-11T22:10:16.0129384Z [ RUN      ] FunctionalTest.MultiLabelSoftMarginLossWeightedNoReduction
2023-01-11T22:10:16.0132979Z [       OK ] FunctionalTest.MultiLabelSoftMarginLossWeightedNoReduction (0 ms)
2023-01-11T22:10:16.0133676Z [ RUN      ] FunctionalTest.PairwiseDistance
2023-01-11T22:10:16.0134380Z [       OK ] FunctionalTest.PairwiseDistance (0 ms)
2023-01-11T22:10:16.0134895Z [ RUN      ] FunctionalTest.PDist
2023-01-11T22:10:16.0136458Z [       OK ] FunctionalTest.PDist (0 ms)
2023-01-11T22:10:16.0136991Z [ RUN      ] FunctionalTest.AdaptiveMaxPool1d
2023-01-11T22:10:16.0137676Z [       OK ] FunctionalTest.AdaptiveMaxPool1d (0 ms)
2023-01-11T22:10:16.0138218Z [ RUN      ] FunctionalTest.AdaptiveMaxPool2d
2023-01-11T22:10:16.0138958Z [       OK ] FunctionalTest.AdaptiveMaxPool2d (0 ms)
2023-01-11T22:10:16.0139522Z [ RUN      ] FunctionalTest.AdaptiveMaxPool3d
2023-01-11T22:10:16.0140343Z [       OK ] FunctionalTest.AdaptiveMaxPool3d (0 ms)
2023-01-11T22:10:16.0140893Z [ RUN      ] FunctionalTest.AdaptiveAvgPool1d
2023-01-11T22:10:16.0141410Z [       OK ] FunctionalTest.AdaptiveAvgPool1d (0 ms)
2023-01-11T22:10:16.0141917Z [ RUN      ] FunctionalTest.AdaptiveAvgPool2d
2023-01-11T22:10:16.0142709Z [       OK ] FunctionalTest.AdaptiveAvgPool2d (0 ms)
2023-01-11T22:10:16.0143251Z [ RUN      ] FunctionalTest.AdaptiveAvgPool3d
2023-01-11T22:10:16.0143775Z [       OK ] FunctionalTest.AdaptiveAvgPool3d (0 ms)
2023-01-11T22:10:16.0144259Z [ RUN      ] FunctionalTest.L1Loss
2023-01-11T22:10:16.0144976Z [       OK ] FunctionalTest.L1Loss (0 ms)
2023-01-11T22:10:16.0146958Z [ RUN      ] FunctionalTest.MSELoss
2023-01-11T22:10:16.0147532Z [       OK ] FunctionalTest.MSELoss (0 ms)
2023-01-11T22:10:16.0148049Z [ RUN      ] FunctionalTest.BCELoss
2023-01-11T22:10:16.0148346Z [       OK ] FunctionalTest.BCELoss (0 ms)
2023-01-11T22:10:16.0148677Z [ RUN      ] FunctionalTest.KLDivLoss
2023-01-11T22:10:16.0149790Z [W loss.h:57] Warning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release. (function kl_div)
2023-01-11T22:10:16.0150605Z [       OK ] FunctionalTest.KLDivLoss (0 ms)
2023-01-11T22:10:16.0150994Z [ RUN      ] FunctionalTest.HingeEmbeddingLoss
2023-01-11T22:10:16.0151620Z [       OK ] FunctionalTest.HingeEmbeddingLoss (0 ms)
2023-01-11T22:10:16.0152053Z [ RUN      ] FunctionalTest.GridSample
2023-01-11T22:10:16.0153813Z [W vision.h:87] Warning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details. (function grid_sample)
2023-01-11T22:10:16.0155793Z [       OK ] FunctionalTest.GridSample (0 ms)
2023-01-11T22:10:16.0156177Z [ RUN      ] FunctionalTest.AffineGrid
2023-01-11T22:10:16.0296416Z [       OK ] FunctionalTest.AffineGrid (14 ms)
2023-01-11T22:10:16.0296849Z [ RUN      ] FunctionalTest.MultiMarginLoss
2023-01-11T22:10:16.0297540Z [       OK ] FunctionalTest.MultiMarginLoss (0 ms)
2023-01-11T22:10:16.0297892Z [ RUN      ] FunctionalTest.CosineEmbeddingLoss
2023-01-11T22:10:16.0299847Z [       OK ] FunctionalTest.CosineEmbeddingLoss (0 ms)
2023-01-11T22:10:16.0300284Z [ RUN      ] FunctionalTest.MultiLabelMarginLossDefaultOptions
2023-01-11T22:10:16.0302012Z [       OK ] FunctionalTest.MultiLabelMarginLossDefaultOptions (0 ms)
2023-01-11T22:10:16.0302864Z [ RUN      ] FunctionalTest.MultiLabelMarginLossNoReduction
2023-01-11T22:10:16.0303656Z [       OK ] FunctionalTest.MultiLabelMarginLossNoReduction (0 ms)
2023-01-11T22:10:16.0304121Z [ RUN      ] FunctionalTest.TripletMarginLoss
2023-01-11T22:10:16.0305213Z [       OK ] FunctionalTest.TripletMarginLoss (0 ms)
2023-01-11T22:10:16.0517169Z [ RUN      ] FunctionalTest.TripletMarginWithDistanceLossDefaultParity
2023-01-11T22:10:16.0517900Z [       OK ] FunctionalTest.TripletMarginWithDistanceLossDefaultParity (21 ms)
2023-01-11T22:10:16.0518373Z [ RUN      ] FunctionalTest.NLLLoss
2023-01-11T22:10:16.0518687Z [       OK ] FunctionalTest.NLLLoss (0 ms)
2023-01-11T22:10:16.0518993Z [ RUN      ] FunctionalTest.CrossEntropy
2023-01-11T22:10:16.0522132Z [       OK ] FunctionalTest.CrossEntropy (0 ms)
2023-01-11T22:10:16.0522798Z [ RUN      ] FunctionalTest.MaxUnpool1d
2023-01-11T22:10:16.0525080Z [       OK ] FunctionalTest.MaxUnpool1d (0 ms)
2023-01-11T22:10:16.0525703Z [ RUN      ] FunctionalTest.MaxUnpool2d
2023-01-11T22:10:16.0527345Z [       OK ] FunctionalTest.MaxUnpool2d (0 ms)
2023-01-11T22:10:16.0527952Z [ RUN      ] FunctionalTest.MaxUnpool3d
2023-01-11T22:10:16.0528916Z [       OK ] FunctionalTest.MaxUnpool3d (0 ms)
2023-01-11T22:10:16.0529444Z [ RUN      ] FunctionalTest.ELU
2023-01-11T22:10:16.0544411Z [       OK ] FunctionalTest.ELU (1 ms)
2023-01-11T22:10:16.0544779Z [ RUN      ] FunctionalTest.SELU
2023-01-11T22:10:16.0548800Z [       OK ] FunctionalTest.SELU (0 ms)
2023-01-11T22:10:16.0549286Z [ RUN      ] FunctionalTest.GLU
2023-01-11T22:10:16.0550418Z [       OK ] FunctionalTest.GLU (0 ms)
2023-01-11T22:10:16.0550929Z [ RUN      ] FunctionalTest.GELU
2023-01-11T22:10:16.0554436Z [       OK ] FunctionalTest.GELU (0 ms)
2023-01-11T22:10:16.0554953Z [ RUN      ] FunctionalTest.TanhGELU
2023-01-11T22:10:16.0556319Z [       OK ] FunctionalTest.TanhGELU (0 ms)
2023-01-11T22:10:16.0556879Z [ RUN      ] FunctionalTest.Hardshrink
2023-01-11T22:10:16.0563671Z [       OK ] FunctionalTest.Hardshrink (0 ms)
2023-01-11T22:10:16.0564229Z [ RUN      ] FunctionalTest.OneHot
2023-01-11T22:10:16.0567168Z [       OK ] FunctionalTest.OneHot (0 ms)
2023-01-11T22:10:16.0567693Z [ RUN      ] FunctionalTest.Hardtanh
2023-01-11T22:10:16.0591563Z [       OK ] FunctionalTest.Hardtanh (2 ms)
2023-01-11T22:10:16.0592118Z [ RUN      ] FunctionalTest.LeakyReLU
2023-01-11T22:10:16.0599921Z [       OK ] FunctionalTest.LeakyReLU (0 ms)
2023-01-11T22:10:16.0600458Z [ RUN      ] FunctionalTest.LogSigmoid
2023-01-11T22:10:16.0601126Z [       OK ] FunctionalTest.LogSigmoid (0 ms)
2023-01-11T22:10:16.0601720Z [ RUN      ] FunctionalTest.GumbelSoftmax
2023-01-11T22:10:16.0633113Z [       OK ] FunctionalTest.GumbelSoftmax (3 ms)
2023-01-11T22:10:16.0633638Z [ RUN      ] FunctionalTest.Softmax
2023-01-11T22:10:16.0634738Z [       OK ] FunctionalTest.Softmax (0 ms)
2023-01-11T22:10:16.0635276Z [ RUN      ] FunctionalTest.Softmin
2023-01-11T22:10:16.0636097Z [       OK ] FunctionalTest.Softmin (0 ms)
2023-01-11T22:10:16.0636652Z [ RUN      ] FunctionalTest.LogSoftmax
2023-01-11T22:10:16.0637736Z [       OK ] FunctionalTest.LogSoftmax (0 ms)
2023-01-11T22:10:16.0638035Z [ RUN      ] FunctionalTest.PReLU
2023-01-11T22:10:16.0640679Z [       OK ] FunctionalTest.PReLU (0 ms)
2023-01-11T22:10:16.0641219Z [ RUN      ] FunctionalTest.LayerNorm
2023-01-11T22:10:16.0641772Z [       OK ] FunctionalTest.LayerNorm (0 ms)
2023-01-11T22:10:16.0642080Z [ RUN      ] FunctionalTest.GroupNorm
2023-01-11T22:10:16.0642754Z [       OK ] FunctionalTest.GroupNorm (0 ms)
2023-01-11T22:10:16.0643081Z [ RUN      ] FunctionalTest.LocalResponseNorm
2023-01-11T22:10:16.0645083Z [       OK ] FunctionalTest.LocalResponseNorm (0 ms)
2023-01-11T22:10:16.0645383Z [ RUN      ] FunctionalTest.Linear
2023-01-11T22:10:16.0649400Z [       OK ] FunctionalTest.Linear (0 ms)
2023-01-11T22:10:16.0650199Z [ RUN      ] FunctionalTest.Embedding
2023-01-11T22:10:16.0650741Z [       OK ] FunctionalTest.Embedding (0 ms)
2023-01-11T22:10:16.0651193Z [ RUN      ] FunctionalTest.EmbeddingBag
2023-01-11T22:10:16.0655494Z [       OK ] FunctionalTest.EmbeddingBag (0 ms)
2023-01-11T22:10:16.0655881Z [ RUN      ] FunctionalTest.Bilinear
2023-01-11T22:10:16.0658378Z [       OK ] FunctionalTest.Bilinear (0 ms)
2023-01-11T22:10:16.0658753Z [ RUN      ] FunctionalTest.Normalize
2023-01-11T22:10:16.0662875Z [       OK ] FunctionalTest.Normalize (0 ms)
2023-01-11T22:10:16.0663279Z [ RUN      ] FunctionalTest.ReLU
2023-01-11T22:10:16.0666298Z [       OK ] FunctionalTest.ReLU (0 ms)
2023-01-11T22:10:16.0666827Z [ RUN      ] FunctionalTest.ReLUDefaultOptions
2023-01-11T22:10:16.0667477Z [       OK ] FunctionalTest.ReLUDefaultOptions (0 ms)
2023-01-11T22:10:16.0667776Z [ RUN      ] FunctionalTest.ReLU6
2023-01-11T22:10:16.0671012Z [       OK ] FunctionalTest.ReLU6 (0 ms)
2023-01-11T22:10:16.0671552Z [ RUN      ] FunctionalTest.ReLU6DefaultOptions
2023-01-11T22:10:16.0672147Z [       OK ] FunctionalTest.ReLU6DefaultOptions (0 ms)
2023-01-11T22:10:16.0672668Z [ RUN      ] FunctionalTest.RReLU
2023-01-11T22:10:16.0705014Z [       OK ] FunctionalTest.RReLU (3 ms)
2023-01-11T22:10:16.0705352Z [ RUN      ] FunctionalTest.RReLUDefaultOptions
2023-01-11T22:10:16.0707006Z [       OK ] FunctionalTest.RReLUDefaultOptions (0 ms)
2023-01-11T22:10:16.0707332Z [ RUN      ] FunctionalTest.CELU
2023-01-11T22:10:16.0717900Z [       OK ] FunctionalTest.CELU (1 ms)
2023-01-11T22:10:16.0718213Z [ RUN      ] FunctionalTest.CELUDefaultOptions
2023-01-11T22:10:16.0719647Z [       OK ] FunctionalTest.CELUDefaultOptions (0 ms)
2023-01-11T22:10:16.0719986Z [ RUN      ] FunctionalTest.PixelShuffle
2023-01-11T22:10:16.0721582Z [       OK ] FunctionalTest.PixelShuffle (0 ms)
2023-01-11T22:10:16.0721901Z [ RUN      ] FunctionalTest.PixelUnshuffle
2023-01-11T22:10:16.0723296Z [       OK ] FunctionalTest.PixelUnshuffle (0 ms)
2023-01-11T22:10:16.0723860Z [ RUN      ] FunctionalTest.Softplus
2023-01-11T22:10:16.0730613Z [       OK ] FunctionalTest.Softplus (0 ms)
2023-01-11T22:10:16.0731226Z [ RUN      ] FunctionalTest.SoftplusDefaultOptions
2023-01-11T22:10:16.0732023Z [       OK ] FunctionalTest.SoftplusDefaultOptions (0 ms)
2023-01-11T22:10:16.0732603Z [ RUN      ] FunctionalTest.Fold
2023-01-11T22:10:16.0733436Z [       OK ] FunctionalTest.Fold (0 ms)
2023-01-11T22:10:16.0733952Z [ RUN      ] FunctionalTest.Unfold
2023-01-11T22:10:16.0734970Z [       OK ] FunctionalTest.Unfold (0 ms)
2023-01-11T22:10:16.0735499Z [ RUN      ] FunctionalTest.Softshrink
2023-01-11T22:10:16.0741231Z [       OK ] FunctionalTest.Softshrink (0 ms)
2023-01-11T22:10:16.0741856Z [ RUN      ] FunctionalTest.SoftshrinkDefaultOptions
2023-01-11T22:10:16.0742668Z [       OK ] FunctionalTest.SoftshrinkDefaultOptions (0 ms)
2023-01-11T22:10:16.0743185Z [ RUN      ] FunctionalTest.Softsign
2023-01-11T22:10:16.0743805Z [       OK ] FunctionalTest.Softsign (0 ms)
2023-01-11T22:10:16.0744990Z [ RUN      ] FunctionalTest.Mish
2023-01-11T22:10:16.0745511Z [       OK ] FunctionalTest.Mish (0 ms)
2023-01-11T22:10:16.0745942Z [ RUN      ] FunctionalTest.Tanhshrink
2023-01-11T22:10:16.0746684Z [       OK ] FunctionalTest.Tanhshrink (0 ms)
2023-01-11T22:10:16.0746986Z [ RUN      ] FunctionalTest.Threshold
2023-01-11T22:10:16.0758687Z [       OK ] FunctionalTest.Threshold (1 ms)
2023-01-11T22:10:16.0759121Z [ RUN      ] FunctionalTest.BatchNorm1d
2023-01-11T22:10:16.0760050Z [       OK ] FunctionalTest.BatchNorm1d (0 ms)
2023-01-11T22:10:16.0760466Z [ RUN      ] FunctionalTest.BatchNorm1dDefaultOptions
2023-01-11T22:10:16.0761642Z [       OK ] FunctionalTest.BatchNorm1dDefaultOptions (0 ms)
2023-01-11T22:10:16.0762077Z [ RUN      ] FunctionalTest.BatchNorm2d
2023-01-11T22:10:16.0763127Z [       OK ] FunctionalTest.BatchNorm2d (0 ms)
2023-01-11T22:10:16.0763598Z [ RUN      ] FunctionalTest.BatchNorm2dDefaultOptions
2023-01-11T22:10:16.0764418Z [       OK ] FunctionalTest.BatchNorm2dDefaultOptions (0 ms)
2023-01-11T22:10:16.0765016Z [ RUN      ] FunctionalTest.BatchNorm3d
2023-01-11T22:10:16.0765814Z [       OK ] FunctionalTest.BatchNorm3d (0 ms)
2023-01-11T22:10:16.0766343Z [ RUN      ] FunctionalTest.BatchNorm3dDefaultOptions
2023-01-11T22:10:16.0767304Z [       OK ] FunctionalTest.BatchNorm3dDefaultOptions (0 ms)
2023-01-11T22:10:16.0767659Z [ RUN      ] FunctionalTest.InstanceNorm1d
2023-01-11T22:10:16.0770255Z [       OK ] FunctionalTest.InstanceNorm1d (0 ms)
2023-01-11T22:10:16.0770618Z [ RUN      ] FunctionalTest.InstanceNorm1dDefaultOptions
2023-01-11T22:10:16.0772408Z [       OK ] FunctionalTest.InstanceNorm1dDefaultOptions (0 ms)
2023-01-11T22:10:16.0772962Z [ RUN      ] FunctionalTest.InstanceNorm2d
2023-01-11T22:10:16.0775108Z [       OK ] FunctionalTest.InstanceNorm2d (0 ms)
2023-01-11T22:10:16.0775642Z [ RUN      ] FunctionalTest.InstanceNorm2dDefaultOptions
2023-01-11T22:10:16.0776899Z [       OK ] FunctionalTest.InstanceNorm2dDefaultOptions (0 ms)
2023-01-11T22:10:16.0777434Z [ RUN      ] FunctionalTest.InstanceNorm3d
2023-01-11T22:10:16.0780191Z [       OK ] FunctionalTest.InstanceNorm3d (0 ms)
2023-01-11T22:10:16.0780712Z [ RUN      ] FunctionalTest.InstanceNorm3dDefaultOptions
2023-01-11T22:10:16.0783303Z [       OK ] FunctionalTest.InstanceNorm3dDefaultOptions (0 ms)
2023-01-11T22:10:16.0783913Z [ RUN      ] FunctionalTest.Interpolate
2023-01-11T22:10:16.0784822Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0786337Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0787522Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0788677Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0789969Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0791138Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0792373Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0793544Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:16.0868051Z [       OK ] FunctionalTest.Interpolate (8 ms)
2023-01-11T22:10:16.0868480Z [ RUN      ] FunctionalTest.Pad1
2023-01-11T22:10:16.0868804Z [       OK ] FunctionalTest.Pad1 (0 ms)
2023-01-11T22:10:16.0869073Z [ RUN      ] FunctionalTest.Pad2
2023-01-11T22:10:16.0870659Z [       OK ] FunctionalTest.Pad2 (0 ms)
2023-01-11T22:10:16.0870939Z [ RUN      ] FunctionalTest.Pad3
2023-01-11T22:10:16.0875495Z [       OK ] FunctionalTest.Pad3 (0 ms)
2023-01-11T22:10:16.0875908Z [ RUN      ] FunctionalTest.Pad4
2023-01-11T22:10:16.0877288Z [       OK ] FunctionalTest.Pad4 (0 ms)
2023-01-11T22:10:16.0877699Z [ RUN      ] FunctionalTest.Pad5
2023-01-11T22:10:16.0880259Z [       OK ] FunctionalTest.Pad5 (0 ms)
2023-01-11T22:10:16.0880668Z [ RUN      ] FunctionalTest.Pad6
2023-01-11T22:10:16.0882387Z [       OK ] FunctionalTest.Pad6 (0 ms)
2023-01-11T22:10:16.0882743Z [ RUN      ] FunctionalTest.Pad7
2023-01-11T22:10:16.0883087Z [       OK ] FunctionalTest.Pad7 (0 ms)
2023-01-11T22:10:16.0883440Z [ RUN      ] FunctionalTest.Pad8
2023-01-11T22:10:16.0883812Z [       OK ] FunctionalTest.Pad8 (0 ms)
2023-01-11T22:10:16.0884158Z [ RUN      ] FunctionalTest.CTCLoss
2023-01-11T22:10:16.0994039Z [       OK ] FunctionalTest.CTCLoss (11 ms)
2023-01-11T22:10:16.0994528Z [ RUN      ] FunctionalTest.PoissonNLLLoss
2023-01-11T22:10:16.0996257Z [       OK ] FunctionalTest.PoissonNLLLoss (0 ms)
2023-01-11T22:10:16.0996734Z [ RUN      ] FunctionalTest.MarginRankingLoss
2023-01-11T22:10:16.0999929Z [       OK ] FunctionalTest.MarginRankingLoss (0 ms)
2023-01-11T22:10:16.1000364Z [ RUN      ] FunctionalTest.ConvTranspose1d
2023-01-11T22:10:16.1003291Z [       OK ] FunctionalTest.ConvTranspose1d (0 ms)
2023-01-11T22:10:16.1003739Z [ RUN      ] FunctionalTest.ConvTranspose2dEven
2023-01-11T22:10:16.1008452Z [       OK ] FunctionalTest.ConvTranspose2dEven (0 ms)
2023-01-11T22:10:16.1008914Z [ RUN      ] FunctionalTest.ConvTranspose2dUneven
2023-01-11T22:10:16.1012983Z [       OK ] FunctionalTest.ConvTranspose2dUneven (0 ms)
2023-01-11T22:10:16.1013367Z [ RUN      ] FunctionalTest.ConvTranspose3d
2023-01-11T22:10:16.1016201Z [       OK ] FunctionalTest.ConvTranspose3d (0 ms)
2023-01-11T22:10:16.1016582Z [ RUN      ] FunctionalTest.AlphaDropout
2023-01-11T22:10:16.1026871Z [       OK ] FunctionalTest.AlphaDropout (1 ms)
2023-01-11T22:10:16.1027333Z [ RUN      ] FunctionalTest.FeatureAlphaDropout
2023-01-11T22:10:16.1035633Z [       OK ] FunctionalTest.FeatureAlphaDropout (0 ms)
2023-01-11T22:10:16.1035988Z [ RUN      ] FunctionalTest.Dropout
2023-01-11T22:10:16.1041917Z [       OK ] FunctionalTest.Dropout (0 ms)
2023-01-11T22:10:16.1042223Z [ RUN      ] FunctionalTest.Dropout2d
2023-01-11T22:10:16.1053180Z [       OK ] FunctionalTest.Dropout2d (1 ms)
2023-01-11T22:10:16.1053507Z [ RUN      ] FunctionalTest.Dropout3d
2023-01-11T22:10:16.1063131Z [       OK ] FunctionalTest.Dropout3d (0 ms)
2023-01-11T22:10:16.1063438Z [ RUN      ] FunctionalTest.isfinite
2023-01-11T22:10:16.1069192Z [       OK ] FunctionalTest.isfinite (0 ms)
2023-01-11T22:10:16.1069493Z [ RUN      ] FunctionalTest.isinf
2023-01-11T22:10:16.1073737Z [       OK ] FunctionalTest.isinf (0 ms)
2023-01-11T22:10:16.1074028Z [ RUN      ] FunctionalTest.AllClose
2023-01-11T22:10:16.1139113Z [       OK ] FunctionalTest.AllClose (6 ms)
2023-01-11T22:10:16.1139478Z [ RUN      ] FunctionalTest.BCEWithLogitsLoss
2023-01-11T22:10:16.1176653Z [       OK ] FunctionalTest.BCEWithLogitsLoss (3 ms)
2023-01-11T22:10:16.1177015Z [----------] 132 tests from FunctionalTest (114 ms total)
2023-01-11T22:10:16.1177184Z 
2023-01-11T22:10:16.1177341Z [----------] 1 test from IntegrationTest
2023-01-11T22:10:16.1177633Z [ RUN      ] IntegrationTest.CartPole
2023-01-11T22:10:29.0932835Z [       OK ] IntegrationTest.CartPole (12975 ms)
2023-01-11T22:10:29.0933566Z [----------] 1 test from IntegrationTest (12975 ms total)
2023-01-11T22:10:29.0933793Z 
2023-01-11T22:10:29.0934144Z [----------] 9 tests from InitTest
2023-01-11T22:10:29.0936065Z [ RUN      ] InitTest.ProducesPyTorchValues_XavierUniform
2023-01-11T22:10:29.0964750Z [       OK ] InitTest.ProducesPyTorchValues_XavierUniform (3 ms)
2023-01-11T22:10:29.0965152Z [ RUN      ] InitTest.ProducesPyTorchValues_XavierNormal
2023-01-11T22:10:29.0976855Z [       OK ] InitTest.ProducesPyTorchValues_XavierNormal (1 ms)
2023-01-11T22:10:29.0977275Z [ RUN      ] InitTest.ProducesPyTorchValues_KaimingNormal
2023-01-11T22:10:29.0989581Z [       OK ] InitTest.ProducesPyTorchValues_KaimingNormal (1 ms)
2023-01-11T22:10:29.0990023Z [ RUN      ] InitTest.ProducesPyTorchValues_KaimingUniform
2023-01-11T22:10:29.1002542Z [       OK ] InitTest.ProducesPyTorchValues_KaimingUniform (1 ms)
2023-01-11T22:10:29.1003014Z [ RUN      ] InitTest.CanInitializeTensorThatRequiresGrad
2023-01-11T22:10:29.1029499Z [       OK ] InitTest.CanInitializeTensorThatRequiresGrad (2 ms)
2023-01-11T22:10:29.1030142Z [ RUN      ] InitTest.CalculateGainWithTanh
2023-01-11T22:10:29.1030663Z [       OK ] InitTest.CalculateGainWithTanh (0 ms)
2023-01-11T22:10:29.1030976Z [ RUN      ] InitTest.CalculateGainWithRelu
2023-01-11T22:10:29.1031304Z [       OK ] InitTest.CalculateGainWithRelu (0 ms)
2023-01-11T22:10:29.1031641Z [ RUN      ] InitTest.CalculateGainWithLeakyRelu
2023-01-11T22:10:29.1031994Z [       OK ] InitTest.CalculateGainWithLeakyRelu (0 ms)
2023-01-11T22:10:29.1032344Z [ RUN      ] InitTest.CanInitializeCnnWithOrthogonal
2023-01-11T22:10:29.1044986Z [       OK ] InitTest.CanInitializeCnnWithOrthogonal (1 ms)
2023-01-11T22:10:29.1045464Z [----------] 9 tests from InitTest (11 ms total)
2023-01-11T22:10:29.1045902Z 
2023-01-11T22:10:29.1046096Z [----------] 6 tests from TorchScriptTest
2023-01-11T22:10:29.1046544Z [ RUN      ] TorchScriptTest.CanCompileMultipleFunctions
2023-01-11T22:10:29.1427987Z [       OK ] TorchScriptTest.CanCompileMultipleFunctions (38 ms)
2023-01-11T22:10:29.1428513Z [ RUN      ] TorchScriptTest.TestNestedIValueModuleArgMatching
2023-01-11T22:10:29.1462330Z [       OK ] TorchScriptTest.TestNestedIValueModuleArgMatching (3 ms)
2023-01-11T22:10:29.1462968Z [ RUN      ] TorchScriptTest.TestDictArgMatching
2023-01-11T22:10:29.1465884Z [       OK ] TorchScriptTest.TestDictArgMatching (0 ms)
2023-01-11T22:10:29.1466417Z [ RUN      ] TorchScriptTest.TestTupleArgMatching
2023-01-11T22:10:29.1467083Z [       OK ] TorchScriptTest.TestTupleArgMatching (0 ms)
2023-01-11T22:10:29.1467686Z [ RUN      ] TorchScriptTest.TestOptionalArgMatching
2023-01-11T22:10:29.1472594Z [       OK ] TorchScriptTest.TestOptionalArgMatching (0 ms)
2023-01-11T22:10:29.1473285Z [ RUN      ] TorchScriptTest.TestPickle
2023-01-11T22:10:29.1473874Z [       OK ] TorchScriptTest.TestPickle (0 ms)
2023-01-11T22:10:29.1474449Z [----------] 6 tests from TorchScriptTest (42 ms total)
2023-01-11T22:10:29.1474747Z 
2023-01-11T22:10:29.1474967Z [----------] 3 tests from MakeUniqueTest
2023-01-11T22:10:29.1475394Z [ RUN      ] MakeUniqueTest.ForwardRvaluesCorrectly
2023-01-11T22:10:29.1476063Z [       OK ] MakeUniqueTest.ForwardRvaluesCorrectly (0 ms)
2023-01-11T22:10:29.1476538Z [ RUN      ] MakeUniqueTest.ForwardLvaluesCorrectly
2023-01-11T22:10:29.1476902Z [       OK ] MakeUniqueTest.ForwardLvaluesCorrectly (0 ms)
2023-01-11T22:10:29.1477288Z [ RUN      ] MakeUniqueTest.CanConstructUniquePtrOfArray
2023-01-11T22:10:29.1477700Z [       OK ] MakeUniqueTest.CanConstructUniquePtrOfArray (0 ms)
2023-01-11T22:10:29.1478087Z [----------] 3 tests from MakeUniqueTest (0 ms total)
2023-01-11T22:10:29.1478229Z 
2023-01-11T22:10:29.1478383Z [----------] 2 tests from MetaTensorTest
2023-01-11T22:10:29.1478681Z [ RUN      ] MetaTensorTest.MetaDeviceApi
2023-01-11T22:10:29.1479016Z [       OK ] MetaTensorTest.MetaDeviceApi (0 ms)
2023-01-11T22:10:29.1479328Z [ RUN      ] MetaTensorTest.MetaNamespaceApi
2023-01-11T22:10:29.1479733Z [       OK ] MetaTensorTest.MetaNamespaceApi (0 ms)
2023-01-11T22:10:29.1480075Z [----------] 2 tests from MetaTensorTest (0 ms total)
2023-01-11T22:10:29.1480231Z 
2023-01-11T22:10:29.1480373Z [----------] 2 tests from UtilsTest
2023-01-11T22:10:29.1480623Z [ RUN      ] UtilsTest.WarnOnce
2023-01-11T22:10:29.1480900Z [       OK ] UtilsTest.WarnOnce (0 ms)
2023-01-11T22:10:29.1481222Z [ RUN      ] UtilsTest.AmbiguousOperatorDefaults
2023-01-11T22:10:29.1481571Z [       OK ] UtilsTest.AmbiguousOperatorDefaults (0 ms)
2023-01-11T22:10:29.1481911Z [----------] 2 tests from UtilsTest (0 ms total)
2023-01-11T22:10:29.1482058Z 
2023-01-11T22:10:29.1482199Z [----------] 1 test from NoGradTest
2023-01-11T22:10:29.1482486Z [ RUN      ] NoGradTest.SetsGradModeCorrectly
2023-01-11T22:10:29.1508580Z [       OK ] NoGradTest.SetsGradModeCorrectly (3 ms)
2023-01-11T22:10:29.1509185Z [----------] 1 test from NoGradTest (3 ms total)
2023-01-11T22:10:29.1509352Z 
2023-01-11T22:10:29.1509503Z [----------] 3 tests from AutogradTest
2023-01-11T22:10:29.1509793Z [ RUN      ] AutogradTest.CanTakeDerivatives
2023-01-11T22:10:29.1510503Z [       OK ] AutogradTest.CanTakeDerivatives (0 ms)
2023-01-11T22:10:29.1511064Z [ RUN      ] AutogradTest.CanTakeDerivativesOfZeroDimTensors
2023-01-11T22:10:29.1511747Z [       OK ] AutogradTest.CanTakeDerivativesOfZeroDimTensors (0 ms)
2023-01-11T22:10:29.1512506Z [ RUN      ] AutogradTest.CanPassCustomGradientInputs
2023-01-11T22:10:29.1513266Z [       OK ] AutogradTest.CanPassCustomGradientInputs (0 ms)
2023-01-11T22:10:29.1513899Z [----------] 3 tests from AutogradTest (0 ms total)
2023-01-11T22:10:29.1514179Z 
2023-01-11T22:10:29.1514423Z [----------] 1 test from OptionalArrayRefTest
2023-01-11T22:10:29.1514757Z [ RUN      ] OptionalArrayRefTest.DanglingPointerFix
2023-01-11T22:10:29.1515289Z [       OK ] OptionalArrayRefTest.DanglingPointerFix (0 ms)
2023-01-11T22:10:29.1515863Z [----------] 1 test from OptionalArrayRefTest (0 ms total)
2023-01-11T22:10:29.1516036Z 
2023-01-11T22:10:29.1516243Z [----------] 52 tests from ModuleTest
2023-01-11T22:10:29.1516634Z [ RUN      ] ModuleTest.CanEnableAndDisableTrainingMode
2023-01-11T22:10:29.1517140Z [       OK ] ModuleTest.CanEnableAndDisableTrainingMode (0 ms)
2023-01-11T22:10:29.1517472Z [ RUN      ] ModuleTest.ZeroGrad
2023-01-11T22:10:29.1517938Z [       OK ] ModuleTest.ZeroGrad (0 ms)
2023-01-11T22:10:29.1518259Z [ RUN      ] ModuleTest.ZeroGradWithUndefined
2023-01-11T22:10:29.1518605Z [       OK ] ModuleTest.ZeroGradWithUndefined (0 ms)
2023-01-11T22:10:29.1518992Z [ RUN      ] ModuleTest.RegisterModuleThrowsForEmptyOrDottedName
2023-01-11T22:10:29.1541504Z [       OK ] ModuleTest.RegisterModuleThrowsForEmptyOrDottedName (2 ms)
2023-01-11T22:10:29.1542040Z [ RUN      ] ModuleTest.RegisterModuleThrowsForDuplicateModuleName
2023-01-11T22:10:29.1558551Z [       OK ] ModuleTest.RegisterModuleThrowsForDuplicateModuleName (1 ms)
2023-01-11T22:10:29.1559067Z [ RUN      ] ModuleTest.ReplaceModuleThrowsForUnknownModuleName
2023-01-11T22:10:29.1571398Z [       OK ] ModuleTest.ReplaceModuleThrowsForUnknownModuleName (1 ms)
2023-01-11T22:10:29.1571777Z [ RUN      ] ModuleTest.ReplaceModule
2023-01-11T22:10:29.1572093Z [       OK ] ModuleTest.ReplaceModule (0 ms)
2023-01-11T22:10:29.1572386Z [ RUN      ] ModuleTest.UnregisterModule
2023-01-11T22:10:29.1588223Z [       OK ] ModuleTest.UnregisterModule (1 ms)
2023-01-11T22:10:29.1588679Z [ RUN      ] ModuleTest.RegisterParameterThrowsForEmptyOrDottedName
2023-01-11T22:10:29.1620501Z [       OK ] ModuleTest.RegisterParameterThrowsForEmptyOrDottedName (3 ms)
2023-01-11T22:10:29.1621006Z [ RUN      ] ModuleTest.RegisterParameterThrowsForDuplicateModuleName
2023-01-11T22:10:29.1642684Z [       OK ] ModuleTest.RegisterParameterThrowsForDuplicateModuleName (2 ms)
2023-01-11T22:10:29.1643142Z [ RUN      ] ModuleTest.RegisterParameterUndefinedTensor
2023-01-11T22:10:29.1643543Z [       OK ] ModuleTest.RegisterParameterUndefinedTensor (0 ms)
2023-01-11T22:10:29.1643974Z [ RUN      ] ModuleTest.RegisterBufferThrowsForEmptyOrDottedName
2023-01-11T22:10:29.1674910Z [       OK ] ModuleTest.RegisterBufferThrowsForEmptyOrDottedName (3 ms)
2023-01-11T22:10:29.1675432Z [ RUN      ] ModuleTest.RegisterBufferThrowsForDuplicateModuleName
2023-01-11T22:10:29.1697106Z [       OK ] ModuleTest.RegisterBufferThrowsForDuplicateModuleName (2 ms)
2023-01-11T22:10:29.1697658Z [ RUN      ] ModuleTest.CanGetName
2023-01-11T22:10:29.1698146Z [       OK ] ModuleTest.CanGetName (0 ms)
2023-01-11T22:10:29.1698706Z [ RUN      ] ModuleTest.AsCastsModulesCorrectly
2023-01-11T22:10:29.1699198Z [       OK ] ModuleTest.AsCastsModulesCorrectly (0 ms)
2023-01-11T22:10:29.1699685Z [ RUN      ] ModuleTest.DeviceOrDtypeConversionSkipsUndefinedTensor
2023-01-11T22:10:29.1700185Z [       OK ] ModuleTest.DeviceOrDtypeConversionSkipsUndefinedTensor (0 ms)
2023-01-11T22:10:29.1700705Z [ RUN      ] ModuleTest.ParametersAndBuffersAccessorSkipsUndefinedTensor
2023-01-11T22:10:29.1701292Z [       OK ] ModuleTest.ParametersAndBuffersAccessorSkipsUndefinedTensor (0 ms)
2023-01-11T22:10:29.1701990Z [ RUN      ] ModuleTest.CallingCloneOnModuleThatDoesNotOverrideCloneThrows
2023-01-11T22:10:29.1715661Z [       OK ] ModuleTest.CallingCloneOnModuleThatDoesNotOverrideCloneThrows (1 ms)
2023-01-11T22:10:29.1716464Z [ RUN      ] ModuleTest.CallingCloneOnModuleThatDoesOverrideCloneDoesNotThrow
2023-01-11T22:10:29.1717025Z [       OK ] ModuleTest.CallingCloneOnModuleThatDoesOverrideCloneDoesNotThrow (0 ms)
2023-01-11T22:10:29.1717485Z [ RUN      ] ModuleTest.CloneCreatesDistinctParameters
2023-01-11T22:10:29.1721405Z [       OK ] ModuleTest.CloneCreatesDistinctParameters (0 ms)
2023-01-11T22:10:29.1722052Z [ RUN      ] ModuleTest.ClonePreservesExternalReferences
2023-01-11T22:10:29.1723262Z [       OK ] ModuleTest.ClonePreservesExternalReferences (0 ms)
2023-01-11T22:10:29.1723909Z [ RUN      ] ModuleTest.CloneCopiesTheValuesOfVariablesOfSubmodules
2023-01-11T22:10:29.1724816Z [       OK ] ModuleTest.CloneCopiesTheValuesOfVariablesOfSubmodules (0 ms)
2023-01-11T22:10:29.1725347Z [ RUN      ] ModuleTest.HasCorrectNumberOfParameters
2023-01-11T22:10:29.1725735Z [       OK ] ModuleTest.HasCorrectNumberOfParameters (0 ms)
2023-01-11T22:10:29.1726128Z [ RUN      ] ModuleTest.ContainsParametersWithTheCorrectName
2023-01-11T22:10:29.1726564Z [       OK ] ModuleTest.ContainsParametersWithTheCorrectName (0 ms)
2023-01-11T22:10:29.1726958Z [ RUN      ] ModuleTest.HasCorrectNumberOfBuffers
2023-01-11T22:10:29.1727321Z [       OK ] ModuleTest.HasCorrectNumberOfBuffers (0 ms)
2023-01-11T22:10:29.1727689Z [ RUN      ] ModuleTest.ContainsBuffersWithTheCorrectName
2023-01-11T22:10:29.1728107Z [       OK ] ModuleTest.ContainsBuffersWithTheCorrectName (0 ms)
2023-01-11T22:10:29.1728624Z [ RUN      ] ModuleTest.DefaultConstructorOfModuleHolderCallsDefaultConstructorOfImpl
2023-01-11T22:10:29.1729232Z [       OK ] ModuleTest.DefaultConstructorOfModuleHolderCallsDefaultConstructorOfImpl (0 ms)
2023-01-11T22:10:29.1729832Z [ RUN      ] ModuleTest.ValueConstructorOfModuleHolderCallsCorrectConstructorInImpl
2023-01-11T22:10:29.1730432Z [       OK ] ModuleTest.ValueConstructorOfModuleHolderCallsCorrectConstructorInImpl (0 ms)
2023-01-11T22:10:29.1730993Z [ RUN      ] ModuleTest.NullptrConstructorLeavesTheModuleHolderInEmptyState
2023-01-11T22:10:29.1737217Z [       OK ] ModuleTest.NullptrConstructorLeavesTheModuleHolderInEmptyState (1 ms)
2023-01-11T22:10:29.1737973Z [ RUN      ] ModuleTest.ModulesReturnsExpectedSubmodulesForFlatModel
2023-01-11T22:10:29.1738702Z [       OK ] ModuleTest.ModulesReturnsExpectedSubmodulesForFlatModel (0 ms)
2023-01-11T22:10:29.1739475Z [ RUN      ] ModuleTest.ModulesExcludesSelfWhenIncludeSelfSetToFalse
2023-01-11T22:10:29.1740116Z [       OK ] ModuleTest.ModulesExcludesSelfWhenIncludeSelfSetToFalse (0 ms)
2023-01-11T22:10:29.1741107Z [ RUN      ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForFlatModel
2023-01-11T22:10:29.1742055Z [       OK ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForFlatModel (0 ms)
2023-01-11T22:10:29.1743071Z [ RUN      ] ModuleTest.NamedModulesExcludesSelfWhenIncludeSelfSetToFalse
2023-01-11T22:10:29.1744001Z [       OK ] ModuleTest.NamedModulesExcludesSelfWhenIncludeSelfSetToFalse (0 ms)
2023-01-11T22:10:29.1744760Z [ RUN      ] ModuleTest.ChildrenReturnsExpectedSubmodulesForFlatModel
2023-01-11T22:10:29.1745570Z [       OK ] ModuleTest.ChildrenReturnsExpectedSubmodulesForFlatModel (0 ms)
2023-01-11T22:10:29.1746214Z [ RUN      ] ModuleTest.NamedChildrenReturnsExpectedNamedSubmodulesForFlatModel
2023-01-11T22:10:29.1746786Z [       OK ] ModuleTest.NamedChildrenReturnsExpectedNamedSubmodulesForFlatModel (0 ms)
2023-01-11T22:10:29.1747318Z [ RUN      ] ModuleTest.ParametersReturnsExpectedTensorsForFlatModel
2023-01-11T22:10:29.1747949Z [       OK ] ModuleTest.ParametersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:10:29.1748441Z [ RUN      ] ModuleTest.NamedParametersReturnsExpectedTensorsForFlatModel
2023-01-11T22:10:29.1748965Z [       OK ] ModuleTest.NamedParametersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:10:29.1749455Z [ RUN      ] ModuleTest.BuffersReturnsExpectedTensorsForFlatModel
2023-01-11T22:10:29.1749906Z [       OK ] ModuleTest.BuffersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:10:29.1750384Z [ RUN      ] ModuleTest.NamedBuffersReturnsExpectedTensorsForFlatModel
2023-01-11T22:10:29.1750883Z [       OK ] ModuleTest.NamedBuffersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:10:29.1751371Z [ RUN      ] ModuleTest.ModulesReturnsExpectedSubmodulesForDeepModel
2023-01-11T22:10:29.1751911Z [       OK ] ModuleTest.ModulesReturnsExpectedSubmodulesForDeepModel (0 ms)
2023-01-11T22:10:29.1752440Z [ RUN      ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForDeepModel
2023-01-11T22:10:29.1753002Z [       OK ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForDeepModel (0 ms)
2023-01-11T22:10:29.1753527Z [ RUN      ] ModuleTest.ChildrensReturnsExpectedSubmodulesForDeepModel
2023-01-11T22:10:29.1754014Z [       OK ] ModuleTest.ChildrensReturnsExpectedSubmodulesForDeepModel (0 ms)
2023-01-11T22:10:29.1754549Z [ RUN      ] ModuleTest.NamedChildrensReturnsExpectedNamedSubmodulesForDeepModel
2023-01-11T22:10:29.1755130Z [       OK ] ModuleTest.NamedChildrensReturnsExpectedNamedSubmodulesForDeepModel (0 ms)
2023-01-11T22:10:29.1755586Z [ RUN      ] ModuleTest.ModuleApplyIteratesCorreclty
2023-01-11T22:10:29.1755972Z [       OK ] ModuleTest.ModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:10:29.1756373Z [ RUN      ] ModuleTest.ConstModuleApplyIteratesCorreclty
2023-01-11T22:10:29.1756780Z [       OK ] ModuleTest.ConstModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:10:29.1757184Z [ RUN      ] ModuleTest.NamedModuleApplyIteratesCorreclty
2023-01-11T22:10:29.1757594Z [       OK ] ModuleTest.NamedModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:10:29.1758020Z [ RUN      ] ModuleTest.ConstNamedModuleApplyIteratesCorreclty
2023-01-11T22:10:29.1758450Z [       OK ] ModuleTest.ConstNamedModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:10:29.1758881Z [ RUN      ] ModuleTest.ModulePointerApplyIteratesCorreclty
2023-01-11T22:10:29.1759308Z [       OK ] ModuleTest.ModulePointerApplyIteratesCorreclty (0 ms)
2023-01-11T22:10:29.1759807Z [ RUN      ] ModuleTest.NamedModulePointerApplyIteratesCorreclty
2023-01-11T22:10:29.1760270Z [       OK ] ModuleTest.NamedModulePointerApplyIteratesCorreclty (0 ms)
2023-01-11T22:10:29.1760772Z [ RUN      ] ModuleTest.ThrowsWhenAttemptingtoGetTopLevelModuleAsSharedPtr
2023-01-11T22:10:29.1775203Z [       OK ] ModuleTest.ThrowsWhenAttemptingtoGetTopLevelModuleAsSharedPtr (2 ms)
2023-01-11T22:10:29.1775906Z [ RUN      ] ModuleTest.PrettyPrint
2023-01-11T22:10:29.1776298Z [       OK ] ModuleTest.PrettyPrint (0 ms)
2023-01-11T22:10:29.1776701Z [ RUN      ] ModuleTest.CanCallForwardOnNonTensorForwardThroughPimpl
2023-01-11T22:10:29.1777195Z [       OK ] ModuleTest.CanCallForwardOnNonTensorForwardThroughPimpl (0 ms)
2023-01-11T22:10:29.1777590Z [----------] 52 tests from ModuleTest (26 ms total)
2023-01-11T22:10:29.1777743Z 
2023-01-11T22:10:29.1777899Z [----------] 11 tests from ModuleDictTest
2023-01-11T22:10:29.1778213Z [ RUN      ] ModuleDictTest.ConstructsFromList
2023-01-11T22:10:29.1778550Z [       OK ] ModuleDictTest.ConstructsFromList (0 ms)
2023-01-11T22:10:29.1778917Z [ RUN      ] ModuleDictTest.ConstructsFromordereddict
2023-01-11T22:10:29.1779392Z [       OK ] ModuleDictTest.ConstructsFromordereddict (0 ms)
2023-01-11T22:10:29.1779757Z [ RUN      ] ModuleDictTest.UpdatePopClearContains
2023-01-11T22:10:29.1789525Z [       OK ] ModuleDictTest.UpdatePopClearContains (1 ms)
2023-01-11T22:10:29.1790068Z [ RUN      ] ModuleDictTest.UpdateExist
2023-01-11T22:10:29.1790399Z [       OK ] ModuleDictTest.UpdateExist (0 ms)
2023-01-11T22:10:29.1790679Z [ RUN      ] ModuleDictTest.Keys
2023-01-11T22:10:29.1801583Z [       OK ] ModuleDictTest.Keys (1 ms)
2023-01-11T22:10:29.1802114Z [ RUN      ] ModuleDictTest.Values
2023-01-11T22:10:29.1802485Z [       OK ] ModuleDictTest.Values (0 ms)
2023-01-11T22:10:29.1802915Z [ RUN      ] ModuleDictTest.SanityCheckForHoldingStandardModules
2023-01-11T22:10:29.1803689Z [       OK ] ModuleDictTest.SanityCheckForHoldingStandardModules (0 ms)
2023-01-11T22:10:29.1804275Z [ RUN      ] ModuleDictTest.HasReferenceSemantics
2023-01-11T22:10:29.1804674Z [       OK ] ModuleDictTest.HasReferenceSemantics (0 ms)
2023-01-11T22:10:29.1805005Z [ RUN      ] ModuleDictTest.IsCloneable
2023-01-11T22:10:29.1807836Z [       OK ] ModuleDictTest.IsCloneable (0 ms)
2023-01-11T22:10:29.1808313Z [ RUN      ] ModuleDictTest.RegistersElementsAsSubmodules
2023-01-11T22:10:29.1809171Z [       OK ] ModuleDictTest.RegistersElementsAsSubmodules (0 ms)
2023-01-11T22:10:29.1809698Z [ RUN      ] ModuleDictTest.PrettyPrintModuleDict
2023-01-11T22:10:29.1810903Z [       OK ] ModuleDictTest.PrettyPrintModuleDict (0 ms)
2023-01-11T22:10:29.1811486Z [----------] 11 tests from ModuleDictTest (3 ms total)
2023-01-11T22:10:29.1811756Z 
2023-01-11T22:10:29.1812015Z [----------] 15 tests from ModuleListTest
2023-01-11T22:10:29.1812486Z [ RUN      ] ModuleListTest.ConstructsFromSharedPointer
2023-01-11T22:10:29.1812906Z [       OK ] ModuleListTest.ConstructsFromSharedPointer (0 ms)
2023-01-11T22:10:29.1813290Z [ RUN      ] ModuleListTest.ConstructsFromConcreteType
2023-01-11T22:10:29.1813688Z [       OK ] ModuleListTest.ConstructsFromConcreteType (0 ms)
2023-01-11T22:10:29.1814075Z [ RUN      ] ModuleListTest.ConstructsFromModuleHolder
2023-01-11T22:10:29.1814454Z [       OK ] ModuleListTest.ConstructsFromModuleHolder (0 ms)
2023-01-11T22:10:29.1814825Z [ RUN      ] ModuleListTest.PushBackAddsAnElement
2023-01-11T22:10:29.1815193Z [       OK ] ModuleListTest.PushBackAddsAnElement (0 ms)
2023-01-11T22:10:29.1815515Z [ RUN      ] ModuleListTest.Insertion
2023-01-11T22:10:29.1815802Z [       OK ] ModuleListTest.Insertion (0 ms)
2023-01-11T22:10:29.1816104Z [ RUN      ] ModuleListTest.AccessWithAt
2023-01-11T22:10:29.1836075Z [       OK ] ModuleListTest.AccessWithAt (2 ms)
2023-01-11T22:10:29.1836390Z [ RUN      ] ModuleListTest.AccessWithPtr
2023-01-11T22:10:29.1858530Z [       OK ] ModuleListTest.AccessWithPtr (2 ms)
2023-01-11T22:10:29.1858966Z [ RUN      ] ModuleListTest.SanityCheckForHoldingStandardModules
2023-01-11T22:10:29.1860375Z [       OK ] ModuleListTest.SanityCheckForHoldingStandardModules (0 ms)
2023-01-11T22:10:29.1861180Z [ RUN      ] ModuleListTest.ExtendPushesModulesFromOtherModuleList
2023-01-11T22:10:29.1861668Z [       OK ] ModuleListTest.ExtendPushesModulesFromOtherModuleList (0 ms)
2023-01-11T22:10:29.1862080Z [ RUN      ] ModuleListTest.HasReferenceSemantics
2023-01-11T22:10:29.1862626Z [       OK ] ModuleListTest.HasReferenceSemantics (0 ms)
2023-01-11T22:10:29.1862961Z [ RUN      ] ModuleListTest.IsCloneable
2023-01-11T22:10:29.1864813Z [       OK ] ModuleListTest.IsCloneable (0 ms)
2023-01-11T22:10:29.1865300Z [ RUN      ] ModuleListTest.RegistersElementsAsSubmodules
2023-01-11T22:10:29.1865901Z [       OK ] ModuleListTest.RegistersElementsAsSubmodules (0 ms)
2023-01-11T22:10:29.1866401Z [ RUN      ] ModuleListTest.NestingIsPossible
2023-01-11T22:10:29.1866786Z [       OK ] ModuleListTest.NestingIsPossible (0 ms)
2023-01-11T22:10:29.1867325Z [ RUN      ] ModuleListTest.PrettyPrintModuleList
2023-01-11T22:10:29.1867963Z [       OK ] ModuleListTest.PrettyPrintModuleList (0 ms)
2023-01-11T22:10:29.1868315Z [ RUN      ] ModuleListTest.RangeBasedForLoop
2023-01-11T22:10:29.1868657Z [       OK ] ModuleListTest.RangeBasedForLoop (0 ms)
2023-01-11T22:10:29.1868990Z [----------] 15 tests from ModuleListTest (5 ms total)
2023-01-11T22:10:29.1869146Z 
2023-01-11T22:10:29.1869295Z [----------] 256 tests from ModulesTest
2023-01-11T22:10:29.1869562Z [ RUN      ] ModulesTest.Conv1d
2023-01-11T22:10:29.1886686Z [       OK ] ModulesTest.Conv1d (1 ms)
2023-01-11T22:10:29.1887360Z [ RUN      ] ModulesTest.Conv1dSameStrided
2023-01-11T22:10:29.1923060Z [       OK ] ModulesTest.Conv1dSameStrided (3 ms)
2023-01-11T22:10:29.1923594Z [ RUN      ] ModulesTest.Conv2dEven
2023-01-11T22:10:29.1926846Z [       OK ] ModulesTest.Conv2dEven (0 ms)
2023-01-11T22:10:29.1927393Z [ RUN      ] ModulesTest.Conv2dUneven
2023-01-11T22:10:29.1929839Z [       OK ] ModulesTest.Conv2dUneven (0 ms)
2023-01-11T22:10:29.1930385Z [ RUN      ] ModulesTest.Conv2dSameStrided
2023-01-11T22:10:29.1977006Z [       OK ] ModulesTest.Conv2dSameStrided (4 ms)
2023-01-11T22:10:29.1977541Z [ RUN      ] ModulesTest.Conv3d
2023-01-11T22:10:29.1982087Z [       OK ] ModulesTest.Conv3d (0 ms)
2023-01-11T22:10:29.1982761Z [ RUN      ] ModulesTest.Conv3dSameStrided
2023-01-11T22:10:29.2037556Z [       OK ] ModulesTest.Conv3dSameStrided (5 ms)
2023-01-11T22:10:29.2038127Z [ RUN      ] ModulesTest.ConvTranspose1d
2023-01-11T22:10:29.2041300Z [       OK ] ModulesTest.ConvTranspose1d (0 ms)
2023-01-11T22:10:29.2041876Z [ RUN      ] ModulesTest.ConvTranspose2dEven
2023-01-11T22:10:29.2046013Z [       OK ] ModulesTest.ConvTranspose2dEven (0 ms)
2023-01-11T22:10:29.2046599Z [ RUN      ] ModulesTest.ConvTranspose2dUneven
2023-01-11T22:10:29.2050574Z [       OK ] ModulesTest.ConvTranspose2dUneven (0 ms)
2023-01-11T22:10:29.2051138Z [ RUN      ] ModulesTest.ConvTranspose3d
2023-01-11T22:10:29.2054377Z [       OK ] ModulesTest.ConvTranspose3d (0 ms)
2023-01-11T22:10:29.2054904Z [ RUN      ] ModulesTest.MaxPool1d
2023-01-11T22:10:29.2069829Z [       OK ] ModulesTest.MaxPool1d (1 ms)
2023-01-11T22:10:29.2070401Z [ RUN      ] ModulesTest.MaxPool1dReturnIndices
2023-01-11T22:10:29.2071274Z [       OK ] ModulesTest.MaxPool1dReturnIndices (0 ms)
2023-01-11T22:10:29.2071827Z [ RUN      ] ModulesTest.MaxPool2dEven
2023-01-11T22:10:29.2072468Z [       OK ] ModulesTest.MaxPool2dEven (0 ms)
2023-01-11T22:10:29.2073044Z [ RUN      ] ModulesTest.MaxPool2dUneven
2023-01-11T22:10:29.2074092Z [       OK ] ModulesTest.MaxPool2dUneven (0 ms)
2023-01-11T22:10:29.2074702Z [ RUN      ] ModulesTest.MaxPool2dReturnIndices
2023-01-11T22:10:29.2075438Z [       OK ] ModulesTest.MaxPool2dReturnIndices (0 ms)
2023-01-11T22:10:29.2076003Z [ RUN      ] ModulesTest.MaxPool3d
2023-01-11T22:10:29.2076945Z [       OK ] ModulesTest.MaxPool3d (0 ms)
2023-01-11T22:10:29.2077526Z [ RUN      ] ModulesTest.MaxPool3dReturnIndices
2023-01-11T22:10:29.2078540Z [       OK ] ModulesTest.MaxPool3dReturnIndices (0 ms)
2023-01-11T22:10:29.2079087Z [ RUN      ] ModulesTest.AvgPool1d
2023-01-11T22:10:29.2080116Z [       OK ] ModulesTest.AvgPool1d (0 ms)
2023-01-11T22:10:29.2080646Z [ RUN      ] ModulesTest.AvgPool2dEven
2023-01-11T22:10:29.2081542Z [       OK ] ModulesTest.AvgPool2dEven (0 ms)
2023-01-11T22:10:29.2082074Z [ RUN      ] ModulesTest.AvgPool2dUneven
2023-01-11T22:10:29.2082649Z [       OK ] ModulesTest.AvgPool2dUneven (0 ms)
2023-01-11T22:10:29.2083142Z [ RUN      ] ModulesTest.AvgPool3d
2023-01-11T22:10:29.2084546Z [       OK ] ModulesTest.AvgPool3d (0 ms)
2023-01-11T22:10:29.2085103Z [ RUN      ] ModulesTest.FractionalMaxPool2d
2023-01-11T22:10:29.2085827Z [       OK ] ModulesTest.FractionalMaxPool2d (0 ms)
2023-01-11T22:10:29.2086402Z [ RUN      ] ModulesTest.FractionalMaxPool2dReturnIndices
2023-01-11T22:10:29.2087244Z [       OK ] ModulesTest.FractionalMaxPool2dReturnIndices (0 ms)
2023-01-11T22:10:29.2087607Z [ RUN      ] ModulesTest.FractionalMaxPool3d
2023-01-11T22:10:29.2089261Z [       OK ] ModulesTest.FractionalMaxPool3d (0 ms)
2023-01-11T22:10:29.2089682Z [ RUN      ] ModulesTest.FractionalMaxPool3dReturnIndices
2023-01-11T22:10:29.2091034Z [       OK ] ModulesTest.FractionalMaxPool3dReturnIndices (0 ms)
2023-01-11T22:10:29.2091544Z [ RUN      ] ModulesTest.LPPool1d
2023-01-11T22:10:29.2092526Z [       OK ] ModulesTest.LPPool1d (0 ms)
2023-01-11T22:10:29.2092996Z [ RUN      ] ModulesTest.LPPool2d
2023-01-11T22:10:29.2093533Z [       OK ] ModulesTest.LPPool2d (0 ms)
2023-01-11T22:10:29.2093974Z [ RUN      ] ModulesTest.Identity
2023-01-11T22:10:29.2094632Z [       OK ] ModulesTest.Identity (0 ms)
2023-01-11T22:10:29.2094892Z [ RUN      ] ModulesTest.Flatten
2023-01-11T22:10:29.2097259Z [       OK ] ModulesTest.Flatten (0 ms)
2023-01-11T22:10:29.2097839Z [ RUN      ] ModulesTest.Unflatten
2023-01-11T22:10:29.2098894Z [W TensorImpl.h:1816] Warning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (function operator())
2023-01-11T22:10:29.2099504Z [       OK ] ModulesTest.Unflatten (0 ms)
2023-01-11T22:10:29.2099805Z [ RUN      ] ModulesTest.AdaptiveMaxPool1d
2023-01-11T22:10:29.2101237Z [       OK ] ModulesTest.AdaptiveMaxPool1d (0 ms)
2023-01-11T22:10:29.2101782Z [ RUN      ] ModulesTest.AdaptiveMaxPool1dReturnIndices
2023-01-11T22:10:29.2102841Z [       OK ] ModulesTest.AdaptiveMaxPool1dReturnIndices (0 ms)
2023-01-11T22:10:29.2103224Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dEven
2023-01-11T22:10:29.2106018Z [       OK ] ModulesTest.AdaptiveMaxPool2dEven (0 ms)
2023-01-11T22:10:29.2106398Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dUneven
2023-01-11T22:10:29.2107435Z [       OK ] ModulesTest.AdaptiveMaxPool2dUneven (0 ms)
2023-01-11T22:10:29.2107900Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dReturnIndicesEven
2023-01-11T22:10:29.2110378Z [       OK ] ModulesTest.AdaptiveMaxPool2dReturnIndicesEven (0 ms)
2023-01-11T22:10:29.2110829Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dReturnIndicesUneven
2023-01-11T22:10:29.2112993Z [       OK ] ModulesTest.AdaptiveMaxPool2dReturnIndicesUneven (0 ms)
2023-01-11T22:10:29.2113379Z [ RUN      ] ModulesTest.AdaptiveMaxPool3d
2023-01-11T22:10:29.2115223Z [       OK ] ModulesTest.AdaptiveMaxPool3d (0 ms)
2023-01-11T22:10:29.2115579Z [ RUN      ] ModulesTest.AdaptiveMaxPool3dReturnIndices
2023-01-11T22:10:29.2118107Z [       OK ] ModulesTest.AdaptiveMaxPool3dReturnIndices (0 ms)
2023-01-11T22:10:29.2118495Z [ RUN      ] ModulesTest.AdaptiveAvgPool1d
2023-01-11T22:10:29.2119584Z [       OK ] ModulesTest.AdaptiveAvgPool1d (0 ms)
2023-01-11T22:10:29.2119950Z [ RUN      ] ModulesTest.AdaptiveAvgPool2dEven
2023-01-11T22:10:29.2121796Z [       OK ] ModulesTest.AdaptiveAvgPool2dEven (0 ms)
2023-01-11T22:10:29.2122174Z [ RUN      ] ModulesTest.AdaptiveAvgPool2dUneven
2023-01-11T22:10:29.2123633Z [       OK ] ModulesTest.AdaptiveAvgPool2dUneven (0 ms)
2023-01-11T22:10:29.2123993Z [ RUN      ] ModulesTest.AdaptiveAvgPool3d
2023-01-11T22:10:29.2125777Z [       OK ] ModulesTest.AdaptiveAvgPool3d (0 ms)
2023-01-11T22:10:29.2126146Z [ RUN      ] ModulesTest.MaxUnpool1d
2023-01-11T22:10:29.2127932Z [       OK ] ModulesTest.MaxUnpool1d (0 ms)
2023-01-11T22:10:29.2128307Z [ RUN      ] ModulesTest.MaxPool1d_MaxUnpool1d
2023-01-11T22:10:29.2130587Z [       OK ] ModulesTest.MaxPool1d_MaxUnpool1d (0 ms)
2023-01-11T22:10:29.2130935Z [ RUN      ] ModulesTest.MaxUnpool2d
2023-01-11T22:10:29.2133098Z [       OK ] ModulesTest.MaxUnpool2d (0 ms)
2023-01-11T22:10:29.2133477Z [ RUN      ] ModulesTest.MaxPool2d_MaxUnpool2d
2023-01-11T22:10:29.2135520Z [       OK ] ModulesTest.MaxPool2d_MaxUnpool2d (0 ms)
2023-01-11T22:10:29.2135822Z [ RUN      ] ModulesTest.MaxUnpool3d
2023-01-11T22:10:29.2137052Z [       OK ] ModulesTest.MaxUnpool3d (0 ms)
2023-01-11T22:10:29.2137390Z [ RUN      ] ModulesTest.MaxUnpool3dOutputSize
2023-01-11T22:10:29.2140006Z [       OK ] ModulesTest.MaxUnpool3dOutputSize (0 ms)
2023-01-11T22:10:29.2140423Z [ RUN      ] ModulesTest.MaxPool3d_MaxUnpool3d
2023-01-11T22:10:29.3906623Z [       OK ] ModulesTest.MaxPool3d_MaxUnpool3d (176 ms)
2023-01-11T22:10:29.3906955Z [ RUN      ] ModulesTest.Linear
2023-01-11T22:10:29.3911531Z [       OK ] ModulesTest.Linear (0 ms)
2023-01-11T22:10:29.3911870Z [ RUN      ] ModulesTest.LocalResponseNorm
2023-01-11T22:10:29.3916093Z [       OK ] ModulesTest.LocalResponseNorm (0 ms)
2023-01-11T22:10:29.3916395Z [ RUN      ] ModulesTest.LayerNorm
2023-01-11T22:10:29.3918403Z [       OK ] ModulesTest.LayerNorm (0 ms)
2023-01-11T22:10:29.3918701Z [ RUN      ] ModulesTest.GroupNorm
2023-01-11T22:10:29.3928272Z [       OK ] ModulesTest.GroupNorm (0 ms)
2023-01-11T22:10:29.3928616Z [ RUN      ] ModulesTest.Bilinear
2023-01-11T22:10:29.3931735Z [       OK ] ModulesTest.Bilinear (0 ms)
2023-01-11T22:10:29.3932220Z [ RUN      ] ModulesTest.Fold
2023-01-11T22:10:29.3957536Z [       OK ] ModulesTest.Fold (2 ms)
2023-01-11T22:10:29.3958064Z [ RUN      ] ModulesTest.Unfold
2023-01-11T22:10:29.4024369Z [       OK ] ModulesTest.Unfold (6 ms)
2023-01-11T22:10:29.4024906Z [ RUN      ] ModulesTest.SimpleContainer
2023-01-11T22:10:29.4045154Z [       OK ] ModulesTest.SimpleContainer (2 ms)
2023-01-11T22:10:29.4045487Z [ RUN      ] ModulesTest.EmbeddingBasic
2023-01-11T22:10:29.4047330Z [       OK ] ModulesTest.EmbeddingBasic (0 ms)
2023-01-11T22:10:29.4047699Z [ RUN      ] ModulesTest.EmbeddingList
2023-01-11T22:10:29.4048793Z [       OK ] ModulesTest.EmbeddingList (0 ms)
2023-01-11T22:10:29.4049612Z [ RUN      ] ModulesTest.EmbeddingFromPretrained
2023-01-11T22:10:29.4050251Z [       OK ] ModulesTest.EmbeddingFromPretrained (0 ms)
2023-01-11T22:10:29.4050629Z [ RUN      ] ModulesTest.EmbeddingBagFromPretrained
2023-01-11T22:10:29.4052695Z [       OK ] ModulesTest.EmbeddingBagFromPretrained (0 ms)
2023-01-11T22:10:29.4053077Z [ RUN      ] ModulesTest.AlphaDropout
2023-01-11T22:10:29.4054148Z [       OK ] ModulesTest.AlphaDropout (0 ms)
2023-01-11T22:10:29.4054505Z [ RUN      ] ModulesTest.FeatureAlphaDropout
2023-01-11T22:10:29.4055807Z [       OK ] ModulesTest.FeatureAlphaDropout (0 ms)
2023-01-11T22:10:29.4056119Z [ RUN      ] ModulesTest.Dropout
2023-01-11T22:10:29.4058112Z [       OK ] ModulesTest.Dropout (0 ms)
2023-01-11T22:10:29.4058644Z [ RUN      ] ModulesTest.Dropout2d
2023-01-11T22:10:29.4063935Z [       OK ] ModulesTest.Dropout2d (0 ms)
2023-01-11T22:10:29.4064480Z [ RUN      ] ModulesTest.Dropout3d
2023-01-11T22:10:29.4071876Z [       OK ] ModulesTest.Dropout3d (0 ms)
2023-01-11T22:10:29.4072296Z [ RUN      ] ModulesTest.Parameters
2023-01-11T22:10:29.4072793Z [       OK ] ModulesTest.Parameters (0 ms)
2023-01-11T22:10:29.4073373Z [ RUN      ] ModulesTest.FunctionalCallsSuppliedFunction
2023-01-11T22:10:29.4074025Z [       OK ] ModulesTest.FunctionalCallsSuppliedFunction (0 ms)
2023-01-11T22:10:29.4074420Z [ RUN      ] ModulesTest.FunctionalWithTorchFunction
2023-01-11T22:10:29.4074818Z [       OK ] ModulesTest.FunctionalWithTorchFunction (0 ms)
2023-01-11T22:10:29.4075195Z [ RUN      ] ModulesTest.FunctionalArgumentBinding
2023-01-11T22:10:29.4075554Z [       OK ] ModulesTest.FunctionalArgumentBinding (0 ms)
2023-01-11T22:10:29.4075902Z [ RUN      ] ModulesTest.BatchNorm1dStateful
2023-01-11T22:10:29.4076238Z [       OK ] ModulesTest.BatchNorm1dStateful (0 ms)
2023-01-11T22:10:29.4076745Z [ RUN      ] ModulesTest.BatchNorm1dStateless
2023-01-11T22:10:29.4077218Z [       OK ] ModulesTest.BatchNorm1dStateless (0 ms)
2023-01-11T22:10:29.4077581Z [ RUN      ] ModulesTest.BatchNorm1d
2023-01-11T22:10:29.4077892Z [       OK ] ModulesTest.BatchNorm1d (0 ms)
2023-01-11T22:10:29.4078190Z [ RUN      ] ModulesTest.BatchNorm2dStateful
2023-01-11T22:10:29.4078522Z [       OK ] ModulesTest.BatchNorm2dStateful (0 ms)
2023-01-11T22:10:29.4078852Z [ RUN      ] ModulesTest.BatchNorm2dStateless
2023-01-11T22:10:29.4079178Z [       OK ] ModulesTest.BatchNorm2dStateless (0 ms)
2023-01-11T22:10:29.4079482Z [ RUN      ] ModulesTest.BatchNorm2d
2023-01-11T22:10:29.4081345Z [       OK ] ModulesTest.BatchNorm2d (0 ms)
2023-01-11T22:10:29.4081921Z [ RUN      ] ModulesTest.BatchNorm3dStateful
2023-01-11T22:10:29.4082391Z [       OK ] ModulesTest.BatchNorm3dStateful (0 ms)
2023-01-11T22:10:29.4082732Z [ RUN      ] ModulesTest.BatchNorm3dStateless
2023-01-11T22:10:29.4083071Z [       OK ] ModulesTest.BatchNorm3dStateless (0 ms)
2023-01-11T22:10:29.4083367Z [ RUN      ] ModulesTest.BatchNorm3d
2023-01-11T22:10:29.4086529Z [       OK ] ModulesTest.BatchNorm3d (0 ms)
2023-01-11T22:10:29.4087060Z [ RUN      ] ModulesTest.InstanceNorm1dStateful
2023-01-11T22:10:29.4087430Z [       OK ] ModulesTest.InstanceNorm1dStateful (0 ms)
2023-01-11T22:10:29.4087820Z [ RUN      ] ModulesTest.InstanceNorm1dStateless
2023-01-11T22:10:29.4088181Z [       OK ] ModulesTest.InstanceNorm1dStateless (0 ms)
2023-01-11T22:10:29.4088506Z [ RUN      ] ModulesTest.InstanceNorm1d
2023-01-11T22:10:29.4088986Z [       OK ] ModulesTest.InstanceNorm1d (0 ms)
2023-01-11T22:10:29.4089552Z [ RUN      ] ModulesTest.InstanceNorm2dStateful
2023-01-11T22:10:29.4089908Z [       OK ] ModulesTest.InstanceNorm2dStateful (0 ms)
2023-01-11T22:10:29.4090260Z [ RUN      ] ModulesTest.InstanceNorm2dStateless
2023-01-11T22:10:29.4090614Z [       OK ] ModulesTest.InstanceNorm2dStateless (0 ms)
2023-01-11T22:10:29.4090983Z [ RUN      ] ModulesTest.InstanceNorm2d
2023-01-11T22:10:29.4091937Z [       OK ] ModulesTest.InstanceNorm2d (0 ms)
2023-01-11T22:10:29.4092532Z [ RUN      ] ModulesTest.InstanceNorm3dStateful
2023-01-11T22:10:29.4092933Z [       OK ] ModulesTest.InstanceNorm3dStateful (0 ms)
2023-01-11T22:10:29.4093286Z [ RUN      ] ModulesTest.InstanceNorm3dStateless
2023-01-11T22:10:29.4093627Z [       OK ] ModulesTest.InstanceNorm3dStateless (0 ms)
2023-01-11T22:10:29.4093953Z [ RUN      ] ModulesTest.InstanceNorm3d
2023-01-11T22:10:29.4096453Z [       OK ] ModulesTest.InstanceNorm3d (0 ms)
2023-01-11T22:10:29.4096985Z [ RUN      ] ModulesTest.L1Loss
2023-01-11T22:10:29.4097727Z [       OK ] ModulesTest.L1Loss (0 ms)
2023-01-11T22:10:29.4098144Z [ RUN      ] ModulesTest.MSELoss
2023-01-11T22:10:29.4099027Z [       OK ] ModulesTest.MSELoss (0 ms)
2023-01-11T22:10:29.4099368Z [ RUN      ] ModulesTest.BCELoss
2023-01-11T22:10:29.4100388Z [       OK ] ModulesTest.BCELoss (0 ms)
2023-01-11T22:10:29.4100728Z [ RUN      ] ModulesTest.KLDivLoss
2023-01-11T22:10:29.4101504Z [W loss.h:57] Warning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release. (function kl_div)
2023-01-11T22:10:29.4102239Z [       OK ] ModulesTest.KLDivLoss (0 ms)
2023-01-11T22:10:29.4102708Z [ RUN      ] ModulesTest.HingeEmbeddingLoss
2023-01-11T22:10:29.4104912Z [       OK ] ModulesTest.HingeEmbeddingLoss (0 ms)
2023-01-11T22:10:29.4105386Z [ RUN      ] ModulesTest.MultiMarginLoss
2023-01-11T22:10:29.4106792Z [       OK ] ModulesTest.MultiMarginLoss (0 ms)
2023-01-11T22:10:29.4107386Z [ RUN      ] ModulesTest.CosineEmbeddingLoss
2023-01-11T22:10:29.4111143Z [       OK ] ModulesTest.CosineEmbeddingLoss (0 ms)
2023-01-11T22:10:29.4111538Z [ RUN      ] ModulesTest.SmoothL1LossDefaultOptions
2023-01-11T22:10:29.4112651Z [       OK ] ModulesTest.SmoothL1LossDefaultOptions (0 ms)
2023-01-11T22:10:29.4113021Z [ RUN      ] ModulesTest.HuberLossDefaultOptions
2023-01-11T22:10:29.4114518Z [       OK ] ModulesTest.HuberLossDefaultOptions (0 ms)
2023-01-11T22:10:29.4115015Z [ RUN      ] ModulesTest.MultiLabelMarginLossDefaultOptions
2023-01-11T22:10:29.4116021Z [       OK ] ModulesTest.MultiLabelMarginLossDefaultOptions (0 ms)
2023-01-11T22:10:29.4116491Z [ RUN      ] ModulesTest.SmoothL1LossNoReduction
2023-01-11T22:10:29.4117382Z [       OK ] ModulesTest.SmoothL1LossNoReduction (0 ms)
2023-01-11T22:10:29.4117732Z [ RUN      ] ModulesTest.HuberLossNoReduction
2023-01-11T22:10:29.4118831Z [       OK ] ModulesTest.HuberLossNoReduction (0 ms)
2023-01-11T22:10:29.4119234Z [ RUN      ] ModulesTest.MultiLabelMarginLossNoReduction
2023-01-11T22:10:29.4120495Z [       OK ] ModulesTest.MultiLabelMarginLossNoReduction (0 ms)
2023-01-11T22:10:29.4120911Z [ RUN      ] ModulesTest.SmoothL1LossBeta
2023-01-11T22:10:29.4122218Z [       OK ] ModulesTest.SmoothL1LossBeta (0 ms)
2023-01-11T22:10:29.4122606Z [ RUN      ] ModulesTest.HuberLossDelta
2023-01-11T22:10:29.4123506Z [       OK ] ModulesTest.HuberLossDelta (0 ms)
2023-01-11T22:10:29.4123918Z [ RUN      ] ModulesTest.TripletMarginLoss
2023-01-11T22:10:29.4126589Z [       OK ] ModulesTest.TripletMarginLoss (0 ms)
2023-01-11T22:10:29.4126988Z [ RUN      ] ModulesTest.TripletMarginWithDistanceLossDefaultParity
2023-01-11T22:10:29.4368714Z [       OK ] ModulesTest.TripletMarginWithDistanceLossDefaultParity (24 ms)
2023-01-11T22:10:29.4369227Z [ RUN      ] ModulesTest.TripletMarginWithDistanceLossFunctionalParity
2023-01-11T22:10:29.4866425Z [       OK ] ModulesTest.TripletMarginWithDistanceLossFunctionalParity (49 ms)
2023-01-11T22:10:29.4866835Z [ RUN      ] ModulesTest.NLLLoss
2023-01-11T22:10:29.4868430Z [       OK ] ModulesTest.NLLLoss (0 ms)
2023-01-11T22:10:29.4868797Z [ RUN      ] ModulesTest.CrossEntropyLoss
2023-01-11T22:10:29.4875338Z [       OK ] ModulesTest.CrossEntropyLoss (0 ms)
2023-01-11T22:10:29.4875674Z [ RUN      ] ModulesTest.CosineSimilarity
2023-01-11T22:10:29.4878898Z [       OK ] ModulesTest.CosineSimilarity (0 ms)
2023-01-11T22:10:29.4879308Z [ RUN      ] ModulesTest.SoftMarginLossDefaultOptions
2023-01-11T22:10:29.4881372Z [       OK ] ModulesTest.SoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:10:29.4882086Z [ RUN      ] ModulesTest.MultiLabelSoftMarginLossDefaultOptions
2023-01-11T22:10:29.4884719Z [       OK ] ModulesTest.MultiLabelSoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:10:29.4885228Z [ RUN      ] ModulesTest.SoftMarginLossNoReduction
2023-01-11T22:10:29.4886715Z [       OK ] ModulesTest.SoftMarginLossNoReduction (0 ms)
2023-01-11T22:10:29.4887180Z [ RUN      ] ModulesTest.MultiLabelSoftMarginLossWeightedNoReduction
2023-01-11T22:10:29.4890141Z [       OK ] ModulesTest.MultiLabelSoftMarginLossWeightedNoReduction (0 ms)
2023-01-11T22:10:29.4890568Z [ RUN      ] ModulesTest.PairwiseDistance
2023-01-11T22:10:29.4892103Z [       OK ] ModulesTest.PairwiseDistance (0 ms)
2023-01-11T22:10:29.4892398Z [ RUN      ] ModulesTest.ELU
2023-01-11T22:10:29.4905019Z [       OK ] ModulesTest.ELU (1 ms)
2023-01-11T22:10:29.4905360Z [ RUN      ] ModulesTest.SELU
2023-01-11T22:10:29.4907993Z [       OK ] ModulesTest.SELU (0 ms)
2023-01-11T22:10:29.4908341Z [ RUN      ] ModulesTest.Hardshrink
2023-01-11T22:10:29.4915281Z [       OK ] ModulesTest.Hardshrink (0 ms)
2023-01-11T22:10:29.4915638Z [ RUN      ] ModulesTest.Hardtanh
2023-01-11T22:10:29.4940698Z [       OK ] ModulesTest.Hardtanh (2 ms)
2023-01-11T22:10:29.4941057Z [ RUN      ] ModulesTest.HardtanhMinValGEMaxVal
2023-01-11T22:10:29.5018610Z [       OK ] ModulesTest.HardtanhMinValGEMaxVal (7 ms)
2023-01-11T22:10:29.5018954Z [ RUN      ] ModulesTest.LeakyReLU
2023-01-11T22:10:29.5030348Z [       OK ] ModulesTest.LeakyReLU (1 ms)
2023-01-11T22:10:29.5030712Z [ RUN      ] ModulesTest.LogSigmoid
2023-01-11T22:10:29.5032202Z [       OK ] ModulesTest.LogSigmoid (0 ms)
2023-01-11T22:10:29.5032519Z [ RUN      ] ModulesTest.Softmax
2023-01-11T22:10:29.5033792Z [       OK ] ModulesTest.Softmax (0 ms)
2023-01-11T22:10:29.5034082Z [ RUN      ] ModulesTest.Softmin
2023-01-11T22:10:29.5035269Z [       OK ] ModulesTest.Softmin (0 ms)
2023-01-11T22:10:29.5035576Z [ RUN      ] ModulesTest.LogSoftmax
2023-01-11T22:10:29.5036644Z [       OK ] ModulesTest.LogSoftmax (0 ms)
2023-01-11T22:10:29.5037000Z [ RUN      ] ModulesTest.AdaptiveLogSoftmaxWithLoss
2023-01-11T22:10:29.5063050Z [       OK ] ModulesTest.AdaptiveLogSoftmaxWithLoss (2 ms)
2023-01-11T22:10:29.5063414Z [ RUN      ] ModulesTest.Softmax2d
2023-01-11T22:10:29.5073917Z [       OK ] ModulesTest.Softmax2d (1 ms)
2023-01-11T22:10:29.5074284Z [ RUN      ] ModulesTest.PReLU
2023-01-11T22:10:29.5079057Z [       OK ] ModulesTest.PReLU (0 ms)
2023-01-11T22:10:29.5079369Z [ RUN      ] ModulesTest.ReLU
2023-01-11T22:10:29.5081968Z [       OK ] ModulesTest.ReLU (0 ms)
2023-01-11T22:10:29.5082239Z [ RUN      ] ModulesTest.ReLU6
2023-01-11T22:10:29.5084681Z [       OK ] ModulesTest.ReLU6 (0 ms)
2023-01-11T22:10:29.5084934Z [ RUN      ] ModulesTest.RReLU
2023-01-11T22:10:29.5121049Z [       OK ] ModulesTest.RReLU (3 ms)
2023-01-11T22:10:29.5121334Z [ RUN      ] ModulesTest.CELU
2023-01-11T22:10:29.5130948Z [       OK ] ModulesTest.CELU (1 ms)
2023-01-11T22:10:29.5131218Z [ RUN      ] ModulesTest.GLU
2023-01-11T22:10:29.5133305Z [       OK ] ModulesTest.GLU (0 ms)
2023-01-11T22:10:29.5133552Z [ RUN      ] ModulesTest.GELU
2023-01-11T22:10:29.5137049Z [       OK ] ModulesTest.GELU (0 ms)
2023-01-11T22:10:29.5137470Z [ RUN      ] ModulesTest.TanhGELU
2023-01-11T22:10:29.5138303Z [       OK ] ModulesTest.TanhGELU (0 ms)
2023-01-11T22:10:29.5138629Z [ RUN      ] ModulesTest.Mish
2023-01-11T22:10:29.5139831Z [       OK ] ModulesTest.Mish (0 ms)
2023-01-11T22:10:29.5140136Z [ RUN      ] ModulesTest.Sigmoid
2023-01-11T22:10:29.5141139Z [       OK ] ModulesTest.Sigmoid (0 ms)
2023-01-11T22:10:29.5141696Z [ RUN      ] ModulesTest.PixelShuffle
2023-01-11T22:10:29.5143451Z [       OK ] ModulesTest.PixelShuffle (0 ms)
2023-01-11T22:10:29.5144114Z [ RUN      ] ModulesTest.PixelUnshuffle
2023-01-11T22:10:29.5145740Z [       OK ] ModulesTest.PixelUnshuffle (0 ms)
2023-01-11T22:10:29.5146057Z [ RUN      ] ModulesTest.Softplus
2023-01-11T22:10:29.5152980Z [       OK ] ModulesTest.Softplus (0 ms)
2023-01-11T22:10:29.5153481Z [ RUN      ] ModulesTest.Softshrink
2023-01-11T22:10:29.5159181Z [       OK ] ModulesTest.Softshrink (0 ms)
2023-01-11T22:10:29.5159755Z [ RUN      ] ModulesTest.Softsign
2023-01-11T22:10:29.5160238Z [       OK ] ModulesTest.Softsign (0 ms)
2023-01-11T22:10:29.5160710Z [ RUN      ] ModulesTest.Tanh
2023-01-11T22:10:29.5161503Z [       OK ] ModulesTest.Tanh (0 ms)
2023-01-11T22:10:29.5162203Z [ RUN      ] ModulesTest.Tanhshrink
2023-01-11T22:10:29.5162738Z [       OK ] ModulesTest.Tanhshrink (0 ms)
2023-01-11T22:10:29.5163082Z [ RUN      ] ModulesTest.Threshold
2023-01-11T22:10:29.5174991Z [       OK ] ModulesTest.Threshold (1 ms)
2023-01-11T22:10:29.5175524Z [ RUN      ] ModulesTest.Upsampling1D
2023-01-11T22:10:29.5177859Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5179316Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5181123Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5182662Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5184924Z [       OK ] ModulesTest.Upsampling1D (0 ms)
2023-01-11T22:10:29.5185487Z [ RUN      ] ModulesTest.Upsampling2D
2023-01-11T22:10:29.5186924Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5188254Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5190116Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5191435Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5193194Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5194609Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5197037Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5198370Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5200341Z [       OK ] ModulesTest.Upsampling2D (1 ms)
2023-01-11T22:10:29.5200659Z [ RUN      ] ModulesTest.Upsampling3D
2023-01-11T22:10:29.5202297Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5203505Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5206526Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5207929Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:10:29.5209821Z [       OK ] ModulesTest.Upsampling3D (0 ms)
2023-01-11T22:10:29.5210341Z [ RUN      ] ModulesTest.CTCLoss
2023-01-11T22:10:29.5212986Z [       OK ] ModulesTest.CTCLoss (0 ms)
2023-01-11T22:10:29.5213302Z [ RUN      ] ModulesTest.PoissonNLLLoss
2023-01-11T22:10:29.5215273Z [       OK ] ModulesTest.PoissonNLLLoss (0 ms)
2023-01-11T22:10:29.5215861Z [ RUN      ] ModulesTest.MarginRankingLoss
2023-01-11T22:10:29.5218413Z [       OK ] ModulesTest.MarginRankingLoss (0 ms)
2023-01-11T22:10:29.5218976Z [ RUN      ] ModulesTest.BCEWithLogitsLoss
2023-01-11T22:10:29.5278109Z [       OK ] ModulesTest.BCEWithLogitsLoss (5 ms)
2023-01-11T22:10:29.5278601Z [ RUN      ] ModulesTest.MultiheadAttention
2023-01-11T22:10:38.8636718Z [       OK ] ModulesTest.MultiheadAttention (9335 ms)
2023-01-11T22:10:38.8637501Z [ RUN      ] ModulesTest.PrettyPrintIdentity
2023-01-11T22:10:38.8637989Z [       OK ] ModulesTest.PrettyPrintIdentity (0 ms)
2023-01-11T22:10:38.8638508Z [ RUN      ] ModulesTest.PrettyPrintFlatten
2023-01-11T22:10:38.8638853Z [       OK ] ModulesTest.PrettyPrintFlatten (0 ms)
2023-01-11T22:10:38.8639238Z [ RUN      ] ModulesTest.PrettyPrintUnflatten
2023-01-11T22:10:38.8639630Z [       OK ] ModulesTest.PrettyPrintUnflatten (0 ms)
2023-01-11T22:10:38.8640054Z [ RUN      ] ModulesTest.ReflectionPad1d
2023-01-11T22:10:38.8640492Z [       OK ] ModulesTest.ReflectionPad1d (0 ms)
2023-01-11T22:10:38.8640874Z [ RUN      ] ModulesTest.ReflectionPad2d
2023-01-11T22:10:38.8641490Z [       OK ] ModulesTest.ReflectionPad2d (0 ms)
2023-01-11T22:10:38.8642053Z [ RUN      ] ModulesTest.ReflectionPad3d
2023-01-11T22:10:38.8644922Z [       OK ] ModulesTest.ReflectionPad3d (0 ms)
2023-01-11T22:10:38.8645510Z [ RUN      ] ModulesTest.ReplicationPad1d
2023-01-11T22:10:38.8646732Z [       OK ] ModulesTest.ReplicationPad1d (0 ms)
2023-01-11T22:10:38.8647306Z [ RUN      ] ModulesTest.ReplicationPad2d
2023-01-11T22:10:38.8649678Z [       OK ] ModulesTest.ReplicationPad2d (0 ms)
2023-01-11T22:10:38.8650267Z [ RUN      ] ModulesTest.ReplicationPad3d
2023-01-11T22:10:38.8654005Z [       OK ] ModulesTest.ReplicationPad3d (0 ms)
2023-01-11T22:10:38.8654294Z [ RUN      ] ModulesTest.ZeroPad2d
2023-01-11T22:10:38.8656588Z [       OK ] ModulesTest.ZeroPad2d (0 ms)
2023-01-11T22:10:38.8656938Z [ RUN      ] ModulesTest.ConstantPad1d
2023-01-11T22:10:38.8658769Z [       OK ] ModulesTest.ConstantPad1d (0 ms)
2023-01-11T22:10:38.8659069Z [ RUN      ] ModulesTest.ConstantPad2d
2023-01-11T22:10:38.8661037Z [       OK ] ModulesTest.ConstantPad2d (0 ms)
2023-01-11T22:10:38.8661592Z [ RUN      ] ModulesTest.ConstantPad3d
2023-01-11T22:10:38.8665673Z [       OK ] ModulesTest.ConstantPad3d (0 ms)
2023-01-11T22:10:38.8665983Z [ RUN      ] ModulesTest.CrossMapLRN2d
2023-01-11T22:10:38.8672546Z [       OK ] ModulesTest.CrossMapLRN2d (0 ms)
2023-01-11T22:10:38.8672834Z [ RUN      ] ModulesTest.RNNCell
2023-01-11T22:10:38.8675742Z [       OK ] ModulesTest.RNNCell (0 ms)
2023-01-11T22:10:38.8676196Z [ RUN      ] ModulesTest.LSTMCell
2023-01-11T22:10:38.8680858Z [       OK ] ModulesTest.LSTMCell (0 ms)
2023-01-11T22:10:38.8681124Z [ RUN      ] ModulesTest.GRUCell
2023-01-11T22:10:38.8685143Z [       OK ] ModulesTest.GRUCell (0 ms)
2023-01-11T22:10:38.8685811Z [ RUN      ] ModulesTest.PrettyPrintLinear
2023-01-11T22:10:38.8686422Z [       OK ] ModulesTest.PrettyPrintLinear (0 ms)
2023-01-11T22:10:38.8686749Z [ RUN      ] ModulesTest.PrettyPrintBilinear
2023-01-11T22:10:38.8687089Z [       OK ] ModulesTest.PrettyPrintBilinear (0 ms)
2023-01-11T22:10:38.8687546Z [ RUN      ] ModulesTest.PrettyPrintConv
2023-01-11T22:10:38.8688028Z [       OK ] ModulesTest.PrettyPrintConv (0 ms)
2023-01-11T22:10:38.8688367Z [ RUN      ] ModulesTest.PrettyPrintConvTranspose
2023-01-11T22:10:38.8690701Z [       OK ] ModulesTest.PrettyPrintConvTranspose (0 ms)
2023-01-11T22:10:38.8691217Z [ RUN      ] ModulesTest.PrettyPrintUpsample
2023-01-11T22:10:38.8691792Z [       OK ] ModulesTest.PrettyPrintUpsample (0 ms)
2023-01-11T22:10:38.8692314Z [ RUN      ] ModulesTest.PrettyPrintFold
2023-01-11T22:10:38.8692826Z [       OK ] ModulesTest.PrettyPrintFold (0 ms)
2023-01-11T22:10:38.8693442Z [ RUN      ] ModulesTest.PrettyPrintUnfold
2023-01-11T22:10:38.8693909Z [       OK ] ModulesTest.PrettyPrintUnfold (0 ms)
2023-01-11T22:10:38.8694423Z [ RUN      ] ModulesTest.PrettyPrintMaxPool
2023-01-11T22:10:38.8694825Z [       OK ] ModulesTest.PrettyPrintMaxPool (0 ms)
2023-01-11T22:10:38.8695278Z [ RUN      ] ModulesTest.PrettyPrintAvgPool
2023-01-11T22:10:38.8695613Z [       OK ] ModulesTest.PrettyPrintAvgPool (0 ms)
2023-01-11T22:10:38.8695954Z [ RUN      ] ModulesTest.PrettyPrinFractionalMaxPool
2023-01-11T22:10:38.8696359Z [       OK ] ModulesTest.PrettyPrinFractionalMaxPool (0 ms)
2023-01-11T22:10:38.8696808Z [ RUN      ] ModulesTest.PrettyPrintLPPool
2023-01-11T22:10:38.8697349Z [       OK ] ModulesTest.PrettyPrintLPPool (0 ms)
2023-01-11T22:10:38.8697893Z [ RUN      ] ModulesTest.PrettyPrintAdaptiveMaxPool
2023-01-11T22:10:38.8698387Z [       OK ] ModulesTest.PrettyPrintAdaptiveMaxPool (0 ms)
2023-01-11T22:10:38.8698864Z [ RUN      ] ModulesTest.PrettyPrintAdaptiveAvgPool
2023-01-11T22:10:38.8699397Z [       OK ] ModulesTest.PrettyPrintAdaptiveAvgPool (0 ms)
2023-01-11T22:10:38.8699933Z [ RUN      ] ModulesTest.PrettyPrintMaxUnpool
2023-01-11T22:10:38.8700429Z [       OK ] ModulesTest.PrettyPrintMaxUnpool (0 ms)
2023-01-11T22:10:38.8700960Z [ RUN      ] ModulesTest.PrettyPrintDropout
2023-01-11T22:10:38.8701468Z [       OK ] ModulesTest.PrettyPrintDropout (0 ms)
2023-01-11T22:10:38.8701965Z [ RUN      ] ModulesTest.PrettyPrintDropout2d
2023-01-11T22:10:38.8702617Z [       OK ] ModulesTest.PrettyPrintDropout2d (0 ms)
2023-01-11T22:10:38.8703149Z [ RUN      ] ModulesTest.PrettyPrintDropout3d
2023-01-11T22:10:38.8703705Z [       OK ] ModulesTest.PrettyPrintDropout3d (0 ms)
2023-01-11T22:10:38.8704299Z [ RUN      ] ModulesTest.PrettyPrintFunctional
2023-01-11T22:10:38.8704872Z [       OK ] ModulesTest.PrettyPrintFunctional (0 ms)
2023-01-11T22:10:38.8705401Z [ RUN      ] ModulesTest.PrettyPrintBatchNorm1d
2023-01-11T22:10:38.8705922Z [       OK ] ModulesTest.PrettyPrintBatchNorm1d (0 ms)
2023-01-11T22:10:38.8706565Z [ RUN      ] ModulesTest.PrettyPrintBatchNorm2d
2023-01-11T22:10:38.8706926Z [       OK ] ModulesTest.PrettyPrintBatchNorm2d (0 ms)
2023-01-11T22:10:38.8707315Z [ RUN      ] ModulesTest.PrettyPrintBatchNorm3d
2023-01-11T22:10:38.8707852Z [       OK ] ModulesTest.PrettyPrintBatchNorm3d (0 ms)
2023-01-11T22:10:38.8708326Z [ RUN      ] ModulesTest.PrettyPrintInstanceNorm1d
2023-01-11T22:10:38.8708865Z [       OK ] ModulesTest.PrettyPrintInstanceNorm1d (0 ms)
2023-01-11T22:10:38.8709412Z [ RUN      ] ModulesTest.PrettyPrintInstanceNorm2d
2023-01-11T22:10:38.8710022Z [       OK ] ModulesTest.PrettyPrintInstanceNorm2d (0 ms)
2023-01-11T22:10:38.8710477Z [ RUN      ] ModulesTest.PrettyPrintInstanceNorm3d
2023-01-11T22:10:38.8710994Z [       OK ] ModulesTest.PrettyPrintInstanceNorm3d (0 ms)
2023-01-11T22:10:38.8711477Z [ RUN      ] ModulesTest.PrettyPrintLayerNorm
2023-01-11T22:10:38.8711863Z [       OK ] ModulesTest.PrettyPrintLayerNorm (0 ms)
2023-01-11T22:10:38.8712202Z [ RUN      ] ModulesTest.PrettyPrintGroupNorm
2023-01-11T22:10:38.8712624Z [       OK ] ModulesTest.PrettyPrintGroupNorm (0 ms)
2023-01-11T22:10:38.8713096Z [ RUN      ] ModulesTest.PrettyPrintLocalResponseNorm
2023-01-11T22:10:38.8713472Z [       OK ] ModulesTest.PrettyPrintLocalResponseNorm (0 ms)
2023-01-11T22:10:38.8713830Z [ RUN      ] ModulesTest.PrettyPrintEmbedding
2023-01-11T22:10:38.8714245Z [       OK ] ModulesTest.PrettyPrintEmbedding (0 ms)
2023-01-11T22:10:38.8714579Z [ RUN      ] ModulesTest.PrettyPrintEmbeddingBag
2023-01-11T22:10:38.8715024Z [       OK ] ModulesTest.PrettyPrintEmbeddingBag (0 ms)
2023-01-11T22:10:38.8715501Z [ RUN      ] ModulesTest.PrettyPrintL1Loss
2023-01-11T22:10:38.8715971Z [       OK ] ModulesTest.PrettyPrintL1Loss (0 ms)
2023-01-11T22:10:38.8716391Z [ RUN      ] ModulesTest.PrettyPrintKLDivLoss
2023-01-11T22:10:38.8716872Z [       OK ] ModulesTest.PrettyPrintKLDivLoss (0 ms)
2023-01-11T22:10:38.8717243Z [ RUN      ] ModulesTest.PrettyPrintMSELoss
2023-01-11T22:10:38.8717586Z [       OK ] ModulesTest.PrettyPrintMSELoss (0 ms)
2023-01-11T22:10:38.8717912Z [ RUN      ] ModulesTest.PrettyPrintBCELoss
2023-01-11T22:10:38.8718244Z [       OK ] ModulesTest.PrettyPrintBCELoss (0 ms)
2023-01-11T22:10:38.8718588Z [ RUN      ] ModulesTest.PrettyPrintHingeEmbeddingLoss
2023-01-11T22:10:38.8718983Z [       OK ] ModulesTest.PrettyPrintHingeEmbeddingLoss (0 ms)
2023-01-11T22:10:38.8719376Z [ RUN      ] ModulesTest.PrettyPrintCosineEmbeddingLoss
2023-01-11T22:10:38.8719848Z [       OK ] ModulesTest.PrettyPrintCosineEmbeddingLoss (0 ms)
2023-01-11T22:10:38.8720239Z [ RUN      ] ModulesTest.PrettyPrintTripletMarginLoss
2023-01-11T22:10:38.8720627Z [       OK ] ModulesTest.PrettyPrintTripletMarginLoss (0 ms)
2023-01-11T22:10:38.8721050Z [ RUN      ] ModulesTest.PrettyPrintTripletMarginWithDistanceLoss
2023-01-11T22:10:38.8721504Z [       OK ] ModulesTest.PrettyPrintTripletMarginWithDistanceLoss (0 ms)
2023-01-11T22:10:38.8721892Z [ RUN      ] ModulesTest.PrettyPrintNLLLoss
2023-01-11T22:10:38.8722222Z [       OK ] ModulesTest.PrettyPrintNLLLoss (0 ms)
2023-01-11T22:10:38.8722559Z [ RUN      ] ModulesTest.PrettyPrinCrossEntropyLoss
2023-01-11T22:10:38.8722935Z [       OK ] ModulesTest.PrettyPrinCrossEntropyLoss (0 ms)
2023-01-11T22:10:38.8723322Z [ RUN      ] ModulesTest.PrettyPrintMultiLabelMarginLoss
2023-01-11T22:10:38.8723716Z [       OK ] ModulesTest.PrettyPrintMultiLabelMarginLoss (0 ms)
2023-01-11T22:10:38.8724133Z [ RUN      ] ModulesTest.PrettyPrintMultiLabelSoftMarginLoss
2023-01-11T22:10:38.8724563Z [       OK ] ModulesTest.PrettyPrintMultiLabelSoftMarginLoss (0 ms)
2023-01-11T22:10:38.8724955Z [ RUN      ] ModulesTest.PrettyPrintSoftMarginLoss
2023-01-11T22:10:38.8725312Z [       OK ] ModulesTest.PrettyPrintSoftMarginLoss (0 ms)
2023-01-11T22:10:38.8725682Z [ RUN      ] ModulesTest.PrettyPrintCosineSimilarity
2023-01-11T22:10:38.8726062Z [       OK ] ModulesTest.PrettyPrintCosineSimilarity (0 ms)
2023-01-11T22:10:38.8726424Z [ RUN      ] ModulesTest.PrettyPrintPairwiseDistance
2023-01-11T22:10:38.8726802Z [       OK ] ModulesTest.PrettyPrintPairwiseDistance (0 ms)
2023-01-11T22:10:38.8727241Z [ RUN      ] ModulesTest.PrettyPrintReflectionPad
2023-01-11T22:10:38.8727609Z [       OK ] ModulesTest.PrettyPrintReflectionPad (0 ms)
2023-01-11T22:10:38.8728032Z [ RUN      ] ModulesTest.PrettyPrintReplicationPad
2023-01-11T22:10:38.8728404Z [       OK ] ModulesTest.PrettyPrintReplicationPad (0 ms)
2023-01-11T22:10:38.8728758Z [ RUN      ] ModulesTest.PrettyPrintZeroPad2d
2023-01-11T22:10:38.8729100Z [       OK ] ModulesTest.PrettyPrintZeroPad2d (0 ms)
2023-01-11T22:10:38.8729488Z [ RUN      ] ModulesTest.PrettyPrintConstantPad
2023-01-11T22:10:38.8729913Z [       OK ] ModulesTest.PrettyPrintConstantPad (0 ms)
2023-01-11T22:10:38.8730251Z [ RUN      ] ModulesTest.PrettyPrintNestedModel
2023-01-11T22:10:38.8730603Z [       OK ] ModulesTest.PrettyPrintNestedModel (0 ms)
2023-01-11T22:10:38.8730924Z [ RUN      ] ModulesTest.PrettyPrintELU
2023-01-11T22:10:38.8731236Z [       OK ] ModulesTest.PrettyPrintELU (0 ms)
2023-01-11T22:10:38.8731532Z [ RUN      ] ModulesTest.PrettyPrintSELU
2023-01-11T22:10:38.8731850Z [       OK ] ModulesTest.PrettyPrintSELU (0 ms)
2023-01-11T22:10:38.8732205Z [ RUN      ] ModulesTest.PrettyPrintGLU
2023-01-11T22:10:38.8732505Z [       OK ] ModulesTest.PrettyPrintGLU (0 ms)
2023-01-11T22:10:38.8732829Z [ RUN      ] ModulesTest.PrettyPrintHardshrink
2023-01-11T22:10:38.8733174Z [       OK ] ModulesTest.PrettyPrintHardshrink (0 ms)
2023-01-11T22:10:38.8733496Z [ RUN      ] ModulesTest.PrettyPrintHardtanh
2023-01-11T22:10:38.8733835Z [       OK ] ModulesTest.PrettyPrintHardtanh (0 ms)
2023-01-11T22:10:38.8734166Z [ RUN      ] ModulesTest.PrettyPrintLeakyReLU
2023-01-11T22:10:38.8737100Z [       OK ] ModulesTest.PrettyPrintLeakyReLU (0 ms)
2023-01-11T22:10:38.8737649Z [ RUN      ] ModulesTest.PrettyPrintLogSigmoid
2023-01-11T22:10:38.8738251Z [       OK ] ModulesTest.PrettyPrintLogSigmoid (0 ms)
2023-01-11T22:10:38.8738852Z [ RUN      ] ModulesTest.PrettyPrintSoftmax
2023-01-11T22:10:38.8739458Z [       OK ] ModulesTest.PrettyPrintSoftmax (0 ms)
2023-01-11T22:10:38.8740060Z [ RUN      ] ModulesTest.PrettyPrintSoftmin
2023-01-11T22:10:38.8740665Z [       OK ] ModulesTest.PrettyPrintSoftmin (0 ms)
2023-01-11T22:10:38.8741251Z [ RUN      ] ModulesTest.PrettyPrintLogSoftmax
2023-01-11T22:10:38.8741836Z [       OK ] ModulesTest.PrettyPrintLogSoftmax (0 ms)
2023-01-11T22:10:38.8742568Z [ RUN      ] ModulesTest.PrettyPrintSoftmax2d
2023-01-11T22:10:38.8742931Z [       OK ] ModulesTest.PrettyPrintSoftmax2d (0 ms)
2023-01-11T22:10:38.8743395Z [ RUN      ] ModulesTest.PrettyPrintPReLU
2023-01-11T22:10:38.8743920Z [       OK ] ModulesTest.PrettyPrintPReLU (0 ms)
2023-01-11T22:10:38.8744460Z [ RUN      ] ModulesTest.PrettyPrintReLU
2023-01-11T22:10:38.8744960Z [       OK ] ModulesTest.PrettyPrintReLU (0 ms)
2023-01-11T22:10:38.8745428Z [ RUN      ] ModulesTest.PrettyPrintReLU6
2023-01-11T22:10:38.8745954Z [       OK ] ModulesTest.PrettyPrintReLU6 (0 ms)
2023-01-11T22:10:38.8746489Z [ RUN      ] ModulesTest.PrettyPrintRReLU
2023-01-11T22:10:38.8747017Z [       OK ] ModulesTest.PrettyPrintRReLU (0 ms)
2023-01-11T22:10:38.8747416Z [ RUN      ] ModulesTest.PrettyPrintCELU
2023-01-11T22:10:38.8747833Z [       OK ] ModulesTest.PrettyPrintCELU (0 ms)
2023-01-11T22:10:38.8748144Z [ RUN      ] ModulesTest.PrettyPrintSigmoid
2023-01-11T22:10:38.8748477Z [       OK ] ModulesTest.PrettyPrintSigmoid (0 ms)
2023-01-11T22:10:38.8748818Z [ RUN      ] ModulesTest.PrettyPrintPixelShuffle
2023-01-11T22:10:38.8749165Z [       OK ] ModulesTest.PrettyPrintPixelShuffle (0 ms)
2023-01-11T22:10:38.8749527Z [ RUN      ] ModulesTest.PrettyPrintPixelUnshuffle
2023-01-11T22:10:38.8749896Z [       OK ] ModulesTest.PrettyPrintPixelUnshuffle (0 ms)
2023-01-11T22:10:38.8750232Z [ RUN      ] ModulesTest.PrettyPrintSoftplus
2023-01-11T22:10:38.8750685Z [       OK ] ModulesTest.PrettyPrintSoftplus (0 ms)
2023-01-11T22:10:38.8751023Z [ RUN      ] ModulesTest.PrettyPrintSoftshrink
2023-01-11T22:10:38.8751372Z [       OK ] ModulesTest.PrettyPrintSoftshrink (0 ms)
2023-01-11T22:10:38.8751693Z [ RUN      ] ModulesTest.PrettyPrintSoftsign
2023-01-11T22:10:38.8752031Z [       OK ] ModulesTest.PrettyPrintSoftsign (0 ms)
2023-01-11T22:10:38.8752348Z [ RUN      ] ModulesTest.PrettyPrintTanh
2023-01-11T22:10:38.8752694Z [       OK ] ModulesTest.PrettyPrintTanh (0 ms)
2023-01-11T22:10:38.8753010Z [ RUN      ] ModulesTest.PrettyPrintTanhshrink
2023-01-11T22:10:38.8753358Z [       OK ] ModulesTest.PrettyPrintTanhshrink (0 ms)
2023-01-11T22:10:38.8753694Z [ RUN      ] ModulesTest.PrettyPrintThreshold
2023-01-11T22:10:38.8754025Z [       OK ] ModulesTest.PrettyPrintThreshold (0 ms)
2023-01-11T22:10:38.8754356Z [ RUN      ] ModulesTest.PrettyPrintCTCLoss
2023-01-11T22:10:38.8754744Z [       OK ] ModulesTest.PrettyPrintCTCLoss (0 ms)
2023-01-11T22:10:38.8755082Z [ RUN      ] ModulesTest.PrettyPrintPoissonNLLLoss
2023-01-11T22:10:38.8755452Z [       OK ] ModulesTest.PrettyPrintPoissonNLLLoss (0 ms)
2023-01-11T22:10:38.8755828Z [ RUN      ] ModulesTest.PrettyPrintMarginRankingLoss
2023-01-11T22:10:38.8756218Z [       OK ] ModulesTest.PrettyPrintMarginRankingLoss (0 ms)
2023-01-11T22:10:38.8756579Z [ RUN      ] ModulesTest.PrettyPrintCrossMapLRN2d
2023-01-11T22:10:38.8756948Z [       OK ] ModulesTest.PrettyPrintCrossMapLRN2d (0 ms)
2023-01-11T22:10:38.8757303Z [ RUN      ] ModulesTest.PrettyPrintAlphaDropout
2023-01-11T22:10:38.8757649Z [       OK ] ModulesTest.PrettyPrintAlphaDropout (0 ms)
2023-01-11T22:10:38.8758029Z [ RUN      ] ModulesTest.PrettyPrintFeatureAlphaDropout
2023-01-11T22:10:38.8758430Z [       OK ] ModulesTest.PrettyPrintFeatureAlphaDropout (0 ms)
2023-01-11T22:10:38.8758813Z [ RUN      ] ModulesTest.PrettyPrintBCEWithLogitsLoss
2023-01-11T22:10:38.8759198Z [       OK ] ModulesTest.PrettyPrintBCEWithLogitsLoss (0 ms)
2023-01-11T22:10:38.8759649Z [ RUN      ] ModulesTest.PrettyPrintMultiheadAttention
2023-01-11T22:10:38.8760047Z [       OK ] ModulesTest.PrettyPrintMultiheadAttention (0 ms)
2023-01-11T22:10:38.8760389Z [ RUN      ] ModulesTest.PrettyPrintRNNCell
2023-01-11T22:10:38.8760721Z [       OK ] ModulesTest.PrettyPrintRNNCell (0 ms)
2023-01-11T22:10:38.8761048Z [ RUN      ] ModulesTest.PrettyPrintLSTMCell
2023-01-11T22:10:38.8761376Z [       OK ] ModulesTest.PrettyPrintLSTMCell (0 ms)
2023-01-11T22:10:38.8761701Z [ RUN      ] ModulesTest.PrettyPrintGRUCell
2023-01-11T22:10:38.8762032Z [       OK ] ModulesTest.PrettyPrintGRUCell (0 ms)
2023-01-11T22:10:38.8762407Z [ RUN      ] ModulesTest.PrettyPrintAdaptiveLogSoftmaxWithLoss
2023-01-11T22:10:38.8762860Z [       OK ] ModulesTest.PrettyPrintAdaptiveLogSoftmaxWithLoss (0 ms)
2023-01-11T22:10:38.8763258Z [----------] 256 tests from ModulesTest (9684 ms total)
2023-01-11T22:10:38.8763414Z 
2023-01-11T22:10:38.8763557Z [----------] 1 test from NestedTest
2023-01-11T22:10:38.8763804Z [ RUN      ] NestedTest.Nested
2023-01-11T22:10:38.8764158Z [W NestedTensorImpl.cpp:179] Warning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (function operator())
2023-01-11T22:10:38.8764552Z [       OK ] NestedTest.Nested (0 ms)
2023-01-11T22:10:38.8764835Z [----------] 1 test from NestedTest (0 ms total)
2023-01-11T22:10:38.8764981Z 
2023-01-11T22:10:38.8765141Z [----------] 10 tests from ParameterDictTest
2023-01-11T22:10:38.8765470Z [ RUN      ] ParameterDictTest.ConstructFromTensor
2023-01-11T22:10:38.8765835Z [       OK ] ParameterDictTest.ConstructFromTensor (0 ms)
2023-01-11T22:10:38.8766261Z [ RUN      ] ParameterDictTest.ConstructFromOrderedDict
2023-01-11T22:10:38.8766661Z [       OK ] ParameterDictTest.ConstructFromOrderedDict (0 ms)
2023-01-11T22:10:38.8767029Z [ RUN      ] ParameterDictTest.InsertAndContains
2023-01-11T22:10:38.8767371Z [       OK ] ParameterDictTest.InsertAndContains (0 ms)
2023-01-11T22:10:38.8767712Z [ RUN      ] ParameterDictTest.InsertAndClear
2023-01-11T22:10:38.8768053Z [       OK ] ParameterDictTest.InsertAndClear (0 ms)
2023-01-11T22:10:38.8772362Z [ RUN      ] ParameterDictTest.InsertAndPop
2023-01-11T22:10:38.8773148Z [       OK ] ParameterDictTest.InsertAndPop (1 ms)
2023-01-11T22:10:38.8773857Z [ RUN      ] ParameterDictTest.SimpleUpdate
2023-01-11T22:10:38.8774568Z [       OK ] ParameterDictTest.SimpleUpdate (1 ms)
2023-01-11T22:10:38.8775191Z [ RUN      ] ParameterDictTest.Keys
2023-01-11T22:10:38.8775950Z [       OK ] ParameterDictTest.Keys (0 ms)
2023-01-11T22:10:38.8776585Z [ RUN      ] ParameterDictTest.Values
2023-01-11T22:10:38.8777204Z [       OK ] ParameterDictTest.Values (0 ms)
2023-01-11T22:10:38.8777809Z [ RUN      ] ParameterDictTest.Get
2023-01-11T22:10:38.8778418Z [       OK ] ParameterDictTest.Get (0 ms)
2023-01-11T22:10:38.8779161Z [ RUN      ] ParameterDictTest.PrettyPrintParameterDict
2023-01-11T22:10:38.8780011Z [       OK ] ParameterDictTest.PrettyPrintParameterDict (0 ms)
2023-01-11T22:10:38.8780830Z [----------] 10 tests from ParameterDictTest (2 ms total)
2023-01-11T22:10:38.8781175Z 
2023-01-11T22:10:38.8781520Z [----------] 8 tests from ParameterListTest
2023-01-11T22:10:38.8782268Z [ RUN      ] ParameterListTest.ConstructsFromSharedPointer
2023-01-11T22:10:38.8783259Z [       OK ] ParameterListTest.ConstructsFromSharedPointer (0 ms)
2023-01-11T22:10:38.8783760Z [ RUN      ] ParameterListTest.isEmpty
2023-01-11T22:10:38.8784157Z [       OK ] ParameterListTest.isEmpty (0 ms)
2023-01-11T22:10:38.8784617Z [ RUN      ] ParameterListTest.PushBackAddsAnElement
2023-01-11T22:10:38.8785068Z [       OK ] ParameterListTest.PushBackAddsAnElement (0 ms)
2023-01-11T22:10:38.8785573Z [ RUN      ] ParameterListTest.ForEachLoop
2023-01-11T22:10:38.8785932Z [       OK ] ParameterListTest.ForEachLoop (0 ms)
2023-01-11T22:10:38.8786369Z [ RUN      ] ParameterListTest.AccessWithAt
2023-01-11T22:10:38.8786921Z [       OK ] ParameterListTest.AccessWithAt (3 ms)
2023-01-11T22:10:38.8787613Z [ RUN      ] ParameterListTest.ExtendPushesParametersFromOtherParameterList
2023-01-11T22:10:38.8788573Z [       OK ] ParameterListTest.ExtendPushesParametersFromOtherParameterList (0 ms)
2023-01-11T22:10:38.8789431Z [ RUN      ] ParameterListTest.PrettyPrintParameterList
2023-01-11T22:10:38.8790120Z [       OK ] ParameterListTest.PrettyPrintParameterList (0 ms)
2023-01-11T22:10:38.8790474Z [ RUN      ] ParameterListTest.IncrementAdd
2023-01-11T22:10:38.8790816Z [       OK ] ParameterListTest.IncrementAdd (0 ms)
2023-01-11T22:10:38.8791167Z [----------] 8 tests from ParameterListTest (4 ms total)
2023-01-11T22:10:38.8791334Z 
2023-01-11T22:10:38.8791489Z [----------] 1 test from NamespaceTests
2023-01-11T22:10:38.8791874Z [ RUN      ] NamespaceTests.NotLeakingSymbolsFromTorchAutogradNamespace
2023-01-11T22:10:38.8792385Z [       OK ] NamespaceTests.NotLeakingSymbolsFromTorchAutogradNamespace (0 ms)
2023-01-11T22:10:38.8792801Z [----------] 1 test from NamespaceTests (0 ms total)
2023-01-11T22:10:38.8792954Z 
2023-01-11T22:10:38.8793089Z [----------] 7 tests from NNUtilsTest
2023-01-11T22:10:38.8793371Z [ RUN      ] NNUtilsTest.ClipGradNorm
2023-01-11T22:10:38.8803347Z [       OK ] NNUtilsTest.ClipGradNorm (1 ms)
2023-01-11T22:10:38.8803833Z [ RUN      ] NNUtilsTest.ClipGradNormErrorIfNonfinite
2023-01-11T22:10:39.0594740Z [       OK ] NNUtilsTest.ClipGradNormErrorIfNonfinite (178 ms)
2023-01-11T22:10:39.0595213Z [ RUN      ] NNUtilsTest.ClipGradValue
2023-01-11T22:10:39.0595714Z [       OK ] NNUtilsTest.ClipGradValue (0 ms)
2023-01-11T22:10:39.0596071Z [ RUN      ] NNUtilsTest.ConvertParameters
2023-01-11T22:10:39.0599814Z [       OK ] NNUtilsTest.ConvertParameters (0 ms)
2023-01-11T22:10:39.0600232Z [ RUN      ] NNUtilsTest.PackSequence
2023-01-11T22:10:39.0997288Z [       OK ] NNUtilsTest.PackSequence (39 ms)
2023-01-11T22:10:39.0997688Z [ RUN      ] NNUtilsTest.PackPaddedSequence
2023-01-11T22:10:39.1155067Z [       OK ] NNUtilsTest.PackPaddedSequence (15 ms)
2023-01-11T22:10:39.1155666Z [ RUN      ] NNUtilsTest.PadSequence
2023-01-11T22:10:39.1219040Z [       OK ] NNUtilsTest.PadSequence (6 ms)
2023-01-11T22:10:39.1220066Z [----------] 7 tests from NNUtilsTest (243 ms total)
2023-01-11T22:10:39.1220344Z 
2023-01-11T22:10:39.1220651Z [----------] 3 tests from PackedSequenceTest
2023-01-11T22:10:39.1221179Z [ RUN      ] PackedSequenceTest.WrongOrder
2023-01-11T22:10:39.1259243Z [       OK ] PackedSequenceTest.WrongOrder (4 ms)
2023-01-11T22:10:39.1259567Z [ RUN      ] PackedSequenceTest.TotalLength
2023-01-11T22:10:39.1334633Z [       OK ] PackedSequenceTest.TotalLength (7 ms)
2023-01-11T22:10:39.1335026Z [ RUN      ] PackedSequenceTest.To
2023-01-11T22:10:39.1336649Z [       OK ] PackedSequenceTest.To (0 ms)
2023-01-11T22:10:39.1337069Z [----------] 3 tests from PackedSequenceTest (11 ms total)
2023-01-11T22:10:39.1337269Z 
2023-01-11T22:10:39.1337419Z [----------] 34 tests from OptimTest
2023-01-11T22:10:39.1337712Z [ RUN      ] OptimTest.OptimizerAccessors
2023-01-11T22:10:39.1366034Z [       OK ] OptimTest.OptimizerAccessors (2 ms)
2023-01-11T22:10:39.1366606Z [ RUN      ] OptimTest.OldInterface
2023-01-11T22:10:39.1367372Z [       OK ] OptimTest.OldInterface (0 ms)
2023-01-11T22:10:39.1367892Z [ RUN      ] OptimTest.XORConvergence_SGD
2023-01-11T22:10:40.5780466Z [       OK ] OptimTest.XORConvergence_SGD (1441 ms)
2023-01-11T22:10:40.5780852Z [ RUN      ] OptimTest.XORConvergence_LBFGS
2023-01-11T22:10:41.5842737Z [       OK ] OptimTest.XORConvergence_LBFGS (1006 ms)
2023-01-11T22:10:41.5843263Z [ RUN      ] OptimTest.XORConvergence_Adagrad
2023-01-11T22:10:42.1359307Z [       OK ] OptimTest.XORConvergence_Adagrad (551 ms)
2023-01-11T22:10:42.1359859Z [ RUN      ] OptimTest.XORConvergence_RMSprop
2023-01-11T22:10:42.6890219Z [       OK ] OptimTest.XORConvergence_RMSprop (552 ms)
2023-01-11T22:10:42.6890765Z [ RUN      ] OptimTest.XORConvergence_RMSpropWithMomentum
2023-01-11T22:10:44.2402189Z [       OK ] OptimTest.XORConvergence_RMSpropWithMomentum (1551 ms)
2023-01-11T22:10:44.2402582Z [ RUN      ] OptimTest.XORConvergence_Adam
2023-01-11T22:10:44.8488501Z [       OK ] OptimTest.XORConvergence_Adam (608 ms)
2023-01-11T22:10:44.8489101Z [ RUN      ] OptimTest.XORConvergence_AdamWithAmsgrad
2023-01-11T22:10:45.4596139Z [       OK ] OptimTest.XORConvergence_AdamWithAmsgrad (610 ms)
2023-01-11T22:10:45.4596842Z [ RUN      ] OptimTest.ProducesPyTorchValues_Adam
2023-01-11T22:10:45.6549689Z [       OK ] OptimTest.ProducesPyTorchValues_Adam (195 ms)
2023-01-11T22:10:45.6550158Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecay
2023-01-11T22:10:45.8557874Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecay (200 ms)
2023-01-11T22:10:45.8558361Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecayAndAMSGrad
2023-01-11T22:10:46.0626586Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecayAndAMSGrad (206 ms)
2023-01-11T22:10:46.0627310Z [ RUN      ] OptimTest.XORConvergence_AdamW
2023-01-11T22:10:46.6760618Z [       OK ] OptimTest.XORConvergence_AdamW (613 ms)
2023-01-11T22:10:46.6761068Z [ RUN      ] OptimTest.XORConvergence_AdamWWithAmsgrad
2023-01-11T22:10:47.2891173Z [       OK ] OptimTest.XORConvergence_AdamWWithAmsgrad (613 ms)
2023-01-11T22:10:47.2891609Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamW
2023-01-11T22:10:47.4904158Z [       OK ] OptimTest.ProducesPyTorchValues_AdamW (201 ms)
2023-01-11T22:10:47.4904728Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWWithoutWeightDecay
2023-01-11T22:10:47.6861174Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWWithoutWeightDecay (195 ms)
2023-01-11T22:10:47.6861630Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWWithAMSGrad
2023-01-11T22:10:47.8932307Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWWithAMSGrad (207 ms)
2023-01-11T22:10:47.8932769Z [ RUN      ] OptimTest.ProducesPyTorchValues_Adagrad
2023-01-11T22:10:48.0560780Z [       OK ] OptimTest.ProducesPyTorchValues_Adagrad (162 ms)
2023-01-11T22:10:48.0561295Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecay
2023-01-11T22:10:48.2250395Z [       OK ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecay (168 ms)
2023-01-11T22:10:48.2250904Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecayAndLRDecay
2023-01-11T22:10:48.3946090Z [       OK ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecayAndLRDecay (169 ms)
2023-01-11T22:10:48.3946551Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSprop
2023-01-11T22:10:48.5655689Z [       OK ] OptimTest.ProducesPyTorchValues_RMSprop (170 ms)
2023-01-11T22:10:48.5656220Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecay
2023-01-11T22:10:48.7428774Z [       OK ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecay (177 ms)
2023-01-11T22:10:48.7429338Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCentered
2023-01-11T22:10:48.9369405Z [       OK ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCentered (194 ms)
2023-01-11T22:10:48.9369983Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCenteredAndMomentum
2023-01-11T22:10:49.1402109Z [       OK ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCenteredAndMomentum (203 ms)
2023-01-11T22:10:49.1402583Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGD
2023-01-11T22:10:49.2724424Z [       OK ] OptimTest.ProducesPyTorchValues_SGD (132 ms)
2023-01-11T22:10:49.2725066Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecay
2023-01-11T22:10:49.4141976Z [       OK ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecay (141 ms)
2023-01-11T22:10:49.4142665Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndMomentum
2023-01-11T22:10:49.5733296Z [       OK ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndMomentum (159 ms)
2023-01-11T22:10:49.5733854Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndNesterovMomentum
2023-01-11T22:10:49.7373604Z [       OK ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndNesterovMomentum (164 ms)
2023-01-11T22:10:49.7374094Z [ RUN      ] OptimTest.ProducesPyTorchValues_LBFGS
2023-01-11T22:10:49.8885937Z [       OK ] OptimTest.ProducesPyTorchValues_LBFGS (151 ms)
2023-01-11T22:10:49.8886502Z [ RUN      ] OptimTest.ProducesPyTorchValues_LBFGS_with_line_search
2023-01-11T22:10:50.5667791Z [       OK ] OptimTest.ProducesPyTorchValues_LBFGS_with_line_search (678 ms)
2023-01-11T22:10:50.5668243Z [ RUN      ] OptimTest.ZeroGrad
2023-01-11T22:10:50.5668545Z [       OK ] OptimTest.ZeroGrad (0 ms)
2023-01-11T22:10:50.5669277Z [ RUN      ] OptimTest.ExternalVectorOfParameters
2023-01-11T22:10:50.5669839Z [       OK ] OptimTest.ExternalVectorOfParameters (0 ms)
2023-01-11T22:10:50.5670281Z [ RUN      ] OptimTest.AddParameter_LBFGS
2023-01-11T22:10:50.5672034Z [       OK ] OptimTest.AddParameter_LBFGS (0 ms)
2023-01-11T22:10:50.5672420Z [ RUN      ] OptimTest.CheckLRChange_StepLR_Adam
2023-01-11T22:10:50.5672805Z [       OK ] OptimTest.CheckLRChange_StepLR_Adam (0 ms)
2023-01-11T22:10:50.5673151Z [----------] 34 tests from OptimTest (11433 ms total)
2023-01-11T22:10:50.5673360Z 
2023-01-11T22:10:50.5673527Z [----------] 29 tests from OrderedDictTest
2023-01-11T22:10:50.5673880Z [ RUN      ] OrderedDictTest.IsEmptyAfterDefaultConstruction
2023-01-11T22:10:50.5674379Z [       OK ] OrderedDictTest.IsEmptyAfterDefaultConstruction (0 ms)
2023-01-11T22:10:50.5675009Z [ RUN      ] OrderedDictTest.InsertAddsElementsWhenTheyAreYetNotPresent
2023-01-11T22:10:50.5675540Z [       OK ] OrderedDictTest.InsertAddsElementsWhenTheyAreYetNotPresent (0 ms)
2023-01-11T22:10:50.5676063Z [ RUN      ] OrderedDictTest.GetReturnsValuesWhenTheyArePresent
2023-01-11T22:10:50.5676574Z [       OK ] OrderedDictTest.GetReturnsValuesWhenTheyArePresent (0 ms)
2023-01-11T22:10:50.5677047Z [ RUN      ] OrderedDictTest.GetThrowsWhenPassedKeysThatAreNotPresent
2023-01-11T22:10:50.5699752Z [       OK ] OrderedDictTest.GetThrowsWhenPassedKeysThatAreNotPresent (2 ms)
2023-01-11T22:10:50.5700175Z [ RUN      ] OrderedDictTest.CanInitializeFromList
2023-01-11T22:10:50.5700553Z [       OK ] OrderedDictTest.CanInitializeFromList (0 ms)
2023-01-11T22:10:50.5701006Z [ RUN      ] OrderedDictTest.InsertThrowsWhenPassedElementsThatArePresent
2023-01-11T22:10:50.5723506Z [       OK ] OrderedDictTest.InsertThrowsWhenPassedElementsThatArePresent (2 ms)
2023-01-11T22:10:50.5724007Z [ RUN      ] OrderedDictTest.FrontReturnsTheFirstItem
2023-01-11T22:10:50.5724403Z [       OK ] OrderedDictTest.FrontReturnsTheFirstItem (0 ms)
2023-01-11T22:10:50.5724775Z [ RUN      ] OrderedDictTest.FrontThrowsWhenEmpty
2023-01-11T22:10:50.5734016Z [       OK ] OrderedDictTest.FrontThrowsWhenEmpty (1 ms)
2023-01-11T22:10:50.5734459Z [ RUN      ] OrderedDictTest.BackReturnsTheLastItem
2023-01-11T22:10:50.5734843Z [       OK ] OrderedDictTest.BackReturnsTheLastItem (0 ms)
2023-01-11T22:10:50.5735195Z [ RUN      ] OrderedDictTest.BackThrowsWhenEmpty
2023-01-11T22:10:50.5744860Z [       OK ] OrderedDictTest.BackThrowsWhenEmpty (1 ms)
2023-01-11T22:10:50.5745398Z [ RUN      ] OrderedDictTest.FindReturnsPointersToValuesWhenPresent
2023-01-11T22:10:50.5745899Z [       OK ] OrderedDictTest.FindReturnsPointersToValuesWhenPresent (0 ms)
2023-01-11T22:10:50.5746431Z [ RUN      ] OrderedDictTest.FindReturnsNullPointersWhenPasesdKeysThatAreNotPresent
2023-01-11T22:10:50.5747044Z [       OK ] OrderedDictTest.FindReturnsNullPointersWhenPasesdKeysThatAreNotPresent (0 ms)
2023-01-11T22:10:50.5747639Z [ RUN      ] OrderedDictTest.SubscriptOperatorThrowsWhenPassedKeysThatAreNotPresent
2023-01-11T22:10:50.5748241Z [       OK ] OrderedDictTest.SubscriptOperatorThrowsWhenPassedKeysThatAreNotPresent (0 ms)
2023-01-11T22:10:50.5748845Z [ RUN      ] OrderedDictTest.SubscriptOperatorReturnsItemsPositionallyWhenPassedIntegers
2023-01-11T22:10:50.5749493Z [       OK ] OrderedDictTest.SubscriptOperatorReturnsItemsPositionallyWhenPassedIntegers (0 ms)
2023-01-11T22:10:50.5750114Z [ RUN      ] OrderedDictTest.SubscriptOperatorsThrowswhenPassedKeysThatAreNotPresent
2023-01-11T22:10:50.5768519Z [       OK ] OrderedDictTest.SubscriptOperatorsThrowswhenPassedKeysThatAreNotPresent (2 ms)
2023-01-11T22:10:50.5769140Z [ RUN      ] OrderedDictTest.UpdateInsertsAllItemsFromAnotherOrderedDict
2023-01-11T22:10:50.5769812Z [       OK ] OrderedDictTest.UpdateInsertsAllItemsFromAnotherOrderedDict (0 ms)
2023-01-11T22:10:50.5770277Z [ RUN      ] OrderedDictTest.UpdateAlsoChecksForDuplicates
2023-01-11T22:10:50.5780275Z [       OK ] OrderedDictTest.UpdateAlsoChecksForDuplicates (1 ms)
2023-01-11T22:10:50.5780931Z [ RUN      ] OrderedDictTest.CanIterateItems
2023-01-11T22:10:50.5781563Z [       OK ] OrderedDictTest.CanIterateItems (0 ms)
2023-01-11T22:10:50.5782139Z [ RUN      ] OrderedDictTest.EraseWorks
2023-01-11T22:10:50.5782816Z [       OK ] OrderedDictTest.EraseWorks (0 ms)
2023-01-11T22:10:50.5783165Z [ RUN      ] OrderedDictTest.ClearMakesTheDictEmpty
2023-01-11T22:10:50.5783546Z [       OK ] OrderedDictTest.ClearMakesTheDictEmpty (0 ms)
2023-01-11T22:10:50.5783890Z [ RUN      ] OrderedDictTest.CanCopyConstruct
2023-01-11T22:10:50.5784336Z [       OK ] OrderedDictTest.CanCopyConstruct (0 ms)
2023-01-11T22:10:50.5784668Z [ RUN      ] OrderedDictTest.CanCopyAssign
2023-01-11T22:10:50.5784985Z [       OK ] OrderedDictTest.CanCopyAssign (0 ms)
2023-01-11T22:10:50.5785317Z [ RUN      ] OrderedDictTest.CanMoveConstruct
2023-01-11T22:10:50.5785663Z [       OK ] OrderedDictTest.CanMoveConstruct (0 ms)
2023-01-11T22:10:50.5785987Z [ RUN      ] OrderedDictTest.CanMoveAssign
2023-01-11T22:10:50.5786303Z [       OK ] OrderedDictTest.CanMoveAssign (0 ms)
2023-01-11T22:10:50.5786642Z [ RUN      ] OrderedDictTest.CanInsertWithBraces
2023-01-11T22:10:50.5787001Z [       OK ] OrderedDictTest.CanInsertWithBraces (0 ms)
2023-01-11T22:10:50.5787402Z [ RUN      ] OrderedDictTest.ErrorMessagesIncludeTheKeyDescription
2023-01-11T22:10:50.5805088Z [       OK ] OrderedDictTest.ErrorMessagesIncludeTheKeyDescription (2 ms)
2023-01-11T22:10:50.5805681Z [ RUN      ] OrderedDictTest.KeysReturnsAllKeys
2023-01-11T22:10:50.5806067Z [       OK ] OrderedDictTest.KeysReturnsAllKeys (0 ms)
2023-01-11T22:10:50.5806419Z [ RUN      ] OrderedDictTest.ValuesReturnsAllValues
2023-01-11T22:10:50.5806796Z [       OK ] OrderedDictTest.ValuesReturnsAllValues (0 ms)
2023-01-11T22:10:50.5807165Z [ RUN      ] OrderedDictTest.ItemsReturnsAllItems
2023-01-11T22:10:50.5807515Z [       OK ] OrderedDictTest.ItemsReturnsAllItems (0 ms)
2023-01-11T22:10:50.5807876Z [----------] 29 tests from OrderedDictTest (13 ms total)
2023-01-11T22:10:50.5808036Z 
2023-01-11T22:10:50.5808177Z [----------] 13 tests from RNNTest
2023-01-11T22:10:50.5808461Z [ RUN      ] RNNTest.CheckOutputSizes
2023-01-11T22:10:50.5886656Z [       OK ] RNNTest.CheckOutputSizes (8 ms)
2023-01-11T22:10:50.5887123Z [ RUN      ] RNNTest.CheckOutputSizesProj
2023-01-11T22:10:50.5965270Z [       OK ] RNNTest.CheckOutputSizesProj (7 ms)
2023-01-11T22:10:50.5965828Z [ RUN      ] RNNTest.CheckOutputValuesMatchPyTorch
2023-01-11T22:10:50.5969751Z [       OK ] RNNTest.CheckOutputValuesMatchPyTorch (0 ms)
2023-01-11T22:10:50.5970328Z [ RUN      ] RNNTest.EndToEndLSTM
2023-01-11T22:10:52.5403493Z [       OK ] RNNTest.EndToEndLSTM (1943 ms)
2023-01-11T22:10:52.5404202Z [ RUN      ] RNNTest.EndToEndLSTMProj
2023-01-11T22:10:54.4102791Z [       OK ] RNNTest.EndToEndLSTMProj (1869 ms)
2023-01-11T22:10:54.4103190Z [ RUN      ] RNNTest.EndToEndGRU
2023-01-11T22:10:56.0850753Z [       OK ] RNNTest.EndToEndGRU (1674 ms)
2023-01-11T22:10:56.0851362Z [ RUN      ] RNNTest.EndToEndRNNRelu
2023-01-11T22:10:56.9697611Z [       OK ] RNNTest.EndToEndRNNRelu (884 ms)
2023-01-11T22:10:56.9698377Z [ RUN      ] RNNTest.EndToEndRNNTanh
2023-01-11T22:10:57.9568840Z [       OK ] RNNTest.EndToEndRNNTanh (987 ms)
2023-01-11T22:10:57.9569776Z [ RUN      ] RNNTest.PrettyPrintRNNs
2023-01-11T22:10:57.9592544Z [       OK ] RNNTest.PrettyPrintRNNs (2 ms)
2023-01-11T22:10:57.9593161Z [ RUN      ] RNNTest.BidirectionalFlattenParameters
2023-01-11T22:10:57.9704534Z [       OK ] RNNTest.BidirectionalFlattenParameters (11 ms)
2023-01-11T22:10:57.9705137Z [ RUN      ] RNNTest.BidirectionalGRUReverseForward
2023-01-11T22:10:57.9718135Z [       OK ] RNNTest.BidirectionalGRUReverseForward (1 ms)
2023-01-11T22:10:57.9718893Z [ RUN      ] RNNTest.BidirectionalLSTMReverseForward
2023-01-11T22:10:57.9730199Z [       OK ] RNNTest.BidirectionalLSTMReverseForward (1 ms)
2023-01-11T22:10:57.9730599Z [ RUN      ] RNNTest.UsePackedSequenceAsInput
2023-01-11T22:10:57.9748061Z [       OK ] RNNTest.UsePackedSequenceAsInput (1 ms)
2023-01-11T22:10:57.9748692Z [----------] 13 tests from RNNTest (7394 ms total)
2023-01-11T22:10:57.9748978Z 
2023-01-11T22:10:57.9749566Z [----------] 19 tests from SequentialTest
2023-01-11T22:10:57.9750106Z [ RUN      ] SequentialTest.CanContainThings
2023-01-11T22:10:57.9750689Z [       OK ] SequentialTest.CanContainThings (0 ms)
2023-01-11T22:10:57.9751123Z [ RUN      ] SequentialTest.ConstructsFromSharedPointer
2023-01-11T22:10:57.9751576Z [       OK ] SequentialTest.ConstructsFromSharedPointer (0 ms)
2023-01-11T22:10:57.9752017Z [ RUN      ] SequentialTest.ConstructsFromConcreteType
2023-01-11T22:10:57.9752474Z [       OK ] SequentialTest.ConstructsFromConcreteType (0 ms)
2023-01-11T22:10:57.9752861Z [ RUN      ] SequentialTest.ConstructsFromModuleHolder
2023-01-11T22:10:57.9753310Z [       OK ] SequentialTest.ConstructsFromModuleHolder (0 ms)
2023-01-11T22:10:57.9753668Z [ RUN      ] SequentialTest.PushBackAddsAnElement
2023-01-11T22:10:57.9754094Z [       OK ] SequentialTest.PushBackAddsAnElement (0 ms)
2023-01-11T22:10:57.9754430Z [ RUN      ] SequentialTest.AccessWithAt
2023-01-11T22:10:57.9783951Z [       OK ] SequentialTest.AccessWithAt (3 ms)
2023-01-11T22:10:57.9784295Z [ RUN      ] SequentialTest.AccessWithPtr
2023-01-11T22:10:57.9807043Z [       OK ] SequentialTest.AccessWithPtr (2 ms)
2023-01-11T22:10:57.9807492Z [ RUN      ] SequentialTest.CallingForwardOnEmptySequentialIsDisallowed
2023-01-11T22:10:57.9819117Z [       OK ] SequentialTest.CallingForwardOnEmptySequentialIsDisallowed (1 ms)
2023-01-11T22:10:57.9819580Z [ RUN      ] SequentialTest.CallingForwardChainsCorrectly
2023-01-11T22:10:57.9819996Z [       OK ] SequentialTest.CallingForwardChainsCorrectly (0 ms)
2023-01-11T22:10:57.9820440Z [ RUN      ] SequentialTest.CallingForwardWithTheWrongReturnTypeThrows
2023-01-11T22:10:57.9830051Z [       OK ] SequentialTest.CallingForwardWithTheWrongReturnTypeThrows (1 ms)
2023-01-11T22:10:57.9830551Z [ RUN      ] SequentialTest.TheReturnTypeOfForwardDefaultsToTensor
2023-01-11T22:10:57.9831035Z [       OK ] SequentialTest.TheReturnTypeOfForwardDefaultsToTensor (0 ms)
2023-01-11T22:10:57.9831447Z [ RUN      ] SequentialTest.ForwardReturnsTheLastValue
2023-01-11T22:10:57.9834901Z [       OK ] SequentialTest.ForwardReturnsTheLastValue (0 ms)
2023-01-11T22:10:57.9835554Z [ RUN      ] SequentialTest.SanityCheckForHoldingStandardModules
2023-01-11T22:10:57.9836706Z [       OK ] SequentialTest.SanityCheckForHoldingStandardModules (0 ms)
2023-01-11T22:10:57.9837521Z [ RUN      ] SequentialTest.ExtendPushesModulesFromOtherSequential
2023-01-11T22:10:57.9838003Z [       OK ] SequentialTest.ExtendPushesModulesFromOtherSequential (0 ms)
2023-01-11T22:10:57.9838409Z [ RUN      ] SequentialTest.HasReferenceSemantics
2023-01-11T22:10:57.9838756Z [       OK ] SequentialTest.HasReferenceSemantics (0 ms)
2023-01-11T22:10:57.9839250Z [ RUN      ] SequentialTest.IsCloneable
2023-01-11T22:10:57.9842431Z [       OK ] SequentialTest.IsCloneable (0 ms)
2023-01-11T22:10:57.9843091Z [ RUN      ] SequentialTest.RegistersElementsAsSubmodules
2023-01-11T22:10:57.9843499Z [       OK ] SequentialTest.RegistersElementsAsSubmodules (0 ms)
2023-01-11T22:10:57.9843881Z [ RUN      ] SequentialTest.PrettyPrintSequential
2023-01-11T22:10:57.9845233Z [       OK ] SequentialTest.PrettyPrintSequential (0 ms)
2023-01-11T22:10:57.9845883Z [ RUN      ] SequentialTest.ModuleForwardMethodOptionalArg
2023-01-11T22:10:57.9877113Z [       OK ] SequentialTest.ModuleForwardMethodOptionalArg (3 ms)
2023-01-11T22:10:57.9877787Z [----------] 19 tests from SequentialTest (12 ms total)
2023-01-11T22:10:57.9877952Z 
2023-01-11T22:10:57.9878114Z [----------] 11 tests from TransformerTest
2023-01-11T22:10:57.9878435Z [ RUN      ] TransformerTest.TransformerEncoderLayer
2023-01-11T22:10:57.9966952Z [       OK ] TransformerTest.TransformerEncoderLayer (8 ms)
2023-01-11T22:10:57.9967501Z [ RUN      ] TransformerTest.TransformerDecoderLayer
2023-01-11T22:10:58.0050470Z [       OK ] TransformerTest.TransformerDecoderLayer (8 ms)
2023-01-11T22:10:58.0051047Z [ RUN      ] TransformerTest.TransformerDecoderLayer_gelu
2023-01-11T22:10:58.0098288Z [       OK ] TransformerTest.TransformerDecoderLayer_gelu (4 ms)
2023-01-11T22:10:58.0098890Z [ RUN      ] TransformerTest.TransformerEncoder
2023-01-11T22:10:58.0262573Z [       OK ] TransformerTest.TransformerEncoder (16 ms)
2023-01-11T22:10:58.0263212Z [ RUN      ] TransformerTest.PrettyPrintTransformerEncoderLayer
2023-01-11T22:10:58.0265836Z [       OK ] TransformerTest.PrettyPrintTransformerEncoderLayer (0 ms)
2023-01-11T22:10:58.0266548Z [ RUN      ] TransformerTest.PrettyPrintTransformerEncoder
2023-01-11T22:10:58.0274653Z [       OK ] TransformerTest.PrettyPrintTransformerEncoder (0 ms)
2023-01-11T22:10:58.0275303Z [ RUN      ] TransformerTest.PrettyPrintTransformerDecoderLayer
2023-01-11T22:10:58.0276912Z [       OK ] TransformerTest.PrettyPrintTransformerDecoderLayer (0 ms)
2023-01-11T22:10:58.0277585Z [ RUN      ] TransformerTest.TransformerDecoder
2023-01-11T22:10:58.0723343Z [       OK ] TransformerTest.TransformerDecoder (44 ms)
2023-01-11T22:10:58.0723827Z [ RUN      ] TransformerTest.PrettyPrintTransformerDecoder
2023-01-11T22:10:58.0733951Z [       OK ] TransformerTest.PrettyPrintTransformerDecoder (1 ms)
2023-01-11T22:10:58.0734382Z [ RUN      ] TransformerTest.Transformer
2023-01-11T22:10:58.0896556Z [       OK ] TransformerTest.Transformer (16 ms)
2023-01-11T22:10:58.0897068Z [ RUN      ] TransformerTest.TransformerArgsCorrectness
2023-01-11T22:10:58.0956301Z [       OK ] TransformerTest.TransformerArgsCorrectness (5 ms)
2023-01-11T22:10:58.0957024Z [----------] 11 tests from TransformerTest (107 ms total)
2023-01-11T22:10:58.0957196Z 
2023-01-11T22:10:58.0957351Z [----------] 23 tests from SerializeTest
2023-01-11T22:10:58.0957635Z [ RUN      ] SerializeTest.KeysFunc
2023-01-11T22:10:58.0961046Z [       OK ] SerializeTest.KeysFunc (0 ms)
2023-01-11T22:10:58.0961595Z [ RUN      ] SerializeTest.TryReadFunc
2023-01-11T22:10:58.0963687Z [       OK ] SerializeTest.TryReadFunc (0 ms)
2023-01-11T22:10:58.0964199Z [ RUN      ] SerializeTest.Basic
2023-01-11T22:10:58.0966042Z [       OK ] SerializeTest.Basic (0 ms)
2023-01-11T22:10:58.0966577Z [ RUN      ] SerializeTest.MathBits
2023-01-11T22:10:58.1041380Z [       OK ] SerializeTest.MathBits (7 ms)
2023-01-11T22:10:58.1041917Z [ RUN      ] SerializeTest.BasicToFile
2023-01-11T22:10:58.1044868Z [       OK ] SerializeTest.BasicToFile (0 ms)
2023-01-11T22:10:58.1045563Z [ RUN      ] SerializeTest.BasicViaFunc
2023-01-11T22:10:58.1048160Z [       OK ] SerializeTest.BasicViaFunc (0 ms)
2023-01-11T22:10:58.1048707Z [ RUN      ] SerializeTest.Resized
2023-01-11T22:10:58.1050330Z [       OK ] SerializeTest.Resized (0 ms)
2023-01-11T22:10:58.1050959Z [ RUN      ] SerializeTest.Sliced
2023-01-11T22:10:58.1052493Z [       OK ] SerializeTest.Sliced (0 ms)
2023-01-11T22:10:58.1053129Z [ RUN      ] SerializeTest.NonContiguous
2023-01-11T22:10:58.1054638Z [       OK ] SerializeTest.NonContiguous (0 ms)
2023-01-11T22:10:58.1055270Z [ RUN      ] SerializeTest.ErrorOnMissingKey
2023-01-11T22:10:58.1133632Z [       OK ] SerializeTest.ErrorOnMissingKey (7 ms)
2023-01-11T22:10:58.1134145Z [ RUN      ] SerializeTest.XOR
2023-01-11T22:10:58.2871051Z [       OK ] SerializeTest.XOR (173 ms)
2023-01-11T22:10:58.2871565Z [ RUN      ] SerializeTest.Optim
2023-01-11T22:10:58.2893553Z [       OK ] SerializeTest.Optim (2 ms)
2023-01-11T22:10:58.2894011Z [ RUN      ] SerializeTest.Optim_Adagrad
2023-01-11T22:10:58.2924003Z [       OK ] SerializeTest.Optim_Adagrad (3 ms)
2023-01-11T22:10:58.2924565Z [ RUN      ] SerializeTest.Optim_SGD
2023-01-11T22:10:58.2951720Z [       OK ] SerializeTest.Optim_SGD (2 ms)
2023-01-11T22:10:58.2952288Z [ RUN      ] SerializeTest.Optim_Adam
2023-01-11T22:10:58.2986647Z [       OK ] SerializeTest.Optim_Adam (3 ms)
2023-01-11T22:10:58.2987206Z [ RUN      ] SerializeTest.Optim_AdamW
2023-01-11T22:10:58.3021327Z [       OK ] SerializeTest.Optim_AdamW (3 ms)
2023-01-11T22:10:58.3021858Z [ RUN      ] SerializeTest.Optim_RMSprop
2023-01-11T22:10:58.3054880Z [       OK ] SerializeTest.Optim_RMSprop (3 ms)
2023-01-11T22:10:58.3055435Z [ RUN      ] SerializeTest.Optim_LBFGS
2023-01-11T22:10:58.3088779Z [       OK ] SerializeTest.Optim_LBFGS (3 ms)
2023-01-11T22:10:58.3089577Z [ RUN      ] SerializeTest.CanSerializeModulesWithIntermediateModulesWithoutParametersOrBuffers
2023-01-11T22:10:58.3093083Z [       OK ] SerializeTest.CanSerializeModulesWithIntermediateModulesWithoutParametersOrBuffers (0 ms)
2023-01-11T22:10:58.3093643Z [ RUN      ] SerializeTest.VectorOfTensors
2023-01-11T22:10:58.3095475Z [       OK ] SerializeTest.VectorOfTensors (0 ms)
2023-01-11T22:10:58.3096035Z [ RUN      ] SerializeTest.IValue
2023-01-11T22:10:58.3098337Z [       OK ] SerializeTest.IValue (0 ms)
2023-01-11T22:10:58.3099030Z [ RUN      ] SerializeTest.UnserializableSubmoduleIsSkippedWhenSavingModule
2023-01-11T22:10:58.3099896Z [       OK ] SerializeTest.UnserializableSubmoduleIsSkippedWhenSavingModule (0 ms)
2023-01-11T22:10:58.3100575Z [ RUN      ] SerializeTest.UnserializableSubmoduleIsIgnoredWhenLoadingModule
2023-01-11T22:10:58.3106022Z [       OK ] SerializeTest.UnserializableSubmoduleIsIgnoredWhenLoadingModule (0 ms)
2023-01-11T22:10:58.3106767Z [----------] 23 tests from SerializeTest (214 ms total)
2023-01-11T22:10:58.3107056Z 
2023-01-11T22:10:58.3107296Z [----------] 1 test from SpecialTest
2023-01-11T22:10:58.3107759Z [ RUN      ] SpecialTest.special
2023-01-11T22:10:58.3108285Z [       OK ] SpecialTest.special (0 ms)
2023-01-11T22:10:58.3108669Z [----------] 1 test from SpecialTest (0 ms total)
2023-01-11T22:10:58.3108823Z 
2023-01-11T22:10:58.3108966Z [----------] 5 tests from TestStatic
2023-01-11T22:10:58.3109226Z [ RUN      ] TestStatic.AllOf
2023-01-11T22:10:58.3109495Z [       OK ] TestStatic.AllOf (0 ms)
2023-01-11T22:10:58.3109740Z [ RUN      ] TestStatic.AnyOf
2023-01-11T22:10:58.3110014Z [       OK ] TestStatic.AnyOf (0 ms)
2023-01-11T22:10:58.3110298Z [ RUN      ] TestStatic.EnableIfModule
2023-01-11T22:10:58.3110595Z [       OK ] TestStatic.EnableIfModule (0 ms)
2023-01-11T22:10:58.3111078Z [ RUN      ] TestStatic.ReturnTypeOfForward
2023-01-11T22:10:58.3111413Z [       OK ] TestStatic.ReturnTypeOfForward (0 ms)
2023-01-11T22:10:58.3111684Z [ RUN      ] TestStatic.Apply
2023-01-11T22:10:58.3111954Z [       OK ] TestStatic.Apply (0 ms)
2023-01-11T22:10:58.3112254Z [----------] 5 tests from TestStatic (0 ms total)
2023-01-11T22:10:58.3112403Z 
2023-01-11T22:10:58.3112548Z [----------] 45 tests from TensorTest
2023-01-11T22:10:58.3112801Z [ RUN      ] TensorTest.ToDtype
2023-01-11T22:10:58.3119032Z [       OK ] TensorTest.ToDtype (1 ms)
2023-01-11T22:10:58.3119648Z [ RUN      ] TensorTest.ToTensorAndTensorAttributes
2023-01-11T22:10:58.3120138Z [       OK ] TensorTest.ToTensorAndTensorAttributes (0 ms)
2023-01-11T22:10:58.3120514Z [ RUN      ] TensorTest.ToOptionsWithRequiresGrad
2023-01-11T22:10:58.3143619Z [       OK ] TensorTest.ToOptionsWithRequiresGrad (2 ms)
2023-01-11T22:10:58.3144359Z [ RUN      ] TensorTest.ToDoesNotCopyWhenOptionsAreAllTheSame
2023-01-11T22:10:58.3145153Z [       OK ] TensorTest.ToDoesNotCopyWhenOptionsAreAllTheSame (0 ms)
2023-01-11T22:10:58.3145694Z [ RUN      ] TensorTest.AtTensorCtorScalar
2023-01-11T22:10:58.3146288Z [       OK ] TensorTest.AtTensorCtorScalar (0 ms)
2023-01-11T22:10:58.3146726Z [ RUN      ] TensorTest.AtTensorCtorSingleDim
2023-01-11T22:10:58.3147078Z [       OK ] TensorTest.AtTensorCtorSingleDim (0 ms)
2023-01-11T22:10:58.3147423Z [ RUN      ] TensorTest.AtTensorCastRealToComplex
2023-01-11T22:10:58.3147794Z [       OK ] TensorTest.AtTensorCastRealToComplex (0 ms)
2023-01-11T22:10:58.3148180Z [ RUN      ] TensorTest.AtTensorCastComplexToRealErrorChecks
2023-01-11T22:10:58.3200268Z [       OK ] TensorTest.AtTensorCastComplexToRealErrorChecks (5 ms)
2023-01-11T22:10:58.3200873Z [ RUN      ] TensorTest.TorchTensorCtorScalarIntegralType
2023-01-11T22:10:58.3201284Z [       OK ] TensorTest.TorchTensorCtorScalarIntegralType (0 ms)
2023-01-11T22:10:58.3201696Z [ RUN      ] TensorTest.TorchTensorCtorScalarFloatingType
2023-01-11T22:10:58.3202105Z [       OK ] TensorTest.TorchTensorCtorScalarFloatingType (0 ms)
2023-01-11T22:10:58.3202500Z [ RUN      ] TensorTest.TorchTensorCtorScalarBoolType
2023-01-11T22:10:58.3202875Z [       OK ] TensorTest.TorchTensorCtorScalarBoolType (0 ms)
2023-01-11T22:10:58.3203275Z [ RUN      ] TensorTest.TorchTensorCtorSingleDimIntegralType
2023-01-11T22:10:58.3207360Z [       OK ] TensorTest.TorchTensorCtorSingleDimIntegralType (0 ms)
2023-01-11T22:10:58.3208027Z [ RUN      ] TensorTest.TorchTensorCtorSingleDimFloatingType
2023-01-11T22:10:58.3208818Z [       OK ] TensorTest.TorchTensorCtorSingleDimFloatingType (0 ms)
2023-01-11T22:10:58.3209269Z [ RUN      ] TensorTest.TorchTensorCtorSingleDimBoolType
2023-01-11T22:10:58.3209674Z [       OK ] TensorTest.TorchTensorCtorSingleDimBoolType (0 ms)
2023-01-11T22:10:58.3210076Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimIntegralType
2023-01-11T22:10:58.3212772Z [       OK ] TensorTest.TorchTensorCtorMultiDimIntegralType (0 ms)
2023-01-11T22:10:58.3213389Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimFloatingType
2023-01-11T22:10:58.3215199Z [       OK ] TensorTest.TorchTensorCtorMultiDimFloatingType (0 ms)
2023-01-11T22:10:58.3215766Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimBoolType
2023-01-11T22:10:58.3216351Z [       OK ] TensorTest.TorchTensorCtorMultiDimBoolType (0 ms)
2023-01-11T22:10:58.3217019Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimWithOptions
2023-01-11T22:10:58.3217592Z [       OK ] TensorTest.TorchTensorCtorMultiDimWithOptions (0 ms)
2023-01-11T22:10:58.3218015Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimErrorChecks
2023-01-11T22:10:58.3274845Z [       OK ] TensorTest.TorchTensorCtorMultiDimErrorChecks (5 ms)
2023-01-11T22:10:58.3275626Z [ RUN      ] TensorTest.TorchTensorCastRealToComplex
2023-01-11T22:10:58.3276335Z [       OK ] TensorTest.TorchTensorCastRealToComplex (0 ms)
2023-01-11T22:10:58.3277095Z [ RUN      ] TensorTest.TorchTensorCastComplexToRealErrorChecks
2023-01-11T22:10:58.3277789Z [W Copy.cpp:276] Warning: Casting complex values to real discards the imaginary part (function operator())
2023-01-11T22:10:58.3289010Z [       OK ] TensorTest.TorchTensorCastComplexToRealErrorChecks (1 ms)
2023-01-11T22:10:58.3289728Z [ RUN      ] TensorTest.TorchTensorCtorZeroSizedDim
2023-01-11T22:10:58.3290356Z [       OK ] TensorTest.TorchTensorCtorZeroSizedDim (0 ms)
2023-01-11T22:10:58.3291043Z [ RUN      ] TensorTest.TorchTensorCtorWithoutSpecifyingDtype
2023-01-11T22:10:58.3291919Z [       OK ] TensorTest.TorchTensorCtorWithoutSpecifyingDtype (0 ms)
2023-01-11T22:10:58.3292694Z [ RUN      ] TensorTest.TorchTensorCtorWithNonDtypeOptions
2023-01-11T22:10:58.3293463Z [       OK ] TensorTest.TorchTensorCtorWithNonDtypeOptions (0 ms)
2023-01-11T22:10:58.3294048Z [ RUN      ] TensorTest.Arange
2023-01-11T22:10:58.3294507Z [       OK ] TensorTest.Arange (0 ms)
2023-01-11T22:10:58.3295109Z [ RUN      ] TensorTest.PrettyPrintTensorDataContainer
2023-01-11T22:10:58.3295833Z [       OK ] TensorTest.PrettyPrintTensorDataContainer (0 ms)
2023-01-11T22:10:58.3296628Z [ RUN      ] TensorTest.TensorDataContainerCallingAccessorOfWrongType
2023-01-11T22:10:58.3358112Z [       OK ] TensorTest.TensorDataContainerCallingAccessorOfWrongType (6 ms)
2023-01-11T22:10:58.3358724Z [ RUN      ] TensorTest.FromBlob
2023-01-11T22:10:58.3359185Z [       OK ] TensorTest.FromBlob (0 ms)
2023-01-11T22:10:58.3359778Z [ RUN      ] TensorTest.FromBlobUsesDeleter
2023-01-11T22:10:58.3360367Z [       OK ] TensorTest.FromBlobUsesDeleter (0 ms)
2023-01-11T22:10:58.3360938Z [ RUN      ] TensorTest.FromBlobWithStrides
2023-01-11T22:10:58.3361504Z [       OK ] TensorTest.FromBlobWithStrides (0 ms)
2023-01-11T22:10:58.3361998Z [ RUN      ] TensorTest.Item
2023-01-11T22:10:58.3362451Z [       OK ] TensorTest.Item (0 ms)
2023-01-11T22:10:58.3362910Z [ RUN      ] TensorTest.DataPtr
2023-01-11T22:10:58.3363374Z [       OK ] TensorTest.DataPtr (0 ms)
2023-01-11T22:10:58.3363822Z [ RUN      ] TensorTest.Data
2023-01-11T22:10:58.3364273Z [       OK ] TensorTest.Data (0 ms)
2023-01-11T22:10:58.3364766Z [ RUN      ] TensorTest.BackwardAndGrad
2023-01-11T22:10:58.3365308Z [       OK ] TensorTest.BackwardAndGrad (0 ms)
2023-01-11T22:10:58.3365887Z [ RUN      ] TensorTest.BackwardCreatesOnesGrad
2023-01-11T22:10:58.3366502Z [       OK ] TensorTest.BackwardCreatesOnesGrad (0 ms)
2023-01-11T22:10:58.3367122Z [ RUN      ] TensorTest.BackwardNonScalarOutputs
2023-01-11T22:10:58.3394603Z [       OK ] TensorTest.BackwardNonScalarOutputs (3 ms)
2023-01-11T22:10:58.3395156Z [ RUN      ] TensorTest.IsLeaf
2023-01-11T22:10:58.3395617Z [       OK ] TensorTest.IsLeaf (0 ms)
2023-01-11T22:10:58.3396089Z [ RUN      ] TensorTest.OutputNr
2023-01-11T22:10:58.3396592Z [       OK ] TensorTest.OutputNr (0 ms)
2023-01-11T22:10:58.3397046Z [ RUN      ] TensorTest.Version
2023-01-11T22:10:58.3397523Z [       OK ] TensorTest.Version (0 ms)
2023-01-11T22:10:58.3397983Z [ RUN      ] TensorTest.Detach
2023-01-11T22:10:58.3398442Z [       OK ] TensorTest.Detach (0 ms)
2023-01-11T22:10:58.3398934Z [ RUN      ] TensorTest.DetachInplace
2023-01-11T22:10:58.3399467Z [       OK ] TensorTest.DetachInplace (0 ms)
2023-01-11T22:10:58.3400252Z [ RUN      ] TensorTest.SetData
2023-01-11T22:10:58.3400717Z [       OK ] TensorTest.SetData (0 ms)
2023-01-11T22:10:58.3401246Z [ RUN      ] TensorTest.RequiresGradInplace
2023-01-11T22:10:58.3423536Z [       OK ] TensorTest.RequiresGradInplace (2 ms)
2023-01-11T22:10:58.3423994Z [ RUN      ] TensorTest.StdDimension
2023-01-11T22:10:58.3424447Z [       OK ] TensorTest.StdDimension (0 ms)
2023-01-11T22:10:58.3424914Z [ RUN      ] TensorTest.ReshapeAlias
2023-01-11T22:10:58.3427526Z [       OK ] TensorTest.ReshapeAlias (0 ms)
2023-01-11T22:10:58.3428106Z [----------] 45 tests from TensorTest (31 ms total)
2023-01-11T22:10:58.3428388Z 
2023-01-11T22:10:58.3428684Z [----------] 36 tests from TensorIndexingTest
2023-01-11T22:10:58.3429202Z [ RUN      ] TensorIndexingTest.Slice
2023-01-11T22:10:58.3429722Z [       OK ] TensorIndexingTest.Slice (0 ms)
2023-01-11T22:10:58.3430421Z [ RUN      ] TensorIndexingTest.TensorIndex
2023-01-11T22:10:58.3441530Z [       OK ] TensorIndexingTest.TensorIndex (1 ms)
2023-01-11T22:10:58.3442166Z [ RUN      ] TensorIndexingTest.TestNoIndices
2023-01-11T22:10:58.3529068Z [       OK ] TensorIndexingTest.TestNoIndices (8 ms)
2023-01-11T22:10:58.3529769Z [ RUN      ] TensorIndexingTest.TestAdvancedIndexingWithListOfTensor
2023-01-11T22:10:58.3530526Z [       OK ] TensorIndexingTest.TestAdvancedIndexingWithListOfTensor (0 ms)
2023-01-11T22:10:58.3531208Z [ RUN      ] TensorIndexingTest.TestSingleInt
2023-01-11T22:10:58.3531758Z [       OK ] TensorIndexingTest.TestSingleInt (0 ms)
2023-01-11T22:10:58.3532286Z [ RUN      ] TensorIndexingTest.TestMultipleInt
2023-01-11T22:10:58.3532840Z [       OK ] TensorIndexingTest.TestMultipleInt (0 ms)
2023-01-11T22:10:58.3533338Z [ RUN      ] TensorIndexingTest.TestNone
2023-01-11T22:10:58.3533840Z [       OK ] TensorIndexingTest.TestNone (0 ms)
2023-01-11T22:10:58.3534363Z [ RUN      ] TensorIndexingTest.TestStep
2023-01-11T22:10:58.3534876Z [       OK ] TensorIndexingTest.TestStep (0 ms)
2023-01-11T22:10:58.3535432Z [ RUN      ] TensorIndexingTest.TestStepAssignment
2023-01-11T22:10:58.3536051Z [       OK ] TensorIndexingTest.TestStepAssignment (0 ms)
2023-01-11T22:10:58.3536639Z [ RUN      ] TensorIndexingTest.TestBoolIndices
2023-01-11T22:10:58.3537213Z [       OK ] TensorIndexingTest.TestBoolIndices (0 ms)
2023-01-11T22:10:58.3537827Z [ RUN      ] TensorIndexingTest.TestBoolIndicesAccumulate
2023-01-11T22:10:58.3538514Z [       OK ] TensorIndexingTest.TestBoolIndicesAccumulate (0 ms)
2023-01-11T22:10:58.3539175Z [ RUN      ] TensorIndexingTest.TestMultipleBoolIndices
2023-01-11T22:10:58.3539847Z [       OK ] TensorIndexingTest.TestMultipleBoolIndices (0 ms)
2023-01-11T22:10:58.3540448Z [ RUN      ] TensorIndexingTest.TestByteMask
2023-01-11T22:10:58.3541031Z [       OK ] TensorIndexingTest.TestByteMask (0 ms)
2023-01-11T22:10:58.3541620Z [ RUN      ] TensorIndexingTest.TestByteMaskAccumulate
2023-01-11T22:10:58.3542274Z [       OK ] TensorIndexingTest.TestByteMaskAccumulate (0 ms)
2023-01-11T22:10:58.3543108Z [ RUN      ] TensorIndexingTest.TestMultipleByteMask
2023-01-11T22:10:58.3543719Z [       OK ] TensorIndexingTest.TestMultipleByteMask (0 ms)
2023-01-11T22:10:58.3544326Z [ RUN      ] TensorIndexingTest.TestByteMask2d
2023-01-11T22:10:58.3544936Z [       OK ] TensorIndexingTest.TestByteMask2d (0 ms)
2023-01-11T22:10:58.3545546Z [ RUN      ] TensorIndexingTest.TestIntIndices
2023-01-11T22:10:58.3546153Z [       OK ] TensorIndexingTest.TestIntIndices (0 ms)
2023-01-11T22:10:58.3546781Z [ RUN      ] TensorIndexingTest.TestIntIndices2d
2023-01-11T22:10:58.3547447Z [       OK ] TensorIndexingTest.TestIntIndices2d (0 ms)
2023-01-11T22:10:58.3548302Z [ RUN      ] TensorIndexingTest.TestIntIndicesBroadcast
2023-01-11T22:10:58.3549026Z [       OK ] TensorIndexingTest.TestIntIndicesBroadcast (0 ms)
2023-01-11T22:10:58.3549692Z [ RUN      ] TensorIndexingTest.TestEmptyIndex
2023-01-11T22:10:58.3550300Z [       OK ] TensorIndexingTest.TestEmptyIndex (0 ms)
2023-01-11T22:10:58.3550945Z [ RUN      ] TensorIndexingTest.TestEmptyNdimIndex
2023-01-11T22:10:58.3605084Z [       OK ] TensorIndexingTest.TestEmptyNdimIndex (5 ms)
2023-01-11T22:10:58.3605507Z [ RUN      ] TensorIndexingTest.TestEmptyNdimIndexBool
2023-01-11T22:10:58.3626277Z [       OK ] TensorIndexingTest.TestEmptyNdimIndexBool (2 ms)
2023-01-11T22:10:58.3626889Z [ RUN      ] TensorIndexingTest.TestEmptySlice
2023-01-11T22:10:58.3627393Z [       OK ] TensorIndexingTest.TestEmptySlice (0 ms)
2023-01-11T22:10:58.3628245Z [ RUN      ] TensorIndexingTest.TestIndexGetitemCopyBoolsSlices
2023-01-11T22:10:58.3628705Z [       OK ] TensorIndexingTest.TestIndexGetitemCopyBoolsSlices (0 ms)
2023-01-11T22:10:58.3629150Z [ RUN      ] TensorIndexingTest.TestIndexSetitemBoolsSlices
2023-01-11T22:10:58.3716562Z [       OK ] TensorIndexingTest.TestIndexSetitemBoolsSlices (8 ms)
2023-01-11T22:10:58.3717174Z [ RUN      ] TensorIndexingTest.TestIndexScalarWithBoolMask
2023-01-11T22:10:58.3718247Z [       OK ] TensorIndexingTest.TestIndexScalarWithBoolMask (0 ms)
2023-01-11T22:10:58.3719050Z [ RUN      ] TensorIndexingTest.TestSetitemExpansionError
2023-01-11T22:10:58.3837480Z [       OK ] TensorIndexingTest.TestSetitemExpansionError (11 ms)
2023-01-11T22:10:58.3946976Z [ RUN      ] TensorIndexingTest.TestGetitemScalars
2023-01-11T22:10:58.3947685Z [       OK ] TensorIndexingTest.TestGetitemScalars (10 ms)
2023-01-11T22:10:58.3948369Z [ RUN      ] TensorIndexingTest.TestSetitemScalars
2023-01-11T22:10:58.4056399Z [       OK ] TensorIndexingTest.TestSetitemScalars (10 ms)
2023-01-11T22:10:58.4056943Z [ RUN      ] TensorIndexingTest.TestBasicAdvancedCombined
2023-01-11T22:10:58.4057528Z [       OK ] TensorIndexingTest.TestBasicAdvancedCombined (0 ms)
2023-01-11T22:10:58.4058134Z [ RUN      ] TensorIndexingTest.TestIntAssignment
2023-01-11T22:10:58.4058612Z [       OK ] TensorIndexingTest.TestIntAssignment (0 ms)
2023-01-11T22:10:58.4059051Z [ RUN      ] TensorIndexingTest.TestByteTensorAssignment
2023-01-11T22:10:58.4060168Z [       OK ] TensorIndexingTest.TestByteTensorAssignment (0 ms)
2023-01-11T22:10:58.4060605Z [ RUN      ] TensorIndexingTest.TestVariableSlicing
2023-01-11T22:10:58.4061196Z [       OK ] TensorIndexingTest.TestVariableSlicing (0 ms)
2023-01-11T22:10:58.4061672Z [ RUN      ] TensorIndexingTest.TestEllipsisTensor
2023-01-11T22:10:58.4062056Z [       OK ] TensorIndexingTest.TestEllipsisTensor (0 ms)
2023-01-11T22:10:58.4062575Z [ RUN      ] TensorIndexingTest.TestOutOfBoundIndex
2023-01-11T22:10:58.4158540Z [       OK ] TensorIndexingTest.TestOutOfBoundIndex (9 ms)
2023-01-11T22:10:58.4159102Z [ RUN      ] TensorIndexingTest.TestZeroDimIndex
2023-01-11T22:10:58.4179751Z [       OK ] TensorIndexingTest.TestZeroDimIndex (2 ms)
2023-01-11T22:10:58.4180477Z [----------] 36 tests from TensorIndexingTest (75 ms total)
2023-01-11T22:10:58.4180779Z 
2023-01-11T22:10:58.4181057Z [----------] 18 tests from NumpyTests
2023-01-11T22:10:58.4181556Z [ RUN      ] NumpyTests.TestNoneIndex
2023-01-11T22:10:58.4181895Z [       OK ] NumpyTests.TestNoneIndex (0 ms)
2023-01-11T22:10:58.4182200Z [ RUN      ] NumpyTests.TestEmptyFancyIndex
2023-01-11T22:10:58.4233217Z [       OK ] NumpyTests.TestEmptyFancyIndex (5 ms)
2023-01-11T22:10:58.4233840Z [ RUN      ] NumpyTests.TestEllipsisIndex
2023-01-11T22:10:58.4234708Z [       OK ] NumpyTests.TestEllipsisIndex (0 ms)
2023-01-11T22:10:58.4235142Z [ RUN      ] NumpyTests.TestSingleIntIndex
2023-01-11T22:10:58.4255733Z [       OK ] NumpyTests.TestSingleIntIndex (2 ms)
2023-01-11T22:10:58.4256173Z [ RUN      ] NumpyTests.TestSingleBoolIndex
2023-01-11T22:10:58.4256787Z [       OK ] NumpyTests.TestSingleBoolIndex (0 ms)
2023-01-11T22:10:58.4257186Z [ RUN      ] NumpyTests.TestBooleanShapeMismatch
2023-01-11T22:10:58.4466711Z [       OK ] NumpyTests.TestBooleanShapeMismatch (20 ms)
2023-01-11T22:10:58.4467204Z [ RUN      ] NumpyTests.TestBooleanIndexingOnedim
2023-01-11T22:10:58.4467781Z [       OK ] NumpyTests.TestBooleanIndexingOnedim (0 ms)
2023-01-11T22:10:58.4468180Z [ RUN      ] NumpyTests.TestBooleanAssignmentValueMismatch
2023-01-11T22:10:58.4618444Z [       OK ] NumpyTests.TestBooleanAssignmentValueMismatch (15 ms)
2023-01-11T22:10:58.4619018Z [ RUN      ] NumpyTests.TestBooleanIndexingTwodim
2023-01-11T22:10:58.4620043Z [       OK ] NumpyTests.TestBooleanIndexingTwodim (0 ms)
2023-01-11T22:10:58.4620419Z [ RUN      ] NumpyTests.TestBooleanIndexingWeirdness
2023-01-11T22:10:58.4729223Z [       OK ] NumpyTests.TestBooleanIndexingWeirdness (10 ms)
2023-01-11T22:10:58.4729637Z [ RUN      ] NumpyTests.TestBooleanIndexingWeirdnessTensors
2023-01-11T22:10:58.4838826Z [       OK ] NumpyTests.TestBooleanIndexingWeirdnessTensors (10 ms)
2023-01-11T22:10:58.4839569Z [ RUN      ] NumpyTests.TestBooleanIndexingAlldims
2023-01-11T22:10:58.4840099Z [       OK ] NumpyTests.TestBooleanIndexingAlldims (0 ms)
2023-01-11T22:10:58.4840485Z [ RUN      ] NumpyTests.TestBooleanListIndexing
2023-01-11T22:10:58.4841022Z [       OK ] NumpyTests.TestBooleanListIndexing (0 ms)
2023-01-11T22:10:58.4841392Z [ RUN      ] NumpyTests.TestEverythingReturnsViews
2023-01-11T22:10:58.4841755Z [       OK ] NumpyTests.TestEverythingReturnsViews (0 ms)
2023-01-11T22:10:58.4842111Z [ RUN      ] NumpyTests.TestBroaderrorsIndexing
2023-01-11T22:10:58.5056937Z [       OK ] NumpyTests.TestBroaderrorsIndexing (21 ms)
2023-01-11T22:10:58.5057385Z [ RUN      ] NumpyTests.TestTrivialFancyOutOfBounds
2023-01-11T22:10:58.5322216Z [       OK ] NumpyTests.TestTrivialFancyOutOfBounds (26 ms)
2023-01-11T22:10:58.5322698Z [ RUN      ] NumpyTests.TestIndexIsLarger
2023-01-11T22:10:58.5323123Z [       OK ] NumpyTests.TestIndexIsLarger (0 ms)
2023-01-11T22:10:58.5323532Z [ RUN      ] NumpyTests.TestBroadcastSubspace
2023-01-11T22:10:58.5325626Z [       OK ] NumpyTests.TestBroadcastSubspace (0 ms)
2023-01-11T22:10:58.5326281Z [----------] 18 tests from NumpyTests (114 ms total)
2023-01-11T22:10:58.5326561Z 
2023-01-11T22:10:58.5326737Z [----------] 5 tests from TensorOptionsTest
2023-01-11T22:10:58.5327093Z [ RUN      ] TensorOptionsTest.DefaultsToTheRightValues
2023-01-11T22:10:58.5327502Z [       OK ] TensorOptionsTest.DefaultsToTheRightValues (0 ms)
2023-01-11T22:10:58.5327972Z [ RUN      ] TensorOptionsTest.UtilityFunctionsReturnTheRightTensorOptions
2023-01-11T22:10:58.5328492Z [       OK ] TensorOptionsTest.UtilityFunctionsReturnTheRightTensorOptions (0 ms)
2023-01-11T22:10:58.5328956Z [ RUN      ] TensorOptionsTest.ConstructsWellFromCPUTypes
2023-01-11T22:10:58.5329376Z [       OK ] TensorOptionsTest.ConstructsWellFromCPUTypes (0 ms)
2023-01-11T22:10:58.5329793Z [ RUN      ] TensorOptionsTest.ConstructsWellFromCPUTensors
2023-01-11T22:10:58.5330210Z [       OK ] TensorOptionsTest.ConstructsWellFromCPUTensors (0 ms)
2023-01-11T22:10:58.5330626Z [ RUN      ] TensorOptionsTest.ConstructsWellFromVariables
2023-01-11T22:10:58.5331045Z [       OK ] TensorOptionsTest.ConstructsWellFromVariables (0 ms)
2023-01-11T22:10:58.5331586Z [----------] 5 tests from TensorOptionsTest (0 ms total)
2023-01-11T22:10:58.5331751Z 
2023-01-11T22:10:58.5331898Z [----------] 1 test from DeviceTest
2023-01-11T22:10:58.5332223Z [ RUN      ] DeviceTest.ParsesCorrectlyFromString
2023-01-11T22:10:58.5410721Z [       OK ] DeviceTest.ParsesCorrectlyFromString (8 ms)
2023-01-11T22:10:58.5411364Z [----------] 1 test from DeviceTest (8 ms total)
2023-01-11T22:10:58.5411631Z 
2023-01-11T22:10:58.5411905Z [----------] 3 tests from DefaultDtypeTest
2023-01-11T22:10:58.5412474Z [ RUN      ] DefaultDtypeTest.CanSetAndGetDefaultDtype
2023-01-11T22:10:58.5413175Z [       OK ] DefaultDtypeTest.CanSetAndGetDefaultDtype (0 ms)
2023-01-11T22:10:58.5413877Z [ RUN      ] DefaultDtypeTest.NewTensorOptionsHasCorrectDefault
2023-01-11T22:10:58.5414469Z [       OK ] DefaultDtypeTest.NewTensorOptionsHasCorrectDefault (0 ms)
2023-01-11T22:10:58.5415203Z [ RUN      ] DefaultDtypeTest.NewTensorsHaveCorrectDefaultDtype
2023-01-11T22:10:58.5415792Z [       OK ] DefaultDtypeTest.NewTensorsHaveCorrectDefaultDtype (0 ms)
2023-01-11T22:10:58.5416311Z [----------] 3 tests from DefaultDtypeTest (0 ms total)
2023-01-11T22:10:58.5416520Z 
2023-01-11T22:10:58.5416709Z [----------] 1 test from TorchIncludeTest
2023-01-11T22:10:58.5417121Z [ RUN      ] TorchIncludeTest.GetSetNumThreads
2023-01-11T22:10:58.5570663Z [       OK ] TorchIncludeTest.GetSetNumThreads (15 ms)
2023-01-11T22:10:58.5571364Z [----------] 1 test from TorchIncludeTest (15 ms total)
2023-01-11T22:10:58.5571584Z 
2023-01-11T22:10:58.5571760Z [----------] 28 tests from InferenceModeTest
2023-01-11T22:10:58.5572084Z [ RUN      ] InferenceModeTest.TestTLSState
2023-01-11T22:10:58.5572408Z [       OK ] InferenceModeTest.TestTLSState (0 ms)
2023-01-11T22:10:58.5572797Z [ RUN      ] InferenceModeTest.TestInferenceTensorCreation
2023-01-11T22:10:58.5573215Z [       OK ] InferenceModeTest.TestInferenceTensorCreation (0 ms)
2023-01-11T22:10:58.5573615Z [ RUN      ] InferenceModeTest.TestExistingAutogradSession
2023-01-11T22:10:58.5642471Z [       OK ] InferenceModeTest.TestExistingAutogradSession (7 ms)
2023-01-11T22:10:58.5643410Z [ RUN      ] InferenceModeTest.TestInferenceTensorInInferenceModeFunctionalOp
2023-01-11T22:10:58.5644463Z [       OK ] InferenceModeTest.TestInferenceTensorInInferenceModeFunctionalOp (0 ms)
2023-01-11T22:10:58.5645456Z [ RUN      ] InferenceModeTest.TestInferenceTensorInInferenceModeInplaceOp
2023-01-11T22:10:58.5646438Z [       OK ] InferenceModeTest.TestInferenceTensorInInferenceModeInplaceOp (0 ms)
2023-01-11T22:10:58.5647361Z [ RUN      ] InferenceModeTest.TestInferenceTensorInInferenceModeViewOp
2023-01-11T22:10:58.5648303Z [       OK ] InferenceModeTest.TestInferenceTensorInInferenceModeViewOp (0 ms)
2023-01-11T22:10:58.5649257Z [ RUN      ] InferenceModeTest.TestInferenceTensorInNormalModeFunctionalOp
2023-01-11T22:10:58.5650216Z [       OK ] InferenceModeTest.TestInferenceTensorInNormalModeFunctionalOp (0 ms)
2023-01-11T22:10:58.5651159Z [ RUN      ] InferenceModeTest.TestInferenceTensorInNormalModeInplaceOp
2023-01-11T22:10:58.5698876Z [       OK ] InferenceModeTest.TestInferenceTensorInNormalModeInplaceOp (5 ms)
2023-01-11T22:10:58.5699780Z [ RUN      ] InferenceModeTest.TestInferenceTensorInNormalModeViewOp
2023-01-11T22:10:58.5700608Z [       OK ] InferenceModeTest.TestInferenceTensorInNormalModeViewOp (0 ms)
2023-01-11T22:10:58.5701127Z [ RUN      ] InferenceModeTest.TestNormalTensorInplaceOutputInInferenceMode
2023-01-11T22:10:58.5701667Z [       OK ] InferenceModeTest.TestNormalTensorInplaceOutputInInferenceMode (0 ms)
2023-01-11T22:10:58.5702199Z [ RUN      ] InferenceModeTest.TestNormalTensorInplaceOutputInNormalMode
2023-01-11T22:10:58.5702976Z [       OK ] InferenceModeTest.TestNormalTensorInplaceOutputInNormalMode (0 ms)
2023-01-11T22:10:58.5703492Z [ RUN      ] InferenceModeTest.TestNormalTensorViewOutputInInferenceMode
2023-01-11T22:10:58.5704005Z [       OK ] InferenceModeTest.TestNormalTensorViewOutputInInferenceMode (0 ms)
2023-01-11T22:10:58.5704490Z [ RUN      ] InferenceModeTest.TestNormalTensorViewOutputInNormalMode
2023-01-11T22:10:58.5732884Z [       OK ] InferenceModeTest.TestNormalTensorViewOutputInNormalMode (3 ms)
2023-01-11T22:10:58.5733536Z [ RUN      ] InferenceModeTest.TestMixInferenceAndNormalTensorFunctionalOp
2023-01-11T22:10:58.5764642Z [       OK ] InferenceModeTest.TestMixInferenceAndNormalTensorFunctionalOp (3 ms)
2023-01-11T22:10:58.5765222Z [ RUN      ] InferenceModeTest.TestMixInferenceAndNormalTensorInplaceOp
2023-01-11T22:10:58.5845842Z [       OK ] InferenceModeTest.TestMixInferenceAndNormalTensorInplaceOp (8 ms)
2023-01-11T22:10:58.5846494Z [ RUN      ] InferenceModeTest.TestMixInferenceAndNormalTensorViewOp
2023-01-11T22:10:58.5846991Z [       OK ] InferenceModeTest.TestMixInferenceAndNormalTensorViewOp (0 ms)
2023-01-11T22:10:58.5847427Z [ RUN      ] InferenceModeTest.TestHandleDirectViewOnRebase
2023-01-11T22:10:58.5877966Z [       OK ] InferenceModeTest.TestHandleDirectViewOnRebase (3 ms)
2023-01-11T22:10:58.5878405Z [ RUN      ] InferenceModeTest.TestHandleInDirectViewOnRebase
2023-01-11T22:10:58.5900025Z [       OK ] InferenceModeTest.TestHandleInDirectViewOnRebase (2 ms)
2023-01-11T22:10:58.5900462Z [ RUN      ] InferenceModeTest.TestCreationMetaPropagation
2023-01-11T22:10:58.5961749Z [       OK ] InferenceModeTest.TestCreationMetaPropagation (6 ms)
2023-01-11T22:10:58.5962190Z [ RUN      ] InferenceModeTest.TestCreationMetaPropagationInput
2023-01-11T22:10:58.6084120Z [       OK ] InferenceModeTest.TestCreationMetaPropagationInput (12 ms)
2023-01-11T22:10:58.6084583Z [ RUN      ] InferenceModeTest.TestInplaceCopyOnInferenceTensor
2023-01-11T22:10:58.6155818Z [       OK ] InferenceModeTest.TestInplaceCopyOnInferenceTensor (7 ms)
2023-01-11T22:10:58.6156273Z [ RUN      ] InferenceModeTest.TestSetRequiresGradInNormalMode
2023-01-11T22:10:58.6166625Z [       OK ] InferenceModeTest.TestSetRequiresGradInNormalMode (1 ms)
2023-01-11T22:10:58.6167048Z [ RUN      ] InferenceModeTest.TestAccessVersionCounter
2023-01-11T22:10:58.6200033Z [       OK ] InferenceModeTest.TestAccessVersionCounter (3 ms)
2023-01-11T22:10:58.6200547Z [ RUN      ] InferenceModeTest.TestInplaceUpdateInferenceTensorWithNormalTensor
2023-01-11T22:10:58.6270970Z [       OK ] InferenceModeTest.TestInplaceUpdateInferenceTensorWithNormalTensor (7 ms)
2023-01-11T22:10:58.6271673Z [ RUN      ] InferenceModeTest.TestComplexViewInInferenceMode
2023-01-11T22:10:58.6272259Z [       OK ] InferenceModeTest.TestComplexViewInInferenceMode (0 ms)
2023-01-11T22:10:58.6272967Z [ RUN      ] InferenceModeTest.TestComplexViewInNormalMode
2023-01-11T22:10:58.6273739Z [       OK ] InferenceModeTest.TestComplexViewInNormalMode (0 ms)
2023-01-11T22:10:58.6274225Z [ RUN      ] InferenceModeTest.TestCustomFunction
2023-01-11T22:10:58.6274602Z [       OK ] InferenceModeTest.TestCustomFunction (0 ms)
2023-01-11T22:10:58.6275025Z [ RUN      ] InferenceModeTest.TestLegacyAutoNonVariableTypeModeWarning
2023-01-11T22:10:58.6275530Z [       OK ] InferenceModeTest.TestLegacyAutoNonVariableTypeModeWarning (0 ms)
2023-01-11T22:10:58.6275963Z [----------] 28 tests from InferenceModeTest (70 ms total)
2023-01-11T22:10:58.6276131Z 
2023-01-11T22:10:58.6276269Z [----------] 4 tests from GradModeTest
2023-01-11T22:10:58.6276609Z [ RUN      ] GradModeTest.TestRequiresGradFunctionalOp
2023-01-11T22:10:58.6277121Z [       OK ] GradModeTest.TestRequiresGradFunctionalOp (0 ms)
2023-01-11T22:10:58.6277503Z [ RUN      ] GradModeTest.TestRequiresGradInplaceOp
2023-01-11T22:10:58.6277867Z [       OK ] GradModeTest.TestRequiresGradInplaceOp (0 ms)
2023-01-11T22:10:58.6278231Z [ RUN      ] GradModeTest.TestRequiresGradViewOp
2023-01-11T22:10:58.6278593Z [       OK ] GradModeTest.TestRequiresGradViewOp (0 ms)
2023-01-11T22:10:58.6278955Z [ RUN      ] GradModeTest.TestRequiresGradViewOpExiting
2023-01-11T22:10:58.6305747Z [       OK ] GradModeTest.TestRequiresGradViewOpExiting (3 ms)
2023-01-11T22:10:58.6306384Z [----------] 4 tests from GradModeTest (3 ms total)
2023-01-11T22:10:58.6306685Z 
2023-01-11T22:10:58.6306947Z [----------] 3 tests from OperationTest
2023-01-11T22:10:58.6307213Z [ RUN      ] OperationTest.Lerp
2023-01-11T22:10:58.6313747Z [       OK ] OperationTest.Lerp (0 ms)
2023-01-11T22:10:58.6314255Z [ RUN      ] OperationTest.Cross
2023-01-11T22:10:58.6347446Z [       OK ] OperationTest.Cross (3 ms)
2023-01-11T22:10:58.6347994Z [ RUN      ] OperationTest.Linear_out
2023-01-11T22:10:58.6350501Z [       OK ] OperationTest.Linear_out (0 ms)
2023-01-11T22:10:58.6351099Z [----------] 3 tests from OperationTest (4 ms total)
2023-01-11T22:10:58.6351284Z 
2023-01-11T22:10:58.6351455Z [----------] Global test environment tear-down
2023-01-11T22:10:58.6451469Z [==========] 992 tests from 48 test suites ran. (43492 ms total)
2023-01-11T22:10:58.6451958Z [  PASSED  ] 992 tests.
2023-01-11T22:10:58.7326480Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_tensorexpr --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_tensorexpr.xml
2023-01-11T22:10:59.1096751Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:10:59.1101234Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:10:59.1101832Z [==========] Running 801 tests from 25 test suites.
2023-01-11T22:10:59.1102216Z [----------] Global test environment set-up.
2023-01-11T22:10:59.1102638Z [----------] 1 test from Approx
2023-01-11T22:10:59.1102889Z [ RUN      ] Approx.log_vml
2023-01-11T22:11:00.3533744Z [       OK ] Approx.log_vml (1242 ms)
2023-01-11T22:11:00.3534298Z [----------] 1 test from Approx (1243 ms total)
2023-01-11T22:11:00.3534487Z 
2023-01-11T22:11:00.3534662Z [----------] 34 tests from ATen
2023-01-11T22:11:00.3534900Z [ RUN      ] ATen._cast_Float
2023-01-11T22:11:00.3535168Z [       OK ] ATen._cast_Float (0 ms)
2023-01-11T22:11:00.3535416Z [ RUN      ] ATen.negInt
2023-01-11T22:11:00.3538080Z [       OK ] ATen.negInt (0 ms)
2023-01-11T22:11:00.3538337Z [ RUN      ] ATen.negFloat
2023-01-11T22:11:00.3541725Z [       OK ] ATen.negFloat (0 ms)
2023-01-11T22:11:00.3541979Z [ RUN      ] ATen.addInt
2023-01-11T22:11:00.3548696Z [       OK ] ATen.addInt (0 ms)
2023-01-11T22:11:00.3548949Z [ RUN      ] ATen.addFloat
2023-01-11T22:11:00.3555280Z [       OK ] ATen.addFloat (0 ms)
2023-01-11T22:11:00.3555648Z [ RUN      ] ATen.subInt
2023-01-11T22:11:00.3561845Z [       OK ] ATen.subInt (0 ms)
2023-01-11T22:11:00.3562095Z [ RUN      ] ATen.subFloat
2023-01-11T22:11:00.3568507Z [       OK ] ATen.subFloat (0 ms)
2023-01-11T22:11:00.3568824Z [ RUN      ] ATen.lerp
2023-01-11T22:11:00.3576312Z [       OK ] ATen.lerp (0 ms)
2023-01-11T22:11:00.3576786Z [ RUN      ] ATen.addcmulInt
2023-01-11T22:11:00.3584016Z [       OK ] ATen.addcmulInt (0 ms)
2023-01-11T22:11:00.3584526Z [ RUN      ] ATen.addcmulFloat
2023-01-11T22:11:00.3591712Z [       OK ] ATen.addcmulFloat (0 ms)
2023-01-11T22:11:00.3592434Z [ RUN      ] ATen.mulInt
2023-01-11T22:11:00.3595992Z [       OK ] ATen.mulInt (0 ms)
2023-01-11T22:11:00.3596454Z [ RUN      ] ATen.mulFloat
2023-01-11T22:11:00.3600442Z [       OK ] ATen.mulFloat (0 ms)
2023-01-11T22:11:00.3600887Z [ RUN      ] ATen.divInt
2023-01-11T22:11:00.3604863Z [       OK ] ATen.divInt (0 ms)
2023-01-11T22:11:00.3605323Z [ RUN      ] ATen.divFloat
2023-01-11T22:11:00.3609283Z [       OK ] ATen.divFloat (0 ms)
2023-01-11T22:11:00.3609738Z [ RUN      ] ATen.maxInt
2023-01-11T22:11:00.3613720Z [       OK ] ATen.maxInt (0 ms)
2023-01-11T22:11:00.3614161Z [ RUN      ] ATen.maxFloat
2023-01-11T22:11:00.3618130Z [       OK ] ATen.maxFloat (0 ms)
2023-01-11T22:11:00.3618590Z [ RUN      ] ATen.minInt
2023-01-11T22:11:00.3622580Z [       OK ] ATen.minInt (0 ms)
2023-01-11T22:11:00.3623022Z [ RUN      ] ATen.minFloat
2023-01-11T22:11:00.3627080Z [       OK ] ATen.minFloat (0 ms)
2023-01-11T22:11:00.3627525Z [ RUN      ] ATen.reluInt
2023-01-11T22:11:00.3630142Z [       OK ] ATen.reluInt (0 ms)
2023-01-11T22:11:00.3630607Z [ RUN      ] ATen.reluFloat
2023-01-11T22:11:00.3633677Z [       OK ] ATen.reluFloat (0 ms)
2023-01-11T22:11:00.3634122Z [ RUN      ] ATen.logFloat
2023-01-11T22:11:00.3637062Z [       OK ] ATen.logFloat (0 ms)
2023-01-11T22:11:00.3637538Z [ RUN      ] ATen.fastLogFloat
2023-01-11T22:11:00.3786008Z [       OK ] ATen.fastLogFloat (14 ms)
2023-01-11T22:11:00.3786495Z [ RUN      ] ATen.fastTanhFloat
2023-01-11T22:11:00.3843075Z [       OK ] ATen.fastTanhFloat (5 ms)
2023-01-11T22:11:00.3843607Z [ RUN      ] ATen.fastSigmoidFloat
2023-01-11T22:11:00.3918872Z [       OK ] ATen.fastSigmoidFloat (7 ms)
2023-01-11T22:11:00.3919395Z [ RUN      ] ATen.log10Float
2023-01-11T22:11:00.3922321Z [       OK ] ATen.log10Float (0 ms)
2023-01-11T22:11:00.3922813Z [ RUN      ] ATen.log2Float
2023-01-11T22:11:00.3925639Z [       OK ] ATen.log2Float (0 ms)
2023-01-11T22:11:00.3926118Z [ RUN      ] ATen.expFloat
2023-01-11T22:11:00.3929005Z [       OK ] ATen.expFloat (0 ms)
2023-01-11T22:11:00.3929456Z [ RUN      ] ATen.erfFloat
2023-01-11T22:11:00.3932434Z [       OK ] ATen.erfFloat (0 ms)
2023-01-11T22:11:00.3932899Z [ RUN      ] ATen.cosFloat
2023-01-11T22:11:00.3935836Z [       OK ] ATen.cosFloat (0 ms)
2023-01-11T22:11:00.3936285Z [ RUN      ] ATen.eqInt
2023-01-11T22:11:00.3940909Z [       OK ] ATen.eqInt (0 ms)
2023-01-11T22:11:00.3941355Z [ RUN      ] ATen.geInt
2023-01-11T22:11:00.3946010Z [       OK ] ATen.geInt (0 ms)
2023-01-11T22:11:00.3946445Z [ RUN      ] ATen.gtInt
2023-01-11T22:11:00.3950967Z [       OK ] ATen.gtInt (0 ms)
2023-01-11T22:11:00.3951409Z [ RUN      ] ATen.leInt
2023-01-11T22:11:00.3955949Z [       OK ] ATen.leInt (0 ms)
2023-01-11T22:11:00.3956401Z [ RUN      ] ATen.ltInt
2023-01-11T22:11:00.3960997Z [       OK ] ATen.ltInt (0 ms)
2023-01-11T22:11:00.3961524Z [----------] 34 tests from ATen (42 ms total)
2023-01-11T22:11:00.3961789Z 
2023-01-11T22:11:00.3961969Z [----------] 26 tests from BoundsInference
2023-01-11T22:11:00.3962253Z [ RUN      ] BoundsInference._1
2023-01-11T22:11:00.3963900Z [       OK ] BoundsInference._1 (0 ms)
2023-01-11T22:11:00.3964512Z [ RUN      ] BoundsInference._2
2023-01-11T22:11:00.3966007Z [       OK ] BoundsInference._2 (0 ms)
2023-01-11T22:11:00.3966482Z [ RUN      ] BoundsInference._3
2023-01-11T22:11:00.3968685Z [       OK ] BoundsInference._3 (0 ms)
2023-01-11T22:11:00.3969180Z [ RUN      ] BoundsInference._4
2023-01-11T22:11:00.3974281Z [       OK ] BoundsInference._4 (0 ms)
2023-01-11T22:11:00.3974978Z [ RUN      ] BoundsInference._5
2023-01-11T22:11:00.3987197Z [       OK ] BoundsInference._5 (1 ms)
2023-01-11T22:11:00.3987707Z [ RUN      ] BoundsInference._6
2023-01-11T22:11:00.3996068Z [       OK ] BoundsInference._6 (0 ms)
2023-01-11T22:11:00.3996598Z [ RUN      ] BoundsInference.Adjacent
2023-01-11T22:11:00.4001841Z [       OK ] BoundsInference.Adjacent (0 ms)
2023-01-11T22:11:00.4002461Z [ RUN      ] BoundsInference.MultipleTopLoopLoad
2023-01-11T22:11:00.4006857Z [       OK ] BoundsInference.MultipleTopLoopLoad (0 ms)
2023-01-11T22:11:00.4007496Z [ RUN      ] BoundsInference.MultipleTopLoopStore
2023-01-11T22:11:00.4011691Z [       OK ] BoundsInference.MultipleTopLoopStore (0 ms)
2023-01-11T22:11:00.4012276Z [ RUN      ] BoundsInference.CacheReads
2023-01-11T22:11:00.4038324Z [       OK ] BoundsInference.CacheReads (2 ms)
2023-01-11T22:11:00.4039012Z [ RUN      ] BoundsInference.Flattened
2023-01-11T22:11:00.4050762Z [       OK ] BoundsInference.Flattened (1 ms)
2023-01-11T22:11:00.4051369Z [ RUN      ] BoundsInference.GetPotentialHazards
2023-01-11T22:11:00.4052114Z [       OK ] BoundsInference.GetPotentialHazards (0 ms)
2023-01-11T22:11:00.4052821Z [ RUN      ] BoundsInference.GetPotentialHazardsLoopNoHazard
2023-01-11T22:11:00.4056100Z [       OK ] BoundsInference.GetPotentialHazardsLoopNoHazard (0 ms)
2023-01-11T22:11:00.4056754Z [ RUN      ] BoundsInference.GetPotentialHazardsLoopCall
2023-01-11T22:11:00.4060881Z [       OK ] BoundsInference.GetPotentialHazardsLoopCall (0 ms)
2023-01-11T22:11:00.4061523Z [ RUN      ] BoundsInference.GetPotentialHazardsLoopSplit
2023-01-11T22:11:00.4072631Z [       OK ] BoundsInference.GetPotentialHazardsLoopSplit (1 ms)
2023-01-11T22:11:00.4073361Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferWithPartialOverlap
2023-01-11T22:11:00.4077891Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferWithPartialOverlap (0 ms)
2023-01-11T22:11:00.4078584Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlap
2023-01-11T22:11:00.4079735Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlap (0 ms)
2023-01-11T22:11:00.4080501Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlapRAW
2023-01-11T22:11:00.4082206Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlapRAW (0 ms)
2023-01-11T22:11:00.4082914Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferNotOverlapping
2023-01-11T22:11:00.4085407Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferNotOverlapping (0 ms)
2023-01-11T22:11:00.4086074Z [ RUN      ] BoundsInference.HasConflictingOverlap2DBufferWithOverlap
2023-01-11T22:11:00.4099352Z [       OK ] BoundsInference.HasConflictingOverlap2DBufferWithOverlap (1 ms)
2023-01-11T22:11:00.4100022Z [ RUN      ] BoundsInference.HasConflictingOverlap2DBufferWithNoOverlap
2023-01-11T22:11:00.4109322Z [       OK ] BoundsInference.HasConflictingOverlap2DBufferWithNoOverlap (0 ms)
2023-01-11T22:11:00.4110213Z [ RUN      ] BoundsInference.HasConflictingOverlapDifferentBuffers
2023-01-11T22:11:00.4112358Z [       OK ] BoundsInference.HasConflictingOverlapDifferentBuffers (0 ms)
2023-01-11T22:11:00.4112830Z [ RUN      ] BoundsInference.HasConflictingOverlapDueToRAWDependence
2023-01-11T22:11:00.4115203Z [       OK ] BoundsInference.HasConflictingOverlapDueToRAWDependence (0 ms)
2023-01-11T22:11:00.4115753Z [ RUN      ] BoundsInference.HasConflictingOverlapDueToWARDependence
2023-01-11T22:11:00.4118011Z [       OK ] BoundsInference.HasConflictingOverlapDueToWARDependence (0 ms)
2023-01-11T22:11:00.4118539Z [ RUN      ] BoundsInference.HasConflictingOverlapWithLoads
2023-01-11T22:11:00.4121021Z [       OK ] BoundsInference.HasConflictingOverlapWithLoads (0 ms)
2023-01-11T22:11:00.4121477Z [ RUN      ] BoundsInference.IsOverlapping
2023-01-11T22:11:00.4143267Z [       OK ] BoundsInference.IsOverlapping (2 ms)
2023-01-11T22:11:00.4143894Z [----------] 26 tests from BoundsInference (18 ms total)
2023-01-11T22:11:00.4144153Z 
2023-01-11T22:11:00.4144293Z [----------] 4 tests from Conv
2023-01-11T22:11:00.4144560Z [ RUN      ] Conv.DepthwiseConv2D
2023-01-11T22:11:00.6757895Z [       OK ] Conv.DepthwiseConv2D (261 ms)
2023-01-11T22:11:00.6758324Z [ RUN      ] Conv.DepthwiseConv2DNoBias
2023-01-11T22:11:00.9305367Z [       OK ] Conv.DepthwiseConv2DNoBias (254 ms)
2023-01-11T22:11:00.9306053Z [ RUN      ] Conv.DepthwiseConv2DDynamicShapes
2023-01-11T22:11:01.1272817Z [       OK ] Conv.DepthwiseConv2DDynamicShapes (196 ms)
2023-01-11T22:11:01.1273491Z [ RUN      ] Conv.Conv2D
2023-01-11T22:11:02.1009589Z [       OK ] Conv.Conv2D (973 ms)
2023-01-11T22:11:02.1010172Z [----------] 4 tests from Conv (1686 ms total)
2023-01-11T22:11:02.1010444Z 
2023-01-11T22:11:02.1010743Z [----------] 28 tests from CppPrinter
2023-01-11T22:11:02.1011157Z [ RUN      ] CppPrinter.IntImm
2023-01-11T22:11:02.1011561Z [       OK ] CppPrinter.IntImm (0 ms)
2023-01-11T22:11:02.1011984Z [ RUN      ] CppPrinter.FloatImm
2023-01-11T22:11:02.1012450Z [       OK ] CppPrinter.FloatImm (0 ms)
2023-01-11T22:11:02.1012846Z [ RUN      ] CppPrinter.FloatImm1
2023-01-11T22:11:02.1013298Z [       OK ] CppPrinter.FloatImm1 (0 ms)
2023-01-11T22:11:02.1013626Z [ RUN      ] CppPrinter.DoubleImm
2023-01-11T22:11:02.1014008Z [       OK ] CppPrinter.DoubleImm (0 ms)
2023-01-11T22:11:02.1014477Z [ RUN      ] CppPrinter.DoubleImm1
2023-01-11T22:11:02.1015004Z [       OK ] CppPrinter.DoubleImm1 (0 ms)
2023-01-11T22:11:02.1015476Z [ RUN      ] CppPrinter.HalfImm
2023-01-11T22:11:02.1015789Z [       OK ] CppPrinter.HalfImm (0 ms)
2023-01-11T22:11:02.1016052Z [ RUN      ] CppPrinter.Add
2023-01-11T22:11:02.1016314Z [       OK ] CppPrinter.Add (0 ms)
2023-01-11T22:11:02.1016567Z [ RUN      ] CppPrinter.AddExpr1
2023-01-11T22:11:02.1016848Z [       OK ] CppPrinter.AddExpr1 (0 ms)
2023-01-11T22:11:02.1017124Z [ RUN      ] CppPrinter.AddExpr2
2023-01-11T22:11:02.1017392Z [       OK ] CppPrinter.AddExpr2 (0 ms)
2023-01-11T22:11:02.1017665Z [ RUN      ] CppPrinter.AddExpr3
2023-01-11T22:11:02.1017945Z [       OK ] CppPrinter.AddExpr3 (0 ms)
2023-01-11T22:11:02.1018204Z [ RUN      ] CppPrinter.Mod
2023-01-11T22:11:02.1018450Z [       OK ] CppPrinter.Mod (0 ms)
2023-01-11T22:11:02.1018712Z [ RUN      ] CppPrinter.ModFloat
2023-01-11T22:11:02.1018995Z [       OK ] CppPrinter.ModFloat (0 ms)
2023-01-11T22:11:02.1019246Z [ RUN      ] CppPrinter.Max
2023-01-11T22:11:02.1019504Z [       OK ] CppPrinter.Max (0 ms)
2023-01-11T22:11:02.1019766Z [ RUN      ] CppPrinter.MaxFloat
2023-01-11T22:11:02.1020028Z [       OK ] CppPrinter.MaxFloat (0 ms)
2023-01-11T22:11:02.1020296Z [ RUN      ] CppPrinter.MaxHalf
2023-01-11T22:11:02.1020569Z [       OK ] CppPrinter.MaxHalf (0 ms)
2023-01-11T22:11:02.1020813Z [ RUN      ] CppPrinter.And
2023-01-11T22:11:02.1021073Z [       OK ] CppPrinter.And (0 ms)
2023-01-11T22:11:02.1021354Z [ RUN      ] CppPrinter.CompareSelect
2023-01-11T22:11:02.1021645Z [       OK ] CppPrinter.CompareSelect (0 ms)
2023-01-11T22:11:02.1021940Z [ RUN      ] CppPrinter.IfThenElse
2023-01-11T22:11:02.1022230Z [       OK ] CppPrinter.IfThenElse (0 ms)
2023-01-11T22:11:02.1022804Z [ RUN      ] CppPrinter.AllocateFree
2023-01-11T22:11:02.1023311Z [       OK ] CppPrinter.AllocateFree (0 ms)
2023-01-11T22:11:02.1023598Z [ RUN      ] CppPrinter.LoadStore
2023-01-11T22:11:02.1023880Z [       OK ] CppPrinter.LoadStore (0 ms)
2023-01-11T22:11:02.1024132Z [ RUN      ] CppPrinter.Var
2023-01-11T22:11:02.1024394Z [       OK ] CppPrinter.Var (0 ms)
2023-01-11T22:11:02.1024648Z [ RUN      ] CppPrinter.Cast
2023-01-11T22:11:02.1024903Z [       OK ] CppPrinter.Cast (0 ms)
2023-01-11T22:11:02.1025165Z [ RUN      ] CppPrinter.BitCast
2023-01-11T22:11:02.1025438Z [       OK ] CppPrinter.BitCast (0 ms)
2023-01-11T22:11:02.1025685Z [ RUN      ] CppPrinter.Let
2023-01-11T22:11:02.1025947Z [       OK ] CppPrinter.Let (0 ms)
2023-01-11T22:11:02.1028641Z [ RUN      ] CppPrinter.For
2023-01-11T22:11:02.1029095Z [       OK ] CppPrinter.For (0 ms)
2023-01-11T22:11:02.1029560Z [ RUN      ] CppPrinter.Cond
2023-01-11T22:11:02.1030192Z [       OK ] CppPrinter.Cond (0 ms)
2023-01-11T22:11:02.1030694Z [ RUN      ] CppPrinter.Intrinsics
2023-01-11T22:11:02.1031200Z [       OK ] CppPrinter.Intrinsics (0 ms)
2023-01-11T22:11:02.1031736Z [ RUN      ] CppPrinter.ExternalCall
2023-01-11T22:11:02.1032172Z [       OK ] CppPrinter.ExternalCall (0 ms)
2023-01-11T22:11:02.1032746Z [----------] 28 tests from CppPrinter (0 ms total)
2023-01-11T22:11:02.1033033Z 
2023-01-11T22:11:02.1033261Z [----------] 8 tests from DynamicShapes
2023-01-11T22:11:02.1033554Z [ RUN      ] DynamicShapes.SimpleGraph
2023-01-11T22:11:02.1867966Z [       OK ] DynamicShapes.SimpleGraph (85 ms)
2023-01-11T22:11:02.1868599Z [ RUN      ] DynamicShapes.GraphWith2InputsSameDims
2023-01-11T22:11:02.2606551Z [       OK ] DynamicShapes.GraphWith2InputsSameDims (73 ms)
2023-01-11T22:11:02.2607147Z [ RUN      ] DynamicShapes.GraphWith2InputsAndBroadcast
2023-01-11T22:11:02.3317618Z [       OK ] DynamicShapes.GraphWith2InputsAndBroadcast (71 ms)
2023-01-11T22:11:02.3318258Z [ RUN      ] DynamicShapes.GraphWithPartiallySymbolicOutput
2023-01-11T22:11:02.3712738Z [       OK ] DynamicShapes.GraphWithPartiallySymbolicOutput (39 ms)
2023-01-11T22:11:02.3713364Z [ RUN      ] DynamicShapes.GraphWithSymbolicStrides
2023-01-11T22:11:02.5370193Z [       OK ] DynamicShapes.GraphWithSymbolicStrides (165 ms)
2023-01-11T22:11:02.5370627Z [ RUN      ] DynamicShapes.GraphWithCatAndBroadcast
2023-01-11T22:11:02.9745329Z [       OK ] DynamicShapes.GraphWithCatAndBroadcast (437 ms)
2023-01-11T22:11:02.9745733Z [ RUN      ] DynamicShapes.GraphFromModel
2023-01-11T22:11:03.3504437Z [       OK ] DynamicShapes.GraphFromModel (375 ms)
2023-01-11T22:11:03.3504825Z [ RUN      ] DynamicShapes.MultiThreadedExecution
2023-01-11T22:11:03.4257947Z [       OK ] DynamicShapes.MultiThreadedExecution (75 ms)
2023-01-11T22:11:03.4258596Z [----------] 8 tests from DynamicShapes (1324 ms total)
2023-01-11T22:11:03.4258851Z 
2023-01-11T22:11:03.4259097Z [----------] 30 tests from Expr
2023-01-11T22:11:03.4259570Z [ RUN      ] Expr.BasicValueTest
2023-01-11T22:11:03.4259973Z [       OK ] Expr.BasicValueTest (0 ms)
2023-01-11T22:11:03.4260398Z [ RUN      ] Expr.BasicValueTest02
2023-01-11T22:11:03.4260803Z [       OK ] Expr.BasicValueTest02 (0 ms)
2023-01-11T22:11:03.4261212Z [ RUN      ] Expr.IsChannelsLastContiguous
2023-01-11T22:11:03.4261559Z [       OK ] Expr.IsChannelsLastContiguous (0 ms)
2023-01-11T22:11:03.4261841Z [ RUN      ] Expr.LetTest01
2023-01-11T22:11:03.4262099Z [       OK ] Expr.LetTest01 (0 ms)
2023-01-11T22:11:03.4262338Z [ RUN      ] Expr.LetTest02
2023-01-11T22:11:03.4262774Z [       OK ] Expr.LetTest02 (0 ms)
2023-01-11T22:11:03.4263240Z [ RUN      ] Expr.LetStmtTest01
2023-01-11T22:11:03.4263501Z [       OK ] Expr.LetStmtTest01 (0 ms)
2023-01-11T22:11:03.4263754Z [ RUN      ] Expr.IntTest
2023-01-11T22:11:03.4264019Z [       OK ] Expr.IntTest (0 ms)
2023-01-11T22:11:03.4264267Z [ RUN      ] Expr.FloatTest
2023-01-11T22:11:03.4264512Z [       OK ] Expr.FloatTest (0 ms)
2023-01-11T22:11:03.4264759Z [ RUN      ] Expr.ByteTest
2023-01-11T22:11:03.4265011Z [       OK ] Expr.ByteTest (0 ms)
2023-01-11T22:11:03.4265243Z [ RUN      ] Expr.CharTest
2023-01-11T22:11:03.4265495Z [       OK ] Expr.CharTest (0 ms)
2023-01-11T22:11:03.4265740Z [ RUN      ] Expr.ShortTest
2023-01-11T22:11:03.4265985Z [       OK ] Expr.ShortTest (0 ms)
2023-01-11T22:11:03.4266231Z [ RUN      ] Expr.LongTest
2023-01-11T22:11:03.4266480Z [       OK ] Expr.LongTest (0 ms)
2023-01-11T22:11:03.4266710Z [ RUN      ] Expr.HalfTest
2023-01-11T22:11:03.4267021Z [       OK ] Expr.HalfTest (0 ms)
2023-01-11T22:11:03.4267273Z [ RUN      ] Expr.DoubleTest
2023-01-11T22:11:03.4267526Z [       OK ] Expr.DoubleTest (0 ms)
2023-01-11T22:11:03.4267780Z [ RUN      ] Expr.VectorAdd01
2023-01-11T22:11:03.4276002Z [       OK ] Expr.VectorAdd01 (1 ms)
2023-01-11T22:11:03.4276264Z [ RUN      ] Expr.CompareSelectEQ
2023-01-11T22:11:03.4312588Z [       OK ] Expr.CompareSelectEQ (3 ms)
2023-01-11T22:11:03.4312888Z [ RUN      ] Expr.CompareSelectDtypes
2023-01-11T22:11:03.4350142Z [       OK ] Expr.CompareSelectDtypes (3 ms)
2023-01-11T22:11:03.4350464Z [ RUN      ] Expr.IntrinsicsDtypes
2023-01-11T22:11:03.4356509Z [       OK ] Expr.IntrinsicsDtypes (0 ms)
2023-01-11T22:11:03.4357041Z [ RUN      ] Expr.Substitute01
2023-01-11T22:11:03.4357513Z [       OK ] Expr.Substitute01 (0 ms)
2023-01-11T22:11:03.4357945Z [ RUN      ] Expr.Math01
2023-01-11T22:11:03.4358376Z [       OK ] Expr.Math01 (0 ms)
2023-01-11T22:11:03.4358786Z [ RUN      ] Expr.UnaryMath01
2023-01-11T22:11:03.4359688Z [       OK ] Expr.UnaryMath01 (0 ms)
2023-01-11T22:11:03.4360204Z [ RUN      ] Expr.BinaryMath01
2023-01-11T22:11:03.4360487Z [       OK ] Expr.BinaryMath01 (0 ms)
2023-01-11T22:11:03.4360844Z [ RUN      ] Expr.LogicalOps01
2023-01-11T22:11:03.4361306Z [       OK ] Expr.LogicalOps01 (0 ms)
2023-01-11T22:11:03.4361761Z [ RUN      ] Expr.LogicalOps02
2023-01-11T22:11:03.4362264Z [       OK ] Expr.LogicalOps02 (0 ms)
2023-01-11T22:11:03.4362591Z [ RUN      ] Expr.LogicalOps03
2023-01-11T22:11:03.4363450Z [       OK ] Expr.LogicalOps03 (0 ms)
2023-01-11T22:11:03.4363925Z [ RUN      ] Expr.BitwiseOps
2023-01-11T22:11:03.4364334Z [       OK ] Expr.BitwiseOps (0 ms)
2023-01-11T22:11:03.4364595Z [ RUN      ] Expr.DynamicShapeAdd
2023-01-11T22:11:03.4366451Z [       OK ] Expr.DynamicShapeAdd (0 ms)
2023-01-11T22:11:03.4366725Z [ RUN      ] Expr.OutOfBounds
2023-01-11T22:11:03.4392379Z [       OK ] Expr.OutOfBounds (2 ms)
2023-01-11T22:11:03.4392667Z [ RUN      ] Expr.OutOfBounds2d
2023-01-11T22:11:03.4400419Z [       OK ] Expr.OutOfBounds2d (0 ms)
2023-01-11T22:11:03.4400735Z [ RUN      ] Expr.OutOfBounds2dFlattenedIndex
2023-01-11T22:11:03.4405181Z [       OK ] Expr.OutOfBounds2dFlattenedIndex (0 ms)
2023-01-11T22:11:03.4405584Z [----------] 30 tests from Expr (14 ms total)
2023-01-11T22:11:03.4405736Z 
2023-01-11T22:11:03.4405889Z [----------] 16 tests from ExternalCall
2023-01-11T22:11:03.4406159Z [ RUN      ] ExternalCall.Conv1d_float
2023-01-11T22:11:03.4683250Z [       OK ] ExternalCall.Conv1d_float (27 ms)
2023-01-11T22:11:03.4683658Z [ RUN      ] ExternalCall.Conv1d_int
2023-01-11T22:11:03.4970584Z [       OK ] ExternalCall.Conv1d_int (28 ms)
2023-01-11T22:11:03.4970984Z [ RUN      ] ExternalCall.Conv1d_nobias_noargs
2023-01-11T22:11:03.5212821Z [       OK ] ExternalCall.Conv1d_nobias_noargs (24 ms)
2023-01-11T22:11:03.5213232Z [ RUN      ] ExternalCall.Conv2d_float
2023-01-11T22:11:03.5528270Z [       OK ] ExternalCall.Conv2d_float (31 ms)
2023-01-11T22:11:03.5528851Z [ RUN      ] ExternalCall.Conv2d_int
2023-01-11T22:11:03.5909941Z [       OK ] ExternalCall.Conv2d_int (38 ms)
2023-01-11T22:11:03.5910406Z [ RUN      ] ExternalCall.Conv2d_nobias_noargs
2023-01-11T22:11:03.6189384Z [       OK ] ExternalCall.Conv2d_nobias_noargs (27 ms)
2023-01-11T22:11:03.6190470Z [ RUN      ] ExternalCall.Addmm_float
2023-01-11T22:11:03.6453601Z [       OK ] ExternalCall.Addmm_float (26 ms)
2023-01-11T22:11:03.6454123Z [ RUN      ] ExternalCall.Embedding
2023-01-11T22:11:03.6703339Z [       OK ] ExternalCall.Embedding (24 ms)
2023-01-11T22:11:03.6703910Z [ RUN      ] ExternalCall.MaxReduction
2023-01-11T22:11:03.6933019Z [       OK ] ExternalCall.MaxReduction (22 ms)
2023-01-11T22:11:03.6933868Z [ RUN      ] ExternalCall.Prepacked_Linear_float
2023-01-11T22:11:03.7282639Z [       OK ] ExternalCall.Prepacked_Linear_float (34 ms)
2023-01-11T22:11:03.7283427Z [ RUN      ] ExternalCall.Prepacked_Conv2d_float
2023-01-11T22:11:03.7765874Z [       OK ] ExternalCall.Prepacked_Conv2d_float (48 ms)
2023-01-11T22:11:03.7766453Z [ RUN      ] ExternalCall.BinaryFloat
2023-01-11T22:11:03.8501486Z [       OK ] ExternalCall.BinaryFloat (74 ms)
2023-01-11T22:11:03.8501927Z [ RUN      ] ExternalCall.UnaryFloat
2023-01-11T22:11:03.8956803Z [       OK ] ExternalCall.UnaryFloat (45 ms)
2023-01-11T22:11:03.8957162Z [ RUN      ] ExternalCall.ComputeInterop
2023-01-11T22:11:04.9803795Z [       OK ] ExternalCall.ComputeInterop (1084 ms)
2023-01-11T22:11:04.9804382Z [ RUN      ] ExternalCall.Inlining
2023-01-11T22:11:05.0539796Z [       OK ] ExternalCall.Inlining (73 ms)
2023-01-11T22:11:05.0540436Z [ RUN      ] ExternalCall.JitCustomFusionOp
2023-01-11T22:11:05.1790322Z [       OK ] ExternalCall.JitCustomFusionOp (125 ms)
2023-01-11T22:11:05.1791040Z [----------] 16 tests from ExternalCall (1738 ms total)
2023-01-11T22:11:05.1791324Z 
2023-01-11T22:11:05.1791590Z [----------] 8 tests from GraphOpt
2023-01-11T22:11:05.1792077Z [ RUN      ] GraphOpt.OptimizeCat
2023-01-11T22:11:05.2122502Z [       OK ] GraphOpt.OptimizeCat (33 ms)
2023-01-11T22:11:05.2123054Z [ RUN      ] GraphOpt.OptimizeCat2
2023-01-11T22:11:05.2490231Z [       OK ] GraphOpt.OptimizeCat2 (36 ms)
2023-01-11T22:11:05.2490775Z [ RUN      ] GraphOpt.OptimizeCat3
2023-01-11T22:11:05.2913030Z [       OK ] GraphOpt.OptimizeCat3 (42 ms)
2023-01-11T22:11:05.2913723Z [ RUN      ] GraphOpt.OptimizeCatWithTypePromotionInUser
2023-01-11T22:11:05.3252370Z [       OK ] GraphOpt.OptimizeCatWithTypePromotionInUser (33 ms)
2023-01-11T22:11:05.3253168Z [ RUN      ] GraphOpt.OptimizeCatWithTypePromotionInCat
2023-01-11T22:11:05.3882969Z [       OK ] GraphOpt.OptimizeCatWithTypePromotionInCat (63 ms)
2023-01-11T22:11:05.3883758Z [ RUN      ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp
2023-01-11T22:11:05.4277886Z [       OK ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp (39 ms)
2023-01-11T22:11:05.4278713Z [ RUN      ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp2
2023-01-11T22:11:05.4700955Z [       OK ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp2 (42 ms)
2023-01-11T22:11:05.4701698Z [ RUN      ] GraphOpt.AOTGraphPrepPasses
2023-01-11T22:11:05.4702286Z [       OK ] GraphOpt.AOTGraphPrepPasses (0 ms)
2023-01-11T22:11:05.4703469Z [----------] 8 tests from GraphOpt (291 ms total)
2023-01-11T22:11:05.4703742Z 
2023-01-11T22:11:05.4704003Z [----------] 4 tests from IRPrinter
2023-01-11T22:11:05.4704490Z [ RUN      ] IRPrinter.BasicValueTest
2023-01-11T22:11:05.4705036Z [       OK ] IRPrinter.BasicValueTest (0 ms)
2023-01-11T22:11:05.4705484Z [ RUN      ] IRPrinter.BasicValueTest02
2023-01-11T22:11:05.4705881Z [       OK ] IRPrinter.BasicValueTest02 (0 ms)
2023-01-11T22:11:05.4706285Z [ RUN      ] IRPrinter.CastTest
2023-01-11T22:11:05.4706605Z [       OK ] IRPrinter.CastTest (0 ms)
2023-01-11T22:11:05.4706994Z [ RUN      ] IRPrinter.FunctionName
2023-01-11T22:11:05.4707649Z [       OK ] IRPrinter.FunctionName (0 ms)
2023-01-11T22:11:05.4708150Z [----------] 4 tests from IRPrinter (0 ms total)
2023-01-11T22:11:05.4708401Z 
2023-01-11T22:11:05.4708622Z [----------] 8 tests from IRVerifier
2023-01-11T22:11:05.4709272Z [ RUN      ] IRVerifier.BitwiseOps
2023-01-11T22:11:05.4709783Z [       OK ] IRVerifier.BitwiseOps (0 ms)
2023-01-11T22:11:05.4710308Z [ RUN      ] IRVerifier.CompareSelect
2023-01-11T22:11:05.4710844Z [       OK ] IRVerifier.CompareSelect (0 ms)
2023-01-11T22:11:05.4711111Z [ RUN      ] IRVerifier.Ramp
2023-01-11T22:11:05.4711382Z [       OK ] IRVerifier.Ramp (0 ms)
2023-01-11T22:11:05.4711636Z [ RUN      ] IRVerifier.Load
2023-01-11T22:11:05.4711896Z [       OK ] IRVerifier.Load (0 ms)
2023-01-11T22:11:05.4712151Z [ RUN      ] IRVerifier.IfThenElse
2023-01-11T22:11:05.4712437Z [       OK ] IRVerifier.IfThenElse (0 ms)
2023-01-11T22:11:05.4712698Z [ RUN      ] IRVerifier.For
2023-01-11T22:11:05.4712945Z [       OK ] IRVerifier.For (0 ms)
2023-01-11T22:11:05.4713202Z [ RUN      ] IRVerifier.Block
2023-01-11T22:11:05.4713471Z [       OK ] IRVerifier.Block (0 ms)
2023-01-11T22:11:05.4713719Z [ RUN      ] IRVerifier.Store
2023-01-11T22:11:05.4713979Z [       OK ] IRVerifier.Store (0 ms)
2023-01-11T22:11:05.4714274Z [----------] 8 tests from IRVerifier (0 ms total)
2023-01-11T22:11:05.4714424Z 
2023-01-11T22:11:05.4714548Z [----------] 37 tests from Kernel
2023-01-11T22:11:05.4714840Z [ RUN      ] Kernel.ParallelExternalCallBuf
2023-01-11T22:11:05.5293523Z [       OK ] Kernel.ParallelExternalCallBuf (58 ms)
2023-01-11T22:11:05.5294165Z [ RUN      ] Kernel.InliningIntermediates
2023-01-11T22:11:05.5985190Z [       OK ] Kernel.InliningIntermediates (69 ms)
2023-01-11T22:11:05.5985915Z [ RUN      ] Kernel.PreAllocIntermediateBufs
2023-01-11T22:11:05.7115579Z [       OK ] Kernel.PreAllocIntermediateBufs (113 ms)
2023-01-11T22:11:05.7116151Z [ RUN      ] Kernel._1
2023-01-11T22:11:05.7420108Z [       OK ] Kernel._1 (30 ms)
2023-01-11T22:11:05.7420558Z [ RUN      ] Kernel._2
2023-01-11T22:11:05.7747033Z [       OK ] Kernel._2 (32 ms)
2023-01-11T22:11:05.7747472Z [ RUN      ] Kernel._3
2023-01-11T22:11:05.8079233Z [       OK ] Kernel._3 (33 ms)
2023-01-11T22:11:05.8079796Z [ RUN      ] Kernel.Huge
2023-01-11T22:11:05.8341161Z [       OK ] Kernel.Huge (26 ms)
2023-01-11T22:11:05.8341695Z [ RUN      ] Kernel.ParallelStrided
2023-01-11T22:11:05.9554612Z [       OK ] Kernel.ParallelStrided (121 ms)
2023-01-11T22:11:05.9555331Z [ RUN      ] Kernel.CatInputTypesPromotion
2023-01-11T22:11:06.0486895Z [       OK ] Kernel.CatInputTypesPromotion (93 ms)
2023-01-11T22:11:06.0487442Z [ RUN      ] Kernel.ToDType
2023-01-11T22:11:06.0762852Z [       OK ] Kernel.ToDType (27 ms)
2023-01-11T22:11:06.0763463Z [ RUN      ] Kernel.CatAndInlineWithAConstantDim
2023-01-11T22:11:06.1041095Z [       OK ] Kernel.CatAndInlineWithAConstantDim (27 ms)
2023-01-11T22:11:06.1042010Z [ RUN      ] Kernel.CatWithEmptyInputs
2023-01-11T22:11:06.1746501Z [       OK ] Kernel.CatWithEmptyInputs (70 ms)
2023-01-11T22:11:06.1746860Z [ RUN      ] Kernel.CatWoConditionals
2023-01-11T22:11:06.2479149Z [       OK ] Kernel.CatWoConditionals (73 ms)
2023-01-11T22:11:06.2479490Z [ RUN      ] Kernel.OptimizeConditionals
2023-01-11T22:11:06.3433262Z [       OK ] Kernel.OptimizeConditionals (95 ms)
2023-01-11T22:11:06.3433924Z [ RUN      ] Kernel.SumAllAxes
2023-01-11T22:11:06.3926502Z [       OK ] Kernel.SumAllAxes (49 ms)
2023-01-11T22:11:06.3927037Z [ RUN      ] Kernel.SumOneAxis
2023-01-11T22:11:06.7943038Z [       OK ] Kernel.SumOneAxis (401 ms)
2023-01-11T22:11:06.7943592Z [ RUN      ] Kernel.SumMultipleAxes
2023-01-11T22:11:07.2234985Z [       OK ] Kernel.SumMultipleAxes (429 ms)
2023-01-11T22:11:07.2235516Z [ RUN      ] Kernel.Softmax2D
2023-01-11T22:11:07.6649221Z [       OK ] Kernel.Softmax2D (441 ms)
2023-01-11T22:11:07.6649752Z [ RUN      ] Kernel.Softmax3D
2023-01-11T22:11:08.5261627Z [       OK ] Kernel.Softmax3D (861 ms)
2023-01-11T22:11:08.5262180Z [ RUN      ] Kernel.Softmax4D
2023-01-11T22:11:09.6552019Z [       OK ] Kernel.Softmax4D (1128 ms)
2023-01-11T22:11:09.6552600Z [ RUN      ] Kernel.SignTest
2023-01-11T22:11:09.7244544Z [       OK ] Kernel.SignTest (69 ms)
2023-01-11T22:11:09.7522771Z [ RUN      ] Kernel.InlineProducerIntoReduction
2023-01-11T22:11:09.7523458Z [       OK ] Kernel.InlineProducerIntoReduction (27 ms)
2023-01-11T22:11:09.7524115Z [ RUN      ] Kernel.InlineReductionIntoConsumer
2023-01-11T22:11:09.7866477Z [       OK ] Kernel.InlineReductionIntoConsumer (34 ms)
2023-01-11T22:11:09.7867073Z [ RUN      ] Kernel.ConstantTensors
2023-01-11T22:11:09.8257652Z [       OK ] Kernel.ConstantTensors (39 ms)
2023-01-11T22:11:09.8258242Z [ RUN      ] Kernel.ConstantTensorsNonContiguous
2023-01-11T22:11:09.8653157Z [       OK ] Kernel.ConstantTensorsNonContiguous (39 ms)
2023-01-11T22:11:09.8653736Z [ RUN      ] Kernel.RunFast
2023-01-11T22:11:09.8968113Z [       OK ] Kernel.RunFast (31 ms)
2023-01-11T22:11:09.8968695Z [ RUN      ] Kernel.RunWithAllocatedOutputs
2023-01-11T22:11:09.9282529Z [       OK ] Kernel.RunWithAllocatedOutputs (31 ms)
2023-01-11T22:11:09.9283052Z [ RUN      ] Kernel.CodegenInspection
2023-01-11T22:11:09.9680972Z [       OK ] Kernel.CodegenInspection (39 ms)
2023-01-11T22:11:09.9681559Z [ RUN      ] Kernel.CustomLowering
2023-01-11T22:11:09.9926551Z [       OK ] Kernel.CustomLowering (24 ms)
2023-01-11T22:11:09.9927133Z [ RUN      ] Kernel.Vectorize
2023-01-11T22:11:10.0233572Z [       OK ] Kernel.Vectorize (30 ms)
2023-01-11T22:11:10.0234169Z [ RUN      ] Kernel.Strided1dWithinBounds
2023-01-11T22:11:10.0460494Z [       OK ] Kernel.Strided1dWithinBounds (22 ms)
2023-01-11T22:11:10.0461132Z [ RUN      ] Kernel.InputAsOutput
2023-01-11T22:11:10.0804187Z [       OK ] Kernel.InputAsOutput (34 ms)
2023-01-11T22:11:10.0804729Z [ RUN      ] Kernel.ScalarOut
2023-01-11T22:11:10.1006288Z [       OK ] Kernel.ScalarOut (20 ms)
2023-01-11T22:11:10.1006654Z [ RUN      ] Kernel.ScalarTensorOut
2023-01-11T22:11:10.1262822Z [       OK ] Kernel.ScalarTensorOut (25 ms)
2023-01-11T22:11:10.1263399Z [ RUN      ] Kernel.FuseLoopsWithVariableBounds
2023-01-11T22:11:10.5658957Z [       OK ] Kernel.FuseLoopsWithVariableBounds (439 ms)
2023-01-11T22:11:10.5659370Z [ RUN      ] Kernel.FuseLoopsWithVariableConcatDim
2023-01-11T22:11:11.0650782Z [       OK ] Kernel.FuseLoopsWithVariableConcatDim (499 ms)
2023-01-11T22:11:11.0651204Z [ RUN      ] Kernel.DoNotFuseLoopsWithMismatchingVariableDims
2023-01-11T22:11:11.3892534Z [       OK ] Kernel.DoNotFuseLoopsWithMismatchingVariableDims (324 ms)
2023-01-11T22:11:11.3892983Z [----------] 37 tests from Kernel (5918 ms total)
2023-01-11T22:11:11.3893138Z 
2023-01-11T22:11:11.3893285Z [----------] 174 tests from LoopNest
2023-01-11T22:11:11.3893556Z [ RUN      ] LoopNest.ExprSimple01
2023-01-11T22:11:11.3907080Z [       OK ] LoopNest.ExprSimple01 (1 ms)
2023-01-11T22:11:11.3907618Z [ RUN      ] LoopNest.ExprLower01
2023-01-11T22:11:11.3922234Z [       OK ] LoopNest.ExprLower01 (1 ms)
2023-01-11T22:11:11.3922749Z [ RUN      ] LoopNest.ExprSimple02
2023-01-11T22:11:11.3944260Z [       OK ] LoopNest.ExprSimple02 (2 ms)
2023-01-11T22:11:11.3944861Z [ RUN      ] LoopNest.ExprSliceHeadWithLoopOptions
2023-01-11T22:11:11.3946213Z [       OK ] LoopNest.ExprSliceHeadWithLoopOptions (0 ms)
2023-01-11T22:11:11.3947057Z [ RUN      ] LoopNest.ExprSliceTailWithLoopOptions
2023-01-11T22:11:11.3948980Z [       OK ] LoopNest.ExprSliceTailWithLoopOptions (0 ms)
2023-01-11T22:11:11.3949667Z [ RUN      ] LoopNest.ExprSliceHeadWhenFactorEqualsSize
2023-01-11T22:11:11.3966021Z [       OK ] LoopNest.ExprSliceHeadWhenFactorEqualsSize (1 ms)
2023-01-11T22:11:11.3966780Z [ RUN      ] LoopNest.ExprSliceHeadWhenFactorLargerThanSize
2023-01-11T22:11:11.3967352Z [       OK ] LoopNest.ExprSliceHeadWhenFactorLargerThanSize (0 ms)
2023-01-11T22:11:11.3967963Z [ RUN      ] LoopNest.ExprSliceHead
2023-01-11T22:11:11.3968363Z [       OK ] LoopNest.ExprSliceHead (0 ms)
2023-01-11T22:11:11.3968730Z [ RUN      ] LoopNest.ExprSliceHeadWithNonZeroStart
2023-01-11T22:11:11.3969453Z [       OK ] LoopNest.ExprSliceHeadWithNonZeroStart (0 ms)
2023-01-11T22:11:11.3970123Z [ RUN      ] LoopNest.ExprSliceTailWhenFactorEqualsSize
2023-01-11T22:11:11.3970856Z [       OK ] LoopNest.ExprSliceTailWhenFactorEqualsSize (0 ms)
2023-01-11T22:11:11.3971436Z [ RUN      ] LoopNest.ExprSliceTailWhenFactorLargerThanSize
2023-01-11T22:11:11.3971940Z [       OK ] LoopNest.ExprSliceTailWhenFactorLargerThanSize (0 ms)
2023-01-11T22:11:11.3972404Z [ RUN      ] LoopNest.ExprSliceTail
2023-01-11T22:11:11.3972701Z [       OK ] LoopNest.ExprSliceTail (0 ms)
2023-01-11T22:11:11.3973001Z [ RUN      ] LoopNest.ExprSplitAndSlice
2023-01-11T22:11:11.3978527Z [       OK ] LoopNest.ExprSplitAndSlice (0 ms)
2023-01-11T22:11:11.3978853Z [ RUN      ] LoopNest.ExprSliceAndNormalize
2023-01-11T22:11:11.3980395Z [       OK ] LoopNest.ExprSliceAndNormalize (0 ms)
2023-01-11T22:11:11.3980786Z [ RUN      ] LoopNest.ExprSliceWithVariableDimension
2023-01-11T22:11:11.3996015Z [       OK ] LoopNest.ExprSliceWithVariableDimension (1 ms)
2023-01-11T22:11:11.3996364Z [ RUN      ] LoopNest.ExprSplitWithTail
2023-01-11T22:11:11.4002960Z [       OK ] LoopNest.ExprSplitWithTail (0 ms)
2023-01-11T22:11:11.4003331Z [ RUN      ] LoopNest.ExprSplitWithTailNone
2023-01-11T22:11:11.4015655Z [       OK ] LoopNest.ExprSplitWithTailNone (1 ms)
2023-01-11T22:11:11.4015988Z [ RUN      ] LoopNest.ExprSplitWithMask01
2023-01-11T22:11:11.4055649Z [       OK ] LoopNest.ExprSplitWithMask01 (3 ms)
2023-01-11T22:11:11.4056023Z [ RUN      ] LoopNest.ExprSplitWithMaskRepeatedNoMask
2023-01-11T22:11:11.4060970Z [       OK ] LoopNest.ExprSplitWithMaskRepeatedNoMask (0 ms)
2023-01-11T22:11:11.4061386Z [ RUN      ] LoopNest.getLoopAt
2023-01-11T22:11:11.4061664Z [       OK ] LoopNest.getLoopAt (0 ms)
2023-01-11T22:11:11.4061942Z [ RUN      ] LoopNest.TileSimple
2023-01-11T22:11:11.4941140Z [       OK ] LoopNest.TileSimple (87 ms)
2023-01-11T22:11:11.4941449Z [ RUN      ] LoopNest.TileWithTails
2023-01-11T22:11:11.5818894Z [       OK ] LoopNest.TileWithTails (87 ms)
2023-01-11T22:11:11.5819233Z [ RUN      ] LoopNest.TileInMiddle
2023-01-11T22:11:11.7226429Z [       OK ] LoopNest.TileInMiddle (140 ms)
2023-01-11T22:11:11.7227092Z [ RUN      ] LoopNest.SplitWithTailWithLoopOptions
2023-01-11T22:11:11.7227599Z [       OK ] LoopNest.SplitWithTailWithLoopOptions (0 ms)
2023-01-11T22:11:11.7227977Z [ RUN      ] LoopNest.SplitWithMaskWithLoopOptions
2023-01-11T22:11:11.7228350Z [       OK ] LoopNest.SplitWithMaskWithLoopOptions (0 ms)
2023-01-11T22:11:11.7228701Z [ RUN      ] LoopNest.ScheduleBroadcastAddBuffer
2023-01-11T22:11:11.7245457Z [       OK ] LoopNest.ScheduleBroadcastAddBuffer (1 ms)
2023-01-11T22:11:11.7245822Z [ RUN      ] LoopNest.ScheduleFunctionCall01
2023-01-11T22:11:11.7321386Z [       OK ] LoopNest.ScheduleFunctionCall01 (7 ms)
2023-01-11T22:11:11.7321730Z [ RUN      ] LoopNest.ScheduleInlineSimple
2023-01-11T22:11:11.7440157Z [       OK ] LoopNest.ScheduleInlineSimple (11 ms)
2023-01-11T22:11:11.7440503Z [ RUN      ] LoopNest.ScheduleInlineFunc01
2023-01-11T22:11:11.7995511Z [       OK ] LoopNest.ScheduleInlineFunc01 (55 ms)
2023-01-11T22:11:11.7995871Z [ RUN      ] LoopNest.ScheduleInlineRandom
2023-01-11T22:11:11.7999187Z [       OK ] LoopNest.ScheduleInlineRandom (0 ms)
2023-01-11T22:11:11.7999618Z [ RUN      ] LoopNest.ScheduleInlineRandomUnrelated
2023-01-11T22:11:11.8002605Z [       OK ] LoopNest.ScheduleInlineRandomUnrelated (0 ms)
2023-01-11T22:11:11.8003030Z [ RUN      ] LoopNest.ScheduleInlineRandomLowerDimensions
2023-01-11T22:11:11.8005733Z [       OK ] LoopNest.ScheduleInlineRandomLowerDimensions (0 ms)
2023-01-11T22:11:11.8006143Z [ RUN      ] LoopNest.ScheduleInlineIntrinsics
2023-01-11T22:11:11.8093145Z [       OK ] LoopNest.ScheduleInlineIntrinsics (8 ms)
2023-01-11T22:11:11.8093546Z [ RUN      ] LoopNest.ScheduleInlineRandWithIntrinsics
2023-01-11T22:11:11.8096211Z [       OK ] LoopNest.ScheduleInlineRandWithIntrinsics (0 ms)
2023-01-11T22:11:11.8096569Z [ RUN      ] LoopNest.ScheduleSplitAThenInline
2023-01-11T22:11:11.8097924Z [       OK ] LoopNest.ScheduleSplitAThenInline (0 ms)
2023-01-11T22:11:11.8098273Z [ RUN      ] LoopNest.ScheduleSplitBThenInline
2023-01-11T22:11:11.8101746Z [       OK ] LoopNest.ScheduleSplitBThenInline (0 ms)
2023-01-11T22:11:11.8102094Z [ RUN      ] LoopNest.ScheduleSplitTwiceThenInline
2023-01-11T22:11:11.8103650Z [       OK ] LoopNest.ScheduleSplitTwiceThenInline (0 ms)
2023-01-11T22:11:11.8104011Z [ RUN      ] LoopNest.ScheduleInlineThenSplit
2023-01-11T22:11:11.8107090Z [       OK ] LoopNest.ScheduleInlineThenSplit (0 ms)
2023-01-11T22:11:11.8107453Z [ RUN      ] LoopNest.ScheduleSplitInlineThenSplit
2023-01-11T22:11:11.8114396Z [       OK ] LoopNest.ScheduleSplitInlineThenSplit (0 ms)
2023-01-11T22:11:11.8114772Z [ RUN      ] LoopNest.ScheduleSplitInlineSimplify
2023-01-11T22:11:11.8121719Z [       OK ] LoopNest.ScheduleSplitInlineSimplify (0 ms)
2023-01-11T22:11:11.8122087Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedOnce
2023-01-11T22:11:11.8126390Z [       OK ] LoopNest.ScheduleInlineThreeMixedOnce (0 ms)
2023-01-11T22:11:11.8126755Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedTwice
2023-01-11T22:11:11.8130938Z [       OK ] LoopNest.ScheduleInlineThreeMixedTwice (0 ms)
2023-01-11T22:11:11.8131315Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedInner
2023-01-11T22:11:11.8135800Z [       OK ] LoopNest.ScheduleInlineThreeMixedInner (0 ms)
2023-01-11T22:11:11.8136160Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedSplit
2023-01-11T22:11:11.8138118Z [       OK ] LoopNest.ScheduleInlineThreeMixedSplit (0 ms)
2023-01-11T22:11:11.8138677Z [ RUN      ] LoopNest.ScheduleInlineOutputTensors
2023-01-11T22:11:11.8142502Z [       OK ] LoopNest.ScheduleInlineOutputTensors (0 ms)
2023-01-11T22:11:11.8142885Z [ RUN      ] LoopNest.ScheduleInlineWithCompoundIndices
2023-01-11T22:11:11.8161191Z [       OK ] LoopNest.ScheduleInlineWithCompoundIndices (1 ms)
2023-01-11T22:11:11.8161726Z [ RUN      ] LoopNest.ScheduleInlineConsumerIndicesWithCast
2023-01-11T22:11:11.8162376Z [       OK ] LoopNest.ScheduleInlineConsumerIndicesWithCast (0 ms)
2023-01-11T22:11:11.8163067Z [ RUN      ] LoopNest.ScheduleInlineProducerIndicesWithCast
2023-01-11T22:11:11.8163763Z [       OK ] LoopNest.ScheduleInlineProducerIndicesWithCast (0 ms)
2023-01-11T22:11:11.8164302Z [ RUN      ] LoopNest.ScheduleFuserStyle
2023-01-11T22:11:11.8211208Z [       OK ] LoopNest.ScheduleFuserStyle (4 ms)
2023-01-11T22:11:11.8211642Z [ RUN      ] LoopNest.ScheduleFuserThreeArg
2023-01-11T22:11:11.8268021Z [       OK ] LoopNest.ScheduleFuserThreeArg (5 ms)
2023-01-11T22:11:11.8268451Z [ RUN      ] LoopNest.ScheduleDynamicShape2D
2023-01-11T22:11:11.8388053Z [       OK ] LoopNest.ScheduleDynamicShape2D (11 ms)
2023-01-11T22:11:11.8388476Z [ RUN      ] LoopNest.LoopNestComputeAt_1
2023-01-11T22:11:11.8396697Z [       OK ] LoopNest.LoopNestComputeAt_1 (0 ms)
2023-01-11T22:11:11.8397088Z [ RUN      ] LoopNest.LoopNestComputeAt_2
2023-01-11T22:11:11.8883984Z [       OK ] LoopNest.LoopNestComputeAt_2 (48 ms)
2023-01-11T22:11:11.8884337Z [ RUN      ] LoopNest.LoopNestComputeAt_3
2023-01-11T22:11:11.9368703Z [       OK ] LoopNest.LoopNestComputeAt_3 (48 ms)
2023-01-11T22:11:11.9369095Z [ RUN      ] LoopNest.Reduce2dComputeAt
2023-01-11T22:11:12.0168923Z [       OK ] LoopNest.Reduce2dComputeAt (79 ms)
2023-01-11T22:11:12.0169333Z [ RUN      ] LoopNest.LoopNestReorderAxis1
2023-01-11T22:11:12.0170798Z [       OK ] LoopNest.LoopNestReorderAxis1 (0 ms)
2023-01-11T22:11:12.0171181Z [ RUN      ] LoopNest.LoopNestReorderPartialAxes
2023-01-11T22:11:12.0181007Z [       OK ] LoopNest.LoopNestReorderPartialAxes (0 ms)
2023-01-11T22:11:12.0181475Z [ RUN      ] LoopNest.LoopNestReorderInternalAxis
2023-01-11T22:11:12.0189728Z [       OK ] LoopNest.LoopNestReorderInternalAxis (0 ms)
2023-01-11T22:11:12.0190343Z [ RUN      ] LoopNest.LoopNestReorderEnclosingAxis
2023-01-11T22:11:12.0198886Z [       OK ] LoopNest.LoopNestReorderEnclosingAxis (0 ms)
2023-01-11T22:11:12.0199475Z [ RUN      ] LoopNest.LoopNestReorderSameAxis
2023-01-11T22:11:12.0200136Z [       OK ] LoopNest.LoopNestReorderSameAxis (0 ms)
2023-01-11T22:11:12.0200695Z [ RUN      ] LoopNest.LoopNestReorderExtraStatements
2023-01-11T22:11:12.0214246Z [       OK ] LoopNest.LoopNestReorderExtraStatements (1 ms)
2023-01-11T22:11:12.0214817Z [ RUN      ] LoopNest.LoopNestReorderLongStringOfPreOrphans
2023-01-11T22:11:12.0755847Z [       OK ] LoopNest.LoopNestReorderLongStringOfPreOrphans (54 ms)
2023-01-11T22:11:12.0756392Z [ RUN      ] LoopNest.LoopNestReorderLongStringOfPostOrphans
2023-01-11T22:11:12.1302216Z [       OK ] LoopNest.LoopNestReorderLongStringOfPostOrphans (54 ms)
2023-01-11T22:11:12.1302918Z [ RUN      ] LoopNest.LoopNestReorderLongStringFull
2023-01-11T22:11:12.1983632Z [       OK ] LoopNest.LoopNestReorderLongStringFull (68 ms)
2023-01-11T22:11:12.1984220Z [ RUN      ] LoopNest.LoopNestReorderInternalLoopNest
2023-01-11T22:11:12.2096675Z [       OK ] LoopNest.LoopNestReorderInternalLoopNest (11 ms)
2023-01-11T22:11:12.2097301Z [ RUN      ] LoopNest.OuterLoopVectorization
2023-01-11T22:11:12.2098152Z [       OK ] LoopNest.OuterLoopVectorization (0 ms)
2023-01-11T22:11:12.2098506Z [ RUN      ] LoopNest.VectorizeLoopNotNormalized
2023-01-11T22:11:12.2100321Z [       OK ] LoopNest.VectorizeLoopNotNormalized (0 ms)
2023-01-11T22:11:12.2100741Z [ RUN      ] LoopNest.Unroll
2023-01-11T22:11:12.2101414Z [       OK ] LoopNest.Unroll (0 ms)
2023-01-11T22:11:12.2101712Z [ RUN      ] LoopNest.UnrollOuter
2023-01-11T22:11:12.2103522Z [       OK ] LoopNest.UnrollOuter (0 ms)
2023-01-11T22:11:12.2103826Z [ RUN      ] LoopNest.UnrollInner
2023-01-11T22:11:12.2105234Z [       OK ] LoopNest.UnrollInner (0 ms)
2023-01-11T22:11:12.2105552Z [ RUN      ] LoopNest.UnrollMultipleStatements
2023-01-11T22:11:12.2106351Z [       OK ] LoopNest.UnrollMultipleStatements (0 ms)
2023-01-11T22:11:12.2106740Z [ RUN      ] LoopNest.UnrollNonLiteralConstantBounds
2023-01-11T22:11:12.2107797Z [       OK ] LoopNest.UnrollNonLiteralConstantBounds (0 ms)
2023-01-11T22:11:12.2108158Z [ RUN      ] LoopNest.UnrollNonConstantBounds
2023-01-11T22:11:12.2122780Z [       OK ] LoopNest.UnrollNonConstantBounds (1 ms)
2023-01-11T22:11:12.2123204Z [ RUN      ] LoopNest.UnrollByFactorsLessThan2
2023-01-11T22:11:12.2123547Z [       OK ] LoopNest.UnrollByFactorsLessThan2 (0 ms)
2023-01-11T22:11:12.2123896Z [ RUN      ] LoopNest.UnrollByFactorEqualToIters
2023-01-11T22:11:12.2126562Z [       OK ] LoopNest.UnrollByFactorEqualToIters (0 ms)
2023-01-11T22:11:12.2127041Z [ RUN      ] LoopNest.UnrollEmpty
2023-01-11T22:11:12.2127441Z [       OK ] LoopNest.UnrollEmpty (0 ms)
2023-01-11T22:11:12.2127716Z [ RUN      ] LoopNest.NoUnroll
2023-01-11T22:11:12.2127990Z [       OK ] LoopNest.NoUnroll (0 ms)
2023-01-11T22:11:12.2128256Z [ RUN      ] LoopNest.UnrollWithLet
2023-01-11T22:11:12.2129863Z [       OK ] LoopNest.UnrollWithLet (0 ms)
2023-01-11T22:11:12.2130167Z [ RUN      ] LoopNest.IsNormalized
2023-01-11T22:11:12.2130449Z [       OK ] LoopNest.IsNormalized (0 ms)
2023-01-11T22:11:12.2130766Z [ RUN      ] LoopNest.NormalizeStartPositive
2023-01-11T22:11:12.2132261Z [       OK ] LoopNest.NormalizeStartPositive (0 ms)
2023-01-11T22:11:12.2132599Z [ RUN      ] LoopNest.NormalizeStartNegative
2023-01-11T22:11:12.2134469Z [       OK ] LoopNest.NormalizeStartNegative (0 ms)
2023-01-11T22:11:12.2134863Z [ RUN      ] LoopNest.NormalizeStartZero
2023-01-11T22:11:12.2135188Z [       OK ] LoopNest.NormalizeStartZero (0 ms)
2023-01-11T22:11:12.2135500Z [ RUN      ] LoopNest.NormalizeStartVariable
2023-01-11T22:11:12.2138124Z [       OK ] LoopNest.NormalizeStartVariable (0 ms)
2023-01-11T22:11:12.2138474Z [ RUN      ] LoopNest.NormalizeOnNestedOuterLoop
2023-01-11T22:11:12.2140365Z [       OK ] LoopNest.NormalizeOnNestedOuterLoop (0 ms)
2023-01-11T22:11:12.2140710Z [ RUN      ] LoopNest.NormalizeOnNestedInnerLoop
2023-01-11T22:11:12.2143160Z [       OK ] LoopNest.NormalizeOnNestedInnerLoop (0 ms)
2023-01-11T22:11:12.2143520Z [ RUN      ] LoopNest.NormalizeAndSplitWithTail
2023-01-11T22:11:12.2147018Z [       OK ] LoopNest.NormalizeAndSplitWithTail (0 ms)
2023-01-11T22:11:12.2147423Z [ RUN      ] LoopNest.NotNormalizeAndSplitWithTail
2023-01-11T22:11:12.2152004Z [       OK ] LoopNest.NotNormalizeAndSplitWithTail (0 ms)
2023-01-11T22:11:12.2152377Z [ RUN      ] LoopNest.FlattenSimpleLoopNest2D
2023-01-11T22:11:12.2161805Z [       OK ] LoopNest.FlattenSimpleLoopNest2D (0 ms)
2023-01-11T22:11:12.2162176Z [ RUN      ] LoopNest.FlattenSimpleLoopNest3D
2023-01-11T22:11:12.2242927Z [       OK ] LoopNest.FlattenSimpleLoopNest3D (8 ms)
2023-01-11T22:11:12.2243302Z [ RUN      ] LoopNest.FlattenLoopNestAfterNormalize
2023-01-11T22:11:12.2268244Z [       OK ] LoopNest.FlattenLoopNestAfterNormalize (2 ms)
2023-01-11T22:11:12.2268680Z [ RUN      ] LoopNest.FlattenLoopNestWithNonLiteralConstantBounds
2023-01-11T22:11:12.2279808Z [       OK ] LoopNest.FlattenLoopNestWithNonLiteralConstantBounds (1 ms)
2023-01-11T22:11:12.2280507Z [ RUN      ] LoopNest.FlattenImperfectLoopNest
2023-01-11T22:11:12.2281183Z [       OK ] LoopNest.FlattenImperfectLoopNest (0 ms)
2023-01-11T22:11:12.2281618Z [ RUN      ] LoopNest.FlattenReductionLoopNest
2023-01-11T22:11:12.2282114Z [       OK ] LoopNest.FlattenReductionLoopNest (0 ms)
2023-01-11T22:11:12.2282695Z [ RUN      ] LoopNest.FlattenReductionLoopNestFromTensor
2023-01-11T22:11:12.2283398Z [       OK ] LoopNest.FlattenReductionLoopNestFromTensor (0 ms)
2023-01-11T22:11:12.2283785Z [ RUN      ] LoopNest.FlattenIncorrectLoopsAsInput
2023-01-11T22:11:12.2284145Z [       OK ] LoopNest.FlattenIncorrectLoopsAsInput (0 ms)
2023-01-11T22:11:12.2284501Z [ RUN      ] LoopNest.DetectInlineRankMismatch
2023-01-11T22:11:12.2284971Z [       OK ] LoopNest.DetectInlineRankMismatch (0 ms)
2023-01-11T22:11:12.2285282Z [ RUN      ] LoopNest.CacheReadsSimple
2023-01-11T22:11:12.2633644Z [       OK ] LoopNest.CacheReadsSimple (35 ms)
2023-01-11T22:11:12.2634170Z [ RUN      ] LoopNest.CacheReadsOuter
2023-01-11T22:11:12.3013259Z [       OK ] LoopNest.CacheReadsOuter (37 ms)
2023-01-11T22:11:12.3013812Z [ RUN      ] LoopNest.CacheReadsInternal
2023-01-11T22:11:12.3417809Z [       OK ] LoopNest.CacheReadsInternal (40 ms)
2023-01-11T22:11:12.3418350Z [ RUN      ] LoopNest.CacheReadsInner
2023-01-11T22:11:12.4052831Z [       OK ] LoopNest.CacheReadsInner (63 ms)
2023-01-11T22:11:12.4053419Z [ RUN      ] LoopNest.CacheWritesSimple
2023-01-11T22:11:12.4766699Z [       OK ] LoopNest.CacheWritesSimple (71 ms)
2023-01-11T22:11:12.4767266Z [ RUN      ] LoopNest.DeadStoreElimination
2023-01-11T22:11:12.4779781Z [       OK ] LoopNest.DeadStoreElimination (1 ms)
2023-01-11T22:11:12.4780485Z [ RUN      ] LoopNest.DeadStoreEliminationWithIntermediates
2023-01-11T22:11:12.4791510Z [       OK ] LoopNest.DeadStoreEliminationWithIntermediates (1 ms)
2023-01-11T22:11:12.4792081Z [ RUN      ] LoopNest.CompoundTensorSimple
2023-01-11T22:11:12.4804280Z [       OK ] LoopNest.CompoundTensorSimple (1 ms)
2023-01-11T22:11:12.4804864Z [ RUN      ] LoopNest.InlineConstantIndex
2023-01-11T22:11:12.4807680Z [       OK ] LoopNest.InlineConstantIndex (0 ms)
2023-01-11T22:11:12.4808228Z [ RUN      ] LoopNest.CompoundTensorUsed
2023-01-11T22:11:12.4829289Z [       OK ] LoopNest.CompoundTensorUsed (2 ms)
2023-01-11T22:11:12.4829839Z [ RUN      ] LoopNest.InlineFromLoad
2023-01-11T22:11:12.4830375Z [       OK ] LoopNest.InlineFromLoad (0 ms)
2023-01-11T22:11:12.4830822Z [ RUN      ] LoopNest.OptimizeConditionalsSimple
2023-01-11T22:11:12.4831586Z [       OK ] LoopNest.OptimizeConditionalsSimple (0 ms)
2023-01-11T22:11:12.4832240Z [ RUN      ] LoopNest.OptimizeConditionalsNestedConditions
2023-01-11T22:11:12.4833830Z [       OK ] LoopNest.OptimizeConditionalsNestedConditions (0 ms)
2023-01-11T22:11:12.4836573Z [ RUN      ] LoopNest.OptimizeConditionalsMultipleStores
2023-01-11T22:11:12.4837026Z [       OK ] LoopNest.OptimizeConditionalsMultipleStores (0 ms)
2023-01-11T22:11:12.4837474Z [ RUN      ] LoopNest.OptimizeConditionalsMultipleStoresInOneLoop
2023-01-11T22:11:12.4840984Z [       OK ] LoopNest.OptimizeConditionalsMultipleStoresInOneLoop (0 ms)
2023-01-11T22:11:12.4841424Z [ RUN      ] LoopNest.OptimizeConditionalsOuterLoopVar
2023-01-11T22:11:12.4844231Z [       OK ] LoopNest.OptimizeConditionalsOuterLoopVar (0 ms)
2023-01-11T22:11:12.4844644Z [ RUN      ] LoopNest.OptimizeConditionalsCompValuesNotOrdered
2023-01-11T22:11:12.4847150Z [       OK ] LoopNest.OptimizeConditionalsCompValuesNotOrdered (0 ms)
2023-01-11T22:11:12.4847824Z [ RUN      ] LoopNest.OptimizeConditionalsCompValuesNotConstants
2023-01-11T22:11:12.4850076Z [       OK ] LoopNest.OptimizeConditionalsCompValuesNotConstants (0 ms)
2023-01-11T22:11:12.4850506Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition
2023-01-11T22:11:12.4852999Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition (0 ms)
2023-01-11T22:11:12.4853429Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition2
2023-01-11T22:11:12.4856014Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition2 (0 ms)
2023-01-11T22:11:12.4856427Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition3
2023-01-11T22:11:12.4858671Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition3 (0 ms)
2023-01-11T22:11:12.4859099Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition4
2023-01-11T22:11:12.4861158Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition4 (0 ms)
2023-01-11T22:11:12.4861622Z [ RUN      ] LoopNest.OptimizeConditionalsNotNormalized
2023-01-11T22:11:12.4862729Z [       OK ] LoopNest.OptimizeConditionalsNotNormalized (0 ms)
2023-01-11T22:11:12.4863164Z [ RUN      ] LoopNest.ColReduceSplitTailEvenReorder
2023-01-11T22:11:12.6293417Z [       OK ] LoopNest.ColReduceSplitTailEvenReorder (142 ms)
2023-01-11T22:11:12.6293869Z [ RUN      ] LoopNest.ColReduceSplitTailUnevenReorder
2023-01-11T22:11:12.7429959Z [       OK ] LoopNest.ColReduceSplitTailUnevenReorder (113 ms)
2023-01-11T22:11:12.7430447Z [ RUN      ] LoopNest.ColReduceSplitMaskEvenReorder
2023-01-11T22:11:12.8853166Z [       OK ] LoopNest.ColReduceSplitMaskEvenReorder (142 ms)
2023-01-11T22:11:12.8853937Z [ RUN      ] LoopNest.ColReduceSplitMaskUnevenReorder
2023-01-11T22:11:13.0132717Z [       OK ] LoopNest.ColReduceSplitMaskUnevenReorder (127 ms)
2023-01-11T22:11:13.0133160Z [ RUN      ] LoopNest.ReorderAxisWithMultipleConds
2023-01-11T22:11:13.0135279Z [       OK ] LoopNest.ReorderAxisWithMultipleConds (0 ms)
2023-01-11T22:11:13.0135850Z [ RUN      ] LoopNest.VectorizeUse
2023-01-11T22:11:13.0136708Z [       OK ] LoopNest.VectorizeUse (0 ms)
2023-01-11T22:11:13.0137194Z [ RUN      ] LoopNest.Int64Direct
2023-01-11T22:11:13.0137700Z [       OK ] LoopNest.Int64Direct (0 ms)
2023-01-11T22:11:13.0138096Z [ RUN      ] LoopNest.Int64Compute
2023-01-11T22:11:13.0138625Z [       OK ] LoopNest.Int64Compute (0 ms)
2023-01-11T22:11:13.0139107Z [ RUN      ] LoopNest.DistributeLoopWithAllStmtsAsPivots
2023-01-11T22:11:13.0139678Z [       OK ] LoopNest.DistributeLoopWithAllStmtsAsPivots (0 ms)
2023-01-11T22:11:13.0140192Z [ RUN      ] LoopNest.DistributeLoopWithOneStmtAsPivot
2023-01-11T22:11:13.0140895Z [       OK ] LoopNest.DistributeLoopWithOneStmtAsPivot (0 ms)
2023-01-11T22:11:13.0141349Z [ RUN      ] LoopNest.DistributeLoopWithoutAnyPivot
2023-01-11T22:11:13.0141827Z [       OK ] LoopNest.DistributeLoopWithoutAnyPivot (0 ms)
2023-01-11T22:11:13.0142201Z [ RUN      ] LoopNest.DistributeLoopOverInnerLoops
2023-01-11T22:11:13.0142739Z [       OK ] LoopNest.DistributeLoopOverInnerLoops (0 ms)
2023-01-11T22:11:13.0143136Z [ RUN      ] LoopNest.DistributeLoopAndParentsWithoutAnyPivot
2023-01-11T22:11:13.0145063Z [       OK ] LoopNest.DistributeLoopAndParentsWithoutAnyPivot (0 ms)
2023-01-11T22:11:13.0145478Z [ RUN      ] LoopNest.fuseLoopsSimple
2023-01-11T22:11:13.0146891Z [       OK ] LoopNest.fuseLoopsSimple (0 ms)
2023-01-11T22:11:13.0147202Z [ RUN      ] LoopNest.fuseLoopsMultiple
2023-01-11T22:11:13.0152122Z [       OK ] LoopNest.fuseLoopsMultiple (0 ms)
2023-01-11T22:11:13.0152441Z [ RUN      ] LoopNest.fuseLoopsNested
2023-01-11T22:11:13.0158437Z [       OK ] LoopNest.fuseLoopsNested (0 ms)
2023-01-11T22:11:13.0158765Z [ RUN      ] LoopNest.fuseLoopsNested2D
2023-01-11T22:11:13.0161816Z [       OK ] LoopNest.fuseLoopsNested2D (0 ms)
2023-01-11T22:11:13.0162189Z [ RUN      ] LoopNest.fuseLoopsNested2DInner
2023-01-11T22:11:13.0164368Z [       OK ] LoopNest.fuseLoopsNested2DInner (0 ms)
2023-01-11T22:11:13.0164786Z [ RUN      ] LoopNest.fuseLoopsDifferentStopBounds
2023-01-11T22:11:13.0165224Z [       OK ] LoopNest.fuseLoopsDifferentStopBounds (0 ms)
2023-01-11T22:11:13.0165642Z [ RUN      ] LoopNest.fuseLoopsDifferentStartBounds
2023-01-11T22:11:13.0166112Z [       OK ] LoopNest.fuseLoopsDifferentStartBounds (0 ms)
2023-01-11T22:11:13.0166547Z [ RUN      ] LoopNest.fuseLoopsNotContiguous
2023-01-11T22:11:13.0166891Z [       OK ] LoopNest.fuseLoopsNotContiguous (0 ms)
2023-01-11T22:11:13.0167378Z [ RUN      ] LoopNest.fuseLoopsWithDifferentParents
2023-01-11T22:11:13.0167767Z [       OK ] LoopNest.fuseLoopsWithDifferentParents (0 ms)
2023-01-11T22:11:13.0168136Z [ RUN      ] LoopNest.fuseLoopsWithVariableBounds
2023-01-11T22:11:13.0169879Z [       OK ] LoopNest.fuseLoopsWithVariableBounds (0 ms)
2023-01-11T22:11:13.0170277Z [ RUN      ] LoopNest.fuseLoopsWithExprBounds
2023-01-11T22:11:13.0174658Z [       OK ] LoopNest.fuseLoopsWithExprBounds (0 ms)
2023-01-11T22:11:13.0175116Z [ RUN      ] LoopNest.fuseLoopsWithDifferentExprBounds
2023-01-11T22:11:13.0178942Z [       OK ] LoopNest.fuseLoopsWithDifferentExprBounds (0 ms)
2023-01-11T22:11:13.0179413Z [ RUN      ] LoopNest.fuseLoopsWithNonOverlappingBufferAccesses
2023-01-11T22:11:13.0182997Z [       OK ] LoopNest.fuseLoopsWithNonOverlappingBufferAccesses (0 ms)
2023-01-11T22:11:13.0183459Z [ RUN      ] LoopNest.fuseLoopsWithNonOverlapping2DBufferAccesses
2023-01-11T22:11:13.0190364Z [       OK ] LoopNest.fuseLoopsWithNonOverlapping2DBufferAccesses (0 ms)
2023-01-11T22:11:13.0190770Z [ RUN      ] LoopNest.fuseLoopsWithReductions
2023-01-11T22:11:13.0195143Z [       OK ] LoopNest.fuseLoopsWithReductions (0 ms)
2023-01-11T22:11:13.0195490Z [ RUN      ] LoopNest.fuseLoopsWith2DReductions
2023-01-11T22:11:13.0204030Z [       OK ] LoopNest.fuseLoopsWith2DReductions (0 ms)
2023-01-11T22:11:13.0204405Z [ RUN      ] LoopNest.fuseLoopsWithComplexIndices
2023-01-11T22:11:13.0211504Z [       OK ] LoopNest.fuseLoopsWithComplexIndices (0 ms)
2023-01-11T22:11:13.0211912Z [ RUN      ] LoopNest.fuseLoopsWithMixedLoopVarsAsIndices
2023-01-11T22:11:13.0221811Z [       OK ] LoopNest.fuseLoopsWithMixedLoopVarsAsIndices (1 ms)
2023-01-11T22:11:13.0222199Z [ RUN      ] LoopNest.fuseLoopsWithTranspose
2023-01-11T22:11:13.0227194Z [       OK ] LoopNest.fuseLoopsWithTranspose (0 ms)
2023-01-11T22:11:13.0227597Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies1
2023-01-11T22:11:13.0231376Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies1 (0 ms)
2023-01-11T22:11:13.0231788Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies2
2023-01-11T22:11:13.0235250Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies2 (0 ms)
2023-01-11T22:11:13.0235658Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies3
2023-01-11T22:11:13.0243029Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies3 (0 ms)
2023-01-11T22:11:13.0243428Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies4
2023-01-11T22:11:13.0252275Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies4 (0 ms)
2023-01-11T22:11:13.0252683Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies5
2023-01-11T22:11:13.0256751Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies5 (0 ms)
2023-01-11T22:11:13.0257351Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies6
2023-01-11T22:11:13.0261424Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies6 (0 ms)
2023-01-11T22:11:13.0261835Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies7
2023-01-11T22:11:13.0266712Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies7 (0 ms)
2023-01-11T22:11:13.0267386Z [ RUN      ] LoopNest.areLoopsPerfectlyNested
2023-01-11T22:11:13.0267925Z [       OK ] LoopNest.areLoopsPerfectlyNested (0 ms)
2023-01-11T22:11:13.0268463Z [ RUN      ] LoopNest.reorderNestedLoops2D
2023-01-11T22:11:13.0269053Z [       OK ] LoopNest.reorderNestedLoops2D (0 ms)
2023-01-11T22:11:13.0269624Z [ RUN      ] LoopNest.reorderNestedLoops3D
2023-01-11T22:11:13.0270094Z [       OK ] LoopNest.reorderNestedLoops3D (0 ms)
2023-01-11T22:11:13.0270446Z [ RUN      ] LoopNest.reorderNestedLoops4D
2023-01-11T22:11:13.0271074Z [       OK ] LoopNest.reorderNestedLoops4D (0 ms)
2023-01-11T22:11:13.0271648Z [ RUN      ] LoopNest.reorderTrivialPermutation
2023-01-11T22:11:13.0272231Z [       OK ] LoopNest.reorderTrivialPermutation (0 ms)
2023-01-11T22:11:13.0272875Z [ RUN      ] LoopNest.reorderInvalidPermutations
2023-01-11T22:11:13.0273266Z [       OK ] LoopNest.reorderInvalidPermutations (0 ms)
2023-01-11T22:11:13.0273655Z [ RUN      ] LoopNest.reorderInvalidLoopNest
2023-01-11T22:11:13.0274076Z [       OK ] LoopNest.reorderInvalidLoopNest (0 ms)
2023-01-11T22:11:13.0274484Z [ RUN      ] LoopNest.compressBufferSimple
2023-01-11T22:11:13.0274814Z [       OK ] LoopNest.compressBufferSimple (0 ms)
2023-01-11T22:11:13.0275156Z [ RUN      ] LoopNest.compressBufferMultipleDims
2023-01-11T22:11:13.0275498Z [       OK ] LoopNest.compressBufferMultipleDims (0 ms)
2023-01-11T22:11:13.0275862Z [ RUN      ] LoopNest.compressBufferMultipleDims2
2023-01-11T22:11:13.0276247Z [       OK ] LoopNest.compressBufferMultipleDims2 (0 ms)
2023-01-11T22:11:13.0276621Z [ RUN      ] LoopNest.compressBufferDifferentOrderIndices
2023-01-11T22:11:13.0277037Z [       OK ] LoopNest.compressBufferDifferentOrderIndices (0 ms)
2023-01-11T22:11:13.0277422Z [ RUN      ] LoopNest.compressBufferVariableBounds
2023-01-11T22:11:13.0277788Z [       OK ] LoopNest.compressBufferVariableBounds (0 ms)
2023-01-11T22:11:13.0278159Z [ RUN      ] LoopNest.compressBufferNoCommonParentLoops
2023-01-11T22:11:13.0278560Z [       OK ] LoopNest.compressBufferNoCommonParentLoops (0 ms)
2023-01-11T22:11:13.0278934Z [ RUN      ] LoopNest.compressBufferIndicesMixed
2023-01-11T22:11:13.0279278Z [       OK ] LoopNest.compressBufferIndicesMixed (0 ms)
2023-01-11T22:11:13.0279620Z [ RUN      ] LoopNest.compressMultipleBuffers
2023-01-11T22:11:13.0280065Z [       OK ] LoopNest.compressMultipleBuffers (0 ms)
2023-01-11T22:11:13.0280368Z [ RUN      ] LoopNest.sanitizeNames
2023-01-11T22:11:13.0290247Z [       OK ] LoopNest.sanitizeNames (1 ms)
2023-01-11T22:11:13.0290852Z [----------] 174 tests from LoopNest (1639 ms total)
2023-01-11T22:11:13.0291094Z 
2023-01-11T22:11:13.0291254Z [----------] 31 tests from MemDependency
2023-01-11T22:11:13.0291534Z [ RUN      ] MemDependency.BoundOverlap
2023-01-11T22:11:13.0301201Z [       OK ] MemDependency.BoundOverlap (1 ms)
2023-01-11T22:11:13.0312263Z [ RUN      ] MemDependency.BoundComparison
2023-01-11T22:11:13.0312856Z [       OK ] MemDependency.BoundComparison (0 ms)
2023-01-11T22:11:13.0313207Z [ RUN      ] MemDependency.BoundOverlapSymbolic
2023-01-11T22:11:13.0317259Z [       OK ] MemDependency.BoundOverlapSymbolic (0 ms)
2023-01-11T22:11:13.0317860Z [ RUN      ] MemDependency.BoundOverlapMultiDim
2023-01-11T22:11:13.0325370Z [       OK ] MemDependency.BoundOverlapMultiDim (0 ms)
2023-01-11T22:11:13.0326051Z [ RUN      ] MemDependency.BoundSubtract
2023-01-11T22:11:13.0332119Z [       OK ] MemDependency.BoundSubtract (0 ms)
2023-01-11T22:11:13.0332738Z [ RUN      ] MemDependency.BoundSubtractSymbolic
2023-01-11T22:11:13.0352774Z [       OK ] MemDependency.BoundSubtractSymbolic (2 ms)
2023-01-11T22:11:13.0353436Z [ RUN      ] MemDependency.BoundSubtractMultiDim
2023-01-11T22:11:13.0375986Z [       OK ] MemDependency.BoundSubtractMultiDim (2 ms)
2023-01-11T22:11:13.0376605Z [ RUN      ] MemDependency.BoundSubtractMultiDimSymbolic
2023-01-11T22:11:13.0407769Z [       OK ] MemDependency.BoundSubtractMultiDimSymbolic (3 ms)
2023-01-11T22:11:13.0408405Z [ RUN      ] MemDependency.MemDependencyCheckerSimple
2023-01-11T22:11:13.0409005Z [       OK ] MemDependency.MemDependencyCheckerSimple (0 ms)
2023-01-11T22:11:13.0409595Z [ RUN      ] MemDependency.MemDependencyCheckerMultiStmt
2023-01-11T22:11:13.0410163Z [       OK ] MemDependency.MemDependencyCheckerMultiStmt (0 ms)
2023-01-11T22:11:13.0410699Z [ RUN      ] MemDependency.MemDependencyCheckerOverlap
2023-01-11T22:11:13.0411170Z [       OK ] MemDependency.MemDependencyCheckerOverlap (0 ms)
2023-01-11T22:11:13.0411547Z [ RUN      ] MemDependency.MemDependencyCheckerLoop
2023-01-11T22:11:13.0411931Z [       OK ] MemDependency.MemDependencyCheckerLoop (0 ms)
2023-01-11T22:11:13.0412388Z [ RUN      ] MemDependency.MemDependencyCheckerLoopReduce
2023-01-11T22:11:13.0413507Z [       OK ] MemDependency.MemDependencyCheckerLoopReduce (0 ms)
2023-01-11T22:11:13.0413939Z [ RUN      ] MemDependency.MemDependencyCheckerLoopReduceExpanded
2023-01-11T22:11:13.0415981Z [       OK ] MemDependency.MemDependencyCheckerLoopReduceExpanded (0 ms)
2023-01-11T22:11:13.0416485Z [ RUN      ] MemDependency.MemDependencyCheckerInputsOutputs
2023-01-11T22:11:13.0417796Z [       OK ] MemDependency.MemDependencyCheckerInputsOutputs (0 ms)
2023-01-11T22:11:13.0418418Z [ RUN      ] MemDependency.MemDependencyCheckerOutputDoesntDepend
2023-01-11T22:11:13.0418921Z [       OK ] MemDependency.MemDependencyCheckerOutputDoesntDepend (0 ms)
2023-01-11T22:11:13.0419361Z [ RUN      ] MemDependency.MemDependencyCheckerLoopBounds
2023-01-11T22:11:13.0429278Z [       OK ] MemDependency.MemDependencyCheckerLoopBounds (1 ms)
2023-01-11T22:11:13.0429749Z [ RUN      ] MemDependency.MemDependencyCheckerLoopBoundsIndexShift
2023-01-11T22:11:13.0455440Z [       OK ] MemDependency.MemDependencyCheckerLoopBoundsIndexShift (2 ms)
2023-01-11T22:11:13.0455933Z [ RUN      ] MemDependency.MemDependencyCheckerLoopSelfDependency
2023-01-11T22:11:13.0585400Z [       OK ] MemDependency.MemDependencyCheckerLoopSelfDependency (12 ms)
2023-01-11T22:11:13.0585917Z [ RUN      ] MemDependency.MemDependencyCheckerLoopDistinctStrides
2023-01-11T22:11:13.0592937Z [       OK ] MemDependency.MemDependencyCheckerLoopDistinctStrides (0 ms)
2023-01-11T22:11:13.0593403Z [ RUN      ] MemDependency.MemDependencyCheckerLoopBoundsCond
2023-01-11T22:11:13.0610196Z [       OK ] MemDependency.MemDependencyCheckerLoopBoundsCond (1 ms)
2023-01-11T22:11:13.0610661Z [ RUN      ] MemDependency.MemDependencyCheckerIfThenElse
2023-01-11T22:11:13.0620889Z [       OK ] MemDependency.MemDependencyCheckerIfThenElse (1 ms)
2023-01-11T22:11:13.0621318Z [ RUN      ] MemDependency.MemDependencyCheckerCutLoop
2023-01-11T22:11:13.0639218Z [       OK ] MemDependency.MemDependencyCheckerCutLoop (1 ms)
2023-01-11T22:11:13.0639657Z [ RUN      ] MemDependency.MemDependencyCheckerDynamicShapes
2023-01-11T22:11:13.0666774Z [       OK ] MemDependency.MemDependencyCheckerDynamicShapes (2 ms)
2023-01-11T22:11:13.0667226Z [ RUN      ] MemDependency.MemDependencyCheckerMultiDim
2023-01-11T22:11:13.0699782Z [       OK ] MemDependency.MemDependencyCheckerMultiDim (3 ms)
2023-01-11T22:11:13.0700276Z [ RUN      ] MemDependency.MemDependencyCheckerComputeAPI
2023-01-11T22:11:13.0710269Z [       OK ] MemDependency.MemDependencyCheckerComputeAPI (1 ms)
2023-01-11T22:11:13.0710700Z [ RUN      ] MemDependency.MemDependencyCheckerComputeInline
2023-01-11T22:11:13.0718015Z [       OK ] MemDependency.MemDependencyCheckerComputeInline (0 ms)
2023-01-11T22:11:13.0718464Z [ RUN      ] MemDependency.MemDependencyCheckerComputeSplit
2023-01-11T22:11:13.0738947Z [       OK ] MemDependency.MemDependencyCheckerComputeSplit (2 ms)
2023-01-11T22:11:13.0739386Z [ RUN      ] MemDependency.MemDependencyCheckerComputeReorder
2023-01-11T22:11:13.0752403Z [       OK ] MemDependency.MemDependencyCheckerComputeReorder (1 ms)
2023-01-11T22:11:13.0752999Z [ RUN      ] MemDependency.MemDependencyCheckerComputeReduce
2023-01-11T22:11:13.0763312Z [       OK ] MemDependency.MemDependencyCheckerComputeReduce (1 ms)
2023-01-11T22:11:13.0763730Z [ RUN      ] MemDependency.MemDependencyCheckerComputeGEMM
2023-01-11T22:11:13.0918299Z [       OK ] MemDependency.MemDependencyCheckerComputeGEMM (15 ms)
2023-01-11T22:11:13.0918718Z [----------] 31 tests from MemDependency (62 ms total)
2023-01-11T22:11:13.0918885Z 
2023-01-11T22:11:13.0919017Z [----------] 2 tests from Ops
2023-01-11T22:11:13.0919239Z [ RUN      ] Ops.Sum
2023-01-11T22:11:13.0958411Z [       OK ] Ops.Sum (4 ms)
2023-01-11T22:11:13.0958678Z [ RUN      ] Ops.ChannelsLastSum
2023-01-11T22:11:13.2041606Z [       OK ] Ops.ChannelsLastSum (108 ms)
2023-01-11T22:11:13.2042039Z [----------] 2 tests from Ops (112 ms total)
2023-01-11T22:11:13.2042251Z 
2023-01-11T22:11:13.2042412Z [----------] 10 tests from Quantization
2023-01-11T22:11:13.2042740Z [ RUN      ] Quantization.QuantDequantInt8
2023-01-11T22:11:13.2315558Z [       OK ] Quantization.QuantDequantInt8 (27 ms)
2023-01-11T22:11:13.2315974Z [ RUN      ] Quantization.QuantDequantUInt8
2023-01-11T22:11:13.2580112Z [       OK ] Quantization.QuantDequantUInt8 (26 ms)
2023-01-11T22:11:13.2580735Z [ RUN      ] Quantization.QuantDequantUInt8_NLC
2023-01-11T22:11:13.2852712Z [       OK ] Quantization.QuantDequantUInt8_NLC (27 ms)
2023-01-11T22:11:13.2853267Z [ RUN      ] Quantization.QuantAddDequantInt8
2023-01-11T22:11:13.3161394Z [       OK ] Quantization.QuantAddDequantInt8 (30 ms)
2023-01-11T22:11:13.3162037Z [ RUN      ] Quantization.QuantAddDequantUInt8
2023-01-11T22:11:13.3468389Z [       OK ] Quantization.QuantAddDequantUInt8 (30 ms)
2023-01-11T22:11:13.3468895Z [ RUN      ] Quantization.QuantSigmoidDequantUInt8
2023-01-11T22:11:13.3823557Z [       OK ] Quantization.QuantSigmoidDequantUInt8 (35 ms)
2023-01-11T22:11:13.3824029Z [ RUN      ] Quantization.QuantMulDequantUInt8
2023-01-11T22:11:13.4229858Z [       OK ] Quantization.QuantMulDequantUInt8 (40 ms)
2023-01-11T22:11:13.4230461Z [ RUN      ] Quantization.QuantUpsampleNearst2dDequantUInt8
2023-01-11T22:11:13.4698897Z [       OK ] Quantization.QuantUpsampleNearst2dDequantUInt8 (46 ms)
2023-01-11T22:11:13.4699363Z [ RUN      ] Quantization.UpsampleNearst2d
2023-01-11T22:11:13.5006685Z [       OK ] Quantization.UpsampleNearst2d (30 ms)
2023-01-11T22:11:13.5007135Z [ RUN      ] Quantization.QuantCatDequantUInt8
2023-01-11T22:11:13.5605112Z [       OK ] Quantization.QuantCatDequantUInt8 (59 ms)
2023-01-11T22:11:13.5606135Z [----------] 10 tests from Quantization (356 ms total)
2023-01-11T22:11:13.5606419Z 
2023-01-11T22:11:13.5606692Z [----------] 2 tests from BufLiveRange
2023-01-11T22:11:13.5607634Z [ RUN      ] BufLiveRange.SingleRangeLine
2023-01-11T22:11:13.5608193Z [       OK ] BufLiveRange.SingleRangeLine (0 ms)
2023-01-11T22:11:13.5608730Z [ RUN      ] BufLiveRange.MulRangeLine
2023-01-11T22:11:13.5609272Z [       OK ] BufLiveRange.MulRangeLine (0 ms)
2023-01-11T22:11:13.5609827Z [----------] 2 tests from BufLiveRange (0 ms total)
2023-01-11T22:11:13.5610081Z 
2023-01-11T22:11:13.5610338Z [----------] 6 tests from MemPlanning
2023-01-11T22:11:13.5610866Z [ RUN      ] MemPlanning.MemReuseWithTypeCast
2023-01-11T22:11:13.6239447Z [       OK ] MemPlanning.MemReuseWithTypeCast (63 ms)
2023-01-11T22:11:13.6240366Z [ RUN      ] MemPlanning.NoMemReuseForLargerType
2023-01-11T22:11:13.6967698Z [       OK ] MemPlanning.NoMemReuseForLargerType (72 ms)
2023-01-11T22:11:13.6968551Z [ RUN      ] MemPlanning.SameBufSizeMemReuse
2023-01-11T22:11:13.9529034Z [       OK ] MemPlanning.SameBufSizeMemReuse (256 ms)
2023-01-11T22:11:13.9530015Z [ RUN      ] MemPlanning.SameBufSizeMultiMemReuses
2023-01-11T22:11:14.2145381Z [       OK ] MemPlanning.SameBufSizeMultiMemReuses (261 ms)
2023-01-11T22:11:14.2146140Z [ RUN      ] MemPlanning.SameBufSizeMultiMemReusesOfOneBuf
2023-01-11T22:11:14.5498421Z [       OK ] MemPlanning.SameBufSizeMultiMemReusesOfOneBuf (335 ms)
2023-01-11T22:11:14.5498862Z [ RUN      ] MemPlanning.SmallerBufSizeNonMemReuse
2023-01-11T22:11:14.6958050Z [       OK ] MemPlanning.SmallerBufSizeNonMemReuse (145 ms)
2023-01-11T22:11:14.6958669Z [----------] 6 tests from MemPlanning (1135 ms total)
2023-01-11T22:11:14.6958870Z 
2023-01-11T22:11:14.6959106Z [----------] 45 tests from Reductions
2023-01-11T22:11:14.6959453Z [ RUN      ] Reductions.ReduceSum0D_1
2023-01-11T22:11:14.6959932Z [       OK ] Reductions.ReduceSum0D_1 (0 ms)
2023-01-11T22:11:14.6960339Z [ RUN      ] Reductions.ReduceSum0D_2
2023-01-11T22:11:14.6960655Z [       OK ] Reductions.ReduceSum0D_2 (0 ms)
2023-01-11T22:11:14.6960931Z [ RUN      ] Reductions.ReduceSum1D
2023-01-11T22:11:14.6961282Z [       OK ] Reductions.ReduceSum1D (0 ms)
2023-01-11T22:11:14.6961568Z [ RUN      ] Reductions.ReduceSum2D
2023-01-11T22:11:14.6965066Z [       OK ] Reductions.ReduceSum2D (0 ms)
2023-01-11T22:11:14.6965358Z [ RUN      ] Reductions.ReduceSum3D
2023-01-11T22:11:14.6995885Z [       OK ] Reductions.ReduceSum3D (3 ms)
2023-01-11T22:11:14.6996303Z [ RUN      ] Reductions.ReduceSum10D
2023-01-11T22:11:15.3623074Z [       OK ] Reductions.ReduceSum10D (662 ms)
2023-01-11T22:11:15.3623468Z [ RUN      ] Reductions.ReduceProduct
2023-01-11T22:11:15.3625193Z [       OK ] Reductions.ReduceProduct (0 ms)
2023-01-11T22:11:15.3625734Z [ RUN      ] Reductions.ReduceMax
2023-01-11T22:11:15.3629291Z [       OK ] Reductions.ReduceMax (0 ms)
2023-01-11T22:11:15.3629912Z [ RUN      ] Reductions.ReduceMinCustomInitializer
2023-01-11T22:11:15.3631768Z [       OK ] Reductions.ReduceMinCustomInitializer (0 ms)
2023-01-11T22:11:15.3632154Z [ RUN      ] Reductions.ReduceAnyAll
2023-01-11T22:11:15.3652717Z [       OK ] Reductions.ReduceAnyAll (2 ms)
2023-01-11T22:11:15.3653104Z [ RUN      ] Reductions.ReduceMatmul2D
2023-01-11T22:11:15.3661795Z [       OK ] Reductions.ReduceMatmul2D (0 ms)
2023-01-11T22:11:15.3662637Z [ RUN      ] Reductions.ReduceRfactorLike
2023-01-11T22:11:15.3673009Z [       OK ] Reductions.ReduceRfactorLike (1 ms)
2023-01-11T22:11:15.3673332Z [ RUN      ] Reductions.ReduceAsProducer
2023-01-11T22:11:15.3694629Z [       OK ] Reductions.ReduceAsProducer (2 ms)
2023-01-11T22:11:15.3694935Z [ RUN      ] Reductions.ReduceAsConsumer
2023-01-11T22:11:15.3728935Z [       OK ] Reductions.ReduceAsConsumer (3 ms)
2023-01-11T22:11:15.3729460Z [ RUN      ] Reductions.SplitReduceAxis
2023-01-11T22:11:15.3744835Z [       OK ] Reductions.SplitReduceAxis (1 ms)
2023-01-11T22:11:15.3745169Z [ RUN      ] Reductions.SplitNonReduceAxis
2023-01-11T22:11:15.3777283Z [       OK ] Reductions.SplitNonReduceAxis (3 ms)
2023-01-11T22:11:15.3777646Z [ RUN      ] Reductions.ReorderedReductionInitializer
2023-01-11T22:11:15.3815720Z [       OK ] Reductions.ReorderedReductionInitializer (3 ms)
2023-01-11T22:11:15.3816079Z [ RUN      ] Reductions.ReduceRfactor
2023-01-11T22:11:15.3826454Z [       OK ] Reductions.ReduceRfactor (1 ms)
2023-01-11T22:11:15.3826793Z [ RUN      ] Reductions.Reduce3DRfactorInner
2023-01-11T22:11:15.3950734Z [       OK ] Reductions.Reduce3DRfactorInner (12 ms)
2023-01-11T22:11:15.3951080Z [ RUN      ] Reductions.Reduce3DRfactorOuter
2023-01-11T22:11:15.4079684Z [       OK ] Reductions.Reduce3DRfactorOuter (12 ms)
2023-01-11T22:11:15.4080111Z [ RUN      ] Reductions.ReduceRepeatedInternalRfactor
2023-01-11T22:11:15.5791009Z [       OK ] Reductions.ReduceRepeatedInternalRfactor (171 ms)
2023-01-11T22:11:15.5791453Z [ RUN      ] Reductions.ReduceSplitTail
2023-01-11T22:11:15.6179622Z [       OK ] Reductions.ReduceSplitTail (38 ms)
2023-01-11T22:11:15.6179966Z [ RUN      ] Reductions.ReduceSplitNoTail
2023-01-11T22:11:15.6627275Z [       OK ] Reductions.ReduceSplitNoTail (44 ms)
2023-01-11T22:11:15.6627629Z [ RUN      ] Reductions.ReduceOverSplitTail
2023-01-11T22:11:15.7002616Z [       OK ] Reductions.ReduceOverSplitTail (37 ms)
2023-01-11T22:11:15.7002960Z [ RUN      ] Reductions.ReduceSplitMask
2023-01-11T22:11:15.7492638Z [       OK ] Reductions.ReduceSplitMask (49 ms)
2023-01-11T22:11:15.7492966Z [ RUN      ] Reductions.ReduceSplitNoMask
2023-01-11T22:11:15.7940878Z [       OK ] Reductions.ReduceSplitNoMask (44 ms)
2023-01-11T22:11:15.7941241Z [ RUN      ] Reductions.ReduceOverSplitMask
2023-01-11T22:11:15.8340886Z [       OK ] Reductions.ReduceOverSplitMask (39 ms)
2023-01-11T22:11:15.8341221Z [ RUN      ] Reductions.ReduceSplitRfactor
2023-01-11T22:11:15.8383497Z [       OK ] Reductions.ReduceSplitRfactor (4 ms)
2023-01-11T22:11:15.8383853Z [ RUN      ] Reductions.ReduceOverSplitRfactor
2023-01-11T22:11:15.8397286Z [       OK ] Reductions.ReduceOverSplitRfactor (1 ms)
2023-01-11T22:11:15.8397690Z [ RUN      ] Reductions.ReduceInlineReduction
2023-01-11T22:11:15.8398483Z [       OK ] Reductions.ReduceInlineReduction (0 ms)
2023-01-11T22:11:15.8398820Z [ RUN      ] Reductions.ReduceInlineConsumer
2023-01-11T22:11:15.8486367Z [       OK ] Reductions.ReduceInlineConsumer (8 ms)
2023-01-11T22:11:15.8486726Z [ RUN      ] Reductions.ReduceInlineReducerInternal
2023-01-11T22:11:15.8575896Z [       OK ] Reductions.ReduceInlineReducerInternal (8 ms)
2023-01-11T22:11:15.8576322Z [ RUN      ] Reductions.ReductionCacheAccessesOperatorAxis
2023-01-11T22:11:15.8622238Z [       OK ] Reductions.ReductionCacheAccessesOperatorAxis (4 ms)
2023-01-11T22:11:15.8622933Z [ RUN      ] Reductions.ReductionCacheAccessesOuterReduceAxis
2023-01-11T22:11:15.8665455Z [       OK ] Reductions.ReductionCacheAccessesOuterReduceAxis (4 ms)
2023-01-11T22:11:15.8665891Z [ RUN      ] Reductions.ReductionCacheAccessesInnerReduceAxis
2023-01-11T22:11:15.8708310Z [       OK ] Reductions.ReductionCacheAccessesInnerReduceAxis (4 ms)
2023-01-11T22:11:15.8708707Z [ RUN      ] Reductions.ReductionCacheBodyAccess
2023-01-11T22:11:15.8723047Z [       OK ] Reductions.ReductionCacheBodyAccess (1 ms)
2023-01-11T22:11:15.8723439Z [ RUN      ] Reductions.ReductionCacheConsumerAccess
2023-01-11T22:11:15.8740194Z [       OK ] Reductions.ReductionCacheConsumerAccess (1 ms)
2023-01-11T22:11:15.8740817Z [ RUN      ] Reductions.ReductionSplitCacheConsumerAccess
2023-01-11T22:11:15.8759973Z [       OK ] Reductions.ReductionSplitCacheConsumerAccess (1 ms)
2023-01-11T22:11:15.8760398Z [ RUN      ] Reductions.ReductionReorderCacheConsumerAccess
2023-01-11T22:11:15.8777367Z [       OK ] Reductions.ReductionReorderCacheConsumerAccess (1 ms)
2023-01-11T22:11:15.8777794Z [ RUN      ] Reductions.ReductionRfactorCacheTempOuter
2023-01-11T22:11:15.8952498Z [       OK ] Reductions.ReductionRfactorCacheTempOuter (17 ms)
2023-01-11T22:11:15.8952958Z [ RUN      ] Reductions.ReductionRfactorCacheTempInner
2023-01-11T22:11:15.9091769Z [       OK ] Reductions.ReductionRfactorCacheTempInner (13 ms)
2023-01-11T22:11:15.9092130Z [ RUN      ] Reductions.ReductionVectorize
2023-01-11T22:11:15.9103151Z [       OK ] Reductions.ReductionVectorize (1 ms)
2023-01-11T22:11:15.9103671Z [ RUN      ] Reductions.ReductionVectorizeInner
2023-01-11T22:11:15.9104028Z [       OK ] Reductions.ReductionVectorizeInner (0 ms)
2023-01-11T22:11:15.9104381Z [ RUN      ] Reductions.ReductionVectorizeRfactor
2023-01-11T22:11:15.9117134Z [       OK ] Reductions.ReductionVectorizeRfactor (1 ms)
2023-01-11T22:11:15.9117527Z [ RUN      ] Reductions.InitFunction
2023-01-11T22:11:15.9119102Z [       OK ] Reductions.InitFunction (0 ms)
2023-01-11T22:11:15.9119473Z [----------] 45 tests from Reductions (1216 ms total)
2023-01-11T22:11:15.9119620Z 
2023-01-11T22:11:15.9119823Z [----------] 69 tests from Registerizer
2023-01-11T22:11:15.9120133Z [ RUN      ] Registerizer.RegisterizerSimple
2023-01-11T22:11:15.9121418Z [       OK ] Registerizer.RegisterizerSimple (0 ms)
2023-01-11T22:11:15.9121818Z [ RUN      ] Registerizer.RegisterizerLoop
2023-01-11T22:11:15.9122656Z [       OK ] Registerizer.RegisterizerLoop (0 ms)
2023-01-11T22:11:15.9123094Z [ RUN      ] Registerizer.RegisterizerLoopFixedLoad
2023-01-11T22:11:15.9124113Z [       OK ] Registerizer.RegisterizerLoopFixedLoad (0 ms)
2023-01-11T22:11:15.9124478Z [ RUN      ] Registerizer.RegisterizerLoopInternal
2023-01-11T22:11:15.9126002Z [       OK ] Registerizer.RegisterizerLoopInternal (0 ms)
2023-01-11T22:11:15.9126410Z [ RUN      ] Registerizer.RegisterizerLoopInternalLoadOverlap
2023-01-11T22:11:15.9128250Z [       OK ] Registerizer.RegisterizerLoopInternalLoadOverlap (0 ms)
2023-01-11T22:11:15.9128763Z [ RUN      ] Registerizer.RegisterizerLoopInternalRepeated
2023-01-11T22:11:15.9132297Z [       OK ] Registerizer.RegisterizerLoopInternalRepeated (0 ms)
2023-01-11T22:11:15.9132810Z [ RUN      ] Registerizer.RegisterizerLoopInternalRepeatedOverlapLoopVar
2023-01-11T22:11:15.9135681Z [       OK ] Registerizer.RegisterizerLoopInternalRepeatedOverlapLoopVar (0 ms)
2023-01-11T22:11:15.9136208Z [ RUN      ] Registerizer.RegisterizerLoopInternalRepeatedOverlapOther
2023-01-11T22:11:15.9139278Z [       OK ] Registerizer.RegisterizerLoopInternalRepeatedOverlapOther (0 ms)
2023-01-11T22:11:15.9139696Z [ RUN      ] Registerizer.RegisterizerMultiVar
2023-01-11T22:11:15.9164591Z [       OK ] Registerizer.RegisterizerMultiVar (2 ms)
2023-01-11T22:11:15.9164959Z [ RUN      ] Registerizer.RegisterizerVariableLoad
2023-01-11T22:11:15.9166740Z [       OK ] Registerizer.RegisterizerVariableLoad (0 ms)
2023-01-11T22:11:15.9167119Z [ RUN      ] Registerizer.RegisterizerSymbolicIndices
2023-01-11T22:11:15.9168623Z [       OK ] Registerizer.RegisterizerSymbolicIndices (0 ms)
2023-01-11T22:11:15.9169024Z [ RUN      ] Registerizer.RegisterizerMultiLoop
2023-01-11T22:11:15.9170979Z [       OK ] Registerizer.RegisterizerMultiLoop (0 ms)
2023-01-11T22:11:15.9171481Z [ RUN      ] Registerizer.RegisterizerRepeated
2023-01-11T22:11:15.9174126Z [       OK ] Registerizer.RegisterizerRepeated (0 ms)
2023-01-11T22:11:15.9174746Z [ RUN      ] Registerizer.RegisterizerNoLoads
2023-01-11T22:11:15.9175603Z [       OK ] Registerizer.RegisterizerNoLoads (0 ms)
2023-01-11T22:11:15.9176206Z [ RUN      ] Registerizer.RegisterizerNoRepeatedStores
2023-01-11T22:11:15.9177757Z [       OK ] Registerizer.RegisterizerNoRepeatedStores (0 ms)
2023-01-11T22:11:15.9178361Z [ RUN      ] Registerizer.RegisterizerMultiVarOverlap
2023-01-11T22:11:15.9181056Z [       OK ] Registerizer.RegisterizerMultiVarOverlap (0 ms)
2023-01-11T22:11:15.9181647Z [ RUN      ] Registerizer.RegisterizerAllocs
2023-01-11T22:11:15.9184291Z [       OK ] Registerizer.RegisterizerAllocs (0 ms)
2023-01-11T22:11:15.9184900Z [ RUN      ] Registerizer.RegisterizerNoInitializer
2023-01-11T22:11:15.9185987Z [       OK ] Registerizer.RegisterizerNoInitializer (0 ms)
2023-01-11T22:11:15.9186571Z [ RUN      ] Registerizer.RegisterizerNoInitializerLoopVar
2023-01-11T22:11:15.9187416Z [       OK ] Registerizer.RegisterizerNoInitializerLoopVar (0 ms)
2023-01-11T22:11:15.9188027Z [ RUN      ] Registerizer.RegisterizerLoadThenStore
2023-01-11T22:11:15.9189410Z [       OK ] Registerizer.RegisterizerLoadThenStore (0 ms)
2023-01-11T22:11:15.9189998Z [ RUN      ] Registerizer.RegisterizerParallelized
2023-01-11T22:11:15.9210888Z [       OK ] Registerizer.RegisterizerParallelized (2 ms)
2023-01-11T22:11:15.9211468Z [ RUN      ] Registerizer.RegisterizerConditionAfter
2023-01-11T22:11:15.9212726Z [       OK ] Registerizer.RegisterizerConditionAfter (0 ms)
2023-01-11T22:11:15.9213338Z [ RUN      ] Registerizer.RegisterizerConditionBefore
2023-01-11T22:11:15.9214429Z [       OK ] Registerizer.RegisterizerConditionBefore (0 ms)
2023-01-11T22:11:15.9215047Z [ RUN      ] Registerizer.RegisterizerConditionInside
2023-01-11T22:11:15.9216775Z [       OK ] Registerizer.RegisterizerConditionInside (0 ms)
2023-01-11T22:11:15.9217424Z [ RUN      ] Registerizer.RegisterizerConditionInsideOverlap1
2023-01-11T22:11:15.9219672Z [       OK ] Registerizer.RegisterizerConditionInsideOverlap1 (0 ms)
2023-01-11T22:11:15.9220309Z [ RUN      ] Registerizer.RegisterizerConditionInsideOverlap2
2023-01-11T22:11:15.9223636Z [       OK ] Registerizer.RegisterizerConditionInsideOverlap2 (0 ms)
2023-01-11T22:11:15.9224238Z [ RUN      ] Registerizer.RegisterizerConditionHidden
2023-01-11T22:11:15.9225130Z [       OK ] Registerizer.RegisterizerConditionHidden (0 ms)
2023-01-11T22:11:15.9225765Z [ RUN      ] Registerizer.RegisterizerConditionUnhidden
2023-01-11T22:11:15.9227453Z [       OK ] Registerizer.RegisterizerConditionUnhidden (0 ms)
2023-01-11T22:11:15.9228055Z [ RUN      ] Registerizer.RegisterizerCondCondition
2023-01-11T22:11:15.9229224Z [       OK ] Registerizer.RegisterizerCondCondition (0 ms)
2023-01-11T22:11:15.9229843Z [ RUN      ] Registerizer.RegisterizerCondConditionUnhidden
2023-01-11T22:11:15.9231582Z [       OK ] Registerizer.RegisterizerCondConditionUnhidden (0 ms)
2023-01-11T22:11:15.9232185Z [ RUN      ] Registerizer.RegisterizerIfThenElseHidden
2023-01-11T22:11:15.9235344Z [       OK ] Registerizer.RegisterizerIfThenElseHidden (0 ms)
2023-01-11T22:11:15.9235955Z [ RUN      ] Registerizer.RegisterizerIfThenElseUnhidden
2023-01-11T22:11:15.9239635Z [       OK ] Registerizer.RegisterizerIfThenElseUnhidden (0 ms)
2023-01-11T22:11:15.9240272Z [ RUN      ] Registerizer.RegisterizerIfThenElseNested
2023-01-11T22:11:15.9241802Z [       OK ] Registerizer.RegisterizerIfThenElseNested (0 ms)
2023-01-11T22:11:15.9242422Z [ RUN      ] Registerizer.RegisterizerIfThenElseInternal
2023-01-11T22:11:15.9244440Z [       OK ] Registerizer.RegisterizerIfThenElseInternal (0 ms)
2023-01-11T22:11:15.9245070Z [ RUN      ] Registerizer.RegisterizerIfThenElseCondition
2023-01-11T22:11:15.9246120Z [       OK ] Registerizer.RegisterizerIfThenElseCondition (0 ms)
2023-01-11T22:11:15.9246783Z [ RUN      ] Registerizer.RegisterizerIfThenElseConditionUnhidden
2023-01-11T22:11:15.9247993Z [       OK ] Registerizer.RegisterizerIfThenElseConditionUnhidden (0 ms)
2023-01-11T22:11:15.9248431Z [ RUN      ] Registerizer.RegisterizerConditionBranchOnly
2023-01-11T22:11:15.9259403Z [       OK ] Registerizer.RegisterizerConditionBranchOnly (1 ms)
2023-01-11T22:11:15.9259856Z [ RUN      ] Registerizer.RegisterizerCondIfThenElse
2023-01-11T22:11:15.9261627Z [       OK ] Registerizer.RegisterizerCondIfThenElse (0 ms)
2023-01-11T22:11:15.9262104Z [ RUN      ] Registerizer.RegisterizerIfThenElseLoop
2023-01-11T22:11:15.9263851Z [       OK ] Registerizer.RegisterizerIfThenElseLoop (0 ms)
2023-01-11T22:11:15.9264249Z [ RUN      ] Registerizer.RegisterizerIfThenElseLoopCut
2023-01-11T22:11:15.9265822Z [       OK ] Registerizer.RegisterizerIfThenElseLoopCut (0 ms)
2023-01-11T22:11:15.9266280Z [ RUN      ] Registerizer.RegisterizerPartialAfter
2023-01-11T22:11:15.9269075Z [       OK ] Registerizer.RegisterizerPartialAfter (0 ms)
2023-01-11T22:11:15.9269513Z [ RUN      ] Registerizer.RegisterizerPartialBefore
2023-01-11T22:11:15.9272090Z [       OK ] Registerizer.RegisterizerPartialBefore (0 ms)
2023-01-11T22:11:15.9272598Z [ RUN      ] Registerizer.RegisterizerPartialInside
2023-01-11T22:11:15.9275958Z [       OK ] Registerizer.RegisterizerPartialInside (0 ms)
2023-01-11T22:11:15.9276415Z [ RUN      ] Registerizer.RegisterizerPartialCondition
2023-01-11T22:11:15.9280295Z [       OK ] Registerizer.RegisterizerPartialCondition (0 ms)
2023-01-11T22:11:15.9280732Z [ RUN      ] Registerizer.RegisterizerPartialConditionInternalCut
2023-01-11T22:11:15.9282160Z [       OK ] Registerizer.RegisterizerPartialConditionInternalCut (0 ms)
2023-01-11T22:11:15.9282681Z [ RUN      ] Registerizer.RegisterizerPartialConditionInternalStart
2023-01-11T22:11:15.9284096Z [       OK ] Registerizer.RegisterizerPartialConditionInternalStart (0 ms)
2023-01-11T22:11:15.9284582Z [ RUN      ] Registerizer.RegisterizerPartialOverlapsTwo
2023-01-11T22:11:15.9287080Z [       OK ] Registerizer.RegisterizerPartialOverlapsTwo (0 ms)
2023-01-11T22:11:15.9287535Z [ RUN      ] Registerizer.RegisterizerNestedBlocks
2023-01-11T22:11:15.9289379Z [       OK ] Registerizer.RegisterizerNestedBlocks (0 ms)
2023-01-11T22:11:15.9289758Z [ RUN      ] Registerizer.RegisterizerNestedConditions
2023-01-11T22:11:15.9291475Z [       OK ] Registerizer.RegisterizerNestedConditions (0 ms)
2023-01-11T22:11:15.9291895Z [ RUN      ] Registerizer.RegisterizerNestedConditionsUnhidden
2023-01-11T22:11:15.9293840Z [       OK ] Registerizer.RegisterizerNestedConditionsUnhidden (0 ms)
2023-01-11T22:11:15.9294288Z [ RUN      ] Registerizer.RegisterizerNestedConditionsHiddenFirst
2023-01-11T22:11:15.9296830Z [       OK ] Registerizer.RegisterizerNestedConditionsHiddenFirst (0 ms)
2023-01-11T22:11:15.9297296Z [ RUN      ] Registerizer.RegisterizerNestedConditionsHiddenSecond
2023-01-11T22:11:15.9299745Z [       OK ] Registerizer.RegisterizerNestedConditionsHiddenSecond (0 ms)
2023-01-11T22:11:15.9300171Z [ RUN      ] Registerizer.RegisterizerNestedConditionsCut
2023-01-11T22:11:15.9302009Z [       OK ] Registerizer.RegisterizerNestedConditionsCut (0 ms)
2023-01-11T22:11:15.9302738Z [ RUN      ] Registerizer.RegisterizerNestedConditionLoopHidden
2023-01-11T22:11:15.9304575Z [       OK ] Registerizer.RegisterizerNestedConditionLoopHidden (0 ms)
2023-01-11T22:11:15.9305027Z [ RUN      ] Registerizer.RegisterizerNestedConditionThreeDeep
2023-01-11T22:11:15.9310225Z [       OK ] Registerizer.RegisterizerNestedConditionThreeDeep (0 ms)
2023-01-11T22:11:15.9310681Z [ RUN      ] Registerizer.RegisterizerNestedLoopSimple
2023-01-11T22:11:15.9312212Z [       OK ] Registerizer.RegisterizerNestedLoopSimple (0 ms)
2023-01-11T22:11:15.9312657Z [ RUN      ] Registerizer.RegisterizerHiddenAccessYes
2023-01-11T22:11:15.9315395Z [       OK ] Registerizer.RegisterizerHiddenAccessYes (0 ms)
2023-01-11T22:11:15.9315841Z [ RUN      ] Registerizer.RegisterizerHiddenAccessNo
2023-01-11T22:11:15.9318354Z [       OK ] Registerizer.RegisterizerHiddenAccessNo (0 ms)
2023-01-11T22:11:15.9318822Z [ RUN      ] Registerizer.RegisterizerHiddenAccessMultiLoop
2023-01-11T22:11:15.9322369Z [       OK ] Registerizer.RegisterizerHiddenAccessMultiLoop (0 ms)
2023-01-11T22:11:15.9322805Z [ RUN      ] Registerizer.RegisterizerTwoConditionalLoops
2023-01-11T22:11:15.9325043Z [       OK ] Registerizer.RegisterizerTwoConditionalLoops (0 ms)
2023-01-11T22:11:15.9325517Z [ RUN      ] Registerizer.RegisterizerTwoConditionalLoopsCut
2023-01-11T22:11:15.9328234Z [       OK ] Registerizer.RegisterizerTwoConditionalLoopsCut (0 ms)
2023-01-11T22:11:15.9328660Z [ RUN      ] Registerizer.RegisterizerLoopLetVar
2023-01-11T22:11:15.9330050Z [       OK ] Registerizer.RegisterizerLoopLetVar (0 ms)
2023-01-11T22:11:15.9330491Z [ RUN      ] Registerizer.RegisterizerLoopLetVarOuter
2023-01-11T22:11:15.9331621Z [       OK ] Registerizer.RegisterizerLoopLetVarOuter (0 ms)
2023-01-11T22:11:15.9332047Z [ RUN      ] Registerizer.RegisterizerMultiDim
2023-01-11T22:11:15.9333822Z [       OK ] Registerizer.RegisterizerMultiDim (0 ms)
2023-01-11T22:11:15.9334267Z [ RUN      ] Registerizer.RegisterizerMultiDimPartial
2023-01-11T22:11:15.9336596Z [       OK ] Registerizer.RegisterizerMultiDimPartial (0 ms)
2023-01-11T22:11:15.9337024Z [ RUN      ] Registerizer.RegisterizerMultiDimOverlap
2023-01-11T22:11:15.9339495Z [       OK ] Registerizer.RegisterizerMultiDimOverlap (0 ms)
2023-01-11T22:11:15.9339955Z [ RUN      ] Registerizer.RegisterizerMultiDimPartialOverlap
2023-01-11T22:11:15.9342592Z [       OK ] Registerizer.RegisterizerMultiDimPartialOverlap (0 ms)
2023-01-11T22:11:15.9343029Z [ RUN      ] Registerizer.RegisterizerMultiDim3DReduction1
2023-01-11T22:11:15.9345950Z [       OK ] Registerizer.RegisterizerMultiDim3DReduction1 (0 ms)
2023-01-11T22:11:15.9346366Z [ RUN      ] Registerizer.RegisterizerMultiDim3DReduction2
2023-01-11T22:11:15.9349907Z [       OK ] Registerizer.RegisterizerMultiDim3DReduction2 (0 ms)
2023-01-11T22:11:15.9350426Z [----------] 69 tests from Registerizer (23 ms total)
2023-01-11T22:11:15.9350612Z 
2023-01-11T22:11:15.9350793Z [----------] 92 tests from Simplify
2023-01-11T22:11:15.9351122Z [ RUN      ] Simplify.ConstantFoldSimple
2023-01-11T22:11:15.9351444Z [       OK ] Simplify.ConstantFoldSimple (0 ms)
2023-01-11T22:11:15.9351762Z [ RUN      ] Simplify.ConstantFoldTwoLayer
2023-01-11T22:11:15.9352089Z [       OK ] Simplify.ConstantFoldTwoLayer (0 ms)
2023-01-11T22:11:15.9352390Z [ RUN      ] Simplify.ConstantFoldShifts
2023-01-11T22:11:15.9373854Z [       OK ] Simplify.ConstantFoldShifts (2 ms)
2023-01-11T22:11:15.9374369Z [ RUN      ] Simplify.ConstantFoldBitwise
2023-01-11T22:11:15.9374769Z [       OK ] Simplify.ConstantFoldBitwise (0 ms)
2023-01-11T22:11:15.9375268Z [ RUN      ] Simplify.ConstantFoldMultiOp
2023-01-11T22:11:15.9375686Z [       OK ] Simplify.ConstantFoldMultiOp (0 ms)
2023-01-11T22:11:15.9376304Z [ RUN      ] Simplify.ConstantFoldMinMax
2023-01-11T22:11:15.9376701Z [       OK ] Simplify.ConstantFoldMinMax (0 ms)
2023-01-11T22:11:15.9377108Z [ RUN      ] Simplify.ConstantFoldIntrinsics
2023-01-11T22:11:15.9377484Z [       OK ] Simplify.ConstantFoldIntrinsics (0 ms)
2023-01-11T22:11:15.9377879Z [ RUN      ] Simplify.ConstantFoldCastToBool
2023-01-11T22:11:15.9378328Z [       OK ] Simplify.ConstantFoldCastToBool (0 ms)
2023-01-11T22:11:15.9378722Z [ RUN      ] Simplify.ConstantFoldWithVar
2023-01-11T22:11:15.9379044Z [       OK ] Simplify.ConstantFoldWithVar (0 ms)
2023-01-11T22:11:15.9379397Z [ RUN      ] Simplify.ConditionalSelectFoldSimple
2023-01-11T22:11:15.9379884Z [       OK ] Simplify.ConditionalSelectFoldSimple (0 ms)
2023-01-11T22:11:15.9380319Z [ RUN      ] Simplify.ConditionalSelectFoldTwoLayer
2023-01-11T22:11:15.9380981Z [       OK ] Simplify.ConditionalSelectFoldTwoLayer (0 ms)
2023-01-11T22:11:15.9381472Z [ RUN      ] Simplify.ConditionalSelectFoldWithVar
2023-01-11T22:11:15.9381996Z [       OK ] Simplify.ConditionalSelectFoldWithVar (0 ms)
2023-01-11T22:11:15.9382667Z [ RUN      ] Simplify.UnFoldableExpr
2023-01-11T22:11:15.9383209Z [       OK ] Simplify.UnFoldableExpr (0 ms)
2023-01-11T22:11:15.9383716Z [ RUN      ] Simplify.HashSimple
2023-01-11T22:11:15.9384018Z [       OK ] Simplify.HashSimple (0 ms)
2023-01-11T22:11:15.9384292Z [ RUN      ] Simplify.HashEquivalence
2023-01-11T22:11:15.9384590Z [       OK ] Simplify.HashEquivalence (0 ms)
2023-01-11T22:11:15.9384895Z [ RUN      ] Simplify.HashEquivalenceRand
2023-01-11T22:11:15.9385201Z [       OK ] Simplify.HashEquivalenceRand (0 ms)
2023-01-11T22:11:15.9385539Z [ RUN      ] Simplify.HashEquivalenceAfterFolding
2023-01-11T22:11:15.9385901Z [       OK ] Simplify.HashEquivalenceAfterFolding (0 ms)
2023-01-11T22:11:15.9386241Z [ RUN      ] Simplify.HashDifferenceTypes
2023-01-11T22:11:15.9386548Z [       OK ] Simplify.HashDifferenceTypes (0 ms)
2023-01-11T22:11:15.9386857Z [ RUN      ] Simplify.HashLargeExpression
2023-01-11T22:11:15.9394551Z [       OK ] Simplify.HashLargeExpression (1 ms)
2023-01-11T22:11:15.9395112Z [ RUN      ] Simplify.HashForLoopOptions
2023-01-11T22:11:15.9395703Z [       OK ] Simplify.HashForLoopOptions (0 ms)
2023-01-11T22:11:15.9396222Z [ RUN      ] Simplify.SimplifyAdd
2023-01-11T22:11:15.9396719Z [       OK ] Simplify.SimplifyAdd (0 ms)
2023-01-11T22:11:15.9397187Z [ RUN      ] Simplify.SimplifySub
2023-01-11T22:11:15.9397656Z [       OK ] Simplify.SimplifySub (0 ms)
2023-01-11T22:11:15.9398179Z [ RUN      ] Simplify.SimplifyMultiLayer
2023-01-11T22:11:15.9398749Z [       OK ] Simplify.SimplifyMultiLayer (0 ms)
2023-01-11T22:11:15.9399324Z [ RUN      ] Simplify.SimplifyMultiTerm
2023-01-11T22:11:15.9399980Z [       OK ] Simplify.SimplifyMultiTerm (0 ms)
2023-01-11T22:11:15.9400325Z [ RUN      ] Simplify.SimplifyCasts
2023-01-11T22:11:15.9400615Z [       OK ] Simplify.SimplifyCasts (0 ms)
2023-01-11T22:11:15.9400930Z [ RUN      ] Simplify.SimplifyEliminatesNoOps
2023-01-11T22:11:15.9401258Z [       OK ] Simplify.SimplifyEliminatesNoOps (0 ms)
2023-01-11T22:11:15.9401575Z [ RUN      ] Simplify.SimplifyMultiVar
2023-01-11T22:11:15.9401880Z [       OK ] Simplify.SimplifyMultiVar (0 ms)
2023-01-11T22:11:15.9402195Z [ RUN      ] Simplify.SimplifyEliminatesVar
2023-01-11T22:11:15.9409878Z [       OK ] Simplify.SimplifyEliminatesVar (1 ms)
2023-01-11T22:11:15.9410458Z [ RUN      ] Simplify.SimplifyAdds
2023-01-11T22:11:15.9412175Z [       OK ] Simplify.SimplifyAdds (0 ms)
2023-01-11T22:11:15.9412682Z [ RUN      ] Simplify.SimplifyMuls
2023-01-11T22:11:15.9416224Z [       OK ] Simplify.SimplifyMuls (0 ms)
2023-01-11T22:11:15.9416530Z [ RUN      ] Simplify.SimplifySubs
2023-01-11T22:11:15.9422013Z [       OK ] Simplify.SimplifySubs (0 ms)
2023-01-11T22:11:15.9422500Z [ RUN      ] Simplify.SimplifyDiv
2023-01-11T22:11:15.9422795Z [       OK ] Simplify.SimplifyDiv (0 ms)
2023-01-11T22:11:15.9423120Z [ RUN      ] Simplify.SimplifyDivWithLoopContext0
2023-01-11T22:11:15.9424115Z [       OK ] Simplify.SimplifyDivWithLoopContext0 (0 ms)
2023-01-11T22:11:15.9424481Z [ RUN      ] Simplify.SimplifyDivWithLoopContext1
2023-01-11T22:11:15.9426058Z [       OK ] Simplify.SimplifyDivWithLoopContext1 (0 ms)
2023-01-11T22:11:15.9426450Z [ RUN      ] Simplify.SimplifyDivWithLoopContext2
2023-01-11T22:11:15.9427956Z [       OK ] Simplify.SimplifyDivWithLoopContext2 (0 ms)
2023-01-11T22:11:15.9428375Z [ RUN      ] Simplify.SimplifyDivWithLoopContext3
2023-01-11T22:11:15.9429263Z [       OK ] Simplify.SimplifyDivWithLoopContext3 (0 ms)
2023-01-11T22:11:15.9429621Z [ RUN      ] Simplify.SimplifyDivWithLoopContext4
2023-01-11T22:11:15.9431756Z [       OK ] Simplify.SimplifyDivWithLoopContext4 (0 ms)
2023-01-11T22:11:15.9432121Z [ RUN      ] Simplify.SimplifyDivWithLoopContext5
2023-01-11T22:11:15.9434640Z [       OK ] Simplify.SimplifyDivWithLoopContext5 (0 ms)
2023-01-11T22:11:15.9434994Z [ RUN      ] Simplify.SimplifyDivWithLoopContext6
2023-01-11T22:11:15.9437892Z [       OK ] Simplify.SimplifyDivWithLoopContext6 (0 ms)
2023-01-11T22:11:15.9438300Z [ RUN      ] Simplify.SimplifyDivWithLoopContext7
2023-01-11T22:11:15.9440001Z [       OK ] Simplify.SimplifyDivWithLoopContext7 (0 ms)
2023-01-11T22:11:15.9440370Z [ RUN      ] Simplify.SimplifyModWithLoopContext0
2023-01-11T22:11:15.9440734Z [       OK ] Simplify.SimplifyModWithLoopContext0 (0 ms)
2023-01-11T22:11:15.9441097Z [ RUN      ] Simplify.SimplifyModWithLoopContext1
2023-01-11T22:11:15.9442756Z [       OK ] Simplify.SimplifyModWithLoopContext1 (0 ms)
2023-01-11T22:11:15.9443115Z [ RUN      ] Simplify.SimplifyModWithLoopContext2
2023-01-11T22:11:15.9444638Z [       OK ] Simplify.SimplifyModWithLoopContext2 (0 ms)
2023-01-11T22:11:15.9445002Z [ RUN      ] Simplify.SimplifyModWithLoopContext3
2023-01-11T22:11:15.9445412Z [       OK ] Simplify.SimplifyModWithLoopContext3 (0 ms)
2023-01-11T22:11:15.9445768Z [ RUN      ] Simplify.SimplifyModWithLoopContext4
2023-01-11T22:11:15.9448082Z [       OK ] Simplify.SimplifyModWithLoopContext4 (0 ms)
2023-01-11T22:11:15.9448431Z [ RUN      ] Simplify.SimplifyModWithLoopContext5
2023-01-11T22:11:15.9450915Z [       OK ] Simplify.SimplifyModWithLoopContext5 (0 ms)
2023-01-11T22:11:15.9451275Z [ RUN      ] Simplify.SimplifyModWithLoopContext6
2023-01-11T22:11:15.9453912Z [       OK ] Simplify.SimplifyModWithLoopContext6 (0 ms)
2023-01-11T22:11:15.9454437Z [ RUN      ] Simplify.SimplifyModWithLoopContext7
2023-01-11T22:11:15.9454877Z [       OK ] Simplify.SimplifyModWithLoopContext7 (0 ms)
2023-01-11T22:11:15.9455191Z [ RUN      ] Simplify.SimplifyMod
2023-01-11T22:11:15.9457625Z [       OK ] Simplify.SimplifyMod (0 ms)
2023-01-11T22:11:15.9457946Z [ RUN      ] Simplify.SimplifyMultiOp
2023-01-11T22:11:15.9459960Z [       OK ] Simplify.SimplifyMultiOp (0 ms)
2023-01-11T22:11:15.9460267Z [ RUN      ] Simplify.SimplifyManyOps
2023-01-11T22:11:15.9463342Z [       OK ] Simplify.SimplifyManyOps (0 ms)
2023-01-11T22:11:15.9463661Z [ RUN      ] Simplify.SimplifyFactorization
2023-01-11T22:11:15.9467819Z [       OK ] Simplify.SimplifyFactorization (0 ms)
2023-01-11T22:11:15.9468177Z [ RUN      ] Simplify.SimplifyFactorizeUneven
2023-01-11T22:11:15.9469300Z [       OK ] Simplify.SimplifyFactorizeUneven (0 ms)
2023-01-11T22:11:15.9469650Z [ RUN      ] Simplify.SimplifyDeeperTerms
2023-01-11T22:11:15.9471430Z [       OK ] Simplify.SimplifyDeeperTerms (0 ms)
2023-01-11T22:11:15.9471802Z [ RUN      ] Simplify.SimplifyDeeperDifference
2023-01-11T22:11:15.9472201Z [       OK ] Simplify.SimplifyDeeperDifference (0 ms)
2023-01-11T22:11:15.9472623Z [ RUN      ] Simplify.SimplifyFoldComplexDifference
2023-01-11T22:11:15.9473116Z [       OK ] Simplify.SimplifyFoldComplexDifference (0 ms)
2023-01-11T22:11:15.9473526Z [ RUN      ] Simplify.SimplifyIfComponents
2023-01-11T22:11:15.9473885Z [       OK ] Simplify.SimplifyIfComponents (0 ms)
2023-01-11T22:11:15.9474339Z [ RUN      ] Simplify.SimplifyOpaqueTerms
2023-01-11T22:11:15.9475694Z [       OK ] Simplify.SimplifyOpaqueTerms (0 ms)
2023-01-11T22:11:15.9476201Z [ RUN      ] Simplify.SimplifySymbolicMinMax
2023-01-11T22:11:15.9476990Z [       OK ] Simplify.SimplifySymbolicMinMax (0 ms)
2023-01-11T22:11:15.9477306Z [ RUN      ] Simplify.SimplifyNestedMax
2023-01-11T22:11:15.9488789Z [       OK ] Simplify.SimplifyNestedMax (1 ms)
2023-01-11T22:11:15.9489344Z [ RUN      ] Simplify.SimplifyNestedMin
2023-01-11T22:11:15.9501072Z [       OK ] Simplify.SimplifyNestedMin (1 ms)
2023-01-11T22:11:15.9501639Z [ RUN      ] Simplify.SimplifyWontReorderFloat
2023-01-11T22:11:15.9503297Z [       OK ] Simplify.SimplifyWontReorderFloat (0 ms)
2023-01-11T22:11:15.9503899Z [ RUN      ] Simplify.SimplifyRoundModPattern
2023-01-11T22:11:15.9511092Z [       OK ] Simplify.SimplifyRoundModPattern (0 ms)
2023-01-11T22:11:15.9511721Z [ RUN      ] Simplify.SimplifyRoundModPatternFactorization
2023-01-11T22:11:15.9515323Z [       OK ] Simplify.SimplifyRoundModPatternFactorization (0 ms)
2023-01-11T22:11:15.9515767Z [ RUN      ] Simplify.SimplifyRoundModPatternMultivar
2023-01-11T22:11:15.9518674Z [       OK ] Simplify.SimplifyRoundModPatternMultivar (0 ms)
2023-01-11T22:11:15.9519058Z [ RUN      ] Simplify.SimplifyModRoundModPattern
2023-01-11T22:11:15.9523305Z [       OK ] Simplify.SimplifyModRoundModPattern (0 ms)
2023-01-11T22:11:15.9523711Z [ RUN      ] Simplify.SimplifyModRoundModPatternFactorization
2023-01-11T22:11:15.9530414Z [       OK ] Simplify.SimplifyModRoundModPatternFactorization (0 ms)
2023-01-11T22:11:15.9530893Z [ RUN      ] Simplify.SimplifyModRoundModPatternMultivar
2023-01-11T22:11:15.9544191Z [       OK ] Simplify.SimplifyModRoundModPatternMultivar (1 ms)
2023-01-11T22:11:15.9544628Z [ RUN      ] Simplify.SimplifyDivisionScalarFactorization
2023-01-11T22:11:15.9545402Z [       OK ] Simplify.SimplifyDivisionScalarFactorization (0 ms)
2023-01-11T22:11:15.9546038Z [ RUN      ] Simplify.SimplifyConstantBranches
2023-01-11T22:11:15.9546845Z [       OK ] Simplify.SimplifyConstantBranches (0 ms)
2023-01-11T22:11:15.9547466Z [ RUN      ] Simplify.SimplifyConstantCond
2023-01-11T22:11:15.9548202Z [       OK ] Simplify.SimplifyConstantCond (0 ms)
2023-01-11T22:11:15.9548807Z [ RUN      ] Simplify.SimplifyEliminateEmptyCond
2023-01-11T22:11:15.9549464Z [       OK ] Simplify.SimplifyEliminateEmptyCond (0 ms)
2023-01-11T22:11:15.9550114Z [ RUN      ] Simplify.SimplifyConstantComparisons
2023-01-11T22:11:15.9554277Z [       OK ] Simplify.SimplifyConstantComparisons (0 ms)
2023-01-11T22:11:15.9554919Z [ RUN      ] Simplify.SimplifySymbolicComparisons
2023-01-11T22:11:15.9561774Z [       OK ] Simplify.SimplifySymbolicComparisons (0 ms)
2023-01-11T22:11:15.9562453Z [ RUN      ] Simplify.SimplifyEliminateZeroLengthFor
2023-01-11T22:11:15.9563107Z [       OK ] Simplify.SimplifyEliminateZeroLengthFor (0 ms)
2023-01-11T22:11:15.9563920Z [ RUN      ] Simplify.SimplifyOneLoopFor
2023-01-11T22:11:15.9564499Z [       OK ] Simplify.SimplifyOneLoopFor (0 ms)
2023-01-11T22:11:15.9565093Z [ RUN      ] Simplify.SimplifyForWontLoseLoopOptions
2023-01-11T22:11:15.9565761Z [       OK ] Simplify.SimplifyForWontLoseLoopOptions (0 ms)
2023-01-11T22:11:15.9566360Z [ RUN      ] Simplify.SimplifyMultilevelFor
2023-01-11T22:11:15.9566936Z [       OK ] Simplify.SimplifyMultilevelFor (0 ms)
2023-01-11T22:11:15.9567487Z [ RUN      ] Simplify.SimplifyForCleansUp
2023-01-11T22:11:15.9570207Z [       OK ] Simplify.SimplifyForCleansUp (0 ms)
2023-01-11T22:11:15.9570816Z [ RUN      ] Simplify.SimplifyEliminateEmptyFor
2023-01-11T22:11:15.9571562Z [       OK ] Simplify.SimplifyEliminateEmptyFor (0 ms)
2023-01-11T22:11:15.9572143Z [ RUN      ] Simplify.SimplifyFlattenBlock
2023-01-11T22:11:15.9572832Z [       OK ] Simplify.SimplifyFlattenBlock (0 ms)
2023-01-11T22:11:15.9573455Z [ RUN      ] Simplify.SimplifyEliminateZeroLengthAlloc
2023-01-11T22:11:15.9574144Z [       OK ] Simplify.SimplifyEliminateZeroLengthAlloc (0 ms)
2023-01-11T22:11:15.9574756Z [ RUN      ] Simplify.DontSimplifyRand
2023-01-11T22:11:15.9575298Z [       OK ] Simplify.DontSimplifyRand (0 ms)
2023-01-11T22:11:15.9575867Z [ RUN      ] Simplify.SimplifyReorderForCond
2023-01-11T22:11:15.9580712Z [       OK ] Simplify.SimplifyReorderForCond (0 ms)
2023-01-11T22:11:15.9581334Z [ RUN      ] Simplify.SimplifyFuseConditions
2023-01-11T22:11:15.9587557Z [       OK ] Simplify.SimplifyFuseConditions (0 ms)
2023-01-11T22:11:15.9588103Z [ RUN      ] Simplify.SimplifySyncThreads
2023-01-11T22:11:15.9588678Z [       OK ] Simplify.SimplifySyncThreads (0 ms)
2023-01-11T22:11:15.9589222Z [ RUN      ] Simplify.SimplifyRampSubBroadcast
2023-01-11T22:11:15.9589863Z [       OK ] Simplify.SimplifyRampSubBroadcast (0 ms)
2023-01-11T22:11:15.9590486Z [ RUN      ] Simplify.SimplifyBroadcastTermExpander
2023-01-11T22:11:15.9591157Z [       OK ] Simplify.SimplifyBroadcastTermExpander (0 ms)
2023-01-11T22:11:15.9591791Z [ RUN      ] Simplify.CompareSelectLoopBounds
2023-01-11T22:11:15.9673445Z [       OK ] Simplify.CompareSelectLoopBounds (8 ms)
2023-01-11T22:11:15.9674105Z [ RUN      ] Simplify.CompareSelectCondAlwaysInLoopBounds
2023-01-11T22:11:15.9674868Z [       OK ] Simplify.CompareSelectCondAlwaysInLoopBounds (0 ms)
2023-01-11T22:11:15.9675462Z [ RUN      ] Simplify.IfThenCondAlwaysInLoopBounds
2023-01-11T22:11:15.9675944Z [       OK ] Simplify.IfThenCondAlwaysInLoopBounds (0 ms)
2023-01-11T22:11:15.9676367Z [ RUN      ] Simplify.MultiClauseCondAlwaysInLoopBounds
2023-01-11T22:11:15.9676992Z [       OK ] Simplify.MultiClauseCondAlwaysInLoopBounds (0 ms)
2023-01-11T22:11:15.9677660Z [----------] 92 tests from Simplify (32 ms total)
2023-01-11T22:11:15.9677824Z 
2023-01-11T22:11:15.9677975Z [----------] 12 tests from TEFuserPass
2023-01-11T22:11:15.9678260Z [ RUN      ] TEFuserPass.FuserPass_1
2023-01-11T22:11:15.9683454Z [       OK ] TEFuserPass.FuserPass_1 (0 ms)
2023-01-11T22:11:15.9683752Z [ RUN      ] TEFuserPass.FuserPass_2
2023-01-11T22:11:15.9686685Z [       OK ] TEFuserPass.FuserPass_2 (0 ms)
2023-01-11T22:11:15.9687030Z [ RUN      ] TEFuserPass.FuserPass_3
2023-01-11T22:11:15.9689284Z [       OK ] TEFuserPass.FuserPass_3 (0 ms)
2023-01-11T22:11:15.9689634Z [ RUN      ] TEFuserPass.FuserPass_0DimInput
2023-01-11T22:11:15.9691688Z [       OK ] TEFuserPass.FuserPass_0DimInput (0 ms)
2023-01-11T22:11:15.9692285Z [ RUN      ] TEFuserPass.FuserPass_UnfusibleDevice
2023-01-11T22:11:15.9692896Z [       OK ] TEFuserPass.FuserPass_UnfusibleDevice (0 ms)
2023-01-11T22:11:15.9693535Z [ RUN      ] TEFuserPass.FuserPass_UnknownShapes
2023-01-11T22:11:15.9694161Z [       OK ] TEFuserPass.FuserPass_UnknownShapes (0 ms)
2023-01-11T22:11:15.9694528Z [ RUN      ] TEFuserPass.FuserPass_Multidevice
2023-01-11T22:11:15.9700808Z [       OK ] TEFuserPass.FuserPass_Multidevice (0 ms)
2023-01-11T22:11:15.9701375Z [ RUN      ] TEFuserPass.FuserPass_MergeGroups
2023-01-11T22:11:15.9703423Z [       OK ] TEFuserPass.FuserPass_MergeGroups (0 ms)
2023-01-11T22:11:15.9704013Z [ RUN      ] TEFuserPass.FuserPass_IgnoreUnknownShapeAtStart
2023-01-11T22:11:15.9704659Z [       OK ] TEFuserPass.FuserPass_IgnoreUnknownShapeAtStart (0 ms)
2023-01-11T22:11:15.9705266Z [ RUN      ] TEFuserPass.FuserPass_Where
2023-01-11T22:11:15.9707296Z [       OK ] TEFuserPass.FuserPass_Where (0 ms)
2023-01-11T22:11:15.9707609Z [ RUN      ] TEFuserPass.FuserPass_WhereList
2023-01-11T22:11:15.9708609Z [       OK ] TEFuserPass.FuserPass_WhereList (0 ms)
2023-01-11T22:11:15.9708944Z [ RUN      ] TEFuserPass.DynamicShapeFusion
2023-01-11T22:11:16.2298782Z [       OK ] TEFuserPass.DynamicShapeFusion (258 ms)
2023-01-11T22:11:16.2299242Z [----------] 12 tests from TEFuserPass (261 ms total)
2023-01-11T22:11:16.2299406Z 
2023-01-11T22:11:16.2299552Z [----------] 3 tests from Type
2023-01-11T22:11:16.2299790Z [ RUN      ] Type.Test01
2023-01-11T22:11:16.2300038Z [       OK ] Type.Test01 (0 ms)
2023-01-11T22:11:16.2300286Z [ RUN      ] Type.BitCasting
2023-01-11T22:11:16.2300546Z [       OK ] Type.BitCasting (0 ms)
2023-01-11T22:11:16.2300793Z [ RUN      ] Type.Propagation
2023-01-11T22:11:16.2301062Z [       OK ] Type.Propagation (0 ms)
2023-01-11T22:11:16.2301347Z [----------] 3 tests from Type (0 ms total)
2023-01-11T22:11:16.2301474Z 
2023-01-11T22:11:16.2301676Z [----------] 1 test from SpecializationsInCustomPasses
2023-01-11T22:11:16.2302030Z [ RUN      ] SpecializationsInCustomPasses.Basic
2023-01-11T22:11:16.2315413Z [       OK ] SpecializationsInCustomPasses.Basic (1 ms)
2023-01-11T22:11:16.2316127Z [----------] 1 test from SpecializationsInCustomPasses (1 ms total)
2023-01-11T22:11:16.2316322Z 
2023-01-11T22:11:16.2316458Z [----------] 150 tests from LLVM
2023-01-11T22:11:16.2316709Z [ RUN      ] LLVM.ByteImmTest
2023-01-11T22:11:16.2520916Z [       OK ] LLVM.ByteImmTest (20 ms)
2023-01-11T22:11:16.2521391Z [ RUN      ] LLVM.CharImmTest
2023-01-11T22:11:16.2709024Z [       OK ] LLVM.CharImmTest (18 ms)
2023-01-11T22:11:16.2709327Z [ RUN      ] LLVM.ShortImmTest
2023-01-11T22:11:16.2901555Z [       OK ] LLVM.ShortImmTest (19 ms)
2023-01-11T22:11:16.2901839Z [ RUN      ] LLVM.IntImmTest
2023-01-11T22:11:16.3090241Z [       OK ] LLVM.IntImmTest (18 ms)
2023-01-11T22:11:16.3090535Z [ RUN      ] LLVM.LongImmTest
2023-01-11T22:11:16.3282016Z [       OK ] LLVM.LongImmTest (19 ms)
2023-01-11T22:11:16.3282329Z [ RUN      ] LLVM.FloatImmTest
2023-01-11T22:11:16.3469241Z [       OK ] LLVM.FloatImmTest (18 ms)
2023-01-11T22:11:16.3469693Z [ RUN      ] LLVM.DoubleImmTest
2023-01-11T22:11:16.3658915Z [       OK ] LLVM.DoubleImmTest (18 ms)
2023-01-11T22:11:16.3659420Z [ RUN      ] LLVM.HalfImmTest
2023-01-11T22:11:16.3846695Z [       OK ] LLVM.HalfImmTest (18 ms)
2023-01-11T22:11:16.3847158Z [ RUN      ] LLVM.ByteAddTest
2023-01-11T22:11:16.4031807Z [       OK ] LLVM.ByteAddTest (18 ms)
2023-01-11T22:11:16.4032096Z [ RUN      ] LLVM.CharAddTest
2023-01-11T22:11:16.4216363Z [       OK ] LLVM.CharAddTest (18 ms)
2023-01-11T22:11:16.4216647Z [ RUN      ] LLVM.ShortAddTest
2023-01-11T22:11:16.4406482Z [       OK ] LLVM.ShortAddTest (18 ms)
2023-01-11T22:11:16.4407042Z [ RUN      ] LLVM.IntAddTest
2023-01-11T22:11:16.4595385Z [       OK ] LLVM.IntAddTest (18 ms)
2023-01-11T22:11:16.4595696Z [ RUN      ] LLVM.LongAddTest
2023-01-11T22:11:16.4786430Z [       OK ] LLVM.LongAddTest (19 ms)
2023-01-11T22:11:16.4786695Z [ RUN      ] LLVM.FloatAddTest
2023-01-11T22:11:16.4976593Z [       OK ] LLVM.FloatAddTest (18 ms)
2023-01-11T22:11:16.4976913Z [ RUN      ] LLVM.DoubleAddTest
2023-01-11T22:11:16.5169670Z [       OK ] LLVM.DoubleAddTest (19 ms)
2023-01-11T22:11:16.5169960Z [ RUN      ] LLVM.HalfAddTest
2023-01-11T22:11:16.5360943Z [       OK ] LLVM.HalfAddTest (19 ms)
2023-01-11T22:11:16.5361333Z [ RUN      ] LLVM.ByteSubTest
2023-01-11T22:11:16.5550219Z [       OK ] LLVM.ByteSubTest (18 ms)
2023-01-11T22:11:16.5550487Z [ RUN      ] LLVM.CharSubTest
2023-01-11T22:11:16.5743800Z [       OK ] LLVM.CharSubTest (19 ms)
2023-01-11T22:11:16.5744276Z [ RUN      ] LLVM.ShortSubTest
2023-01-11T22:11:16.5933301Z [       OK ] LLVM.ShortSubTest (18 ms)
2023-01-11T22:11:16.5933581Z [ RUN      ] LLVM.IntSubTest
2023-01-11T22:11:16.6127035Z [       OK ] LLVM.IntSubTest (19 ms)
2023-01-11T22:11:16.6127314Z [ RUN      ] LLVM.LongSubTest
2023-01-11T22:11:16.6318015Z [       OK ] LLVM.LongSubTest (19 ms)
2023-01-11T22:11:16.6318291Z [ RUN      ] LLVM.FloatSubTest
2023-01-11T22:11:16.6508289Z [       OK ] LLVM.FloatSubTest (19 ms)
2023-01-11T22:11:16.6508559Z [ RUN      ] LLVM.DoubleSubTest
2023-01-11T22:11:16.6700746Z [       OK ] LLVM.DoubleSubTest (19 ms)
2023-01-11T22:11:16.6701020Z [ RUN      ] LLVM.HalfSubTest
2023-01-11T22:11:16.6891504Z [       OK ] LLVM.HalfSubTest (19 ms)
2023-01-11T22:11:16.6891944Z [ RUN      ] LLVM.ByteMulTest
2023-01-11T22:11:16.7081099Z [       OK ] LLVM.ByteMulTest (18 ms)
2023-01-11T22:11:16.7081453Z [ RUN      ] LLVM.CharMulTest
2023-01-11T22:11:16.7271031Z [       OK ] LLVM.CharMulTest (19 ms)
2023-01-11T22:11:16.7271536Z [ RUN      ] LLVM.ShortMulTest
2023-01-11T22:11:16.7461885Z [       OK ] LLVM.ShortMulTest (18 ms)
2023-01-11T22:11:16.7462625Z [ RUN      ] LLVM.IntMulTest
2023-01-11T22:11:16.7652515Z [       OK ] LLVM.IntMulTest (19 ms)
2023-01-11T22:11:16.7652794Z [ RUN      ] LLVM.LongMulTest
2023-01-11T22:11:16.7844714Z [       OK ] LLVM.LongMulTest (19 ms)
2023-01-11T22:11:16.7844972Z [ RUN      ] LLVM.FloatMulTest
2023-01-11T22:11:16.8035954Z [       OK ] LLVM.FloatMulTest (19 ms)
2023-01-11T22:11:16.8036235Z [ RUN      ] LLVM.DoubleMulTest
2023-01-11T22:11:16.8226873Z [       OK ] LLVM.DoubleMulTest (19 ms)
2023-01-11T22:11:16.8227143Z [ RUN      ] LLVM.HalfMulTest
2023-01-11T22:11:16.8417419Z [       OK ] LLVM.HalfMulTest (19 ms)
2023-01-11T22:11:16.8417714Z [ RUN      ] LLVM.ByteDivTest
2023-01-11T22:11:16.8607139Z [       OK ] LLVM.ByteDivTest (18 ms)
2023-01-11T22:11:16.8607426Z [ RUN      ] LLVM.CharDivTest
2023-01-11T22:11:16.8798689Z [       OK ] LLVM.CharDivTest (19 ms)
2023-01-11T22:11:16.8799083Z [ RUN      ] LLVM.ShortDivTest
2023-01-11T22:11:16.8990483Z [       OK ] LLVM.ShortDivTest (19 ms)
2023-01-11T22:11:16.8990757Z [ RUN      ] LLVM.IntDivTest
2023-01-11T22:11:16.9182872Z [       OK ] LLVM.IntDivTest (19 ms)
2023-01-11T22:11:16.9183386Z [ RUN      ] LLVM.LongDivTest
2023-01-11T22:11:16.9376317Z [       OK ] LLVM.LongDivTest (19 ms)
2023-01-11T22:11:16.9568440Z [ RUN      ] LLVM.FloatDivTest
2023-01-11T22:11:16.9568958Z [       OK ] LLVM.FloatDivTest (19 ms)
2023-01-11T22:11:16.9569415Z [ RUN      ] LLVM.DoubleDivTest
2023-01-11T22:11:16.9762006Z [       OK ] LLVM.DoubleDivTest (19 ms)
2023-01-11T22:11:16.9762796Z [ RUN      ] LLVM.HalfDivTest
2023-01-11T22:11:16.9954769Z [       OK ] LLVM.HalfDivTest (19 ms)
2023-01-11T22:11:16.9955287Z [ RUN      ] LLVM.IntToFloatCastTest
2023-01-11T22:11:17.0147582Z [       OK ] LLVM.IntToFloatCastTest (19 ms)
2023-01-11T22:11:17.0148120Z [ RUN      ] LLVM.FloatToIntCastTest
2023-01-11T22:11:17.0338007Z [       OK ] LLVM.FloatToIntCastTest (19 ms)
2023-01-11T22:11:17.0338562Z [ RUN      ] LLVM.IntToLongCastTest
2023-01-11T22:11:17.0529459Z [       OK ] LLVM.IntToLongCastTest (19 ms)
2023-01-11T22:11:17.0530048Z [ RUN      ] LLVM.ByteToCharCastTest
2023-01-11T22:11:17.0721887Z [       OK ] LLVM.ByteToCharCastTest (19 ms)
2023-01-11T22:11:17.0722434Z [ RUN      ] LLVM.HalfToLongCastTest
2023-01-11T22:11:17.0913245Z [       OK ] LLVM.HalfToLongCastTest (19 ms)
2023-01-11T22:11:17.0913802Z [ RUN      ] LLVM.ByteToDoubleCastTest
2023-01-11T22:11:17.1105387Z [       OK ] LLVM.ByteToDoubleCastTest (19 ms)
2023-01-11T22:11:17.1105935Z [ RUN      ] LLVM.FloatToByteCastTest
2023-01-11T22:11:17.1295998Z [       OK ] LLVM.FloatToByteCastTest (19 ms)
2023-01-11T22:11:17.1296558Z [ RUN      ] LLVM.FloatToCharCastTest
2023-01-11T22:11:17.1487548Z [       OK ] LLVM.FloatToCharCastTest (19 ms)
2023-01-11T22:11:17.1488084Z [ RUN      ] LLVM.ByteToFloatCastTest
2023-01-11T22:11:17.1678915Z [       OK ] LLVM.ByteToFloatCastTest (19 ms)
2023-01-11T22:11:17.1679475Z [ RUN      ] LLVM.CharToFloatCastTest
2023-01-11T22:11:17.1870705Z [       OK ] LLVM.CharToFloatCastTest (19 ms)
2023-01-11T22:11:17.1871044Z [ RUN      ] LLVM.BitCast
2023-01-11T22:11:17.2630413Z [       OK ] LLVM.BitCast (75 ms)
2023-01-11T22:11:17.2630884Z [ RUN      ] LLVM.fastLogFloat
2023-01-11T22:11:17.3248303Z [       OK ] LLVM.fastLogFloat (61 ms)
2023-01-11T22:11:17.3248827Z [ RUN      ] LLVM.LetTest01
2023-01-11T22:11:17.3440743Z [       OK ] LLVM.LetTest01 (19 ms)
2023-01-11T22:11:17.3441206Z [ RUN      ] LLVM.LetTest02
2023-01-11T22:11:17.3631751Z [       OK ] LLVM.LetTest02 (19 ms)
2023-01-11T22:11:17.3632283Z [ RUN      ] LLVM.LetTestMultitype
2023-01-11T22:11:17.3823444Z [       OK ] LLVM.LetTestMultitype (19 ms)
2023-01-11T22:11:17.3823970Z [ RUN      ] LLVM.BufferTest
2023-01-11T22:11:17.4014987Z [       OK ] LLVM.BufferTest (19 ms)
2023-01-11T22:11:17.4015600Z [ RUN      ] LLVM.BlockTest
2023-01-11T22:11:17.4208565Z [       OK ] LLVM.BlockTest (19 ms)
2023-01-11T22:11:17.4208878Z [ RUN      ] LLVM.LoadStoreTest
2023-01-11T22:11:17.4403194Z [       OK ] LLVM.LoadStoreTest (19 ms)
2023-01-11T22:11:17.4403547Z [ RUN      ] LLVM.IfThenElseTest
2023-01-11T22:11:17.4611952Z [       OK ] LLVM.IfThenElseTest (20 ms)
2023-01-11T22:11:17.4612288Z [ RUN      ] LLVM.CondNoFalseBlockTest
2023-01-11T22:11:17.5219281Z [       OK ] LLVM.CondNoFalseBlockTest (60 ms)
2023-01-11T22:11:17.5219561Z [ RUN      ] LLVM.CondTest
2023-01-11T22:11:17.5834396Z [       OK ] LLVM.CondTest (61 ms)
2023-01-11T22:11:17.5834680Z [ RUN      ] LLVM.CondNestedTest
2023-01-11T22:11:17.6697128Z [       OK ] LLVM.CondNestedTest (86 ms)
2023-01-11T22:11:17.6697475Z [ RUN      ] LLVM.DirectVectorization
2023-01-11T22:11:17.6965799Z [       OK ] LLVM.DirectVectorization (26 ms)
2023-01-11T22:11:17.6966111Z [ RUN      ] LLVM.VecLoadStoreTest
2023-01-11T22:11:17.7168748Z [       OK ] LLVM.VecLoadStoreTest (20 ms)
2023-01-11T22:11:17.7169061Z [ RUN      ] LLVM.VecFloat_erfLane4Test
2023-01-11T22:11:17.7363189Z [       OK ] LLVM.VecFloat_erfLane4Test (19 ms)
2023-01-11T22:11:17.7363495Z [ RUN      ] LLVM.VecFloat_erfcLane4Test
2023-01-11T22:11:17.7559513Z [       OK ] LLVM.VecFloat_erfcLane4Test (19 ms)
2023-01-11T22:11:17.7559956Z [ RUN      ] LLVM.VecFloat_acosLane4Test
2023-01-11T22:11:17.7759923Z [       OK ] LLVM.VecFloat_acosLane4Test (20 ms)
2023-01-11T22:11:17.7760292Z [ RUN      ] LLVM.VecFloat_asinLane4Test
2023-01-11T22:11:17.7957710Z [       OK ] LLVM.VecFloat_asinLane4Test (19 ms)
2023-01-11T22:11:17.7958073Z [ RUN      ] LLVM.VecFloat_atanLane4Test
2023-01-11T22:11:17.8157423Z [       OK ] LLVM.VecFloat_atanLane4Test (19 ms)
2023-01-11T22:11:17.8157711Z [ RUN      ] LLVM.VecFloat_coshLane4Test
2023-01-11T22:11:17.8357842Z [       OK ] LLVM.VecFloat_coshLane4Test (20 ms)
2023-01-11T22:11:17.8358151Z [ RUN      ] LLVM.VecFloat_sinhLane4Test
2023-01-11T22:11:17.8557649Z [       OK ] LLVM.VecFloat_sinhLane4Test (19 ms)
2023-01-11T22:11:17.8557938Z [ RUN      ] LLVM.VecFloat_tanhLane4Test
2023-01-11T22:11:17.8758652Z [       OK ] LLVM.VecFloat_tanhLane4Test (20 ms)
2023-01-11T22:11:17.8758975Z [ RUN      ] LLVM.VecFloat_expm1Lane4Test
2023-01-11T22:11:17.8958526Z [       OK ] LLVM.VecFloat_expm1Lane4Test (19 ms)
2023-01-11T22:11:17.8958976Z [ RUN      ] LLVM.VecFloat_lgammaLane4Test
2023-01-11T22:11:17.9158984Z [       OK ] LLVM.VecFloat_lgammaLane4Test (19 ms)
2023-01-11T22:11:17.9159299Z [ RUN      ] LLVM.VecFloat_erfLane8Test
2023-01-11T22:11:17.9358802Z [       OK ] LLVM.VecFloat_erfLane8Test (19 ms)
2023-01-11T22:11:17.9359115Z [ RUN      ] LLVM.VecFloat_erfcLane8Test
2023-01-11T22:11:17.9556704Z [       OK ] LLVM.VecFloat_erfcLane8Test (19 ms)
2023-01-11T22:11:17.9556993Z [ RUN      ] LLVM.VecFloat_acosLane8Test
2023-01-11T22:11:17.9752317Z [       OK ] LLVM.VecFloat_acosLane8Test (19 ms)
2023-01-11T22:11:17.9752620Z [ RUN      ] LLVM.VecFloat_asinLane8Test
2023-01-11T22:11:17.9946886Z [       OK ] LLVM.VecFloat_asinLane8Test (19 ms)
2023-01-11T22:11:17.9947304Z [ RUN      ] LLVM.VecFloat_atanLane8Test
2023-01-11T22:11:18.0142923Z [       OK ] LLVM.VecFloat_atanLane8Test (19 ms)
2023-01-11T22:11:18.0143273Z [ RUN      ] LLVM.VecFloat_coshLane8Test
2023-01-11T22:11:18.0339988Z [       OK ] LLVM.VecFloat_coshLane8Test (19 ms)
2023-01-11T22:11:18.0340279Z [ RUN      ] LLVM.VecFloat_sinhLane8Test
2023-01-11T22:11:18.0535623Z [       OK ] LLVM.VecFloat_sinhLane8Test (19 ms)
2023-01-11T22:11:18.0535933Z [ RUN      ] LLVM.VecFloat_tanhLane8Test
2023-01-11T22:11:18.0732111Z [       OK ] LLVM.VecFloat_tanhLane8Test (19 ms)
2023-01-11T22:11:18.0732506Z [ RUN      ] LLVM.VecFloat_expm1Lane8Test
2023-01-11T22:11:18.0932892Z [       OK ] LLVM.VecFloat_expm1Lane8Test (20 ms)
2023-01-11T22:11:18.0933208Z [ RUN      ] LLVM.VecFloat_lgammaLane8Test
2023-01-11T22:11:18.1133518Z [       OK ] LLVM.VecFloat_lgammaLane8Test (19 ms)
2023-01-11T22:11:18.1133892Z [ RUN      ] LLVM.VecDouble_erfLane2Test
2023-01-11T22:11:18.1348724Z [       OK ] LLVM.VecDouble_erfLane2Test (21 ms)
2023-01-11T22:11:18.1349098Z [ RUN      ] LLVM.VecDouble_erfcLane2Test
2023-01-11T22:11:18.1550389Z [       OK ] LLVM.VecDouble_erfcLane2Test (20 ms)
2023-01-11T22:11:18.1550707Z [ RUN      ] LLVM.VecDouble_acosLane2Test
2023-01-11T22:11:18.1751432Z [       OK ] LLVM.VecDouble_acosLane2Test (20 ms)
2023-01-11T22:11:18.1751752Z [ RUN      ] LLVM.VecDouble_asinLane2Test
2023-01-11T22:11:18.1951809Z [       OK ] LLVM.VecDouble_asinLane2Test (19 ms)
2023-01-11T22:11:18.1952119Z [ RUN      ] LLVM.VecDouble_atanLane2Test
2023-01-11T22:11:18.2147986Z [       OK ] LLVM.VecDouble_atanLane2Test (19 ms)
2023-01-11T22:11:18.2148335Z [ RUN      ] LLVM.VecDouble_coshLane2Test
2023-01-11T22:11:18.2348161Z [       OK ] LLVM.VecDouble_coshLane2Test (19 ms)
2023-01-11T22:11:18.2348479Z [ RUN      ] LLVM.VecDouble_sinhLane2Test
2023-01-11T22:11:18.2546168Z [       OK ] LLVM.VecDouble_sinhLane2Test (19 ms)
2023-01-11T22:11:18.2546464Z [ RUN      ] LLVM.VecDouble_tanhLane2Test
2023-01-11T22:11:18.2746196Z [       OK ] LLVM.VecDouble_tanhLane2Test (19 ms)
2023-01-11T22:11:18.2746529Z [ RUN      ] LLVM.VecDouble_expm1Lane2Test
2023-01-11T22:11:18.2946467Z [       OK ] LLVM.VecDouble_expm1Lane2Test (19 ms)
2023-01-11T22:11:18.2946782Z [ RUN      ] LLVM.VecDouble_lgammaLane2Test
2023-01-11T22:11:18.3147119Z [       OK ] LLVM.VecDouble_lgammaLane2Test (19 ms)
2023-01-11T22:11:18.3147429Z [ RUN      ] LLVM.VecDouble_erfLane4Test
2023-01-11T22:11:18.3346606Z [       OK ] LLVM.VecDouble_erfLane4Test (19 ms)
2023-01-11T22:11:18.3346937Z [ RUN      ] LLVM.VecDouble_erfcLane4Test
2023-01-11T22:11:18.3547041Z [       OK ] LLVM.VecDouble_erfcLane4Test (19 ms)
2023-01-11T22:11:18.3547353Z [ RUN      ] LLVM.VecDouble_acosLane4Test
2023-01-11T22:11:18.3745836Z [       OK ] LLVM.VecDouble_acosLane4Test (19 ms)
2023-01-11T22:11:18.3746162Z [ RUN      ] LLVM.VecDouble_asinLane4Test
2023-01-11T22:11:18.3944985Z [       OK ] LLVM.VecDouble_asinLane4Test (19 ms)
2023-01-11T22:11:18.3945299Z [ RUN      ] LLVM.VecDouble_atanLane4Test
2023-01-11T22:11:18.4145319Z [       OK ] LLVM.VecDouble_atanLane4Test (19 ms)
2023-01-11T22:11:18.4145637Z [ RUN      ] LLVM.VecDouble_coshLane4Test
2023-01-11T22:11:18.4344638Z [       OK ] LLVM.VecDouble_coshLane4Test (19 ms)
2023-01-11T22:11:18.4344936Z [ RUN      ] LLVM.VecDouble_sinhLane4Test
2023-01-11T22:11:18.4545137Z [       OK ] LLVM.VecDouble_sinhLane4Test (19 ms)
2023-01-11T22:11:18.4545500Z [ RUN      ] LLVM.VecDouble_tanhLane4Test
2023-01-11T22:11:18.4742094Z [       OK ] LLVM.VecDouble_tanhLane4Test (19 ms)
2023-01-11T22:11:18.4742556Z [ RUN      ] LLVM.VecDouble_expm1Lane4Test
2023-01-11T22:11:18.4936935Z [       OK ] LLVM.VecDouble_expm1Lane4Test (19 ms)
2023-01-11T22:11:18.4937252Z [ RUN      ] LLVM.VecDouble_lgammaLane4Test
2023-01-11T22:11:18.5130542Z [       OK ] LLVM.VecDouble_lgammaLane4Test (19 ms)
2023-01-11T22:11:18.5130870Z [ RUN      ] LLVM.VectorizerLoadStoreTest
2023-01-11T22:11:18.5320578Z [       OK ] LLVM.VectorizerLoadStoreTest (18 ms)
2023-01-11T22:11:18.5320891Z [ RUN      ] LLVM.VectorizeBitCast
2023-01-11T22:11:18.5529235Z [       OK ] LLVM.VectorizeBitCast (20 ms)
2023-01-11T22:11:18.5529526Z [ RUN      ] LLVM.MemcpyTest
2023-01-11T22:11:18.5733434Z [       OK ] LLVM.MemcpyTest (20 ms)
2023-01-11T22:11:18.5733715Z [ RUN      ] LLVM.BzeroTest
2023-01-11T22:11:18.5931039Z [       OK ] LLVM.BzeroTest (19 ms)
2023-01-11T22:11:18.5931314Z [ RUN      ] LLVM.ElemwiseAdd
2023-01-11T22:11:18.6412609Z [       OK ] LLVM.ElemwiseAdd (48 ms)
2023-01-11T22:11:18.6412901Z [ RUN      ] LLVM.ElemwiseAddFloat
2023-01-11T22:11:18.6878075Z [       OK ] LLVM.ElemwiseAddFloat (46 ms)
2023-01-11T22:11:18.6878387Z [ RUN      ] LLVM.ElemwiseLog10Float
2023-01-11T22:11:18.7109115Z [       OK ] LLVM.ElemwiseLog10Float (23 ms)
2023-01-11T22:11:18.7109480Z [ RUN      ] LLVM.ElemwiseLog1pFloat
2023-01-11T22:11:18.7338039Z [       OK ] LLVM.ElemwiseLog1pFloat (22 ms)
2023-01-11T22:11:18.7338368Z [ RUN      ] LLVM.ElemwiseMaxInt
2023-01-11T22:11:18.7661349Z [       OK ] LLVM.ElemwiseMaxInt (32 ms)
2023-01-11T22:11:18.7661647Z [ RUN      ] LLVM.ElemwiseMinInt
2023-01-11T22:11:18.7989073Z [       OK ] LLVM.ElemwiseMinInt (32 ms)
2023-01-11T22:11:18.7989406Z [ RUN      ] LLVM.ElemwiseMaxFloat
2023-01-11T22:11:18.8277438Z [       OK ] LLVM.ElemwiseMaxFloat (28 ms)
2023-01-11T22:11:18.8277747Z [ RUN      ] LLVM.ElemwiseMaxNaNFloat
2023-01-11T22:11:18.8565905Z [       OK ] LLVM.ElemwiseMaxNaNFloat (28 ms)
2023-01-11T22:11:18.8566212Z [ RUN      ] LLVM.ElemwiseMinFloat
2023-01-11T22:11:18.8853818Z [       OK ] LLVM.ElemwiseMinFloat (28 ms)
2023-01-11T22:11:18.8854138Z [ RUN      ] LLVM.ElemwiseMinNaNFloat
2023-01-11T22:11:18.9141699Z [       OK ] LLVM.ElemwiseMinNaNFloat (28 ms)
2023-01-11T22:11:18.9142035Z [ RUN      ] LLVM.ElemwiseMod
2023-01-11T22:11:18.9413441Z [       OK ] LLVM.ElemwiseMod (27 ms)
2023-01-11T22:11:18.9413758Z [ RUN      ] LLVM.CompareSelectIntEQ
2023-01-11T22:11:18.9752083Z [       OK ] LLVM.CompareSelectIntEQ (33 ms)
2023-01-11T22:11:18.9752408Z [ RUN      ] LLVM.CompareSelectFloatEQ
2023-01-11T22:11:19.0084036Z [       OK ] LLVM.CompareSelectFloatEQ (33 ms)
2023-01-11T22:11:19.0084653Z [ RUN      ] LLVM.CompareSelectByteGT
2023-01-11T22:11:19.0418882Z [       OK ] LLVM.CompareSelectByteGT (33 ms)
2023-01-11T22:11:19.0419184Z [ RUN      ] LLVM.CompareSelectByteGE
2023-01-11T22:11:19.0751193Z [       OK ] LLVM.CompareSelectByteGE (33 ms)
2023-01-11T22:11:19.0751523Z [ RUN      ] LLVM.CompareSelectByteLT
2023-01-11T22:11:19.1081007Z [       OK ] LLVM.CompareSelectByteLT (32 ms)
2023-01-11T22:11:19.1081312Z [ RUN      ] LLVM.CompareSelectByteLE
2023-01-11T22:11:19.1413414Z [       OK ] LLVM.CompareSelectByteLE (33 ms)
2023-01-11T22:11:19.1413940Z [ RUN      ] LLVM.StoreFloat
2023-01-11T22:11:19.1595396Z [       OK ] LLVM.StoreFloat (18 ms)
2023-01-11T22:11:19.1595662Z [ RUN      ] LLVM.SimpleMath01
2023-01-11T22:11:19.1964046Z [       OK ] LLVM.SimpleMath01 (36 ms)
2023-01-11T22:11:19.1964320Z [ RUN      ] LLVM.ComputeMul
2023-01-11T22:11:19.2425295Z [       OK ] LLVM.ComputeMul (46 ms)
2023-01-11T22:11:19.2425651Z [ RUN      ] LLVM.BroadcastAdd
2023-01-11T22:11:19.3532990Z [       OK ] LLVM.BroadcastAdd (110 ms)
2023-01-11T22:11:19.3533346Z [ RUN      ] LLVM.BitwiseOps
2023-01-11T22:11:19.3717147Z [       OK ] LLVM.BitwiseOps (18 ms)
2023-01-11T22:11:19.3717500Z [ RUN      ] LLVM.ArithmeticRightShift
2023-01-11T22:11:19.3903687Z [       OK ] LLVM.ArithmeticRightShift (18 ms)
2023-01-11T22:11:19.3904012Z [ RUN      ] LLVM.LogicalRightShift
2023-01-11T22:11:19.4089385Z [       OK ] LLVM.LogicalRightShift (18 ms)
2023-01-11T22:11:19.4089784Z [ RUN      ] LLVM.DynamicShapeAdd
2023-01-11T22:11:19.5390033Z [       OK ] LLVM.DynamicShapeAdd (130 ms)
2023-01-11T22:11:19.5390350Z [ RUN      ] LLVM.BindDynamicShapeAdd
2023-01-11T22:11:19.6673406Z [       OK ] LLVM.BindDynamicShapeAdd (128 ms)
2023-01-11T22:11:19.6673749Z [ RUN      ] LLVM.TensorDynamicShapeAdd
2023-01-11T22:11:19.7951871Z [       OK ] LLVM.TensorDynamicShapeAdd (127 ms)
2023-01-11T22:11:19.7952186Z [ RUN      ] LLVM.DynamicShape2D
2023-01-11T22:11:20.0084754Z [       OK ] LLVM.DynamicShape2D (213 ms)
2023-01-11T22:11:20.0085145Z [ RUN      ] LLVM.EmptyStmt
2023-01-11T22:11:20.0257324Z [       OK ] LLVM.EmptyStmt (17 ms)
2023-01-11T22:11:20.0257622Z [ RUN      ] LLVM.EliminatedStmt
2023-01-11T22:11:20.0428893Z [       OK ] LLVM.EliminatedStmt (17 ms)
2023-01-11T22:11:20.0429200Z [ RUN      ] LLVM.SimpleReduction
2023-01-11T22:11:20.1237723Z [       OK ] LLVM.SimpleReduction (80 ms)
2023-01-11T22:11:20.1238035Z [ RUN      ] LLVM.RFactorReduction
2023-01-11T22:11:20.1549630Z [       OK ] LLVM.RFactorReduction (31 ms)
2023-01-11T22:11:20.1549956Z [ RUN      ] LLVM.RFactorVectorizedReduction
2023-01-11T22:11:20.2030177Z [       OK ] LLVM.RFactorVectorizedReduction (48 ms)
2023-01-11T22:11:20.2030747Z [ RUN      ] LLVM.SimpleParallelSS
2023-01-11T22:11:20.2263582Z [       OK ] LLVM.SimpleParallelSS (23 ms)
2023-01-11T22:11:20.2263884Z [ RUN      ] LLVM.SimpleParallelSP
2023-01-11T22:11:20.2521813Z [       OK ] LLVM.SimpleParallelSP (25 ms)
2023-01-11T22:11:20.2522332Z [ RUN      ] LLVM.SimpleParallelPS
2023-01-11T22:11:20.2787189Z [       OK ] LLVM.SimpleParallelPS (26 ms)
2023-01-11T22:11:20.2787601Z [ RUN      ] LLVM.SimpleParallelPP
2023-01-11T22:11:20.3052369Z [       OK ] LLVM.SimpleParallelPP (26 ms)
2023-01-11T22:11:23.3184040Z [ RUN      ] LLVM.CompositeParallel
2023-01-11T22:11:23.3184586Z [       OK ] LLVM.CompositeParallel (3013 ms)
2023-01-11T22:11:23.3185093Z [ RUN      ] LLVM.VectorizedGEMM
2023-01-11T22:11:23.3982675Z [       OK ] LLVM.VectorizedGEMM (79 ms)
2023-01-11T22:11:23.3983007Z [ RUN      ] LLVM.CallRaw
2023-01-11T22:11:24.0543925Z [       OK ] LLVM.CallRaw (656 ms)
2023-01-11T22:11:24.0544248Z [ RUN      ] LLVM.CustomTarget
2023-01-11T22:11:24.0769234Z [       OK ] LLVM.CustomTarget (22 ms)
2023-01-11T22:11:24.0769558Z [ RUN      ] LLVM.CodeGenKernelFuncName
2023-01-11T22:11:24.1128155Z [       OK ] LLVM.CodeGenKernelFuncName (35 ms)
2023-01-11T22:11:24.1128803Z [----------] 150 tests from LLVM (7881 ms total)
2023-01-11T22:11:24.1129013Z 
2023-01-11T22:11:24.1129188Z [----------] Global test environment tear-down
2023-01-11T22:11:24.1205641Z [==========] 801 tests from 25 test suites ran. (25002 ms total)
2023-01-11T22:11:24.1206113Z [  PASSED  ] 801 tests.
2023-01-11T22:11:24.1206350Z 
2023-01-11T22:11:24.1206575Z   YOU HAVE 5 DISABLED TESTS
2023-01-11T22:11:24.1206711Z 
2023-01-11T22:11:24.2028985Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *android* ]]
2023-01-11T22:11:24.2029434Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *cuda* ]]
2023-01-11T22:11:24.2029738Z + assert_git_not_dirty
2023-01-11T22:11:24.2030032Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:11:24.2030405Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:11:24.2030821Z ++ git status --porcelain
2023-01-11T22:11:24.2856406Z + git_status=
2023-01-11T22:11:24.2856940Z + [[ -n '' ]]
2023-01-11T22:11:24.2857222Z + test_aot_compilation
2023-01-11T22:11:24.2857503Z + echo 'Testing Ahead of Time compilation'
2023-01-11T22:11:24.2857718Z Testing Ahead of Time compilation
2023-01-11T22:11:24.2858356Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10d_cuda_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:11:24.2868518Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda_linalg.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorchbind_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:11:24.2878358Z + TEST_REPORTS_DIR=test/test-reports/cpp-unittest/test_aot_compilation
2023-01-11T22:11:24.2878804Z + mkdir -p test/test-reports/cpp-unittest/test_aot_compilation
2023-01-11T22:11:24.2889420Z + '[' -f /opt/conda/lib/python3.10/site-packages/torch/bin/test_mobile_nnc ']'
2023-01-11T22:11:24.2890285Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_mobile_nnc --gtest_output=xml:test/test-reports/cpp-unittest/test_aot_compilation/test_mobile_nnc.xml
2023-01-11T22:11:24.6276497Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:11:24.6277030Z [==========] Running 6 tests from 2 test suites.
2023-01-11T22:11:24.6277491Z [----------] Global test environment set-up.
2023-01-11T22:11:24.6277786Z [----------] 4 tests from Function
2023-01-11T22:11:24.6278052Z [ RUN      ] Function.ExecuteSlowMul
2023-01-11T22:11:24.6280843Z [       OK ] Function.ExecuteSlowMul (0 ms)
2023-01-11T22:11:24.6281307Z [ RUN      ] Function.Serialization
2023-01-11T22:11:24.6281611Z [       OK ] Function.Serialization (0 ms)
2023-01-11T22:11:24.6281884Z [ RUN      ] Function.ValidInput
2023-01-11T22:11:24.6282167Z [       OK ] Function.ValidInput (0 ms)
2023-01-11T22:11:24.6282452Z [ RUN      ] Function.InvalidInput
2023-01-11T22:11:24.6290989Z [       OK ] Function.InvalidInput (0 ms)
2023-01-11T22:11:24.6291515Z [----------] 4 tests from Function (1 ms total)
2023-01-11T22:11:24.6291806Z 
2023-01-11T22:11:24.6292119Z [----------] 2 tests from MobileNNCRegistryTest
2023-01-11T22:11:24.6292592Z [ RUN      ] MobileNNCRegistryTest.FindAndRun
2023-01-11T22:11:24.6292973Z [       OK ] MobileNNCRegistryTest.FindAndRun (0 ms)
2023-01-11T22:11:24.6293350Z [ RUN      ] MobileNNCRegistryTest.NoKernel
2023-01-11T22:11:24.6293758Z [       OK ] MobileNNCRegistryTest.NoKernel (0 ms)
2023-01-11T22:11:24.6294169Z [----------] 2 tests from MobileNNCRegistryTest (0 ms total)
2023-01-11T22:11:24.6294377Z 
2023-01-11T22:11:24.6294549Z [----------] Global test environment tear-down
2023-01-11T22:11:24.6294869Z [==========] 6 tests from 2 test suites ran. (1 ms total)
2023-01-11T22:11:24.6295127Z [  PASSED  ] 6 tests.
2023-01-11T22:11:24.6295232Z 
2023-01-11T22:11:24.6295356Z   YOU HAVE 1 DISABLED TEST
2023-01-11T22:11:24.6295479Z 
2023-01-11T22:11:24.6989813Z + '[' -f /opt/conda/lib/python3.10/site-packages/torch/bin/aot_model_compiler_test ']'
2023-01-11T22:11:24.6990143Z + source test/mobile/nnc/test_aot_compile.sh
2023-01-11T22:11:24.6994539Z ++ set -e -o pipefail
2023-01-11T22:11:24.6998672Z +++ python -c 'import site; print(site.getsitepackages()[0])'
2023-01-11T22:11:24.7162297Z ++ TORCH_INSTALL_DIR=/opt/conda/lib/python3.10/site-packages/torch
2023-01-11T22:11:24.7162672Z ++ TORCH_BIN_DIR=/opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:11:24.7165717Z +++ dirname test/mobile/nnc/test_aot_compile.sh
2023-01-11T22:11:24.7177179Z ++ CURRENT_DIR=test/mobile/nnc
2023-01-11T22:11:24.7177477Z ++ MODEL=aot_test_model.pt
2023-01-11T22:11:24.7177720Z ++ COMPILED_MODEL=aot_test_model.compiled.pt
2023-01-11T22:11:24.7177950Z ++ COMPILED_CODE=aot_test_model.compiled.ll
2023-01-11T22:11:24.7180621Z +++ mktemp -d -t build_XXX
2023-01-11T22:11:24.7228723Z ++ TMP_DIR=/tmp/build_RrY
2023-01-11T22:11:24.7229726Z + test_custom_script_ops
2023-01-11T22:11:24.7230170Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:11:24.7230564Z + echo 'Testing custom script operators'
2023-01-11T22:11:24.7230791Z Testing custom script operators
2023-01-11T22:11:24.7231150Z + CUSTOM_OP_BUILD=/var/lib/jenkins/workspace/build/custom_test_artifacts/custom-op-build
2023-01-11T22:11:24.7231422Z + pushd test/custom_operator
2023-01-11T22:11:24.7231648Z ~/workspace/test/custom_operator ~/workspace
2023-01-11T22:11:24.7231994Z + cp -a /var/lib/jenkins/workspace/build/custom_test_artifacts/custom-op-build build
2023-01-11T22:11:24.8539384Z + python test_custom_ops.py -v
2023-01-11T22:11:26.1634508Z Test results will be stored in test-reports/python-unittest/test_custom_ops
2023-01-11T22:11:26.1646948Z 
2023-01-11T22:11:26.1647230Z Running tests...
2023-01-11T22:11:26.1647623Z ----------------------------------------------------------------------
2023-01-11T22:11:26.1686955Z   test_calling_custom_op (__main__.TestCustomOperators) ... ok (0.004s)
2023-01-11T22:11:26.2131129Z   test_calling_custom_op_inside_script_module (__main__.TestCustomOperators) ... ok (0.044s)
2023-01-11T22:11:26.2137468Z   test_calling_custom_op_string (__main__.TestCustomOperators) ... ok (0.001s)
2023-01-11T22:11:26.2156380Z   test_calling_custom_op_with_autograd (__main__.TestCustomOperators) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:197: UserWarning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/autograd/engine.cpp:1134.)
2023-01-11T22:11:26.2157232Z   Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
2023-01-11T22:11:26.2170558Z ok (0.003s)
2023-01-11T22:11:26.2178319Z   test_calling_custom_op_with_autograd_in_nograd_mode (__main__.TestCustomOperators) ... ok (0.001s)
2023-01-11T22:11:26.2182291Z   test_custom_library_is_loaded (__main__.TestCustomOperators) ... ok (0.000s)
2023-01-11T22:11:26.2260063Z   test_saving_and_loading_script_module_with_custom_op (__main__.TestCustomOperators) ... ok (0.008s)
2023-01-11T22:11:26.2260431Z 
2023-01-11T22:11:26.2260887Z ----------------------------------------------------------------------
2023-01-11T22:11:26.2261304Z Ran 7 tests in 0.061s
2023-01-11T22:11:26.2261420Z 
2023-01-11T22:11:26.2261469Z OK
2023-01-11T22:11:26.2261561Z 
2023-01-11T22:11:26.2261652Z Generating XML reports...
2023-01-11T22:11:26.2289688Z Generated XML report: test-reports/python-unittest/test_custom_ops/TEST-TestCustomOperators-20230111221126.xml
2023-01-11T22:11:26.4876094Z + python model.py --export-script-module=model.pt
2023-01-11T22:11:27.8332007Z + build/test_custom_ops ./model.pt
2023-01-11T22:11:28.1970454Z [W engine.cpp:1134] Warning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (function operator())
2023-01-11T22:11:28.2550005Z ok
2023-01-11T22:11:28.3308588Z + popd
2023-01-11T22:11:28.3308853Z ~/workspace
2023-01-11T22:11:28.3309187Z + assert_git_not_dirty
2023-01-11T22:11:28.3309803Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:11:28.3310132Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:11:28.3312772Z ++ git status --porcelain
2023-01-11T22:11:28.4127131Z + git_status=
2023-01-11T22:11:28.4127573Z + [[ -n '' ]]
2023-01-11T22:11:28.4127799Z + test_custom_backend
2023-01-11T22:11:28.4128144Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:11:28.4128481Z + echo 'Testing custom backends'
2023-01-11T22:11:28.4128685Z Testing custom backends
2023-01-11T22:11:28.4129066Z + CUSTOM_BACKEND_BUILD=/var/lib/jenkins/workspace/build/custom_test_artifacts/custom-backend-build
2023-01-11T22:11:28.4129342Z + pushd test/custom_backend
2023-01-11T22:11:28.4129560Z ~/workspace/test/custom_backend ~/workspace
2023-01-11T22:11:28.4129928Z + cp -a /var/lib/jenkins/workspace/build/custom_test_artifacts/custom-backend-build build
2023-01-11T22:11:28.5183640Z + python test_custom_backend.py -v
2023-01-11T22:11:29.8256235Z Test results will be stored in test-reports/python-unittest/test_custom_backend
2023-01-11T22:11:29.8267206Z 
2023-01-11T22:11:29.8267299Z Running tests...
2023-01-11T22:11:29.8268242Z ----------------------------------------------------------------------
2023-01-11T22:11:29.8273667Z   test_execute (__main__.TestCustomBackend)
2023-01-11T22:11:29.8794161Z Test execution using the custom backend. ... ok (0.052s)
2023-01-11T22:11:29.8801399Z   test_save_load (__main__.TestCustomBackend)
2023-01-11T22:11:29.8976430Z Test that a lowered module can be executed correctly ... ok (0.018s)
2023-01-11T22:11:29.8977649Z 
2023-01-11T22:11:29.8978357Z ----------------------------------------------------------------------
2023-01-11T22:11:29.8978813Z Ran 2 tests in 0.071s
2023-01-11T22:11:29.8978938Z 
2023-01-11T22:11:29.8978999Z OK
2023-01-11T22:11:29.8979090Z 
2023-01-11T22:11:29.8979180Z Generating XML reports...
2023-01-11T22:11:29.9006166Z Generated XML report: test-reports/python-unittest/test_custom_backend/TEST-TestCustomBackend-20230111221129.xml
2023-01-11T22:11:30.1786779Z + python backend.py --export-module-to=model.pt
2023-01-11T22:11:31.5762545Z + build/test_custom_backend ./model.pt
2023-01-11T22:11:31.9666897Z Testing custom_backend
2023-01-11T22:11:32.0405985Z OK
2023-01-11T22:11:32.1143995Z + rm -f ./model.pt
2023-01-11T22:11:32.1170236Z + popd
2023-01-11T22:11:32.1170485Z ~/workspace
2023-01-11T22:11:32.1170662Z + assert_git_not_dirty
2023-01-11T22:11:32.1171075Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:11:32.1171407Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:11:32.1173302Z ++ git status --porcelain
2023-01-11T22:11:32.1982241Z + git_status=
2023-01-11T22:11:32.1982711Z + [[ -n '' ]]
2023-01-11T22:11:32.1982896Z + test_torch_function_benchmark
2023-01-11T22:11:32.1983229Z + echo 'Testing __torch_function__ benchmarks'
2023-01-11T22:11:32.1983475Z Testing __torch_function__ benchmarks
2023-01-11T22:11:32.1983695Z + pushd benchmarks/overrides_benchmark
2023-01-11T22:11:32.1984008Z ~/workspace/benchmarks/overrides_benchmark ~/workspace
2023-01-11T22:11:32.1984289Z + python bench.py -n 1 -m 2
2023-01-11T22:11:33.3157175Z Type tensor had a minimum time of 0.004291534423828125 us and a standard deviation of 0.0532736157765612 us.
2023-01-11T22:11:33.3157714Z Type SubTensor had a minimum time of 0.015735626220703125 us and a standard deviation of 0.03371747880009934 us.
2023-01-11T22:11:33.3158232Z Type WithTorchFunction had a minimum time of 0.010728836059570312 us and a standard deviation of 0.018207438188255765 us.
2023-01-11T22:11:33.3159592Z Type SubWithTorchFunction had a minimum time of 0.016689300537109375 us and a standard deviation of 0.010115243640029803 us.
2023-01-11T22:11:33.5403953Z + python pyspybench.py Tensor -n 1
2023-01-11T22:11:34.8818493Z + python pyspybench.py SubTensor -n 1
2023-01-11T22:11:36.2290826Z + python pyspybench.py WithTorchFunction -n 1
2023-01-11T22:11:37.5766694Z + python pyspybench.py SubWithTorchFunction -n 1
2023-01-11T22:11:38.9272944Z + popd
2023-01-11T22:11:38.9273317Z ~/workspace
2023-01-11T22:11:38.9273644Z + assert_git_not_dirty
2023-01-11T22:11:38.9274295Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:11:38.9274864Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:11:38.9276436Z ++ git status --porcelain
2023-01-11T22:11:39.0113161Z + git_status=
2023-01-11T22:11:39.0113715Z + [[ -n '' ]]
2023-01-11T22:11:39.0114034Z + test_benchmarks
2023-01-11T22:11:39.0114472Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T22:11:39.0114723Z + [[ nogpu_NO_AVX2 != *nogpu* ]]
2023-01-11T22:11:39.0114913Z + test_executorch
2023-01-11T22:11:39.0115189Z + echo 'Testing Executorch op registration'
2023-01-11T22:11:39.0115422Z Testing Executorch op registration
2023-01-11T22:11:39.0115645Z + build/bin/test_edge_op_registration
2023-01-11T22:11:39.2750458Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:11:39.2751028Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:11:39.2751486Z [----------] Global test environment set-up.
2023-01-11T22:11:39.2752008Z [----------] 1 test from OperatorRegistrationTest
2023-01-11T22:11:39.2752488Z [ RUN      ] OperatorRegistrationTest.Add
2023-01-11T22:11:39.2753005Z [       OK ] OperatorRegistrationTest.Add (0 ms)
2023-01-11T22:11:39.2753512Z [----------] 1 test from OperatorRegistrationTest (0 ms total)
2023-01-11T22:11:39.2753740Z 
2023-01-11T22:11:39.2753964Z [----------] Global test environment tear-down
2023-01-11T22:11:39.2754367Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:11:39.2755084Z [  PASSED  ] 1 test.
2023-01-11T22:11:39.3376841Z + assert_git_not_dirty
2023-01-11T22:11:39.3377430Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:11:39.3377759Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:11:39.3378946Z ++ git status --porcelain
2023-01-11T22:11:39.4198649Z + git_status=
2023-01-11T22:11:39.4199112Z + [[ -n '' ]]
2023-01-11T22:11:39.4329908Z Prepare all required actions
2023-01-11T22:11:39.4330240Z Getting action download info
2023-01-11T22:11:39.6516129Z ##[group]Run ./.github/actions/get-workflow-job-id
2023-01-11T22:11:39.6516335Z with:
2023-01-11T22:11:39.6516658Z   github-token: ***
2023-01-11T22:11:39.6516828Z env:
2023-01-11T22:11:39.6516988Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:39.6517255Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:39.6517504Z ##[endgroup]
2023-01-11T22:11:39.6541800Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
2023-01-11T22:11:39.6542044Z with:
2023-01-11T22:11:39.6542206Z   shell: bash
2023-01-11T22:11:39.6542554Z   timeout_minutes: 10
2023-01-11T22:11:39.6542744Z   max_attempts: 5
2023-01-11T22:11:39.6542926Z   retry_wait_seconds: 30
2023-01-11T22:11:39.6543339Z   command: set -eux
python3 -m pip install requests==2.26.0
GHA_WORKFLOW_JOB_ID=$(python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}")
echo "job-id=${GHA_WORKFLOW_JOB_ID}" >> "${GITHUB_OUTPUT}"

2023-01-11T22:11:39.6543721Z   polling_interval_seconds: 1
2023-01-11T22:11:39.6543903Z   warning_on_retry: true
2023-01-11T22:11:39.6544093Z   continue_on_error: false
2023-01-11T22:11:39.6544265Z env:
2023-01-11T22:11:39.6544423Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:39.6544686Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:39.6545048Z   GITHUB_TOKEN: ***
2023-01-11T22:11:39.6545221Z ##[endgroup]
2023-01-11T22:11:39.9651365Z + python3 -m pip install requests==2.26.0
2023-01-11T22:11:40.6178411Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T22:11:40.6468877Z Requirement already satisfied: requests==2.26.0 in /home/ec2-user/.local/lib/python3.7/site-packages (2.26.0)
2023-01-11T22:11:40.6601946Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (3.4)
2023-01-11T22:11:40.6620602Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (2022.12.7)
2023-01-11T22:11:40.6636567Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (1.26.14)
2023-01-11T22:11:40.6799960Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (2.0.12)
2023-01-11T22:11:40.8839559Z ++ python3 .github/scripts/get_workflow_job_id.py 3896346758 i-0c70567fa7dfd6397
2023-01-11T22:11:47.5580149Z + GHA_WORKFLOW_JOB_ID=10589559916
2023-01-11T22:11:47.5580924Z + echo job-id=10589559916
2023-01-11T22:11:47.9699318Z Command completed after 1 attempt(s).
2023-01-11T22:11:47.9804426Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2023-01-11T22:11:47.9804664Z kill "$MONITOR_SCRIPT_PID"
2023-01-11T22:11:48.0328298Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:11:48.0328529Z env:
2023-01-11T22:11:48.0328707Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:48.0328968Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:48.0329232Z   MONITOR_SCRIPT_PID: 30785
2023-01-11T22:11:48.0329419Z ##[endgroup]
2023-01-11T22:11:48.0411219Z Prepare all required actions
2023-01-11T22:11:48.0411490Z Getting action download info
2023-01-11T22:11:48.2160696Z Download action repository 'actions/upload-artifact@v3' (SHA:0b7f8abb1508181956e8e162db84b466c27e18ce)
2023-01-11T22:11:48.4276574Z ##[group]Run ./.github/actions/upload-test-artifacts
2023-01-11T22:11:48.4276789Z with:
2023-01-11T22:11:48.4277016Z   file-suffix: test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916
2023-01-11T22:11:48.4277239Z env:
2023-01-11T22:11:48.4277401Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:48.4277668Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:48.4277915Z ##[endgroup]
2023-01-11T22:11:48.4300433Z ##[group]Run # Remove any previous test jsons if they exist
2023-01-11T22:11:48.4300698Z # Remove any previous test jsons if they exist
2023-01-11T22:11:48.4300929Z rm -f test-jsons-*.zip
2023-01-11T22:11:48.4301208Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json'
2023-01-11T22:11:48.4312321Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:11:48.4312540Z env:
2023-01-11T22:11:48.4312721Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:48.4312976Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:48.4313290Z   FILE_SUFFIX: test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916
2023-01-11T22:11:48.4313513Z ##[endgroup]
2023-01-11T22:11:48.4562471Z   adding: test/allowlist_for_publicAPI.json (deflated 78%)
2023-01-11T22:11:48.4621581Z   adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%)
2023-01-11T22:11:48.4635158Z   adding: test/profiler/profiler_utils_mock_events.json (deflated 87%)
2023-01-11T22:11:48.4636801Z   adding: test/.pytorch-slow-tests.json (deflated 77%)
2023-01-11T22:11:48.4641379Z   adding: test/.pytorch-disabled-tests.json (deflated 84%)
2023-01-11T22:11:48.4660443Z ##[group]Run # Remove any previous test reports if they exist
2023-01-11T22:11:48.4660726Z # Remove any previous test reports if they exist
2023-01-11T22:11:48.4660963Z rm -f test-reports-*.zip
2023-01-11T22:11:48.4661226Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv'
2023-01-11T22:11:48.4672545Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:11:48.4672886Z env:
2023-01-11T22:11:48.4673209Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:48.4673608Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:48.4673920Z   FILE_SUFFIX: test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916
2023-01-11T22:11:48.4674149Z ##[endgroup]
2023-01-11T22:11:48.4830584Z   adding: test/custom_backend/test-reports/python-unittest/test_custom_backend/TEST-TestCustomBackend-20230111221129.xml (deflated 56%)
2023-01-11T22:11:48.4831310Z   adding: test/custom_operator/test-reports/python-unittest/test_custom_ops/TEST-TestCustomOperators-20230111221126.xml (deflated 65%)
2023-01-11T22:11:48.4839528Z   adding: test/test-reports/python-unittest/dynamo.test_export/TEST-ExportTests-20230111212231.xml (deflated 92%)
2023-01-11T22:11:48.4854908Z   adding: test/test-reports/python-unittest/dynamo.test_misc/TEST-MiscTests-20230111212231.xml (deflated 89%)
2023-01-11T22:11:48.4859989Z   adding: test/test-reports/python-unittest/dynamo.test_misc/TEST-TestTracer-20230111212231.xml (deflated 39%)
2023-01-11T22:11:48.4864710Z   adding: test/test-reports/python-unittest/dynamo.test_optimizations/TEST-NormalizeIRTests-20230111212242.xml (deflated 42%)
2023-01-11T22:11:48.4869124Z   adding: test/test-reports/python-unittest/dynamo.test_optimizations/TEST-TestOptimizations-20230111212242.xml (deflated 77%)
2023-01-11T22:11:48.4879060Z   adding: test/test-reports/python-unittest/dynamo.test_repros/TEST-ReproTests-20230111212244.xml (deflated 87%)
2023-01-11T22:11:48.4883496Z   adding: test/test-reports/python-unittest/dynamo.test_torchxla_integration/TEST-TorchXLAReuseGraphTest-20230111212247.xml (deflated 76%)
2023-01-11T22:11:48.4892055Z   adding: test/test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecNNModuleTests-20230111212251.xml (deflated 91%)
2023-01-11T22:11:48.4902621Z   adding: test/test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecReproTests-20230111212251.xml (deflated 88%)
2023-01-11T22:11:48.4907831Z   adding: test/test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecTests-20230111212251.xml (deflated 75%)
2023-01-11T22:11:48.4912837Z   adding: test/test-reports/python-unittest/profiler.test_profiler_tree/TEST-TestProfilerTree-20230111212302.xml (deflated 82%)
2023-01-11T22:11:48.4918698Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_activation_sparsifier.TestActivationSparsifier-20230111212306.xml (deflated 54%)
2023-01-11T22:11:48.4923482Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_scheduler.TestBaseDataScheduler-20230111212306.xml (deflated 71%)
2023-01-11T22:11:48.4928288Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier-20230111212306.xml (deflated 65%)
2023-01-11T22:11:48.4932574Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestBaseSparsifier-20230111212306.xml (deflated 82%)
2023-01-11T22:11:48.4937949Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier-20230111212306.xml (deflated 85%)
2023-01-11T22:11:48.4942870Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestComposability-20230111212306.xml (deflated 74%)
2023-01-11T22:11:48.4949433Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestCubicScheduler-20230111212306.xml (deflated 58%)
2023-01-11T22:11:48.4952735Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_parametrization.TestFakeSparsity-20230111212306.xml (deflated 73%)
2023-01-11T22:11:48.4956182Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestFxComposability-20230111212306.xml (deflated 80%)
2023-01-11T22:11:48.4960086Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier-20230111212306.xml (deflated 76%)
2023-01-11T22:11:48.4963548Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers-20230111212306.xml (deflated 70%)
2023-01-11T22:11:48.4967564Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestQuantizationUtils-20230111212306.xml (deflated 71%)
2023-01-11T22:11:48.4971627Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseKernels-20230111212306.xml (deflated 50%)
2023-01-11T22:11:48.4975401Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseLayers-20230111212306.xml (deflated 62%)
2023-01-11T22:11:48.4979770Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestScheduler-20230111212306.xml (deflated 70%)
2023-01-11T22:11:48.4983806Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions-20230111212306.xml (deflated 78%)
2023-01-11T22:11:48.4987875Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestWeightNormSparsifier-20230111212306.xml (deflated 78%)
2023-01-11T22:11:48.4994902Z   adding: test/test-reports/python-unittest/test_function_schema/TEST-TestFunctionSchema-20230111212321.xml (deflated 82%)
2023-01-11T22:11:48.5000509Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_alias_analysis.TestAliasAnalysis-20230111212326.xml (deflated 66%)
2023-01-11T22:11:48.5005508Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_async.TestAsync-20230111212326.xml (deflated 85%)
2023-01-11T22:11:48.5009540Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_aten_pow.TestAtenPow-20230111212326.xml (deflated 48%)
2023-01-11T22:11:48.5017111Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_autodiff.TestAutodiffJit-20230111212326.xml (deflated 74%)
2023-01-11T22:11:48.5033617Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing-20230111212326.xml (deflated 86%)
2023-01-11T22:11:48.5038495Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackends-20230111212326.xml (deflated 73%)
2023-01-11T22:11:48.5043893Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackendsWithCompiler-20230111212326.xml (deflated 59%)
2023-01-11T22:11:48.5050351Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_batch_mm.TestBatchMM-20230111212326.xml (deflated 82%)
2023-01-11T22:11:48.5053994Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_builtins.TestBuiltins-20230111212326.xml (deflated 71%)
2023-01-11T22:11:48.5063239Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_class_type.TestClassType-20230111212326.xml (deflated 83%)
2023-01-11T22:11:48.5067257Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_complex.TestComplex-20230111212326.xml (deflated 84%)
2023-01-11T22:11:48.5071278Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_custom_operators.TestCustomOperators-20230111212326.xml (deflated 83%)
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2023-01-11T22:11:48.7655091Z   adding: test/test-reports/python-unittest/test_nn/TEST-TestAddRelu-20230111215758.xml (deflated 52%)
2023-01-11T22:11:48.7660910Z   adding: test/test-reports/python-unittest/test_nn/TEST-TestConstantPadNd-20230111215758.xml (deflated 53%)
2023-01-11T22:11:48.7665384Z   adding: test/test-reports/python-unittest/test_nn/TEST-TestFunctionalPickle-20230111215758.xml (deflated 41%)
2023-01-11T22:11:48.7669502Z   adding: test/test-reports/python-unittest/test_nn/TEST-TestFusionEval-20230111215758.xml (deflated 39%)
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2023-01-11T22:11:48.7729927Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestBroadcastAllOverride-20230111215838.xml (deflated 42%)
2023-01-11T22:11:48.7733833Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestDisabledTorchFunction-20230111215838.xml (deflated 41%)
2023-01-11T22:11:48.7737986Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestEinsumOverride-20230111215838.xml (deflated 41%)
2023-01-11T22:11:48.7742584Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestGradCheckOverride-20230111215838.xml (deflated 41%)
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2023-01-11T22:11:48.7751150Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestIndexing-20230111215838.xml (deflated 73%)
2023-01-11T22:11:48.7757378Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestIterator-20230111215838.xml (deflated 40%)
2023-01-11T22:11:48.7764983Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestNamedTuple-20230111215838.xml (deflated 39%)
2023-01-11T22:11:48.7769171Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestPickle-20230111215838.xml (deflated 39%)
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2023-01-11T22:11:48.7780484Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestResolveName-20230111215838.xml (deflated 40%)
2023-01-11T22:11:48.7796657Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionMode-20230111215838.xml (deflated 85%)
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2023-01-11T22:11:48.7827622Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionWarning-20230111215838.xml (deflated 42%)
2023-01-11T22:11:48.7831969Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestWrapTorchFunction-20230111215838.xml (deflated 41%)
2023-01-11T22:11:48.7845710Z   adding: test/test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRSampler-20230111215842.xml (deflated 40%)
2023-01-11T22:11:48.7853210Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestBasicVitalSigns-20230111215845.xml (deflated 63%)
2023-01-11T22:11:48.7872952Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestTorch-20230111215845.xml (deflated 93%)
2023-01-11T22:11:48.7878185Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestAgainstScipy-20230111215849.xml (deflated 62%)
2023-01-11T22:11:48.7882548Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestConstraints-20230111215849.xml (deflated 56%)
2023-01-11T22:11:48.7887138Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributionShapes-20230111215849.xml (deflated 91%)
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2023-01-11T22:11:48.7902099Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestFunctors-20230111215849.xml (deflated 70%)
2023-01-11T22:11:48.7908017Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestJit-20230111215849.xml (deflated 73%)
2023-01-11T22:11:48.7912430Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestKL-20230111215849.xml (deflated 82%)
2023-01-11T22:11:48.7916837Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestLazyLogitsInitialization-20230111215849.xml (deflated 58%)
2023-01-11T22:11:48.7921386Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestNumericalStability-20230111215849.xml (deflated 83%)
2023-01-11T22:11:48.7926378Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestRsample-20230111215849.xml (deflated 75%)
2023-01-11T22:11:48.7931344Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestValidation-20230111215849.xml (deflated 68%)
2023-01-11T22:11:48.7938829Z   adding: test/test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNN-20230111215931.xml (deflated 84%)
2023-01-11T22:11:48.7944225Z   adding: test/test-reports/python-unittest/nn.test_pooling/TEST-TestAvgPool-20230111215939.xml (deflated 76%)
2023-01-11T22:11:48.7949302Z   adding: test/test-reports/python-unittest/nn.test_pooling/TEST-TestPoolingNN-20230111215939.xml (deflated 80%)
2023-01-11T22:11:48.7984810Z   adding: test/test-reports/python-unittest/test_cpp_api_parity/TEST-TestCppApiParity-20230111215947.xml (deflated 96%)
2023-01-11T22:11:48.7990014Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestCppExtensionAOT-20230111220005.xml (deflated 80%)
2023-01-11T22:11:48.7994812Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestORTTensor-20230111220005.xml (deflated 67%)
2023-01-11T22:11:48.7999402Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestPybindTypeCasters-20230111220005.xml (deflated 41%)
2023-01-11T22:11:48.8004793Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestRNGExtension-20230111220005.xml (deflated 40%)
2023-01-11T22:11:48.8011601Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestTorchLibrary-20230111220005.xml (deflated 41%)
2023-01-11T22:11:48.8016566Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestCppExtensionAOT-20230111220015.xml (deflated 81%)
2023-01-11T22:11:48.8022796Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestORTTensor-20230111220015.xml (deflated 68%)
2023-01-11T22:11:48.8027178Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestPybindTypeCasters-20230111220015.xml (deflated 41%)
2023-01-11T22:11:48.8031427Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestRNGExtension-20230111220015.xml (deflated 40%)
2023-01-11T22:11:48.8035780Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestTorchLibrary-20230111220015.xml (deflated 41%)
2023-01-11T22:11:48.8040970Z   adding: test/test-reports/python-unittest/test_cpp_extensions_open_device_registration/TEST-TestCppExtensionOpenRgistration-20230111220017.xml (deflated 55%)
2023-01-11T22:11:48.8046058Z   adding: test/test-reports/python-unittest/test_dispatch/TEST-TestDispatch-20230111220045.xml (deflated 81%)
2023-01-11T22:11:48.8055336Z   adding: test/test-reports/python-unittest/test_dispatch/TEST-TestPythonDispatcher-20230111220045.xml (deflated 75%)
2023-01-11T22:11:48.8055994Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.AnnotationsTest-20230111220111.xml (deflated 78%)
2023-01-11T22:11:48.8056583Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_cse_pass.TestCSEPass-20230111220111.xml (deflated 84%)
2023-01-11T22:11:48.8057125Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_common_passes.TestCommonPass-20230111220111.xml (deflated 82%)
2023-01-11T22:11:48.8058939Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_fx_const_fold.TestConstFold-20230111220111.xml (deflated 83%)
2023-01-11T22:11:48.8059681Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow-20230111220111.xml (deflated 82%)
2023-01-11T22:11:48.8060359Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_dce_pass.TestDCE-20230111220111.xml (deflated 82%)
2023-01-11T22:11:48.8063508Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestFX-20230111220111.xml (deflated 86%)
2023-01-11T22:11:48.8064112Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestFXAPIBackwardCompatibility-20230111220111.xml (deflated 63%)
2023-01-11T22:11:48.8065667Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestFunctionalTracing-20230111220111.xml (deflated 94%)
2023-01-11T22:11:48.8066294Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_pass_infra.TestPassManager-20230111220111.xml (deflated 75%)
2023-01-11T22:11:48.8067391Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_subgraph_rewriter.TestSubgraphRewriter-20230111220111.xml (deflated 86%)
2023-01-11T22:11:48.8069654Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestVisionTracing-20230111220111.xml (deflated 88%)
2023-01-11T22:11:48.8070397Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.TypeCheckerTest-20230111220111.xml (deflated 90%)
2023-01-11T22:11:48.8071084Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestEnableDisableCudaFuser-20230111220548.xml (deflated 69%)
2023-01-11T22:11:48.8071683Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-jit.test_fuser_common.TestFuserCommon-20230111220548.xml (deflated 48%)
2023-01-11T22:11:48.8074396Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestCudaFuser-20230111220548.xml (deflated 92%)
2023-01-11T22:11:48.8074997Z   adding: test/test-reports/python-unittest/test_jit_disabled/TEST-TestJitDisabled-20230111220550.xml (deflated 61%)
2023-01-11T22:11:48.8075524Z   adding: test/test-reports/python-unittest/test_mobile_optimizer/TEST-TestOptimizer-20230111220554.xml (deflated 64%)
2023-01-11T22:11:48.8076828Z   adding: test/test-reports/python-unittest/test_multiprocessing/TEST-TestMultiprocessing-20230111220600.xml (deflated 86%)
2023-01-11T22:11:48.8077445Z   adding: test/test-reports/python-unittest/test_multiprocessing_spawn/TEST-ErrorTest-20230111220612.xml (deflated 39%)
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2023-01-11T22:11:48.8079259Z   adding: test/test-reports/python-unittest/test_namedtuple_return_api/TEST-TestNamedTupleAPI-20230111220631.xml (deflated 77%)
2023-01-11T22:11:48.8079840Z   adding: test/test-reports/python-unittest/test_prims/TEST-TestPrimsBasic-20230111220706.xml (deflated 53%)
2023-01-11T22:11:48.8080340Z   adding: test/test-reports/python-unittest/test_show_pickle/TEST-TestShowPickle-20230111220710.xml (deflated 40%)
2023-01-11T22:11:48.8081060Z   adding: test/test-reports/python-unittest/test_tensorexpr/TEST-TestTensorExprFuser-20230111220717.xml (deflated 88%)
2023-01-11T22:11:48.8086033Z   adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-484174d0a16e76ad.xml (deflated 28%)
2023-01-11T22:11:48.8089992Z   adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e42ebd2ecf6eaa23.xml (deflated 28%)
2023-01-11T22:11:48.8108208Z   adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-b7995a19757f5615.xml (deflated 28%)
2023-01-11T22:11:48.8108858Z   adding: test/test-reports/python-pytest/test_ops/test_ops-b5301370d4336bb2.xml (deflated 28%)
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2023-01-11T22:11:48.8111110Z   adding: test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-1e0b07ffbf426a50.xml (deflated 28%)
2023-01-11T22:11:48.8111629Z   adding: test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-27a816f975c007fa.xml (deflated 28%)
2023-01-11T22:11:48.8112131Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-57f53faeab72d232.xml (deflated 28%)
2023-01-11T22:11:48.8112630Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-fac064cd5b4e0e08.xml (deflated 28%)
2023-01-11T22:11:48.8113203Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-94cc7b66651244d1.xml (deflated 28%)
2023-01-11T22:11:48.8113688Z   adding: test/test-reports/python-pytest/test_ops_jit/test_ops_jit-fe16d0ecf9be8ab0.xml (deflated 29%)
2023-01-11T22:11:48.8114150Z   adding: test/test-reports/python-pytest/test_ops_jit/test_ops_jit-f889cd96ee3679f1.xml (deflated 28%)
2023-01-11T22:11:48.8114604Z   adding: test/test-reports/python-pytest/test_ops_jit/test_ops_jit-0627f0d222682e84.xml (deflated 28%)
2023-01-11T22:11:48.8123792Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_jit.xml (deflated 92%)
2023-01-11T22:11:48.8131655Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_lazy.xml (deflated 93%)
2023-01-11T22:11:48.8148314Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_api.xml (deflated 92%)
2023-01-11T22:11:48.8161937Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_tensorexpr.xml (deflated 92%)
2023-01-11T22:11:48.8162440Z   adding: test/test-reports/cpp-unittest/test_aot_compilation/test_mobile_nnc.xml (deflated 75%)
2023-01-11T22:11:48.8186620Z ##[group]Run # Remove any previous test reports if they exist
2023-01-11T22:11:48.8186957Z # Remove any previous test reports if they exist
2023-01-11T22:11:48.8187233Z rm -f usage-log-*.zip
2023-01-11T22:11:48.8187545Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2023-01-11T22:11:48.8187857Z # so check to see if the file exists first
2023-01-11T22:11:48.8188120Z if [ -f 'usage_log.txt' ]; then
2023-01-11T22:11:48.8188399Z     zip "usage-log-${FILE_SUFFIX}.zip" 'usage_log.txt'
2023-01-11T22:11:48.8188633Z fi
2023-01-11T22:11:48.8199498Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:11:48.8199826Z env:
2023-01-11T22:11:48.8200027Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:48.8200338Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:48.8200692Z   FILE_SUFFIX: test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916
2023-01-11T22:11:48.8200958Z ##[endgroup]
2023-01-11T22:11:48.8570368Z   adding: usage_log.txt (deflated 97%)
2023-01-11T22:11:48.8616516Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T22:11:48.8616731Z with:
2023-01-11T22:11:48.8616921Z   s3-prefix: pytorch/pytorch/3896346758/1/artifact

2023-01-11T22:11:48.8617140Z   retention-days: 14
2023-01-11T22:11:48.8617335Z   if-no-files-found: warn
2023-01-11T22:11:48.8617534Z   path: test-jsons-*.zip
2023-01-11T22:11:48.8617704Z   name: artifact
2023-01-11T22:11:48.8618127Z   s3-bucket: gha-artifacts
2023-01-11T22:11:48.8618317Z   region: us-east-1
2023-01-11T22:11:48.8618468Z env:
2023-01-11T22:11:48.8618640Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:48.8618907Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:48.8619141Z ##[endgroup]
2023-01-11T22:11:49.2196369Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T22:11:49.2196753Z With the provided path, there will be 1 file uploaded
2023-01-11T22:11:49.2197076Z Uploading to s3 prefix: pytorch/pytorch/3896346758/1/artifact
2023-01-11T22:11:49.2262776Z Starting upload of test-jsons-test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916.zip
2023-01-11T22:11:49.3661586Z Finished upload of test-jsons-test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916.zip
2023-01-11T22:11:49.3789779Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T22:11:49.3790027Z with:
2023-01-11T22:11:49.3790248Z   s3-prefix: pytorch/pytorch/3896346758/1/artifact

2023-01-11T22:11:49.3790494Z   retention-days: 14
2023-01-11T22:11:49.3790722Z   if-no-files-found: error
2023-01-11T22:11:49.3790942Z   path: test-reports-*.zip
2023-01-11T22:11:49.3791157Z   name: artifact
2023-01-11T22:11:49.3791368Z   s3-bucket: gha-artifacts
2023-01-11T22:11:49.3791574Z   region: us-east-1
2023-01-11T22:11:49.3791770Z env:
2023-01-11T22:11:49.3791975Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:49.3792258Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:49.3792533Z ##[endgroup]
2023-01-11T22:11:49.7109305Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T22:11:49.7109831Z With the provided path, there will be 1 file uploaded
2023-01-11T22:11:49.7110274Z Uploading to s3 prefix: pytorch/pytorch/3896346758/1/artifact
2023-01-11T22:11:49.7118311Z Starting upload of test-reports-test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916.zip
2023-01-11T22:11:49.9601993Z Finished upload of test-reports-test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916.zip
2023-01-11T22:11:49.9723718Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T22:11:49.9723935Z with:
2023-01-11T22:11:49.9724139Z   s3-prefix: pytorch/pytorch/3896346758/1/artifact

2023-01-11T22:11:49.9724347Z   retention-days: 14
2023-01-11T22:11:49.9724541Z   if-no-files-found: ignore
2023-01-11T22:11:49.9724739Z   path: usage-log-*.zip
2023-01-11T22:11:49.9724911Z   name: artifact
2023-01-11T22:11:49.9725090Z   s3-bucket: gha-artifacts
2023-01-11T22:11:49.9725279Z   region: us-east-1
2023-01-11T22:11:49.9725428Z env:
2023-01-11T22:11:49.9725597Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:49.9725866Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:49.9726101Z ##[endgroup]
2023-01-11T22:11:50.3027395Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T22:11:50.3027987Z With the provided path, there will be 1 file uploaded
2023-01-11T22:11:50.3028375Z Uploading to s3 prefix: pytorch/pytorch/3896346758/1/artifact
2023-01-11T22:11:50.3035162Z Starting upload of usage-log-test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916.zip
2023-01-11T22:11:50.6047231Z Finished upload of usage-log-test-nogpu_NO_AVX2-1-1-linux.2xlarge_10589559916.zip
2023-01-11T22:11:50.6166430Z ##[group]Run # shellcheck disable=SC2156
2023-01-11T22:11:50.6166659Z # shellcheck disable=SC2156
2023-01-11T22:11:50.6166967Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2023-01-11T22:11:50.6177828Z shell: /usr/bin/bash -e {0}
2023-01-11T22:11:50.6178004Z env:
2023-01-11T22:11:50.6178188Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:50.6178459Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:11:50.6178701Z ##[endgroup]
2023-01-11T22:11:52.2034778Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:11:52.2035171Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:11:52.2035653Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:11:52.2035958Z This is free software: you are free to change and redistribute it.
2023-01-11T22:11:52.2036323Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:11:52.2036582Z and "show warranty" for details.
2023-01-11T22:11:52.2036876Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:11:52.2037131Z Type "show configuration" for configuration details.
2023-01-11T22:11:52.2037373Z For bug reporting instructions, please see:
2023-01-11T22:11:52.2037613Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:11:52.2038069Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:11:52.2038340Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:11:52.2038565Z For help, type "help".
2023-01-11T22:11:52.2038802Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:11:52.3843137Z Reading symbols from python...done.
2023-01-11T22:11:52.9453352Z 
2023-01-11T22:11:52.9453746Z warning: core file may not match specified executable file.
2023-01-11T22:11:53.0304948Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:53.0305595Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:53.0306078Z [New LWP 8157]
2023-01-11T22:11:53.0306315Z [New LWP 8164]
2023-01-11T22:11:53.0306599Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:53.0306940Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:53.0307296Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:53.0307644Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:53.0307875Z [New LWP 8161]
2023-01-11T22:11:53.0308041Z [New LWP 8162]
2023-01-11T22:11:53.0308205Z [New LWP 8171]
2023-01-11T22:11:53.0308368Z [New LWP 8167]
2023-01-11T22:11:53.0308540Z [New LWP 8169]
2023-01-11T22:11:53.0308687Z [New LWP 8173]
2023-01-11T22:11:53.0381413Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:53.0381980Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:53.0423767Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:53.0424353Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:53.0831277Z [Thread debugging using libthread_db enabled]
2023-01-11T22:11:53.0831745Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:12:00.5877768Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:12:00.5878782Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:12:00.5879314Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:12:00.5879660Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:12:00.5880016Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:00.5880284Z [Current thread is 1 (Thread 0x7fcb46f39200 (LWP 8157))]
2023-01-11T22:12:00.5956290Z To enable execution of this file add
2023-01-11T22:12:00.5956869Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:12:00.5957405Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:00.5957758Z To completely disable this security protection add
2023-01-11T22:12:00.5958025Z 	set auto-load safe-path /
2023-01-11T22:12:00.5958256Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:00.5958531Z For more information about this security protection see the
2023-01-11T22:12:00.5958888Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:12:00.5959189Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:12:00.6021477Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:00.6024215Z #1  0x00007fcb0f5ca65b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:12:00.6024768Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:12:00.6033874Z #2  <signal handler called>
2023-01-11T22:12:00.6034271Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:12:00.6177005Z #4  0x00007fcb459f703b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:12:00.6179168Z #5  0x00007fcb46dee052 in ffi_call_unix64 ()
2023-01-11T22:12:00.6179485Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:00.6188075Z #6  0x00007fcb46dec8cd in ffi_call_int ()
2023-01-11T22:12:00.6188399Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:00.6195599Z #7  0x00007fcb459ff879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:12:00.6196035Z     resmem=0x7fff9a96d270, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:12:00.6196338Z     avalues=<optimized out>, pProc=0x7fcb459f7002 <string_at>, flags=4357)
2023-01-11T22:12:00.6196718Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:12:00.6196945Z #8  _ctypes_callproc ()
2023-01-11T22:12:00.6197249Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:12:00.6197534Z #9  0x00007fcb459ff3fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:12:00.6199059Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:00.6199403Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:00.6230212Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:00.6230671Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6231056Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fca9ff90358, 
2023-01-11T22:12:00.6231442Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6231811Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6232221Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:00.6235045Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:00.6235382Z     args=0x7fca9ff90358, callable=0x7fcb45cb0880, tstate=<optimized out>)
2023-01-11T22:12:00.6235751Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:00.6239615Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:00.6239927Z     args=0x7fca9ff90358, callable=0x7fcb45cb0880)
2023-01-11T22:12:00.6240274Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6243572Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:00.6243928Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96d580, 
2023-01-11T22:12:00.6244171Z     tstate=<optimized out>)
2023-01-11T22:12:00.6244484Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6244719Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6245026Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:00.6255861Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6256376Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6256971Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6257337Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6257736Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6261955Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6262782Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6263523Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6263932Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcb45c6f890, 
2023-01-11T22:12:00.6264338Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6264717Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6265125Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6265365Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6265663Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6271143Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:00.6271478Z     nargsf=<optimized out>, args=0x7fca9ff901c0, callable=0x7fcb45c6f880, 
2023-01-11T22:12:00.6271720Z     tstate=0x1b2bbf0)
2023-01-11T22:12:00.6272028Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6275332Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:00.6275700Z     args=0x7fca9ff901c0, callable=0x7fcb45c6f880)
2023-01-11T22:12:00.6276045Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6278468Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:00.6278871Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96d740, 
2023-01-11T22:12:00.6279169Z     tstate=<optimized out>)
2023-01-11T22:12:00.6279473Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6279713Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6280076Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:12:00.6291658Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:12:00.6292009Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6292372Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6292731Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6293113Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6297603Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:00.6298050Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6298461Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7fca9feb8680, tstate=0x1b2bbf0)
2023-01-11T22:12:00.6298875Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6300690Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:00.6301027Z     stack=<optimized out>, func=0x7fca9feb8670)
2023-01-11T22:12:00.6301347Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6306499Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:00.6306952Z     args=<optimized out>, callable=0x7fca9feb8670, tstate=0x1b2bbf0)
2023-01-11T22:12:00.6307477Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:00.6307709Z #27 vectorcall_unbound (
2023-01-11T22:12:00.6308020Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:12:00.6308401Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6308764Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6309197Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6309585Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:12:00.6309817Z #28 vectorcall_method ()
2023-01-11T22:12:00.6310103Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:12:00.6311939Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:12:00.6312384Z     arg1=<optimized out>)
2023-01-11T22:12:00.6312957Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:12:00.6313464Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6313847Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:12:00.6315775Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6316317Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6316866Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6317229Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6317632Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6322276Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6322900Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6323617Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6324020Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcaa062c170, 
2023-01-11T22:12:00.6324414Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6324803Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6325209Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6325439Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6325745Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6330085Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:00.6330631Z     nargsf=<optimized out>, args=0x7fcaa0122a48, callable=0x7fcaa062c160, 
2023-01-11T22:12:00.6330929Z     tstate=0x1b2bbf0)
2023-01-11T22:12:00.6331376Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6334405Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:00.6334867Z     args=0x7fcaa0122a48, callable=0x7fcaa062c160)
2023-01-11T22:12:00.6335384Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6337488Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:00.6337979Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96db40, 
2023-01-11T22:12:00.6338363Z     tstate=<optimized out>)
2023-01-11T22:12:00.6338751Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6339037Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6339457Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:00.6339765Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6340072Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6340429Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6340777Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6341159Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6345797Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6346431Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6347010Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6347414Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcaa2379400, 
2023-01-11T22:12:00.6347820Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6348210Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6348593Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6348834Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6349138Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6353585Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:00.6354092Z     nargsf=<optimized out>, args=0x6adf088, callable=0x7fcaa23793f0, 
2023-01-11T22:12:00.6354458Z     tstate=0x1b2bbf0)
2023-01-11T22:12:00.6354772Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6358032Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x6adf088, 
2023-01-11T22:12:00.6358287Z     callable=0x7fcaa23793f0)
2023-01-11T22:12:00.6358628Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6361285Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:00.6361777Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96dd00, 
2023-01-11T22:12:00.6362161Z     tstate=<optimized out>)
2023-01-11T22:12:00.6362516Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6362752Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6363170Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:00.6363533Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6363856Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6364221Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6364577Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6365066Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6369758Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6370315Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6371086Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6371496Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcaa2378710, 
2023-01-11T22:12:00.6371885Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6372271Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6372678Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6372908Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6373207Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6377849Z #48 0x00000000004f141c in do_call_core (kwdict=0x7fcb45dba780, 
2023-01-11T22:12:00.6378250Z     callargs=0x7fcaa0016520, func=0x7fcaa2378700, trace_info=0x7fff9a96dec0, 
2023-01-11T22:12:00.6378485Z     tstate=<optimized out>)
2023-01-11T22:12:00.6378911Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:00.6379159Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6379455Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:00.6380281Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6380804Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6381184Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6381555Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6382067Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6386327Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6386989Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6387557Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6387951Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcb45de55b0, 
2023-01-11T22:12:00.6388357Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6388748Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6389164Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6389397Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6389703Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6394315Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:00.6394812Z     nargsf=<optimized out>, args=0x6a51af8, callable=0x7fcb45de55a0, 
2023-01-11T22:12:00.6395176Z     tstate=0x1b2bbf0)
2023-01-11T22:12:00.6395504Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6399045Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x6a51af8, 
2023-01-11T22:12:00.6399485Z     callable=0x7fcb45de55a0)
2023-01-11T22:12:00.6400020Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6402484Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:00.6403128Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96e080, 
2023-01-11T22:12:00.6403490Z     tstate=<optimized out>)
2023-01-11T22:12:00.6403861Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6404178Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6404478Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:00.6404808Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6405343Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6406001Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6406663Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6407181Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6411099Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6411736Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6412295Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6412697Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcb45de5f40, 
2023-01-11T22:12:00.6413192Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6413565Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6413961Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6414201Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6414502Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6419012Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:00.6419386Z     nargsf=<optimized out>, args=0x1ba85a0, callable=0x7fcb45de5f30, 
2023-01-11T22:12:00.6419621Z     tstate=0x1b2bbf0)
2023-01-11T22:12:00.6419924Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6424008Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x1ba85a0, 
2023-01-11T22:12:00.6424287Z     callable=0x7fcb45de5f30)
2023-01-11T22:12:00.6424627Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6426738Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:00.6427197Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96e240, 
2023-01-11T22:12:00.6427624Z     tstate=<optimized out>)
2023-01-11T22:12:00.6428178Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6428618Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6429132Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:00.6429599Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6430135Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6430808Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6431315Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6431685Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6435369Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6435993Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6436818Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6437327Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcb45c66de0, 
2023-01-11T22:12:00.6437804Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6438327Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6438746Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6439044Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6439414Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6443466Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:00.6443923Z     nargsf=<optimized out>, args=0x7fcb45f30200, callable=0x7fcb45c66dd0, 
2023-01-11T22:12:00.6444223Z     tstate=0x1b2bbf0)
2023-01-11T22:12:00.6444601Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6448122Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:00.6448555Z     args=0x7fcb45f30200, callable=0x7fcb45c66dd0)
2023-01-11T22:12:00.6448954Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6451441Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:00.6451799Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96e400, 
2023-01-11T22:12:00.6452180Z     tstate=<optimized out>)
2023-01-11T22:12:00.6452611Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6452913Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6453337Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:00.6453708Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:00.6454116Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6454574Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6454984Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6455424Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:00.6460358Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:00.6461167Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:00.6461670Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:00.6462128Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fcb45c66d50, 
2023-01-11T22:12:00.6462705Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1b2bbf0, 
2023-01-11T22:12:00.6463159Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6463652Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6463907Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:12:00.6464264Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:00.6467087Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fcb45f09e40, 
2023-01-11T22:12:00.6467617Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fcb45c66d40, 
2023-01-11T22:12:00.6467871Z     tstate=0x1b2bbf0)
2023-01-11T22:12:00.6468252Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6470090Z #75 PyObject_Vectorcall (kwnames=0x7fcb45f09e40, nargsf=<optimized out>, 
2023-01-11T22:12:00.6470403Z     args=<optimized out>, callable=0x7fcb45c66d40)
2023-01-11T22:12:00.6470837Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:00.6473332Z #76 call_function (kwnames=0x7fcb45f09e40, oparg=<optimized out>, 
2023-01-11T22:12:00.6473685Z     pp_stack=<synthetic pointer>, trace_info=0x7fff9a96e5c0, 
2023-01-11T22:12:00.6473937Z     tstate=<optimized out>)
2023-01-11T22:12:00.6474356Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:00.6474655Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:00.6474980Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:00.6476334Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:12:00.6476772Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6477525Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:00.6477904Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6478417Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:12:00.6478744Z #79 _PyEval_Vector ()
2023-01-11T22:12:00.6479133Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:00.6484826Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7fcb45ecc920, 
2023-01-11T22:12:00.6485442Z     globals=globals@entry=0x7fcb45ebe600, locals=locals@entry=0x7fcb45ebe600)
2023-01-11T22:12:00.6485951Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:12:00.6486598Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:12:00.6487042Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:12:00.6489162Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:12:00.6489544Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:12:00.6491600Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:12:00.6492045Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:12:00.6494481Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:12:00.6495148Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:12:00.6508317Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:12:00.6508971Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:00.6509599Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:12:00.6511066Z #86 pymain_run_python (exitcode=0x7fff9a96e820)
2023-01-11T22:12:00.6511768Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:12:00.6512209Z #87 Py_RunMain.localalias ()
2023-01-11T22:12:00.6512633Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:12:00.6527990Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:12:00.6528495Z     argv=<optimized out>)
2023-01-11T22:12:00.6528965Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:12:00.6545174Z #89 0x00007fcb45f9dc87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:12:00.6545714Z     argv=0x7fff9a96ea28, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:12:00.6546150Z     rtld_fini=<optimized out>, stack_end=0x7fff9a96ea18)
2023-01-11T22:12:00.6546444Z     at ../csu/libc-start.c:310
2023-01-11T22:12:00.6546651Z #90 0x0000000000587ade in _start ()
2023-01-11T22:12:00.6546968Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:12:00.8130164Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:12:00.8130663Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:12:00.8130981Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:12:00.8131297Z This is free software: you are free to change and redistribute it.
2023-01-11T22:12:00.8131650Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:12:00.8132110Z and "show warranty" for details.
2023-01-11T22:12:00.8132404Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:12:00.8132647Z Type "show configuration" for configuration details.
2023-01-11T22:12:00.8132902Z For bug reporting instructions, please see:
2023-01-11T22:12:00.8133145Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:12:00.8133396Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:12:00.8133679Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:12:00.8133903Z For help, type "help".
2023-01-11T22:12:00.8134133Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:12:00.9269927Z Reading symbols from python...done.
2023-01-11T22:12:01.4753083Z 
2023-01-11T22:12:01.4753427Z warning: core file may not match specified executable file.
2023-01-11T22:12:01.5613577Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:01.5613993Z [New LWP 8156]
2023-01-11T22:12:01.5614447Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:01.5614912Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:01.5615272Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:01.5615766Z [New LWP 8163]
2023-01-11T22:12:01.5615929Z [New LWP 8168]
2023-01-11T22:12:01.5616097Z [New LWP 8165]
2023-01-11T22:12:01.5616267Z [New LWP 8160]
2023-01-11T22:12:01.5616421Z [New LWP 8170]
2023-01-11T22:12:01.5616587Z [New LWP 8166]
2023-01-11T22:12:01.5616752Z [New LWP 8172]
2023-01-11T22:12:01.5617021Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:01.5617356Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:01.5630258Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:01.5631064Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:01.5631612Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:01.5632147Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:01.5936864Z [Thread debugging using libthread_db enabled]
2023-01-11T22:12:01.5937352Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:12:08.5232872Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:12:08.5233630Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:12:08.5234155Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:12:08.5234484Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:12:08.5234777Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:08.5235065Z [Current thread is 1 (Thread 0x7f8762811200 (LWP 8156))]
2023-01-11T22:12:08.5262798Z To enable execution of this file add
2023-01-11T22:12:08.5263375Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:12:08.5263821Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:08.5264086Z To completely disable this security protection add
2023-01-11T22:12:08.5264338Z 	set auto-load safe-path /
2023-01-11T22:12:08.5264581Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:08.5265071Z For more information about this security protection see the
2023-01-11T22:12:08.5265423Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:12:08.5265732Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:12:08.5308372Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:08.5311028Z #1  0x00007f872aea265b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:12:08.5311482Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:12:08.5321008Z #2  <signal handler called>
2023-01-11T22:12:08.5321412Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:12:08.5465425Z #4  0x00007f87612cf03b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:12:08.5468517Z #5  0x00007f87626c6052 in ffi_call_unix64 ()
2023-01-11T22:12:08.5469209Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:08.5477525Z #6  0x00007f87626c48cd in ffi_call_int ()
2023-01-11T22:12:08.5478144Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:08.5485954Z #7  0x00007f87612d7879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:12:08.5486434Z     resmem=0x7ffd22900310, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:12:08.5486858Z     avalues=<optimized out>, pProc=0x7f87612cf002 <string_at>, flags=4357)
2023-01-11T22:12:08.5487665Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:12:08.5488016Z #8  _ctypes_callproc ()
2023-01-11T22:12:08.5488488Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:12:08.5488932Z #9  0x00007f87612d73fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:12:08.5489467Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:08.5489847Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:08.5495022Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:08.5495567Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5496126Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f86bb868358, 
2023-01-11T22:12:08.5496510Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5496883Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5497278Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:08.5499906Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:08.5500426Z     args=0x7f86bb868358, callable=0x7f8761588880, tstate=<optimized out>)
2023-01-11T22:12:08.5500929Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:08.5504890Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:08.5505203Z     args=0x7f86bb868358, callable=0x7f8761588880)
2023-01-11T22:12:08.5505546Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5508904Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:08.5509328Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd22900620, 
2023-01-11T22:12:08.5509749Z     tstate=<optimized out>)
2023-01-11T22:12:08.5510224Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5510455Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5510848Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:08.5511525Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5512044Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5512613Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5513128Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5513527Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5517922Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5518551Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5519226Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5519628Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f8761547890, 
2023-01-11T22:12:08.5520070Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5520454Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5520863Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5521103Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5521393Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5526110Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:08.5526451Z     nargsf=<optimized out>, args=0x7f86bb8681c0, callable=0x7f8761547880, 
2023-01-11T22:12:08.5526802Z     tstate=0x1596bf0)
2023-01-11T22:12:08.5527109Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5530543Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:08.5530855Z     args=0x7f86bb8681c0, callable=0x7f8761547880)
2023-01-11T22:12:08.5531189Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5533626Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:08.5533983Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd229007e0, 
2023-01-11T22:12:08.5534224Z     tstate=<optimized out>)
2023-01-11T22:12:08.5534524Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5534771Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5535080Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:12:08.5536163Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:12:08.5536555Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5536922Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5537279Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5537650Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5542167Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:08.5542727Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5543125Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7f86bb794680, tstate=0x1596bf0)
2023-01-11T22:12:08.5543548Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5545388Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:08.5545735Z     stack=<optimized out>, func=0x7f86bb794670)
2023-01-11T22:12:08.5546074Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5550918Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:08.5551430Z     args=<optimized out>, callable=0x7f86bb794670, tstate=0x1596bf0)
2023-01-11T22:12:08.5552054Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:08.5552484Z #27 vectorcall_unbound (
2023-01-11T22:12:08.5553186Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:12:08.5553775Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5554135Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5554496Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5554892Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:12:08.5555128Z #28 vectorcall_method ()
2023-01-11T22:12:08.5555416Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:12:08.5556062Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:12:08.5556405Z     arg1=<optimized out>)
2023-01-11T22:12:08.5556824Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:12:08.5557242Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5557578Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:12:08.5559727Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5560124Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5560488Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5560945Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5561341Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5566091Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5566767Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5567587Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5568319Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f86bbf04170, 
2023-01-11T22:12:08.5568722Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5569108Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5569551Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5569782Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5570094Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5573499Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:08.5574043Z     nargsf=<optimized out>, args=0x7f86bb9faa48, callable=0x7f86bbf04160, 
2023-01-11T22:12:08.5574370Z     tstate=0x1596bf0)
2023-01-11T22:12:08.5574697Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5577875Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:08.5578311Z     args=0x7f86bb9faa48, callable=0x7f86bbf04160)
2023-01-11T22:12:08.5578854Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5580999Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:08.5581489Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd22900be0, 
2023-01-11T22:12:08.5581899Z     tstate=<optimized out>)
2023-01-11T22:12:08.5582565Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5582862Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5583283Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:08.5583585Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5583891Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5584387Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5584733Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5585119Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5589254Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5589794Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5590571Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5591304Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f86bdc51400, 
2023-01-11T22:12:08.5592018Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5592391Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5592789Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5593028Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5593334Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5597369Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:08.5597870Z     nargsf=<optimized out>, args=0x654a958, callable=0x7f86bdc513f0, 
2023-01-11T22:12:08.5598279Z     tstate=0x1596bf0)
2023-01-11T22:12:08.5598588Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5601837Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x654a958, 
2023-01-11T22:12:08.5602309Z     callable=0x7f86bdc513f0)
2023-01-11T22:12:08.5602750Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5604978Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:08.5605444Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd22900da0, 
2023-01-11T22:12:08.5605872Z     tstate=<optimized out>)
2023-01-11T22:12:08.5606187Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5606434Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5606863Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:08.5607320Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5607787Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5608152Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5608512Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5608890Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5612933Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5613398Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5613950Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5614339Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f86bdc50710, 
2023-01-11T22:12:08.5614738Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5615127Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5615520Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5615746Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5616120Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5621505Z #48 0x00000000004f141c in do_call_core (kwdict=0x7f8761692780, 
2023-01-11T22:12:08.5621857Z     callargs=0x7f86bb8ee520, func=0x7f86bdc50700, trace_info=0x7ffd22900f60, 
2023-01-11T22:12:08.5622113Z     tstate=<optimized out>)
2023-01-11T22:12:08.5622621Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:08.5622886Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5623241Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:08.5623895Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5624429Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5624948Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5625312Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5625699Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5630849Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5631492Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5632217Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5632617Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f87616bd5b0, 
2023-01-11T22:12:08.5633019Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5633392Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5633827Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5634075Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5634382Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5638758Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:08.5639275Z     nargsf=<optimized out>, args=0x64bd348, callable=0x7f87616bd5a0, 
2023-01-11T22:12:08.5639613Z     tstate=0x1596bf0)
2023-01-11T22:12:08.5639981Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5643457Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x64bd348, 
2023-01-11T22:12:08.5643911Z     callable=0x7f87616bd5a0)
2023-01-11T22:12:08.5644394Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5646778Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:08.5647258Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd22901120, 
2023-01-11T22:12:08.5647679Z     tstate=<optimized out>)
2023-01-11T22:12:08.5647987Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5648231Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5648723Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:08.5649009Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5649377Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5649744Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5650104Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5650471Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5655701Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5656532Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5656980Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5657372Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f87616bdf40, 
2023-01-11T22:12:08.5657773Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5658166Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5658589Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5658819Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5659123Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5663698Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:08.5664009Z     nargsf=<optimized out>, args=0x16135a0, callable=0x7f87616bdf30, 
2023-01-11T22:12:08.5664246Z     tstate=0x1596bf0)
2023-01-11T22:12:08.5664572Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5668238Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x16135a0, 
2023-01-11T22:12:08.5668503Z     callable=0x7f87616bdf30)
2023-01-11T22:12:08.5668939Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5671508Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:08.5671836Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd229012e0, 
2023-01-11T22:12:08.5672131Z     tstate=<optimized out>)
2023-01-11T22:12:08.5672450Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5672698Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5673004Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:08.5673722Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5674129Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5674479Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5674838Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5675230Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5680257Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5680749Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5681179Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5681580Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f876153ede0, 
2023-01-11T22:12:08.5681975Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5682350Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5682744Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5682992Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5683295Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5688735Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:08.5689256Z     nargsf=<optimized out>, args=0x7f8761808200, callable=0x7f876153edd0, 
2023-01-11T22:12:08.5689687Z     tstate=0x1596bf0)
2023-01-11T22:12:08.5690057Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5693005Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:08.5693623Z     args=0x7f8761808200, callable=0x7f876153edd0)
2023-01-11T22:12:08.5694036Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5695920Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:08.5696368Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd229014a0, 
2023-01-11T22:12:08.5696781Z     tstate=<optimized out>)
2023-01-11T22:12:08.5697150Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5697464Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5697832Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:08.5698181Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:08.5698742Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5699201Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5699551Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5699934Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:08.5705119Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:08.5705766Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:08.5706610Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:08.5707011Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f876153ed50, 
2023-01-11T22:12:08.5707410Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1596bf0, 
2023-01-11T22:12:08.5707798Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5708202Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5708443Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:12:08.5708747Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:08.5711652Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7f87617e1e40, 
2023-01-11T22:12:08.5712207Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7f876153ed40, 
2023-01-11T22:12:08.5712562Z     tstate=0x1596bf0)
2023-01-11T22:12:08.5712882Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5714999Z #75 PyObject_Vectorcall (kwnames=0x7f87617e1e40, nargsf=<optimized out>, 
2023-01-11T22:12:08.5715315Z     args=<optimized out>, callable=0x7f876153ed40)
2023-01-11T22:12:08.5715671Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:08.5718187Z #76 call_function (kwnames=0x7f87617e1e40, oparg=<optimized out>, 
2023-01-11T22:12:08.5718663Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd22901660, 
2023-01-11T22:12:08.5719094Z     tstate=<optimized out>)
2023-01-11T22:12:08.5719486Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:08.5719716Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:08.5720080Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:08.5720859Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:12:08.5721369Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5721954Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:08.5722316Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5722777Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:12:08.5723032Z #79 _PyEval_Vector ()
2023-01-11T22:12:08.5723447Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:08.5728838Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7f87617a4920, 
2023-01-11T22:12:08.5729236Z     globals=globals@entry=0x7f8761796600, locals=locals@entry=0x7f8761796600)
2023-01-11T22:12:08.5729581Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:12:08.5730941Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:12:08.5731357Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:12:08.5733337Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:12:08.5733749Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:12:08.5735607Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:12:08.5736017Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:12:08.5737987Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:12:08.5738404Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:12:08.5740440Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:12:08.5740811Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:08.5741215Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:12:08.5743108Z #86 pymain_run_python (exitcode=0x7ffd229018c0)
2023-01-11T22:12:08.5743520Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:12:08.5743760Z #87 Py_RunMain.localalias ()
2023-01-11T22:12:08.5744076Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:12:08.5760046Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:12:08.5760333Z     argv=<optimized out>)
2023-01-11T22:12:08.5760658Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:12:08.5765666Z #89 0x00007f8761875c87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:12:08.5765974Z     argv=0x7ffd22901ac8, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:12:08.5766238Z     rtld_fini=<optimized out>, stack_end=0x7ffd22901ab8)
2023-01-11T22:12:08.5766513Z     at ../csu/libc-start.c:310
2023-01-11T22:12:08.5767250Z #90 0x0000000000587ade in _start ()
2023-01-11T22:12:08.5767653Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:12:08.7367271Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:12:08.7367743Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:12:08.7368216Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:12:08.7368735Z This is free software: you are free to change and redistribute it.
2023-01-11T22:12:08.7369212Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:12:08.7369601Z and "show warranty" for details.
2023-01-11T22:12:08.7370108Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:12:08.7370517Z Type "show configuration" for configuration details.
2023-01-11T22:12:08.7370921Z For bug reporting instructions, please see:
2023-01-11T22:12:08.7371304Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:12:08.7371736Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:12:08.7372201Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:12:08.7372552Z For help, type "help".
2023-01-11T22:12:08.7372940Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:12:08.8510807Z Reading symbols from python...done.
2023-01-11T22:12:09.3944944Z 
2023-01-11T22:12:09.3945560Z warning: core file may not match specified executable file.
2023-01-11T22:12:09.4746154Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:09.4747020Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:09.4747477Z [New LWP 10634]
2023-01-11T22:12:09.4747789Z [New LWP 10644]
2023-01-11T22:12:09.4748310Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:09.4748965Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:09.4749393Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:09.4749743Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:09.4749994Z [New LWP 10645]
2023-01-11T22:12:09.4750148Z [New LWP 10651]
2023-01-11T22:12:09.4750313Z [New LWP 10648]
2023-01-11T22:12:09.4750481Z [New LWP 10656]
2023-01-11T22:12:09.4750633Z [New LWP 10653]
2023-01-11T22:12:09.4750795Z [New LWP 10655]
2023-01-11T22:12:09.4763339Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:09.4764408Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:09.4765462Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:09.4766788Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:09.5047808Z [Thread debugging using libthread_db enabled]
2023-01-11T22:12:09.5048781Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:12:16.4853408Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:12:16.4854070Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:12:16.4854599Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:12:16.4854931Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:12:16.4855226Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:16.4855516Z [Current thread is 1 (Thread 0x7efc8d5be200 (LWP 10634))]
2023-01-11T22:12:16.4882976Z To enable execution of this file add
2023-01-11T22:12:16.4883487Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:12:16.4883829Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:16.4884101Z To completely disable this security protection add
2023-01-11T22:12:16.4884360Z 	set auto-load safe-path /
2023-01-11T22:12:16.4884603Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:16.4884874Z For more information about this security protection see the
2023-01-11T22:12:16.4885239Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:12:16.4885528Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:12:16.4928569Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:16.4930907Z #1  0x00007efc55c4f65b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:12:16.4931278Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:12:16.4940502Z #2  <signal handler called>
2023-01-11T22:12:16.4940834Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:12:16.5084901Z #4  0x00007efc8c07c03b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:12:16.5086661Z #5  0x00007efc8d473052 in ffi_call_unix64 ()
2023-01-11T22:12:16.5087066Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:16.5094966Z #6  0x00007efc8d4718cd in ffi_call_int ()
2023-01-11T22:12:16.5095567Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:16.5102850Z #7  0x00007efc8c084879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:12:16.5103430Z     resmem=0x7ffe60c85840, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:12:16.5103910Z     avalues=<optimized out>, pProc=0x7efc8c07c002 <string_at>, flags=4357)
2023-01-11T22:12:16.5104307Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:12:16.5104558Z #8  _ctypes_callproc ()
2023-01-11T22:12:16.5104869Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:12:16.5105296Z #9  0x00007efc8c0843fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:12:16.5105686Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:16.5106078Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:16.5110765Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:16.5111314Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5111887Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7efbe647c358, 
2023-01-11T22:12:16.5112271Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5112634Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5113184Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:16.5115513Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:16.5115910Z     args=0x7efbe647c358, callable=0x7efc8c338880, tstate=<optimized out>)
2023-01-11T22:12:16.5116309Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:16.5119759Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:16.5120167Z     args=0x7efbe647c358, callable=0x7efc8c338880)
2023-01-11T22:12:16.5120529Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5123676Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:16.5124052Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c85b50, 
2023-01-11T22:12:16.5124292Z     tstate=<optimized out>)
2023-01-11T22:12:16.5124607Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5124845Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5125155Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:16.5125911Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5126292Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5126667Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5127032Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5127417Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5132189Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5132777Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5133457Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5133861Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efc8c2f7890, 
2023-01-11T22:12:16.5134251Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5134639Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5135158Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5135388Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5135692Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5140185Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:16.5140704Z     nargsf=<optimized out>, args=0x7efbe647c1c0, callable=0x7efc8c2f7880, 
2023-01-11T22:12:16.5141059Z     tstate=0xf7cbf0)
2023-01-11T22:12:16.5141373Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5145921Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:16.5146209Z     args=0x7efbe647c1c0, callable=0x7efc8c2f7880)
2023-01-11T22:12:16.5146557Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5148976Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:16.5149348Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c85d10, 
2023-01-11T22:12:16.5149639Z     tstate=<optimized out>)
2023-01-11T22:12:16.5149950Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5150192Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5150516Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:12:16.5151308Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:12:16.5151755Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5152483Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5152832Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5153223Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5157413Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:16.5157775Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5158174Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7efbe65a0680, tstate=0xf7cbf0)
2023-01-11T22:12:16.5158601Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5160436Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:16.5160713Z     stack=<optimized out>, func=0x7efbe65a0670)
2023-01-11T22:12:16.5161048Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5165888Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:16.5166529Z     args=<optimized out>, callable=0x7efbe65a0670, tstate=0xf7cbf0)
2023-01-11T22:12:16.5167062Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:16.5167431Z #27 vectorcall_unbound (
2023-01-11T22:12:16.5167976Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:12:16.5168570Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5168920Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5169283Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5169723Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:12:16.5170061Z #28 vectorcall_method ()
2023-01-11T22:12:16.5170369Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:12:16.5171081Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:12:16.5171355Z     arg1=<optimized out>)
2023-01-11T22:12:16.5171795Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:12:16.5172074Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5172505Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:12:16.5174493Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5175021Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5175562Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5175912Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5176302Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5181112Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5181705Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5182656Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5183286Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efbe6cf8170, 
2023-01-11T22:12:16.5183694Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5184069Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5184598Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5184841Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5185141Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5189520Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:16.5189891Z     nargsf=<optimized out>, args=0x7efbe6802a48, callable=0x7efbe6cf8160, 
2023-01-11T22:12:16.5190125Z     tstate=0xf7cbf0)
2023-01-11T22:12:16.5190430Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5194578Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:16.5195077Z     args=0x7efbe6802a48, callable=0x7efbe6cf8160)
2023-01-11T22:12:16.5195554Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5197973Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:16.5198319Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c86110, 
2023-01-11T22:12:16.5198564Z     tstate=<optimized out>)
2023-01-11T22:12:16.5198893Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5199143Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5199503Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:16.5200043Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5200543Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5201109Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5201469Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5201844Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5206574Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5207043Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5207487Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5207875Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efbe8a05400, 
2023-01-11T22:12:16.5208276Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5208770Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5209175Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5209401Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5209704Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5214390Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:16.5214723Z     nargsf=<optimized out>, args=0x5f30068, callable=0x7efbe8a053f0, 
2023-01-11T22:12:16.5214943Z     tstate=0xf7cbf0)
2023-01-11T22:12:16.5215259Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5218820Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x5f30068, 
2023-01-11T22:12:16.5219125Z     callable=0x7efbe8a053f0)
2023-01-11T22:12:16.5219455Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5222145Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:16.5222757Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c862d0, 
2023-01-11T22:12:16.5223163Z     tstate=<optimized out>)
2023-01-11T22:12:16.5223697Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5224105Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5224583Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:16.5225186Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5225744Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5226408Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5226817Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5227202Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5230689Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5231299Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5232100Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5232838Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efbe8a04710, 
2023-01-11T22:12:16.5233315Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5233688Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5234095Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5234333Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5234635Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5238550Z #48 0x00000000004f141c in do_call_core (kwdict=0x7efc8c442800, 
2023-01-11T22:12:16.5239053Z     callargs=0x7efbe66fa520, func=0x7efbe8a04700, trace_info=0x7ffe60c86490, 
2023-01-11T22:12:16.5239468Z     tstate=<optimized out>)
2023-01-11T22:12:16.5239892Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:16.5240145Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5240451Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:16.5240779Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5241324Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5241798Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5242154Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5242615Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5246769Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5247403Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5248197Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5248590Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efc8c46d5b0, 
2023-01-11T22:12:16.5248990Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5249374Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5249766Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5249994Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5250291Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5255032Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:16.5255673Z     nargsf=<optimized out>, args=0x5ea2ad8, callable=0x7efc8c46d5a0, 
2023-01-11T22:12:16.5255914Z     tstate=0xf7cbf0)
2023-01-11T22:12:16.5256345Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5259174Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x5ea2ad8, 
2023-01-11T22:12:16.5259614Z     callable=0x7efc8c46d5a0)
2023-01-11T22:12:16.5260105Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5262627Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:16.5263136Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c86650, 
2023-01-11T22:12:16.5263454Z     tstate=<optimized out>)
2023-01-11T22:12:16.5263977Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5264229Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5264702Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:16.5265178Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5265522Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5265876Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5266233Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5266615Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5270830Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5271498Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5271967Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5272367Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efc8c46df40, 
2023-01-11T22:12:16.5272768Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5273144Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5273543Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5273781Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5274072Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5278520Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:16.5279131Z     nargsf=<optimized out>, args=0xff95a0, callable=0x7efc8c46df30, 
2023-01-11T22:12:16.5279532Z     tstate=0xf7cbf0)
2023-01-11T22:12:16.5279908Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5283310Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0xff95a0, 
2023-01-11T22:12:16.5283783Z     callable=0x7efc8c46df30)
2023-01-11T22:12:16.5284202Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5286334Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:16.5286800Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c86810, 
2023-01-11T22:12:16.5287228Z     tstate=<optimized out>)
2023-01-11T22:12:16.5287552Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5287900Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5288291Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:16.5288744Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5289174Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5289535Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5289890Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5290257Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5294324Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5294942Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5295613Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5296000Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efc8c2eede0, 
2023-01-11T22:12:16.5296402Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5296789Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5297183Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5297408Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5297713Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5302112Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:16.5302749Z     nargsf=<optimized out>, args=0x7efc8c5b4200, callable=0x7efc8c2eedd0, 
2023-01-11T22:12:16.5303167Z     tstate=0xf7cbf0)
2023-01-11T22:12:16.5303501Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5306676Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:16.5307142Z     args=0x7efc8c5b4200, callable=0x7efc8c2eedd0)
2023-01-11T22:12:16.5307603Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5309829Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:16.5310279Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c869d0, 
2023-01-11T22:12:16.5310714Z     tstate=<optimized out>)
2023-01-11T22:12:16.5311061Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5311402Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5311746Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:16.5312149Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:16.5312684Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5313031Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5313471Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5313855Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:16.5318222Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:16.5318717Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:16.5319151Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:16.5319551Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7efc8c2eed50, 
2023-01-11T22:12:16.5320003Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0xf7cbf0, 
2023-01-11T22:12:16.5320377Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5320781Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5321018Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:12:16.5321305Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:16.5325002Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7efc8c58de40, 
2023-01-11T22:12:16.5325406Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7efc8c2eed40, 
2023-01-11T22:12:16.5325738Z     tstate=0xf7cbf0)
2023-01-11T22:12:16.5326043Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5328391Z #75 PyObject_Vectorcall (kwnames=0x7efc8c58de40, nargsf=<optimized out>, 
2023-01-11T22:12:16.5328764Z     args=<optimized out>, callable=0x7efc8c2eed40)
2023-01-11T22:12:16.5329132Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:16.5331840Z #76 call_function (kwnames=0x7efc8c58de40, oparg=<optimized out>, 
2023-01-11T22:12:16.5332436Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe60c86b90, 
2023-01-11T22:12:16.5332817Z     tstate=<optimized out>)
2023-01-11T22:12:16.5333122Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:16.5333369Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:16.5333672Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:16.5335210Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:12:16.5335728Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5336342Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:16.5336696Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5337147Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:12:16.5337414Z #79 _PyEval_Vector ()
2023-01-11T22:12:16.5337708Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:16.5343597Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7efc8c550920, 
2023-01-11T22:12:16.5344119Z     globals=globals@entry=0x7efc8c542600, locals=locals@entry=0x7efc8c542600)
2023-01-11T22:12:16.5344564Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:12:16.5345712Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:12:16.5346309Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:12:16.5348227Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:12:16.5348932Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:12:16.5350617Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:12:16.5351255Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:12:16.5353084Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:12:16.5353834Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:12:16.5355696Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:12:16.5356245Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:16.5356770Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:12:16.5358305Z #86 pymain_run_python (exitcode=0x7ffe60c86df0)
2023-01-11T22:12:16.5358915Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:12:16.5359301Z #87 Py_RunMain.localalias ()
2023-01-11T22:12:16.5359616Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:12:16.5375014Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:12:16.5375411Z     argv=<optimized out>)
2023-01-11T22:12:16.5375887Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:12:16.5380416Z #89 0x00007efc8c622c87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:12:16.5380907Z     argv=0x7ffe60c86ff8, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:12:16.5381315Z     rtld_fini=<optimized out>, stack_end=0x7ffe60c86fe8)
2023-01-11T22:12:16.5381633Z     at ../csu/libc-start.c:310
2023-01-11T22:12:16.5382192Z #90 0x0000000000587ade in _start ()
2023-01-11T22:12:16.5382695Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:12:16.6968700Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:12:16.6969109Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:12:16.6969715Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:12:16.6970393Z This is free software: you are free to change and redistribute it.
2023-01-11T22:12:16.6970762Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:12:16.6971020Z and "show warranty" for details.
2023-01-11T22:12:16.6971313Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:12:16.6971566Z Type "show configuration" for configuration details.
2023-01-11T22:12:16.6971821Z For bug reporting instructions, please see:
2023-01-11T22:12:16.6972062Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:12:16.6972327Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:12:16.6972587Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:12:16.6972813Z For help, type "help".
2023-01-11T22:12:16.6973055Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:12:16.8107903Z Reading symbols from python...done.
2023-01-11T22:12:17.3735448Z 
2023-01-11T22:12:17.3735754Z warning: core file may not match specified executable file.
2023-01-11T22:12:17.4507699Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:17.4508181Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:17.4508539Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:17.4508898Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:17.4509160Z [New LWP 10635]
2023-01-11T22:12:17.4509334Z [New LWP 10643]
2023-01-11T22:12:17.4509488Z [New LWP 10646]
2023-01-11T22:12:17.4509653Z [New LWP 10649]
2023-01-11T22:12:17.4509827Z [New LWP 10647]
2023-01-11T22:12:17.4509979Z [New LWP 10654]
2023-01-11T22:12:17.4510145Z [New LWP 10650]
2023-01-11T22:12:17.4510311Z [New LWP 10652]
2023-01-11T22:12:17.4510968Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:17.4511355Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:17.4522805Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:17.4524011Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:17.4524601Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:17.4525151Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:17.4814851Z [Thread debugging using libthread_db enabled]
2023-01-11T22:12:17.4815493Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:12:24.5098250Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:12:24.5099271Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:12:24.5100200Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:12:24.5100813Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:12:24.5101312Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:24.5101812Z [Current thread is 1 (Thread 0x7fddb82d6200 (LWP 10635))]
2023-01-11T22:12:24.5131534Z To enable execution of this file add
2023-01-11T22:12:24.5132598Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:12:24.5133141Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:24.5133533Z To completely disable this security protection add
2023-01-11T22:12:24.5133808Z 	set auto-load safe-path /
2023-01-11T22:12:24.5134040Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:24.5134313Z For more information about this security protection see the
2023-01-11T22:12:24.5134684Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:12:24.5134978Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:12:24.5178313Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:12:24.5180748Z #1  0x00007fdd8096765b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:12:24.5181446Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:12:24.5191425Z #2  <signal handler called>
2023-01-11T22:12:24.5191972Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:12:24.5346028Z #4  0x00007fddb6d9403b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:12:24.5347667Z #5  0x00007fddb818b052 in ffi_call_unix64 ()
2023-01-11T22:12:24.5348196Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:24.5356515Z #6  0x00007fddb81898cd in ffi_call_int ()
2023-01-11T22:12:24.5357093Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:12:24.5364656Z #7  0x00007fddb6d9c879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:12:24.5365231Z     resmem=0x7ffd6cef99c0, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:12:24.5365806Z     avalues=<optimized out>, pProc=0x7fddb6d94002 <string_at>, flags=4357)
2023-01-11T22:12:24.5366186Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:12:24.5366412Z #8  _ctypes_callproc ()
2023-01-11T22:12:24.5366727Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:12:24.5367015Z #9  0x00007fddb6d9c3fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:12:24.5368114Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:24.5368463Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:24.5373973Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:24.5374405Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5374972Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fdd11194358, 
2023-01-11T22:12:24.5375364Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5375734Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5376160Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:24.5379151Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:24.5379458Z     args=0x7fdd11194358, callable=0x7fddb7050880, tstate=<optimized out>)
2023-01-11T22:12:24.5379833Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:24.5383748Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:24.5384077Z     args=0x7fdd11194358, callable=0x7fddb7050880)
2023-01-11T22:12:24.5384431Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5387977Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:24.5388430Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cef9cd0, 
2023-01-11T22:12:24.5388852Z     tstate=<optimized out>)
2023-01-11T22:12:24.5389185Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5389555Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5389867Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:24.5390666Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5391092Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5391443Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5391797Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5392194Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5397232Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5397876Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5398478Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5398886Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fddb700f890, 
2023-01-11T22:12:24.5399272Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5399654Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5400112Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5400360Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5400649Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5406568Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:24.5406956Z     nargsf=<optimized out>, args=0x7fdd111941c0, callable=0x7fddb700f880, 
2023-01-11T22:12:24.5407200Z     tstate=0x2695bf0)
2023-01-11T22:12:24.5407513Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5411567Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:24.5411871Z     args=0x7fdd111941c0, callable=0x7fddb700f880)
2023-01-11T22:12:24.5412207Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5414880Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:24.5415349Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cef9e90, 
2023-01-11T22:12:24.5415770Z     tstate=<optimized out>)
2023-01-11T22:12:24.5416202Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5416452Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5416764Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:12:24.5418027Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:12:24.5418398Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5418752Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5419105Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5419490Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5424643Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:24.5424999Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5425414Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7fdd112bc680, tstate=0x2695bf0)
2023-01-11T22:12:24.5425837Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5427839Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:24.5428282Z     stack=<optimized out>, func=0x7fdd112bc670)
2023-01-11T22:12:24.5428901Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5433781Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:24.5434305Z     args=<optimized out>, callable=0x7fdd112bc670, tstate=0x2695bf0)
2023-01-11T22:12:24.5434867Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:24.5435275Z #27 vectorcall_unbound (
2023-01-11T22:12:24.5435829Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:12:24.5436207Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5436578Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5436938Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5437322Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:12:24.5437546Z #28 vectorcall_method ()
2023-01-11T22:12:24.5437839Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:12:24.5439345Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:12:24.5439616Z     arg1=<optimized out>)
2023-01-11T22:12:24.5440103Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:12:24.5441048Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5441379Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:12:24.5443727Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5444120Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5444479Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5444824Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5445218Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5449926Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5450512Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5451192Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5451756Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fdd11a14170, 
2023-01-11T22:12:24.5452166Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5452553Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5452943Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5453190Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5453492Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5458583Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:24.5459107Z     nargsf=<optimized out>, args=0x7fdd1151aa48, callable=0x7fdd11a14160, 
2023-01-11T22:12:24.5459351Z     tstate=0x2695bf0)
2023-01-11T22:12:24.5459738Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5462928Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:24.5463455Z     args=0x7fdd1151aa48, callable=0x7fdd11a14160)
2023-01-11T22:12:24.5463885Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5466116Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:24.5466578Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cefa290, 
2023-01-11T22:12:24.5467174Z     tstate=<optimized out>)
2023-01-11T22:12:24.5467499Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5467860Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5468250Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:24.5468558Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5468861Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5469211Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5469576Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5469959Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5474778Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5475584Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5476386Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5476808Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fdd1371d400, 
2023-01-11T22:12:24.5477196Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5477581Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5477996Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5478236Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5478525Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5483412Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:24.5483724Z     nargsf=<optimized out>, args=0x7649028, callable=0x7fdd1371d3f0, 
2023-01-11T22:12:24.5483957Z     tstate=0x2695bf0)
2023-01-11T22:12:24.5484295Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5487901Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7649028, 
2023-01-11T22:12:24.5488230Z     callable=0x7fdd1371d3f0)
2023-01-11T22:12:24.5488550Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5491379Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:24.5491924Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cefa450, 
2023-01-11T22:12:24.5492169Z     tstate=<optimized out>)
2023-01-11T22:12:24.5492475Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5492717Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5493140Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:24.5493590Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5494111Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5494710Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5495072Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5495449Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5500474Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5501165Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5501667Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5502072Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fdd1371c710, 
2023-01-11T22:12:24.5502788Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5503176Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5503591Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5503833Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5504138Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5508752Z #48 0x00000000004f141c in do_call_core (kwdict=0x7fddb715a800, 
2023-01-11T22:12:24.5509207Z     callargs=0x7fdd11412520, func=0x7fdd1371c700, trace_info=0x7ffd6cefa610, 
2023-01-11T22:12:24.5509644Z     tstate=<optimized out>)
2023-01-11T22:12:24.5510092Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:24.5510394Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5510792Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:24.5511275Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5511668Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5512019Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5512376Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5512763Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5517085Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5518018Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5518522Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5519292Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fddb71855b0, 
2023-01-11T22:12:24.5519931Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5520320Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5520747Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5520990Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5521399Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5525236Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:24.5525737Z     nargsf=<optimized out>, args=0x75bba18, callable=0x7fddb71855a0, 
2023-01-11T22:12:24.5526124Z     tstate=0x2695bf0)
2023-01-11T22:12:24.5526453Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5529798Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x75bba18, 
2023-01-11T22:12:24.5530266Z     callable=0x7fddb71855a0)
2023-01-11T22:12:24.5530697Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5532908Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:24.5533393Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cefa7d0, 
2023-01-11T22:12:24.5533798Z     tstate=<optimized out>)
2023-01-11T22:12:24.5534122Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5534424Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5534817Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:24.5535202Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5535768Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5536281Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5536625Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5537009Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5541408Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5542089Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5542808Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5543211Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fddb7185f40, 
2023-01-11T22:12:24.5543615Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5544004Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5544423Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5544664Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5544965Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5549331Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:24.5549815Z     nargsf=<optimized out>, args=0x27125a0, callable=0x7fddb7185f30, 
2023-01-11T22:12:24.5550225Z     tstate=0x2695bf0)
2023-01-11T22:12:24.5550567Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5553627Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x27125a0, 
2023-01-11T22:12:24.5554081Z     callable=0x7fddb7185f30)
2023-01-11T22:12:24.5554562Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5556657Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:24.5557104Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cefa990, 
2023-01-11T22:12:24.5557538Z     tstate=<optimized out>)
2023-01-11T22:12:24.5557892Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5558156Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5558626Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:24.5559037Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5559753Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5560180Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5560536Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5560928Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5565473Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5566104Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5566743Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5567147Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fddb7006de0, 
2023-01-11T22:12:24.5567543Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5567925Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5568326Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5568567Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5568959Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5573717Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:24.5574263Z     nargsf=<optimized out>, args=0x7fddb72cc200, callable=0x7fddb7006dd0, 
2023-01-11T22:12:24.5574529Z     tstate=0x2695bf0)
2023-01-11T22:12:24.5574857Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5577894Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:24.5578362Z     args=0x7fddb72cc200, callable=0x7fddb7006dd0)
2023-01-11T22:12:24.5578903Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5581197Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:24.5581687Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cefab50, 
2023-01-11T22:12:24.5582062Z     tstate=<optimized out>)
2023-01-11T22:12:24.5582557Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5582812Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5583175Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:24.5584007Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:24.5584573Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5585263Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5585923Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5586561Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:24.5591250Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:24.5591848Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:24.5592557Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:24.5593303Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fddb7006d50, 
2023-01-11T22:12:24.5594038Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2695bf0, 
2023-01-11T22:12:24.5594684Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5595449Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5596023Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:12:24.5596621Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:24.5598984Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fddb72a5e40, 
2023-01-11T22:12:24.5599521Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fddb7006d40, 
2023-01-11T22:12:24.5599980Z     tstate=0x2695bf0)
2023-01-11T22:12:24.5600597Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5602971Z #75 PyObject_Vectorcall (kwnames=0x7fddb72a5e40, nargsf=<optimized out>, 
2023-01-11T22:12:24.5603483Z     args=<optimized out>, callable=0x7fddb7006d40)
2023-01-11T22:12:24.5604081Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:24.5606935Z #76 call_function (kwnames=0x7fddb72a5e40, oparg=<optimized out>, 
2023-01-11T22:12:24.5607406Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd6cefad10, 
2023-01-11T22:12:24.5607789Z     tstate=<optimized out>)
2023-01-11T22:12:24.5608302Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:24.5608718Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:24.5609257Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:24.5610267Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:12:24.5610756Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5611524Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:24.5612095Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5612888Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:12:24.5613291Z #79 _PyEval_Vector ()
2023-01-11T22:12:24.5613739Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:24.5620753Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7fddb7268920, 
2023-01-11T22:12:24.5621254Z     globals=globals@entry=0x7fddb725a600, locals=locals@entry=0x7fddb725a600)
2023-01-11T22:12:24.5621854Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:12:24.5622926Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:12:24.5623548Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:12:24.5626762Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:12:24.5627376Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:12:24.5630148Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:12:24.5630857Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:12:24.5633608Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:12:24.5634315Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:12:24.5637134Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:12:24.5637718Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:24.5638481Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:12:24.5639705Z #86 pymain_run_python (exitcode=0x7ffd6cefaf70)
2023-01-11T22:12:24.5640426Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:12:24.5640871Z #87 Py_RunMain.localalias ()
2023-01-11T22:12:24.5641374Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:12:24.5658996Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:12:24.5659416Z     argv=<optimized out>)
2023-01-11T22:12:24.5660037Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:12:24.5665305Z #89 0x00007fddb733ac87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:12:24.5666010Z     argv=0x7ffd6cefb178, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:12:24.5666424Z     rtld_fini=<optimized out>, stack_end=0x7ffd6cefb168)
2023-01-11T22:12:24.5666811Z     at ../csu/libc-start.c:310
2023-01-11T22:12:24.5667072Z #90 0x0000000000587ade in _start ()
2023-01-11T22:12:24.5667522Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:12:24.7342818Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:12:24.7343320Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:12:24.7343881Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:12:24.7344482Z This is free software: you are free to change and redistribute it.
2023-01-11T22:12:24.7344923Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:12:24.7345170Z and "show warranty" for details.
2023-01-11T22:12:24.7345463Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:12:24.7345735Z Type "show configuration" for configuration details.
2023-01-11T22:12:24.7345978Z For bug reporting instructions, please see:
2023-01-11T22:12:24.7346221Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:12:24.7346488Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:12:24.7346752Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:12:24.7346975Z For help, type "help".
2023-01-11T22:12:24.7347450Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:12:24.8491623Z Reading symbols from python...done.
2023-01-11T22:12:25.4230762Z 
2023-01-11T22:12:25.4231585Z warning: core file may not match specified executable file.
2023-01-11T22:12:25.4263225Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:25.4263698Z [New LWP 23769]
2023-01-11T22:12:25.4264184Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:25.4265639Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:25.4266273Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:25.4266869Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:25.4267489Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:25.4281263Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:25.4282261Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:25.4282807Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:25.4283360Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:25.4596890Z [Thread debugging using libthread_db enabled]
2023-01-11T22:12:25.4597472Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:12:33.0412236Z 78	../sysdeps/unix/syscall-template.S: No such file or directory.
2023-01-11T22:12:33.0413135Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:12:33.0413663Z Core was generated by `/opt/conda/bin/python -bb test_multiprocessing_spawn.py -v --import-slow-tests'.
2023-01-11T22:12:33.0413983Z Program terminated with signal SIGABRT, Aborted.
2023-01-11T22:12:33.0414339Z #0  0x00007fc8511c6177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:12:33.0440854Z To enable execution of this file add
2023-01-11T22:12:33.0441753Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:12:33.0442054Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:33.0442324Z To completely disable this security protection add
2023-01-11T22:12:33.0442594Z 	set auto-load safe-path /
2023-01-11T22:12:33.0442828Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:33.0443106Z For more information about this security protection see the
2023-01-11T22:12:33.0443466Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:12:33.0443769Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:12:33.0488652Z #0  0x00007fc8511c6177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:12:33.0504705Z #1  0x00000000004cb0d3 in os_kill_impl (
2023-01-11T22:12:33.0505203Z     module=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0505822Z     signal=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0506186Z     pid=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0506666Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/sys/_iomodule.c:7929
2023-01-11T22:12:33.0510709Z #2  os_kill (module=<optimized out>, args=args@entry=0x7fc7f030c358, 
2023-01-11T22:12:33.0511311Z     nargs=<optimized out>)
2023-01-11T22:12:33.0511786Z     at /usr/local/src/conda/python-3.10.8/Modules/codecs.c:3581
2023-01-11T22:12:33.0519452Z #3  0x00000000004fe7d4 in cfunction_vectorcall_FASTCALL (func=0x7fc852109a30, 
2023-01-11T22:12:33.0520078Z     args=0x7fc7f030c358, nargsf=<optimized out>, kwnames=<optimized out>)
2023-01-11T22:12:33.0520513Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_bitutils.h:430
2023-01-11T22:12:33.0527637Z #4  0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.0528181Z     nargsf=<optimized out>, args=0x7fc7f030c358, callable=0x7fc852109a30, 
2023-01-11T22:12:33.0528518Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0528840Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0532458Z #5  PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0532756Z     args=0x7fc7f030c358, callable=0x7fc852109a30)
2023-01-11T22:12:33.0533167Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0535156Z #6  call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0535520Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69d5c0, 
2023-01-11T22:12:33.0535747Z     tstate=<optimized out>)
2023-01-11T22:12:33.0536069Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0536438Z #7  _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0536853Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:12:33.0537184Z #8  0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0537570Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0537933Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0538279Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0538669Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0543593Z #9  _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0544299Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0544737Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0545304Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7ab1f36e0, 
2023-01-11T22:12:33.0545721Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0546116Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0546537Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0546789Z #10 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0547098Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0551084Z #11 0x00000000004f141c in do_call_core (kwdict=0x0, callargs=0x7fc7a4f1bd60, 
2023-01-11T22:12:33.0551563Z     func=0x7fc7ab1f36d0, trace_info=0x7ffe1b69d780, tstate=<optimized out>)
2023-01-11T22:12:33.0552131Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:33.0552377Z #12 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0552755Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:33.0553339Z #13 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0553910Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0554546Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0555209Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0556120Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0560915Z #14 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0561519Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0562324Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0562999Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7adfef1d0, 
2023-01-11T22:12:33.0563768Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0564455Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0565129Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0565528Z #15 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0566068Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0570184Z #16 0x00000000004f141c in do_call_core (kwdict=0x7fc7ae4aa740, 
2023-01-11T22:12:33.0570682Z     callargs=0x7fc7a50013f0, func=0x7fc7adfef1c0, trace_info=0x7ffe1b69d940, 
2023-01-11T22:12:33.0571111Z     tstate=<optimized out>)
2023-01-11T22:12:33.0571632Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:33.0572002Z #17 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0572555Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:33.0573012Z #18 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0573506Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0574191Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0574848Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0575504Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0580031Z #19 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0580661Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0581607Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0582275Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7adf9b650, 
2023-01-11T22:12:33.0583269Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0583882Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0584656Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0585109Z #20 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0585598Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0589655Z #21 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.0590120Z     nargsf=<optimized out>, args=0x744db48, callable=0x7fc7adf9b640, 
2023-01-11T22:12:33.0590481Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0591101Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0594814Z #22 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x744db48, 
2023-01-11T22:12:33.0595251Z     callable=0x7fc7adf9b640)
2023-01-11T22:12:33.0595795Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0598509Z #23 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0599125Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69db00, 
2023-01-11T22:12:33.0599360Z     tstate=<optimized out>)
2023-01-11T22:12:33.0611983Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0612231Z #24 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0612521Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:33.0618614Z #25 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:12:33.0619127Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0619771Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0620451Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0621118Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0627054Z #26 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:12:33.0627617Z     args=0x7fc7ab48f1d8, locals=0x0, con=0x7fc7adfc4050, tstate=0x1f51b80)
2023-01-11T22:12:33.0628187Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0631013Z #27 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:12:33.0631558Z     stack=0x7fc7ab48f1d8, func=0x7fc7adfc4040)
2023-01-11T22:12:33.0632105Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0636602Z #28 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:12:33.0637096Z     nargsf=<optimized out>, args=0x7fc7ab48f1d8, callable=0x7fc7adfc4040, 
2023-01-11T22:12:33.0637429Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0637964Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:33.0638416Z #29 method_vectorcall ()
2023-01-11T22:12:33.0639023Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:12:33.0644254Z #30 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fc7a4f40400, 
2023-01-11T22:12:33.0644771Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fc7aaeb9200, 
2023-01-11T22:12:33.0645160Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0645750Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0647639Z #31 PyObject_Vectorcall (kwnames=0x7fc7a4f40400, nargsf=<optimized out>, 
2023-01-11T22:12:33.0648175Z     args=<optimized out>, callable=0x7fc7aaeb9200)
2023-01-11T22:12:33.0648908Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0651741Z #32 call_function (kwnames=0x7fc7a4f40400, oparg=<optimized out>, 
2023-01-11T22:12:33.0652277Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69dd10, 
2023-01-11T22:12:33.0652618Z     tstate=<optimized out>)
2023-01-11T22:12:33.0652939Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0653185Z #33 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0653681Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:33.0653952Z #34 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0654244Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0654606Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0654962Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0655339Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0660024Z #35 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0660445Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0660880Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0661399Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7a4ffb9b0, 
2023-01-11T22:12:33.0661799Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0662180Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0662713Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0662943Z #36 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0663245Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0668459Z #37 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.0668778Z     nargsf=<optimized out>, args=0x7fc7a50fa578, callable=0x7fc7a4ffb9a0, 
2023-01-11T22:12:33.0669016Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0669331Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0672874Z #38 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0673125Z     args=0x7fc7a50fa578, callable=0x7fc7a4ffb9a0)
2023-01-11T22:12:33.0673468Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0676288Z #39 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0676822Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69ded0, 
2023-01-11T22:12:33.0677148Z     tstate=<optimized out>)
2023-01-11T22:12:33.0677493Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0677764Z #40 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0678099Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:33.0693979Z #41 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:12:33.0694512Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0695044Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0695405Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0695806Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0701052Z #42 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:33.0701687Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7fc7a4ffb5c0, 
2023-01-11T22:12:33.0702244Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0702688Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0705050Z #43 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0705540Z     stack=<optimized out>, func=0x7fc7a4ffb5b0)
2023-01-11T22:12:33.0706007Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0706276Z #44 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:12:33.0706610Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:12:33.0713656Z #45 0x0000000000507af8 in _PyObject_Call_Prepend (kwargs=0x0, 
2023-01-11T22:12:33.0714260Z     kwargs@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fc7ab1da530, 
2023-01-11T22:12:33.0714808Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, obj=<optimized out>, 
2023-01-11T22:12:33.0715225Z     obj@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, callable=0x7fc7a4ffb5b0, 
2023-01-11T22:12:33.0715635Z     callable@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0716015Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0716527Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:12:33.0716754Z #46 slot_tp_init ()
2023-01-11T22:12:33.0717052Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7734
2023-01-11T22:12:33.0719454Z #47 0x00000000004f7bdb in type_call (kwds=0x0, args=0x7fc7ab1da530, 
2023-01-11T22:12:33.0719864Z     type=<optimized out>)
2023-01-11T22:12:33.0720449Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:33.0720695Z #48 _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:33.0721024Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:215
2023-01-11T22:12:33.0723184Z #49 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:33.0723618Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0723999Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fc7a50fa3d8, 
2023-01-11T22:12:33.0724393Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0724764Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0725170Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:33.0727762Z #50 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0728299Z     args=0x7fc7a50fa3d8, callable=0x74f77a0, tstate=<optimized out>)
2023-01-11T22:12:33.0728794Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:33.0732219Z #51 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0732695Z     args=0x7fc7a50fa3d8, callable=0x74f77a0)
2023-01-11T22:12:33.0733181Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0736239Z #52 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0736720Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69e1b0, 
2023-01-11T22:12:33.0737128Z     tstate=<optimized out>)
2023-01-11T22:12:33.0737508Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0737853Z #53 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0738378Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:33.0738841Z #54 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0739165Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0739613Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0739977Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0740362Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0745604Z #55 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0746227Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0746873Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0747276Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7adfec200, 
2023-01-11T22:12:33.0747668Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0748059Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0748462Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0748705Z #56 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0748993Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0753491Z #57 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.0753918Z     nargsf=<optimized out>, args=0x7fc7a50fa098, callable=0x7fc7adfec1f0, 
2023-01-11T22:12:33.0754162Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0754467Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0757905Z #58 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0758370Z     args=0x7fc7a50fa098, callable=0x7fc7adfec1f0)
2023-01-11T22:12:33.0758872Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0761273Z #59 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0761744Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69e370, 
2023-01-11T22:12:33.0762179Z     tstate=<optimized out>)
2023-01-11T22:12:33.0762616Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0762887Z #60 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0763408Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:12:33.0763872Z #61 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0764220Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0764584Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0764930Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0765313Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0769321Z #62 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0769994Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0770521Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0770931Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7adf9b6e0, 
2023-01-11T22:12:33.0771337Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0771724Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0772103Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0772342Z #63 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0772733Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0777086Z #64 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.0777579Z     nargsf=<optimized out>, args=0x72a77f8, callable=0x7fc7adf9b6d0, 
2023-01-11T22:12:33.0777958Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0778285Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0781485Z #65 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x72a77f8, 
2023-01-11T22:12:33.0782076Z     callable=0x7fc7adf9b6d0)
2023-01-11T22:12:33.0782640Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0784887Z #66 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0785355Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69e530, 
2023-01-11T22:12:33.0785757Z     tstate=<optimized out>)
2023-01-11T22:12:33.0786118Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0786398Z #67 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0786801Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:33.0787230Z #68 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0787774Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0788126Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0788588Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0788975Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0793011Z #69 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0793668Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0794250Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0794655Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7ae009b50, 
2023-01-11T22:12:33.0795047Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0795434Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0795827Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0796068Z #70 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0796359Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0799735Z #71 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fc850fbdb80, 
2023-01-11T22:12:33.0800342Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fc7ae009b40, 
2023-01-11T22:12:33.0800662Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0800990Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0803107Z #72 PyObject_Vectorcall (kwnames=0x7fc850fbdb80, nargsf=<optimized out>, 
2023-01-11T22:12:33.0803736Z     args=<optimized out>, callable=0x7fc7ae009b40)
2023-01-11T22:12:33.0804137Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0806674Z #73 call_function (kwnames=0x7fc850fbdb80, oparg=<optimized out>, 
2023-01-11T22:12:33.0807220Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69e6f0, 
2023-01-11T22:12:33.0807502Z     tstate=<optimized out>)
2023-01-11T22:12:33.0807883Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0808147Z #74 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0808457Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:33.0809033Z #75 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:12:33.0809685Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0810396Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0811035Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0811749Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0817154Z #76 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:12:33.0817738Z     args=0x7fc7a5029150, locals=0x0, con=0x7fc7ab1f3e30, tstate=0x1f51b80)
2023-01-11T22:12:33.0818303Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0821264Z #77 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:12:33.0821802Z     stack=0x7fc7a5029150, func=0x7fc7ab1f3e20)
2023-01-11T22:12:33.0822577Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0827059Z #78 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:12:33.0827510Z     nargsf=<optimized out>, args=0x7fc7a5029150, callable=0x7fc7ab1f3e20, 
2023-01-11T22:12:33.0827946Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0828558Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:33.0828939Z #79 method_vectorcall ()
2023-01-11T22:12:33.0829484Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:12:33.0835848Z #80 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.0836343Z     nargsf=<optimized out>, args=0x7fc7a5029158, callable=0x7fc7aae938c0, 
2023-01-11T22:12:33.0836711Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0837317Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0840921Z #81 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0841410Z     args=0x7fc7a5029158, callable=0x7fc7aae938c0)
2023-01-11T22:12:33.0841988Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0844909Z #82 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0845363Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69e900, 
2023-01-11T22:12:33.0845780Z     tstate=<optimized out>)
2023-01-11T22:12:33.0846294Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0846702Z #83 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0847193Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:33.0847650Z #84 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.0848187Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0848802Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0849479Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0850165Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0854856Z #85 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.0855472Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0856295Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.0856935Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc850f18d40, 
2023-01-11T22:12:33.0857698Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.0858350Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0859097Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0859700Z #86 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.0860212Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0864301Z #87 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.0864779Z     nargsf=<optimized out>, args=0x6fefc20, callable=0x7fc850f18d30, 
2023-01-11T22:12:33.0865149Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0865744Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0869477Z #88 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x6fefc20, 
2023-01-11T22:12:33.0869924Z     callable=0x7fc850f18d30)
2023-01-11T22:12:33.0870460Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0873136Z #89 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.0873610Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69eac0, 
2023-01-11T22:12:33.0874033Z     tstate=<optimized out>)
2023-01-11T22:12:33.0874534Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0874930Z #90 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0875442Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:33.0875875Z #91 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:12:33.0876370Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0877225Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0877829Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0878549Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0883928Z #92 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:12:33.0884502Z     args=0x6fe1d78, locals=0x0, con=0x7fc850f18ef0, tstate=0x1f51b80)
2023-01-11T22:12:33.0885064Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0887826Z #93 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:12:33.0888346Z     stack=0x6fe1d78, func=0x7fc850f18ee0)
2023-01-11T22:12:33.0888905Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0893327Z #94 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:12:33.0893815Z     nargsf=<optimized out>, args=0x6fe1d78, callable=0x7fc850f18ee0, 
2023-01-11T22:12:33.0894140Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0894692Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:33.0895100Z #95 method_vectorcall ()
2023-01-11T22:12:33.0895699Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:12:33.0900932Z #96 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fc7abb9a2c0, 
2023-01-11T22:12:33.0901444Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fc7aaf1b400, 
2023-01-11T22:12:33.0901833Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0902684Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0904582Z #97 PyObject_Vectorcall (kwnames=0x7fc7abb9a2c0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0905128Z     args=<optimized out>, callable=0x7fc7aaf1b400)
2023-01-11T22:12:33.0905684Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0908730Z #98 call_function (kwnames=0x7fc7abb9a2c0, oparg=<optimized out>, 
2023-01-11T22:12:33.0909242Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69ecd0, 
2023-01-11T22:12:33.0909643Z     tstate=<optimized out>)
2023-01-11T22:12:33.0910157Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0910517Z #99 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0911061Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:33.0911488Z #100 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:12:33.0912180Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0912865Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0913445Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0914206Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0919281Z #101 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:12:33.0919846Z     args=0x7fc7ab4e6b08, locals=0x0, con=0x7fc7ab1f0320, tstate=0x1f51b80)
2023-01-11T22:12:33.0920496Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0923380Z #102 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:12:33.0923911Z     stack=0x7fc7ab4e6b08, func=0x7fc7ab1f0310)
2023-01-11T22:12:33.0924477Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0928907Z #103 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:12:33.0929380Z     nargsf=<optimized out>, args=0x7fc7ab4e6b08, callable=0x7fc7ab1f0310, 
2023-01-11T22:12:33.0929716Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0930282Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:33.0930875Z #104 method_vectorcall ()
2023-01-11T22:12:33.0931475Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:12:33.0936465Z #105 0x00000000004efd83 in _PyObject_VectorcallTstate (
2023-01-11T22:12:33.0936942Z     kwnames=0x7fc7ab9ab970, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0937354Z     callable=0x7fc7aad1e3c0, tstate=0x1f51b80)
2023-01-11T22:12:33.0938009Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0940161Z #106 PyObject_Vectorcall (kwnames=0x7fc7ab9ab970, nargsf=<optimized out>, 
2023-01-11T22:12:33.0940678Z     args=<optimized out>, callable=0x7fc7aad1e3c0)
2023-01-11T22:12:33.0941242Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.0944258Z #107 call_function (kwnames=0x7fc7ab9ab970, oparg=<optimized out>, 
2023-01-11T22:12:33.0944750Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69eee0, 
2023-01-11T22:12:33.0945170Z     tstate=<optimized out>)
2023-01-11T22:12:33.0945723Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.0946155Z #108 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0946629Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:33.0947084Z #109 0x0000000000509f16 in _PyEval_EvalFrame (
2023-01-11T22:12:33.0947648Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0948225Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0948905Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0949583Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0954151Z #110 _PyEval_Vector (
2023-01-11T22:12:33.0954670Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0955406Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7ffe1b69efd0, locals=0x0, con=0x7fc7ab1f03b0, 
2023-01-11T22:12:33.0956000Z     tstate=0x1f51b80, 
2023-01-11T22:12:33.0956464Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0957156Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0959540Z #111 _PyFunction_Vectorcall (
2023-01-11T22:12:33.0960197Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0961049Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, stack=0x7ffe1b69efd0, func=0x7fc7ab1f03a0)
2023-01-11T22:12:33.0961789Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0964066Z #112 _PyObject_VectorcallTstate (
2023-01-11T22:12:33.0964614Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.0965237Z     callable=0x7fc7ab1f03a0, tstate=0x1f51b80)
2023-01-11T22:12:33.0965892Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:33.0966250Z #113 method_vectorcall ()
2023-01-11T22:12:33.0966828Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:83
2023-01-11T22:12:33.0973054Z #114 0x00000000004f141c in do_call_core (kwdict=0x7fc7ab220580, 
2023-01-11T22:12:33.0973574Z     callargs=0x7fc7ab1b7e20, func=0x7fc7ac65fa00, trace_info=0x7ffe1b69f0f0, 
2023-01-11T22:12:33.0973993Z     tstate=<optimized out>)
2023-01-11T22:12:33.0974509Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:33.0974966Z #115 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.0975542Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:33.0975982Z #116 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:12:33.0976641Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0977248Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.0977867Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.0978618Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.0983553Z #117 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:33.0984147Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7fc850f190a0, 
2023-01-11T22:12:33.0984669Z     tstate=0x1f51b80)
2023-01-11T22:12:33.0985206Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.0987650Z #118 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.0988093Z     stack=<optimized out>, func=0x7fc850f19090)
2023-01-11T22:12:33.0988619Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.0989082Z #119 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:12:33.0989643Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:12:33.0990054Z #120 0x00000000005084a6 in _PyObject_Call_Prepend ()
2023-01-11T22:12:33.0990579Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:12:33.0993291Z #121 0x00000000005d04d3 in slot_tp_call ()
2023-01-11T22:12:33.0993916Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7494
2023-01-11T22:12:33.0995407Z #122 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:33.0995860Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:33.0999964Z #123 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:33.1000511Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1001187Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fc7ab435970, 
2023-01-11T22:12:33.1001577Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1001947Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1002353Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:33.1004950Z #124 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1005632Z     args=0x7fc7ab435970, callable=0x7fc7ab1d94e0, tstate=<optimized out>)
2023-01-11T22:12:33.1006084Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:33.1009546Z #125 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1010034Z     args=0x7fc7ab435970, callable=0x7fc7ab1d94e0)
2023-01-11T22:12:33.1010501Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1013855Z #126 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.1014252Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69f3f0, 
2023-01-11T22:12:33.1014496Z     tstate=<optimized out>)
2023-01-11T22:12:33.1014818Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.1015167Z #127 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1015638Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:33.1016217Z #128 0x0000000000509f16 in _PyEval_EvalFrame (
2023-01-11T22:12:33.1016546Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1016918Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1017262Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1017815Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1022619Z #129 _PyEval_Vector (
2023-01-11T22:12:33.1023048Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1023455Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7ffe1b69f4e0, locals=0x0, con=0x7fc850f24200, 
2023-01-11T22:12:33.1023766Z     tstate=0x1f51b80, 
2023-01-11T22:12:33.1024061Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1024471Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1027330Z #130 _PyFunction_Vectorcall (
2023-01-11T22:12:33.1027705Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1028150Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, stack=0x7ffe1b69f4e0, func=0x7fc850f241f0)
2023-01-11T22:12:33.1028579Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1031236Z #131 _PyObject_VectorcallTstate (
2023-01-11T22:12:33.1031739Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.1032109Z     callable=0x7fc850f241f0, tstate=0x1f51b80)
2023-01-11T22:12:33.1032445Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:33.1032698Z #132 method_vectorcall ()
2023-01-11T22:12:33.1033016Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:83
2023-01-11T22:12:33.1039437Z #133 0x00000000004f141c in do_call_core (kwdict=0x7fc7ab261180, 
2023-01-11T22:12:33.1039817Z     callargs=0x7fc7ab1a4100, func=0x7fc7ab3fb9c0, trace_info=0x7ffe1b69f600, 
2023-01-11T22:12:33.1040147Z     tstate=<optimized out>)
2023-01-11T22:12:33.1040474Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:33.1040737Z #134 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1041227Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:33.1041663Z #135 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:12:33.1042034Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1042386Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1042874Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1043271Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1048834Z #136 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:33.1049295Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7fc850f240e0, 
2023-01-11T22:12:33.1049623Z     tstate=0x1f51b80)
2023-01-11T22:12:33.1049932Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1052510Z #137 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1052964Z     stack=<optimized out>, func=0x7fc850f240d0)
2023-01-11T22:12:33.1053542Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1053916Z #138 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:12:33.1054308Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:12:33.1054574Z #139 0x00000000005084a6 in _PyObject_Call_Prepend ()
2023-01-11T22:12:33.1054888Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:12:33.1057250Z #140 0x00000000005d04d3 in slot_tp_call ()
2023-01-11T22:12:33.1057637Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7494
2023-01-11T22:12:33.1059251Z #141 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:33.1059744Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:33.1063492Z #142 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:33.1064047Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1064634Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fc7ab435060, 
2023-01-11T22:12:33.1065010Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1065381Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1065788Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:33.1068375Z #143 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1068718Z     args=0x7fc7ab435060, callable=0x7fc7ab1dbd90, tstate=<optimized out>)
2023-01-11T22:12:33.1069098Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:33.1072914Z #144 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1073209Z     args=0x7fc7ab435060, callable=0x7fc7ab1dbd90)
2023-01-11T22:12:33.1073544Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1077091Z #145 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.1077602Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69f900, 
2023-01-11T22:12:33.1077945Z     tstate=<optimized out>)
2023-01-11T22:12:33.1078266Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.1078640Z #146 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1079056Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:33.1079533Z #147 0x0000000000509f16 in _PyEval_EvalFrame (
2023-01-11T22:12:33.1080001Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1080370Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1080714Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1081099Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1085482Z #148 _PyEval_Vector (
2023-01-11T22:12:33.1085954Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1086747Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7ffe1b69f9f0, locals=0x0, con=0x7fc850f24200, 
2023-01-11T22:12:33.1087067Z     tstate=0x1f51b80, 
2023-01-11T22:12:33.1087353Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1087742Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1090317Z #149 _PyFunction_Vectorcall (
2023-01-11T22:12:33.1090843Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1091417Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, stack=0x7ffe1b69f9f0, func=0x7fc850f241f0)
2023-01-11T22:12:33.1091822Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1094254Z #150 _PyObject_VectorcallTstate (
2023-01-11T22:12:33.1094912Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.1095332Z     callable=0x7fc850f241f0, tstate=0x1f51b80)
2023-01-11T22:12:33.1095666Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:12:33.1095911Z #151 method_vectorcall ()
2023-01-11T22:12:33.1096230Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:83
2023-01-11T22:12:33.1102019Z #152 0x00000000004f141c in do_call_core (kwdict=0x7fc7ac6fb000, 
2023-01-11T22:12:33.1102475Z     callargs=0x7fc7ab1c2560, func=0x7fc7ac24cf00, trace_info=0x7ffe1b69fb10, 
2023-01-11T22:12:33.1102728Z     tstate=<optimized out>)
2023-01-11T22:12:33.1103048Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:33.1103283Z #153 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1103731Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:33.1104002Z #154 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:12:33.1104538Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1105049Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1105411Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1105798Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1111087Z #155 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:12:33.1111736Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7fc850f240e0, 
2023-01-11T22:12:33.1112109Z     tstate=0x1f51b80)
2023-01-11T22:12:33.1112402Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1114543Z #156 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1115006Z     stack=<optimized out>, func=0x7fc850f240d0)
2023-01-11T22:12:33.1116091Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1116666Z #157 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:12:33.1117010Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:12:33.1117267Z #158 0x00000000005084a6 in _PyObject_Call_Prepend ()
2023-01-11T22:12:33.1117566Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:12:33.1119092Z #159 0x00000000005d04d3 in slot_tp_call ()
2023-01-11T22:12:33.1119525Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7494
2023-01-11T22:12:33.1120895Z #160 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:33.1121297Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:33.1125001Z #161 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:12:33.1125416Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1125927Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x6fe43d0, 
2023-01-11T22:12:33.1126299Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1126667Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1127097Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:33.1129815Z #162 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1130493Z     args=0x6fe43d0, callable=0x7fc7ab1b5fc0, tstate=<optimized out>)
2023-01-11T22:12:33.1130889Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:33.1134395Z #163 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1134833Z     args=0x6fe43d0, callable=0x7fc7ab1b5fc0)
2023-01-11T22:12:33.1135373Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1138731Z #164 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.1139108Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69fe10, 
2023-01-11T22:12:33.1139332Z     tstate=<optimized out>)
2023-01-11T22:12:33.1139676Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.1140179Z #165 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1140506Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:33.1140930Z #166 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.1141492Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1141854Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1142214Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1142725Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1147773Z #167 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.1148383Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.1149032Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.1149441Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7ab21c7a0, 
2023-01-11T22:12:33.1149845Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.1150221Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1150626Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1150874Z #168 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.1151179Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1156377Z #169 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.1156715Z     nargsf=<optimized out>, args=0x6eb4108, callable=0x7fc7ab21c790, 
2023-01-11T22:12:33.1156953Z     tstate=0x1f51b80)
2023-01-11T22:12:33.1157290Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1160931Z #170 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1161192Z     args=0x6eb4108, callable=0x7fc7ab21c790)
2023-01-11T22:12:33.1161546Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1164205Z #171 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.1164652Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b69ffd0, 
2023-01-11T22:12:33.1164896Z     tstate=<optimized out>)
2023-01-11T22:12:33.1165346Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.1165709Z #172 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1166060Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:33.1166410Z #173 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.1166783Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1167155Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1167519Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1167888Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1172803Z #174 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.1173417Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.1174148Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.1174535Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc850d723c0, 
2023-01-11T22:12:33.1174938Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.1175424Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1175825Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1176055Z #175 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.1176356Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1181152Z #176 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.1181682Z     nargsf=<optimized out>, args=0x6e52cc8, callable=0x7fc850d723b0, 
2023-01-11T22:12:33.1181974Z     tstate=0x1f51b80)
2023-01-11T22:12:33.1182300Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1186006Z #177 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1186442Z     args=0x6e52cc8, callable=0x7fc850d723b0)
2023-01-11T22:12:33.1186901Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1189220Z #178 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.1189666Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b6a0190, 
2023-01-11T22:12:33.1190089Z     tstate=<optimized out>)
2023-01-11T22:12:33.1190417Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.1190664Z #179 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1190960Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:33.1191288Z #180 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.1191723Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1192075Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1192434Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1192821Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1198054Z #181 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.1198593Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.1199323Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.1199781Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc850d71e20, 
2023-01-11T22:12:33.1200332Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.1200705Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1201116Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1201361Z #182 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.1201652Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1201950Z #183 0x00000000004f711d in _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:12:33.1202302Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:153
2023-01-11T22:12:33.1209129Z #184 0x0000000000507af8 in _PyObject_Call_Prepend (kwargs=0x7fc7ac65fe00, 
2023-01-11T22:12:33.1209684Z     kwargs@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fc852018070, 
2023-01-11T22:12:33.1210391Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, obj=<optimized out>, 
2023-01-11T22:12:33.1210802Z     obj@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, callable=0x7fc850d71e10, 
2023-01-11T22:12:33.1211210Z     callable@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.1211695Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1212092Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:12:33.1212319Z #185 slot_tp_init ()
2023-01-11T22:12:33.1212612Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7734
2023-01-11T22:12:33.1215324Z #186 0x00000000004f7bdb in type_call (kwds=0x7fc7ac65fe00, 
2023-01-11T22:12:33.1215746Z     args=0x7fc852018070, type=<optimized out>)
2023-01-11T22:12:33.1216284Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:12:33.1216531Z #187 _PyObject_MakeTpCall.localalias ()
2023-01-11T22:12:33.1216851Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:215
2023-01-11T22:12:33.1219195Z #188 0x00000000004f428d in _PyObject_VectorcallTstate (
2023-01-11T22:12:33.1219551Z     kwnames=0x7fc7ab9a50c0, 
2023-01-11T22:12:33.1220145Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:12:33.1220564Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1220930Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1221323Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:12:33.1224123Z #189 _PyObject_VectorcallTstate (kwnames=0x7fc7ab9a50c0, 
2023-01-11T22:12:33.1224433Z     nargsf=<optimized out>, args=<optimized out>, callable=0x20eeb40, 
2023-01-11T22:12:33.1224673Z     tstate=0x1f51b80)
2023-01-11T22:12:33.1224980Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:12:33.1227263Z #190 PyObject_Vectorcall (kwnames=0x7fc7ab9a50c0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1227613Z     args=<optimized out>, callable=0x20eeb40)
2023-01-11T22:12:33.1227949Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1230611Z #191 call_function (kwnames=0x7fc7ab9a50c0, oparg=<optimized out>, 
2023-01-11T22:12:33.1231087Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b6a04b0, 
2023-01-11T22:12:33.1231518Z     tstate=<optimized out>)
2023-01-11T22:12:33.1231862Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.1232153Z #192 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1232523Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:33.1232943Z #193 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:33.1233582Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1233951Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1234308Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1234684Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:33.1238773Z #194 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:33.1239322Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:33.1239940Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:33.1240336Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fc7ab96e8d0, 
2023-01-11T22:12:33.1240748Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f51b80, 
2023-01-11T22:12:33.1241140Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1241538Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1241850Z #195 _PyFunction_Vectorcall ()
2023-01-11T22:12:33.1242159Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:33.1247071Z #196 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:33.1247376Z     nargsf=<optimized out>, args=0x7fc85203dba8, callable=0x7fc7ab96e8c0, 
2023-01-11T22:12:33.1247618Z     tstate=0x1f51b80)
2023-01-11T22:12:33.1247933Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1251712Z #197 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:33.1251966Z     args=0x7fc85203dba8, callable=0x7fc7ab96e8c0)
2023-01-11T22:12:33.1252311Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:33.1255065Z #198 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:33.1255382Z     pp_stack=<synthetic pointer>, trace_info=0x7ffe1b6a0670, 
2023-01-11T22:12:33.1255618Z     tstate=<optimized out>)
2023-01-11T22:12:33.1255927Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:33.1256158Z #199 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:33.1256469Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:33.1257868Z #200 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:12:33.1258310Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1258663Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1259021Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:33.1259484Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:12:33.1259752Z #201 _PyEval_Vector ()
2023-01-11T22:12:33.1260033Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:33.1266281Z #202 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7fc850f8f730, 
2023-01-11T22:12:33.1266595Z     globals=globals@entry=0x7fc8510ca480, locals=locals@entry=0x7fc8510ca480)
2023-01-11T22:12:33.1266943Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:12:33.1268048Z #203 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:12:33.1268382Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:12:33.1270474Z #204 0x00000000005c1d40 in run_mod ()
2023-01-11T22:12:33.1270783Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:12:33.1287209Z #205 0x000000000045adf2 in pyrun_file (
2023-01-11T22:12:33.1287689Z     fp=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1288354Z     filename=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1288768Z     start=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1289196Z     globals=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1289617Z     locals=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:33.1289991Z     closeit=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, flags=0x7ffe1b6a0968)
2023-01-11T22:12:33.1290421Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1208
2023-01-11T22:12:33.1290729Z #206 0x00000000005bc25f in _PyRun_SimpleFileObject.localalias ()
2023-01-11T22:12:33.1291266Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:456
2023-01-11T22:12:33.1291675Z #207 0x00000000005bc063 in _PyRun_AnyFileObject.localalias ()
2023-01-11T22:12:33.1292087Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:90
2023-01-11T22:12:33.1298180Z #208 0x00000000005b8e7d in pymain_run_file_obj (skip_source_first_line=0, 
2023-01-11T22:12:33.1298549Z     filename=0x7fc851145840, program_name=0x7fc8510b7a00)
2023-01-11T22:12:33.1298887Z     at /croot/python-split_1669298683653/work/build-static/python.c:357
2023-01-11T22:12:33.1300044Z #209 pymain_run_file (config=0x1f35e50)
2023-01-11T22:12:33.1300615Z     at /croot/python-split_1669298683653/work/build-static/python.c:376
2023-01-11T22:12:33.1302155Z #210 pymain_run_python (exitcode=0x7ffe1b6a0960)
2023-01-11T22:12:33.1302916Z     at /croot/python-split_1669298683653/work/build-static/python.c:591
2023-01-11T22:12:33.1303268Z #211 Py_RunMain.localalias ()
2023-01-11T22:12:33.1303589Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:12:33.1318028Z #212 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:12:33.1318317Z     argv=<optimized out>)
2023-01-11T22:12:33.1318636Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:12:33.1323878Z #213 0x00007fc8511a8c87 in __libc_start_main (main=0x587be0 <main>, argc=6, 
2023-01-11T22:12:33.1324232Z     argv=0x7ffe1b6a0b68, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:12:33.1324507Z     rtld_fini=<optimized out>, stack_end=0x7ffe1b6a0b58)
2023-01-11T22:12:33.1324781Z     at ../csu/libc-start.c:310
2023-01-11T22:12:33.1326087Z #214 0x0000000000587ade in _start ()
2023-01-11T22:12:33.1326419Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:12:33.2931170Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:12:33.2931642Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:12:33.2932080Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:12:33.2932656Z This is free software: you are free to change and redistribute it.
2023-01-11T22:12:33.2933131Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:12:33.2933376Z and "show warranty" for details.
2023-01-11T22:12:33.2933672Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:12:33.2933941Z Type "show configuration" for configuration details.
2023-01-11T22:12:33.2934182Z For bug reporting instructions, please see:
2023-01-11T22:12:33.2934423Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:12:33.2934691Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:12:33.2934970Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:12:33.2935183Z For help, type "help".
2023-01-11T22:12:33.2935424Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:12:33.4071675Z Reading symbols from python...done.
2023-01-11T22:12:33.9656349Z 
2023-01-11T22:12:33.9656668Z warning: core file may not match specified executable file.
2023-01-11T22:12:33.9689353Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:33.9689984Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:33.9690451Z [New LWP 23909]
2023-01-11T22:12:33.9690745Z [New LWP 23921]
2023-01-11T22:12:33.9691249Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:33.9691888Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:33.9692363Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:33.9692716Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:33.9692975Z [New LWP 23916]
2023-01-11T22:12:33.9693143Z [New LWP 23913]
2023-01-11T22:12:33.9693299Z [New LWP 23918]
2023-01-11T22:12:33.9693467Z [New LWP 23911]
2023-01-11T22:12:33.9693633Z [New LWP 23923]
2023-01-11T22:12:33.9693785Z [New LWP 23924]
2023-01-11T22:12:33.9706427Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:33.9707530Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:33.9708083Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:12:33.9708613Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:12:33.9932354Z [Thread debugging using libthread_db enabled]
2023-01-11T22:12:33.9932823Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:12:40.8850040Z 78	../sysdeps/unix/syscall-template.S: No such file or directory.
2023-01-11T22:12:40.8850901Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:12:40.8851445Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:12:40.8851776Z Program terminated with signal SIGABRT, Aborted.
2023-01-11T22:12:40.8852122Z #0  0x00007fd4cad5d177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:12:40.8852411Z [Current thread is 1 (Thread 0x7fd4cbcdb200 (LWP 23909))]
2023-01-11T22:12:40.8879231Z To enable execution of this file add
2023-01-11T22:12:40.8879817Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:12:40.8880371Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:40.8880868Z To completely disable this security protection add
2023-01-11T22:12:40.8881356Z 	set auto-load safe-path /
2023-01-11T22:12:40.8881746Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:12:40.8882027Z For more information about this security protection see the
2023-01-11T22:12:40.8882389Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:12:40.8882685Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:12:40.8921860Z #0  0x00007fd4cad5d177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:12:40.8924549Z #1  0x00000000004cb0d3 in os_kill_impl (
2023-01-11T22:12:40.8924921Z     module=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8925287Z     signal=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8925874Z     pid=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.8926424Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/sys/_iomodule.c:7929
2023-01-11T22:12:40.8929858Z #2  os_kill (module=<optimized out>, args=args@entry=0x7fd424f8ebf8, 
2023-01-11T22:12:40.8930208Z     nargs=<optimized out>)
2023-01-11T22:12:40.8930532Z     at /usr/local/src/conda/python-3.10.8/Modules/codecs.c:3581
2023-01-11T22:12:40.8938204Z #3  0x00000000004fe7d4 in cfunction_vectorcall_FASTCALL (func=0x7fd4cbca18f0, 
2023-01-11T22:12:40.8938777Z     args=0x7fd424f8ebf8, nargsf=<optimized out>, kwnames=<optimized out>)
2023-01-11T22:12:40.8939197Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_bitutils.h:430
2023-01-11T22:12:40.8946141Z #4  0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:40.8946518Z     nargsf=<optimized out>, args=0x7fd424f8ebf8, callable=0x7fd4cbca18f0, 
2023-01-11T22:12:40.8946765Z     tstate=0x2096d10)
2023-01-11T22:12:40.8947113Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.8950716Z #5  PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:40.8950996Z     args=0x7fd424f8ebf8, callable=0x7fd4cbca18f0)
2023-01-11T22:12:40.8951354Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.8953908Z #6  call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:40.8954462Z     pp_stack=<synthetic pointer>, trace_info=0x7ffcf67604f0, 
2023-01-11T22:12:40.8954705Z     tstate=<optimized out>)
2023-01-11T22:12:40.8955030Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:40.8955277Z #7  _PyEval_EvalFrameDefault ()
2023-01-11T22:12:40.8955678Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:12:40.8956163Z #8  0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:40.8956518Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8956895Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8957264Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.8957676Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:40.8962377Z #9  _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:40.8963002Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:40.8963710Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:40.8964111Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fd424dc7890, 
2023-01-11T22:12:40.8964501Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2096d10, 
2023-01-11T22:12:40.8964888Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.8965300Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:40.8965542Z #10 _PyFunction_Vectorcall ()
2023-01-11T22:12:40.8965832Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:40.8970165Z #11 0x00000000004f141c in do_call_core (kwdict=0x0, callargs=0x7fd4caa53be0, 
2023-01-11T22:12:40.8970570Z     func=0x7fd424dc7880, trace_info=0x7ffcf67606b0, tstate=<optimized out>)
2023-01-11T22:12:40.8970930Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:40.8971213Z #12 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:40.8971648Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:40.8972130Z #13 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:40.8972852Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8973510Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8974169Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.8974652Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:40.8978241Z #14 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:40.8978838Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:40.8979540Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:40.8979942Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fd427b629f0, 
2023-01-11T22:12:40.8980349Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2096d10, 
2023-01-11T22:12:40.8980721Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.8981118Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:40.8981360Z #15 _PyFunction_Vectorcall ()
2023-01-11T22:12:40.8981728Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:40.8986642Z #16 0x00000000004f141c in do_call_core (kwdict=0x7fd4cab627c0, 
2023-01-11T22:12:40.8987176Z     callargs=0x7fd424dca9d0, func=0x7fd427b629e0, trace_info=0x7ffcf6760870, 
2023-01-11T22:12:40.8987524Z     tstate=<optimized out>)
2023-01-11T22:12:40.8987864Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:12:40.8988220Z #17 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:40.8988594Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:12:40.8988849Z #18 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:40.8989158Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8989520Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.8989878Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.8990249Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:40.8994448Z #19 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:40.8994953Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:40.8995395Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:40.8995788Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fd4cab8d5b0, 
2023-01-11T22:12:40.8996192Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2096d10, 
2023-01-11T22:12:40.8996581Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.8996980Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:40.8997214Z #20 _PyFunction_Vectorcall ()
2023-01-11T22:12:40.8997514Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:40.9003096Z #21 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:40.9003576Z     nargsf=<optimized out>, args=0x6fa5608, callable=0x7fd4cab8d5a0, 
2023-01-11T22:12:40.9003988Z     tstate=0x2096d10)
2023-01-11T22:12:40.9004313Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9008221Z #22 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x6fa5608, 
2023-01-11T22:12:40.9008671Z     callable=0x7fd4cab8d5a0)
2023-01-11T22:12:40.9009262Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9011725Z #23 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:40.9012194Z     pp_stack=<synthetic pointer>, trace_info=0x7ffcf6760a30, 
2023-01-11T22:12:40.9012626Z     tstate=<optimized out>)
2023-01-11T22:12:40.9013336Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:40.9013867Z #24 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:40.9014404Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:40.9014823Z #25 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:40.9015134Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9015483Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9015846Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9016225Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:40.9020200Z #26 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:40.9020984Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:40.9021565Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:40.9021977Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fd4cab8df40, 
2023-01-11T22:12:40.9022712Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2096d10, 
2023-01-11T22:12:40.9023170Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9023605Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:40.9023854Z #27 _PyFunction_Vectorcall ()
2023-01-11T22:12:40.9024162Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:40.9028675Z #28 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:40.9029072Z     nargsf=<optimized out>, args=0x21136c0, callable=0x7fd4cab8df30, 
2023-01-11T22:12:40.9029314Z     tstate=0x2096d10)
2023-01-11T22:12:40.9029624Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9033893Z #29 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x21136c0, 
2023-01-11T22:12:40.9034369Z     callable=0x7fd4cab8df30)
2023-01-11T22:12:40.9034828Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9037239Z #30 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:40.9037732Z     pp_stack=<synthetic pointer>, trace_info=0x7ffcf6760bf0, 
2023-01-11T22:12:40.9038137Z     tstate=<optimized out>)
2023-01-11T22:12:40.9038443Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:40.9038688Z #31 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:40.9038994Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:12:40.9039350Z #32 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:40.9039945Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9040479Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9040839Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9041215Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:40.9045973Z #33 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:40.9046527Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:40.9046963Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:40.9047351Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fd4caa12de0, 
2023-01-11T22:12:40.9047759Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2096d10, 
2023-01-11T22:12:40.9048151Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9048546Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:40.9048775Z #34 _PyFunction_Vectorcall ()
2023-01-11T22:12:40.9049077Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:40.9054219Z #35 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:12:40.9054638Z     nargsf=<optimized out>, args=0x7fd4cacd4200, callable=0x7fd4caa12dd0, 
2023-01-11T22:12:40.9054889Z     tstate=0x2096d10)
2023-01-11T22:12:40.9055204Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9059003Z #36 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:12:40.9059386Z     args=0x7fd4cacd4200, callable=0x7fd4caa12dd0)
2023-01-11T22:12:40.9059735Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9062876Z #37 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:12:40.9063335Z     pp_stack=<synthetic pointer>, trace_info=0x7ffcf6760db0, 
2023-01-11T22:12:40.9063792Z     tstate=<optimized out>)
2023-01-11T22:12:40.9064208Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:40.9064523Z #38 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:40.9065089Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:12:40.9065579Z #39 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:12:40.9065947Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9066298Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9066655Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9067041Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:12:40.9071091Z #40 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:12:40.9071736Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:12:40.9072290Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:12:40.9072699Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fd4caa12d50, 
2023-01-11T22:12:40.9073103Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2096d10, 
2023-01-11T22:12:40.9073475Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9073872Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:40.9074113Z #41 _PyFunction_Vectorcall ()
2023-01-11T22:12:40.9074402Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:12:40.9077895Z #42 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fd4cacadd80, 
2023-01-11T22:12:40.9078464Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fd4caa12d40, 
2023-01-11T22:12:40.9078796Z     tstate=0x2096d10)
2023-01-11T22:12:40.9079111Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9081422Z #43 PyObject_Vectorcall (kwnames=0x7fd4cacadd80, nargsf=<optimized out>, 
2023-01-11T22:12:40.9081934Z     args=<optimized out>, callable=0x7fd4caa12d40)
2023-01-11T22:12:40.9082289Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:12:40.9084469Z #44 call_function (kwnames=0x7fd4cacadd80, oparg=<optimized out>, 
2023-01-11T22:12:40.9084989Z     pp_stack=<synthetic pointer>, trace_info=0x7ffcf6760f70, 
2023-01-11T22:12:40.9085361Z     tstate=<optimized out>)
2023-01-11T22:12:40.9085668Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:12:40.9085909Z #45 _PyEval_EvalFrameDefault ()
2023-01-11T22:12:40.9086212Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:12:40.9087384Z #46 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:12:40.9087786Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9088161Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:12:40.9088513Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9088960Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:12:40.9089225Z #47 _PyEval_Vector ()
2023-01-11T22:12:40.9089622Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:12:40.9096110Z #48 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7fd4cac70920, 
2023-01-11T22:12:40.9096669Z     globals=globals@entry=0x7fd4cac625c0, locals=locals@entry=0x7fd4cac625c0)
2023-01-11T22:12:40.9097148Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:12:40.9097831Z #49 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:12:40.9098430Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:12:40.9100776Z #50 0x00000000005c1d40 in run_mod ()
2023-01-11T22:12:40.9101199Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:12:40.9103224Z #51 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:12:40.9103625Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:12:40.9105849Z #52 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:12:40.9106470Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:12:40.9108400Z #53 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:12:40.9108771Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:12:40.9109189Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:12:40.9110992Z #54 pymain_run_python (exitcode=0x7ffcf67611d0)
2023-01-11T22:12:40.9111611Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:12:40.9111948Z #55 Py_RunMain.localalias ()
2023-01-11T22:12:40.9112272Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:12:40.9128184Z #56 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:12:40.9128467Z     argv=<optimized out>)
2023-01-11T22:12:40.9128792Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:12:40.9133903Z #57 0x00007fd4cad3fc87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:12:40.9134240Z     argv=0x7ffcf67613d8, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:12:40.9134515Z     rtld_fini=<optimized out>, stack_end=0x7ffcf67613c8)
2023-01-11T22:12:40.9134793Z     at ../csu/libc-start.c:310
2023-01-11T22:12:40.9135719Z #58 0x0000000000587ade in _start ()
2023-01-11T22:12:41.6774989Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:12:41.6800602Z ##[group]Run set -x
2023-01-11T22:12:41.6800837Z set -x
2023-01-11T22:12:41.6801092Z python3 -m pip install -r requirements.txt
2023-01-11T22:12:41.6801361Z python3 -m pip install boto3==1.19.12
2023-01-11T22:12:41.6801685Z python3 -m tools.stats.print_test_stats --upload-to-s3 --compare-with-s3 test
2023-01-11T22:12:41.6812630Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:12:41.6812865Z env:
2023-01-11T22:12:41.6813072Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:12:41.6813379Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:12:41.6813660Z   AWS_DEFAULT_REGION: us-east-1
2023-01-11T22:12:41.6813876Z   BRANCH: 
2023-01-11T22:12:41.6814083Z   TEST_CONFIG: nogpu_NO_AVX2
2023-01-11T22:12:41.6814302Z   SHARD_NUMBER: 1
2023-01-11T22:12:41.6814552Z   BUILD_ENVIRONMENT: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T22:12:41.6814813Z   PR_NUMBER: 
2023-01-11T22:12:41.6815048Z   PYTORCH_RETRY_TEST_CASES: 1
2023-01-11T22:12:41.6815293Z   PYTORCH_OVERRIDE_FLAKY_SIGNAL: 1
2023-01-11T22:12:41.6815560Z   SHA1: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T22:12:41.6815808Z   TAG: ciflow/trunk/91627
2023-01-11T22:12:41.6816012Z   WORKFLOW_ID: 3896346758
2023-01-11T22:12:41.6816366Z   GITHUB_TOKEN: ***
2023-01-11T22:12:41.6816592Z   GHA_WORKFLOW_JOB_ID: 10589559916
2023-01-11T22:12:41.6816797Z ##[endgroup]
2023-01-11T22:12:41.6841554Z + python3 -m pip install -r requirements.txt
2023-01-11T22:12:41.8994417Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T22:12:41.9283991Z Requirement already satisfied: astunparse in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 2)) (1.6.3)
2023-01-11T22:12:41.9311475Z Requirement already satisfied: expecttest in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 3)) (0.1.4)
2023-01-11T22:12:41.9319418Z Requirement already satisfied: future in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (0.18.2)
2023-01-11T22:12:41.9327529Z Requirement already satisfied: hypothesis in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 5)) (6.62.0)
2023-01-11T22:12:41.9691392Z Requirement already satisfied: numpy in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 6)) (1.21.6)
2023-01-11T22:12:41.9700327Z Requirement already satisfied: psutil in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 7)) (5.9.1)
2023-01-11T22:12:41.9776116Z Requirement already satisfied: pyyaml in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 8)) (6.0)
2023-01-11T22:12:41.9784214Z Requirement already satisfied: requests in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 9)) (2.26.0)
2023-01-11T22:12:42.0010683Z Requirement already satisfied: setuptools in /usr/lib/python3.7/site-packages (from -r requirements.txt (line 10)) (49.1.3)
2023-01-11T22:12:42.0176506Z Requirement already satisfied: six in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 11)) (1.16.0)
2023-01-11T22:12:42.0185237Z Requirement already satisfied: types-dataclasses in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 12)) (0.6.6)
2023-01-11T22:12:42.0190703Z Requirement already satisfied: typing_extensions in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 13)) (4.4.0)
2023-01-11T22:12:42.0199971Z Requirement already satisfied: sympy in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 14)) (1.10.1)
2023-01-11T22:12:42.0218250Z Requirement already satisfied: filelock in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 15)) (3.9.0)
2023-01-11T22:12:42.0289389Z Requirement already satisfied: networkx in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 16)) (2.6.3)
2023-01-11T22:12:42.0445516Z Requirement already satisfied: jinja2 in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 17)) (3.1.2)
2023-01-11T22:12:42.0470476Z Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from astunparse->-r requirements.txt (line 2)) (0.38.4)
2023-01-11T22:12:42.0486284Z Requirement already satisfied: attrs>=19.2.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (22.2.0)
2023-01-11T22:12:42.0741013Z Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (2.4.0)
2023-01-11T22:12:42.0752485Z Requirement already satisfied: exceptiongroup>=1.0.0; python_version < "3.11" in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (1.1.0)
2023-01-11T22:12:42.0769075Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (2022.12.7)
2023-01-11T22:12:42.0777473Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (3.4)
2023-01-11T22:12:42.0788572Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (1.26.14)
2023-01-11T22:12:42.0942691Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (2.0.12)
2023-01-11T22:12:42.0961264Z Requirement already satisfied: mpmath>=0.19 in /home/ec2-user/.local/lib/python3.7/site-packages (from sympy->-r requirements.txt (line 14)) (1.2.1)
2023-01-11T22:12:42.1016886Z Requirement already satisfied: MarkupSafe>=2.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from jinja2->-r requirements.txt (line 17)) (2.1.1)
2023-01-11T22:12:42.1651380Z + python3 -m pip install boto3==1.19.12
2023-01-11T22:12:42.3770955Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T22:12:42.3950164Z Requirement already satisfied: boto3==1.19.12 in /home/ec2-user/.local/lib/python3.7/site-packages (1.19.12)
2023-01-11T22:12:42.3998664Z Requirement already satisfied: botocore<1.23.0,>=1.22.12 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (1.22.12)
2023-01-11T22:12:42.4040783Z Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (0.5.2)
2023-01-11T22:12:42.4073784Z Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (0.10.0)
2023-01-11T22:12:42.4085957Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /home/ec2-user/.local/lib/python3.7/site-packages (from botocore<1.23.0,>=1.22.12->boto3==1.19.12) (1.26.14)
2023-01-11T22:12:42.4236718Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from botocore<1.23.0,>=1.22.12->boto3==1.19.12) (2.8.2)
2023-01-11T22:12:42.4254313Z Requirement already satisfied: six>=1.5 in /home/ec2-user/.local/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.23.0,>=1.22.12->boto3==1.19.12) (1.16.0)
2023-01-11T22:12:42.6051766Z + python3 -m tools.stats.print_test_stats --upload-to-s3 --compare-with-s3 test
2023-01-11T22:15:03.8392290Z [scribe] Scribe access token not provided, sending report via boto3...
2023-01-11T22:15:03.8393433Z ERROR ENCOUNTERED WHEN UPLOADING TO SCRIBE: {"errorMessage":"2023-01-11T22:14:47.494Z 00aa022d-ed99-4cd4-8cab-bb5449156722 Task timed out after 60.01 seconds"}
2023-01-11T22:15:03.8393917Z 
2023-01-11T22:15:03.8395103Z ----- Historic stats comparison result ------
2023-01-11T22:15:03.8395609Z 
2023-01-11T22:15:03.8395802Z     job: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T22:15:03.8396078Z     commit: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T22:15:03.8396230Z 
2023-01-11T22:15:03.8396380Z Commit graph (base is most recent master ancestor with at least one S3 report):
2023-01-11T22:15:03.8396545Z 
2023-01-11T22:15:03.8396613Z     : (master)
2023-01-11T22:15:03.8396774Z     |
2023-01-11T22:15:03.8396973Z     | * 8419ddda87 (HEAD)            total time  3511.30s
2023-01-11T22:15:03.8397159Z     | |
2023-01-11T22:15:03.8401078Z     | : (2 commits)
2023-01-11T22:15:03.8401313Z     |/
2023-01-11T22:15:03.8401834Z     * db2a237763 (base)  11 reports, total time  4966.48s ± 3495.23s
2023-01-11T22:15:03.8402329Z     * 2b0abd4ce3         11 reports, total time  4990.75s ± 3463.36s
2023-01-11T22:15:03.8402871Z     * f7939b21e1         33 reports, total time  3500.97s ± 3537.57s
2023-01-11T22:15:03.8403359Z     * cb3204823e         11 reports, total time  4951.99s ± 3458.09s
2023-01-11T22:15:03.8403685Z     * 6e236553f5         11 reports, total time  4964.39s ± 3513.19s
2023-01-11T22:15:03.8404867Z     * cce577b391         11 reports, total time  4938.35s ± 3358.29s
2023-01-11T22:15:03.8405186Z     * fae821c2f1         11 reports, total time  4751.06s ± 3169.39s
2023-01-11T22:15:03.8405497Z     * 0c3659586d         11 reports, total time  4713.33s ± 3185.77s
2023-01-11T22:15:03.8405808Z     * 122245985a         11 reports, total time  4767.91s ± 3184.40s
2023-01-11T22:15:03.8406104Z     * b797a24259         11 reports, total time  4784.26s ± 3253.10s
2023-01-11T22:15:03.8406487Z     |
2023-01-11T22:15:03.8406636Z     :
2023-01-11T22:15:03.8406714Z 
2023-01-11T22:15:03.8406834Z Removed  (across  859 suites)     0 tests, totaling     0.00s
2023-01-11T22:15:03.8407095Z Modified (across    0 suites)     0 tests, totaling     0.00s
2023-01-11T22:15:03.8407360Z Added    (across  682 suites) 17743 tests, totaling +3511.30s
2023-01-11T22:15:03.9233633Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2023-01-11T22:15:03.9233874Z with:
2023-01-11T22:15:03.9234012Z env:
2023-01-11T22:15:03.9234416Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:15:03.9234685Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:15:03.9234921Z ##[endgroup]
2023-01-11T22:15:03.9248149Z ##[group]Run set -eou pipefail
2023-01-11T22:15:03.9248363Z set -eou pipefail
2023-01-11T22:15:03.9248542Z 
2023-01-11T22:15:03.9248777Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2023-01-11T22:15:03.9249022Z for _ in $(seq 1440); do
2023-01-11T22:15:03.9249238Z     # Break if no ssh session exists anymore
2023-01-11T22:15:03.9249453Z     if [ "$(who)" = "" ]; then
2023-01-11T22:15:03.9249633Z       break
2023-01-11T22:15:03.9249785Z     fi
2023-01-11T22:15:03.9249981Z     echo "."
2023-01-11T22:15:03.9250140Z     sleep 5
2023-01-11T22:15:03.9250305Z done
2023-01-11T22:15:03.9261313Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:15:03.9261531Z env:
2023-01-11T22:15:03.9261703Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:15:03.9261958Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:15:03.9262205Z ##[endgroup]
2023-01-11T22:15:03.9286089Z Holding runner for 2 hours until all ssh sessions have logged out
2023-01-11T22:15:03.9373873Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2023-01-11T22:15:03.9374178Z # ignore expansion of "docker ps -q" since it could be empty
2023-01-11T22:15:03.9374426Z # shellcheck disable=SC2046
2023-01-11T22:15:03.9374650Z docker stop $(docker ps -q) || true
2023-01-11T22:15:03.9374875Z # Prune all of the docker images
2023-01-11T22:15:03.9375079Z docker system prune -af
2023-01-11T22:15:03.9385687Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:15:03.9385905Z env:
2023-01-11T22:15:03.9386082Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:15:03.9386338Z   DOCKER_CONTAINER_ID: a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:15:03.9386588Z ##[endgroup]
2023-01-11T22:15:04.3461790Z a71a466242c1
2023-01-11T22:15:06.7688971Z Deleted Containers:
2023-01-11T22:15:06.7689276Z a71a466242c1f37c1e0011471b01ee39ce7e013772770194af88dbad63e7c256
2023-01-11T22:15:06.7689447Z 
2023-01-11T22:15:12.5395334Z Deleted Images:
2023-01-11T22:15:12.5396468Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T22:15:12.5397853Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7@sha256:0da23f4faf0ce20770149c4a783e08eaa91c07112511dc5511c77937c66edb24
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2023-01-11T22:15:12.6593176Z [command]/usr/bin/git version
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2023-01-11T22:15:13.0516373Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
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